diff --git a/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb b/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb
index 3a34dc0e14..3dd987d4a0 100644
--- a/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb
+++ b/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb
@@ -97389,7 +97389,7 @@
         }
       ],
       "source": [
-        "records = linker.inference.prediction_errors_from_labels_column(\n",
+        "records = linker.evaluation.prediction_errors_from_labels_column(\n",
         "    \"cluster\",\n",
         "    threshold=0.999,\n",
         "    include_false_negatives=False,\n",
@@ -109042,7 +109042,7 @@
       ],
       "source": [
         "# Some of the false negatives will be because they weren't detected by the blocking rules\n",
-        "records = linker.inference.prediction_errors_from_labels_column(\n",
+        "records = linker.evaluation.prediction_errors_from_labels_column(\n",
         "    \"cluster\",\n",
         "    threshold=0.5,\n",
         "    include_false_negatives=True,\n",
diff --git a/docs/demos/examples/duckdb/accuracy_analysis_from_labels_column.ipynb b/docs/demos/examples/duckdb/accuracy_analysis_from_labels_column.ipynb
index c0d1e55324..2575f82b77 100644
--- a/docs/demos/examples/duckdb/accuracy_analysis_from_labels_column.ipynb
+++ b/docs/demos/examples/duckdb/accuracy_analysis_from_labels_column.ipynb
@@ -1,272 +1,1337 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Evaluation when you have fully labelled data\n",
-        "\n",
-        "In this example, our data contains a fully-populated ground-truth column called `cluster` that enables us to perform accuracy analysis of the final model\n"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation when you have fully labelled data\n",
+    "\n",
+    "In this example, our data contains a fully-populated ground-truth column called `cluster` that enables us to perform accuracy analysis of the final model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/accuracy_analysis_from_labels_column.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:16.264709Z",
+     "iopub.status.busy": "2024-06-07T09:09:16.264397Z",
+     "iopub.status.idle": "2024-06-07T09:09:16.269613Z",
+     "shell.execute_reply": "2024-06-07T09:09:16.268968Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:16.273849Z",
+     "iopub.status.busy": "2024-06-07T09:09:16.273306Z",
+     "iopub.status.idle": "2024-06-07T09:09:17.467426Z",
+     "shell.execute_reply": "2024-06-07T09:09:17.466787Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>city</th>\n",
+       "      <th>email</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Robert</td>\n",
+       "      <td>Alan</td>\n",
+       "      <td>1971-06-24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>robert255@smith.net</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Robert</td>\n",
+       "      <td>Allen</td>\n",
+       "      <td>1971-05-24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roberta25@smith.net</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unique_id first_name surname         dob city                email  cluster\n",
+       "0          0     Robert    Alan  1971-06-24  NaN  robert255@smith.net        0\n",
+       "1          1     Robert   Allen  1971-05-24  NaN  roberta25@smith.net        0"
       ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import splink_datasets\n",
+    "\n",
+    "df = splink_datasets.fake_1000\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:17.501913Z",
+     "iopub.status.busy": "2024-06-07T09:09:17.501641Z",
+     "iopub.status.idle": "2024-06-07T09:09:17.581434Z",
+     "shell.execute_reply": "2024-06-07T09:09:17.580667Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from splink import SettingsCreator, Linker, block_on, DuckDBAPI\n",
+    "import splink.comparison_template_library as ctl\n",
+    "import splink.comparison_library as cl\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    link_type=\"dedupe_only\",\n",
+    "    blocking_rules_to_generate_predictions=[\n",
+    "        block_on(\"first_name\"),\n",
+    "        block_on(\"surname\"),\n",
+    "    ],\n",
+    "    comparisons=[\n",
+    "        ctl.NameComparison(\"first_name\"),\n",
+    "        ctl.NameComparison(\"surname\"),\n",
+    "        ctl.DateComparison(\n",
+    "            \"dob\",\n",
+    "            input_is_string=True,\n",
+    "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
+    "            datetime_thresholds=[1, 1, 10],\n",
+    "        ),\n",
+    "        cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
+    "        ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
+    "    ],\n",
+    "    retain_intermediate_calculation_columns=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:17.585114Z",
+     "iopub.status.busy": "2024-06-07T09:09:17.584837Z",
+     "iopub.status.idle": "2024-06-07T09:09:17.847471Z",
+     "shell.execute_reply": "2024-06-07T09:09:17.846845Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Probability two random records match is estimated to be  0.00333.\n",
+      "This means that amongst all possible pairwise record comparisons, one in 300.13 are expected to match.  With 499,500 total possible comparisons, we expect a total of around 1,664.29 matching pairs\n"
+     ]
+    }
+   ],
+   "source": [
+    "db_api = DuckDBAPI()\n",
+    "linker = Linker(df, settings, database_api=db_api)\n",
+    "deterministic_rules = [\n",
+    "    \"l.first_name = r.first_name and levenshtein(r.dob, l.dob) <= 1\",\n",
+    "    \"l.surname = r.surname and levenshtein(r.dob, l.dob) <= 1\",\n",
+    "    \"l.first_name = r.first_name and levenshtein(r.surname, l.surname) <= 2\",\n",
+    "    \"l.email = r.email\",\n",
+    "]\n",
+    "\n",
+    "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:17.850459Z",
+     "iopub.status.busy": "2024-06-07T09:09:17.850216Z",
+     "iopub.status.idle": "2024-06-07T09:09:18.931010Z",
+     "shell.execute_reply": "2024-06-07T09:09:18.930397Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default value for `max_pairs`, which may be too small and thus lead to inaccurate estimates for your model's u-parameters. Consider increasing to 1e8 or 1e9, which will result in more accurate estimates, but with a longer run time.\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/accuracy_analysis_from_labels_column.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:53.238834Z",
-          "iopub.status.busy": "2024-03-27T15:10:53.238466Z",
-          "iopub.status.idle": "2024-03-27T15:10:53.243675Z",
-          "shell.execute_reply": "2024-03-27T15:10:53.243004Z"
-        }
-      },
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:53.247564Z",
-          "iopub.status.busy": "2024-03-27T15:10:53.247269Z",
-          "iopub.status.idle": "2024-03-27T15:10:55.196205Z",
-          "shell.execute_reply": "2024-03-27T15:10:55.195428Z"
-        }
-      },
-      "source": [
-        "from splink import splink_datasets\n",
-        "\n",
-        "df = splink_datasets.fake_1000\n",
-        "df.head(2)"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:55.242600Z",
-          "iopub.status.busy": "2024-03-27T15:10:55.242267Z",
-          "iopub.status.idle": "2024-03-27T15:10:55.601924Z",
-          "shell.execute_reply": "2024-03-27T15:10:55.601113Z"
-        }
-      },
-      "source": [
-        "from splink import SettingsCreator, Linker, block_on, DuckDBAPI\n",
-        "import splink.comparison_template_library as ctl\n",
-        "import splink.comparison_library as cl\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    link_type=\"dedupe_only\",\n",
-        "    blocking_rules_to_generate_predictions=[\n",
-        "        block_on(\"first_name\"),\n",
-        "        block_on(\"surname\"),\n",
-        "    ],\n",
-        "    comparisons=[\n",
-        "        ctl.NameComparison(\"first_name\"),\n",
-        "        ctl.NameComparison(\"surname\"),\n",
-        "        ctl.DateComparison(\n",
-        "            \"dob\",\n",
-        "            input_is_string=True,\n",
-        "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
-        "            datetime_thresholds=[1, 1, 10],\n",
-        "        ),\n",
-        "        cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
-        "        ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
-        "    ],\n",
-        "    retain_intermediate_calculation_columns=True,\n",
-        ")"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:55.606354Z",
-          "iopub.status.busy": "2024-03-27T15:10:55.606011Z",
-          "iopub.status.idle": "2024-03-27T15:10:55.966147Z",
-          "shell.execute_reply": "2024-03-27T15:10:55.965434Z"
-        }
-      },
-      "source": [
-        "db_api = DuckDBAPI()\n",
-        "linker = Linker(df, settings, database_api=db_api)\n",
-        "deterministic_rules = [\n",
-        "    \"l.first_name = r.first_name and levenshtein(r.dob, l.dob) <= 1\",\n",
-        "    \"l.surname = r.surname and levenshtein(r.dob, l.dob) <= 1\",\n",
-        "    \"l.first_name = r.first_name and levenshtein(r.surname, l.surname) <= 2\",\n",
-        "    \"l.email = r.email\",\n",
-        "]\n",
-        "\n",
-        "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:55.970182Z",
-          "iopub.status.busy": "2024-03-27T15:10:55.969667Z",
-          "iopub.status.idle": "2024-03-27T15:10:57.008471Z",
-          "shell.execute_reply": "2024-03-27T15:10:57.007360Z"
-        }
-      },
-      "source": [
-        "linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=5)"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:57.012299Z",
-          "iopub.status.busy": "2024-03-27T15:10:57.012041Z",
-          "iopub.status.idle": "2024-03-27T15:10:58.591902Z",
-          "shell.execute_reply": "2024-03-27T15:10:58.591381Z"
-        }
-      },
-      "source": [
-        "session_dob = linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"dob\"))\n",
-        "session_email = linker.training.estimate_parameters_using_expectation_maximisation(\n",
-        "    block_on(\"email\")\n",
-        ")"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:58.594711Z",
-          "iopub.status.busy": "2024-03-27T15:10:58.594499Z",
-          "iopub.status.idle": "2024-03-27T15:10:58.945354Z",
-          "shell.execute_reply": "2024-03-27T15:10:58.944711Z"
-        }
-      },
-      "source": [
-        "linker.accuracy_analysis_from_labels_column(\n",
-        "    \"cluster\", output_type=\"table\"\n",
-        ").as_pandas_dataframe(limit=5)"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:10:58.948920Z",
-          "iopub.status.busy": "2024-03-27T15:10:58.948640Z",
-          "iopub.status.idle": "2024-03-27T15:11:01.154581Z",
-          "shell.execute_reply": "2024-03-27T15:11:01.153881Z"
-        }
-      },
-      "source": [
-        "linker.accuracy_analysis_from_labels_column(\"cluster\", output_type=\"roc\")"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - first_name (no m values are trained).\n",
+      "    - surname (no m values are trained).\n",
+      "    - dob (no m values are trained).\n",
+      "    - city (no m values are trained).\n",
+      "    - email (no m values are trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:18.934824Z",
+     "iopub.status.busy": "2024-06-07T09:09:18.934551Z",
+     "iopub.status.idle": "2024-06-07T09:09:20.495494Z",
+     "shell.execute_reply": "2024-06-07T09:09:20.494833Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"dob\" = r.\"dob\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - first_name\n",
+      "    - surname\n",
+      "    - city\n",
+      "    - email\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - dob\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.417 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.121 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 0.0354 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.0127 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was 0.00539 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was 0.0025 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was 0.0012 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was 0.000599 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was 0.000313 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was 0.000186 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 11: Largest change in params was 0.000147 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 12: Largest change in params was 0.000158 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 13: Largest change in params was 0.000184 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 14: Largest change in params was 0.000195 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 15: Largest change in params was 0.000179 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 16: Largest change in params was 0.000144 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 17: Largest change in params was 0.000105 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 18: Largest change in params was 7.27e-05 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 18 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - dob (no m values are trained).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"email\" = r.\"email\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - first_name\n",
+      "    - surname\n",
+      "    - dob\n",
+      "    - city\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - email\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.466 in the m_probability of dob, level `Exact match on dob`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.0884 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 0.0193 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.00688 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was 0.00294 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was 0.00138 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was 0.000681 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was 0.000346 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was 0.000178 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was 9.26e-05 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 10 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
+     ]
+    }
+   ],
+   "source": [
+    "session_dob = linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"dob\"))\n",
+    "session_email = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+    "    block_on(\"email\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:20.498372Z",
+     "iopub.status.busy": "2024-06-07T09:09:20.498155Z",
+     "iopub.status.idle": "2024-06-07T09:09:20.768827Z",
+     "shell.execute_reply": "2024-06-07T09:09:20.768326Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>truth_threshold</th>\n",
+       "      <th>match_probability</th>\n",
+       "      <th>total_clerical_labels</th>\n",
+       "      <th>p</th>\n",
+       "      <th>n</th>\n",
+       "      <th>tp</th>\n",
+       "      <th>tn</th>\n",
+       "      <th>fp</th>\n",
+       "      <th>fn</th>\n",
+       "      <th>P_rate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>specificity</th>\n",
+       "      <th>npv</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>f2</th>\n",
+       "      <th>f0_5</th>\n",
+       "      <th>p4</th>\n",
+       "      <th>phi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-19.3</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>499500.0</td>\n",
+       "      <td>2031.0</td>\n",
+       "      <td>497469.0</td>\n",
+       "      <td>1027.0</td>\n",
+       "      <td>495147.0</td>\n",
+       "      <td>2322.0</td>\n",
+       "      <td>1004.0</td>\n",
+       "      <td>0.004066</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.306659</td>\n",
+       "      <td>0.505662</td>\n",
+       "      <td>0.995332</td>\n",
+       "      <td>0.997976</td>\n",
+       "      <td>0.993341</td>\n",
+       "      <td>0.381784</td>\n",
+       "      <td>0.447573</td>\n",
+       "      <td>0.332858</td>\n",
+       "      <td>0.552084</td>\n",
+       "      <td>0.390667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-19.2</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>499500.0</td>\n",
+       "      <td>2031.0</td>\n",
+       "      <td>497469.0</td>\n",
+       "      <td>1027.0</td>\n",
+       "      <td>495383.0</td>\n",
+       "      <td>2086.0</td>\n",
+       "      <td>1004.0</td>\n",
+       "      <td>0.004066</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.329907</td>\n",
+       "      <td>0.505662</td>\n",
+       "      <td>0.995807</td>\n",
+       "      <td>0.997977</td>\n",
+       "      <td>0.993814</td>\n",
+       "      <td>0.399300</td>\n",
+       "      <td>0.456973</td>\n",
+       "      <td>0.354554</td>\n",
+       "      <td>0.570207</td>\n",
+       "      <td>0.405492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-18.0</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>499500.0</td>\n",
+       "      <td>2031.0</td>\n",
+       "      <td>497469.0</td>\n",
+       "      <td>1027.0</td>\n",
+       "      <td>495584.0</td>\n",
+       "      <td>1885.0</td>\n",
+       "      <td>1004.0</td>\n",
+       "      <td>0.004066</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.352679</td>\n",
+       "      <td>0.505662</td>\n",
+       "      <td>0.996211</td>\n",
+       "      <td>0.997978</td>\n",
+       "      <td>0.994216</td>\n",
+       "      <td>0.415537</td>\n",
+       "      <td>0.465295</td>\n",
+       "      <td>0.375393</td>\n",
+       "      <td>0.586607</td>\n",
+       "      <td>0.419506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-17.1</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>499500.0</td>\n",
+       "      <td>2031.0</td>\n",
+       "      <td>497469.0</td>\n",
+       "      <td>1027.0</td>\n",
+       "      <td>495836.0</td>\n",
+       "      <td>1633.0</td>\n",
+       "      <td>1004.0</td>\n",
+       "      <td>0.004066</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.386090</td>\n",
+       "      <td>0.505662</td>\n",
+       "      <td>0.996717</td>\n",
+       "      <td>0.997979</td>\n",
+       "      <td>0.994721</td>\n",
+       "      <td>0.437860</td>\n",
+       "      <td>0.476168</td>\n",
+       "      <td>0.405256</td>\n",
+       "      <td>0.608551</td>\n",
+       "      <td>0.439259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-17.0</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>499500.0</td>\n",
+       "      <td>2031.0</td>\n",
+       "      <td>497469.0</td>\n",
+       "      <td>1027.0</td>\n",
+       "      <td>495957.0</td>\n",
+       "      <td>1512.0</td>\n",
+       "      <td>1004.0</td>\n",
+       "      <td>0.004066</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.404490</td>\n",
+       "      <td>0.505662</td>\n",
+       "      <td>0.996961</td>\n",
+       "      <td>0.997980</td>\n",
+       "      <td>0.994963</td>\n",
+       "      <td>0.449453</td>\n",
+       "      <td>0.481572</td>\n",
+       "      <td>0.421351</td>\n",
+       "      <td>0.619682</td>\n",
+       "      <td>0.449767</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:11:01.168311Z",
-          "iopub.status.busy": "2024-03-27T15:11:01.167847Z",
-          "iopub.status.idle": "2024-03-27T15:11:03.812090Z",
-          "shell.execute_reply": "2024-03-27T15:11:03.811329Z"
-        }
-      },
-      "source": [
-        "linker.accuracy_analysis_from_labels_column(\n",
-        "    \"cluster\",\n",
-        "    output_type=\"threshold_selection\",\n",
-        "    threshold_actual=0.5,\n",
-        "    add_metrics=[\"f1\"],\n",
-        ")"
+      "text/plain": [
+       "   truth_threshold  match_probability  total_clerical_labels       p  \\\n",
+       "0            -19.3           0.000002               499500.0  2031.0   \n",
+       "1            -19.2           0.000002               499500.0  2031.0   \n",
+       "2            -18.0           0.000004               499500.0  2031.0   \n",
+       "3            -17.1           0.000007               499500.0  2031.0   \n",
+       "4            -17.0           0.000008               499500.0  2031.0   \n",
+       "\n",
+       "          n      tp        tn      fp      fn    P_rate  ...  precision  \\\n",
+       "0  497469.0  1027.0  495147.0  2322.0  1004.0  0.004066  ...   0.306659   \n",
+       "1  497469.0  1027.0  495383.0  2086.0  1004.0  0.004066  ...   0.329907   \n",
+       "2  497469.0  1027.0  495584.0  1885.0  1004.0  0.004066  ...   0.352679   \n",
+       "3  497469.0  1027.0  495836.0  1633.0  1004.0  0.004066  ...   0.386090   \n",
+       "4  497469.0  1027.0  495957.0  1512.0  1004.0  0.004066  ...   0.404490   \n",
+       "\n",
+       "     recall  specificity       npv  accuracy        f1        f2      f0_5  \\\n",
+       "0  0.505662     0.995332  0.997976  0.993341  0.381784  0.447573  0.332858   \n",
+       "1  0.505662     0.995807  0.997977  0.993814  0.399300  0.456973  0.354554   \n",
+       "2  0.505662     0.996211  0.997978  0.994216  0.415537  0.465295  0.375393   \n",
+       "3  0.505662     0.996717  0.997979  0.994721  0.437860  0.476168  0.405256   \n",
+       "4  0.505662     0.996961  0.997980  0.994963  0.449453  0.481572  0.421351   \n",
+       "\n",
+       "         p4       phi  \n",
+       "0  0.552084  0.390667  \n",
+       "1  0.570207  0.405492  \n",
+       "2  0.586607  0.419506  \n",
+       "3  0.608551  0.439259  \n",
+       "4  0.619682  0.449767  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.evaluation.accuracy_analysis_from_labels_column(\n",
+    "    \"cluster\", output_type=\"table\"\n",
+    ").as_pandas_dataframe(limit=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:20.771736Z",
+     "iopub.status.busy": "2024-06-07T09:09:20.771453Z",
+     "iopub.status.idle": "2024-06-07T09:09:21.322647Z",
+     "shell.execute_reply": "2024-06-07T09:09:21.322088Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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0.999507631708518, \"precision\": 1.0, \"recall\": 0.0004923682914820286, \"specificity\": 1.0, \"npv\": 0.9959359277996552, \"accuracy\": 0.995935935935936, \"f1\": 0.000984251968503937, \"f2\": 0.0006153846153846154, \"f0_5\": 0.002457002457002457, \"p4\": 0.001966564392884295, \"phi\": 0.0221442378779737}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:11:03.822254Z",
-          "iopub.status.busy": "2024-03-27T15:11:03.821939Z",
-          "iopub.status.idle": "2024-03-27T15:11:04.205976Z",
-          "shell.execute_reply": "2024-03-27T15:11:04.205179Z"
-        }
-      },
-      "source": [
-        "# Plot some false positives\n",
-        "linker.inference.prediction_errors_from_labels_column(\n",
-        "    \"cluster\", include_false_negatives=True, include_false_positives=True\n",
-        ").as_pandas_dataframe(limit=5)"
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.evaluation.accuracy_analysis_from_labels_column(\"cluster\", output_type=\"roc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:21.327370Z",
+     "iopub.status.busy": "2024-06-07T09:09:21.327111Z",
+     "iopub.status.idle": "2024-06-07T09:09:22.635682Z",
+     "shell.execute_reply": "2024-06-07T09:09:22.635098Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-6b3f7f85ab904d128c8414728e959752.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-6b3f7f85ab904d128c8414728e959752.vega-embed details,\n",
+       "  #altair-viz-6b3f7f85ab904d128c8414728e959752.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-6b3f7f85ab904d128c8414728e959752\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-6b3f7f85ab904d128c8414728e959752\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-6b3f7f85ab904d128c8414728e959752\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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0.5980140146049732}, {\"truth_threshold\": -6.600000098347664, \"match_probability\": 0.010203470791514735, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1025.0, \"tn\": 497107.0, \"fp\": 362.0, \"fn\": 1006.0, \"P_rate\": 0.004066066066066066, \"N_rate\": 0.9959339339339339, \"tp_rate\": 0.5046774987690793, \"tn_rate\": 0.9992723164659506, \"fp_rate\": 0.0007276835340493578, \"fn_rate\": 0.49532250123092075, \"precision\": 0.7390050468637347, \"recall\": 0.5046774987690793, \"specificity\": 0.9992723164659506, \"npv\": 0.9979803779463696, \"accuracy\": 0.9972612612612612, \"f1\": 0.5997659449970744, \"f2\": 0.5388497529176742, \"f0_5\": 0.6762105818709593, \"p4\": 0.7494305167951033, \"phi\": 0.6094289693343778}, {\"truth_threshold\": -6.000000089406967, \"match_probability\": 0.015384614445865122, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1025.0, \"tn\": 497123.0, \"fp\": 346.0, \"fn\": 1006.0, \"P_rate\": 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\"specificity\": 0.9993929269964561, \"npv\": 0.9979766146434486, \"accuracy\": 0.9973773773773774, \"f1\": 0.6096543504171633, \"f2\": 0.5413271245634459, \"f0_5\": 0.6977220024553267, \"p4\": 0.7571194297807958, \"phi\": 0.6224164076915241}, {\"truth_threshold\": -5.600000083446503, \"match_probability\": 0.02020082327925431, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1023.0, \"tn\": 497194.0, \"fp\": 275.0, \"fn\": 1008.0, \"P_rate\": 0.004066066066066066, \"N_rate\": 0.9959339339339339, \"tp_rate\": 0.5036927621861153, \"tn_rate\": 0.9994472017351835, \"fp_rate\": 0.0005527982648165012, \"fn_rate\": 0.4963072378138848, \"precision\": 0.788135593220339, \"recall\": 0.5036927621861153, \"specificity\": 0.9994472017351835, \"npv\": 0.9979767243005849, \"accuracy\": 0.9974314314314314, \"f1\": 0.614598978672274, \"f2\": 0.542878369772872, \"f0_5\": 0.7081545064377682, \"p4\": 0.7609286094405532, \"phi\": 0.6289074042142978}, {\"truth_threshold\": -5.000000074505806, \"match_probability\": 0.030303028785498974, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1022.0, \"tn\": 497295.0, \"fp\": 174.0, \"fn\": 1009.0, \"P_rate\": 0.004066066066066066, \"N_rate\": 0.9959339339339339, \"tp_rate\": 0.5032003938946332, \"tn_rate\": 0.9996502294615343, \"fp_rate\": 0.00034977053846571343, \"fn_rate\": 0.49679960610536683, \"precision\": 0.8545150501672241, \"recall\": 0.5032003938946332, \"specificity\": 0.9996502294615343, \"npv\": 0.997975131646545, \"accuracy\": 0.9976316316316316, \"f1\": 0.6334056399132321, \"f2\": 0.5482832618025751, \"f0_5\": 0.7498165810711666, \"p4\": 0.7752068513314447, \"phi\": 0.6547329374104119}, {\"truth_threshold\": -4.500000067055225, \"match_probability\": 0.04232371044088178, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1021.0, \"tn\": 497295.0, \"fp\": 174.0, \"fn\": 1010.0, \"P_rate\": 0.004066066066066066, \"N_rate\": 0.9959339339339339, \"tp_rate\": 0.5027080256031512, \"tn_rate\": 0.9996502294615343, \"fp_rate\": 0.00034977053846571343, \"fn_rate\": 0.49729197439684886, \"precision\": 0.8543933054393306, \"recall\": 0.5027080256031512, \"specificity\": 0.9996502294615343, \"npv\": 0.9979731289069947, \"accuracy\": 0.9976296296296296, \"f1\": 0.6329820210787352, \"f2\": 0.5478055585363236, \"f0_5\": 0.7495228307150198, \"p4\": 0.7748892061614011, \"phi\": 0.6543648176926443}, {\"truth_threshold\": -4.400000065565109, \"match_probability\": 0.04522405175894309, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1021.0, \"tn\": 497297.0, \"fp\": 172.0, \"fn\": 1010.0, \"P_rate\": 0.004066066066066066, \"N_rate\": 0.9959339339339339, \"tp_rate\": 0.5027080256031512, \"tn_rate\": 0.9996542498125511, \"fp_rate\": 0.00034575018744886614, \"fn_rate\": 0.49729197439684886, \"precision\": 0.8558256496227996, \"recall\": 0.5027080256031512, \"specificity\": 0.9996542498125511, \"npv\": 0.9979731370420243, \"accuracy\": 0.9976336336336337, \"f1\": 0.6333746898263027, \"f2\": 0.5479231512289363, \"f0_5\": 0.7504042334264295, \"p4\": 0.7751839749617189, \"phi\": 0.6549170185726637}, {\"truth_threshold\": -4.300000064074993, \"match_probability\": 0.048313119674570026, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1016.0, \"tn\": 497331.0, \"fp\": 138.0, \"fn\": 1015.0, \"P_rate\": 0.004066066066066066, \"N_rate\": 0.9959339339339339, \"tp_rate\": 0.5002461841457411, \"tn_rate\": 0.9997225957798376, \"fp_rate\": 0.0002774042201624624, \"fn_rate\": 0.499753815854259, \"precision\": 0.8804159445407279, \"recall\": 0.5002461841457411, \"specificity\": 0.9997225957798376, \"npv\": 0.9979632624722582, \"accuracy\": 0.9976916916916917, \"f1\": 0.6379905808477238, \"f2\": 0.5475317956456133, \"f0_5\": 0.7642545509252294, \"p4\": 0.7786402025597812, \"phi\": 0.6626931269013433}, {\"truth_threshold\": -3.500000052154064, \"match_probability\": 0.08121030044424019, \"total_clerical_labels\": 499500.0, \"p\": 2031.0, \"n\": 497469.0, \"tp\": 1016.0, \"tn\": 497358.0, \"fp\": 111.0, \"fn\": 1015.0, \"P_rate\": 0.004066066066066066, \"N_rate\": 0.9959339339339339, \"tp_rate\": 0.5002461841457411, \"tn_rate\": 0.999776870518565, \"fp_rate\": 0.0002231294814350241, \"fn_rate\": 0.499753815854259, \"precision\": 0.90150842945874, \"recall\": 0.5002461841457411, \"specificity\": 0.999776870518565, \"npv\": 0.9979633728151405, \"accuracy\": 0.9977457457457457, \"f1\": 0.643445218492717, \"f2\": 0.5491298238028322, \"f0_5\": 0.7768771983483713, \"p4\": 0.7826974277823744, \"phi\": 0.6706389775112944}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:11:04.209998Z",
-          "iopub.status.busy": "2024-03-27T15:11:04.209676Z",
-          "iopub.status.idle": "2024-03-27T15:11:05.510086Z",
-          "shell.execute_reply": "2024-03-27T15:11:05.509348Z"
-        }
-      },
-      "source": [
-        "records = linker.inference.prediction_errors_from_labels_column(\n",
-        "    \"cluster\", include_false_negatives=True, include_false_positives=True\n",
-        ").as_record_dict(limit=5)\n",
-        "\n",
-        "linker.visualisations.waterfall_chart(records)"
+      "text/plain": [
+       "alt.HConcatChart(...)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.evaluation.accuracy_analysis_from_labels_column(\n",
+    "    \"cluster\",\n",
+    "    output_type=\"threshold_selection\",\n",
+    "    threshold_actual=0.5,\n",
+    "    add_metrics=[\"f1\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:22.638822Z",
+     "iopub.status.busy": "2024-06-07T09:09:22.638569Z",
+     "iopub.status.idle": "2024-06-07T09:09:22.853941Z",
+     "shell.execute_reply": "2024-06-07T09:09:22.853250Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clerical_match_score</th>\n",
+       "      <th>found_by_blocking_rules</th>\n",
+       "      <th>match_weight</th>\n",
+       "      <th>match_probability</th>\n",
+       "      <th>unique_id_l</th>\n",
+       "      <th>unique_id_r</th>\n",
+       "      <th>first_name_l</th>\n",
+       "      <th>first_name_r</th>\n",
+       "      <th>gamma_first_name</th>\n",
+       "      <th>bf_first_name</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tf_city_r</th>\n",
+       "      <th>bf_city</th>\n",
+       "      <th>bf_tf_adj_city</th>\n",
+       "      <th>email_l</th>\n",
+       "      <th>email_r</th>\n",
+       "      <th>gamma_email</th>\n",
+       "      <th>bf_email</th>\n",
+       "      <th>cluster_l</th>\n",
+       "      <th>cluster_r</th>\n",
+       "      <th>match_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-24.165914</td>\n",
+       "      <td>5.312940e-08</td>\n",
+       "      <td>417</td>\n",
+       "      <td>418</td>\n",
+       "      <td>Florence</td>\n",
+       "      <td>Brown</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.213986</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00123</td>\n",
+       "      <td>0.427845</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>fb@reose.cem</td>\n",
+       "      <td>f@b@reese.com</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001023</td>\n",
+       "      <td>108</td>\n",
+       "      <td>108</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-21.941506</td>\n",
+       "      <td>2.482839e-07</td>\n",
+       "      <td>796</td>\n",
+       "      <td>797</td>\n",
+       "      <td>Taylor</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00738</td>\n",
+       "      <td>0.427845</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>jt40o@combs.net</td>\n",
+       "      <td>jt40@cotbs.nm</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001023</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-19.517277</td>\n",
+       "      <td>1.332642e-06</td>\n",
+       "      <td>452</td>\n",
+       "      <td>454</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Davies</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.01599</td>\n",
+       "      <td>0.427845</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>rd@lewis.com</td>\n",
+       "      <td>idlewrs.cocm</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001023</td>\n",
+       "      <td>115</td>\n",
+       "      <td>115</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-17.978364</td>\n",
+       "      <td>3.872323e-06</td>\n",
+       "      <td>717</td>\n",
+       "      <td>718</td>\n",
+       "      <td>Mia</td>\n",
+       "      <td>Jones</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.213986</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00615</td>\n",
+       "      <td>0.427845</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>mia.j63@martinez.biz</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>182</td>\n",
+       "      <td>182</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-15.518690</td>\n",
+       "      <td>2.130097e-05</td>\n",
+       "      <td>594</td>\n",
+       "      <td>595</td>\n",
+       "      <td>Grace</td>\n",
+       "      <td>Grace</td>\n",
+       "      <td>3</td>\n",
+       "      <td>85.794621</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00123</td>\n",
+       "      <td>0.427845</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>gk@frey-robinson.org</td>\n",
+       "      <td>rgk@frey-robinon.org</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001023</td>\n",
+       "      <td>146</td>\n",
+       "      <td>146</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 32 columns</p>\n",
+       "</div>"
       ],
-      "outputs": []
+      "text/plain": [
+       "   clerical_match_score  found_by_blocking_rules  match_weight  \\\n",
+       "0                   1.0                    False    -24.165914   \n",
+       "1                   1.0                    False    -21.941506   \n",
+       "2                   1.0                    False    -19.517277   \n",
+       "3                   1.0                    False    -17.978364   \n",
+       "4                   1.0                     True    -15.518690   \n",
+       "\n",
+       "   match_probability  unique_id_l  unique_id_r first_name_l first_name_r  \\\n",
+       "0       5.312940e-08          417          418     Florence        Brown   \n",
+       "1       2.482839e-07          796          797       Taylor         None   \n",
+       "2       1.332642e-06          452          454         None       Davies   \n",
+       "3       3.872323e-06          717          718          Mia        Jones   \n",
+       "4       2.130097e-05          594          595        Grace        Grace   \n",
+       "\n",
+       "   gamma_first_name  bf_first_name  ... tf_city_r   bf_city  bf_tf_adj_city  \\\n",
+       "0                 0       0.213986  ...   0.00123  0.427845             1.0   \n",
+       "1                -1       1.000000  ...   0.00738  0.427845             1.0   \n",
+       "2                -1       1.000000  ...   0.01599  0.427845             1.0   \n",
+       "3                 0       0.213986  ...   0.00615  0.427845             1.0   \n",
+       "4                 3      85.794621  ...   0.00123  0.427845             1.0   \n",
+       "\n",
+       "                email_l               email_r gamma_email  bf_email  \\\n",
+       "0          fb@reose.cem         f@b@reese.com           0  0.001023   \n",
+       "1       jt40o@combs.net         jt40@cotbs.nm           0  0.001023   \n",
+       "2          rd@lewis.com          idlewrs.cocm           0  0.001023   \n",
+       "3  mia.j63@martinez.biz                  None          -1  1.000000   \n",
+       "4  gk@frey-robinson.org  rgk@frey-robinon.org           0  0.001023   \n",
+       "\n",
+       "   cluster_l cluster_r match_key  \n",
+       "0        108       108         2  \n",
+       "1        201       201         2  \n",
+       "2        115       115         2  \n",
+       "3        182       182         2  \n",
+       "4        146       146         0  \n",
+       "\n",
+       "[5 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot some false positives\n",
+    "linker.evaluation.prediction_errors_from_labels_column(\n",
+    "    \"cluster\", include_false_negatives=True, include_false_positives=True\n",
+    ").as_pandas_dataframe(limit=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:22.857193Z",
+     "iopub.status.busy": "2024-06-07T09:09:22.856931Z",
+     "iopub.status.idle": "2024-06-07T09:09:23.602967Z",
+     "shell.execute_reply": "2024-06-07T09:09:23.602410Z"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": ".venv",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
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\"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.22629686446829, \"u_probability\": 0.9878019291222225, \"bayes_factor\": 0.2290913368324571, \"log2_bayes_factor\": -2.126005191221481, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  4.37 times less likely to be a match\", \"column_name\": \"surname\", \"value_l\": \"Daves\", \"value_r\": \"Reuben\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 2, \"record_number\": 2}, {\"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"dob_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"dob_r\\\", '%Y-%m-%d'))) <= 31557600.0\", \"label_for_charts\": \"Abs difference of 'transformed dob <= 1 year'\", \"m_probability\": 0.1313648732932813, \"u_probability\": 0.033053053053053054, \"bayes_factor\": 3.9743642768015754, \"log2_bayes_factor\": 1.990724111201016, \"comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed dob <= 1 year'` then comparison is 3.97 times more likely to be a match\", \"column_name\": \"dob\", \"value_l\": \"1986-12-21\", \"value_r\": \"1987-11-21\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 2}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.40425084595448124, \"u_probability\": 0.9448524288198547, \"bayes_factor\": 0.42784548530970185, \"log2_bayes_factor\": -1.2248382277597478, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  2.34 times less likely to be a match\", \"column_name\": \"city\", \"value_l\": \"Swanse\", \"value_r\": \"Swansea\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 4, \"record_number\": 2}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.40425084595448124, \"u_probability\": 0.9448524288198547, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  2.34 times less likely to be a match\", \"column_name\": \"tf_city\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 5, \"record_number\": 2}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.00101907095234151, \"u_probability\": 0.9958535188795172, \"bayes_factor\": 0.0010233141049580423, \"log2_bayes_factor\": -9.932535238200462, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  977.22 times less likely to be a match\", \"column_name\": \"email\", \"value_l\": \"rd@lewis.com\", \"value_r\": \"idlewrs.cocm\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 2}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": -19.517277339720344, \"bayes_factor\": 1.3326438120564402e-06, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 7, \"record_number\": 2}, {\"column_name\": \"Prior\", \"label_for_charts\": \"Starting match weight (prior)\", \"sql_condition\": null, \"log2_bayes_factor\": -8.224622793739668, \"bayes_factor\": 0.0033430420247643373, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 0, \"record_number\": 3}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.21075181163122353, \"u_probability\": 0.9848839400924999, \"bayes_factor\": 0.2139864435310315, \"log2_bayes_factor\": -2.22440869298082, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  4.67 times less likely to be a match\", \"column_name\": \"first_name\", \"value_l\": \"Mia\", \"value_r\": \"Jones\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 1, \"record_number\": 3}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.22629686446829, \"u_probability\": 0.9878019291222225, \"bayes_factor\": 0.2290913368324571, \"log2_bayes_factor\": -2.126005191221481, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  4.37 times less likely to be a match\", \"column_name\": \"surname\", \"value_l\": \"Jones\", \"value_r\": \"Mia\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 2, \"record_number\": 3}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.03609994786816682, \"u_probability\": 0.6536676676676677, \"bayes_factor\": 0.05522676071309138, \"log2_bayes_factor\": -4.178488680149763, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  18.11 times less likely to be a match\", \"column_name\": \"dob\", \"value_l\": \"2031-02-20\", \"value_r\": \"2021-01-21\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 3}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.40425084595448124, \"u_probability\": 0.9448524288198547, \"bayes_factor\": 0.42784548530970185, \"log2_bayes_factor\": -1.2248382277597478, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  2.34 times less likely to be a match\", \"column_name\": \"city\", \"value_l\": \"Ipsich\", \"value_r\": \"Ipswich\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 4, \"record_number\": 3}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.40425084595448124, \"u_probability\": 0.9448524288198547, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  2.34 times less likely to be a match\", \"column_name\": \"tf_city\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 5, \"record_number\": 3}, {\"sql_condition\": \"\\\"email_l\\\" IS NULL OR \\\"email_r\\\" IS NULL\", \"label_for_charts\": \"email is NULL\", \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": -1, \"bayes_factor_description\": \"If comparison level is `email is null` then comparison is 1.00 times more likely to be a match\", \"column_name\": \"email\", \"value_l\": \"mia.j63@martinez.biz\", \"value_r\": \"None\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 3}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": -17.97836358585148, \"bayes_factor\": 3.872338264700716e-06, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 7, \"record_number\": 3}, {\"column_name\": \"Prior\", \"label_for_charts\": \"Starting match weight (prior)\", \"sql_condition\": null, \"log2_bayes_factor\": -8.224622793739668, \"bayes_factor\": 0.0033430420247643373, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 0, \"record_number\": 4}, {\"sql_condition\": \"\\\"first_name_l\\\" = \\\"first_name_r\\\"\", \"label_for_charts\": \"Exact match on first_name\", \"m_probability\": 0.49705726325538413, \"u_probability\": 0.0057935713975033705, \"bayes_factor\": 85.79462116745148, \"log2_bayes_factor\": 6.422815296711335, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on first_name` then comparison is 85.79 times more likely to be a match\", \"column_name\": \"first_name\", \"value_l\": \"Grace\", \"value_r\": \"Grace\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 1, \"record_number\": 4}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.22629686446829, \"u_probability\": 0.9878019291222225, \"bayes_factor\": 0.2290913368324571, \"log2_bayes_factor\": -2.126005191221481, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  4.37 times less likely to be a match\", \"column_name\": \"surname\", \"value_l\": \"Kik\", \"value_r\": \"Kiirk\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 2, \"record_number\": 4}, {\"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"dob_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"dob_r\\\", '%Y-%m-%d'))) <= 315576000.0\", \"label_for_charts\": \"Abs difference of 'transformed dob <= 10 year'\", \"m_probability\": 0.22767659077874, \"u_probability\": 0.30747947947947946, \"bayes_factor\": 0.7404610908154431, \"log2_bayes_factor\": -0.43350416701999833, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed dob <= 10 year'` then comparison is  1.35 times less likely to be a match\", \"column_name\": \"dob\", \"value_l\": \"2020-05-12\", \"value_r\": \"2011-03-17\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 4}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.40425084595448124, \"u_probability\": 0.9448524288198547, \"bayes_factor\": 0.42784548530970185, \"log2_bayes_factor\": -1.2248382277597478, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  2.34 times less likely to be a match\", \"column_name\": \"city\", \"value_l\": \"Edinburgh\", \"value_r\": \"Edhibnurgh\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 4, \"record_number\": 4}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.40425084595448124, \"u_probability\": 0.9448524288198547, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  2.34 times less likely to be a match\", \"column_name\": \"tf_city\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 5, \"record_number\": 4}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.00101907095234151, \"u_probability\": 0.9958535188795172, \"bayes_factor\": 0.0010233141049580423, \"log2_bayes_factor\": -9.932535238200462, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  977.22 times less likely to be a match\", \"column_name\": \"email\", \"value_l\": \"gk@frey-robinson.org\", \"value_r\": \"rgk@frey-robinon.org\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 4}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": -15.518690321230022, \"bayes_factor\": 2.130142806601533e-05, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 7, \"record_number\": 4}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "records = linker.evaluation.prediction_errors_from_labels_column(\n",
+    "    \"cluster\", include_false_negatives=True, include_false_positives=True\n",
+    ").as_record_dict(limit=5)\n",
+    "\n",
+    "linker.visualisations.waterfall_chart(records)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb b/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb
index 383ea109a2..3e30041105 100644
--- a/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb
+++ b/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb
@@ -1,2135 +1,2343 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linking a dataset of real historical persons\n",
+    "\n",
+    "In this example, we deduplicate a more realistic dataset. The data is based on historical persons scraped from wikidata. Duplicate records are introduced with a variety of errors introduced.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:25.613571Z",
+     "iopub.status.busy": "2024-06-07T09:09:25.613270Z",
+     "iopub.status.idle": "2024-06-07T09:09:25.618664Z",
+     "shell.execute_reply": "2024-06-07T09:09:25.617985Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:25.622132Z",
+     "iopub.status.busy": "2024-06-07T09:09:25.621861Z",
+     "iopub.status.idle": "2024-06-07T09:09:28.057830Z",
+     "shell.execute_reply": "2024-06-07T09:09:28.057112Z"
+    }
+   },
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Linking a dataset of real historical persons\n",
-        "\n",
-        "In this example, we deduplicate a more realistic dataset. The data is based on historical persons scraped from wikidata. Duplicate records are introduced with a variety of errors introduced.\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloading: https://raw.githubusercontent.com/moj-analytical-services/splink_datasets/master/data/historical_figures_with_errors_50k.parquet\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
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     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:03.040913Z",
-          "iopub.status.busy": "2024-05-15T16:07:03.040529Z",
-          "iopub.status.idle": "2024-05-15T16:07:03.045834Z",
-          "shell.execute_reply": "2024-05-15T16:07:03.045063Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>full_name</th>\n",
+       "      <th>first_and_surname</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>birth_place</th>\n",
+       "      <th>postcode_fake</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>occupation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Q2296770-1</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas clifford, 1st baron clifford of chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q2296770-2</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas of chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Q2296770-3</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>tom 1st baron clifford of chudleigh</td>\n",
+       "      <td>tom chudleigh</td>\n",
+       "      <td>tom</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Q2296770-4</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas 1st chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8hu</td>\n",
+       "      <td>None</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Q2296770-5</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas clifford, 1st baron chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>None</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    unique_id   cluster                                         full_name  \\\n",
+       "0  Q2296770-1  Q2296770  thomas clifford, 1st baron clifford of chudleigh   \n",
+       "1  Q2296770-2  Q2296770                               thomas of chudleigh   \n",
+       "2  Q2296770-3  Q2296770               tom 1st baron clifford of chudleigh   \n",
+       "3  Q2296770-4  Q2296770                              thomas 1st chudleigh   \n",
+       "4  Q2296770-5  Q2296770              thomas clifford, 1st baron chudleigh   \n",
+       "\n",
+       "  first_and_surname first_name    surname         dob birth_place  \\\n",
+       "0  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "1  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "2     tom chudleigh        tom  chudleigh  1630-08-01       devon   \n",
+       "3  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "4  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "\n",
+       "  postcode_fake gender  occupation  \n",
+       "0      tq13 8df   male  politician  \n",
+       "1      tq13 8df   male  politician  \n",
+       "2      tq13 8df   male  politician  \n",
+       "3      tq13 8hu   None  politician  \n",
+       "4      tq13 8df   None  politician  "
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import splink_datasets\n",
+    "\n",
+    "df = splink_datasets.historical_50k\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:28.061677Z",
+     "iopub.status.busy": "2024-06-07T09:09:28.061319Z",
+     "iopub.status.idle": "2024-06-07T09:09:28.892623Z",
+     "shell.execute_reply": "2024-06-07T09:09:28.891638Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:03.049635Z",
-          "iopub.status.busy": "2024-05-15T16:07:03.049337Z",
-          "iopub.status.idle": "2024-05-15T16:07:04.275040Z",
-          "shell.execute_reply": "2024-05-15T16:07:04.274317Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>unique_id</th>\n",
-              "      <th>cluster</th>\n",
-              "      <th>full_name</th>\n",
-              "      <th>first_and_surname</th>\n",
-              "      <th>first_name</th>\n",
-              "      <th>surname</th>\n",
-              "      <th>dob</th>\n",
-              "      <th>birth_place</th>\n",
-              "      <th>postcode_fake</th>\n",
-              "      <th>gender</th>\n",
-              "      <th>occupation</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Q2296770-1</td>\n",
-              "      <td>Q2296770</td>\n",
-              "      <td>thomas clifford, 1st baron clifford of chudleigh</td>\n",
-              "      <td>thomas chudleigh</td>\n",
-              "      <td>thomas</td>\n",
-              "      <td>chudleigh</td>\n",
-              "      <td>1630-08-01</td>\n",
-              "      <td>devon</td>\n",
-              "      <td>tq13 8df</td>\n",
-              "      <td>male</td>\n",
-              "      <td>politician</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Q2296770-2</td>\n",
-              "      <td>Q2296770</td>\n",
-              "      <td>thomas of chudleigh</td>\n",
-              "      <td>thomas chudleigh</td>\n",
-              "      <td>thomas</td>\n",
-              "      <td>chudleigh</td>\n",
-              "      <td>1630-08-01</td>\n",
-              "      <td>devon</td>\n",
-              "      <td>tq13 8df</td>\n",
-              "      <td>male</td>\n",
-              "      <td>politician</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Q2296770-3</td>\n",
-              "      <td>Q2296770</td>\n",
-              "      <td>tom 1st baron clifford of chudleigh</td>\n",
-              "      <td>tom chudleigh</td>\n",
-              "      <td>tom</td>\n",
-              "      <td>chudleigh</td>\n",
-              "      <td>1630-08-01</td>\n",
-              "      <td>devon</td>\n",
-              "      <td>tq13 8df</td>\n",
-              "      <td>male</td>\n",
-              "      <td>politician</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Q2296770-4</td>\n",
-              "      <td>Q2296770</td>\n",
-              "      <td>thomas 1st chudleigh</td>\n",
-              "      <td>thomas chudleigh</td>\n",
-              "      <td>thomas</td>\n",
-              "      <td>chudleigh</td>\n",
-              "      <td>1630-08-01</td>\n",
-              "      <td>devon</td>\n",
-              "      <td>tq13 8hu</td>\n",
-              "      <td>None</td>\n",
-              "      <td>politician</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Q2296770-5</td>\n",
-              "      <td>Q2296770</td>\n",
-              "      <td>thomas clifford, 1st baron chudleigh</td>\n",
-              "      <td>thomas chudleigh</td>\n",
-              "      <td>thomas</td>\n",
-              "      <td>chudleigh</td>\n",
-              "      <td>1630-08-01</td>\n",
-              "      <td>devon</td>\n",
-              "      <td>tq13 8df</td>\n",
-              "      <td>None</td>\n",
-              "      <td>politician</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "    unique_id   cluster                                         full_name  \\\n",
-              "0  Q2296770-1  Q2296770  thomas clifford, 1st baron clifford of chudleigh   \n",
-              "1  Q2296770-2  Q2296770                               thomas of chudleigh   \n",
-              "2  Q2296770-3  Q2296770               tom 1st baron clifford of chudleigh   \n",
-              "3  Q2296770-4  Q2296770                              thomas 1st chudleigh   \n",
-              "4  Q2296770-5  Q2296770              thomas clifford, 1st baron chudleigh   \n",
-              "\n",
-              "  first_and_surname first_name    surname         dob birth_place  \\\n",
-              "0  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
-              "1  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
-              "2     tom chudleigh        tom  chudleigh  1630-08-01       devon   \n",
-              "3  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
-              "4  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
-              "\n",
-              "  postcode_fake gender  occupation  \n",
-              "0      tq13 8df   male  politician  \n",
-              "1      tq13 8df   male  politician  \n",
-              "2      tq13 8df   male  politician  \n",
-              "3      tq13 8hu   None  politician  \n",
-              "4      tq13 8df   None  politician  "
-            ]
-          },
-          "execution_count": 2,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
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+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"vconcat\": [{\"hconcat\": [{\"mark\": {\"type\": \"line\", \"interpolate\": \"step-after\"}, \"data\": {\"values\": [{\"percentile_ex_nulls\": 0.25410306453704834, \"percentile_inc_nulls\": 0.25509113073349, \"value_count\": 24, \"group_name\": \"first_name\", \"total_non_null_rows\": 50511, \"total_rows_inc_nulls\": 50578, \"sum_tokens_in_value_count_group\": 192.0, \"distinct_value_count\": 4413}, {\"percentile_ex_nulls\": 0.25091564655303955, \"percentile_inc_nulls\": 0.25190794467926025, \"value_count\": 23, \"group_name\": \"first_name\", \"total_non_null_rows\": 50511, \"total_rows_inc_nulls\": 50578, \"sum_tokens_in_value_count_group\": 161.0, \"distinct_value_count\": 4413}, {\"percentile_ex_nulls\": 0.24612462520599365, \"percentile_inc_nulls\": 0.24712324142456055, \"value_count\": 22, \"group_name\": \"first_name\", \"total_non_null_rows\": 50511, \"total_rows_inc_nulls\": 50578, 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{\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"hj\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}, {\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"ih\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}, {\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"fz\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}, {\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"wj\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}]}, \"encoding\": {\"tooltip\": [{\"field\": \"value\", \"type\": \"nominal\"}, {\"field\": \"value_count\", \"type\": \"quantitative\"}, {\"field\": \"total_non_null_rows\", \"type\": \"quantitative\"}, {\"field\": \"total_rows_inc_nulls\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"value\", \"sort\": \"-y\", \"title\": null, \"type\": \"nominal\"}, \"y\": {\"field\": \"value_count\", \"scale\": {\"domain\": [0, 1748]}, \"title\": \"Value count\", \"type\": \"quantitative\"}}, \"title\": \"Bottom 10 values by value count\"}]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "source": [
-        "from splink import splink_datasets\n",
-        "\n",
-        "df = splink_datasets.historical_50k\n",
-        "df.head()"
+      "text/plain": [
+       "alt.VConcatChart(...)"
       ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import DuckDBAPI\n",
+    "from splink.exploratory import profile_columns\n",
+    "\n",
+    "db_api = DuckDBAPI()\n",
+    "profile_columns(df, db_api, column_expressions=[\"first_name\", \"substr(surname,1,2)\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:28.898009Z",
+     "iopub.status.busy": "2024-06-07T09:09:28.897643Z",
+     "iopub.status.idle": "2024-06-07T09:09:29.356811Z",
+     "shell.execute_reply": "2024-06-07T09:09:29.356107Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:04.316719Z",
-          "iopub.status.busy": "2024-05-15T16:07:04.315783Z",
-          "iopub.status.idle": "2024-05-15T16:07:05.112833Z",
-          "shell.execute_reply": "2024-05-15T16:07:05.112087Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
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-              "<style>\n",
-              "  #altair-viz-f5640dad6c1542f2bb897a0cf9710349.vega-embed {\n",
-              "    width: 100%;\n",
-              "    display: flex;\n",
-              "  }\n",
-              "\n",
-              "  #altair-viz-f5640dad6c1542f2bb897a0cf9710349.vega-embed details,\n",
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-              "  }\n",
-              "</style>\n",
-              "<div id=\"altair-viz-f5640dad6c1542f2bb897a0cf9710349\"></div>\n",
-              "<script type=\"text/javascript\">\n",
-              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-              "  (function(spec, embedOpt){\n",
-              "    let outputDiv = document.currentScript.previousElementSibling;\n",
-              "    if (outputDiv.id !== \"altair-viz-f5640dad6c1542f2bb897a0cf9710349\") {\n",
-              "      outputDiv = document.getElementById(\"altair-viz-f5640dad6c1542f2bb897a0cf9710349\");\n",
-              "    }\n",
-              "    const paths = {\n",
-              "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
-              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
-              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
-              "    };\n",
-              "\n",
-              "    function maybeLoadScript(lib, version) {\n",
-              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
-              "      return (VEGA_DEBUG[key] == version) ?\n",
-              "        Promise.resolve(paths[lib]) :\n",
-              "        new Promise(function(resolve, reject) {\n",
-              "          var s = document.createElement('script');\n",
-              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-              "          s.async = true;\n",
-              "          s.onload = () => {\n",
-              "            VEGA_DEBUG[key] = version;\n",
-              "            return resolve(paths[lib]);\n",
-              "          };\n",
-              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-              "          s.src = paths[lib];\n",
-              "        });\n",
-              "    }\n",
-              "\n",
-              "    function showError(err) {\n",
-              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-              "      throw err;\n",
-              "    }\n",
-              "\n",
-              "    function displayChart(vegaEmbed) {\n",
-              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-              "    }\n",
-              "\n",
-              "    if(typeof define === \"function\" && define.amd) {\n",
-              "      requirejs.config({paths});\n",
-              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-              "    } else {\n",
-              "      maybeLoadScript(\"vega\", \"5\")\n",
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-              "        .catch(showError)\n",
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{\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"0e\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}, {\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"v4\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}, {\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"c0\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}, {\"value_count\": 1, \"group_name\": \"substr_surname_1_2_\", \"value\": \"tn\", \"total_non_null_rows\": 46063, \"total_rows_inc_nulls\": 50578, \"distinct_value_count\": 447}]}, \"encoding\": {\"tooltip\": [{\"field\": \"value\", \"type\": \"nominal\"}, {\"field\": \"value_count\", \"type\": \"quantitative\"}, {\"field\": \"total_non_null_rows\", \"type\": \"quantitative\"}, {\"field\": \"total_rows_inc_nulls\", \"type\": \"quantitative\"}], \"x\": {\"field\": 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-              "</script>"
-            ],
-            "text/plain": [
-              "alt.VConcatChart(...)"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-e46490fce23e4ef2b152e2a8990fbed1.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
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+       "  #altair-viz-e46490fce23e4ef2b152e2a8990fbed1.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-e46490fce23e4ef2b152e2a8990fbed1\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-e46490fce23e4ef2b152e2a8990fbed1\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-e46490fce23e4ef2b152e2a8990fbed1\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
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+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-8232b7c328acd1a8eec0ecfe8a013878\"}, \"mark\": \"bar\", \"encoding\": {\"order\": {\"field\": \"cumulative_rows\"}, \"tooltip\": [{\"field\": \"blocking_rule\", \"title\": \"SQL Condition\", \"type\": \"nominal\"}, {\"field\": \"row_count\", \"format\": \",\", \"title\": \"Comparisons Generated\", \"type\": \"quantitative\"}, {\"field\": \"cumulative_rows\", \"format\": \",\", \"title\": \"Cumulative Comparisons\", \"type\": \"quantitative\"}, {\"field\": \"cartesian\", \"format\": \",\", \"title\": \"Total comparisons in Cartesian product\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"start\", \"title\": \"Comparisons Generated by Rule(s)\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"cumulative_rows\"}, \"y\": {\"field\": \"blocking_rule\", \"sort\": [\"-x2\"], \"title\": \"SQL Blocking Rule\"}}, \"height\": {\"step\": 20}, \"title\": {\"text\": \"Count of Additional Comparisons Generated by Each Blocking Rule\", \"subtitle\": \"(Counts exclude comparisons already generated by previous rules)\"}, \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-8232b7c328acd1a8eec0ecfe8a013878\": [{\"blocking_rule\": \"(l.\\\"first_name\\\" = r.\\\"first_name\\\") AND (l.\\\"surname\\\" = r.\\\"surname\\\")\", \"row_count\": 243656, \"cumulative_rows\": 243656, \"cartesian\": 1279041753, \"match_key\": \"0\", \"start\": 0}, {\"blocking_rule\": \"(l.\\\"surname\\\" = r.\\\"surname\\\") AND (l.\\\"dob\\\" = r.\\\"dob\\\")\", \"row_count\": 25041, \"cumulative_rows\": 268697, \"cartesian\": 1279041753, \"match_key\": \"1\", \"start\": 243656}, {\"blocking_rule\": \"(l.\\\"first_name\\\" = r.\\\"first_name\\\") AND (l.\\\"dob\\\" = r.\\\"dob\\\")\", \"row_count\": 29905, \"cumulative_rows\": 298602, \"cartesian\": 1279041753, \"match_key\": \"2\", \"start\": 268697}, {\"blocking_rule\": \"(l.\\\"postcode_fake\\\" = r.\\\"postcode_fake\\\") AND (l.\\\"first_name\\\" = r.\\\"first_name\\\")\", \"row_count\": 8421, \"cumulative_rows\": 307023, \"cartesian\": 1279041753, \"match_key\": \"3\", \"start\": 298602}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "source": [
-        "from splink import DuckDBAPI\n",
-        "from splink.exploratory import profile_columns\n",
-        "\n",
-        "db_api = DuckDBAPI()\n",
-        "profile_columns(df, db_api, column_expressions=[\"first_name\", \"substr(surname,1,2)\"])"
+      "text/plain": [
+       "alt.Chart(...)"
       ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import DuckDBAPI, block_on\n",
+    "from splink.blocking_analysis import (\n",
+    "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
+    ")\n",
+    "\n",
+    "blocking_rules =  [block_on(\"first_name\", \"surname\"),\n",
+    "        block_on(\"surname\", \"dob\"),\n",
+    "        block_on(\"first_name\", \"dob\"),\n",
+    "        block_on(\"postcode_fake\", \"first_name\")]\n",
+    "\n",
+    "db_api = DuckDBAPI()\n",
+    "\n",
+    "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
+    "    table_or_tables=df,\n",
+    "    blocking_rules=blocking_rules,\n",
+    "    db_api=db_api,\n",
+    "    link_type=\"dedupe_only\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:29.359888Z",
+     "iopub.status.busy": "2024-06-07T09:09:29.359671Z",
+     "iopub.status.idle": "2024-06-07T09:09:29.491413Z",
+     "shell.execute_reply": "2024-06-07T09:09:29.490645Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import splink.comparison_library as cl\n",
+    "import splink.comparison_template_library as ctl\n",
+    "from splink import Linker, SettingsCreator\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    link_type=\"dedupe_only\",\n",
+    "    blocking_rules_to_generate_predictions=blocking_rules,\n",
+    "    comparisons=[\n",
+    "        ctl.NameComparison(\"first_name\").configure(term_frequency_adjustments=True),\n",
+    "        ctl.NameComparison(\"surname\").configure(term_frequency_adjustments=True),\n",
+    "        ctl.DateComparison(\n",
+    "            \"dob\",\n",
+    "            input_is_string=True,\n",
+    "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
+    "            datetime_thresholds=[1, 1, 10],\n",
+    "        ),\n",
+    "        # TODO: Restore ctl.PostcodeComparison level here\n",
+    "        cl.LevenshteinAtThresholds(\"postcode_fake\"),\n",
+    "        cl.ExactMatch(\"birth_place\").configure(term_frequency_adjustments=True),\n",
+    "        cl.ExactMatch(\"occupation\").configure(term_frequency_adjustments=True),\n",
+    "    ],\n",
+    "    retain_intermediate_calculation_columns=True,\n",
+    ")\n",
+    "\n",
+    "linker = Linker(df, settings, database_api=db_api)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:29.494674Z",
+     "iopub.status.busy": "2024-06-07T09:09:29.494441Z",
+     "iopub.status.idle": "2024-06-07T09:09:29.778569Z",
+     "shell.execute_reply": "2024-06-07T09:09:29.778006Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Probability two random records match is estimated to be  0.000136.\n",
+      "This means that amongst all possible pairwise record comparisons, one in 7,362.31 are expected to match.  With 1,279,041,753 total possible comparisons, we expect a total of around 173,728.33 matching pairs\n"
+     ]
+    }
+   ],
+   "source": [
+    "linker.training.estimate_probability_two_random_records_match(\n",
+    "    [\n",
+    "        \"l.first_name = r.first_name and l.surname = r.surname and l.dob = r.dob\",\n",
+    "        \"substr(l.first_name,1,2) = substr(r.first_name,1,2) and l.surname = r.surname and substr(l.postcode_fake,1,2) = substr(r.postcode_fake,1,2)\",\n",
+    "        \"l.dob = r.dob and l.postcode_fake = r.postcode_fake\",\n",
+    "    ],\n",
+    "    recall=0.6,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:29.781900Z",
+     "iopub.status.busy": "2024-06-07T09:09:29.781630Z",
+     "iopub.status.idle": "2024-06-07T09:09:37.047025Z",
+     "shell.execute_reply": "2024-06-07T09:09:37.046527Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:05.117580Z",
-          "iopub.status.busy": "2024-05-15T16:07:05.117224Z",
-          "iopub.status.idle": "2024-05-15T16:07:05.620193Z",
-          "shell.execute_reply": "2024-05-15T16:07:05.619557Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "<style>\n",
-              "  #altair-viz-04b0c3cd9e0b44cb8f4f105e72f86332.vega-embed {\n",
-              "    width: 100%;\n",
-              "    display: flex;\n",
-              "  }\n",
-              "\n",
-              "  #altair-viz-04b0c3cd9e0b44cb8f4f105e72f86332.vega-embed details,\n",
-              "  #altair-viz-04b0c3cd9e0b44cb8f4f105e72f86332.vega-embed details summary {\n",
-              "    position: relative;\n",
-              "  }\n",
-              "</style>\n",
-              "<div id=\"altair-viz-04b0c3cd9e0b44cb8f4f105e72f86332\"></div>\n",
-              "<script type=\"text/javascript\">\n",
-              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-              "  (function(spec, embedOpt){\n",
-              "    let outputDiv = document.currentScript.previousElementSibling;\n",
-              "    if (outputDiv.id !== \"altair-viz-04b0c3cd9e0b44cb8f4f105e72f86332\") {\n",
-              "      outputDiv = document.getElementById(\"altair-viz-04b0c3cd9e0b44cb8f4f105e72f86332\");\n",
-              "    }\n",
-              "    const paths = {\n",
-              "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
-              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
-              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
-              "    };\n",
-              "\n",
-              "    function maybeLoadScript(lib, version) {\n",
-              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
-              "      return (VEGA_DEBUG[key] == version) ?\n",
-              "        Promise.resolve(paths[lib]) :\n",
-              "        new Promise(function(resolve, reject) {\n",
-              "          var s = document.createElement('script');\n",
-              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-              "          s.async = true;\n",
-              "          s.onload = () => {\n",
-              "            VEGA_DEBUG[key] = version;\n",
-              "            return resolve(paths[lib]);\n",
-              "          };\n",
-              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-              "          s.src = paths[lib];\n",
-              "        });\n",
-              "    }\n",
-              "\n",
-              "    function showError(err) {\n",
-              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-              "      throw err;\n",
-              "    }\n",
-              "\n",
-              "    function displayChart(vegaEmbed) {\n",
-              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-              "    }\n",
-              "\n",
-              "    if(typeof define === \"function\" && define.amd) {\n",
-              "      requirejs.config({paths});\n",
-              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-              "    } else {\n",
-              "      maybeLoadScript(\"vega\", \"5\")\n",
-              "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
-              "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
-              "        .catch(showError)\n",
-              "        .then(() => displayChart(vegaEmbed));\n",
-              "    }\n",
-              "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-8232b7c328acd1a8eec0ecfe8a013878\"}, \"mark\": \"bar\", \"encoding\": {\"order\": {\"field\": \"cumulative_rows\"}, \"tooltip\": [{\"field\": \"blocking_rule\", \"title\": \"SQL Condition\", \"type\": \"nominal\"}, {\"field\": \"row_count\", \"format\": \",\", \"title\": \"Comparisons Generated\", \"type\": \"quantitative\"}, {\"field\": \"cumulative_rows\", \"format\": \",\", \"title\": \"Cumulative Comparisons\", \"type\": \"quantitative\"}, {\"field\": \"cartesian\", \"format\": \",\", \"title\": \"Total comparisons in Cartesian product\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"start\", \"title\": \"Comparisons Generated by Rule(s)\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"cumulative_rows\"}, \"y\": {\"field\": \"blocking_rule\", \"sort\": [\"-x2\"], \"title\": \"SQL Blocking Rule\"}}, \"height\": {\"step\": 20}, \"title\": {\"text\": \"Count of Additional Comparisons Generated by Each Blocking Rule\", \"subtitle\": \"(Counts exclude comparisons already generated by previous rules)\"}, \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-8232b7c328acd1a8eec0ecfe8a013878\": [{\"blocking_rule\": \"(l.\\\"first_name\\\" = r.\\\"first_name\\\") AND (l.\\\"surname\\\" = r.\\\"surname\\\")\", \"row_count\": 243656, \"cumulative_rows\": 243656, \"cartesian\": 1279041753, \"match_key\": \"0\", \"start\": 0}, {\"blocking_rule\": \"(l.\\\"surname\\\" = r.\\\"surname\\\") AND (l.\\\"dob\\\" = r.\\\"dob\\\")\", \"row_count\": 25041, \"cumulative_rows\": 268697, \"cartesian\": 1279041753, \"match_key\": \"1\", \"start\": 243656}, {\"blocking_rule\": \"(l.\\\"first_name\\\" = r.\\\"first_name\\\") AND (l.\\\"dob\\\" = r.\\\"dob\\\")\", \"row_count\": 29905, \"cumulative_rows\": 298602, \"cartesian\": 1279041753, \"match_key\": \"2\", \"start\": 268697}, {\"blocking_rule\": \"(l.\\\"postcode_fake\\\" = r.\\\"postcode_fake\\\") AND (l.\\\"first_name\\\" = r.\\\"first_name\\\")\", \"row_count\": 8421, \"cumulative_rows\": 307023, \"cartesian\": 1279041753, \"match_key\": \"3\", \"start\": 298602}]}}, {\"mode\": \"vega-lite\"});\n",
-              "</script>"
-            ],
-            "text/plain": [
-              "alt.Chart(...)"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from splink import DuckDBAPI, block_on\n",
-        "from splink.blocking_analysis import (\n",
-        "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
-        ")\n",
-        "\n",
-        "blocking_rules =  [block_on(\"first_name\", \"surname\"),\n",
-        "        block_on(\"surname\", \"dob\"),\n",
-        "        block_on(\"first_name\", \"dob\"),\n",
-        "        block_on(\"postcode_fake\", \"first_name\")]\n",
-        "\n",
-        "db_api = DuckDBAPI()\n",
-        "\n",
-        "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
-        "    table_or_tables=df,\n",
-        "    blocking_rules=blocking_rules,\n",
-        "    db_api=db_api,\n",
-        "    link_type=\"dedupe_only\",\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:05.623477Z",
-          "iopub.status.busy": "2024-05-15T16:07:05.623213Z",
-          "iopub.status.idle": "2024-05-15T16:07:05.768956Z",
-          "shell.execute_reply": "2024-05-15T16:07:05.768275Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "import splink.comparison_library as cl\n",
-        "import splink.comparison_template_library as ctl\n",
-        "from splink import Linker, SettingsCreator\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    link_type=\"dedupe_only\",\n",
-        "    blocking_rules_to_generate_predictions=blocking_rules,\n",
-        "    comparisons=[\n",
-        "        ctl.NameComparison(\"first_name\").configure(term_frequency_adjustments=True),\n",
-        "        ctl.NameComparison(\"surname\").configure(term_frequency_adjustments=True),\n",
-        "        ctl.DateComparison(\n",
-        "            \"dob\",\n",
-        "            input_is_string=True,\n",
-        "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
-        "            datetime_thresholds=[1, 1, 10],\n",
-        "        ),\n",
-        "        # TODO: Restore ctl.PostcodeComparison level here\n",
-        "        cl.LevenshteinAtThresholds(\"postcode_fake\"),\n",
-        "        cl.ExactMatch(\"birth_place\").configure(term_frequency_adjustments=True),\n",
-        "        cl.ExactMatch(\"occupation\").configure(term_frequency_adjustments=True),\n",
-        "    ],\n",
-        "    retain_intermediate_calculation_columns=True,\n",
-        ")\n",
-        "\n",
-        "linker = Linker(df, settings, database_api=db_api)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - first_name (no m values are trained).\n",
+      "    - surname (no m values are trained).\n",
+      "    - dob (no m values are trained).\n",
+      "    - postcode_fake (no m values are trained).\n",
+      "    - birth_place (no m values are trained).\n",
+      "    - occupation (no m values are trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "linker.training.estimate_u_using_random_sampling(max_pairs=5e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:37.049884Z",
+     "iopub.status.busy": "2024-06-07T09:09:37.049671Z",
+     "iopub.status.idle": "2024-06-07T09:09:40.142517Z",
+     "shell.execute_reply": "2024-06-07T09:09:40.141723Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:05.772775Z",
-          "iopub.status.busy": "2024-05-15T16:07:05.772497Z",
-          "iopub.status.idle": "2024-05-15T16:07:06.084481Z",
-          "shell.execute_reply": "2024-05-15T16:07:06.083929Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Probability two random records match is estimated to be  0.000136.\n",
-            "This means that amongst all possible pairwise record comparisons, one in 7,362.31 are expected to match.  With 1,279,041,753 total possible comparisons, we expect a total of around 173,728.33 matching pairs\n"
-          ]
-        }
-      ],
-      "source": [
-        "linker.training.estimate_probability_two_random_records_match(\n",
-        "    [\n",
-        "        \"l.first_name = r.first_name and l.surname = r.surname and l.dob = r.dob\",\n",
-        "        \"substr(l.first_name,1,2) = substr(r.first_name,1,2) and l.surname = r.surname and substr(l.postcode_fake,1,2) = substr(r.postcode_fake,1,2)\",\n",
-        "        \"l.dob = r.dob and l.postcode_fake = r.postcode_fake\",\n",
-        "    ],\n",
-        "    recall=0.6,\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "(l.\"first_name\" = r.\"first_name\") AND (l.\"surname\" = r.\"surname\")\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - dob\n",
+      "    - postcode_fake\n",
+      "    - birth_place\n",
+      "    - occupation\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - first_name\n",
+      "    - surname\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:06.087609Z",
-          "iopub.status.busy": "2024-05-15T16:07:06.087384Z",
-          "iopub.status.idle": "2024-05-15T16:07:13.105199Z",
-          "shell.execute_reply": "2024-05-15T16:07:13.104695Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "----- Estimating u probabilities using random sampling -----\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Estimated u probabilities using random sampling\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Your model is not yet fully trained. Missing estimates for:\n",
-            "    - first_name (no m values are trained).\n",
-            "    - surname (no m values are trained).\n",
-            "    - dob (no m values are trained).\n",
-            "    - postcode_fake (no m values are trained).\n",
-            "    - birth_place (no m values are trained).\n",
-            "    - occupation (no m values are trained).\n"
-          ]
-        }
-      ],
-      "source": [
-        "linker.training.estimate_u_using_random_sampling(max_pairs=5e6)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:13.108034Z",
-          "iopub.status.busy": "2024-05-15T16:07:13.107820Z",
-          "iopub.status.idle": "2024-05-15T16:07:16.289385Z",
-          "shell.execute_reply": "2024-05-15T16:07:16.288708Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "----- Starting EM training session -----\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Estimating the m probabilities of the model by blocking on:\n",
-            "(l.\"first_name\" = r.\"first_name\") AND (l.\"surname\" = r.\"surname\")\n",
-            "\n",
-            "Parameter estimates will be made for the following comparison(s):\n",
-            "    - dob\n",
-            "    - postcode_fake\n",
-            "    - birth_place\n",
-            "    - occupation\n",
-            "\n",
-            "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
-            "    - first_name\n",
-            "    - surname\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 1: Largest change in params was -0.533 in probability_two_random_records_match\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 2: Largest change in params was -0.034 in probability_two_random_records_match\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 3: Largest change in params was 0.0136 in the m_probability of birth_place, level `Exact match on birth_place`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 4: Largest change in params was -0.00579 in the m_probability of birth_place, level `All other comparisons`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 5: Largest change in params was 0.00268 in the m_probability of birth_place, level `Exact match on birth_place`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 6: Largest change in params was 0.00129 in the m_probability of birth_place, level `Exact match on birth_place`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 7: Largest change in params was -0.000682 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 8: Largest change in params was -0.000373 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 9: Largest change in params was -0.000203 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 10: Largest change in params was -0.000111 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 11: Largest change in params was -6.05e-05 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "EM converged after 11 iterations\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Your model is not yet fully trained. Missing estimates for:\n",
-            "    - first_name (no m values are trained).\n",
-            "    - surname (no m values are trained).\n"
-          ]
-        }
-      ],
-      "source": [
-        "training_blocking_rule = block_on(\"first_name\", \"surname\")\n",
-        "training_session_names = linker.training.estimate_parameters_using_expectation_maximisation(\n",
-        "    training_blocking_rule\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.524 in probability_two_random_records_match\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:16.292730Z",
-          "iopub.status.busy": "2024-05-15T16:07:16.292472Z",
-          "iopub.status.idle": "2024-05-15T16:07:26.076237Z",
-          "shell.execute_reply": "2024-05-15T16:07:26.075402Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "----- Starting EM training session -----\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Estimating the m probabilities of the model by blocking on:\n",
-            "l.\"dob\" = r.\"dob\"\n",
-            "\n",
-            "Parameter estimates will be made for the following comparison(s):\n",
-            "    - first_name\n",
-            "    - surname\n",
-            "    - postcode_fake\n",
-            "    - birth_place\n",
-            "    - occupation\n",
-            "\n",
-            "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
-            "    - dob\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 1: Largest change in params was -0.362 in the m_probability of first_name, level `Exact match on first_name`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 2: Largest change in params was 0.0343 in the m_probability of first_name, level `All other comparisons`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 3: Largest change in params was 0.00489 in the m_probability of first_name, level `All other comparisons`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 4: Largest change in params was 0.00109 in the m_probability of surname, level `All other comparisons`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 5: Largest change in params was 0.000261 in the m_probability of surname, level `All other comparisons`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Iteration 6: Largest change in params was 6.1e-05 in the m_probability of surname, level `All other comparisons`\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "EM converged after 6 iterations\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
-          ]
-        }
-      ],
-      "source": [
-        "training_blocking_rule = block_on(\"dob\")\n",
-        "training_session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
-        "    training_blocking_rule\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was -0.0289 in probability_two_random_records_match\n"
+     ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The final match weights can be viewed in the match weights chart:\n"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was -0.0106 in the m_probability of birth_place, level `All other comparisons`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:26.079934Z",
-          "iopub.status.busy": "2024-05-15T16:07:26.079660Z",
-          "iopub.status.idle": "2024-05-15T16:07:26.364087Z",
-          "shell.execute_reply": "2024-05-15T16:07:26.363559Z"
-        }
-      },
-      "outputs": [
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false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.15675637973870843, \"log2_bayes_factor\": -2.673403935240592, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.38 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 4}, {\"comparison_name\": \"occupation\", \"sql_condition\": \"\\\"occupation_l\\\" = \\\"occupation_r\\\"\", \"label_for_charts\": \"Exact match on occupation\", \"m_probability\": 0.8989343034777655, \"u_probability\": 0.03736864790951383, \"m_probability_description\": \"Amongst matching record comparisons, 89.89% of records are in the exact match on occupation comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 3.74% of records are in the exact match on occupation comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"occupation\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 24.055842364285873, \"log2_bayes_factor\": 4.58831541409548, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on occupation` then comparison is 24.06 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 5}, {\"comparison_name\": \"occupation\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.10106569652223449, \"u_probability\": 0.9626313520904862, \"m_probability_description\": \"Amongst matching record comparisons, 10.11% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 96.26% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.10498899324518825, \"log2_bayes_factor\": -3.251690007214343, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  9.52 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 5}]}}, {\"mode\": \"vega-lite\"});\n",
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-            "text/plain": [
-              "alt.VConcatChart(...)"
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-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "linker.visualisations.match_weights_chart()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.00439 in the m_probability of birth_place, level `Exact match on birth_place`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:26.367083Z",
-          "iopub.status.busy": "2024-05-15T16:07:26.366860Z",
-          "iopub.status.idle": "2024-05-15T16:07:28.387226Z",
-          "shell.execute_reply": "2024-05-15T16:07:28.386186Z"
-        }
-      },
-      "outputs": [
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\"match_probability\": 0.99991, \"prop\": 0.0014037723885849118, \"cum_prop\": 0.054272608325845795}, {\"match_weight\": 13.7, \"match_probability\": 0.99992, \"prop\": 0.0016014868160709739, \"cum_prop\": 0.05587409514191677}, {\"match_weight\": 13.91, \"match_probability\": 0.99993, \"prop\": 0.002333030104637146, \"cum_prop\": 0.058207125246553915}, {\"match_weight\": 14.15, \"match_probability\": 0.99994, \"prop\": 0.002135315677151084, \"cum_prop\": 0.060342440923705}, {\"match_weight\": 14.43, \"match_probability\": 0.99995, \"prop\": 0.0036774883046746254, \"cum_prop\": 0.06401992922837962}, {\"match_weight\": 14.8, \"match_probability\": 0.99996, \"prop\": 0.00340068805962801, \"cum_prop\": 0.06742061728800763}, {\"match_weight\": 15.29, \"match_probability\": 0.99997, \"prop\": 0.005140575114637613, \"cum_prop\": 0.07256119240264525}, {\"match_weight\": 16.02, \"match_probability\": 0.99998, \"prop\": 0.00836331956088543, \"cum_prop\": 0.08092451196353068}, {\"match_weight\": 17.61, \"match_probability\": 0.99999, \"prop\": 0.019929613918066025, \"cum_prop\": 0.1008541258815967}]}}, {\"mode\": \"vega-lite\"});\n",
-              "</script>"
-            ],
-            "text/plain": [
-              "alt.LayerChart(...)"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "linker.evaluation.unlinkables_chart()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was -0.00199 in the m_probability of birth_place, level `All other comparisons`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:28.393039Z",
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-          "shell.execute_reply": "2024-05-15T16:07:30.730612Z"
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-      "outputs": [
-        {
-          "data": {
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-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "\n",
-              "    .dataframe tbody tr th {\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>match_weight</th>\n",
-              "      <th>match_probability</th>\n",
-              "      <th>unique_id_l</th>\n",
-              "      <th>unique_id_r</th>\n",
-              "      <th>first_name_l</th>\n",
-              "      <th>first_name_r</th>\n",
-              "      <th>gamma_first_name</th>\n",
-              "      <th>tf_first_name_l</th>\n",
-              "      <th>tf_first_name_r</th>\n",
-              "      <th>bf_first_name</th>\n",
-              "      <th>...</th>\n",
-              "      <th>bf_birth_place</th>\n",
-              "      <th>bf_tf_adj_birth_place</th>\n",
-              "      <th>occupation_l</th>\n",
-              "      <th>occupation_r</th>\n",
-              "      <th>gamma_occupation</th>\n",
-              "      <th>tf_occupation_l</th>\n",
-              "      <th>tf_occupation_r</th>\n",
-              "      <th>bf_occupation</th>\n",
-              "      <th>bf_tf_adj_occupation</th>\n",
-              "      <th>match_key</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>-15.840427</td>\n",
-              "      <td>0.000017</td>\n",
-              "      <td>Q5971253-3</td>\n",
-              "      <td>Q75867928-4</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>44.906565</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.156756</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>naval officer</td>\n",
-              "      <td>military officer</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.009451</td>\n",
-              "      <td>0.010756</td>\n",
-              "      <td>0.104989</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
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-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>-15.840427</td>\n",
-              "      <td>0.000017</td>\n",
-              "      <td>Q5971253-3</td>\n",
-              "      <td>Q75867928-7</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>44.906565</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.156756</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>naval officer</td>\n",
-              "      <td>military officer</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.009451</td>\n",
-              "      <td>0.010756</td>\n",
-              "      <td>0.104989</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
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-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>-15.840427</td>\n",
-              "      <td>0.000017</td>\n",
-              "      <td>Q5971253-2</td>\n",
-              "      <td>Q75867928-4</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>44.906565</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.156756</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>naval officer</td>\n",
-              "      <td>military officer</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.009451</td>\n",
-              "      <td>0.010756</td>\n",
-              "      <td>0.104989</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>-15.840427</td>\n",
-              "      <td>0.000017</td>\n",
-              "      <td>Q5971253-2</td>\n",
-              "      <td>Q75867928-7</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>44.906565</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.156756</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>naval officer</td>\n",
-              "      <td>military officer</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.009451</td>\n",
-              "      <td>0.010756</td>\n",
-              "      <td>0.104989</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>-15.840427</td>\n",
-              "      <td>0.000017</td>\n",
-              "      <td>Q5971253-1</td>\n",
-              "      <td>Q75867928-4</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>sir</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>0.024985</td>\n",
-              "      <td>44.906565</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.156756</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>naval officer</td>\n",
-              "      <td>military officer</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.009451</td>\n",
-              "      <td>0.010756</td>\n",
-              "      <td>0.104989</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>5 rows × 41 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   match_weight  match_probability unique_id_l  unique_id_r first_name_l  \\\n",
-              "0    -15.840427           0.000017  Q5971253-3  Q75867928-4          sir   \n",
-              "1    -15.840427           0.000017  Q5971253-3  Q75867928-7          sir   \n",
-              "2    -15.840427           0.000017  Q5971253-2  Q75867928-4          sir   \n",
-              "3    -15.840427           0.000017  Q5971253-2  Q75867928-7          sir   \n",
-              "4    -15.840427           0.000017  Q5971253-1  Q75867928-4          sir   \n",
-              "\n",
-              "  first_name_r  gamma_first_name  tf_first_name_l  tf_first_name_r  \\\n",
-              "0          sir                 3         0.024985         0.024985   \n",
-              "1          sir                 3         0.024985         0.024985   \n",
-              "2          sir                 3         0.024985         0.024985   \n",
-              "3          sir                 3         0.024985         0.024985   \n",
-              "4          sir                 3         0.024985         0.024985   \n",
-              "\n",
-              "   bf_first_name  ...  bf_birth_place bf_tf_adj_birth_place   occupation_l  \\\n",
-              "0      44.906565  ...        0.156756                   1.0  naval officer   \n",
-              "1      44.906565  ...        0.156756                   1.0  naval officer   \n",
-              "2      44.906565  ...        0.156756                   1.0  naval officer   \n",
-              "3      44.906565  ...        0.156756                   1.0  naval officer   \n",
-              "4      44.906565  ...        0.156756                   1.0  naval officer   \n",
-              "\n",
-              "       occupation_r  gamma_occupation  tf_occupation_l  tf_occupation_r  \\\n",
-              "0  military officer                 0         0.009451         0.010756   \n",
-              "1  military officer                 0         0.009451         0.010756   \n",
-              "2  military officer                 0         0.009451         0.010756   \n",
-              "3  military officer                 0         0.009451         0.010756   \n",
-              "4  military officer                 0         0.009451         0.010756   \n",
-              "\n",
-              "   bf_occupation bf_tf_adj_occupation match_key  \n",
-              "0       0.104989                  1.0         0  \n",
-              "1       0.104989                  1.0         0  \n",
-              "2       0.104989                  1.0         0  \n",
-              "3       0.104989                  1.0         0  \n",
-              "4       0.104989                  1.0         0  \n",
-              "\n",
-              "[5 rows x 41 columns]"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df_predict = linker.inference.predict()\n",
-        "df_e = df_predict.as_pandas_dataframe(limit=5)\n",
-        "df_e"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was -0.000951 in the m_probability of birth_place, level `All other comparisons`\n"
+     ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also view rows in this dataset as a waterfall chart as follows:\n"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was -0.000495 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was -0.000267 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was -0.000144 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was -7.75e-05 in the m_probability of dob, level `Abs difference of 'transformed dob <= 10 year'`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 10 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - first_name (no m values are trained).\n",
+      "    - surname (no m values are trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_blocking_rule = block_on(\"first_name\", \"surname\")\n",
+    "training_session_names = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+    "    training_blocking_rule\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:40.146047Z",
+     "iopub.status.busy": "2024-06-07T09:09:40.145755Z",
+     "iopub.status.idle": "2024-06-07T09:09:49.809293Z",
+     "shell.execute_reply": "2024-06-07T09:09:49.808482Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"dob\" = r.\"dob\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - first_name\n",
+      "    - surname\n",
+      "    - postcode_fake\n",
+      "    - birth_place\n",
+      "    - occupation\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - dob\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.362 in the m_probability of first_name, level `Exact match on first_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.0336 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 0.00479 in the m_probability of first_name, level `All other comparisons`\n"
+     ]
     },
     {
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-          "shell.execute_reply": "2024-05-15T16:07:31.360879Z"
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-              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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-              "    if(typeof define === \"function\" && define.amd) {\n",
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\"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 4}]}}, {\"mode\": \"vega-lite\"});\n",
-              "</script>"
-            ],
-            "text/plain": [
-              "alt.LayerChart(...)"
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.00106 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was 0.000254 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was 5.93e-05 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 6 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_blocking_rule = block_on(\"dob\")\n",
+    "training_session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+    "    training_blocking_rule\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final match weights can be viewed in the match weights chart:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:49.813047Z",
+     "iopub.status.busy": "2024-06-07T09:09:49.812768Z",
+     "iopub.status.idle": "2024-06-07T09:09:50.107589Z",
+     "shell.execute_reply": "2024-06-07T09:09:50.106949Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-2d8e4c090b4346dea97561b47e263662.vega-embed {\n",
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+       "    display: flex;\n",
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+       "</style>\n",
+       "<div id=\"altair-viz-2d8e4c090b4346dea97561b47e263662\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-2d8e4c090b4346dea97561b47e263662\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-2d8e4c090b4346dea97561b47e263662\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"discreteHeight\": 60, \"discreteWidth\": 400}, \"header\": {\"title\": null}, \"mark\": {\"tooltip\": null}, \"title\": {\"anchor\": \"middle\"}}, \"vconcat\": [{\"mark\": {\"type\": \"bar\", \"clip\": true, \"height\": 15}, \"encoding\": {\"color\": {\"field\": \"log2_bayes_factor\", \"scale\": {\"domain\": [-10, 0, 10], \"interpolate\": \"lab\", \"range\": [\"red\", \"#bbbbbb\", \"green\"]}, \"title\": \"Match weight\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"comparison_name\", \"title\": \"Comparison name\", \"type\": \"nominal\"}, {\"field\": \"probability_two_random_records_match\", \"format\": \".4f\", \"title\": \"Probability two random records match\", \"type\": \"nominal\"}, {\"field\": \"log2_bayes_factor\", \"format\": \",.4f\", \"title\": \"Equivalent match weight\", \"type\": \"quantitative\"}, {\"field\": \"bayes_factor_description\", \"title\": \"Match weight 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3.462017226277954, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.8` then comparison is 11.02 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.06694967393729967, \"u_probability\": 0.9956450257234429, \"m_probability_description\": \"Amongst matching record comparisons, 6.69% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.56% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.06724251335324409, \"log2_bayes_factor\": -3.894482539940456, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  14.87 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 1}, {\"comparison_name\": \"dob\", \"sql_condition\": \"\\\"dob_l\\\" = \\\"dob_r\\\"\", \"label_for_charts\": \"Exact match on dob\", \"m_probability\": 0.6137512443354165, \"u_probability\": 0.001891143163086508, \"m_probability_description\": \"Amongst matching record comparisons, 61.38% of records are in the exact match on dob comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.19% of records are in the exact match on dob comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 324.5398107955623, \"log2_bayes_factor\": 8.342251652019065, \"comparison_vector_value\": 5, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `exact match on dob` then comparison is 324.54 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 2}, {\"comparison_name\": \"dob\", \"sql_condition\": \"damerau_levenshtein(\\\"dob_l\\\", \\\"dob_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of dob <= 1\", \"m_probability\": 0.3396908201996453, \"u_probability\": 0.019230847232137062, \"m_probability_description\": \"Amongst matching record comparisons, 33.97% of records are in the damerau-levenshtein distance of dob <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.92% of records are in the damerau-levenshtein distance of dob <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, 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records are in the abs difference of 'transformed dob <= 1 month' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 1.1742872164743157, \"log2_bayes_factor\": 0.2317853173956578, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed dob <= 1 month'` then comparison is 1.17 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 2}, {\"comparison_name\": \"dob\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"dob_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"dob_r\\\", '%Y-%m-%d'))) <= 31557600.0\", \"label_for_charts\": \"Abs difference of 'transformed dob <= 1 year'\", \"m_probability\": 0.004710483499763359, \"u_probability\": 0.02643442955415666, \"m_probability_description\": \"Amongst matching record comparisons, 0.47% of records are in the abs difference of 'transformed dob <= 1 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 2.64% of records are in the abs difference of 'transformed dob <= 1 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.1781950123082063, \"log2_bayes_factor\": -2.4884711386050786, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed dob <= 1 year'` then comparison is  5.61 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 2}, {\"comparison_name\": \"dob\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"dob_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"dob_r\\\", '%Y-%m-%d'))) <= 315576000.0\", \"label_for_charts\": \"Abs difference of 'transformed dob <= 10 year'\", \"m_probability\": 0.013464734404494298, \"u_probability\": 0.20047773757760617, \"m_probability_description\": \"Amongst matching record comparisons, 1.35% of records are in the abs difference of 'transformed dob <= 10 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 20.05% of records are in the abs difference of 'transformed dob <= 10 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.0671632400045517, \"log2_bayes_factor\": -3.896184361141375, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed dob <= 10 year'` then comparison is  14.89 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 2}, {\"comparison_name\": \"dob\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.026077429203334265, \"u_probability\": 0.7500027040474182, \"m_probability_description\": \"Amongst matching record comparisons, 2.61% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 75.00% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.03476978024559968, \"log2_bayes_factor\": -4.84602224090261, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  28.76 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 2}, {\"comparison_name\": \"postcode_fake\", \"sql_condition\": \"\\\"postcode_fake_l\\\" = \\\"postcode_fake_r\\\"\", \"label_for_charts\": \"Exact match on postcode_fake\", \"m_probability\": 0.6870506533855032, \"u_probability\": 0.00014536287350888695, \"m_probability_description\": \"Amongst matching record comparisons, 68.71% of records are in the exact match on postcode_fake comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.01% of records are in the exact match on postcode_fake comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4726.452063040013, \"log2_bayes_factor\": 12.206541907608122, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode_fake` then comparison is 4,726.45 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 3}, {\"comparison_name\": \"postcode_fake\", \"sql_condition\": \"levenshtein(\\\"postcode_fake_l\\\", \\\"postcode_fake_r\\\") <= 1\", \"label_for_charts\": \"Levenshtein distance of postcode_fake <= 1\", \"m_probability\": 0.08641184751415396, \"u_probability\": 8.199956967167982e-05, \"m_probability_description\": \"Amongst matching record comparisons, 8.64% of records are in the levenshtein distance of postcode_fake <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.01% of records are in the levenshtein distance of postcode_fake <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 1053.8085487538601, \"log2_bayes_factor\": 10.041397073030216, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `levenshtein distance of postcode_fake <= 1` then comparison is 1,053.81 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 3}, {\"comparison_name\": \"postcode_fake\", \"sql_condition\": \"levenshtein(\\\"postcode_fake_l\\\", \\\"postcode_fake_r\\\") <= 2\", \"label_for_charts\": \"Levenshtein distance of postcode_fake <= 2\", \"m_probability\": 0.056075932948771445, \"u_probability\": 0.0005255426965321298, \"m_probability_description\": \"Amongst matching record comparisons, 5.61% of records are in the levenshtein distance of postcode_fake <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.05% of records are in the levenshtein distance of postcode_fake <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 106.70100320829626, \"log2_bayes_factor\": 6.737429930293483, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `levenshtein distance of postcode_fake <= 2` then comparison is 106.70 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 3}, {\"comparison_name\": \"postcode_fake\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.17046156615157143, \"u_probability\": 0.9992470948602873, \"m_probability_description\": \"Amongst matching record comparisons, 17.05% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.17059000424254925, \"log2_bayes_factor\": -2.5513949807762453, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.86 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 3}, {\"comparison_name\": \"birth_place\", \"sql_condition\": \"\\\"birth_place_l\\\" = \\\"birth_place_r\\\"\", \"label_for_charts\": \"Exact match on birth_place\", \"m_probability\": 0.8438248944830462, \"u_probability\": 0.005352664822329286, \"m_probability_description\": \"Amongst matching record comparisons, 84.38% of records are in the exact match on birth_place comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.54% of records are in the exact match on birth_place comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"birth_place\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 157.64575636474171, \"log2_bayes_factor\": 7.300542524693252, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on birth_place` then comparison is 157.65 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 4}, {\"comparison_name\": \"birth_place\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.1561751055169538, \"u_probability\": 0.9946473351776707, \"m_probability_description\": \"Amongst matching record comparisons, 15.62% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.1570155571663566, \"log2_bayes_factor\": -2.671020585867125, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.37 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 4}, {\"comparison_name\": \"occupation\", \"sql_condition\": \"\\\"occupation_l\\\" = \\\"occupation_r\\\"\", \"label_for_charts\": \"Exact match on occupation\", \"m_probability\": 0.8988237439900274, \"u_probability\": 0.03667032589185728, \"m_probability_description\": \"Amongst matching record comparisons, 89.88% of records are in the exact match on occupation comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 3.67% of records are in the exact match on occupation comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"occupation\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 24.510928717696864, \"log2_bayes_factor\": 4.615353243760895, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on occupation` then comparison is 24.51 times more likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 5}, {\"comparison_name\": \"occupation\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.10117625600997256, \"u_probability\": 0.9633296741081427, \"m_probability_description\": \"Amongst matching record comparisons, 10.12% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 96.33% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.10502765432160308, \"log2_bayes_factor\": -3.251158847951538, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  9.52 times less likely to be a match\", \"probability_two_random_records_match\": 0.00013582694460587586, \"comparison_sort_order\": 5}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "source": [
-        "\n",
-        "records_to_plot = df_e.to_dict(orient=\"records\")\n",
-        "linker.visualisations.waterfall_chart(records_to_plot, filter_nulls=False)"
+      "text/plain": [
+       "alt.VConcatChart(...)"
       ]
-    },
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.visualisations.match_weights_chart()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:50.110966Z",
+     "iopub.status.busy": "2024-06-07T09:09:50.110700Z",
+     "iopub.status.idle": "2024-06-07T09:09:52.080683Z",
+     "shell.execute_reply": "2024-06-07T09:09:52.080017Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:31.364481Z",
-          "iopub.status.busy": "2024-05-15T16:07:31.364255Z",
-          "iopub.status.idle": "2024-05-15T16:07:31.746356Z",
-          "shell.execute_reply": "2024-05-15T16:07:31.745671Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Completed iteration 1, root rows count 625\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Completed iteration 2, root rows count 93\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Completed iteration 3, root rows count 19\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Completed iteration 4, root rows count 4\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Completed iteration 5, root rows count 0\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-a01a24c47dd94853a01167d1e29c13ab.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-a01a24c47dd94853a01167d1e29c13ab.vega-embed details,\n",
+       "  #altair-viz-a01a24c47dd94853a01167d1e29c13ab.vega-embed details summary {\n",
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+       "</style>\n",
+       "<div id=\"altair-viz-a01a24c47dd94853a01167d1e29c13ab\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-a01a24c47dd94853a01167d1e29c13ab\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-a01a24c47dd94853a01167d1e29c13ab\");\n",
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+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
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+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"line\"}, \"encoding\": {\"x\": {\"axis\": {\"format\": \"+\", \"title\": \"Threshold match weight\"}, \"field\": \"match_weight\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"format\": \"%\", \"title\": \"Percentage of unlinkable records\"}, \"field\": \"cum_prop\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"point\"}, \"encoding\": {\"opacity\": {\"condition\": {\"param\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"value\": 1, \"empty\": false}, \"value\": 0}, \"tooltip\": [{\"field\": \"match_weight\", \"format\": \"+.5\", \"title\": \"Match weight\", \"type\": \"quantitative\"}, {\"field\": \"match_probability\", \"format\": \".5\", \"title\": \"Match probability\", \"type\": \"quantitative\"}, {\"field\": \"cum_prop\", \"format\": \".3%\", \"title\": \"Proportion of unlinkable records\", \"type\": \"quantitative\"}], \"x\": 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{\"match_weight\": 11.83, \"match_probability\": 0.99972, \"prop\": 0.0002965716412290931, \"cum_prop\": 0.03910791215457721}, {\"match_weight\": 11.88, \"match_probability\": 0.99973, \"prop\": 0.0003954288549721241, \"cum_prop\": 0.03950334100954933}, {\"match_weight\": 11.93, \"match_probability\": 0.99974, \"prop\": 0.0004942860687151551, \"cum_prop\": 0.039997627078264486}, {\"match_weight\": 11.99, \"match_probability\": 0.99975, \"prop\": 0.0003954288549721241, \"cum_prop\": 0.04039305593323661}, {\"match_weight\": 12.05, \"match_probability\": 0.99976, \"prop\": 0.0004942860687151551, \"cum_prop\": 0.040887342001951765}, {\"match_weight\": 12.12, \"match_probability\": 0.99977, \"prop\": 0.0006920004962012172, \"cum_prop\": 0.04157934249815298}, {\"match_weight\": 12.18, \"match_probability\": 0.99978, \"prop\": 0.0009094863198697567, \"cum_prop\": 0.04248882881802274}, {\"match_weight\": 12.25, \"match_probability\": 0.99979, \"prop\": 0.0008304005605168641, \"cum_prop\": 0.0433192293785396}, {\"match_weight\": 12.32, \"match_probability\": 0.9998, \"prop\": 0.0003756574005819857, \"cum_prop\": 0.04369488677912159}, {\"match_weight\": 12.4, \"match_probability\": 0.99981, \"prop\": 0.0005140575231052935, \"cum_prop\": 0.04420894430222688}, {\"match_weight\": 12.48, \"match_probability\": 0.99982, \"prop\": 0.0003954288549721241, \"cum_prop\": 0.044604373157199007}, {\"match_weight\": 12.56, \"match_probability\": 0.99983, \"prop\": 0.0008106291061267257, \"cum_prop\": 0.04541500226332573}, {\"match_weight\": 12.65, \"match_probability\": 0.99984, \"prop\": 0.0010083435336127877, \"cum_prop\": 0.04642334579693852}, {\"match_weight\": 12.74, \"match_probability\": 0.99985, \"prop\": 0.0005536003736779094, \"cum_prop\": 0.04697694617061643}, {\"match_weight\": 12.85, \"match_probability\": 0.99986, \"prop\": 0.0009292577742598951, \"cum_prop\": 0.047906203944876324}, {\"match_weight\": 12.96, \"match_probability\": 0.99987, \"prop\": 0.0010478864423930645, \"cum_prop\": 0.04895409038726939}, {\"match_weight\": 13.09, \"match_probability\": 0.99988, \"prop\": 0.0008501720149070024, \"cum_prop\": 0.04980426240217639}, {\"match_weight\": 13.22, \"match_probability\": 0.99989, \"prop\": 0.0016805726336315274, \"cum_prop\": 0.05148483503580792}, {\"match_weight\": 13.36, \"match_probability\": 0.9999, \"prop\": 0.0011269721435382962, \"cum_prop\": 0.052611807179346215}, {\"match_weight\": 13.52, \"match_probability\": 0.99991, \"prop\": 0.001561943907290697, \"cum_prop\": 0.05417375108663691}, {\"match_weight\": 13.7, \"match_probability\": 0.99992, \"prop\": 0.0015224009985104203, \"cum_prop\": 0.05569615208514733}, {\"match_weight\": 13.91, \"match_probability\": 0.99993, \"prop\": 0.0018585155485197902, \"cum_prop\": 0.05755466763366712}, {\"match_weight\": 14.14, \"match_probability\": 0.99994, \"prop\": 0.0032820594497025013, \"cum_prop\": 0.060836727083369624}, {\"match_weight\": 14.44, \"match_probability\": 0.99995, \"prop\": 0.0028470875695347786, \"cum_prop\": 0.0636838146529044}, {\"match_weight\": 14.8, \"match_probability\": 0.99996, \"prop\": 0.0032425164245069027, \"cum_prop\": 0.0669263310774113}, {\"match_weight\": 15.29, \"match_probability\": 0.99997, \"prop\": 0.005338289309293032, \"cum_prop\": 0.07226462038670434}, {\"match_weight\": 16.02, \"match_probability\": 0.99998, \"prop\": 0.008442405611276627, \"cum_prop\": 0.08070702599798096}, {\"match_weight\": 17.61, \"match_probability\": 0.99999, \"prop\": 0.019494641572237015, \"cum_prop\": 0.10020166757021798}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "source": [
-        "clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(\n",
-        "    df_predict, threshold_match_probability=0.95\n",
-        ")"
+      "text/plain": [
+       "alt.LayerChart(...)"
       ]
-    },
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.evaluation.unlinkables_chart()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:52.085817Z",
+     "iopub.status.busy": "2024-06-07T09:09:52.085551Z",
+     "iopub.status.idle": "2024-06-07T09:09:54.516650Z",
+     "shell.execute_reply": "2024-06-07T09:09:54.515921Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:31.749625Z",
-          "iopub.status.busy": "2024-05-15T16:07:31.749370Z",
-          "iopub.status.idle": "2024-05-15T16:07:31.898014Z",
-          "shell.execute_reply": "2024-05-15T16:07:31.897301Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "        <iframe\n",
-              "            width=\"100%\"\n",
-              "            height=\"1200\"\n",
-              "            src=\"./dashboards/50k_cluster.html\"\n",
-              "            frameborder=\"0\"\n",
-              "            allowfullscreen\n",
-              "            \n",
-              "        ></iframe>\n",
-              "        "
-            ],
-            "text/plain": [
-              "<IPython.lib.display.IFrame at 0x12b838ca0>"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>match_weight</th>\n",
+       "      <th>match_probability</th>\n",
+       "      <th>unique_id_l</th>\n",
+       "      <th>unique_id_r</th>\n",
+       "      <th>first_name_l</th>\n",
+       "      <th>first_name_r</th>\n",
+       "      <th>gamma_first_name</th>\n",
+       "      <th>tf_first_name_l</th>\n",
+       "      <th>tf_first_name_r</th>\n",
+       "      <th>bf_first_name</th>\n",
+       "      <th>...</th>\n",
+       "      <th>bf_birth_place</th>\n",
+       "      <th>bf_tf_adj_birth_place</th>\n",
+       "      <th>occupation_l</th>\n",
+       "      <th>occupation_r</th>\n",
+       "      <th>gamma_occupation</th>\n",
+       "      <th>tf_occupation_l</th>\n",
+       "      <th>tf_occupation_r</th>\n",
+       "      <th>bf_occupation</th>\n",
+       "      <th>bf_tf_adj_occupation</th>\n",
+       "      <th>match_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-15.829333</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>Q7528564-9</td>\n",
+       "      <td>Q75867928-1</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>38.34881</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.157016</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>historian</td>\n",
+       "      <td>military officer</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.012456</td>\n",
+       "      <td>0.010756</td>\n",
+       "      <td>0.105028</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-15.829333</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>Q7528564-9</td>\n",
+       "      <td>Q75867928-2</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>38.34881</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.157016</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>historian</td>\n",
+       "      <td>military officer</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.012456</td>\n",
+       "      <td>0.010756</td>\n",
+       "      <td>0.105028</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-15.829333</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>Q7528564-9</td>\n",
+       "      <td>Q75867928-3</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>38.34881</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.157016</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>historian</td>\n",
+       "      <td>military officer</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.012456</td>\n",
+       "      <td>0.010756</td>\n",
+       "      <td>0.105028</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-15.829333</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>Q7528564-9</td>\n",
+       "      <td>Q75867928-4</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>38.34881</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.157016</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>historian</td>\n",
+       "      <td>military officer</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.012456</td>\n",
+       "      <td>0.010756</td>\n",
+       "      <td>0.105028</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-15.829333</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>Q7528564-9</td>\n",
+       "      <td>Q75867928-6</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>sir</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>0.024985</td>\n",
+       "      <td>38.34881</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.157016</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>historian</td>\n",
+       "      <td>military officer</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.012456</td>\n",
+       "      <td>0.010756</td>\n",
+       "      <td>0.105028</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "from IPython.display import IFrame\n",
-        "\n",
-        "linker.visualisations.cluster_studio_dashboard(\n",
-        "    df_predict,\n",
-        "    clusters,\n",
-        "    \"dashboards/50k_cluster.html\",\n",
-        "    sampling_method=\"by_cluster_size\",\n",
-        "    overwrite=True,\n",
-        ")\n",
-        "\n",
-        "\n",
-        "IFrame(src=\"./dashboards/50k_cluster.html\", width=\"100%\", height=1200)"
+      "text/plain": [
+       "   match_weight  match_probability unique_id_l  unique_id_r first_name_l  \\\n",
+       "0    -15.829333           0.000017  Q7528564-9  Q75867928-1          sir   \n",
+       "1    -15.829333           0.000017  Q7528564-9  Q75867928-2          sir   \n",
+       "2    -15.829333           0.000017  Q7528564-9  Q75867928-3          sir   \n",
+       "3    -15.829333           0.000017  Q7528564-9  Q75867928-4          sir   \n",
+       "4    -15.829333           0.000017  Q7528564-9  Q75867928-6          sir   \n",
+       "\n",
+       "  first_name_r  gamma_first_name  tf_first_name_l  tf_first_name_r  \\\n",
+       "0          sir                 3         0.024985         0.024985   \n",
+       "1          sir                 3         0.024985         0.024985   \n",
+       "2          sir                 3         0.024985         0.024985   \n",
+       "3          sir                 3         0.024985         0.024985   \n",
+       "4          sir                 3         0.024985         0.024985   \n",
+       "\n",
+       "   bf_first_name  ...  bf_birth_place bf_tf_adj_birth_place occupation_l  \\\n",
+       "0       38.34881  ...        0.157016                   1.0    historian   \n",
+       "1       38.34881  ...        0.157016                   1.0    historian   \n",
+       "2       38.34881  ...        0.157016                   1.0    historian   \n",
+       "3       38.34881  ...        0.157016                   1.0    historian   \n",
+       "4       38.34881  ...        0.157016                   1.0    historian   \n",
+       "\n",
+       "       occupation_r  gamma_occupation  tf_occupation_l  tf_occupation_r  \\\n",
+       "0  military officer                 0         0.012456         0.010756   \n",
+       "1  military officer                 0         0.012456         0.010756   \n",
+       "2  military officer                 0         0.012456         0.010756   \n",
+       "3  military officer                 0         0.012456         0.010756   \n",
+       "4  military officer                 0         0.012456         0.010756   \n",
+       "\n",
+       "   bf_occupation bf_tf_adj_occupation match_key  \n",
+       "0       0.105028                  1.0         0  \n",
+       "1       0.105028                  1.0         0  \n",
+       "2       0.105028                  1.0         0  \n",
+       "3       0.105028                  1.0         0  \n",
+       "4       0.105028                  1.0         0  \n",
+       "\n",
+       "[5 rows x 41 columns]"
       ]
-    },
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_predict = linker.inference.predict()\n",
+    "df_e = df_predict.as_pandas_dataframe(limit=5)\n",
+    "df_e"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also view rows in this dataset as a waterfall chart as follows:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:54.520577Z",
+     "iopub.status.busy": "2024-06-07T09:09:54.520273Z",
+     "iopub.status.idle": "2024-06-07T09:09:55.151653Z",
+     "shell.execute_reply": "2024-06-07T09:09:55.150935Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:31.901400Z",
-          "iopub.status.busy": "2024-05-15T16:07:31.901154Z",
-          "iopub.status.idle": "2024-05-15T16:07:44.228710Z",
-          "shell.execute_reply": "2024-05-15T16:07:44.227315Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "<style>\n",
-              "  #altair-viz-71296cea16554d339cdb18eb244e7549.vega-embed {\n",
-              "    width: 100%;\n",
-              "    display: flex;\n",
-              "  }\n",
-              "\n",
-              "  #altair-viz-71296cea16554d339cdb18eb244e7549.vega-embed details,\n",
-              "  #altair-viz-71296cea16554d339cdb18eb244e7549.vega-embed details summary {\n",
-              "    position: relative;\n",
-              "  }\n",
-              "</style>\n",
-              "<div id=\"altair-viz-71296cea16554d339cdb18eb244e7549\"></div>\n",
-              "<script type=\"text/javascript\">\n",
-              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-              "  (function(spec, embedOpt){\n",
-              "    let outputDiv = document.currentScript.previousElementSibling;\n",
-              "    if (outputDiv.id !== \"altair-viz-71296cea16554d339cdb18eb244e7549\") {\n",
-              "      outputDiv = document.getElementById(\"altair-viz-71296cea16554d339cdb18eb244e7549\");\n",
-              "    }\n",
-              "    const paths = {\n",
-              "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
-              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
-              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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-              "\n",
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-              "alt.Chart(...)"
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+       "  #altair-viz-8d620cac45c8488a8ffe95fec42c0393.vega-embed details summary {\n",
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\"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 4}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "source": [
-        "linker.accuracy_analysis_from_labels_column(\n",
-        "    \"cluster\", output_type=\"roc\", match_weight_round_to_nearest=0.02\n",
-        ")"
+      "text/plain": [
+       "alt.LayerChart(...)"
       ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "records_to_plot = df_e.to_dict(orient=\"records\")\n",
+    "linker.visualisations.waterfall_chart(records_to_plot, filter_nulls=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:55.155050Z",
+     "iopub.status.busy": "2024-06-07T09:09:55.154811Z",
+     "iopub.status.idle": "2024-06-07T09:09:55.525689Z",
+     "shell.execute_reply": "2024-06-07T09:09:55.524936Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 1, root rows count 623\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 2, root rows count 100\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 3, root rows count 22\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 4, root rows count 4\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:44.268428Z",
-          "iopub.status.busy": "2024-05-15T16:07:44.268099Z",
-          "iopub.status.idle": "2024-05-15T16:07:47.826572Z",
-          "shell.execute_reply": "2024-05-15T16:07:47.826055Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
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surname` then comparison is 1,252.36 times more likely to be a match\", \"column_name\": \"surname\", \"value_l\": \"cartwright\", \"value_r\": \"cartwright\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 53}, {\"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Term freq adjustment on surname with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 0.7179905696918997, \"log2_bayes_factor\": -0.47796319951323696, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"Term frequency adjustment on surname makes comparison  1.39 times less likely to be a match\", \"column_name\": \"tf_surname\", \"value_l\": \"cartwright\", \"value_r\": \"cartwright\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 4, \"record_number\": 53}, {\"sql_condition\": \"damerau_levenshtein(\\\"dob_l\\\", \\\"dob_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein 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to be a match\", \"column_name\": \"postcode_fake\", \"value_l\": \"sy10 8ra\", \"value_r\": \"sy10 8ra\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 53}, {\"sql_condition\": \"\\\"birth_place_l\\\" = \\\"birth_place_r\\\"\", \"label_for_charts\": \"Exact match on birth_place\", \"m_probability\": 0.8440643138505439, \"u_probability\": 0.005235471695763575, \"bayes_factor\": 161.22029931582702, \"log2_bayes_factor\": 7.332889595417968, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on birth_place` then comparison is 161.22 times more likely to be a match\", \"column_name\": \"birth_place\", \"value_l\": \"shropshire\", \"value_r\": \"shropshire\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 7, \"record_number\": 53}, {\"sql_condition\": \"\\\"birth_place_l\\\" = \\\"birth_place_r\\\"\", \"label_for_charts\": \"Term freq adjustment on birth_place with weight 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\"value_l\": \"actor\", \"value_r\": \"actor\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 9, \"record_number\": 53}, {\"sql_condition\": \"\\\"occupation_l\\\" = \\\"occupation_r\\\"\", \"label_for_charts\": \"Term freq adjustment on occupation with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 2.8723882583091043, \"log2_bayes_factor\": 1.5222507702190105, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"Term frequency adjustment on occupation makes comparison 2.87 times more likely to be a match\", \"column_name\": \"tf_occupation\", \"value_l\": \"actor\", \"value_r\": \"actor\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 10, \"record_number\": 53}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": 30.381858498471228, \"bayes_factor\": 1399106364.0769026, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 53}, {\"column_name\": \"Prior\", \"label_for_charts\": \"Starting match weight (prior)\", \"sql_condition\": null, \"log2_bayes_factor\": -12.845746707461347, \"bayes_factor\": 0.00013584539607096294, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 0, \"record_number\": 54}, {\"sql_condition\": \"\\\"first_name_l\\\" = \\\"first_name_r\\\"\", \"label_for_charts\": \"Exact match on first_name\", \"m_probability\": 0.5520331483113572, \"u_probability\": 0.012292927619903934, \"bayes_factor\": 44.90656460203507, \"log2_bayes_factor\": 5.4888544535850965, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on first_name` then comparison is 44.91 times more likely to be a match\", \"column_name\": \"first_name\", \"value_l\": \"william\", \"value_r\": \"william\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 1, \"record_number\": 54}, {\"sql_condition\": \"\\\"first_name_l\\\" = \\\"first_name_r\\\"\", \"label_for_charts\": \"Term freq adjustment on first_name with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 0.22335541978739842, \"log2_bayes_factor\": -2.1625868323861748, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"Term frequency adjustment on first_name makes comparison  4.48 times less likely to be a match\", \"column_name\": \"tf_first_name\", \"value_l\": \"william\", \"value_r\": \"william\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 2, \"record_number\": 54}, {\"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on 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\"tf_surname\", \"value_l\": \"cartwright\", \"value_r\": \"cartwright\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 4, \"record_number\": 54}, {\"sql_condition\": \"damerau_levenshtein(\\\"dob_l\\\", \\\"dob_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of dob <= 1\", \"m_probability\": 0.3387321638685843, \"u_probability\": 0.022242783244398753, \"bayes_factor\": 15.228856935153782, \"log2_bayes_factor\": 3.9287357533328375, \"comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of dob <= 1` then comparison is 15.23 times more likely to be a match\", \"column_name\": \"dob\", \"value_l\": \"1600-01-01\", \"value_r\": \"1606-01-01\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 5, \"record_number\": 54}, {\"sql_condition\": \"\\\"postcode_fake_l\\\" = \\\"postcode_fake_r\\\"\", \"label_for_charts\": \"Exact match on postcode_fake\", \"m_probability\": 0.6872360790281722, \"u_probability\": 0.00013724762297795306, \"bayes_factor\": 5007.271267193948, \"log2_bayes_factor\": 12.28980889970718, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode_fake` then comparison is 5,007.27 times more likely to be a match\", \"column_name\": \"postcode_fake\", \"value_l\": \"sy10 8ra\", \"value_r\": \"sy10 8ra\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 54}, {\"sql_condition\": \"\\\"birth_place_l\\\" = \\\"birth_place_r\\\"\", \"label_for_charts\": \"Exact match on birth_place\", \"m_probability\": 0.8440643138505439, \"u_probability\": 0.005235471695763575, \"bayes_factor\": 161.22029931582702, \"log2_bayes_factor\": 7.332889595417968, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on birth_place` then comparison is 161.22 times more likely to be a match\", \"column_name\": \"birth_place\", \"value_l\": \"shropshire\", \"value_r\": \"shropshire\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 7, \"record_number\": 54}, {\"sql_condition\": \"\\\"birth_place_l\\\" = \\\"birth_place_r\\\"\", \"label_for_charts\": \"Term freq adjustment on birth_place with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 1.3443151470104457, \"log2_bayes_factor\": 0.4268713879172663, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"Term frequency adjustment on birth_place makes comparison 1.34 times more likely to be a match\", \"column_name\": \"tf_birth_place\", \"value_l\": \"shropshire\", \"value_r\": \"shropshire\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 8, \"record_number\": 54}, {\"sql_condition\": \"\\\"occupation_l\\\" = \\\"occupation_r\\\"\", \"label_for_charts\": \"Exact match on occupation\", \"m_probability\": 0.8989343034777655, \"u_probability\": 0.03736864790951383, \"bayes_factor\": 24.055842364285873, \"log2_bayes_factor\": 4.58831541409548, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on occupation` then comparison is 24.06 times more likely to be a match\", \"column_name\": \"occupation\", \"value_l\": \"actor\", \"value_r\": \"actor\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 9, \"record_number\": 54}, {\"sql_condition\": \"\\\"occupation_l\\\" = \\\"occupation_r\\\"\", \"label_for_charts\": \"Term freq adjustment on occupation with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 2.8723882583091043, \"log2_bayes_factor\": 1.5222507702190105, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"Term frequency adjustment on occupation makes comparison 2.87 times more likely to be a match\", \"column_name\": \"tf_occupation\", \"value_l\": \"actor\", \"value_r\": \"actor\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 10, \"record_number\": 54}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": 30.381858498471228, \"bayes_factor\": 1399106364.0769026, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 54}]}}, {\"mode\": \"vega-lite\"});\n",
-              "</script>"
-            ],
-            "text/plain": [
-              "alt.LayerChart(...)"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 5, root rows count 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(\n",
+    "    df_predict, threshold_match_probability=0.95\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:55.528997Z",
+     "iopub.status.busy": "2024-06-07T09:09:55.528732Z",
+     "iopub.status.idle": "2024-06-07T09:09:55.705059Z",
+     "shell.execute_reply": "2024-06-07T09:09:55.704305Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"1200\"\n",
+       "            src=\"./dashboards/50k_cluster.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
       ],
-      "source": [
-        "records = linker.inference.prediction_errors_from_labels_column(\n",
-        "    \"cluster\",\n",
-        "    threshold=0.999,\n",
-        "    include_false_negatives=False,\n",
-        "    include_false_positives=True,\n",
-        ").as_record_dict()\n",
-        "linker.visualisations.waterfall_chart(records)"
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x12e35bc10>"
       ]
-    },
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import IFrame\n",
+    "\n",
+    "linker.visualisations.cluster_studio_dashboard(\n",
+    "    df_predict,\n",
+    "    clusters,\n",
+    "    \"dashboards/50k_cluster.html\",\n",
+    "    sampling_method=\"by_cluster_size\",\n",
+    "    overwrite=True,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "IFrame(src=\"./dashboards/50k_cluster.html\", width=\"100%\", height=1200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:09:55.708587Z",
+     "iopub.status.busy": "2024-06-07T09:09:55.708313Z",
+     "iopub.status.idle": "2024-06-07T09:10:07.358895Z",
+     "shell.execute_reply": "2024-06-07T09:10:07.358097Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T16:07:47.834324Z",
-          "iopub.status.busy": "2024-05-15T16:07:47.834092Z",
-          "iopub.status.idle": "2024-05-15T16:07:51.080047Z",
-          "shell.execute_reply": "2024-05-15T16:07:51.079464Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "<style>\n",
-              "  #altair-viz-2d2a1a64c2944c6188676a4e65fbe399.vega-embed {\n",
-              "    width: 100%;\n",
-              "    display: flex;\n",
-              "  }\n",
-              "\n",
-              "  #altair-viz-2d2a1a64c2944c6188676a4e65fbe399.vega-embed details,\n",
-              "  #altair-viz-2d2a1a64c2944c6188676a4e65fbe399.vega-embed details summary {\n",
-              "    position: relative;\n",
-              "  }\n",
-              "</style>\n",
-              "<div id=\"altair-viz-2d2a1a64c2944c6188676a4e65fbe399\"></div>\n",
-              "<script type=\"text/javascript\">\n",
-              "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
-              "  (function(spec, embedOpt){\n",
-              "    let outputDiv = document.currentScript.previousElementSibling;\n",
-              "    if (outputDiv.id !== \"altair-viz-2d2a1a64c2944c6188676a4e65fbe399\") {\n",
-              "      outputDiv = document.getElementById(\"altair-viz-2d2a1a64c2944c6188676a4e65fbe399\");\n",
-              "    }\n",
-              "    const paths = {\n",
-              "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
-              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
-              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
-              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
-              "    };\n",
-              "\n",
-              "    function maybeLoadScript(lib, version) {\n",
-              "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
-              "      return (VEGA_DEBUG[key] == version) ?\n",
-              "        Promise.resolve(paths[lib]) :\n",
-              "        new Promise(function(resolve, reject) {\n",
-              "          var s = document.createElement('script');\n",
-              "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-              "          s.async = true;\n",
-              "          s.onload = () => {\n",
-              "            VEGA_DEBUG[key] = version;\n",
-              "            return resolve(paths[lib]);\n",
-              "          };\n",
-              "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
-              "          s.src = paths[lib];\n",
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-              "\n",
-              "    function showError(err) {\n",
-              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
-              "      throw err;\n",
-              "    }\n",
-              "\n",
-              "    function displayChart(vegaEmbed) {\n",
-              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
-              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
-              "    }\n",
-              "\n",
-              "    if(typeof define === \"function\" && define.amd) {\n",
-              "      requirejs.config({paths});\n",
-              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
-              "    } else {\n",
-              "      maybeLoadScript(\"vega\", \"5\")\n",
-              "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
-              "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
-              "        .catch(showError)\n",
-              "        .then(() => displayChart(vegaEmbed));\n",
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+       "    width: 100%;\n",
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+       "\n",
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+       "</style>\n",
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
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+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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+       "          s.src = paths[lib];\n",
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+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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0.9997623533404699, \"f1\": 6.579769839651009e-06, \"f2\": 4.112366296690779e-06, \"f0_5\": 1.644926225058806e-05, \"p4\": 1.3159453082838614e-05, \"phi\": 0.0018135914397872834}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "source": [
-        "# Some of the false negatives will be because they weren't detected by the blocking rules\n",
-        "records = linker.inference.prediction_errors_from_labels_column(\n",
-        "    \"cluster\",\n",
-        "    threshold=0.5,\n",
-        "    include_false_negatives=True,\n",
-        "    include_false_positives=False,\n",
-        ").as_record_dict(limit=50)\n",
-        "\n",
-        "linker.visualisations.waterfall_chart(records)"
+      "text/plain": [
+       "alt.Chart(...)"
       ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.8"
-    },
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\"label_for_charts\": \"Term freq adjustment on occupation with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 2.818710855559814, \"log2_bayes_factor\": 1.4950354932879772, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"Term frequency adjustment on occupation makes comparison 2.82 times more likely to be a match\", \"column_name\": \"tf_occupation\", \"value_l\": \"actor\", \"value_r\": \"actor\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 10, \"record_number\": 57}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": 30.512415828493186, \"bayes_factor\": 1531624851.981952, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 57}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "records = linker.evaluation.prediction_errors_from_labels_column(\n",
+    "    \"cluster\",\n",
+    "    threshold=0.999,\n",
+    "    include_false_negatives=False,\n",
+    "    include_false_positives=True,\n",
+    ").as_record_dict()\n",
+    "linker.visualisations.waterfall_chart(records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:10:10.819376Z",
+     "iopub.status.busy": "2024-06-07T09:10:10.818967Z",
+     "iopub.status.idle": "2024-06-07T09:10:13.601958Z",
+     "shell.execute_reply": "2024-06-07T09:10:13.601341Z"
     }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-6a9dc7eacd4649e5be0b4d280ff5f037.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-6a9dc7eacd4649e5be0b4d280ff5f037.vega-embed details,\n",
+       "  #altair-viz-6a9dc7eacd4649e5be0b4d280ff5f037.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-6a9dc7eacd4649e5be0b4d280ff5f037\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-6a9dc7eacd4649e5be0b4d280ff5f037\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-6a9dc7eacd4649e5be0b4d280ff5f037\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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{\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.026077429203334265, \"u_probability\": 0.7500027040474182, \"bayes_factor\": 0.03476978024559968, \"log2_bayes_factor\": -4.84602224090261, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  28.76 times less likely to be a match\", \"column_name\": \"dob\", \"value_l\": \"1859-03-04\", \"value_r\": \"1859-83-01\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 5, \"record_number\": 49}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.17046156615157143, \"u_probability\": 0.9992470948602873, \"bayes_factor\": 0.17059000424254925, \"log2_bayes_factor\": -2.5513949807762453, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.86 times less likely to be a match\", \"column_name\": \"postcode_fake\", \"value_l\": \"en3 7ne\", \"value_r\": \"cm2 8wq\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 49}, {\"sql_condition\": \"\\\"birth_place_l\\\" IS NULL OR \\\"birth_place_r\\\" IS NULL\", \"label_for_charts\": \"birth_place is NULL\", \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": -1, \"bayes_factor_description\": \"If comparison level is `birth_place is null` then comparison is 1.00 times more likely to be a match\", \"column_name\": \"birth_place\", \"value_l\": \"None\", \"value_r\": \"None\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 7, \"record_number\": 49}, {\"sql_condition\": \"\\\"birth_place_l\\\" IS NULL OR \\\"birth_place_r\\\" IS NULL\", \"label_for_charts\": \"birth_place is NULL\", \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": -1, \"bayes_factor_description\": \"If comparison level is `birth_place is null` then comparison is 1.00 times more likely to be a match\", \"column_name\": \"tf_birth_place\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 8, \"record_number\": 49}, {\"sql_condition\": \"\\\"occupation_l\\\" IS NULL OR \\\"occupation_r\\\" IS NULL\", \"label_for_charts\": \"occupation is NULL\", \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": -1, \"bayes_factor_description\": \"If comparison level is `occupation is null` then comparison is 1.00 times more likely to be a match\", \"column_name\": \"occupation\", \"value_l\": \"politician\", \"value_r\": \"None\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 9, \"record_number\": 49}, {\"sql_condition\": \"\\\"occupation_l\\\" IS NULL OR \\\"occupation_r\\\" IS NULL\", \"label_for_charts\": \"occupation is NULL\", \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": -1, \"bayes_factor_description\": \"If comparison level is `occupation is null` then comparison is 1.00 times more likely to be a match\", \"column_name\": \"tf_occupation\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 10, \"record_number\": 49}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": -26.194002346074125, \"bayes_factor\": 1.3026255351966673e-08, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 49}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Some of the false negatives will be because they weren't detected by the blocking rules\n",
+    "records = linker.evaluation.prediction_errors_from_labels_column(\n",
+    "    \"cluster\",\n",
+    "    threshold=0.5,\n",
+    "    include_false_negatives=True,\n",
+    "    include_false_positives=False,\n",
+    ").as_record_dict(limit=50)\n",
+    "\n",
+    "linker.visualisations.waterfall_chart(records)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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diff --git a/docs/demos/examples/duckdb/deterministic_dedupe.ipynb b/docs/demos/examples/duckdb/deterministic_dedupe.ipynb
index b3afdff7b1..0065baeda9 100644
--- a/docs/demos/examples/duckdb/deterministic_dedupe.ipynb
+++ b/docs/demos/examples/duckdb/deterministic_dedupe.ipynb
@@ -1,267 +1,826 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Linking a dataset of real historical persons with Deterrministic Rules\n",
-        "\n",
-        "While Splink is primarily a tool for probabilistic records linkage, it includes functionality to perform deterministic (i.e. rules based) linkage.\n",
-        "\n",
-        "Significant work has gone into optimising the performance of rules based matching, so Splink is likely to be significantly faster than writing the basic SQL by hand.\n",
-        "\n",
-        "In this example, we deduplicate a 50k row dataset based on historical persons scraped from wikidata. Duplicate records are introduced with a variety of errors introduced. The probabilistic dedupe of the same dataset can be found at `Deduplicate 50k rows historical persons`.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/deterministic_dedupe.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:50.508953Z",
-          "iopub.status.busy": "2024-05-15T15:43:50.508620Z",
-          "iopub.status.idle": "2024-05-15T15:43:50.514416Z",
-          "shell.execute_reply": "2024-05-15T15:43:50.513604Z"
-        }
-      },
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
-      ],
-      "outputs": []
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linking a dataset of real historical persons with Deterrministic Rules\n",
+    "\n",
+    "While Splink is primarily a tool for probabilistic records linkage, it includes functionality to perform deterministic (i.e. rules based) linkage.\n",
+    "\n",
+    "Significant work has gone into optimising the performance of rules based matching, so Splink is likely to be significantly faster than writing the basic SQL by hand.\n",
+    "\n",
+    "In this example, we deduplicate a 50k row dataset based on historical persons scraped from wikidata. Duplicate records are introduced with a variety of errors introduced. The probabilistic dedupe of the same dataset can be found at `Deduplicate 50k rows historical persons`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/deterministic_dedupe.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:10:59.567669Z",
+     "iopub.status.busy": "2024-06-07T09:10:59.567311Z",
+     "iopub.status.idle": "2024-06-07T09:10:59.591784Z",
+     "shell.execute_reply": "2024-06-07T09:10:59.590923Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:10:59.595969Z",
+     "iopub.status.busy": "2024-06-07T09:10:59.595667Z",
+     "iopub.status.idle": "2024-06-07T09:11:01.007136Z",
+     "shell.execute_reply": "2024-06-07T09:11:01.006553Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:50.519532Z",
-          "iopub.status.busy": "2024-05-15T15:43:50.519159Z",
-          "iopub.status.idle": "2024-05-15T15:43:53.171104Z",
-          "shell.execute_reply": "2024-05-15T15:43:53.170070Z"
-        }
-      },
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "from splink import splink_datasets\n",
-        "\n",
-        "pd.options.display.max_rows = 1000\n",
-        "df = splink_datasets.historical_50k\n",
-        "df.head()"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>full_name</th>\n",
+       "      <th>first_and_surname</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>birth_place</th>\n",
+       "      <th>postcode_fake</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>occupation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Q2296770-1</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas clifford, 1st baron clifford of chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q2296770-2</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas of chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Q2296770-3</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>tom 1st baron clifford of chudleigh</td>\n",
+       "      <td>tom chudleigh</td>\n",
+       "      <td>tom</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Q2296770-4</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas 1st chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8hu</td>\n",
+       "      <td>None</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Q2296770-5</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas clifford, 1st baron chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>None</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "outputs": []
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When defining the settings object, specity your deterministic rules in the `blocking_rules_to_generate_predictions` key.\n",
-        "\n",
-        "For a deterministic linkage, the linkage methodology is based solely on these rules, so there is no need to define `comparisons` nor any other parameters required for model training in a probabilistic model.\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Prior to running the linkage, it's usually a good idea to check how many record comparisons will be generated by your deterministic rules:\n"
+      "text/plain": [
+       "    unique_id   cluster                                         full_name  \\\n",
+       "0  Q2296770-1  Q2296770  thomas clifford, 1st baron clifford of chudleigh   \n",
+       "1  Q2296770-2  Q2296770                               thomas of chudleigh   \n",
+       "2  Q2296770-3  Q2296770               tom 1st baron clifford of chudleigh   \n",
+       "3  Q2296770-4  Q2296770                              thomas 1st chudleigh   \n",
+       "4  Q2296770-5  Q2296770              thomas clifford, 1st baron chudleigh   \n",
+       "\n",
+       "  first_and_surname first_name    surname         dob birth_place  \\\n",
+       "0  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "1  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "2     tom chudleigh        tom  chudleigh  1630-08-01       devon   \n",
+       "3  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "4  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "\n",
+       "  postcode_fake gender  occupation  \n",
+       "0      tq13 8df   male  politician  \n",
+       "1      tq13 8df   male  politician  \n",
+       "2      tq13 8df   male  politician  \n",
+       "3      tq13 8hu   None  politician  \n",
+       "4      tq13 8df   None  politician  "
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from splink import splink_datasets\n",
+    "\n",
+    "pd.options.display.max_rows = 1000\n",
+    "df = splink_datasets.historical_50k\n",
+    "df.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When defining the settings object, specity your deterministic rules in the `blocking_rules_to_generate_predictions` key.\n",
+    "\n",
+    "For a deterministic linkage, the linkage methodology is based solely on these rules, so there is no need to define `comparisons` nor any other parameters required for model training in a probabilistic model.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prior to running the linkage, it's usually a good idea to check how many record comparisons will be generated by your deterministic rules:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:01.050336Z",
+     "iopub.status.busy": "2024-06-07T09:11:01.049679Z",
+     "iopub.status.idle": "2024-06-07T09:11:01.602823Z",
+     "shell.execute_reply": "2024-06-07T09:11:01.601902Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:53.273619Z",
-          "iopub.status.busy": "2024-05-15T15:43:53.271060Z",
-          "iopub.status.idle": "2024-05-15T15:43:54.139302Z",
-          "shell.execute_reply": "2024-05-15T15:43:54.138451Z"
-        }
-      },
-      "source": [
-        "from splink import DuckDBAPI, block_on\n",
-        "from splink.blocking_analysis import (\n",
-        "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
-        ")\n",
-        "\n",
-        "db_api = DuckDBAPI()\n",
-        "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
-        "    table_or_tables=df,\n",
-        "    blocking_rules=[\n",
-        "        block_on(\"first_name\", \"surname\", \"dob\"),\n",
-        "        block_on(\"surname\", \"dob\", \"postcode_fake\"),\n",
-        "        block_on(\"first_name\", \"dob\", \"occupation\"),\n",
-        "    ],\n",
-        "    db_api=db_api,\n",
-        "    link_type=\"dedupe_only\",\n",
-        ")"
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-7573355cdaeb4eda997ecaf67fa26c1e.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-7573355cdaeb4eda997ecaf67fa26c1e.vega-embed details,\n",
+       "  #altair-viz-7573355cdaeb4eda997ecaf67fa26c1e.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-7573355cdaeb4eda997ecaf67fa26c1e\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-7573355cdaeb4eda997ecaf67fa26c1e\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-7573355cdaeb4eda997ecaf67fa26c1e\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-b9acee0cea6dec7a8f0f5f5c44419bdf\"}, \"mark\": \"bar\", \"encoding\": {\"order\": {\"field\": \"cumulative_rows\"}, \"tooltip\": [{\"field\": \"blocking_rule\", \"title\": \"SQL Condition\", \"type\": \"nominal\"}, {\"field\": \"row_count\", \"format\": \",\", \"title\": \"Comparisons Generated\", \"type\": \"quantitative\"}, {\"field\": \"cumulative_rows\", \"format\": \",\", \"title\": \"Cumulative Comparisons\", \"type\": \"quantitative\"}, {\"field\": \"cartesian\", \"format\": \",\", \"title\": \"Total comparisons in Cartesian product\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"start\", \"title\": \"Comparisons Generated by Rule(s)\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"cumulative_rows\"}, \"y\": {\"field\": \"blocking_rule\", \"sort\": [\"-x2\"], \"title\": \"SQL Blocking Rule\"}}, \"height\": {\"step\": 20}, \"title\": {\"text\": \"Count of Additional Comparisons Generated by Each Blocking Rule\", \"subtitle\": \"(Counts exclude comparisons already generated by previous rules)\"}, \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-b9acee0cea6dec7a8f0f5f5c44419bdf\": [{\"blocking_rule\": \"(l.\\\"first_name\\\" = r.\\\"first_name\\\") AND (l.\\\"surname\\\" = r.\\\"surname\\\") AND (l.\\\"dob\\\" = r.\\\"dob\\\")\", \"row_count\": 37852, \"cumulative_rows\": 37852, \"cartesian\": 1279041753, \"match_key\": \"0\", \"start\": 0}, {\"blocking_rule\": \"(l.\\\"surname\\\" = r.\\\"surname\\\") AND (l.\\\"dob\\\" = r.\\\"dob\\\") AND (l.\\\"postcode_fake\\\" = r.\\\"postcode_fake\\\")\", \"row_count\": 9568, \"cumulative_rows\": 47420, \"cartesian\": 1279041753, \"match_key\": \"1\", \"start\": 37852}, {\"blocking_rule\": \"(l.\\\"first_name\\\" = r.\\\"first_name\\\") AND (l.\\\"dob\\\" = r.\\\"dob\\\") AND (l.\\\"occupation\\\" = r.\\\"occupation\\\")\", \"row_count\": 4306, \"cumulative_rows\": 51726, \"cartesian\": 1279041753, \"match_key\": \"2\", \"start\": 47420}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "outputs": []
-    },
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import DuckDBAPI, block_on\n",
+    "from splink.blocking_analysis import (\n",
+    "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
+    ")\n",
+    "\n",
+    "db_api = DuckDBAPI()\n",
+    "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
+    "    table_or_tables=df,\n",
+    "    blocking_rules=[\n",
+    "        block_on(\"first_name\", \"surname\", \"dob\"),\n",
+    "        block_on(\"surname\", \"dob\", \"postcode_fake\"),\n",
+    "        block_on(\"first_name\", \"dob\", \"occupation\"),\n",
+    "    ],\n",
+    "    db_api=db_api,\n",
+    "    link_type=\"dedupe_only\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:01.606853Z",
+     "iopub.status.busy": "2024-06-07T09:11:01.606539Z",
+     "iopub.status.idle": "2024-06-07T09:11:01.691839Z",
+     "shell.execute_reply": "2024-06-07T09:11:01.690988Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from splink import Linker, SettingsCreator\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    link_type=\"dedupe_only\",\n",
+    "    blocking_rules_to_generate_predictions=[\n",
+    "        block_on(\"first_name\", \"surname\", \"dob\"),\n",
+    "        block_on(\"surname\", \"dob\", \"postcode_fake\"),\n",
+    "        block_on(\"first_name\", \"dob\", \"occupation\"),\n",
+    "    ],\n",
+    "    retain_intermediate_calculation_columns=True,\n",
+    ")\n",
+    "\n",
+    "linker = Linker(df, settings, database_api=db_api)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results of the linkage can be viewed with the `deterministic_link` function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:01.695906Z",
+     "iopub.status.busy": "2024-06-07T09:11:01.695600Z",
+     "iopub.status.idle": "2024-06-07T09:11:01.995020Z",
+     "shell.execute_reply": "2024-06-07T09:11:01.994289Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:54.144031Z",
-          "iopub.status.busy": "2024-05-15T15:43:54.143555Z",
-          "iopub.status.idle": "2024-05-15T15:43:54.254120Z",
-          "shell.execute_reply": "2024-05-15T15:43:54.252360Z"
-        }
-      },
-      "source": [
-        "from splink import Linker, SettingsCreator\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    link_type=\"dedupe_only\",\n",
-        "    blocking_rules_to_generate_predictions=[\n",
-        "        block_on(\"first_name\", \"surname\", \"dob\"),\n",
-        "        block_on(\"surname\", \"dob\", \"postcode_fake\"),\n",
-        "        block_on(\"first_name\", \"dob\", \"occupation\"),\n",
-        "    ],\n",
-        "    retain_intermediate_calculation_columns=True,\n",
-        ")\n",
-        "\n",
-        "linker = Linker(df, settings, database_api=db_api)\n"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id_l</th>\n",
+       "      <th>unique_id_r</th>\n",
+       "      <th>occupation_l</th>\n",
+       "      <th>occupation_r</th>\n",
+       "      <th>first_name_l</th>\n",
+       "      <th>first_name_r</th>\n",
+       "      <th>dob_l</th>\n",
+       "      <th>dob_r</th>\n",
+       "      <th>surname_l</th>\n",
+       "      <th>surname_r</th>\n",
+       "      <th>postcode_fake_l</th>\n",
+       "      <th>postcode_fake_r</th>\n",
+       "      <th>match_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Q55455287-12</td>\n",
+       "      <td>Q55455287-2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>writer</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>ta4 2ug</td>\n",
+       "      <td>ta4 2uu</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q55455287-12</td>\n",
+       "      <td>Q55455287-3</td>\n",
+       "      <td>None</td>\n",
+       "      <td>writer</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>ta4 2ug</td>\n",
+       "      <td>ta4 2uu</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Q55455287-12</td>\n",
+       "      <td>Q55455287-4</td>\n",
+       "      <td>None</td>\n",
+       "      <td>writer</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>ta4 2ug</td>\n",
+       "      <td>ta4 2sz</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Q55455287-12</td>\n",
+       "      <td>Q55455287-5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>ta4 2ug</td>\n",
+       "      <td>ta4 2ug</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Q55455287-12</td>\n",
+       "      <td>Q55455287-6</td>\n",
+       "      <td>None</td>\n",
+       "      <td>writer</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>jaido</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>1836-01-01</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>morata</td>\n",
+       "      <td>ta4 2ug</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "outputs": []
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The results of the linkage can be viewed with the `deterministic_link` function.\n"
+      "text/plain": [
+       "    unique_id_l  unique_id_r occupation_l occupation_r first_name_l  \\\n",
+       "0  Q55455287-12  Q55455287-2         None       writer        jaido   \n",
+       "1  Q55455287-12  Q55455287-3         None       writer        jaido   \n",
+       "2  Q55455287-12  Q55455287-4         None       writer        jaido   \n",
+       "3  Q55455287-12  Q55455287-5         None         None        jaido   \n",
+       "4  Q55455287-12  Q55455287-6         None       writer        jaido   \n",
+       "\n",
+       "  first_name_r       dob_l       dob_r surname_l surname_r postcode_fake_l  \\\n",
+       "0        jaido  1836-01-01  1836-01-01    morata    morata         ta4 2ug   \n",
+       "1        jaido  1836-01-01  1836-01-01    morata    morata         ta4 2ug   \n",
+       "2        jaido  1836-01-01  1836-01-01    morata    morata         ta4 2ug   \n",
+       "3        jaido  1836-01-01  1836-01-01    morata    morata         ta4 2ug   \n",
+       "4        jaido  1836-01-01  1836-01-01    morata    morata         ta4 2ug   \n",
+       "\n",
+       "  postcode_fake_r match_key  \n",
+       "0         ta4 2uu         0  \n",
+       "1         ta4 2uu         0  \n",
+       "2         ta4 2sz         0  \n",
+       "3         ta4 2ug         0  \n",
+       "4            None         0  "
       ]
-    },
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_predict = linker.inference.deterministic_link()\n",
+    "df_predict.as_pandas_dataframe().head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which can be used to generate clusters.\n",
+    "\n",
+    "Note, for deterministic linkage, each comparison has been assigned a match probability of 1, so to generate clusters, set `threshold_match_probability=1` in the `cluster_pairwise_predictions_at_threshold` function.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:01.998965Z",
+     "iopub.status.busy": "2024-06-07T09:11:01.998665Z",
+     "iopub.status.idle": "2024-06-07T09:11:02.348788Z",
+     "shell.execute_reply": "2024-06-07T09:11:02.348039Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:54.259538Z",
-          "iopub.status.busy": "2024-05-15T15:43:54.258905Z",
-          "iopub.status.idle": "2024-05-15T15:43:54.922593Z",
-          "shell.execute_reply": "2024-05-15T15:43:54.921796Z"
-        }
-      },
-      "source": [
-        "df_predict = linker.deterministic_link()\n",
-        "df_predict.as_pandas_dataframe().head()"
-      ],
-      "outputs": []
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 1, root rows count 94\n"
+     ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Which can be used to generate clusters.\n",
-        "\n",
-        "Note, for deterministic linkage, each comparison has been assigned a match probability of 1, so to generate clusters, set `threshold_match_probability=1` in the `cluster_pairwise_predictions_at_threshold` function.\n"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 2, root rows count 10\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:54.928175Z",
-          "iopub.status.busy": "2024-05-15T15:43:54.927807Z",
-          "iopub.status.idle": "2024-05-15T15:43:55.547697Z",
-          "shell.execute_reply": "2024-05-15T15:43:55.543024Z"
-        }
-      },
-      "source": [
-        "clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(\n",
-        "    df_predict, threshold_match_probability=1\n",
-        ")"
-      ],
-      "outputs": []
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 3, root rows count 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(\n",
+    "    df_predict, threshold_match_probability=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:02.352872Z",
+     "iopub.status.busy": "2024-06-07T09:11:02.352366Z",
+     "iopub.status.idle": "2024-06-07T09:11:02.367858Z",
+     "shell.execute_reply": "2024-06-07T09:11:02.367179Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:55.555934Z",
-          "iopub.status.busy": "2024-05-15T15:43:55.554006Z",
-          "iopub.status.idle": "2024-05-15T15:43:55.592918Z",
-          "shell.execute_reply": "2024-05-15T15:43:55.589688Z"
-        }
-      },
-      "source": [
-        "clusters.as_pandas_dataframe(limit=5)"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cluster_id</th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>full_name</th>\n",
+       "      <th>first_and_surname</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>birth_place</th>\n",
+       "      <th>postcode_fake</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>__splink_salt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Q16025107-1</td>\n",
+       "      <td>Q5497940-9</td>\n",
+       "      <td>Q5497940</td>\n",
+       "      <td>frederick hall</td>\n",
+       "      <td>frederick hall</td>\n",
+       "      <td>frederick</td>\n",
+       "      <td>hall</td>\n",
+       "      <td>1855-01-01</td>\n",
+       "      <td>bristol, city of</td>\n",
+       "      <td>bs11 9pn</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.002739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q1149445-1</td>\n",
+       "      <td>Q1149445-9</td>\n",
+       "      <td>Q1149445</td>\n",
+       "      <td>earl egerton</td>\n",
+       "      <td>earl egerton</td>\n",
+       "      <td>earl</td>\n",
+       "      <td>egerton</td>\n",
+       "      <td>1800-01-01</td>\n",
+       "      <td>westminster</td>\n",
+       "      <td>w1d 2hf</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.991459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Q20664532-1</td>\n",
+       "      <td>Q21466387-2</td>\n",
+       "      <td>Q21466387</td>\n",
+       "      <td>harry brooker</td>\n",
+       "      <td>harry brooker</td>\n",
+       "      <td>harry</td>\n",
+       "      <td>brooker</td>\n",
+       "      <td>1848-01-01</td>\n",
+       "      <td>plymouth</td>\n",
+       "      <td>pl4 9hx</td>\n",
+       "      <td>male</td>\n",
+       "      <td>painter</td>\n",
+       "      <td>0.506127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Q1124636-1</td>\n",
+       "      <td>Q1124636-12</td>\n",
+       "      <td>Q1124636</td>\n",
+       "      <td>tom stapleton</td>\n",
+       "      <td>tom stapleton</td>\n",
+       "      <td>tom</td>\n",
+       "      <td>stapleton</td>\n",
+       "      <td>1535-01-01</td>\n",
+       "      <td>None</td>\n",
+       "      <td>bn6 9na</td>\n",
+       "      <td>male</td>\n",
+       "      <td>theologian</td>\n",
+       "      <td>0.612694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Q18508292-1</td>\n",
+       "      <td>Q21466711-4</td>\n",
+       "      <td>Q21466711</td>\n",
+       "      <td>harry s0ence</td>\n",
+       "      <td>harry s0ence</td>\n",
+       "      <td>harry</td>\n",
+       "      <td>s0ence</td>\n",
+       "      <td>1860-01-01</td>\n",
+       "      <td>london</td>\n",
+       "      <td>se1 7pb</td>\n",
+       "      <td>male</td>\n",
+       "      <td>painter</td>\n",
+       "      <td>0.488917</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "outputs": []
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "These results can then be passed into the `Cluster Studio Dashboard`.\n"
+      "text/plain": [
+       "    cluster_id    unique_id    cluster       full_name first_and_surname  \\\n",
+       "0  Q16025107-1   Q5497940-9   Q5497940  frederick hall    frederick hall   \n",
+       "1   Q1149445-1   Q1149445-9   Q1149445    earl egerton      earl egerton   \n",
+       "2  Q20664532-1  Q21466387-2  Q21466387   harry brooker     harry brooker   \n",
+       "3   Q1124636-1  Q1124636-12   Q1124636   tom stapleton     tom stapleton   \n",
+       "4  Q18508292-1  Q21466711-4  Q21466711    harry s0ence      harry s0ence   \n",
+       "\n",
+       "  first_name    surname         dob       birth_place postcode_fake gender  \\\n",
+       "0  frederick       hall  1855-01-01  bristol, city of      bs11 9pn   None   \n",
+       "1       earl    egerton  1800-01-01       westminster       w1d 2hf   None   \n",
+       "2      harry    brooker  1848-01-01          plymouth       pl4 9hx   male   \n",
+       "3        tom  stapleton  1535-01-01              None       bn6 9na   male   \n",
+       "4      harry     s0ence  1860-01-01            london       se1 7pb   male   \n",
+       "\n",
+       "   occupation  __splink_salt  \n",
+       "0        None       0.002739  \n",
+       "1        None       0.991459  \n",
+       "2     painter       0.506127  \n",
+       "3  theologian       0.612694  \n",
+       "4     painter       0.488917  "
       ]
-    },
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clusters.as_pandas_dataframe(limit=5)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results can then be passed into the `Cluster Studio Dashboard`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:02.371850Z",
+     "iopub.status.busy": "2024-06-07T09:11:02.371545Z",
+     "iopub.status.idle": "2024-06-07T09:11:02.462645Z",
+     "shell.execute_reply": "2024-06-07T09:11:02.461886Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:43:55.600959Z",
-          "iopub.status.busy": "2024-05-15T15:43:55.600358Z",
-          "iopub.status.idle": "2024-05-15T15:43:55.761150Z",
-          "shell.execute_reply": "2024-05-15T15:43:55.759988Z"
-        }
-      },
-      "source": [
-        "linker.visualisations.cluster_studio_dashboard(\n",
-        "    df_predict,\n",
-        "    clusters,\n",
-        "    \"dashboards/50k_deterministic_cluster.html\",\n",
-        "    sampling_method=\"by_cluster_size\",\n",
-        "    overwrite=True,\n",
-        ")\n",
-        "\n",
-        "from IPython.display import IFrame\n",
-        "\n",
-        "IFrame(src=\"./dashboards/50k_deterministic_cluster.html\", width=\"100%\", height=1200)"
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"1200\"\n",
+       "            src=\"./dashboards/50k_deterministic_cluster.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
       ],
-      "outputs": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.8"
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x12edeee60>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "linker.visualisations.cluster_studio_dashboard(\n",
+    "    df_predict,\n",
+    "    clusters,\n",
+    "    \"dashboards/50k_deterministic_cluster.html\",\n",
+    "    sampling_method=\"by_cluster_size\",\n",
+    "    overwrite=True,\n",
+    ")\n",
+    "\n",
+    "from IPython.display import IFrame\n",
+    "\n",
+    "IFrame(src=\"./dashboards/50k_deterministic_cluster.html\", width=\"100%\", height=1200)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/demos/examples/duckdb/febrl3.ipynb b/docs/demos/examples/duckdb/febrl3.ipynb
index 080b7c6607..9a102d4519 100644
--- a/docs/demos/examples/duckdb/febrl3.ipynb
+++ b/docs/demos/examples/duckdb/febrl3.ipynb
@@ -1,630 +1,1802 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deduplicating the febrl3 dataset\n",
-        "\n",
-        "See A.2 [here](https://arxiv.org/pdf/2008.04443.pdf) and [here](https://recordlinkage.readthedocs.io/en/latest/ref-datasets.html) for the source of this data\n"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deduplicating the febrl3 dataset\n",
+    "\n",
+    "See A.2 [here](https://arxiv.org/pdf/2008.04443.pdf) and [here](https://recordlinkage.readthedocs.io/en/latest/ref-datasets.html) for the source of this data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/febrl3.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:24.420657Z",
+     "iopub.status.busy": "2024-06-07T09:11:24.420336Z",
+     "iopub.status.idle": "2024-06-07T09:11:24.443364Z",
+     "shell.execute_reply": "2024-06-07T09:11:24.442120Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:24.447798Z",
+     "iopub.status.busy": "2024-06-07T09:11:24.447495Z",
+     "iopub.status.idle": "2024-06-07T09:11:26.149918Z",
+     "shell.execute_reply": "2024-06-07T09:11:26.149230Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloading: https://raw.githubusercontent.com/moj-analytical-services/splink_datasets/master/data/febrl/dataset3.csv\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/febrl3.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  download progress: 0 %\t(..........)\r",
+      "  download progress: 2 %\t(..........)\r",
+      "  download progress: 3 %\t(..........)\r",
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+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:53.970752Z",
-          "iopub.status.busy": "2024-05-15T15:50:53.970419Z",
-          "iopub.status.idle": "2024-05-15T15:50:53.975673Z",
-          "shell.execute_reply": "2024-05-15T15:50:53.974958Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rec_id</th>\n",
+       "      <th>given_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>street_number</th>\n",
+       "      <th>address_1</th>\n",
+       "      <th>address_2</th>\n",
+       "      <th>suburb</th>\n",
+       "      <th>postcode</th>\n",
+       "      <th>state</th>\n",
+       "      <th>date_of_birth</th>\n",
+       "      <th>soc_sec_id</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rec-1496-org</td>\n",
+       "      <td>mitchell</td>\n",
+       "      <td>green</td>\n",
+       "      <td>7</td>\n",
+       "      <td>wallaby place</td>\n",
+       "      <td>delmar</td>\n",
+       "      <td>cleveland</td>\n",
+       "      <td>2119</td>\n",
+       "      <td>sa</td>\n",
+       "      <td>19560409</td>\n",
+       "      <td>1804974</td>\n",
+       "      <td>rec-1496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rec-552-dup-3</td>\n",
+       "      <td>harley</td>\n",
+       "      <td>mccarthy</td>\n",
+       "      <td>177</td>\n",
+       "      <td>pridhamstreet</td>\n",
+       "      <td>milton</td>\n",
+       "      <td>marsden</td>\n",
+       "      <td>3165</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>19080419</td>\n",
+       "      <td>6089216</td>\n",
+       "      <td>rec-552</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          rec_id given_name    surname street_number       address_1  \\\n",
+       "0   rec-1496-org   mitchell      green             7   wallaby place   \n",
+       "1  rec-552-dup-3     harley   mccarthy           177   pridhamstreet   \n",
+       "\n",
+       "  address_2      suburb  postcode state date_of_birth soc_sec_id   cluster  \n",
+       "0    delmar   cleveland      2119    sa      19560409    1804974  rec-1496  \n",
+       "1    milton     marsden      3165   nsw      19080419    6089216   rec-552  "
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink.datasets import splink_datasets\n",
+    "\n",
+    "df = splink_datasets.febrl3\n",
+    "df = df.rename(columns=lambda x: x.strip())\n",
+    "\n",
+    "df[\"cluster\"] = df[\"rec_id\"].apply(lambda x: \"-\".join(x.split(\"-\")[:2]))\n",
+    "\n",
+    "df[\"date_of_birth\"] = df[\"date_of_birth\"].astype(str).str.strip()\n",
+    "df[\"soc_sec_id\"] = df[\"soc_sec_id\"].astype(str).str.strip()\n",
+    "\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:26.153666Z",
+     "iopub.status.busy": "2024-06-07T09:11:26.153378Z",
+     "iopub.status.idle": "2024-06-07T09:11:26.160666Z",
+     "shell.execute_reply": "2024-06-07T09:11:26.159911Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df[\"date_of_birth\"] = df[\"date_of_birth\"].astype(str).str.strip()\n",
+    "df[\"soc_sec_id\"] = df[\"soc_sec_id\"].astype(str).str.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:26.164000Z",
+     "iopub.status.busy": "2024-06-07T09:11:26.163726Z",
+     "iopub.status.idle": "2024-06-07T09:11:26.170794Z",
+     "shell.execute_reply": "2024-06-07T09:11:26.170146Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df[\"date_of_birth\"] = df[\"date_of_birth\"].astype(str).str.strip()\n",
+    "df[\"soc_sec_id\"] = df[\"soc_sec_id\"].astype(str).str.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:26.174301Z",
+     "iopub.status.busy": "2024-06-07T09:11:26.174024Z",
+     "iopub.status.idle": "2024-06-07T09:11:26.331196Z",
+     "shell.execute_reply": "2024-06-07T09:11:26.330465Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from splink import DuckDBAPI, Linker, SettingsCreator\n",
+    "\n",
+    "# TODO:  Allow missingness to be analysed without a linker\n",
+    "settings = SettingsCreator(\n",
+    "    unique_id_column_name=\"rec_id\",\n",
+    "    link_type=\"dedupe_only\",\n",
+    ")\n",
+    "\n",
+    "linker = Linker(df, settings, database_api=DuckDBAPI())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's usually a good idea to perform exploratory analysis on your data so you understand what's in each column and how often it's missing:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:26.334644Z",
+     "iopub.status.busy": "2024-06-07T09:11:26.334398Z",
+     "iopub.status.idle": "2024-06-07T09:11:26.630134Z",
+     "shell.execute_reply": "2024-06-07T09:11:26.629629Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:53.979321Z",
-          "iopub.status.busy": "2024-05-15T15:50:53.979040Z",
-          "iopub.status.idle": "2024-05-15T15:50:55.403280Z",
-          "shell.execute_reply": "2024-05-15T15:50:55.402512Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink.datasets import splink_datasets\n",
-        "\n",
-        "df = splink_datasets.febrl3\n",
-        "df = df.rename(columns=lambda x: x.strip())\n",
-        "\n",
-        "df[\"cluster\"] = df[\"rec_id\"].apply(lambda x: \"-\".join(x.split(\"-\")[:2]))\n",
-        "\n",
-        "df[\"date_of_birth\"] = df[\"date_of_birth\"].astype(str).str.strip()\n",
-        "df[\"soc_sec_id\"] = df[\"soc_sec_id\"].astype(str).str.strip()\n",
-        "\n",
-        "df.head(2)"
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-20f1041d514c4b2595dc3e87d49f0cb9.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-20f1041d514c4b2595dc3e87d49f0cb9.vega-embed details,\n",
+       "  #altair-viz-20f1041d514c4b2595dc3e87d49f0cb9.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-20f1041d514c4b2595dc3e87d49f0cb9\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-20f1041d514c4b2595dc3e87d49f0cb9\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-20f1041d514c4b2595dc3e87d49f0cb9\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"rect\", \"encoding\": {\"color\": {\"field\": \"completeness\", \"legend\": null, \"scale\": {\"scheme\": \"darkred\", \"zero\": true}, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"source_dataset\", \"title\": \"Source dataset\", \"type\": \"nominal\"}, {\"field\": \"total_rows_inc_nulls\", \"format\": \",\", \"title\": \"# of records\", \"type\": \"quantitative\"}, {\"field\": \"column_name\", \"title\": \"Column name\", \"type\": \"nominal\"}, {\"field\": \"total_null_rows\", \"format\": \",\", \"title\": \"# of nulls\", \"type\": \"quantitative\"}, {\"field\": \"completeness\", \"format\": \".1%\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"labelAngle\": 20}, \"field\": \"column_name\", \"sort\": {\"field\": \"mean_comp\", \"order\": \"descending\"}, \"title\": \"Column name\", \"type\": \"nominal\"}, \"y\": {\"field\": \"source_dataset\", \"title\": \"Source dataset\", \"type\": \"nominal\"}}, \"title\": \"Column completeness by source dataset\", \"transform\": [{\"joinaggregate\": [{\"op\": \"mean\", \"field\": \"completeness\", \"as\": \"mean_comp\"}], \"groupby\": [\"column_name\"]}]}, {\"mark\": {\"type\": \"text\"}, \"encoding\": {\"color\": {\"condition\": {\"test\": \"datum['completeness'] < 0.5\", \"value\": \"white\"}, \"value\": \"black\"}, \"text\": {\"field\": \"completeness\", \"format\": \".0%\", \"type\": \"quantitative\"}, \"x\": {\"axis\": {\"labelAngle\": 0}, \"field\": \"column_name\", \"sort\": {\"field\": \"mean_comp\", \"order\": \"descending\"}, \"type\": \"nominal\"}, \"y\": {\"field\": \"source_dataset\", \"type\": \"nominal\"}}, \"transform\": [{\"joinaggregate\": [{\"op\": \"mean\", \"field\": \"completeness\", \"as\": \"mean_comp\"}], \"groupby\": [\"column_name\"]}]}], \"data\": {\"name\": \"data-f284bee0b7d37d94cca34fa76f5ff473\"}, \"height\": {\"step\": 40}, \"width\": {\"step\": 40}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-f284bee0b7d37d94cca34fa76f5ff473\": [{\"source_dataset\": \"input_data_1\", \"column_name\": \"rec_id\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"given_name\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"surname\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"street_number\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"address_1\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"address_2\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"suburb\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"postcode\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"state\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"date_of_birth\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"soc_sec_id\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"cluster\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
       ]
-    },
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink.exploratory import completeness_chart\n",
+    "\n",
+    "completeness_chart(df, db_api=DuckDBAPI())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:26.633200Z",
+     "iopub.status.busy": "2024-06-07T09:11:26.632979Z",
+     "iopub.status.idle": "2024-06-07T09:11:27.047469Z",
+     "shell.execute_reply": "2024-06-07T09:11:27.046951Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:55.445888Z",
-          "iopub.status.busy": "2024-05-15T15:50:55.445564Z",
-          "iopub.status.idle": "2024-05-15T15:50:55.453559Z",
-          "shell.execute_reply": "2024-05-15T15:50:55.452728Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "df[\"date_of_birth\"] = df[\"date_of_birth\"].astype(str).str.strip()\n",
-        "df[\"soc_sec_id\"] = df[\"soc_sec_id\"].astype(str).str.strip()"
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-9223ba00ba0e49cdbd4dd81daf2b9b48.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-9223ba00ba0e49cdbd4dd81daf2b9b48.vega-embed details,\n",
+       "  #altair-viz-9223ba00ba0e49cdbd4dd81daf2b9b48.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-9223ba00ba0e49cdbd4dd81daf2b9b48\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-9223ba00ba0e49cdbd4dd81daf2b9b48\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-9223ba00ba0e49cdbd4dd81daf2b9b48\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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count\"}, {\"mark\": \"bar\", \"data\": {\"values\": [{\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" tuttleby\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" matthiessen\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" fittock\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" sabieray\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" flockhdart\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" kothe\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" georfe\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" woodmansee\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" vincdnt\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" rocheq\", \"total_non_null_rows\": 5000, \"total_rows_inc_nulls\": 5000, \"distinct_value_count\": 1741}]}, \"encoding\": {\"tooltip\": [{\"field\": \"value\", \"type\": \"nominal\"}, {\"field\": \"value_count\", \"type\": \"quantitative\"}, {\"field\": \"total_non_null_rows\", \"type\": \"quantitative\"}, {\"field\": \"total_rows_inc_nulls\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"value\", \"sort\": \"-y\", \"title\": null, \"type\": \"nominal\"}, \"y\": {\"field\": \"value_count\", \"scale\": {\"domain\": [0, 123]}, \"title\": \"Value count\", \"type\": \"quantitative\"}}, \"title\": \"Bottom 10 values by value count\"}]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.VConcatChart(...)"
       ]
-    },
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink.exploratory import profile_columns\n",
+    "\n",
+    "profile_columns(df, db_api=DuckDBAPI(), column_expressions=[\"given_name\", \"surname\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:27.050491Z",
+     "iopub.status.busy": "2024-06-07T09:11:27.050266Z",
+     "iopub.status.idle": "2024-06-07T09:11:27.428593Z",
+     "shell.execute_reply": "2024-06-07T09:11:27.428055Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:55.457023Z",
-          "iopub.status.busy": "2024-05-15T15:50:55.456741Z",
-          "iopub.status.idle": "2024-05-15T15:50:55.464209Z",
-          "shell.execute_reply": "2024-05-15T15:50:55.463386Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "df[\"date_of_birth\"] = df[\"date_of_birth\"].astype(str).str.strip()\n",
-        "df[\"soc_sec_id\"] = df[\"soc_sec_id\"].astype(str).str.strip()"
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-9b8e638ece9642d5bfa73787633a74da.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-9b8e638ece9642d5bfa73787633a74da.vega-embed details,\n",
+       "  #altair-viz-9b8e638ece9642d5bfa73787633a74da.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-9b8e638ece9642d5bfa73787633a74da\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-9b8e638ece9642d5bfa73787633a74da\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-9b8e638ece9642d5bfa73787633a74da\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-4ab5b177c099ba5baa7c18ee8b59ed20\"}, \"mark\": \"bar\", \"encoding\": {\"order\": {\"field\": \"cumulative_rows\"}, \"tooltip\": [{\"field\": \"blocking_rule\", \"title\": \"SQL Condition\", \"type\": \"nominal\"}, {\"field\": \"row_count\", \"format\": \",\", \"title\": \"Comparisons Generated\", \"type\": \"quantitative\"}, {\"field\": \"cumulative_rows\", \"format\": \",\", \"title\": \"Cumulative Comparisons\", \"type\": \"quantitative\"}, {\"field\": \"cartesian\", \"format\": \",\", \"title\": \"Total comparisons in Cartesian product\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"start\", \"title\": \"Comparisons Generated by Rule(s)\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"cumulative_rows\"}, \"y\": {\"field\": \"blocking_rule\", \"sort\": [\"-x2\"], \"title\": \"SQL Blocking Rule\"}}, \"height\": {\"step\": 20}, \"title\": {\"text\": \"Count of Additional Comparisons Generated by Each Blocking Rule\", \"subtitle\": \"(Counts exclude comparisons already generated by previous rules)\"}, \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-4ab5b177c099ba5baa7c18ee8b59ed20\": [{\"blocking_rule\": \"l.\\\"soc_sec_id\\\" = r.\\\"soc_sec_id\\\"\", \"row_count\": 5601, \"cumulative_rows\": 5601, \"cartesian\": 12497500, \"match_key\": \"0\", \"start\": 0}, {\"blocking_rule\": \"l.\\\"given_name\\\" = r.\\\"given_name\\\"\", \"row_count\": 48681, \"cumulative_rows\": 54282, \"cartesian\": 12497500, \"match_key\": \"1\", \"start\": 5601}, {\"blocking_rule\": \"l.\\\"surname\\\" = r.\\\"surname\\\"\", \"row_count\": 36675, \"cumulative_rows\": 90957, \"cartesian\": 12497500, \"match_key\": \"2\", \"start\": 54282}, {\"blocking_rule\": \"l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"row_count\": 12256, \"cumulative_rows\": 103213, \"cartesian\": 12497500, \"match_key\": \"3\", \"start\": 90957}, {\"blocking_rule\": \"l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"row_count\": 11037, \"cumulative_rows\": 114250, \"cartesian\": 12497500, \"match_key\": \"4\", \"start\": 103213}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
       ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import DuckDBAPI, block_on\n",
+    "from splink.blocking_analysis import (\n",
+    "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
+    ")\n",
+    "\n",
+    "blocking_rules = [\n",
+    "    block_on(\"soc_sec_id\"),\n",
+    "    block_on(\"given_name\"),\n",
+    "    block_on(\"surname\"),\n",
+    "    block_on(\"date_of_birth\"),\n",
+    "    block_on(\"postcode\"),\n",
+    "]\n",
+    "\n",
+    "db_api = DuckDBAPI()\n",
+    "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
+    "    table_or_tables=df,\n",
+    "    blocking_rules=blocking_rules,\n",
+    "    db_api=db_api,\n",
+    "    link_type=\"dedupe_only\",\n",
+    "    unique_id_column_name=\"rec_id\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:27.431702Z",
+     "iopub.status.busy": "2024-06-07T09:11:27.431466Z",
+     "iopub.status.idle": "2024-06-07T09:11:27.591229Z",
+     "shell.execute_reply": "2024-06-07T09:11:27.590491Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import splink.comparison_library as cl\n",
+    "import splink.comparison_template_library as ctl\n",
+    "from splink import Linker\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    unique_id_column_name=\"rec_id\",\n",
+    "    link_type=\"dedupe_only\",\n",
+    "    blocking_rules_to_generate_predictions=blocking_rules,\n",
+    "    comparisons=[\n",
+    "        ctl.NameComparison(\"given_name\").configure(term_frequency_adjustments=True),\n",
+    "        ctl.NameComparison(\"surname\").configure(term_frequency_adjustments=True),\n",
+    "        ctl.DateComparison(\n",
+    "            \"date_of_birth\",\n",
+    "            input_is_string=True,\n",
+    "            datetime_format=\"%Y%m%d\",\n",
+    "            invalid_dates_as_null=True,\n",
+    "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
+    "            datetime_thresholds=[1, 1, 10],\n",
+    "        ),\n",
+    "        cl.LevenshteinAtThresholds(\"soc_sec_id\", [2]),\n",
+    "        cl.ExactMatch(\"street_number\").configure(term_frequency_adjustments=True),\n",
+    "        cl.ExactMatch(\"postcode\").configure(term_frequency_adjustments=True),\n",
+    "    ],\n",
+    "    retain_intermediate_calculation_columns=True,\n",
+    ")\n",
+    "\n",
+    "linker = Linker(df, settings, database_api=DuckDBAPI())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:27.594493Z",
+     "iopub.status.busy": "2024-06-07T09:11:27.594264Z",
+     "iopub.status.idle": "2024-06-07T09:11:27.787352Z",
+     "shell.execute_reply": "2024-06-07T09:11:27.786769Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Probability two random records match is estimated to be  0.000528.\n",
+      "This means that amongst all possible pairwise record comparisons, one in 1,893.56 are expected to match.  With 12,497,500 total possible comparisons, we expect a total of around 6,600.00 matching pairs\n"
+     ]
+    }
+   ],
+   "source": [
+    "from splink import block_on\n",
+    "\n",
+    "deterministic_rules = [\n",
+    "    block_on(\"soc_sec_id\"),\n",
+    "    block_on(\"given_name\", \"surname\", \"date_of_birth\"),\n",
+    "    \"l.given_name = r.surname and l.surname = r.given_name and l.date_of_birth = r.date_of_birth\",\n",
+    "]\n",
+    "\n",
+    "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:27.790368Z",
+     "iopub.status.busy": "2024-06-07T09:11:27.790145Z",
+     "iopub.status.idle": "2024-06-07T09:11:35.433199Z",
+     "shell.execute_reply": "2024-06-07T09:11:35.431006Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default value for `max_pairs`, which may be too small and thus lead to inaccurate estimates for your model's u-parameters. Consider increasing to 1e8 or 1e9, which will result in more accurate estimates, but with a longer run time.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:55.467779Z",
-          "iopub.status.busy": "2024-05-15T15:50:55.467486Z",
-          "iopub.status.idle": "2024-05-15T15:50:55.617978Z",
-          "shell.execute_reply": "2024-05-15T15:50:55.617331Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink import DuckDBAPI, Linker, SettingsCreator\n",
-        "\n",
-        "# TODO:  Allow missingness to be analysed without a linker\n",
-        "settings = SettingsCreator(\n",
-        "    unique_id_column_name=\"rec_id\",\n",
-        "    link_type=\"dedupe_only\",\n",
-        ")\n",
-        "\n",
-        "linker = Linker(df, settings, database_api=DuckDBAPI())"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "It's usually a good idea to perform exploratory analysis on your data so you understand what's in each column and how often it's missing:\n"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 month' (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:55.621604Z",
-          "iopub.status.busy": "2024-05-15T15:50:55.621314Z",
-          "iopub.status.idle": "2024-05-15T15:50:55.930689Z",
-          "shell.execute_reply": "2024-05-15T15:50:55.929809Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink.exploratory import completeness_chart\n",
-        "\n",
-        "completeness_chart(df, db_api=DuckDBAPI())"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 year' (comparison vector value: 2). This usually means the comparison level was never observed in the training data.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:55.933815Z",
-          "iopub.status.busy": "2024-05-15T15:50:55.933588Z",
-          "iopub.status.idle": "2024-05-15T15:50:56.393881Z",
-          "shell.execute_reply": "2024-05-15T15:50:56.393363Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink.exploratory import profile_columns\n",
-        "\n",
-        "profile_columns(df, db_api=DuckDBAPI(), column_expressions=[\"given_name\", \"surname\"])"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 10 year' (comparison vector value: 1). This usually means the comparison level was never observed in the training data.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:56.397337Z",
-          "iopub.status.busy": "2024-05-15T15:50:56.396993Z",
-          "iopub.status.idle": "2024-05-15T15:50:56.749566Z",
-          "shell.execute_reply": "2024-05-15T15:50:56.748922Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink import DuckDBAPI, block_on\n",
-        "from splink.blocking_analysis import (\n",
-        "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
-        ")\n",
-        "\n",
-        "blocking_rules = [\n",
-        "    block_on(\"soc_sec_id\"),\n",
-        "    block_on(\"given_name\"),\n",
-        "    block_on(\"surname\"),\n",
-        "    block_on(\"date_of_birth\"),\n",
-        "    block_on(\"postcode\"),\n",
-        "]\n",
-        "\n",
-        "db_api = DuckDBAPI()\n",
-        "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
-        "    table_or_tables=df,\n",
-        "    blocking_rules=blocking_rules,\n",
-        "    db_api=db_api,\n",
-        "    link_type=\"dedupe_only\",\n",
-        "    unique_id_column_name=\"rec_id\",\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:56.752854Z",
-          "iopub.status.busy": "2024-05-15T15:50:56.752596Z",
-          "iopub.status.idle": "2024-05-15T15:50:56.907514Z",
-          "shell.execute_reply": "2024-05-15T15:50:56.906772Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "import splink.comparison_library as cl\n",
-        "import splink.comparison_template_library as ctl\n",
-        "from splink import Linker\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    unique_id_column_name=\"rec_id\",\n",
-        "    link_type=\"dedupe_only\",\n",
-        "    blocking_rules_to_generate_predictions=blocking_rules,\n",
-        "    comparisons=[\n",
-        "        ctl.NameComparison(\"given_name\").configure(term_frequency_adjustments=True),\n",
-        "        ctl.NameComparison(\"surname\").configure(term_frequency_adjustments=True),\n",
-        "        ctl.DateComparison(\n",
-        "            \"date_of_birth\",\n",
-        "            input_is_string=True,\n",
-        "            datetime_format=\"%Y%m%d\",\n",
-        "            invalid_dates_as_null=True,\n",
-        "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
-        "            datetime_thresholds=[1, 1, 10],\n",
-        "        ),\n",
-        "        cl.LevenshteinAtThresholds(\"soc_sec_id\", [2]),\n",
-        "        cl.ExactMatch(\"street_number\").configure(term_frequency_adjustments=True),\n",
-        "        cl.ExactMatch(\"postcode\").configure(term_frequency_adjustments=True),\n",
-        "    ],\n",
-        "    retain_intermediate_calculation_columns=True,\n",
-        ")\n",
-        "\n",
-        "linker = Linker(df, settings, database_api=DuckDBAPI())"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - given_name (no m values are trained).\n",
+      "    - surname (no m values are trained).\n",
+      "    - date_of_birth (some u values are not trained, no m values are trained).\n",
+      "    - soc_sec_id (no m values are trained).\n",
+      "    - street_number (no m values are trained).\n",
+      "    - postcode (no m values are trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:35.446472Z",
+     "iopub.status.busy": "2024-06-07T09:11:35.440198Z",
+     "iopub.status.idle": "2024-06-07T09:11:36.895235Z",
+     "shell.execute_reply": "2024-06-07T09:11:36.894603Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:56.910709Z",
-          "iopub.status.busy": "2024-05-15T15:50:56.910470Z",
-          "iopub.status.idle": "2024-05-15T15:50:57.119744Z",
-          "shell.execute_reply": "2024-05-15T15:50:57.119133Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink import block_on\n",
-        "\n",
-        "deterministic_rules = [\n",
-        "    block_on(\"soc_sec_id\"),\n",
-        "    block_on(\"given_name\", \"surname\", \"date_of_birth\"),\n",
-        "    \"l.given_name = r.surname and l.surname = r.given_name and l.date_of_birth = r.date_of_birth\",\n",
-        "]\n",
-        "\n",
-        "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.9)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"date_of_birth\" = r.\"date_of_birth\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - given_name\n",
+      "    - surname\n",
+      "    - soc_sec_id\n",
+      "    - street_number\n",
+      "    - postcode\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - date_of_birth\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:50:57.122905Z",
-          "iopub.status.busy": "2024-05-15T15:50:57.122623Z",
-          "iopub.status.idle": "2024-05-15T15:51:01.161828Z",
-          "shell.execute_reply": "2024-05-15T15:51:01.161251Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:51:01.165539Z",
-          "iopub.status.busy": "2024-05-15T15:51:01.165298Z",
-          "iopub.status.idle": "2024-05-15T15:51:01.704281Z",
-          "shell.execute_reply": "2024-05-15T15:51:01.703690Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "em_blocking_rule_1 = block_on(\"date_of_birth\")\n",
-        "session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
-        "    em_blocking_rule_1\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.376 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:51:01.707325Z",
-          "iopub.status.busy": "2024-05-15T15:51:01.707114Z",
-          "iopub.status.idle": "2024-05-15T15:51:02.290513Z",
-          "shell.execute_reply": "2024-05-15T15:51:02.290020Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "em_blocking_rule_2 = block_on(\"postcode\")\n",
-        "session_postcode = linker.training.estimate_parameters_using_expectation_maximisation(\n",
-        "    em_blocking_rule_2\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.0158 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:51:02.294783Z",
-          "iopub.status.busy": "2024-05-15T15:51:02.294498Z",
-          "iopub.status.idle": "2024-05-15T15:51:02.665651Z",
-          "shell.execute_reply": "2024-05-15T15:51:02.665073Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "linker.visualisations.match_weights_chart()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was -0.000688 in the m_probability of postcode, level `Exact match on postcode`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:51:02.668752Z",
-          "iopub.status.busy": "2024-05-15T15:51:02.668512Z",
-          "iopub.status.idle": "2024-05-15T15:51:09.240685Z",
-          "shell.execute_reply": "2024-05-15T15:51:09.240109Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "results = linker.inference.predict(threshold_match_probability=0.2)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 3.65e-05 in the m_probability of postcode, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 4 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - date_of_birth (some u values are not trained, no m values are trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "em_blocking_rule_1 = block_on(\"date_of_birth\")\n",
+    "session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+    "    em_blocking_rule_1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:36.898638Z",
+     "iopub.status.busy": "2024-06-07T09:11:36.898156Z",
+     "iopub.status.idle": "2024-06-07T09:11:37.517318Z",
+     "shell.execute_reply": "2024-06-07T09:11:37.516459Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"postcode\" = r.\"postcode\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - given_name\n",
+      "    - surname\n",
+      "    - date_of_birth\n",
+      "    - soc_sec_id\n",
+      "    - street_number\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - postcode\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 1 month' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 1 year' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 10 year' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was 0.0627 in probability_two_random_records_match\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:51:09.243955Z",
-          "iopub.status.busy": "2024-05-15T15:51:09.243667Z",
-          "iopub.status.idle": "2024-05-15T15:51:11.811265Z",
-          "shell.execute_reply": "2024-05-15T15:51:11.810638Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "linker.accuracy_analysis_from_labels_column(\n",
-        "    \"cluster\", match_weight_round_to_nearest=0.1, output_type=\"roc\"\n",
-        ")"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was -0.00188 in the m_probability of date_of_birth, level `Exact match on date_of_birth`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 5.26e-05 in the m_probability of soc_sec_id, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 3 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 month' (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 year' (comparison vector value: 2). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 10 year' (comparison vector value: 1). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - date_of_birth (some u values are not trained, some m values are not trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "em_blocking_rule_2 = block_on(\"postcode\")\n",
+    "session_postcode = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+    "    em_blocking_rule_2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:37.523135Z",
+     "iopub.status.busy": "2024-06-07T09:11:37.522810Z",
+     "iopub.status.idle": "2024-06-07T09:11:37.957335Z",
+     "shell.execute_reply": "2024-06-07T09:11:37.956712Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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date_of_birth <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 12.07057129971884, \"log2_bayes_factor\": 3.5934220553022502, \"comparison_vector_value\": 4, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of date_of_birth <= 1` then comparison is 12.07 times more likely to be a match\", \"probability_two_random_records_match\": 0.0005281056211242248, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 2629800.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 1 month'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.0050000000000000044, \"m_probability_description\": \"Amongst matching record 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0.0005281056211242248, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 315576000.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 10 year'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.08000000000000007, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 8.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.125, \"log2_bayes_factor\": -3.0, \"comparison_vector_value\": 1, 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\"comparison_vector_value\": 0, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  17.10 times less likely to be a match\", \"probability_two_random_records_match\": 0.0005281056211242248, \"comparison_sort_order\": 2}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.8589485966871453, \"u_probability\": 0.00045325284132268303, \"m_probability_description\": \"Amongst matching record comparisons, 85.89% of records are in the exact match on soc_sec_id comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.05% of records are in the exact match on soc_sec_id comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 1895.0760334575298, \"log2_bayes_factor\": 10.888040017433948, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 1,895.08 times more likely to be a match\", \"probability_two_random_records_match\": 0.0005281056211242248, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"label_for_charts\": \"Levenshtein distance of soc_sec_id <= 2\", \"m_probability\": 0.07683393545412412, \"u_probability\": 0.00026981646656896335, \"m_probability_description\": \"Amongst matching record comparisons, 7.68% of records are in the levenshtein distance of soc_sec_id <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.03% of records are in the levenshtein distance of soc_sec_id <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 284.7637004188766, \"log2_bayes_factor\": 8.153621443651533, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `levenshtein distance of soc_sec_id <= 2` then comparison is 284.76 times more likely to be a match\", \"probability_two_random_records_match\": 0.0005281056211242248, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.0642174678587305, \"u_probability\": 0.9992769306921083, \"m_probability_description\": \"Amongst matching record comparisons, 6.42% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.93% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.06426393513783302, \"log2_bayes_factor\": -3.9598468642717424, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  15.56 times less likely to be a match\", \"probability_two_random_records_match\": 0.0005281056211242248, \"comparison_sort_order\": 3}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.7681604748522775, \"u_probability\": 0.015875496200246524, \"m_probability_description\": \"Amongst matching record comparisons, 76.82% of records are in the exact match on street_number comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.59% of records are in the exact match on 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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.VConcatChart(...)"
       ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.visualisations.match_weights_chart()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:37.960629Z",
+     "iopub.status.busy": "2024-06-07T09:11:37.960358Z",
+     "iopub.status.idle": "2024-06-07T09:11:44.496784Z",
+     "shell.execute_reply": "2024-06-07T09:11:44.496254Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'date_of_birth':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    u values not fully trained\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = linker.inference.predict(threshold_match_probability=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:44.499943Z",
+     "iopub.status.busy": "2024-06-07T09:11:44.499693Z",
+     "iopub.status.idle": "2024-06-07T09:11:47.310831Z",
+     "shell.execute_reply": "2024-06-07T09:11:47.310208Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'date_of_birth':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    u values not fully trained\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:51:11.820946Z",
-          "iopub.status.busy": "2024-05-15T15:51:11.820644Z",
-          "iopub.status.idle": "2024-05-15T15:51:12.084284Z",
-          "shell.execute_reply": "2024-05-15T15:51:12.083443Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "pred_errors_df = linker.inference.prediction_errors_from_labels_column(\n",
-        "    \"cluster\"\n",
-        ").as_pandas_dataframe()\n",
-        "len(pred_errors_df)\n",
-        "pred_errors_df.head()"
+     "data": {
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+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
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+       "<div id=\"altair-viz-39962d2d39cc4922babc9cd251a1a02e\"></div>\n",
+       "<script type=\"text/javascript\">\n",
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+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
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+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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+       "      maybeLoadScript(\"vega\", \"5\")\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
       ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.evaluation.accuracy_analysis_from_labels_column(\n",
+    "    \"cluster\", match_weight_round_to_nearest=0.1, output_type=\"roc\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:47.319625Z",
+     "iopub.status.busy": "2024-06-07T09:11:47.319347Z",
+     "iopub.status.idle": "2024-06-07T09:11:47.588558Z",
+     "shell.execute_reply": "2024-06-07T09:11:47.587940Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'date_of_birth':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    u values not fully trained\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-15T15:51:12.087291Z",
-          "iopub.status.busy": "2024-05-15T15:51:12.087021Z",
-          "iopub.status.idle": "2024-05-15T15:51:13.092062Z",
-          "shell.execute_reply": "2024-05-15T15:51:13.091503Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "records = linker.inference.prediction_errors_from_labels_column(\"cluster\").as_record_dict(\n",
-        "    limit=10\n",
-        ")\n",
-        "linker.visualisations.waterfall_chart(records)"
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>clerical_match_score</th>\n",
+       "      <th>found_by_blocking_rules</th>\n",
+       "      <th>match_weight</th>\n",
+       "      <th>match_probability</th>\n",
+       "      <th>rec_id_l</th>\n",
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+       "      <th>2</th>\n",
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+       "      <td>1.483873e-06</td>\n",
+       "      <td>rec-829-dup-0</td>\n",
+       "      <td>rec-829-dup-1</td>\n",
+       "      <td>wilde</td>\n",
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+       "      <td>-15.233122</td>\n",
+       "      <td>2.596344e-05</td>\n",
+       "      <td>rec-721-dup-0</td>\n",
+       "      <td>rec-721-dup-1</td>\n",
+       "      <td>mikhaili</td>\n",
+       "      <td>elly</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4806</td>\n",
+       "      <td>4860</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0014</td>\n",
+       "      <td>0.230037</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>rec-721</td>\n",
+       "      <td>rec-721</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-12.600328</td>\n",
+       "      <td>1.610102e-04</td>\n",
+       "      <td>rec-401-dup-1</td>\n",
+       "      <td>rec-401-dup-3</td>\n",
+       "      <td>whitbe</td>\n",
+       "      <td>alexa-ose</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0002</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3040</td>\n",
+       "      <td>3041</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>0.0004</td>\n",
+       "      <td>0.230037</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>rec-401</td>\n",
+       "      <td>rec-401</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   clerical_match_score  found_by_blocking_rules  match_weight  \\\n",
+       "0                   1.0                    False    -27.448215   \n",
+       "1                   1.0                    False    -27.448215   \n",
+       "2                   1.0                    False    -19.362199   \n",
+       "3                   1.0                     True    -15.233122   \n",
+       "4                   1.0                     True    -12.600328   \n",
+       "\n",
+       "   match_probability       rec_id_l       rec_id_r given_name_l given_name_r  \\\n",
+       "0       5.460897e-09  rec-993-dup-1  rec-993-dup-3    westbrook         jake   \n",
+       "1       5.460897e-09  rec-829-dup-0  rec-829-dup-2        wilde         kyra   \n",
+       "2       1.483873e-06  rec-829-dup-0  rec-829-dup-1        wilde         kyra   \n",
+       "3       2.596344e-05  rec-721-dup-0  rec-721-dup-1     mikhaili         elly   \n",
+       "4       1.610102e-04  rec-401-dup-1  rec-401-dup-3       whitbe    alexa-ose   \n",
+       "\n",
+       "   gamma_given_name  tf_given_name_l  ...  postcode_l  postcode_r  \\\n",
+       "0                 0           0.0004  ...        2704        2074   \n",
+       "1                 0           0.0002  ...        3859        3595   \n",
+       "2                 0           0.0002  ...        3859        3889   \n",
+       "3                 0           0.0008  ...        4806        4860   \n",
+       "4                 0           0.0002  ...        3040        3041   \n",
+       "\n",
+       "   gamma_postcode tf_postcode_l tf_postcode_r  bf_postcode  \\\n",
+       "0               0        0.0002        0.0014     0.230037   \n",
+       "1               0        0.0004        0.0006     0.230037   \n",
+       "2               0        0.0004        0.0002     0.230037   \n",
+       "3               0        0.0008        0.0014     0.230037   \n",
+       "4               0        0.0020        0.0004     0.230037   \n",
+       "\n",
+       "   bf_tf_adj_postcode  cluster_l  cluster_r  match_key  \n",
+       "0                 1.0    rec-993    rec-993          5  \n",
+       "1                 1.0    rec-829    rec-829          5  \n",
+       "2                 1.0    rec-829    rec-829          5  \n",
+       "3                 1.0    rec-721    rec-721          2  \n",
+       "4                 1.0    rec-401    rec-401          0  \n",
+       "\n",
+       "[5 rows x 45 columns]"
       ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
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+   ],
+   "source": [
+    "pred_errors_df = linker.evaluation.prediction_errors_from_labels_column(\n",
+    "    \"cluster\"\n",
+    ").as_pandas_dataframe()\n",
+    "len(pred_errors_df)\n",
+    "pred_errors_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:11:47.591674Z",
+     "iopub.status.busy": "2024-06-07T09:11:47.591437Z",
+     "iopub.status.idle": "2024-06-07T09:11:48.630581Z",
+     "shell.execute_reply": "2024-06-07T09:11:48.629955Z"
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+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'date_of_birth':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    u values not fully trained\n"
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\"bar_sort_order\": 11, \"record_number\": 9}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "records = linker.evaluation.prediction_errors_from_labels_column(\"cluster\").as_record_dict(\n",
+    "    limit=10\n",
+    ")\n",
+    "linker.visualisations.waterfall_chart(records)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
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+       "layout": "IPY_MODEL_bdf3a462cd3d48bda4269ac1cc8ed9ef",
+       "max": 100,
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+       "_view_module_version": "2.0.0",
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+       "bar_color": "black",
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+}
diff --git a/docs/demos/examples/duckdb/febrl4.ipynb b/docs/demos/examples/duckdb/febrl4.ipynb
index 1a162201ff..a7cf929e46 100644
--- a/docs/demos/examples/duckdb/febrl4.ipynb
+++ b/docs/demos/examples/duckdb/febrl4.ipynb
@@ -30,17 +30,17 @@
    "id": "9c2be649",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:42.115992Z",
-     "iopub.status.busy": "2024-05-15T15:56:42.115623Z",
-     "iopub.status.idle": "2024-05-15T15:56:42.138818Z",
-     "shell.execute_reply": "2024-05-15T15:56:42.137554Z"
+     "iopub.execute_input": "2024-06-07T09:16:39.973571Z",
+     "iopub.status.busy": "2024-06-07T09:16:39.973235Z",
+     "iopub.status.idle": "2024-06-07T09:16:39.993885Z",
+     "shell.execute_reply": "2024-06-07T09:16:39.992799Z"
     }
    },
+   "outputs": [],
    "source": [
     "# Uncomment and run this cell if you're running in Google Colab.\n",
     "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -64,12 +64,184 @@
    "id": "832113c9-13b2-43b7-86d0-6051a9db79e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:42.144735Z",
-     "iopub.status.busy": "2024-05-15T15:56:42.144299Z",
-     "iopub.status.idle": "2024-05-15T15:56:44.123585Z",
-     "shell.execute_reply": "2024-05-15T15:56:44.122726Z"
+     "iopub.execute_input": "2024-06-07T09:16:39.999281Z",
+     "iopub.status.busy": "2024-06-07T09:16:39.998928Z",
+     "iopub.status.idle": "2024-06-07T09:16:41.957056Z",
+     "shell.execute_reply": "2024-06-07T09:16:41.956423Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rec_id</th>\n",
+       "      <th>given_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>street_number</th>\n",
+       "      <th>address_1</th>\n",
+       "      <th>address_2</th>\n",
+       "      <th>suburb</th>\n",
+       "      <th>postcode</th>\n",
+       "      <th>state</th>\n",
+       "      <th>date_of_birth</th>\n",
+       "      <th>soc_sec_id</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rec-1070-org</td>\n",
+       "      <td>michaela</td>\n",
+       "      <td>neumann</td>\n",
+       "      <td>8</td>\n",
+       "      <td>stanley street</td>\n",
+       "      <td>miami</td>\n",
+       "      <td>winston hills</td>\n",
+       "      <td>4223</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>19151111</td>\n",
+       "      <td>5304218</td>\n",
+       "      <td>rec-1070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rec-1016-org</td>\n",
+       "      <td>courtney</td>\n",
+       "      <td>painter</td>\n",
+       "      <td>12</td>\n",
+       "      <td>pinkerton circuit</td>\n",
+       "      <td>bega flats</td>\n",
+       "      <td>richlands</td>\n",
+       "      <td>4560</td>\n",
+       "      <td>vic</td>\n",
+       "      <td>19161214</td>\n",
+       "      <td>4066625</td>\n",
+       "      <td>rec-1016</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         rec_id given_name   surname street_number           address_1  \\\n",
+       "0  rec-1070-org   michaela   neumann             8      stanley street   \n",
+       "1  rec-1016-org   courtney   painter            12   pinkerton circuit   \n",
+       "\n",
+       "     address_2          suburb postcode state date_of_birth soc_sec_id  \\\n",
+       "0        miami   winston hills     4223   nsw      19151111    5304218   \n",
+       "1   bega flats       richlands     4560   vic      19161214    4066625   \n",
+       "\n",
+       "    cluster  \n",
+       "0  rec-1070  \n",
+       "1  rec-1016  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rec_id</th>\n",
+       "      <th>given_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>street_number</th>\n",
+       "      <th>address_1</th>\n",
+       "      <th>address_2</th>\n",
+       "      <th>suburb</th>\n",
+       "      <th>postcode</th>\n",
+       "      <th>state</th>\n",
+       "      <th>date_of_birth</th>\n",
+       "      <th>soc_sec_id</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rec-561-dup-0</td>\n",
+       "      <td>elton</td>\n",
+       "      <td></td>\n",
+       "      <td>3</td>\n",
+       "      <td>light setreet</td>\n",
+       "      <td>pinehill</td>\n",
+       "      <td>windermere</td>\n",
+       "      <td>3212</td>\n",
+       "      <td>vic</td>\n",
+       "      <td>19651013</td>\n",
+       "      <td>1551941</td>\n",
+       "      <td>rec-561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rec-2642-dup-0</td>\n",
+       "      <td>mitchell</td>\n",
+       "      <td>maxon</td>\n",
+       "      <td>47</td>\n",
+       "      <td>edkins street</td>\n",
+       "      <td>lochaoair</td>\n",
+       "      <td>north ryde</td>\n",
+       "      <td>3355</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>19390212</td>\n",
+       "      <td>8859999</td>\n",
+       "      <td>rec-2642</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           rec_id given_name surname street_number       address_1  \\\n",
+       "0   rec-561-dup-0      elton                     3   light setreet   \n",
+       "1  rec-2642-dup-0   mitchell   maxon            47   edkins street   \n",
+       "\n",
+       "    address_2       suburb postcode state date_of_birth soc_sec_id   cluster  \n",
+       "0    pinehill   windermere     3212   vic      19651013    1551941   rec-561  \n",
+       "1   lochaoair   north ryde     3355   nsw      19390212    8859999  rec-2642  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from splink import splink_datasets\n",
     "\n",
@@ -90,8 +262,7 @@
     "\n",
     "display(dfs[0].head(2))\n",
     "display(dfs[1].head(2))"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -107,12 +278,13 @@
    "id": "3233c3e1-3e6b-4abc-8bed-c26e8b463c2a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:44.128064Z",
-     "iopub.status.busy": "2024-05-15T15:56:44.127470Z",
-     "iopub.status.idle": "2024-05-15T15:56:44.412449Z",
-     "shell.execute_reply": "2024-05-15T15:56:44.410927Z"
+     "iopub.execute_input": "2024-06-07T09:16:41.960684Z",
+     "iopub.status.busy": "2024-06-07T09:16:41.960330Z",
+     "iopub.status.idle": "2024-06-07T09:16:42.175342Z",
+     "shell.execute_reply": "2024-06-07T09:16:42.174611Z"
     }
    },
+   "outputs": [],
    "source": [
     "from splink import DuckDBAPI, Linker, SettingsCreator\n",
     "\n",
@@ -127,8 +299,7 @@
     ")\n",
     "\n",
     "linker = Linker(dfs, basic_settings, database_api=DuckDBAPI())"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -144,18 +315,97 @@
    "id": "319ffdbc-7853-40a9-b331-e635d96b6fdc",
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-     "iopub.execute_input": "2024-05-15T15:56:44.418048Z",
-     "iopub.status.busy": "2024-05-15T15:56:44.417174Z",
-     "iopub.status.idle": "2024-05-15T15:56:45.018140Z",
-     "shell.execute_reply": "2024-05-15T15:56:45.017233Z"
+     "iopub.execute_input": "2024-06-07T09:16:42.178669Z",
+     "iopub.status.busy": "2024-06-07T09:16:42.178397Z",
+     "iopub.status.idle": "2024-06-07T09:16:42.558301Z",
+     "shell.execute_reply": "2024-06-07T09:16:42.557736Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-6c21de0e1e5042339c8389bce84c2804.vega-embed {\n",
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+       "\n",
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
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+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
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+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"rect\", \"encoding\": {\"color\": {\"field\": \"completeness\", \"legend\": null, \"scale\": {\"scheme\": \"darkred\", \"zero\": true}, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"source_dataset\", \"title\": \"Source dataset\", \"type\": \"nominal\"}, {\"field\": \"total_rows_inc_nulls\", \"format\": \",\", \"title\": \"# of records\", \"type\": \"quantitative\"}, {\"field\": \"column_name\", \"title\": \"Column name\", \"type\": \"nominal\"}, {\"field\": \"total_null_rows\", \"format\": \",\", \"title\": \"# of nulls\", \"type\": \"quantitative\"}, {\"field\": \"completeness\", \"format\": \".1%\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"labelAngle\": 20}, \"field\": \"column_name\", \"sort\": {\"field\": \"mean_comp\", \"order\": \"descending\"}, \"title\": \"Column name\", \"type\": \"nominal\"}, \"y\": {\"field\": \"source_dataset\", \"title\": \"Source dataset\", \"type\": \"nominal\"}}, \"title\": \"Column completeness by source dataset\", \"transform\": [{\"joinaggregate\": [{\"op\": \"mean\", \"field\": \"completeness\", \"as\": \"mean_comp\"}], \"groupby\": [\"column_name\"]}]}, {\"mark\": {\"type\": \"text\"}, \"encoding\": {\"color\": {\"condition\": {\"test\": \"datum['completeness'] < 0.5\", \"value\": \"white\"}, \"value\": \"black\"}, \"text\": {\"field\": \"completeness\", \"format\": \".0%\", \"type\": \"quantitative\"}, \"x\": {\"axis\": {\"labelAngle\": 0}, \"field\": \"column_name\", \"sort\": {\"field\": \"mean_comp\", \"order\": \"descending\"}, \"type\": \"nominal\"}, \"y\": {\"field\": \"source_dataset\", \"type\": \"nominal\"}}, \"transform\": [{\"joinaggregate\": [{\"op\": \"mean\", \"field\": \"completeness\", \"as\": \"mean_comp\"}], \"groupby\": [\"column_name\"]}]}], \"data\": {\"name\": \"data-6ecbf39aaf27ee3ce97421ad43ab4cc8\"}, \"height\": {\"step\": 40}, \"width\": {\"step\": 40}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-6ecbf39aaf27ee3ce97421ad43ab4cc8\": [{\"source_dataset\": \"input_data_2\", \"column_name\": \"rec_id\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"rec_id\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"given_name\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"given_name\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"surname\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"surname\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"street_number\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"street_number\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"address_1\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"address_1\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"address_2\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"address_2\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"suburb\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"suburb\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"postcode\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"postcode\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"state\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"state\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"date_of_birth\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"date_of_birth\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"soc_sec_id\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"soc_sec_id\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_1\", \"column_name\": \"cluster\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}, {\"source_dataset\": \"input_data_2\", \"column_name\": \"cluster\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 5000, \"completeness\": 1.0}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from splink.exploratory import completeness_chart\n",
     "\n",
     "completeness_chart(dfs, db_api=DuckDBAPI())"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -163,18 +413,97 @@
    "id": "dff8dfca-57c8-42bf-878c-da9dd23d2682",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:45.022368Z",
-     "iopub.status.busy": "2024-05-15T15:56:45.021805Z",
-     "iopub.status.idle": "2024-05-15T15:56:45.760354Z",
-     "shell.execute_reply": "2024-05-15T15:56:45.759671Z"
+     "iopub.execute_input": "2024-06-07T09:16:42.561536Z",
+     "iopub.status.busy": "2024-06-07T09:16:42.561314Z",
+     "iopub.status.idle": "2024-06-07T09:16:43.066015Z",
+     "shell.execute_reply": "2024-06-07T09:16:43.065469Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-7552b29ce90d424188ef8de791c0d3c5.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-7552b29ce90d424188ef8de791c0d3c5.vega-embed details,\n",
+       "  #altair-viz-7552b29ce90d424188ef8de791c0d3c5.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-7552b29ce90d424188ef8de791c0d3c5\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-7552b29ce90d424188ef8de791c0d3c5\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-7552b29ce90d424188ef8de791c0d3c5\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"vconcat\": [{\"hconcat\": [{\"mark\": {\"type\": \"line\", \"interpolate\": \"step-after\"}, \"data\": {\"values\": [{\"percentile_ex_nulls\": 0.965399980545044, \"percentile_inc_nulls\": 0.965399980545044, \"value_count\": 346, \"group_name\": \"given_name\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"sum_tokens_in_value_count_group\": 346.0, \"distinct_value_count\": 1768}, {\"percentile_ex_nulls\": 0.9501000046730042, \"percentile_inc_nulls\": 0.9501000046730042, \"value_count\": 153, \"group_name\": \"given_name\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"sum_tokens_in_value_count_group\": 153.0, \"distinct_value_count\": 1768}, {\"percentile_ex_nulls\": 0.9352999925613403, \"percentile_inc_nulls\": 0.9352999925613403, \"value_count\": 148, \"group_name\": \"given_name\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, 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10000, \"sum_tokens_in_value_count_group\": 1535.0, \"distinct_value_count\": 3060}, {\"percentile_ex_nulls\": 1.0, \"percentile_inc_nulls\": 1.0, \"value_count\": 256, \"group_name\": \"surname\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"sum_tokens_in_value_count_group\": 256.0, \"distinct_value_count\": 3060}]}, \"encoding\": {\"tooltip\": [{\"field\": \"value_count\", \"type\": \"quantitative\"}, {\"field\": \"percentile_ex_nulls\", \"type\": \"quantitative\"}, {\"field\": \"percentile_inc_nulls\", \"type\": \"quantitative\"}, {\"field\": \"total_non_null_rows\", \"type\": \"quantitative\"}, {\"field\": \"total_rows_inc_nulls\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"percentile_ex_nulls\", \"sort\": \"descending\", \"title\": \"Percentile\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"value_count\", \"title\": \"Count of values\", \"type\": \"quantitative\"}}, \"title\": {\"text\": \"Distribution of counts of values in column surname\", \"subtitle\": \"In this col, 0 values (0.0%) are null and there are 3060 distinct values\"}}, {\"mark\": \"bar\", \"data\": {\"values\": [{\"value_count\": 256, \"group_name\": \"surname\", \"value\": \" white\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 194, \"group_name\": \"surname\", \"value\": \" clarke\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 175, \"group_name\": \"surname\", \"value\": \" ryan\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 166, \"group_name\": \"surname\", \"value\": \" campbell\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 165, \"group_name\": \"surname\", \"value\": \" green\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 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\"value\": \" kilby\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" murabito\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" quan\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" thaker\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" ben-gurion\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" roselli\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}, {\"value_count\": 1, \"group_name\": \"surname\", \"value\": \" mcard\", \"total_non_null_rows\": 10000, \"total_rows_inc_nulls\": 10000, \"distinct_value_count\": 3060}]}, \"encoding\": {\"tooltip\": [{\"field\": \"value\", \"type\": \"nominal\"}, {\"field\": \"value_count\", \"type\": \"quantitative\"}, {\"field\": \"total_non_null_rows\", \"type\": \"quantitative\"}, {\"field\": \"total_rows_inc_nulls\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"value\", \"sort\": \"-y\", \"title\": null, \"type\": \"nominal\"}, \"y\": {\"field\": \"value_count\", \"scale\": {\"domain\": [0, 256]}, \"title\": \"Value count\", \"type\": \"quantitative\"}}, \"title\": \"Bottom 10 values by value count\"}]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.VConcatChart(...)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from splink.exploratory import profile_columns\n",
     "\n",
     "profile_columns(dfs, db_api=DuckDBAPI(), column_expressions=[\"given_name\", \"surname\"])"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -192,12 +521,92 @@
    "id": "e745280e-fe2f-4563-bd7e-6e4c70d0c9de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:45.764541Z",
-     "iopub.status.busy": "2024-05-15T15:56:45.764220Z",
-     "iopub.status.idle": "2024-05-15T15:56:46.595508Z",
-     "shell.execute_reply": "2024-05-15T15:56:46.594573Z"
+     "iopub.execute_input": "2024-06-07T09:16:43.069224Z",
+     "iopub.status.busy": "2024-06-07T09:16:43.068982Z",
+     "iopub.status.idle": "2024-06-07T09:16:43.684745Z",
+     "shell.execute_reply": "2024-06-07T09:16:43.684041Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-19cf4b97f9304024b2c462ed95801f2a.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-19cf4b97f9304024b2c462ed95801f2a.vega-embed details,\n",
+       "  #altair-viz-19cf4b97f9304024b2c462ed95801f2a.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-19cf4b97f9304024b2c462ed95801f2a\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-19cf4b97f9304024b2c462ed95801f2a\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-19cf4b97f9304024b2c462ed95801f2a\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-48ed1d41860949bc1f837892bc2717ad\"}, \"mark\": \"bar\", \"encoding\": {\"order\": {\"field\": \"cumulative_rows\"}, \"tooltip\": [{\"field\": \"blocking_rule\", \"title\": \"SQL Condition\", \"type\": \"nominal\"}, {\"field\": \"row_count\", \"format\": \",\", \"title\": \"Comparisons Generated\", \"type\": \"quantitative\"}, {\"field\": \"cumulative_rows\", \"format\": \",\", \"title\": \"Cumulative Comparisons\", \"type\": \"quantitative\"}, {\"field\": \"cartesian\", \"format\": \",\", \"title\": \"Total comparisons in Cartesian product\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"start\", \"title\": \"Comparisons Generated by Rule(s)\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"cumulative_rows\"}, \"y\": {\"field\": \"blocking_rule\", \"sort\": [\"-x2\"], \"title\": \"SQL Blocking Rule\"}}, \"height\": {\"step\": 20}, \"title\": {\"text\": \"Count of Additional Comparisons Generated by Each Blocking Rule\", \"subtitle\": \"(Counts exclude comparisons already generated by previous rules)\"}, \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-48ed1d41860949bc1f837892bc2717ad\": [{\"blocking_rule\": \"(l.\\\"given_name\\\" = r.\\\"given_name\\\") AND (l.\\\"surname\\\" = r.\\\"surname\\\")\", \"row_count\": 2788, \"cumulative_rows\": 2788, \"cartesian\": 25000000, \"match_key\": \"0\", \"start\": 0}, {\"blocking_rule\": \"l.given_name = r.surname and l.surname = r.given_name\", \"row_count\": 238, \"cumulative_rows\": 3026, \"cartesian\": 25000000, \"match_key\": \"1\", \"start\": 2788}, {\"blocking_rule\": \"l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"row_count\": 21412, \"cumulative_rows\": 24438, \"cartesian\": 25000000, \"match_key\": \"2\", \"start\": 3026}, {\"blocking_rule\": \"l.\\\"soc_sec_id\\\" = r.\\\"soc_sec_id\\\"\", \"row_count\": 197, \"cumulative_rows\": 24635, \"cartesian\": 25000000, \"match_key\": \"3\", \"start\": 24438}, {\"blocking_rule\": \"(l.\\\"state\\\" = r.\\\"state\\\") AND (l.\\\"address_1\\\" = r.\\\"address_1\\\")\", \"row_count\": 5992, \"cumulative_rows\": 30627, \"cartesian\": 25000000, \"match_key\": \"4\", \"start\": 24635}, {\"blocking_rule\": \"(l.\\\"street_number\\\" = r.\\\"street_number\\\") AND (l.\\\"address_1\\\" = r.\\\"address_1\\\")\", \"row_count\": 311, \"cumulative_rows\": 30938, \"cartesian\": 25000000, \"match_key\": \"5\", \"start\": 30627}, {\"blocking_rule\": \"l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"row_count\": 24376, \"cumulative_rows\": 55314, \"cartesian\": 25000000, \"match_key\": \"6\", \"start\": 30938}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from splink import DuckDBAPI, block_on\n",
     "from splink.blocking_analysis import (\n",
@@ -225,8 +634,7 @@
     "    unique_id_column_name=\"rec_id\",\n",
     "    source_dataset_column_name=\"source_dataset\",\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -253,12 +661,13 @@
    "id": "f6360b69-2d52-4f1a-9199-2edf2339ec63",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:46.600071Z",
-     "iopub.status.busy": "2024-05-15T15:56:46.599766Z",
-     "iopub.status.idle": "2024-05-15T15:56:47.112399Z",
-     "shell.execute_reply": "2024-05-15T15:56:47.111220Z"
+     "iopub.execute_input": "2024-06-07T09:16:43.687914Z",
+     "iopub.status.busy": "2024-06-07T09:16:43.687640Z",
+     "iopub.status.idle": "2024-06-07T09:16:44.021204Z",
+     "shell.execute_reply": "2024-06-07T09:16:44.020435Z"
     }
    },
+   "outputs": [],
    "source": [
     "import splink.comparison_level_library as cll\n",
     "import splink.comparison_library as cl\n",
@@ -307,8 +716,7 @@
     "\n",
     "linker_simple = Linker(dfs, simple_model_settings, database_api=DuckDBAPI())\n",
     "linker_detailed = Linker(dfs, detailed_model_settings, database_api=DuckDBAPI())"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -342,23 +750,32 @@
    "id": "7ad48419-4eda-4fe5-b00f-2ec9f798e0e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:47.118143Z",
-     "iopub.status.busy": "2024-05-15T15:56:47.117804Z",
-     "iopub.status.idle": "2024-05-15T15:56:47.491169Z",
-     "shell.execute_reply": "2024-05-15T15:56:47.489974Z"
+     "iopub.execute_input": "2024-06-07T09:16:44.024887Z",
+     "iopub.status.busy": "2024-06-07T09:16:44.024650Z",
+     "iopub.status.idle": "2024-06-07T09:16:44.225016Z",
+     "shell.execute_reply": "2024-06-07T09:16:44.224395Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Probability two random records match is estimated to be  0.000239.\n",
+      "This means that amongst all possible pairwise record comparisons, one in 4,185.85 are expected to match.  With 25,000,000 total possible comparisons, we expect a total of around 5,972.50 matching pairs\n"
+     ]
+    }
+   ],
    "source": [
     "deterministic_rules = [\n",
     "    block_on(\"soc_sec_id\"),\n",
     "    block_on(\"given_name\", \"surname\", \"date_of_birth\"),\n",
     "]\n",
     "\n",
-    "linker_detailed.estimate_probability_two_random_records_match(\n",
+    "linker_detailed.training.estimate_probability_two_random_records_match(\n",
     "    deterministic_rules, recall=0.8\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -382,18 +799,76 @@
    "id": "e40ee288-0c42-4cda-aaf1-3ffb2ea02383",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:47.497349Z",
-     "iopub.status.busy": "2024-05-15T15:56:47.496965Z",
-     "iopub.status.idle": "2024-05-15T15:56:59.095072Z",
-     "shell.execute_reply": "2024-05-15T15:56:59.094337Z"
+     "iopub.execute_input": "2024-06-07T09:16:44.228813Z",
+     "iopub.status.busy": "2024-06-07T09:16:44.228526Z",
+     "iopub.status.idle": "2024-06-07T09:16:50.708588Z",
+     "shell.execute_reply": "2024-06-07T09:16:50.707955Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default value for `max_pairs`, which may be too small and thus lead to inaccurate estimates for your model's u-parameters. Consider increasing to 1e8 or 1e9, which will result in more accurate estimates, but with a longer run time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 month' (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 year' (comparison vector value: 2). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 10 year' (comparison vector value: 1). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - given_name (no m values are trained).\n",
+      "    - surname (no m values are trained).\n",
+      "    - date_of_birth (some u values are not trained, no m values are trained).\n",
+      "    - soc_sec_id (no m values are trained).\n",
+      "    - street_number (no m values are trained).\n",
+      "    - postcode (no m values are trained).\n"
+     ]
+    }
+   ],
    "source": [
     "# We generally recommend setting max pairs higher (e.g. 1e7 or more)\n",
     "# But this will run faster for the purpose of this demo\n",
-    "linker_detailed.estimate_u_using_random_sampling(max_pairs=1e6)"
-   ],
-   "outputs": []
+    "linker_detailed.training.estimate_u_using_random_sampling(max_pairs=1e6)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -411,21 +886,214 @@
    "id": "9ee0f49b-084c-45aa-8c6b-ec5da11c2cc4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:56:59.100504Z",
-     "iopub.status.busy": "2024-05-15T15:56:59.100174Z",
-     "iopub.status.idle": "2024-05-15T15:57:01.059609Z",
-     "shell.execute_reply": "2024-05-15T15:57:01.058521Z"
+     "iopub.execute_input": "2024-06-07T09:16:50.712950Z",
+     "iopub.status.busy": "2024-06-07T09:16:50.712681Z",
+     "iopub.status.idle": "2024-06-07T09:16:52.276811Z",
+     "shell.execute_reply": "2024-06-07T09:16:52.276216Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"date_of_birth\" = r.\"date_of_birth\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - given_name\n",
+      "    - surname\n",
+      "    - soc_sec_id\n",
+      "    - street_number\n",
+      "    - postcode\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - date_of_birth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.316 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.00365 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 8.84e-05 in the m_probability of soc_sec_id, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 3 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - date_of_birth (some u values are not trained, no m values are trained).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"postcode\" = r.\"postcode\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - given_name\n",
+      "    - surname\n",
+      "    - date_of_birth\n",
+      "    - soc_sec_id\n",
+      "    - street_number\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - postcode\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 1 month' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 1 year' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 10 year' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was 0.0374 in the m_probability of date_of_birth, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.000489 in the m_probability of date_of_birth, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 9.4e-06 in the m_probability of soc_sec_id, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 3 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 month' (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 year' (comparison vector value: 2). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 10 year' (comparison vector value: 1). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - date_of_birth (some u values are not trained, some m values are not trained).\n"
+     ]
+    }
+   ],
    "source": [
-    "session_dob = linker_detailed.estimate_parameters_using_expectation_maximisation(\n",
+    "session_dob = linker_detailed.training.estimate_parameters_using_expectation_maximisation(\n",
     "    block_on(\"date_of_birth\"), estimate_without_term_frequencies=True\n",
     ")\n",
-    "session_pc = linker_detailed.estimate_parameters_using_expectation_maximisation(\n",
+    "session_pc = linker_detailed.training.estimate_parameters_using_expectation_maximisation(\n",
     "    block_on(\"postcode\"), estimate_without_term_frequencies=True\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -441,16 +1109,95 @@
    "id": "31ef6844-6be8-4f01-9ff7-5dfebcf12ae1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:01.065654Z",
-     "iopub.status.busy": "2024-05-15T15:57:01.065325Z",
-     "iopub.status.idle": "2024-05-15T15:57:01.389061Z",
-     "shell.execute_reply": "2024-05-15T15:57:01.388339Z"
+     "iopub.execute_input": "2024-06-07T09:16:52.281563Z",
+     "iopub.status.busy": "2024-06-07T09:16:52.281303Z",
+     "iopub.status.idle": "2024-06-07T09:16:52.513958Z",
+     "shell.execute_reply": "2024-06-07T09:16:52.513314Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-17976b80813b4e15845d2b4ee677d095.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-17976b80813b4e15845d2b4ee677d095.vega-embed details,\n",
+       "  #altair-viz-17976b80813b4e15845d2b4ee677d095.vega-embed details summary {\n",
+       "    position: relative;\n",
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+       "</style>\n",
+       "<div id=\"altair-viz-17976b80813b4e15845d2b4ee677d095\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-17976b80813b4e15845d2b4ee677d095\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-17976b80813b4e15845d2b4ee677d095\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
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+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"discreteHeight\": 300, \"discreteWidth\": 400}, \"header\": {\"title\": null}, \"title\": {\"anchor\": \"middle\", \"offset\": 10}}, \"hconcat\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"green\"}, \"row\": {\"field\": \"comparison_name\", \"header\": {\"labelAlign\": \"left\", \"labelAnchor\": \"middle\", \"labelAngle\": 0}, \"sort\": {\"field\": \"comparison_sort_order\"}, \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"m_probability_description\", \"title\": \"m probability description\", \"type\": \"nominal\"}, {\"field\": \"comparison_name\", \"title\": \"Comparison column name\", \"type\": \"nominal\"}, {\"field\": \"label_for_charts\", \"title\": \"Label\", \"type\": \"ordinal\"}, {\"field\": \"sql_condition\", \"title\": \"SQL condition\", \"type\": \"nominal\"}, {\"field\": \"m_probability\", \"format\": \".4p\", \"title\": \"m probability\", \"type\": 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\"is_null_level\": false, \"bayes_factor\": 6.194402311303725, \"log2_bayes_factor\": 2.630965085575408, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.9` then comparison is 6.19 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 0}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.8\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.015306013382073153, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.53% of records are in the 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\"Amongst non-matching record comparisons, 0.31% of records are in the exact match on surname comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"surname\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 302.4067738942159, \"log2_bayes_factor\": 8.240346645980994, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 302.41 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 0}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.9\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.001474049155701179, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of 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0.01666666666666668, \"u_probability\": 0.00909207511529066, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.91% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 1.8330982152399289, \"log2_bayes_factor\": 0.8742840858470302, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.8` then comparison is 1.83 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 0}, {\"comparison_name\": \"surname\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.9862924117076621, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.63% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.016898301628226146, \"log2_bayes_factor\": -5.886977934726584, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  59.18 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 0}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.95, \"u_probability\": 0.00018947896200509552, \"m_probability_description\": \"Amongst matching record comparisons, 95.00% of records are in the exact match on soc_sec_id comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on soc_sec_id comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 5013.749230769231, \"log2_bayes_factor\": 12.291674124357469, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 5,013.75 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 0}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"m_probability\": 0.01666666666666668, \"u_probability\": 1.8462052708188793e-05, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 902.7526315789482, \"log2_bayes_factor\": 9.818186910498753, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 1` then comparison is 902.75 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 0}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.00024195005917573736, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 68.88473895582335, \"log2_bayes_factor\": 6.106112491874257, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 2` then comparison is 68.88 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 0}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.999550108926111, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.96% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.016674168226116136, \"log2_bayes_factor\": -5.906241393941308, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  59.97 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 0}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.95, \"u_probability\": 0.015193297691854736, \"m_probability_description\": \"Amongst matching record comparisons, 95.00% of records are in the exact match on street_number comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.52% of records are in the exact match on street_number comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"street_number\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 62.52757099002302, \"log2_bayes_factor\": 5.966420568817788, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on street_number` then comparison is 62.53 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 3, \"iteration\": 0}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.050000000000000044, \"u_probability\": 0.9848067023081453, \"m_probability_description\": \"Amongst matching record comparisons, 5.00% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.48% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.05077138476293095, \"log2_bayes_factor\": -4.299840580418856, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  19.70 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 3, \"iteration\": 0}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.95, \"u_probability\": 0.0011708828164930262, \"m_probability_description\": \"Amongst matching record comparisons, 95.00% of records are in the exact match on postcode comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.12% of records are in the exact match on postcode comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"postcode\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 811.3536099585061, \"log2_bayes_factor\": 9.664187006989755, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 811.35 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 0}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.012730071185788495, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.27% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 1.3092359361880783, \"log2_bayes_factor\": 0.38872510744092714, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 1.31 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 0}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 2\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.11116293441695865, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 11.12% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.14993007115260235, \"log2_bayes_factor\": -2.737638324331581, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 2` then comparison is  6.67 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 0}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.01666666666666668, \"u_probability\": 0.8749361115807598, \"m_probability_description\": \"Amongst matching record comparisons, 1.67% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.49% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.019049009917483886, \"log2_bayes_factor\": -5.714140174956706, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  52.50 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 0}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Exact match on given_name\", \"m_probability\": 0.6784454818290848, \"u_probability\": 0.004473646877289537, \"m_probability_description\": \"Amongst matching record comparisons, 67.84% of records are in the exact match on given_name comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.45% of records are in the exact match on given_name comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"given_name\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 151.65378503010876, \"log2_bayes_factor\": 7.244637695491655, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on given_name` then comparison is 151.65 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 1}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.9\", \"m_probability\": 0.13970621127016486, \"u_probability\": 0.0026906012604723563, \"m_probability_description\": \"Amongst matching record comparisons, 13.97% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.27% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 51.9237886797237, \"log2_bayes_factor\": 5.69832374998926, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.9` then comparison is 51.92 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 1}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.8\", \"m_probability\": 0.006404198593396558, \"u_probability\": 0.015306013382073153, \"m_probability_description\": \"Amongst matching record comparisons, 0.64% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.53% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.4184106229057229, \"log2_bayes_factor\": -1.2570086146723554, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.8` then comparison is  2.39 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 1}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.1754441083073538, \"u_probability\": 0.977529738480165, \"m_probability_description\": \"Amongst matching record comparisons, 17.54% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 97.75% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.17947700351308926, \"log2_bayes_factor\": -2.4781290923043366, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.57 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on surname\", \"m_probability\": 0.6766849515138509, \"u_probability\": 0.0031414640213460196, \"m_probability_description\": \"Amongst matching record comparisons, 67.67% of records are in the exact match on surname comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.31% of records are in the exact match on surname comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"surname\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 215.40432961059742, \"log2_bayes_factor\": 7.7509034380085735, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 215.40 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.9\", \"m_probability\": 0.1857241263330773, \"u_probability\": 0.001474049155701179, \"m_probability_description\": \"Amongst matching record comparisons, 18.57% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.15% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 125.99588393287443, \"log2_bayes_factor\": 6.9772327939235295, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.9` then comparison is 126.00 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.8\", \"m_probability\": 0.0037558880987553977, \"u_probability\": 0.00909207511529066, \"m_probability_description\": \"Amongst matching record comparisons, 0.38% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.91% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.41309470622816424, \"log2_bayes_factor\": -1.2754555225832116, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.8` then comparison is  2.42 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13383503405431652, \"u_probability\": 0.9862924117076621, \"m_probability_description\": \"Amongst matching record comparisons, 13.38% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.63% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.1356950864324254, \"log2_bayes_factor\": -2.881559613687817, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.37 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 1}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.9150112420191042, \"u_probability\": 0.00018947896200509552, \"m_probability_description\": \"Amongst matching record comparisons, 91.50% of records are in the exact match on soc_sec_id comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on soc_sec_id comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4829.0914850720865, \"log2_bayes_factor\": 12.23753607967185, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 4,829.09 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 1}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"m_probability\": 0.05033762845764586, \"u_probability\": 1.8462052708188793e-05, \"m_probability_description\": \"Amongst matching record comparisons, 5.03% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2726.545593454987, \"log2_bayes_factor\": 11.412858565151893, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 1` then comparison is 2,726.55 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 1}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"m_probability\": 0.0006624825645740673, \"u_probability\": 0.00024195005917573736, \"m_probability_description\": \"Amongst matching record comparisons, 0.07% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.7380963114081385, \"log2_bayes_factor\": 1.453173193691194, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 2` then comparison is 2.74 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 1}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.03398864695867587, \"u_probability\": 0.999550108926111, \"m_probability_description\": \"Amongst matching record comparisons, 3.40% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.96% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.034003945030021895, \"log2_bayes_factor\": -4.878154056791674, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  29.41 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 1}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.8472166613645412, \"u_probability\": 0.015193297691854736, \"m_probability_description\": \"Amongst matching record comparisons, 84.72% of records are in the exact match on street_number comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.52% of records are in the exact match on street_number comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"street_number\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 55.762526249896474, \"log2_bayes_factor\": 5.801224017026971, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on street_number` then comparison is 55.76 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 3, \"iteration\": 1}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.1527833386354587, \"u_probability\": 0.9848067023081453, \"m_probability_description\": \"Amongst matching record comparisons, 15.28% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.48% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.15514043342452083, \"log2_bayes_factor\": -2.6883533575858327, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.45 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 3, \"iteration\": 1}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.8428270888044572, \"u_probability\": 0.0011708828164930262, \"m_probability_description\": \"Amongst matching record comparisons, 84.28% of records are in the exact match on postcode comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.12% of records are in the exact match on postcode comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"postcode\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 719.8218958655946, \"log2_bayes_factor\": 9.491496177250285, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 719.82 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 1}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14427178876945038, \"u_probability\": 0.012730071185788495, \"m_probability_description\": \"Amongst matching record comparisons, 14.43% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.27% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.333148626105993, \"log2_bayes_factor\": 3.502476827731619, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.33 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 1}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 2\", \"m_probability\": 0.004913159060189934, \"u_probability\": 0.11116293441695865, \"m_probability_description\": \"Amongst matching record comparisons, 0.49% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 11.12% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.04419781724869975, \"log2_bayes_factor\": -4.499881067267393, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 2` then comparison is  22.63 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 1}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.007987963365902375, \"u_probability\": 0.8749361115807598, \"m_probability_description\": \"Amongst matching record comparisons, 0.80% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.49% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.00912976760265433, \"log2_bayes_factor\": -6.775206147647038, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  109.53 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 1}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Exact match on given_name\", \"m_probability\": 0.6753807158066163, \"u_probability\": 0.004473646877289537, \"m_probability_description\": \"Amongst matching record comparisons, 67.54% of records are in the exact match on given_name comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.45% of records are in the exact match on given_name comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"given_name\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 150.9687139669395, \"log2_bayes_factor\": 7.23810579309112, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on given_name` then comparison is 150.97 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 2}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.9\", \"m_probability\": 0.1391512873996204, \"u_probability\": 0.0026906012604723563, \"m_probability_description\": \"Amongst matching record comparisons, 13.92% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.27% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 51.71754337734581, \"log2_bayes_factor\": 5.692581842551108, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.9` then comparison is 51.72 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 2}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.8\", \"m_probability\": 0.006374699190209097, \"u_probability\": 0.015306013382073153, \"m_probability_description\": \"Amongst matching record comparisons, 0.64% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.53% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.41648331483071416, \"log2_bayes_factor\": -1.2636693954385743, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.8` then comparison is  2.40 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 2}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.17909329760355414, \"u_probability\": 0.977529738480165, \"m_probability_description\": \"Amongst matching record comparisons, 17.91% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 97.75% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.18321007592260388, \"log2_bayes_factor\": -2.4484292460167114, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.46 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 2}, {\"comparison_name\": \"surname\", \"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on surname\", \"m_probability\": 0.6738696848092763, \"u_probability\": 0.0031414640213460196, \"m_probability_description\": \"Amongst matching record comparisons, 67.39% of records are in the exact match on surname comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.31% of records are in the exact match on surname comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"surname\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 214.50816569293195, \"log2_bayes_factor\": 7.744888757688213, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 214.51 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 2}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.9\", \"m_probability\": 0.1851322244112338, \"u_probability\": 0.001474049155701179, \"m_probability_description\": \"Amongst matching record comparisons, 18.51% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.15% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 125.5943356401637, \"log2_bayes_factor\": 6.972627589289917, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.9` then comparison is 125.59 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 2}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.8\", \"m_probability\": 0.0037372790994608363, \"u_probability\": 0.00909207511529066, \"m_probability_description\": \"Amongst matching record comparisons, 0.37% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.91% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.4110479788245085, \"log2_bayes_factor\": -1.2826212952083622, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.8` then comparison is  2.43 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 2}, {\"comparison_name\": \"surname\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13726081168002915, \"u_probability\": 0.9862924117076621, \"m_probability_description\": \"Amongst matching record comparisons, 13.73% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.63% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.13916847585025663, \"log2_bayes_factor\": -2.8450956429327676, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.19 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 2}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.9123048362871907, \"u_probability\": 0.00018947896200509552, \"m_probability_description\": \"Amongst matching record comparisons, 91.23% of records are in the exact match on soc_sec_id comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on soc_sec_id comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4814.808074907318, \"log2_bayes_factor\": 12.233262576002273, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 4,814.81 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 2}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"m_probability\": 0.050123195220372105, \"u_probability\": 1.8462052708188793e-05, \"m_probability_description\": \"Amongst matching record comparisons, 5.01% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2714.9307833001744, \"log2_bayes_factor\": 11.406699701895219, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 1` then comparison is 2,714.93 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 2}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"m_probability\": 0.0006594105240815229, \"u_probability\": 0.00024195005917573736, \"m_probability_description\": \"Amongst matching record comparisons, 0.07% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.7253993089647, \"log2_bayes_factor\": 1.4464676202965892, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 2` then comparison is 2.73 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 2}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.0369125579683556, \"u_probability\": 0.999550108926111, \"m_probability_description\": \"Amongst matching record comparisons, 3.69% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.96% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.03692917207323747, \"log2_bayes_factor\": -4.759095271104032, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  27.08 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 2, \"iteration\": 2}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.8461159207936304, \"u_probability\": 0.015193297691854736, \"m_probability_description\": \"Amongst matching record comparisons, 84.61% of records are in the exact match on street_number comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.52% of records are in the exact match on street_number comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"street_number\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 55.690077161276236, \"log2_bayes_factor\": 5.7993483864469315, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on street_number` then comparison is 55.69 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 3, \"iteration\": 2}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.15388407920636946, \"u_probability\": 0.9848067023081453, \"m_probability_description\": \"Amongst matching record comparisons, 15.39% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.48% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.1562581558855184, \"log2_bayes_factor\": -2.677996601762165, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.40 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 3, \"iteration\": 2}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.8416909960803431, \"u_probability\": 0.0011708828164930262, \"m_probability_description\": \"Amongst matching record comparisons, 84.17% of records are in the exact match on postcode comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.12% of records are in the exact match on postcode comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"postcode\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 718.8516085677445, \"log2_bayes_factor\": 9.489550177854628, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 718.85 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 2}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14505428248647406, \"u_probability\": 0.012730071185788495, \"m_probability_description\": \"Amongst matching record comparisons, 14.51% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.27% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.394616759756122, \"log2_bayes_factor\": 3.5102804974987203, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.39 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 2}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 2\", \"m_probability\": 0.0049354606434434236, \"u_probability\": 0.11116293441695865, \"m_probability_description\": \"Amongst matching record comparisons, 0.49% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 11.12% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.044398437926540434, \"log2_bayes_factor\": -4.493347270750635, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 2` then comparison is  22.52 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 2}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.008319260789739383, \"u_probability\": 0.8749361115807598, \"m_probability_description\": \"Amongst matching record comparisons, 0.83% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.49% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.009508420877392813, \"log2_bayes_factor\": -6.716578521023216, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  105.17 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 2}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Exact match on given_name\", \"m_probability\": 0.6753271300685155, \"u_probability\": 0.004473646877289537, \"m_probability_description\": \"Amongst matching record comparisons, 67.53% of records are in the exact match on given_name comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.45% of records are in the exact match on given_name comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"given_name\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 150.9567358784648, \"log2_bayes_factor\": 7.237991322920688, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on given_name` then comparison is 150.96 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 3}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.9\", \"m_probability\": 0.13913968381577332, \"u_probability\": 0.0026906012604723563, \"m_probability_description\": \"Amongst matching record comparisons, 13.91% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.27% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 51.713230741349705, \"log2_bayes_factor\": 5.692461533704512, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.9` then comparison is 51.71 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 3}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.8\", \"m_probability\": 0.006374130721386753, \"u_probability\": 0.015306013382073153, \"m_probability_description\": \"Amongst matching record comparisons, 0.64% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.53% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.41644617460300404, \"log2_bayes_factor\": -1.2637980546421446, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.8` then comparison is  2.40 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 3}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.17915905539432436, \"u_probability\": 0.977529738480165, \"m_probability_description\": \"Amongst matching record comparisons, 17.92% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 97.75% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.18327734527327597, \"log2_bayes_factor\": -2.447899628059339, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.46 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 0, \"iteration\": 3}, {\"comparison_name\": \"surname\", \"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on surname\", \"m_probability\": 0.6738051277674583, \"u_probability\": 0.0031414640213460196, \"m_probability_description\": \"Amongst matching record comparisons, 67.38% of records are in the exact match on surname comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.31% of records are in the exact match on surname comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"surname\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 214.48761570688106, \"log2_bayes_factor\": 7.744750540184572, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 214.49 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 3}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.9\", \"m_probability\": 0.18512210772531942, \"u_probability\": 0.001474049155701179, \"m_probability_description\": \"Amongst matching record comparisons, 18.51% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.15% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 125.58747244576124, \"log2_bayes_factor\": 6.972548750009262, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.9` then comparison is 125.59 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 3}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.8\", \"m_probability\": 0.003736987993781811, \"u_probability\": 0.00909207511529066, \"m_probability_description\": \"Amongst matching record comparisons, 0.37% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.91% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.41101596130646845, \"log2_bayes_factor\": -1.2827336745800058, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.8` then comparison is  2.43 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 3}, {\"comparison_name\": \"surname\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13733577651344064, \"u_probability\": 0.9862924117076621, \"m_probability_description\": \"Amongst matching record comparisons, 13.73% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.63% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.13924448255224647, \"log2_bayes_factor\": -2.8443079317903814, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.18 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 1, \"iteration\": 3}, {\"comparison_name\": \"soc_sec_id\", 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exact match on postcode comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"postcode\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 718.8240034559097, \"log2_bayes_factor\": 9.489494774872734, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 718.82 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 3}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14506690077074827, \"u_probability\": 0.012730071185788495, \"m_probability_description\": \"Amongst matching record comparisons, 14.51% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.27% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.395607978429611, \"log2_bayes_factor\": 3.510405992203754, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.40 times more likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 3}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 2\", \"m_probability\": 0.004943322055731211, \"u_probability\": 0.11116293441695865, \"m_probability_description\": \"Amongst matching record comparisons, 0.49% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 11.12% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.04446915765276051, \"log2_bayes_factor\": -4.491051112760025, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 2` then comparison is  22.49 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 3}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.008331103444272117, \"u_probability\": 0.8749361115807598, \"m_probability_description\": \"Amongst matching record comparisons, 0.83% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.49% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.009521956328011415, \"log2_bayes_factor\": -6.714526272603148, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  105.02 times less likely to be a match\", \"probability_two_random_records_match\": 0.1910771142442733, \"comparison_sort_order\": 4, \"iteration\": 3}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.HConcatChart(...)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "session_dob.m_u_values_interactive_history_chart()"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -466,16 +1213,95 @@
    "id": "8d260a60-a4fa-4c0d-9853-8b8256a24257",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:01.393145Z",
-     "iopub.status.busy": "2024-05-15T15:57:01.392842Z",
-     "iopub.status.idle": "2024-05-15T15:57:01.561233Z",
-     "shell.execute_reply": "2024-05-15T15:57:01.560475Z"
+     "iopub.execute_input": "2024-06-07T09:16:52.517168Z",
+     "iopub.status.busy": "2024-06-07T09:16:52.516948Z",
+     "iopub.status.idle": "2024-06-07T09:16:52.637604Z",
+     "shell.execute_reply": "2024-06-07T09:16:52.636662Z"
     }
    },
-   "source": [
-    "linker_detailed.parameter_estimate_comparisons_chart()"
+   "outputs": [
+    {
+     "data": {
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\"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -7.1233292579677245, \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.8\", \"comparison_level_label\": \"Jaro-Winkler distance of given_name >= 0.8\", \"comparison_vector_value\": 1, \"comparison_name\": \"given_name\", \"comparison_sort_order\": 0}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.17915905539432436, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -2.1958617314295936, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"given_name\", \"comparison_sort_order\": 0}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.17647671087774758, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", 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surname\", \"comparison_vector_value\": 3, \"comparison_name\": \"surname\", \"comparison_sort_order\": 1}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.18512210772531942, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -2.1381066951521026, \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"comparison_level_label\": \"Jaro-Winkler distance of surname >= 0.9\", \"comparison_vector_value\": 2, \"comparison_name\": \"surname\", \"comparison_sort_order\": 1}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.1886371285558509, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -2.104733622460238, \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"comparison_level_label\": \"Jaro-Winkler distance of surname >= 0.9\", \"comparison_vector_value\": 2, \"comparison_name\": \"surname\", \"comparison_sort_order\": 1}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.003736987993781811, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -8.058506923146409, \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.8\", \"comparison_level_label\": \"Jaro-Winkler distance of surname >= 0.8\", \"comparison_vector_value\": 1, \"comparison_name\": \"surname\", \"comparison_sort_order\": 1}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.004022485431406998, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -7.951882165979119, \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.8\", \"comparison_level_label\": \"Jaro-Winkler distance of surname >= 0.8\", \"comparison_vector_value\": 1, \"comparison_name\": \"surname\", \"comparison_sort_order\": 1}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.13733577651344064, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -2.6510916233826425, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"surname\", \"comparison_sort_order\": 1}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.1367865110120646, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -2.6577914411244494, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"surname\", \"comparison_sort_order\": 1}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.9413247834677082, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": 4.003869413197312, \"sql_condition\": \"\\\"date_of_birth_l\\\" = \\\"date_of_birth_r\\\"\", \"comparison_level_label\": \"Exact match on date_of_birth\", \"comparison_vector_value\": 5, \"comparison_name\": \"date_of_birth\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.010781837446462814, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -6.51961375958237, \"sql_condition\": \"damerau_levenshtein(\\\"date_of_birth_l\\\", \\\"date_of_birth_r\\\") <= 1\", \"comparison_level_label\": \"Damerau-Levenshtein distance of date_of_birth <= 1\", \"comparison_vector_value\": 4, \"comparison_name\": \"date_of_birth\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": \"level not observed in training dataset\", \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": null, \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 2629800.0\", \"comparison_level_label\": \"Abs difference of 'transformed date_of_birth <= 1 month'\", \"comparison_vector_value\": 3, \"comparison_name\": \"date_of_birth\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": \"level not observed in training dataset\", \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": null, \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 31557600.0\", \"comparison_level_label\": \"Abs difference of 'transformed date_of_birth <= 1 year'\", \"comparison_vector_value\": 2, \"comparison_name\": \"date_of_birth\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": \"level not observed in training dataset\", \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": null, \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 315576000.0\", \"comparison_level_label\": \"Abs difference of 'transformed date_of_birth <= 10 year'\", \"comparison_vector_value\": 1, \"comparison_name\": \"date_of_birth\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.04789337908582895, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -4.313225008977755, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"date_of_birth\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.9122207117443828, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": 3.377430449105042, \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"comparison_level_label\": \"Exact match on soc_sec_id\", \"comparison_vector_value\": 3, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.9111614766980942, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": 3.3584494348200287, \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"comparison_level_label\": \"Exact match on soc_sec_id\", \"comparison_vector_value\": 3, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.05011897568364008, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -4.244317987807588, \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"comparison_level_label\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"comparison_vector_value\": 2, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.05100307287518862, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -4.217747340276537, \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"comparison_level_label\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"comparison_vector_value\": 2, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.0006593447559218409, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -10.565727818459225, \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"comparison_level_label\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"comparison_vector_value\": 1, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.0004746887729168959, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -11.040045457493472, \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"comparison_level_label\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"comparison_vector_value\": 1, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.03700096781605524, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -4.70189943594794, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.03736076165380017, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -4.687399458596249, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"soc_sec_id\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.8460925240982402, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": 2.458752126454589, \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"comparison_level_label\": \"Exact match on street_number\", \"comparison_vector_value\": 1, \"comparison_name\": \"street_number\", \"comparison_sort_order\": 4}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.8383397070506713, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": 2.374569595615257, \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"comparison_level_label\": \"Exact match on street_number\", \"comparison_vector_value\": 1, \"comparison_name\": \"street_number\", \"comparison_sort_order\": 4}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.1539074759017598, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -2.458752126454589, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"street_number\", \"comparison_sort_order\": 4}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.1616602929493287, \"estimate_description\": \"EM, blocked on: l.\\\"postcode\\\" = r.\\\"postcode\\\"\", \"estimated_probability_as_log_odds\": -2.374569595615257, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"street_number\", \"comparison_sort_order\": 4}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.8416586737292484, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": 2.4101974407296693, \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"comparison_level_label\": \"Exact match on postcode\", \"comparison_vector_value\": 3, \"comparison_name\": \"postcode\", \"comparison_sort_order\": 5}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.14506690077074827, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -2.5590931456802997, \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"comparison_level_label\": \"Damerau-Levenshtein distance of postcode <= 1\", \"comparison_vector_value\": 2, \"comparison_name\": \"postcode\", \"comparison_sort_order\": 5}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.004943322055731211, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -7.653153992353945, \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"comparison_level_label\": \"Damerau-Levenshtein distance of postcode <= 2\", \"comparison_vector_value\": 1, \"comparison_name\": \"postcode\", \"comparison_sort_order\": 5}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.008331103444272117, \"estimate_description\": \"EM, blocked on: l.\\\"date_of_birth\\\" = r.\\\"date_of_birth\\\"\", \"estimated_probability_as_log_odds\": -6.89520710503466, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"postcode\", \"comparison_sort_order\": 5}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "outputs": []
+   "source": [
+    "linker_detailed.visualisations.parameter_estimate_comparisons_chart()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -491,26 +1317,577 @@
    "id": "71f2f166-05cd-4038-a289-a053a1f0b5c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:01.565611Z",
-     "iopub.status.busy": "2024-05-15T15:57:01.565220Z",
-     "iopub.status.idle": "2024-05-15T15:57:04.177024Z",
-     "shell.execute_reply": "2024-05-15T15:57:04.176371Z"
+     "iopub.execute_input": "2024-06-07T09:16:52.640970Z",
+     "iopub.status.busy": "2024-06-07T09:16:52.640725Z",
+     "iopub.status.idle": "2024-06-07T09:16:54.701590Z",
+     "shell.execute_reply": "2024-06-07T09:16:54.701058Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Probability two random records match is estimated to be  0.000239.\n",
+      "This means that amongst all possible pairwise record comparisons, one in 4,185.85 are expected to match.  With 25,000,000 total possible comparisons, we expect a total of around 5,972.50 matching pairs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - given_name (no m values are trained).\n",
+      "    - surname (no m values are trained).\n",
+      "    - street_number (no m values are trained).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"given_name\" = r.\"given_name\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - surname\n",
+      "    - street_number\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - given_name\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.0816 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.0263 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was -0.0249 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.0229 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was -0.02 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was -0.0165 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was -0.0132 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was 0.0102 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was -0.00772 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was 0.00577 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 11: Largest change in params was -0.00428 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 12: Largest change in params was 0.00316 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 13: Largest change in params was -0.00233 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 14: Largest change in params was -0.00172 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 15: Largest change in params was 0.00127 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 16: Largest change in params was 0.000936 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 17: Largest change in params was -0.000691 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 18: Largest change in params was -0.000511 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 19: Largest change in params was 0.000378 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 20: Largest change in params was -0.00028 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 21: Largest change in params was 0.000208 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 22: Largest change in params was -0.000154 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 23: Largest change in params was 0.000114 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 24: Largest change in params was -8.48e-05 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 24 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - given_name (no m values are trained).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"street_number\" = r.\"street_number\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - given_name\n",
+      "    - surname\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - street_number\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was 0.0445 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.0288 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was -0.0278 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.0269 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was -0.0245 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was 0.0209 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was -0.0169 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was 0.0132 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was -0.00995 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was 0.00738 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 11: Largest change in params was -0.00541 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 12: Largest change in params was -0.00396 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 13: Largest change in params was -0.0029 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 14: Largest change in params was 0.00213 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 15: Largest change in params was -0.00158 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 16: Largest change in params was 0.00118 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 17: Largest change in params was -0.000894 in the m_probability of given_name, level `Exact match on given_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 18: Largest change in params was 0.000683 in the m_probability of given_name, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 19: Largest change in params was -0.000561 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 20: Largest change in params was 0.000469 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 21: Largest change in params was -0.000389 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 22: Largest change in params was -0.000321 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 23: Largest change in params was 0.000264 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 24: Largest change in params was 0.000217 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 25: Largest change in params was 0.000177 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 25 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "linker_simple.estimate_probability_two_random_records_match(\n",
+    "linker_simple.training.estimate_probability_two_random_records_match(\n",
     "    deterministic_rules, recall=0.8\n",
     ")\n",
-    "linker_simple.estimate_u_using_random_sampling(max_pairs=1e7)\n",
-    "session_ssid = linker_simple.estimate_parameters_using_expectation_maximisation(\n",
+    "linker_simple.training.estimate_u_using_random_sampling(max_pairs=1e7)\n",
+    "session_ssid = linker_simple.training.estimate_parameters_using_expectation_maximisation(\n",
     "    block_on(\"given_name\"), estimate_without_term_frequencies=True\n",
     ")\n",
-    "session_pc = linker_simple.estimate_parameters_using_expectation_maximisation(\n",
+    "session_pc = linker_simple.training.estimate_parameters_using_expectation_maximisation(\n",
     "    block_on(\"street_number\"), estimate_without_term_frequencies=True\n",
     ")\n",
-    "linker_simple.parameter_estimate_comparisons_chart()"
-   ],
-   "outputs": []
+    "linker_simple.visualisations.parameter_estimate_comparisons_chart()"
+   ]
   },
   {
    "cell_type": "code",
@@ -518,12 +1895,13 @@
    "id": "3a87cb78-0e97-40a3-b757-6c99bb19d7b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:04.180496Z",
-     "iopub.status.busy": "2024-05-15T15:57:04.180247Z",
-     "iopub.status.idle": "2024-05-15T15:57:04.183145Z",
-     "shell.execute_reply": "2024-05-15T15:57:04.182523Z"
+     "iopub.execute_input": "2024-06-07T09:16:54.704569Z",
+     "iopub.status.busy": "2024-06-07T09:16:54.704327Z",
+     "iopub.status.idle": "2024-06-07T09:16:54.707573Z",
+     "shell.execute_reply": "2024-06-07T09:16:54.707000Z"
     }
    },
+   "outputs": [],
    "source": [
     "# import json\n",
     "# we can have a look at the full settings if we wish, including the values of our estimated parameters:\n",
@@ -531,8 +1909,7 @@
     "# we can also get a handy summary of of the model in an easily readable format if we wish:\n",
     "# print(linker_detailed._settings_obj.human_readable_description)\n",
     "# (we suppress output here for brevity)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -550,16 +1927,95 @@
    "id": "b17b131c-c83e-4c32-bfad-c12021d2c3b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:04.186220Z",
-     "iopub.status.busy": "2024-05-15T15:57:04.185782Z",
-     "iopub.status.idle": "2024-05-15T15:57:04.541188Z",
-     "shell.execute_reply": "2024-05-15T15:57:04.540169Z"
+     "iopub.execute_input": "2024-06-07T09:16:54.710434Z",
+     "iopub.status.busy": "2024-06-07T09:16:54.710226Z",
+     "iopub.status.idle": "2024-06-07T09:16:54.974408Z",
+     "shell.execute_reply": "2024-06-07T09:16:54.973855Z"
     }
    },
-   "source": [
-    "linker_simple.match_weights_chart()"
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
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+       "      outputDiv = document.getElementById(\"altair-viz-e4a7d13e59014c1ebaf77ce0ce00567b\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
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+       "\n",
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+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.VConcatChart(...)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "outputs": []
+   "source": [
+    "linker_simple.visualisations.match_weights_chart()"
+   ]
   },
   {
    "cell_type": "code",
@@ -567,16 +2023,95 @@
    "id": "c095ff2b-405b-427c-849f-1468f6ca98e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:04.545921Z",
-     "iopub.status.busy": "2024-05-15T15:57:04.545071Z",
-     "iopub.status.idle": "2024-05-15T15:57:04.888788Z",
-     "shell.execute_reply": "2024-05-15T15:57:04.887944Z"
+     "iopub.execute_input": "2024-06-07T09:16:54.977562Z",
+     "iopub.status.busy": "2024-06-07T09:16:54.977352Z",
+     "iopub.status.idle": "2024-06-07T09:16:55.252915Z",
+     "shell.execute_reply": "2024-06-07T09:16:55.251950Z"
     }
    },
-   "source": [
-    "linker_detailed.match_weights_chart()"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-4e3a61129ff6454cbf1cb4a803396ad2.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-4e3a61129ff6454cbf1cb4a803396ad2.vega-embed details,\n",
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+       "</style>\n",
+       "<div id=\"altair-viz-4e3a61129ff6454cbf1cb4a803396ad2\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-4e3a61129ff6454cbf1cb4a803396ad2\") {\n",
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+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
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+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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+       "\n",
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+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
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+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"discreteHeight\": 60, \"discreteWidth\": 400}, \"header\": {\"title\": null}, \"mark\": {\"tooltip\": null}, \"title\": {\"anchor\": \"middle\"}}, \"vconcat\": [{\"mark\": {\"type\": \"bar\", \"clip\": true, \"height\": 15}, \"encoding\": {\"color\": {\"field\": \"log2_bayes_factor\", \"scale\": {\"domain\": [-10, 0, 10], \"interpolate\": \"lab\", \"range\": [\"red\", \"#bbbbbb\", \"green\"]}, \"title\": \"Match weight\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"comparison_name\", \"title\": \"Comparison name\", \"type\": \"nominal\"}, {\"field\": \"probability_two_random_records_match\", \"format\": \".4f\", \"title\": \"Probability two random records match\", \"type\": \"nominal\"}, {\"field\": \"log2_bayes_factor\", \"format\": \",.4f\", \"title\": \"Equivalent match weight\", \"type\": \"quantitative\"}, {\"field\": \"bayes_factor_description\", \"title\": \"Match weight 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\"m_probability\": null, \"u_probability\": null, \"m_probability_description\": null, \"u_probability_description\": null, \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": null, \"is_null_level\": false, \"bayes_factor\": 0.00023895708684804802, \"log2_bayes_factor\": -12.030960824733759, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 0, \"bayes_factor_description\": \"The probability that two random records drawn at random match is 0.000 or one in  4,185.9 records.This is equivalent to a starting match weight of -12.031.\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": -1}, {\"comparison_name\": \"given_name\", \"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Exact match on given_name\", \"m_probability\": 0.6761102648451272, \"u_probability\": 0.004473646877289537, \"m_probability_description\": \"Amongst matching record comparisons, 67.61% of 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\"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.8\", \"m_probability\": 0.006747724612959055, \"u_probability\": 0.015306013382073153, \"m_probability_description\": \"Amongst matching record comparisons, 0.67% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.53% of records are in the jaro-winkler distance of given_name >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.4408544827787872, \"log2_bayes_factor\": -1.1816255652215457, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.8` then comparison is  2.27 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, 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\"surname\", \"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on surname\", \"m_probability\": 0.6721795013840679, \"u_probability\": 0.0031414640213460196, \"m_probability_description\": \"Amongst matching record comparisons, 67.22% of records are in the exact match on surname comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.31% of records are in the exact match on surname comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"surname\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 213.97014156987223, \"log2_bayes_factor\": 7.741265679791364, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 213.97 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.9\", \"m_probability\": 0.18687961814058518, \"u_probability\": 0.001474049155701179, \"m_probability_description\": \"Amongst matching record comparisons, 18.69% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.15% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 126.77977353590346, \"log2_bayes_factor\": 6.986180785827256, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.9` then comparison is 126.78 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.8\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.8\", \"m_probability\": 0.0038797367125944044, \"u_probability\": 0.00909207511529066, \"m_probability_description\": \"Amongst matching record comparisons, 0.39% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.91% of records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.4267163066074576, \"log2_bayes_factor\": -1.2286508519082064, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, 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\"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"\\\"date_of_birth_l\\\" = \\\"date_of_birth_r\\\"\", \"label_for_charts\": \"Exact match on date_of_birth\", \"m_probability\": 0.9413247834677082, \"u_probability\": 0.00022182603061405572, \"m_probability_description\": \"Amongst matching record comparisons, 94.13% of records are in the exact match on date_of_birth comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on date_of_birth comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4243.527149910883, \"log2_bayes_factor\": 12.051048192450033, \"comparison_vector_value\": 5, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `exact match on date_of_birth` then comparison is 4,243.53 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"damerau_levenshtein(\\\"date_of_birth_l\\\", \\\"date_of_birth_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of date_of_birth <= 1\", \"m_probability\": 0.010781837446462814, \"u_probability\": 0.0009770662836814455, \"m_probability_description\": \"Amongst matching record comparisons, 1.08% of records are in the damerau-levenshtein distance of date_of_birth <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.10% of records are in the damerau-levenshtein distance of date_of_birth <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.034908917170284, \"log2_bayes_factor\": 3.4640028164587586, \"comparison_vector_value\": 4, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of date_of_birth <= 1` then comparison is 11.03 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 2629800.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 1 month'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.0050000000000000044, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 1 month' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.50% of records are in the abs difference of 'transformed date_of_birth <= 1 month' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.0, \"log2_bayes_factor\": 1.0, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 1 month'` then comparison is 2.00 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 31557600.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 1 year'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.020000000000000018, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 1 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 2.00% of records are in the abs difference of 'transformed date_of_birth <= 1 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.5, \"log2_bayes_factor\": -1.0, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 1 year'` then comparison is  2.00 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 315576000.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 10 year'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.08000000000000007, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 8.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.125, \"log2_bayes_factor\": -3.0, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 10 year'` then comparison is  8.00 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.04789337908582895, \"u_probability\": 0.9988011076857045, \"m_probability_description\": \"Amongst matching record comparisons, 4.79% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.88% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.04795086701175314, \"log2_bayes_factor\": -4.382299288561595, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  20.85 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.9116910942212385, \"u_probability\": 0.00018947896200509552, \"m_probability_description\": \"Amongst matching record comparisons, 91.17% of records are in the exact match on soc_sec_id comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on soc_sec_id comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4811.568970895677, \"log2_bayes_factor\": 12.23229169371735, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 4,811.57 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"m_probability\": 0.05056102427941435, \"u_probability\": 1.8462052708188793e-05, \"m_probability_description\": \"Amongst matching record comparisons, 5.06% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2738.6458634141013, \"log2_bayes_factor\": 11.419247006664481, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 1` then comparison is 2,738.65 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"m_probability\": 0.0005670167644193685, \"u_probability\": 0.00024195005917573736, \"m_probability_description\": \"Amongst matching record comparisons, 0.06% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.343528108036225, \"log2_bayes_factor\": 1.2286820984573408, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 2` then comparison is 2.34 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.03718086473492771, \"u_probability\": 0.999550108926111, \"m_probability_description\": \"Amongst matching record comparisons, 3.72% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.96% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.03719759960295918, \"log2_bayes_factor\": -4.748646663942527, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  26.88 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.8422161155744557, \"u_probability\": 0.015193297691854736, \"m_probability_description\": \"Amongst matching record comparisons, 84.22% of records are in the exact match on street_number comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.52% of records are in the exact match on street_number comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"street_number\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 55.433397847919174, \"log2_bayes_factor\": 5.79268353671938, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on street_number` then comparison is 55.43 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.15778388442554425, \"u_probability\": 0.9848067023081453, \"m_probability_description\": \"Amongst matching record comparisons, 15.78% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.48% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.16021812611118258, \"log2_bayes_factor\": -2.6418907201032495, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.24 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.8416586737292484, \"u_probability\": 0.0011708828164930262, \"m_probability_description\": \"Amongst matching record comparisons, 84.17% of records are in the exact match on postcode comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.12% of records are in the exact match on postcode comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"postcode\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 718.8240034559097, \"log2_bayes_factor\": 9.489494774872734, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 718.82 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14506690077074827, \"u_probability\": 0.012730071185788495, \"m_probability_description\": \"Amongst matching record comparisons, 14.51% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.27% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.395607978429611, \"log2_bayes_factor\": 3.510405992203754, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.40 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 2\", \"m_probability\": 0.004943322055731211, \"u_probability\": 0.11116293441695865, \"m_probability_description\": \"Amongst matching record comparisons, 0.49% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 11.12% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.04446915765276051, \"log2_bayes_factor\": -4.491051112760025, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 2` then comparison is  22.49 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.008331103444272117, \"u_probability\": 0.8749361115807598, \"m_probability_description\": \"Amongst matching record comparisons, 0.83% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.49% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.009521956328011415, \"log2_bayes_factor\": -6.714526272603148, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  105.02 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.VConcatChart(...)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "outputs": []
+   "source": [
+    "linker_detailed.visualisations.match_weights_chart()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -594,17 +2129,96 @@
    "id": "26e5dbe5-a621-44ab-bdb4-0bcd53b220b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:04.893722Z",
-     "iopub.status.busy": "2024-05-15T15:57:04.893207Z",
-     "iopub.status.idle": "2024-05-15T15:57:05.067224Z",
-     "shell.execute_reply": "2024-05-15T15:57:05.066686Z"
+     "iopub.execute_input": "2024-06-07T09:16:55.256437Z",
+     "iopub.status.busy": "2024-06-07T09:16:55.256148Z",
+     "iopub.status.idle": "2024-06-07T09:16:55.408274Z",
+     "shell.execute_reply": "2024-06-07T09:16:55.407631Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-80ac4a3e01564838b5b3b063ea4d2f65.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-80ac4a3e01564838b5b3b063ea4d2f65.vega-embed details,\n",
+       "  #altair-viz-80ac4a3e01564838b5b3b063ea4d2f65.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-80ac4a3e01564838b5b3b063ea4d2f65\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-80ac4a3e01564838b5b3b063ea4d2f65\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-80ac4a3e01564838b5b3b063ea4d2f65\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"discreteHeight\": 300, \"discreteWidth\": 400}, \"header\": {\"title\": null}, \"title\": {\"anchor\": \"middle\", \"offset\": 10}}, \"hconcat\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"green\"}, \"row\": {\"field\": \"comparison_name\", \"header\": {\"labelAlign\": \"left\", \"labelAnchor\": \"middle\", \"labelAngle\": 0}, \"sort\": {\"field\": \"comparison_sort_order\"}, \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"m_probability_description\", \"title\": \"m probability description\", \"type\": \"nominal\"}, {\"field\": \"comparison_name\", \"title\": \"Comparison column name\", \"type\": \"nominal\"}, {\"field\": \"label_for_charts\", \"title\": \"Label\", \"type\": \"ordinal\"}, {\"field\": \"sql_condition\", \"title\": \"SQL condition\", \"type\": \"nominal\"}, {\"field\": \"m_probability\", \"format\": \".4p\", \"title\": \"m probability\", \"type\": \"quantitative\"}, {\"field\": \"u_probability\", \"format\": \".4p\", \"title\": \"u probability\", \"type\": \"quantitative\"}, {\"field\": \"bayes_factor\", \"format\": \",.4f\", \"title\": \"Bayes factor = m/u\", \"type\": \"quantitative\"}, {\"field\": \"log2_bayes_factor\", \"format\": \",.4f\", \"title\": \"Match weight = log2(m/u)\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"title\": \"Proportion of record comparisons\"}, \"field\": \"m_probability\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"title\": null}, \"field\": \"label_for_charts\", \"sort\": {\"field\": \"comparison_vector_value\", \"order\": \"descending\"}, \"type\": \"nominal\"}}, \"height\": {\"step\": 12}, \"resolve\": {\"scale\": {\"y\": \"independent\"}}, \"title\": {\"text\": \"Amongst matching record comparisons:\", \"fontSize\": 12, \"fontWeight\": \"bold\"}, \"transform\": [{\"filter\": \"(datum.bayes_factor != 'no-op filter due to vega lite issue 4680')\"}], \"width\": 150}, {\"mark\": 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{\"data-d965af20286f260db497fd4c3b306fc4\": [{\"comparison_name\": \"given_name\", \"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Exact match on given_name\", \"m_probability\": 0.6761102648451272, \"u_probability\": 0.004473646877289537, \"m_probability_description\": \"Amongst matching record comparisons, 67.61% of records are in the exact match on given_name comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.45% of records are in the exact match on given_name comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"given_name\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 151.1317909952616, \"log2_bayes_factor\": 7.239663357140374, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on given_name` then comparison is 151.13 times more likely to be a match\", 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\"log2_bayes_factor\": -2.458740169624494, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.50 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"surname\", \"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on surname\", \"m_probability\": 0.6721795013840679, \"u_probability\": 0.0031414640213460196, \"m_probability_description\": \"Amongst matching record comparisons, 67.22% of records are in the exact match on surname comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.31% of records are in the exact match on surname comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"surname\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 213.97014156987223, \"log2_bayes_factor\": 7.741265679791364, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 213.97 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.9\", \"m_probability\": 0.18687961814058518, \"u_probability\": 0.001474049155701179, \"m_probability_description\": \"Amongst matching record comparisons, 18.69% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.15% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"has_tf_adjustments\": false, 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records are in the jaro-winkler distance of surname >= 0.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.4267163066074576, \"log2_bayes_factor\": -1.2286508519082064, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.8` then comparison is  2.34 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"surname\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13706114376275264, \"u_probability\": 0.9862924117076621, \"m_probability_description\": \"Amongst matching record comparisons, 13.71% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.63% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.13896603292875953, \"log2_bayes_factor\": -2.84719580268472, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"\\\"date_of_birth_l\\\" = \\\"date_of_birth_r\\\"\", \"label_for_charts\": \"Exact match on date_of_birth\", \"m_probability\": 0.9413247834677082, \"u_probability\": 0.00022182603061405572, \"m_probability_description\": \"Amongst matching record comparisons, 94.13% of records are in the exact match on date_of_birth comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on date_of_birth comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4243.527149910883, \"log2_bayes_factor\": 12.051048192450033, \"comparison_vector_value\": 5, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `exact match on date_of_birth` then comparison is 4,243.53 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"damerau_levenshtein(\\\"date_of_birth_l\\\", \\\"date_of_birth_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of date_of_birth <= 1\", \"m_probability\": 0.010781837446462814, \"u_probability\": 0.0009770662836814455, \"m_probability_description\": \"Amongst matching record comparisons, 1.08% of records are in the damerau-levenshtein distance of date_of_birth <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.10% of records are in the damerau-levenshtein distance of date_of_birth <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.034908917170284, \"log2_bayes_factor\": 3.4640028164587586, \"comparison_vector_value\": 4, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of date_of_birth <= 1` then comparison is 11.03 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 2629800.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 1 month'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.0050000000000000044, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 1 month' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.50% of records are in the abs difference of 'transformed date_of_birth <= 1 month' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.0, \"log2_bayes_factor\": 1.0, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 1 month'` then comparison is 2.00 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 31557600.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 1 year'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.020000000000000018, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 1 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 2.00% of records are in the abs difference of 'transformed date_of_birth <= 1 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.5, \"log2_bayes_factor\": -1.0, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 1 year'` then comparison is  2.00 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 315576000.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 10 year'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.08000000000000007, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 8.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.125, \"log2_bayes_factor\": -3.0, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 10 year'` then comparison is  8.00 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.04789337908582895, \"u_probability\": 0.9988011076857045, \"m_probability_description\": \"Amongst matching record comparisons, 4.79% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.88% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.04795086701175314, \"log2_bayes_factor\": -4.382299288561595, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  20.85 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.9116910942212385, \"u_probability\": 0.00018947896200509552, \"m_probability_description\": \"Amongst matching record comparisons, 91.17% of records are in the exact match on soc_sec_id comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on soc_sec_id comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4811.568970895677, \"log2_bayes_factor\": 12.23229169371735, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 4,811.57 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"m_probability\": 0.05056102427941435, \"u_probability\": 1.8462052708188793e-05, \"m_probability_description\": \"Amongst matching record comparisons, 5.06% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2738.6458634141013, \"log2_bayes_factor\": 11.419247006664481, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 1` then comparison is 2,738.65 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"m_probability\": 0.0005670167644193685, \"u_probability\": 0.00024195005917573736, \"m_probability_description\": \"Amongst matching record comparisons, 0.06% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.343528108036225, \"log2_bayes_factor\": 1.2286820984573408, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 2` then comparison is 2.34 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"soc_sec_id\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.03718086473492771, \"u_probability\": 0.999550108926111, \"m_probability_description\": \"Amongst matching record comparisons, 3.72% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.96% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.03719759960295918, \"log2_bayes_factor\": -4.748646663942527, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  26.88 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.8422161155744557, \"u_probability\": 0.015193297691854736, \"m_probability_description\": \"Amongst matching record comparisons, 84.22% of records are in the exact match on street_number comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.52% of records are in the exact match on street_number comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"street_number\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 55.433397847919174, \"log2_bayes_factor\": 5.79268353671938, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on street_number` then comparison is 55.43 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}, {\"comparison_name\": \"street_number\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.15778388442554425, \"u_probability\": 0.9848067023081453, \"m_probability_description\": \"Amongst matching record comparisons, 15.78% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 98.48% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.16021812611118258, \"log2_bayes_factor\": -2.6418907201032495, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.24 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.8416586737292484, \"u_probability\": 0.0011708828164930262, \"m_probability_description\": \"Amongst matching record comparisons, 84.17% of records are in the exact match on postcode comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.12% of records are in the exact match on postcode comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"postcode\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 718.8240034559097, \"log2_bayes_factor\": 9.489494774872734, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 718.82 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14506690077074827, \"u_probability\": 0.012730071185788495, \"m_probability_description\": \"Amongst matching record comparisons, 14.51% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.27% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.395607978429611, \"log2_bayes_factor\": 3.510405992203754, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.40 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 2\", \"m_probability\": 0.004943322055731211, \"u_probability\": 0.11116293441695865, \"m_probability_description\": \"Amongst matching record comparisons, 0.49% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 11.12% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.04446915765276051, \"log2_bayes_factor\": -4.491051112760025, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 2` then comparison is  22.49 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}, {\"comparison_name\": \"postcode\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.008331103444272117, \"u_probability\": 0.8749361115807598, \"m_probability_description\": \"Amongst matching record comparisons, 0.83% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.49% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.009521956328011415, \"log2_bayes_factor\": -6.714526272603148, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  105.02 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 5}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.HConcatChart(...)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# linker_simple.m_u_parameters_chart()\n",
-    "linker_detailed.m_u_parameters_chart()"
-   ],
-   "outputs": []
+    "linker_detailed.visualisations.m_u_parameters_chart()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -622,16 +2236,95 @@
    "id": "149962d6-a2ad-412f-aa05-8697beb12ed0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:05.070283Z",
-     "iopub.status.busy": "2024-05-15T15:57:05.070040Z",
-     "iopub.status.idle": "2024-05-15T15:57:06.960773Z",
-     "shell.execute_reply": "2024-05-15T15:57:06.959848Z"
+     "iopub.execute_input": "2024-06-07T09:16:55.411718Z",
+     "iopub.status.busy": "2024-06-07T09:16:55.411484Z",
+     "iopub.status.idle": "2024-06-07T09:16:57.179378Z",
+     "shell.execute_reply": "2024-06-07T09:16:57.178861Z"
     }
    },
-   "source": [
-    "linker_simple.unlinkables_chart()"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-b48a9f701ae64134b9d83d3e0c958dbf.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-b48a9f701ae64134b9d83d3e0c958dbf.vega-embed details,\n",
+       "  #altair-viz-b48a9f701ae64134b9d83d3e0c958dbf.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-b48a9f701ae64134b9d83d3e0c958dbf\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-b48a9f701ae64134b9d83d3e0c958dbf\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-b48a9f701ae64134b9d83d3e0c958dbf\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"line\"}, \"encoding\": {\"x\": {\"axis\": {\"format\": \"+\", \"title\": \"Threshold match weight\"}, \"field\": \"match_weight\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"format\": \"%\", \"title\": \"Percentage of unlinkable records\"}, \"field\": \"cum_prop\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"point\"}, \"encoding\": {\"opacity\": {\"condition\": {\"param\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"value\": 1, \"empty\": false}, \"value\": 0}, \"tooltip\": [{\"field\": \"match_weight\", \"format\": \"+.5\", \"title\": \"Match weight\", \"type\": \"quantitative\"}, {\"field\": \"match_probability\", \"format\": \".5\", \"title\": \"Match probability\", \"type\": \"quantitative\"}, {\"field\": \"cum_prop\", \"format\": \".3%\", \"title\": \"Proportion of unlinkable records\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"title\": \"Threshold match weight\"}, \"field\": \"match_weight\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"format\": \"%\", \"title\": \"Percentage of unlinkable records\"}, \"field\": \"cum_prop\", \"type\": \"quantitative\"}}, \"name\": \"mouse_coords\"}, {\"mark\": {\"type\": \"rule\", \"color\": \"gray\"}, \"encoding\": {\"x\": {\"field\": \"match_weight\", \"type\": \"quantitative\"}}, \"transform\": [{\"filter\": {\"param\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"empty\": false}}]}, {\"mark\": {\"type\": \"rule\", \"color\": \"gray\"}, \"encoding\": {\"y\": {\"field\": \"cum_prop\", \"type\": \"quantitative\"}}, \"transform\": [{\"filter\": {\"param\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"empty\": false}}]}], \"data\": {\"name\": \"data-8050ee5d0680a4eb8328b28f42ca9941\"}, \"height\": 400, \"params\": [{\"name\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"select\": {\"type\": \"point\", \"fields\": [\"match_weight\", 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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "outputs": []
+   "source": [
+    "linker_simple.evaluation.unlinkables_chart()"
+   ]
   },
   {
    "cell_type": "code",
@@ -639,16 +2332,95 @@
    "id": "cac493dd-ea43-4550-8fd4-f758ae90ed75",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:06.965159Z",
-     "iopub.status.busy": "2024-05-15T15:57:06.964863Z",
-     "iopub.status.idle": "2024-05-15T15:57:07.337075Z",
-     "shell.execute_reply": "2024-05-15T15:57:07.336337Z"
+     "iopub.execute_input": "2024-06-07T09:16:57.182832Z",
+     "iopub.status.busy": "2024-06-07T09:16:57.182595Z",
+     "iopub.status.idle": "2024-06-07T09:16:57.517285Z",
+     "shell.execute_reply": "2024-06-07T09:16:57.516677Z"
     }
    },
-   "source": [
-    "linker_detailed.unlinkables_chart()"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-249c2d3da60a40fbbbde7755d56bfc70.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-249c2d3da60a40fbbbde7755d56bfc70.vega-embed details,\n",
+       "  #altair-viz-249c2d3da60a40fbbbde7755d56bfc70.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-249c2d3da60a40fbbbde7755d56bfc70\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-249c2d3da60a40fbbbde7755d56bfc70\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-249c2d3da60a40fbbbde7755d56bfc70\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"line\"}, \"encoding\": {\"x\": {\"axis\": {\"format\": \"+\", \"title\": \"Threshold match weight\"}, \"field\": \"match_weight\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"format\": \"%\", \"title\": \"Percentage of unlinkable records\"}, \"field\": \"cum_prop\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"point\"}, \"encoding\": {\"opacity\": {\"condition\": {\"param\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"value\": 1, \"empty\": false}, \"value\": 0}, \"tooltip\": [{\"field\": \"match_weight\", \"format\": \"+.5\", \"title\": \"Match weight\", \"type\": \"quantitative\"}, {\"field\": \"match_probability\", \"format\": \".5\", \"title\": \"Match probability\", \"type\": \"quantitative\"}, {\"field\": \"cum_prop\", \"format\": \".3%\", \"title\": \"Proportion of unlinkable records\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"title\": \"Threshold match weight\"}, \"field\": \"match_weight\", \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"format\": \"%\", \"title\": \"Percentage of unlinkable records\"}, \"field\": \"cum_prop\", \"type\": \"quantitative\"}}, \"name\": \"mouse_coords\"}, {\"mark\": {\"type\": \"rule\", \"color\": \"gray\"}, \"encoding\": {\"x\": {\"field\": \"match_weight\", \"type\": \"quantitative\"}}, \"transform\": [{\"filter\": {\"param\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"empty\": false}}]}, {\"mark\": {\"type\": \"rule\", \"color\": \"gray\"}, \"encoding\": {\"y\": {\"field\": \"cum_prop\", \"type\": \"quantitative\"}}, \"transform\": [{\"filter\": {\"param\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"empty\": false}}]}], \"data\": {\"name\": \"data-4f53cda18c2baa0c0354bb5f9a3ecbe5\"}, \"height\": 400, \"params\": [{\"name\": \"x_match_weight_y_cum_prop_coords_of_mouse\", \"select\": {\"type\": \"point\", \"fields\": [\"match_weight\", \"cum_prop\"], \"nearest\": true, \"on\": \"mouseover\"}, \"views\": [\"mouse_coords\"]}], \"title\": {\"text\": \"Unlinkable records\", \"subtitle\": \"Records with insufficient information to exceed a given match threshold\"}, \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-4f53cda18c2baa0c0354bb5f9a3ecbe5\": []}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "outputs": []
+   "source": [
+    "linker_detailed.evaluation.unlinkables_chart()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -676,18 +2448,252 @@
    "id": "03348477-c3c1-42e7-a8af-8f678acc9d58",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:07.340733Z",
-     "iopub.status.busy": "2024-05-15T15:57:07.340494Z",
-     "iopub.status.idle": "2024-05-15T15:57:12.239689Z",
-     "shell.execute_reply": "2024-05-15T15:57:12.238900Z"
+     "iopub.execute_input": "2024-06-07T09:16:57.520557Z",
+     "iopub.status.busy": "2024-06-07T09:16:57.520288Z",
+     "iopub.status.idle": "2024-06-07T09:17:01.939499Z",
+     "shell.execute_reply": "2024-06-07T09:17:01.938793Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'date_of_birth':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    u values not fully trained\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>match_weight</th>\n",
+       "      <th>match_probability</th>\n",
+       "      <th>source_dataset_l</th>\n",
+       "      <th>source_dataset_r</th>\n",
+       "      <th>rec_id_l</th>\n",
+       "      <th>rec_id_r</th>\n",
+       "      <th>given_name_l</th>\n",
+       "      <th>given_name_r</th>\n",
+       "      <th>gamma_given_name</th>\n",
+       "      <th>tf_given_name_l</th>\n",
+       "      <th>...</th>\n",
+       "      <th>gamma_postcode</th>\n",
+       "      <th>tf_postcode_l</th>\n",
+       "      <th>tf_postcode_r</th>\n",
+       "      <th>bf_postcode</th>\n",
+       "      <th>bf_tf_adj_postcode</th>\n",
+       "      <th>address_1_l</th>\n",
+       "      <th>address_1_r</th>\n",
+       "      <th>state_l</th>\n",
+       "      <th>state_r</th>\n",
+       "      <th>match_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.825003</td>\n",
+       "      <td>0.220115</td>\n",
+       "      <td>__splink__input_table_0</td>\n",
+       "      <td>__splink__input_table_1</td>\n",
+       "      <td>rec-760-org</td>\n",
+       "      <td>rec-3951-dup-0</td>\n",
+       "      <td>lachlan</td>\n",
+       "      <td>lachlan</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0113</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0007</td>\n",
+       "      <td>0.0007</td>\n",
+       "      <td>718.824003</td>\n",
+       "      <td>1.672690</td>\n",
+       "      <td>bushby close</td>\n",
+       "      <td>templestoew avenue</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>vic</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.637366</td>\n",
+       "      <td>0.243251</td>\n",
+       "      <td>__splink__input_table_0</td>\n",
+       "      <td>__splink__input_table_1</td>\n",
+       "      <td>rec-4980-org</td>\n",
+       "      <td>rec-4980-dup-0</td>\n",
+       "      <td>isabella</td>\n",
+       "      <td>ctercteko</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0069</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0004</td>\n",
+       "      <td>0.0004</td>\n",
+       "      <td>718.824003</td>\n",
+       "      <td>2.927207</td>\n",
+       "      <td>sturt avenue</td>\n",
+       "      <td>sturta venue</td>\n",
+       "      <td>vic</td>\n",
+       "      <td>vic</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.091445</td>\n",
+       "      <td>0.319400</td>\n",
+       "      <td>__splink__input_table_0</td>\n",
+       "      <td>__splink__input_table_1</td>\n",
+       "      <td>rec-585-org</td>\n",
+       "      <td>rec-585-dup-0</td>\n",
+       "      <td>danny</td>\n",
+       "      <td>stephenson</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0016</td>\n",
+       "      <td>0.0012</td>\n",
+       "      <td>11.395608</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>o'shanassy street</td>\n",
+       "      <td>o'shanassy street</td>\n",
+       "      <td>tas</td>\n",
+       "      <td>tas</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.942148</td>\n",
+       "      <td>0.342303</td>\n",
+       "      <td>__splink__input_table_0</td>\n",
+       "      <td>__splink__input_table_1</td>\n",
+       "      <td>rec-1250-org</td>\n",
+       "      <td>rec-1250-dup-0</td>\n",
+       "      <td>luke</td>\n",
+       "      <td>gazzola</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0055</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0015</td>\n",
+       "      <td>0.0002</td>\n",
+       "      <td>11.395608</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>newman morris circuit</td>\n",
+       "      <td>newman morr is circuit</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.186499</td>\n",
+       "      <td>0.467727</td>\n",
+       "      <td>__splink__input_table_0</td>\n",
+       "      <td>__splink__input_table_1</td>\n",
+       "      <td>rec-4763-org</td>\n",
+       "      <td>rec-4763-dup-0</td>\n",
+       "      <td>max</td>\n",
+       "      <td>alisha</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0021</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0004</td>\n",
+       "      <td>0.0016</td>\n",
+       "      <td>0.044469</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>duffy street</td>\n",
+       "      <td>duffy s treet</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>nsw</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   match_weight  match_probability         source_dataset_l  \\\n",
+       "0     -1.825003           0.220115  __splink__input_table_0   \n",
+       "1     -1.637366           0.243251  __splink__input_table_0   \n",
+       "2     -1.091445           0.319400  __splink__input_table_0   \n",
+       "3     -0.942148           0.342303  __splink__input_table_0   \n",
+       "4     -0.186499           0.467727  __splink__input_table_0   \n",
+       "\n",
+       "          source_dataset_r      rec_id_l        rec_id_r given_name_l  \\\n",
+       "0  __splink__input_table_1   rec-760-org  rec-3951-dup-0      lachlan   \n",
+       "1  __splink__input_table_1  rec-4980-org  rec-4980-dup-0     isabella   \n",
+       "2  __splink__input_table_1   rec-585-org   rec-585-dup-0        danny   \n",
+       "3  __splink__input_table_1  rec-1250-org  rec-1250-dup-0         luke   \n",
+       "4  __splink__input_table_1  rec-4763-org  rec-4763-dup-0          max   \n",
+       "\n",
+       "  given_name_r  gamma_given_name  tf_given_name_l  ...  gamma_postcode  \\\n",
+       "0      lachlan                 3           0.0113  ...               3   \n",
+       "1    ctercteko                 0           0.0069  ...               3   \n",
+       "2   stephenson                 0           0.0001  ...               2   \n",
+       "3      gazzola                 0           0.0055  ...               2   \n",
+       "4       alisha                 0           0.0021  ...               1   \n",
+       "\n",
+       "   tf_postcode_l  tf_postcode_r bf_postcode bf_tf_adj_postcode  \\\n",
+       "0         0.0007         0.0007  718.824003           1.672690   \n",
+       "1         0.0004         0.0004  718.824003           2.927207   \n",
+       "2         0.0016         0.0012   11.395608           1.000000   \n",
+       "3         0.0015         0.0002   11.395608           1.000000   \n",
+       "4         0.0004         0.0016    0.044469           1.000000   \n",
+       "\n",
+       "              address_1_l              address_1_r  state_l  state_r  \\\n",
+       "0            bushby close       templestoew avenue      nsw      vic   \n",
+       "1            sturt avenue             sturta venue      vic      vic   \n",
+       "2       o'shanassy street        o'shanassy street      tas      tas   \n",
+       "3   newman morris circuit   newman morr is circuit      nsw      nsw   \n",
+       "4            duffy street            duffy s treet      nsw      nsw   \n",
+       "\n",
+       "   match_key  \n",
+       "0          0  \n",
+       "1          2  \n",
+       "2          1  \n",
+       "3          1  \n",
+       "4          2  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "predictions = linker_detailed.predict(threshold_match_probability=0.2)\n",
+    "predictions = linker_detailed.inference.predict(threshold_match_probability=0.2)\n",
     "df_predictions = predictions.as_pandas_dataframe()\n",
     "df_predictions.head(5)"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -703,18 +2709,110 @@
    "id": "ce8d409c-7ef5-4485-9ec0-8b539fdecb1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:12.244377Z",
-     "iopub.status.busy": "2024-05-15T15:57:12.243938Z",
-     "iopub.status.idle": "2024-05-15T15:57:15.174716Z",
-     "shell.execute_reply": "2024-05-15T15:57:15.173769Z"
+     "iopub.execute_input": "2024-06-07T09:17:01.942896Z",
+     "iopub.status.busy": "2024-06-07T09:17:01.942661Z",
+     "iopub.status.idle": "2024-06-07T09:17:04.159161Z",
+     "shell.execute_reply": "2024-06-07T09:17:04.158614Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'date_of_birth':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    u values not fully trained\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-481e6faf093645ea885bc17ed13ee2ff.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-481e6faf093645ea885bc17ed13ee2ff.vega-embed details,\n",
+       "  #altair-viz-481e6faf093645ea885bc17ed13ee2ff.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-481e6faf093645ea885bc17ed13ee2ff\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-481e6faf093645ea885bc17ed13ee2ff\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-481e6faf093645ea885bc17ed13ee2ff\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
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+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-7c38d47f8425a87ed7e8b6de0ba70b25\"}, \"mark\": {\"type\": \"line\", \"clip\": true, \"point\": true}, \"encoding\": {\"tooltip\": [{\"field\": \"truth_threshold\", \"format\": \".4f\", \"type\": \"quantitative\"}, {\"field\": \"match_probability\", \"format\": \".4%\", \"type\": \"quantitative\"}, {\"field\": \"fp_rate\", \"format\": \".4f\", \"title\": \"FP_rate\", \"type\": \"quantitative\"}, {\"field\": \"tp_rate\", \"format\": \".4f\", \"title\": \"TP_rate\", \"type\": \"quantitative\"}, {\"field\": \"tp\", \"format\": \",.0f\", \"title\": \"TP\", \"type\": \"quantitative\"}, {\"field\": \"tn\", \"format\": \",.0f\", \"title\": \"TN\", \"type\": \"quantitative\"}, {\"field\": \"fp\", \"format\": \",.0f\", \"title\": \"FP\", \"type\": \"quantitative\"}, {\"field\": \"fn\", \"format\": \",.0f\", \"title\": \"FN\", \"type\": \"quantitative\"}, {\"field\": 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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "linker_detailed.accuracy_analysis_from_labels_column(\n",
+    "linker_detailed.evaluation.accuracy_analysis_from_labels_column(\n",
     "    \"cluster\", output_type=\"precision_recall\"\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -730,20 +2828,40 @@
    "id": "ade53248-212f-4776-8d7d-4632b1749425",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:15.183049Z",
-     "iopub.status.busy": "2024-05-15T15:57:15.182695Z",
-     "iopub.status.idle": "2024-05-15T15:57:15.493444Z",
-     "shell.execute_reply": "2024-05-15T15:57:15.492713Z"
+     "iopub.execute_input": "2024-06-07T09:17:04.165374Z",
+     "iopub.status.busy": "2024-06-07T09:17:04.165099Z",
+     "iopub.status.idle": "2024-06-07T09:17:04.301694Z",
+     "shell.execute_reply": "2024-06-07T09:17:04.301045Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 1, root rows count 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2    4958\n",
+       "1      84\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "clusters = linker_detailed.cluster_pairwise_predictions_at_threshold(\n",
+    "clusters = linker_detailed.clustering.cluster_pairwise_predictions_at_threshold(\n",
     "    predictions, threshold_match_probability=0.99\n",
     ")\n",
     "df_clusters = clusters.as_pandas_dataframe().sort_values(\"cluster_id\")\n",
     "df_clusters.groupby(\"cluster_id\").size().value_counts()"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -763,12 +2881,13 @@
    "id": "ef77a8b1-1119-4cb0-b299-343a4022d65e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:15.500107Z",
-     "iopub.status.busy": "2024-05-15T15:57:15.499499Z",
-     "iopub.status.idle": "2024-05-15T15:57:15.523366Z",
-     "shell.execute_reply": "2024-05-15T15:57:15.522625Z"
+     "iopub.execute_input": "2024-06-07T09:17:04.305169Z",
+     "iopub.status.busy": "2024-06-07T09:17:04.304886Z",
+     "iopub.status.idle": "2024-06-07T09:17:04.322035Z",
+     "shell.execute_reply": "2024-06-07T09:17:04.321351Z"
     }
    },
+   "outputs": [],
    "source": [
     "df_predictions[\"cluster_l\"] = df_predictions[\"rec_id_l\"].apply(\n",
     "    lambda x: \"-\".join(x.split(\"-\")[:2])\n",
@@ -779,8 +2898,7 @@
     "df_true_links = df_predictions[\n",
     "    df_predictions[\"cluster_l\"] == df_predictions[\"cluster_r\"]\n",
     "].sort_values(\"match_probability\")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -788,19 +2906,98 @@
    "id": "bc531ca3-fe0d-480d-b059-a7125474fb22",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:15.527453Z",
-     "iopub.status.busy": "2024-05-15T15:57:15.527121Z",
-     "iopub.status.idle": "2024-05-15T15:57:16.507088Z",
-     "shell.execute_reply": "2024-05-15T15:57:16.506251Z"
+     "iopub.execute_input": "2024-06-07T09:17:04.325739Z",
+     "iopub.status.busy": "2024-06-07T09:17:04.325483Z",
+     "iopub.status.idle": "2024-06-07T09:17:04.966790Z",
+     "shell.execute_reply": "2024-06-07T09:17:04.966182Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-e16c961acf90456990d18243b02cf59b.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-e16c961acf90456990d18243b02cf59b.vega-embed details,\n",
+       "  #altair-viz-e16c961acf90456990d18243b02cf59b.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-e16c961acf90456990d18243b02cf59b\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-e16c961acf90456990d18243b02cf59b\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-e16c961acf90456990d18243b02cf59b\");\n",
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+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
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+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
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+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"column_name\": \"surname\", \"value_l\": \" ctercteko\", \"value_r\": \" isabela\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 0}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13706114376275264, \"u_probability\": 0.9862924117076621, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"column_name\": \"tf_surname\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 4, \"record_number\": 0}, {\"sql_condition\": \"\\\"date_of_birth_l\\\" = \\\"date_of_birth_r\\\"\", \"label_for_charts\": \"Exact match on date_of_birth\", \"m_probability\": 0.9413247834677082, \"u_probability\": 0.00022182603061405572, \"bayes_factor\": 4243.527149910883, \"log2_bayes_factor\": 12.051048192450033, \"comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `exact match on date_of_birth` then comparison is 4,243.53 times more likely to be a match\", \"column_name\": \"date_of_birth\", \"value_l\": \"19810626\", \"value_r\": \"19810626\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 5, \"record_number\": 0}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.03718086473492771, \"u_probability\": 0.999550108926111, \"bayes_factor\": 0.03719759960295918, \"log2_bayes_factor\": -4.748646663942527, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  26.88 times less likely to be a match\", \"column_name\": \"soc_sec_id\", \"value_l\": \"8794181\", \"value_r\": \"7979662\", \"term_frequency_adjustment\": false, 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match\", \"column_name\": \"tf_street_number\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 8, \"record_number\": 0}, {\"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.8416586737292484, \"u_probability\": 0.0011708828164930262, \"bayes_factor\": 718.8240034559097, \"log2_bayes_factor\": 9.489494774872734, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 718.82 times more likely to be a match\", \"column_name\": \"postcode\", \"value_l\": \"7216\", \"value_r\": \"7216\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 9, \"record_number\": 0}, {\"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Term freq adjustment on postcode with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, 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0.977529738480165, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.50 times less likely to be a match\", \"column_name\": \"tf_given_name\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 2, \"record_number\": 1}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13706114376275264, \"u_probability\": 0.9862924117076621, \"bayes_factor\": 0.13896603292875953, \"log2_bayes_factor\": -2.84719580268472, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"column_name\": \"surname\", \"value_l\": \" stephenson\", \"value_r\": \" danny\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 1}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13706114376275264, \"u_probability\": 0.9862924117076621, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"column_name\": \"tf_surname\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 4, \"record_number\": 1}, {\"sql_condition\": \"\\\"date_of_birth_l\\\" = \\\"date_of_birth_r\\\"\", \"label_for_charts\": \"Exact match on date_of_birth\", \"m_probability\": 0.9413247834677082, \"u_probability\": 0.00022182603061405572, \"bayes_factor\": 4243.527149910883, \"log2_bayes_factor\": 12.051048192450033, \"comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `exact match on date_of_birth` then comparison is 4,243.53 times more likely to be a match\", \"column_name\": \"date_of_birth\", \"value_l\": \"19831019\", \"value_r\": \"19831019\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 5, \"record_number\": 1}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.03718086473492771, \"u_probability\": 0.999550108926111, \"bayes_factor\": 0.03719759960295918, \"log2_bayes_factor\": -4.748646663942527, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  26.88 times less likely to be a match\", \"column_name\": \"soc_sec_id\", \"value_l\": \"7199358\", \"value_r\": \"8561604\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 1}, {\"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.8422161155744557, \"u_probability\": 0.015193297691854736, \"bayes_factor\": 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\"bar_sort_order\": 8, \"record_number\": 1}, {\"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14506690077074827, \"u_probability\": 0.012730071185788495, \"bayes_factor\": 11.395607978429611, \"log2_bayes_factor\": 3.510405992203754, \"comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.40 times more likely to be a match\", \"column_name\": \"postcode\", \"value_l\": \"3130\", \"value_r\": \"3133\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 9, \"record_number\": 1}, {\"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14506690077074827, \"u_probability\": 0.012730071185788495, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.40 times more likely to be a match\", \"column_name\": \"tf_postcode\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 10, \"record_number\": 1}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": -1.0914448240740604, \"bayes_factor\": 0.46929115548569683, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 1}, {\"column_name\": \"Prior\", \"label_for_charts\": \"Starting match weight (prior)\", \"sql_condition\": null, \"log2_bayes_factor\": -12.030960824733759, \"bayes_factor\": 0.00023895708684804802, 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\"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  5.50 times less likely to be a match\", \"column_name\": \"tf_given_name\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 2, \"record_number\": 2}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13706114376275264, \"u_probability\": 0.9862924117076621, \"bayes_factor\": 0.13896603292875953, \"log2_bayes_factor\": -2.84719580268472, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"column_name\": \"surname\", \"value_l\": \" gazzola\", \"value_r\": \" luke\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 2}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.13706114376275264, \"u_probability\": 0.9862924117076621, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.20 times less likely to be a match\", \"column_name\": \"tf_surname\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 4, \"record_number\": 2}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.04789337908582895, \"u_probability\": 0.9988011076857045, \"bayes_factor\": 0.04795086701175314, \"log2_bayes_factor\": -4.382299288561595, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  20.85 times less likely to be a match\", \"column_name\": \"date_of_birth\", \"value_l\": \"19420929\", \"value_r\": \"19590318\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 5, \"record_number\": 2}, {\"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.9116910942212385, \"u_probability\": 0.00018947896200509552, \"bayes_factor\": 4811.568970895677, \"log2_bayes_factor\": 12.23229169371735, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 4,811.57 times more likely to be a match\", \"column_name\": \"soc_sec_id\", \"value_l\": \"2904508\", \"value_r\": \"2904508\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 2}, {\"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.8422161155744557, \"u_probability\": 0.015193297691854736, \"bayes_factor\": 55.433397847919174, \"log2_bayes_factor\": 5.79268353671938, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"If comparison level is `exact match on street_number` then comparison is 55.43 times more likely to be a match\", \"column_name\": \"street_number\", \"value_l\": \" 10\", \"value_r\": \" 10\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 7, \"record_number\": 2}, {\"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Term freq adjustment on street_number with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 0.5911788985157485, \"log2_bayes_factor\": -0.758333319905994, \"comparison_vector_value\": 1, \"bayes_factor_description\": \"Term frequency adjustment on street_number makes comparison  1.69 times less likely to be a match\", \"column_name\": \"tf_street_number\", \"value_l\": \" 10\", \"value_r\": \" 10\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 8, \"record_number\": 2}, {\"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14506690077074827, \"u_probability\": 0.012730071185788495, \"bayes_factor\": 11.395607978429611, \"log2_bayes_factor\": 3.510405992203754, \"comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.40 times more likely to be a match\", \"column_name\": \"postcode\", \"value_l\": \"3046\", \"value_r\": \"3036\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 9, \"record_number\": 2}, {\"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.14506690077074827, \"u_probability\": 0.012730071185788495, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.40 times more likely to be a match\", \"column_name\": \"tf_postcode\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 10, \"record_number\": 2}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": -0.9421481828700788, \"bayes_factor\": 0.5204573386021022, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 2}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "records_to_view = 3\n",
-    "linker_detailed.waterfall_chart(\n",
+    "linker_detailed.visualisations.waterfall_chart(\n",
     "    df_true_links.head(records_to_view).to_dict(orient=\"records\")\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -808,21 +3005,100 @@
    "id": "aacd9042-5672-4bc4-aa98-940d1f5fd28a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:16.510992Z",
-     "iopub.status.busy": "2024-05-15T15:57:16.510681Z",
-     "iopub.status.idle": "2024-05-15T15:57:17.322254Z",
-     "shell.execute_reply": "2024-05-15T15:57:17.321456Z"
+     "iopub.execute_input": "2024-06-07T09:17:04.969789Z",
+     "iopub.status.busy": "2024-06-07T09:17:04.969553Z",
+     "iopub.status.idle": "2024-06-07T09:17:05.445307Z",
+     "shell.execute_reply": "2024-06-07T09:17:05.444530Z"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-334df376a16a4fbb9ea365fa4053a4dd.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-334df376a16a4fbb9ea365fa4053a4dd.vega-embed details,\n",
+       "  #altair-viz-334df376a16a4fbb9ea365fa4053a4dd.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-334df376a16a4fbb9ea365fa4053a4dd\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-334df376a16a4fbb9ea365fa4053a4dd\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-334df376a16a4fbb9ea365fa4053a4dd\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"layer\": [{\"mark\": \"rule\", \"encoding\": {\"color\": {\"value\": \"black\"}, \"size\": {\"value\": 0.5}, \"y\": {\"field\": \"zero\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"bar\", \"width\": 60}, \"encoding\": {\"color\": {\"condition\": {\"test\": \"(datum.log2_bayes_factor < 0)\", \"value\": \"red\"}, \"value\": \"green\"}, \"opacity\": {\"condition\": {\"test\": \"datum.column_name == 'Prior match weight' || datum.column_name == 'Final score'\", \"value\": 1}, \"value\": 0.5}, \"tooltip\": [{\"field\": \"column_name\", \"title\": \"Comparison column\", \"type\": \"nominal\"}, {\"field\": \"value_l\", \"title\": \"Value (L)\", \"type\": \"nominal\"}, {\"field\": \"value_r\", \"title\": \"Value (R)\", \"type\": \"nominal\"}, {\"field\": \"label_for_charts\", \"title\": \"Label\", \"type\": \"ordinal\"}, {\"field\": \"sql_condition\", \"title\": \"SQL condition\", \"type\": \"nominal\"}, {\"field\": \"comparison_vector_value\", \"title\": \"Comparison vector value\", \"type\": \"nominal\"}, {\"field\": \"bayes_factor\", \"format\": \",.4f\", \"title\": \"Bayes factor = m/u\", \"type\": \"quantitative\"}, {\"field\": \"log2_bayes_factor\", \"format\": \",.4f\", \"title\": \"Match weight = log2(m/u)\", \"type\": \"quantitative\"}, {\"field\": \"prob\", \"format\": \".4f\", \"title\": \"Cumulative match probability\", \"type\": \"quantitative\"}, {\"field\": \"bayes_factor_description\", \"title\": \"Match weight description\", \"type\": \"nominal\"}], \"x\": {\"axis\": {\"grid\": true, \"labelAlign\": \"center\", \"labelAngle\": -20, \"labelExpr\": \"datum.value == 'Prior' || datum.value == 'Final score' ? '' : datum.value\", \"labelPadding\": 10, \"tickBand\": \"extent\", \"title\": \"Column\"}, \"field\": \"column_name\", \"sort\": {\"field\": \"bar_sort_order\", \"order\": \"ascending\"}, \"type\": \"nominal\"}, \"y\": {\"axis\": {\"grid\": false, \"orient\": \"left\", \"title\": \"Match Weight\"}, \"field\": \"previous_sum\", \"type\": \"quantitative\"}, \"y2\": {\"field\": \"sum\"}}}, {\"mark\": {\"type\": \"text\", \"fontWeight\": \"bold\"}, \"encoding\": {\"color\": {\"value\": \"white\"}, \"text\": {\"condition\": {\"test\": \"abs(datum.log2_bayes_factor) > 1\", \"field\": \"log2_bayes_factor\", \"format\": \".2f\", \"type\": \"nominal\"}, \"value\": \"\"}, \"x\": {\"axis\": {\"labelAngle\": -20, \"title\": \"Column\"}, \"field\": \"column_name\", \"sort\": {\"field\": \"bar_sort_order\", \"order\": \"ascending\"}, \"type\": \"nominal\"}, \"y\": {\"axis\": {\"orient\": \"left\"}, \"field\": \"center\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"text\", \"baseline\": \"bottom\", \"dy\": -25, \"fontWeight\": \"bold\"}, \"encoding\": {\"color\": {\"value\": \"black\"}, \"text\": {\"field\": \"column_name\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"labelAngle\": -20, \"title\": \"Column\"}, \"field\": \"column_name\", \"sort\": {\"field\": \"bar_sort_order\", \"order\": \"ascending\"}, \"type\": \"nominal\"}, \"y\": {\"field\": \"sum_top\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"text\", \"baseline\": \"bottom\", \"dy\": -13, \"fontSize\": 8}, \"encoding\": {\"color\": {\"value\": \"grey\"}, \"text\": {\"field\": \"value_l\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"labelAngle\": -20, \"title\": \"Column\"}, \"field\": \"column_name\", \"sort\": {\"field\": \"bar_sort_order\", \"order\": \"ascending\"}, \"type\": \"nominal\"}, \"y\": {\"field\": \"sum_top\", \"type\": \"quantitative\"}}}, {\"mark\": {\"type\": \"text\", \"baseline\": \"bottom\", \"dy\": -5, \"fontSize\": 8}, \"encoding\": {\"color\": {\"value\": \"grey\"}, \"text\": {\"field\": \"value_r\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"labelAngle\": -20, \"title\": \"Column\"}, \"field\": \"column_name\", \"sort\": {\"field\": \"bar_sort_order\", \"order\": \"ascending\"}, \"type\": \"nominal\"}, \"y\": {\"field\": \"sum_top\", \"type\": \"quantitative\"}}}]}, {\"mark\": {\"type\": \"rule\", \"color\": \"black\", \"strokeWidth\": 2, \"x2Offset\": 30, \"xOffset\": -30}, \"encoding\": {\"x\": {\"axis\": {\"labelAngle\": -20, \"title\": \"Column\"}, \"field\": \"column_name\", \"sort\": {\"field\": \"bar_sort_order\", \"order\": \"ascending\"}, \"type\": \"nominal\"}, \"x2\": {\"field\": \"lead\"}, \"y\": {\"axis\": {\"labelExpr\": \"format(1 / (1 + pow(2, -1*datum.value)), '.2r')\", \"orient\": \"right\", \"title\": \"Probability\"}, \"field\": \"sum\", \"scale\": {\"zero\": false}, \"type\": \"quantitative\"}}}], \"data\": {\"name\": \"data-93cbba1e7028b08160fd30d4e2ab8182\"}, \"height\": 450, \"params\": [{\"name\": \"record_number\", \"bind\": {\"input\": \"range\", \"max\": 0, \"min\": 0, \"step\": 1}, \"value\": 0}], \"resolve\": {\"axis\": {\"y\": \"independent\"}}, \"title\": {\"text\": \"Match weights waterfall chart\", \"subtitle\": \"How each comparison contributes to the final match score\"}, \"transform\": [{\"filter\": \"(datum.record_number == record_number)\"}, {\"filter\": \"(datum.bayes_factor !== 1.0)\"}, {\"window\": [{\"op\": \"sum\", \"field\": \"log2_bayes_factor\", \"as\": \"sum\"}, {\"op\": \"lead\", \"field\": \"column_name\", \"as\": \"lead\"}], \"frame\": [null, 0]}, {\"calculate\": \"datum.column_name === \\\"Final score\\\" ? datum.sum - datum.log2_bayes_factor : datum.sum\", \"as\": \"sum\"}, {\"calculate\": \"datum.lead === null ? datum.column_name : datum.lead\", \"as\": \"lead\"}, {\"calculate\": \"datum.column_name === \\\"Final score\\\" || datum.column_name === \\\"Prior match weight\\\" ? 0 : datum.sum - datum.log2_bayes_factor\", \"as\": \"previous_sum\"}, {\"calculate\": \"datum.sum > datum.previous_sum ? datum.column_name : \\\"\\\"\", \"as\": \"top_label\"}, {\"calculate\": \"datum.sum < datum.previous_sum ? datum.column_name : \\\"\\\"\", \"as\": \"bottom_label\"}, {\"calculate\": \"datum.sum > datum.previous_sum ? datum.sum : datum.previous_sum\", \"as\": \"sum_top\"}, {\"calculate\": \"datum.sum < datum.previous_sum ? datum.sum : datum.previous_sum\", \"as\": \"sum_bottom\"}, {\"calculate\": \"(datum.sum + datum.previous_sum) / 2\", \"as\": \"center\"}, {\"calculate\": \"(datum.log2_bayes_factor > 0 ? \\\"+\\\" : \\\"\\\") + datum.log2_bayes_factor\", \"as\": \"text_log2_bayes_factor\"}, {\"calculate\": \"datum.sum < datum.previous_sum ? 4 : -4\", \"as\": \"dy\"}, {\"calculate\": \"datum.sum < datum.previous_sum ? \\\"top\\\" : \\\"bottom\\\"\", \"as\": \"baseline\"}, {\"calculate\": \"1. / (1 + pow(2, -1.*datum.sum))\", \"as\": \"prob\"}, {\"calculate\": \"0*datum.sum\", \"as\": \"zero\"}], \"width\": {\"step\": 75}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-93cbba1e7028b08160fd30d4e2ab8182\": [{\"column_name\": \"Prior\", \"label_for_charts\": \"Starting match weight (prior)\", \"sql_condition\": null, \"log2_bayes_factor\": -12.030960824733759, \"bayes_factor\": 0.00023895708684804802, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 0, \"record_number\": 0}, {\"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Exact match on given_name\", \"m_probability\": 0.6761102648451272, \"u_probability\": 0.004473646877289537, \"bayes_factor\": 151.1317909952616, \"log2_bayes_factor\": 7.239663357140374, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on given_name` then comparison is 151.13 times more likely to be a match\", \"column_name\": \"given_name\", \"value_l\": \" lachlan\", \"value_r\": \" lachlan\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 1, \"record_number\": 0}, {\"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Term freq adjustment on given_name with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 0.39589795374243697, \"log2_bayes_factor\": -1.3367994842843636, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"Term frequency adjustment on given_name makes comparison  2.53 times less likely to be a match\", \"column_name\": \"tf_given_name\", \"value_l\": \" lachlan\", \"value_r\": \" lachlan\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 2, \"record_number\": 0}, {\"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on surname\", \"m_probability\": 0.6721795013840679, \"u_probability\": 0.0031414640213460196, \"bayes_factor\": 213.97014156987223, \"log2_bayes_factor\": 7.741265679791364, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 213.97 times more likely to be a match\", \"column_name\": \"surname\", \"value_l\": \" nguyen\", \"value_r\": \" nguyen\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 3, \"record_number\": 0}, {\"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Term freq adjustment on surname with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 0.2685011984056427, \"log2_bayes_factor\": -1.8969995674587254, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"Term frequency adjustment on surname makes comparison  3.72 times less likely to be a match\", \"column_name\": \"tf_surname\", \"value_l\": \" nguyen\", \"value_r\": \" nguyen\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 4, \"record_number\": 0}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.04789337908582895, \"u_probability\": 0.9988011076857045, \"bayes_factor\": 0.04795086701175314, \"log2_bayes_factor\": -4.382299288561595, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  20.85 times less likely to be a match\", \"column_name\": \"date_of_birth\", \"value_l\": \"19660926\", \"value_r\": \"19931013\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 5, \"record_number\": 0}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.03718086473492771, \"u_probability\": 0.999550108926111, \"bayes_factor\": 0.03719759960295918, \"log2_bayes_factor\": -4.748646663942527, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  26.88 times less likely to be a match\", \"column_name\": \"soc_sec_id\", \"value_l\": \"4360586\", \"value_r\": \"8311385\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 6, \"record_number\": 0}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.15778388442554425, \"u_probability\": 0.9848067023081453, \"bayes_factor\": 0.16021812611118258, \"log2_bayes_factor\": -2.6418907201032495, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.24 times less likely to be a match\", \"column_name\": \"street_number\", \"value_l\": \" 70\", \"value_r\": \" 23\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 7, \"record_number\": 0}, {\"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.15778388442554425, \"u_probability\": 0.9848067023081453, \"bayes_factor\": 1.0, \"log2_bayes_factor\": 0.0, \"comparison_vector_value\": 0, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  6.24 times less likely to be a match\", \"column_name\": \"tf_street_number\", \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 8, \"record_number\": 0}, {\"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.8416586737292484, \"u_probability\": 0.0011708828164930262, \"bayes_factor\": 718.8240034559097, \"log2_bayes_factor\": 9.489494774872734, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 718.82 times more likely to be a match\", \"column_name\": \"postcode\", \"value_l\": \"5046\", \"value_r\": \"5046\", \"term_frequency_adjustment\": false, \"bar_sort_order\": 9, \"record_number\": 0}, {\"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Term freq adjustment on postcode with weight {cl.tf_adjustment_weight}\", \"m_probability\": null, \"u_probability\": null, \"bayes_factor\": 1.6726897378471803, \"log2_bayes_factor\": 0.7421698690583135, \"comparison_vector_value\": 3, \"bayes_factor_description\": \"Term frequency adjustment on postcode makes comparison 1.67 times more likely to be a match\", \"column_name\": \"tf_postcode\", \"value_l\": \"5046\", \"value_r\": \"5046\", \"term_frequency_adjustment\": true, \"bar_sort_order\": 10, \"record_number\": 0}, {\"column_name\": \"Final score\", \"label_for_charts\": \"Final score\", \"sql_condition\": null, \"log2_bayes_factor\": -1.825002868221434, \"bayes_factor\": 0.2822405400786701, \"comparison_vector_value\": null, \"m_probability\": null, \"u_probability\": null, \"bayes_factor_description\": null, \"value_l\": \"\", \"value_r\": \"\", \"term_frequency_adjustment\": null, \"bar_sort_order\": 11, \"record_number\": 0}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_non_links = df_predictions[\n",
     "    df_predictions[\"cluster_l\"] != df_predictions[\"cluster_r\"]\n",
     "].sort_values(\"match_probability\", ascending=False)\n",
-    "linker_detailed.waterfall_chart(\n",
+    "linker_detailed.visualisations.waterfall_chart(\n",
     "    df_non_links.head(records_to_view).to_dict(orient=\"records\")\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -848,12 +3124,13 @@
    "id": "2a7229da-9f79-4151-a6b1-018d17205f5f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:17.327035Z",
-     "iopub.status.busy": "2024-05-15T15:57:17.326665Z",
-     "iopub.status.idle": "2024-05-15T15:57:17.342204Z",
-     "shell.execute_reply": "2024-05-15T15:57:17.341227Z"
+     "iopub.execute_input": "2024-06-07T09:17:05.448836Z",
+     "iopub.status.busy": "2024-06-07T09:17:05.448543Z",
+     "iopub.status.idle": "2024-06-07T09:17:05.460100Z",
+     "shell.execute_reply": "2024-06-07T09:17:05.459191Z"
     }
    },
+   "outputs": [],
    "source": [
     "# we need to append a full name column to our source data frames\n",
     "# so that we can use it for term frequency adjustments\n",
@@ -945,8 +3222,7 @@
     "    ],\n",
     "    \"retain_intermediate_calculation_columns\": True,\n",
     "}"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -954,25 +3230,83 @@
    "id": "1581eeeb-246b-46de-be88-ba4dc821fce7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:57:17.346493Z",
-     "iopub.status.busy": "2024-05-15T15:57:17.346091Z",
-     "iopub.status.idle": "2024-05-15T15:58:52.238122Z",
-     "shell.execute_reply": "2024-05-15T15:58:52.237374Z"
+     "iopub.execute_input": "2024-06-07T09:17:05.463764Z",
+     "iopub.status.busy": "2024-06-07T09:17:05.463499Z",
+     "iopub.status.idle": "2024-06-07T09:18:25.606071Z",
+     "shell.execute_reply": "2024-06-07T09:18:25.605371Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Probability two random records match is estimated to be  0.000239.\n",
+      "This means that amongst all possible pairwise record comparisons, one in 4,185.85 are expected to match.  With 25,000,000 total possible comparisons, we expect a total of around 5,972.50 matching pairs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 month' (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 year' (comparison vector value: 2). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "u probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 10 year' (comparison vector value: 1). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - Full name (no m values are trained).\n",
+      "    - date_of_birth (some u values are not trained, no m values are trained).\n",
+      "    - Social security ID (no m values are trained).\n",
+      "    - Street number (no m values are trained).\n",
+      "    - Postcode (no m values are trained).\n"
+     ]
+    }
+   ],
    "source": [
     "# train\n",
     "linker_advanced = Linker(dfs, extended_model_settings, database_api=DuckDBAPI())\n",
-    "linker_advanced.estimate_probability_two_random_records_match(\n",
+    "linker_advanced.training.estimate_probability_two_random_records_match(\n",
     "    deterministic_rules, recall=0.8\n",
     ")\n",
     "# We recommend increasing target rows to 1e8 improve accuracy for u\n",
     "# values in full name comparison, as we have subdivided the data more finely\n",
     "\n",
     "# Here, 1e7 for speed\n",
-    "linker_advanced.estimate_u_using_random_sampling(max_pairs=1e7)"
-   ],
-   "outputs": []
+    "linker_advanced.training.estimate_u_using_random_sampling(max_pairs=1e7)"
+   ]
   },
   {
    "cell_type": "code",
@@ -980,18 +3314,107 @@
    "id": "265f0651",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:58:52.244579Z",
-     "iopub.status.busy": "2024-05-15T15:58:52.244307Z",
-     "iopub.status.idle": "2024-05-15T15:58:53.189566Z",
-     "shell.execute_reply": "2024-05-15T15:58:53.188815Z"
+     "iopub.execute_input": "2024-06-07T09:18:25.610698Z",
+     "iopub.status.busy": "2024-06-07T09:18:25.610416Z",
+     "iopub.status.idle": "2024-06-07T09:18:26.522700Z",
+     "shell.execute_reply": "2024-06-07T09:18:26.522017Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.date_of_birth = r.date_of_birth\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - Full name\n",
+      "    - Social security ID\n",
+      "    - Street number\n",
+      "    - Postcode\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - date_of_birth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level single name cross-matches on comparison Full name not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.465 in the m_probability of Full name, level `Exact match on full_name`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.00249 in the m_probability of Social security ID, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 4.89e-05 in the m_probability of Social security ID, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 3 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for Full name - single name cross-matches (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - Full name (some m values are not trained).\n",
+      "    - date_of_birth (some u values are not trained, no m values are trained).\n"
+     ]
+    }
+   ],
    "source": [
-    "session_dob = linker_advanced.estimate_parameters_using_expectation_maximisation(\n",
+    "session_dob = linker_advanced.training.estimate_parameters_using_expectation_maximisation(\n",
     "    \"l.date_of_birth = r.date_of_birth\", estimate_without_term_frequencies=True\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -999,18 +3422,155 @@
    "id": "ebcb15c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:58:53.193304Z",
-     "iopub.status.busy": "2024-05-15T15:58:53.193012Z",
-     "iopub.status.idle": "2024-05-15T15:58:54.287492Z",
-     "shell.execute_reply": "2024-05-15T15:58:54.286732Z"
+     "iopub.execute_input": "2024-06-07T09:18:26.526171Z",
+     "iopub.status.busy": "2024-06-07T09:18:26.525914Z",
+     "iopub.status.idle": "2024-06-07T09:18:27.518982Z",
+     "shell.execute_reply": "2024-06-07T09:18:27.518364Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.postcode = r.postcode\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - Full name\n",
+      "    - date_of_birth\n",
+      "    - Social security ID\n",
+      "    - Street number\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - Postcode\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level single name cross-matches on comparison Full name not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 1 month' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 1 year' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:\n",
+      "Level Abs difference of 'transformed date_of_birth <= 10 year' on comparison date_of_birth not observed in dataset, unable to train m value\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was 0.0375 in the m_probability of date_of_birth, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.000645 in the m_probability of date_of_birth, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 1.72e-05 in the m_probability of Social security ID, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 3 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for Full name - single name cross-matches (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 month' (comparison vector value: 3). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 1 year' (comparison vector value: 2). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "m probability not trained for date_of_birth - Abs difference of 'transformed date_of_birth <= 10 year' (comparison vector value: 1). This usually means the comparison level was never observed in the training data.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - Full name (some m values are not trained).\n",
+      "    - date_of_birth (some u values are not trained, some m values are not trained).\n"
+     ]
+    }
+   ],
    "source": [
-    "session_pc = linker_advanced.estimate_parameters_using_expectation_maximisation(\n",
+    "session_pc = linker_advanced.training.estimate_parameters_using_expectation_maximisation(\n",
     "    \"l.postcode = r.postcode\", estimate_without_term_frequencies=True\n",
     ")"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
@@ -1018,16 +3578,95 @@
    "id": "d9d21e85-b89b-435a-8b75-142166ac3f31",
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-     "iopub.execute_input": "2024-05-15T15:58:54.292571Z",
-     "iopub.status.busy": "2024-05-15T15:58:54.292308Z",
-     "iopub.status.idle": "2024-05-15T15:58:54.443712Z",
-     "shell.execute_reply": "2024-05-15T15:58:54.443023Z"
+     "iopub.execute_input": "2024-06-07T09:18:27.523341Z",
+     "iopub.status.busy": "2024-06-07T09:18:27.523109Z",
+     "iopub.status.idle": "2024-06-07T09:18:27.711081Z",
+     "shell.execute_reply": "2024-06-07T09:18:27.710381Z"
     }
    },
-   "source": [
-    "linker_advanced.parameter_estimate_comparisons_chart()"
+   "outputs": [
+    {
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l.date_of_birth = r.date_of_birth\", \"estimated_probability_as_log_odds\": -6.965454114772173, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"Full name\", \"comparison_sort_order\": 0}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.00771378692900449, \"estimate_description\": \"EM, blocked on: l.postcode = r.postcode\", \"estimated_probability_as_log_odds\": -7.007173202263621, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"Full name\", \"comparison_sort_order\": 0}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.9410929806525783, \"estimate_description\": \"EM, blocked on: l.postcode = r.postcode\", \"estimated_probability_as_log_odds\": 3.9978258086526863, \"sql_condition\": \"\\\"date_of_birth_l\\\" = \\\"date_of_birth_r\\\"\", \"comparison_level_label\": \"Exact match on date_of_birth\", 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\"comparison_vector_value\": 3, \"comparison_name\": \"Social security ID\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.9111317764821681, \"estimate_description\": \"EM, blocked on: l.postcode = r.postcode\", \"estimated_probability_as_log_odds\": 3.357920171304859, \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"comparison_level_label\": \"Exact match on soc_sec_id\", \"comparison_vector_value\": 3, \"comparison_name\": \"Social security ID\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.05007561544480814, \"estimate_description\": \"EM, blocked on: l.date_of_birth = r.date_of_birth\", \"estimated_probability_as_log_odds\": -4.245632524837669, \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"comparison_level_label\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"comparison_vector_value\": 2, \"comparison_name\": \"Social security ID\", 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l.postcode = r.postcode\", \"estimated_probability_as_log_odds\": -4.6848774256274135, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"Social security ID\", \"comparison_sort_order\": 2}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.8461351911072892, \"estimate_description\": \"EM, blocked on: l.date_of_birth = r.date_of_birth\", \"estimated_probability_as_log_odds\": 2.4592248839469666, \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"comparison_level_label\": \"Exact match on street_number\", \"comparison_vector_value\": 2, \"comparison_name\": \"Street number\", \"comparison_sort_order\": 3}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.837851092871102, \"estimate_description\": \"EM, blocked on: l.postcode = r.postcode\", \"estimated_probability_as_log_odds\": 2.3693745634822343, \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", 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0.8415408380900732, \"estimate_description\": \"EM, blocked on: l.date_of_birth = r.date_of_birth\", \"estimated_probability_as_log_odds\": 2.408922207060853, \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"comparison_level_label\": \"Exact match on postcode\", \"comparison_vector_value\": 3, \"comparison_name\": \"Postcode\", \"comparison_sort_order\": 4}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.1450325134778176, \"estimate_description\": \"EM, blocked on: l.date_of_birth = r.date_of_birth\", \"estimated_probability_as_log_odds\": -2.55949319617083, \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"comparison_level_label\": \"Damerau-Levenshtein distance of postcode <= 1\", \"comparison_vector_value\": 2, \"comparison_name\": \"Postcode\", \"comparison_sort_order\": 4}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.005048507461186791, \"estimate_description\": \"EM, blocked on: l.date_of_birth = r.date_of_birth\", \"estimated_probability_as_log_odds\": -7.622625447486967, \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"comparison_level_label\": \"Damerau-Levenshtein distance of postcode <= 2\", \"comparison_vector_value\": 1, \"comparison_name\": \"Postcode\", \"comparison_sort_order\": 4}, {\"m_or_u\": \"m\", \"estimated_probability\": 0.008378140970922567, \"estimate_description\": \"EM, blocked on: l.date_of_birth = r.date_of_birth\", \"estimated_probability_as_log_odds\": -6.887016104656398, \"sql_condition\": \"ELSE\", \"comparison_level_label\": \"All other comparisons\", \"comparison_vector_value\": 0, \"comparison_name\": \"Postcode\", \"comparison_sort_order\": 4}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "outputs": []
+   "source": [
+    "linker_advanced.visualisations.parameter_estimate_comparisons_chart()"
+   ]
   },
   {
    "cell_type": "code",
@@ -1035,16 +3674,95 @@
    "id": "4a857c18-b0d5-48dc-b7f1-1f6389db5089",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:58:54.447134Z",
-     "iopub.status.busy": "2024-05-15T15:58:54.446857Z",
-     "iopub.status.idle": "2024-05-15T15:58:54.770678Z",
-     "shell.execute_reply": "2024-05-15T15:58:54.770024Z"
+     "iopub.execute_input": "2024-06-07T09:18:27.746299Z",
+     "iopub.status.busy": "2024-06-07T09:18:27.744495Z",
+     "iopub.status.idle": "2024-06-07T09:18:28.388134Z",
+     "shell.execute_reply": "2024-06-07T09:18:28.387392Z"
     }
    },
-   "source": [
-    "linker_advanced.match_weights_chart()"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-daaf187b2d124d87b353d55a930edf65.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-daaf187b2d124d87b353d55a930edf65.vega-embed details,\n",
+       "  #altair-viz-daaf187b2d124d87b353d55a930edf65.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-daaf187b2d124d87b353d55a930edf65\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-daaf187b2d124d87b353d55a930edf65\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-daaf187b2d124d87b353d55a930edf65\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"discreteHeight\": 60, \"discreteWidth\": 400}, \"header\": {\"title\": null}, \"mark\": {\"tooltip\": null}, \"title\": {\"anchor\": \"middle\"}}, \"vconcat\": [{\"mark\": {\"type\": \"bar\", \"clip\": true, \"height\": 15}, \"encoding\": {\"color\": {\"field\": \"log2_bayes_factor\", \"scale\": {\"domain\": [-10, 0, 10], \"interpolate\": \"lab\", \"range\": [\"red\", \"#bbbbbb\", \"green\"]}, \"title\": \"Match weight\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"comparison_name\", \"title\": \"Comparison name\", \"type\": \"nominal\"}, {\"field\": \"probability_two_random_records_match\", \"format\": \".4f\", \"title\": \"Probability two random records match\", \"type\": \"nominal\"}, {\"field\": \"log2_bayes_factor\", \"format\": \",.4f\", \"title\": \"Equivalent match weight\", \"type\": \"quantitative\"}, {\"field\": \"bayes_factor_description\", \"title\": \"Match weight 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\"sort\": {\"field\": \"comparison_vector_value\", \"order\": \"descending\"}, \"type\": \"nominal\"}}, \"height\": {\"step\": 12}, \"resolve\": {\"axis\": {\"y\": \"independent\"}, \"scale\": {\"y\": \"independent\"}}, \"transform\": [{\"filter\": \"(datum.comparison_name != 'probability_two_random_records_match')\"}]}], \"data\": {\"name\": \"data-fcf91be1b44857e850b162f64c29a420\"}, \"params\": [{\"name\": \"mouse_zoom\", \"select\": {\"type\": \"interval\", \"encodings\": [\"x\"]}, \"bind\": \"scales\", \"views\": []}], \"resolve\": {\"axis\": {\"y\": \"independent\"}, \"scale\": {\"y\": \"independent\"}}, \"title\": {\"text\": \"Model parameters (components of final match weight)\", \"subtitle\": \"Use mousewheel to zoom\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-fcf91be1b44857e850b162f64c29a420\": [{\"comparison_name\": \"probability_two_random_records_match\", \"sql_condition\": null, \"label_for_charts\": \"\", \"m_probability\": null, \"u_probability\": null, \"m_probability_description\": null, \"u_probability_description\": null, \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": null, \"is_null_level\": false, \"bayes_factor\": 0.00023895708684804802, \"log2_bayes_factor\": -12.030960824733759, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 0, \"bayes_factor_description\": \"The probability that two random records drawn at random match is 0.000 or one in  4,185.9 records.This is equivalent to a starting match weight of -12.031.\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": -1}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"\\\"full_name_l\\\" = \\\"full_name_r\\\"\", \"label_for_charts\": \"Exact match on full_name\", \"m_probability\": 0.4827616941913051, \"u_probability\": 0.00011478065288267096, \"m_probability_description\": \"Amongst matching record comparisons, 48.28% of records are in the exact match on full_name comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.01% of records are in the exact match on full_name comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"full_name\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4205.95006272342, \"log2_bayes_factor\": 12.038216005704445, \"comparison_vector_value\": 11, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `exact match on full_name` then comparison is 4,205.95 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"full_name_l\\\", \\\"full_name_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of full_name >= 0.9\", \"m_probability\": 0.30007462886582736, \"u_probability\": 0.0008349126833712694, \"m_probability_description\": \"Amongst matching record comparisons, 30.01% of records are in the jaro-winkler distance of full_name >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.08% of records are in the jaro-winkler distance of full_name >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 359.4083966411491, \"log2_bayes_factor\": 8.489480303828593, \"comparison_vector_value\": 10, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of full_name >= 0.9` then comparison is 359.41 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"full_name_l\\\", \\\"full_name_r\\\") >= 0.7\", \"label_for_charts\": \"Jaro-Winkler distance of full_name >= 0.7\", \"m_probability\": 0.1322141827959491, \"u_probability\": 0.03480998593664187, \"m_probability_description\": \"Amongst matching record comparisons, 13.22% of records are in the jaro-winkler distance of full_name >= 0.7 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 3.48% of records are in the jaro-winkler distance of full_name >= 0.7 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 3.798168233580845, \"log2_bayes_factor\": 1.925303808693704, \"comparison_vector_value\": 9, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of full_name >= 0.7` then comparison is 3.80 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"\\\"given_name_l\\\" = \\\"surname_r\\\" AND \\\"given_name_r\\\" = \\\"surname_l\\\"\", \"label_for_charts\": \"Match on reversed cols: given_name and surname\", \"m_probability\": 0.028510497253270775, \"u_probability\": 6.15073507236439e-06, \"m_probability_description\": \"Amongst matching record comparisons, 2.85% of records are in the match on reversed cols: given_name and surname comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the match on reversed cols: given_name and surname comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4635.299182592028, \"log2_bayes_factor\": 12.178446744400038, \"comparison_vector_value\": 8, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `match on reversed cols: given_name and surname` then comparison is 4,635.30 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"jaro_winkler_similarity(given_name_l, surname_r) + jaro_winkler_similarity(surname_l, given_name_r) >= 1.8\", \"label_for_charts\": \"switched + jaro_winkler_similarity >= 1.8\", \"m_probability\": 0.012760805866857659, \"u_probability\": 1.9047437643451016e-05, \"m_probability_description\": \"Amongst matching record comparisons, 1.28% of records are in the switched + jaro_winkler_similarity >= 1.8 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the switched + jaro_winkler_similarity >= 1.8 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 669.9486884129605, \"log2_bayes_factor\": 9.387906793095933, \"comparison_vector_value\": 7, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `switched + jaro_winkler_similarity >= 1.8` then comparison is 669.95 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"jaro_winkler_similarity(given_name_l, surname_r) + jaro_winkler_similarity(surname_l, given_name_r) >= 1.4\", \"label_for_charts\": \"switched + jaro_winkler_similarity >= 1.4\", \"m_probability\": 0.015183159429609669, \"u_probability\": 0.026536552826481017, \"m_probability_description\": \"Amongst matching record comparisons, 1.52% of records are in the switched + jaro_winkler_similarity >= 1.4 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 2.65% of records are in the switched + jaro_winkler_similarity >= 1.4 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.5721602021517386, \"log2_bayes_factor\": -0.8055089435302362, \"comparison_vector_value\": 6, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `switched + jaro_winkler_similarity >= 1.4` then comparison is  1.75 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"\\\"given_name_l\\\" = \\\"given_name_r\\\"\", \"label_for_charts\": \"Exact match on given_name\", \"m_probability\": 0.001379050398762352, \"u_probability\": 6.557477230375584e-05, \"m_probability_description\": \"Amongst matching record comparisons, 0.14% of records are in the exact match on given_name comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.01% of records are in the exact match on given_name comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"given_name\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 21.030197289504976, \"log2_bayes_factor\": 4.394390479301104, \"comparison_vector_value\": 5, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `exact match on given_name` then comparison is 21.03 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"\\\"surname_l\\\" = \\\"surname_r\\\"\", \"label_for_charts\": \"Exact match on surname\", \"m_probability\": 0.013480767665554321, \"u_probability\": 0.0007364017168090463, \"m_probability_description\": \"Amongst matching record comparisons, 1.35% of records are in the exact match on surname comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.07% of records are in the exact match on surname comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"surname\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 18.306268654517506, \"log2_bayes_factor\": 4.194265853132868, \"comparison_vector_value\": 4, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `exact match on surname` then comparison is 18.31 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"given_name_l = surname_r OR surname_l = given_name_r\", \"label_for_charts\": \"single name cross-matches\", \"m_probability\": 0.00454545454545455, \"u_probability\": 5.952324263578442e-07, \"m_probability_description\": \"Amongst matching record comparisons, 0.45% of records are in the single name cross-matches comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the single name cross-matches comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 7636.4363636363705, \"log2_bayes_factor\": 12.898683828690894, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `single name cross-matches` then comparison is 7,636.44 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"given_name_l\\\", \\\"given_name_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of given_name >= 0.9\", \"m_probability\": 0.001374506194244919, \"u_probability\": 0.0006707277391008974, \"m_probability_description\": \"Amongst matching record comparisons, 0.14% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.07% of records are in the jaro-winkler distance of given_name >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.0492759045382978, \"log2_bayes_factor\": 1.035114234867687, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of given_name >= 0.9` then comparison is 2.05 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"jaro_winkler_similarity(\\\"surname_l\\\", \\\"surname_r\\\") >= 0.9\", \"label_for_charts\": \"Jaro-Winkler distance of surname >= 0.9\", \"m_probability\": 0.004434658851983499, \"u_probability\": 0.0005478122430580026, \"m_probability_description\": \"Amongst matching record comparisons, 0.44% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.05% of records are in the jaro-winkler distance of surname >= 0.9 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 8.095216761181359, \"log2_bayes_factor\": 3.0170697112992553, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `jaro-winkler distance of surname >= 0.9` then comparison is 8.10 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"Full name\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.007826048486635245, \"u_probability\": 0.9356574580242093, \"m_probability_description\": \"Amongst matching record comparisons, 0.78% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 93.57% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.008364223914979742, \"log2_bayes_factor\": -6.901552600459747, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 11, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  119.56 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 0}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"\\\"date_of_birth_l\\\" = \\\"date_of_birth_r\\\"\", \"label_for_charts\": \"Exact match on date_of_birth\", \"m_probability\": 0.9410929806525783, \"u_probability\": 0.00021768707482993198, \"m_probability_description\": \"Amongst matching record comparisons, 94.11% of records are in the exact match on date_of_birth comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on date_of_birth comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4323.145879872782, \"log2_bayes_factor\": 12.077865803898012, \"comparison_vector_value\": 5, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `exact match on date_of_birth` then comparison is 4,323.15 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"damerau_levenshtein(\\\"date_of_birth_l\\\", \\\"date_of_birth_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of date_of_birth <= 1\", \"m_probability\": 0.010781525322577031, \"u_probability\": 0.0010004251700680273, \"m_probability_description\": \"Amongst matching record comparisons, 1.08% of records are in the damerau-levenshtein distance of date_of_birth <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.10% of records are in the damerau-levenshtein distance of date_of_birth <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 10.776943288865779, \"log2_bayes_factor\": 3.429876133150744, \"comparison_vector_value\": 4, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of date_of_birth <= 1` then comparison is 10.78 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 2629800.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 1 month'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.0050000000000000044, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 1 month' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.50% of records are in the abs difference of 'transformed date_of_birth <= 1 month' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.0, \"log2_bayes_factor\": 1.0, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 1 month'` then comparison is 2.00 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 31557600.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 1 year'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.020000000000000018, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 1 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 2.00% of records are in the abs difference of 'transformed date_of_birth <= 1 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.5, \"log2_bayes_factor\": -1.0, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 1 year'` then comparison is  2.00 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ABS(EPOCH(try_strptime(\\\"date_of_birth_l\\\", '%Y-%m-%d')) - EPOCH(try_strptime(\\\"date_of_birth_r\\\", '%Y-%m-%d'))) <= 315576000.0\", \"label_for_charts\": \"Abs difference of 'transformed date_of_birth <= 10 year'\", \"m_probability\": 0.010000000000000009, \"u_probability\": 0.08000000000000007, \"m_probability_description\": \"Amongst matching record comparisons, 1.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 8.00% of records are in the abs difference of 'transformed date_of_birth <= 10 year' comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.125, \"log2_bayes_factor\": -3.0, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `abs difference of 'transformed date_of_birth <= 10 year'` then comparison is  8.00 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"date_of_birth\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.04812549402484467, \"u_probability\": 0.998781887755102, \"m_probability_description\": \"Amongst matching record comparisons, 4.81% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.88% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.048184187773982624, \"log2_bayes_factor\": -4.375296403541578, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 5, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  20.75 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 1}, {\"comparison_name\": \"Social security ID\", \"sql_condition\": \"\\\"soc_sec_id_l\\\" = \\\"soc_sec_id_r\\\"\", \"label_for_charts\": \"Exact match on soc_sec_id\", \"m_probability\": 0.9113602928239442, \"u_probability\": 0.00018352999812700196, \"m_probability_description\": \"Amongst matching record comparisons, 91.14% of records are in the exact match on soc_sec_id comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the exact match on soc_sec_id comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"soc_sec_id\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 4965.72932013701, \"log2_bayes_factor\": 12.277789907606756, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on soc_sec_id` then comparison is 4,965.73 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"Social security ID\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 1\", \"m_probability\": 0.05052296639946095, \"u_probability\": 1.6269686320447742e-05, \"m_probability_description\": \"Amongst matching record comparisons, 5.05% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.00% of records are in the damerau-levenshtein distance of soc_sec_id <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 3105.343606776468, \"log2_bayes_factor\": 11.600537196381813, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 1` then comparison is 3,105.34 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"Social security ID\", \"sql_condition\": \"damerau_levenshtein(\\\"soc_sec_id_l\\\", \\\"soc_sec_id_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of soc_sec_id <= 2\", \"m_probability\": 0.0005665365003123153, \"u_probability\": 0.00023005733278730678, \"m_probability_description\": \"Amongst matching record comparisons, 0.06% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.02% of records are in the damerau-levenshtein distance of soc_sec_id <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 2.462588318521849, \"log2_bayes_factor\": 1.3001754664608394, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of soc_sec_id <= 2` then comparison is 2.46 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"Social security ID\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.0375502042762827, \"u_probability\": 0.9995701429827653, \"m_probability_description\": \"Amongst matching record comparisons, 3.76% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 99.96% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.03756635243648944, \"log2_bayes_factor\": -4.734415147717224, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  26.62 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 2}, {\"comparison_name\": \"Street number\", \"sql_condition\": \"\\\"street_number_l\\\" = \\\"street_number_r\\\"\", \"label_for_charts\": \"Exact match on street_number\", \"m_probability\": 0.8419931419891956, \"u_probability\": 0.014819799335244426, \"m_probability_description\": \"Amongst matching record comparisons, 84.20% of records are in the exact match on street_number comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.48% of records are in the exact match on street_number comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"street_number\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 56.815421244386805, \"log2_bayes_factor\": 5.828210664285494, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `exact match on street_number` then comparison is 56.82 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"Street number\", \"sql_condition\": \"damerau_levenshtein(\\\"street_number_l\\\", \\\"street_number_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of street_number <= 1\", \"m_probability\": 0.05242542729770825, \"u_probability\": 0.22343120541709127, \"m_probability_description\": \"Amongst matching record comparisons, 5.24% of records are in the damerau-levenshtein distance of street_number <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 22.34% of records are in the damerau-levenshtein distance of street_number <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.2346378931261765, \"log2_bayes_factor\": -2.091492072950498, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of street_number <= 1` then comparison is  4.26 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"Street number\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.10558143071309614, \"u_probability\": 0.7617489952476643, \"m_probability_description\": \"Amongst matching record comparisons, 10.56% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 76.17% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.13860396452346996, \"log2_bayes_factor\": -2.8509595710590663, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 2, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  7.21 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 3}, {\"comparison_name\": \"Postcode\", \"sql_condition\": \"\\\"postcode_l\\\" = \\\"postcode_r\\\"\", \"label_for_charts\": \"Exact match on postcode\", \"m_probability\": 0.8415408380900732, \"u_probability\": 0.0011553461395605756, \"m_probability_description\": \"Amongst matching record comparisons, 84.15% of records are in the exact match on postcode comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.12% of records are in the exact match on postcode comparison level\", \"has_tf_adjustments\": true, \"tf_adjustment_column\": \"postcode\", \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 728.3884969833758, \"log2_bayes_factor\": 9.508564328624239, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `exact match on postcode` then comparison is 728.39 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}, {\"comparison_name\": \"Postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 1\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 1\", \"m_probability\": 0.1450325134778176, \"u_probability\": 0.01267904591384844, \"m_probability_description\": \"Amongst matching record comparisons, 14.50% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.27% of records are in the damerau-levenshtein distance of postcode <= 1 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.438756075440082, \"log2_bayes_factor\": 3.5158582675133916, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 1` then comparison is 11.44 times more likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}, {\"comparison_name\": \"Postcode\", \"sql_condition\": \"damerau_levenshtein(\\\"postcode_l\\\", \\\"postcode_r\\\") <= 2\", \"label_for_charts\": \"Damerau-Levenshtein distance of postcode <= 2\", \"m_probability\": 0.005048507461186791, \"u_probability\": 0.1116170917419834, \"m_probability_description\": \"Amongst matching record comparisons, 0.50% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 11.16% of records are in the damerau-levenshtein distance of postcode <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.04523059490617293, \"log2_bayes_factor\": -4.466557218290451, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `damerau-levenshtein distance of postcode <= 2` then comparison is  22.11 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}, {\"comparison_name\": \"Postcode\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.008378140970922567, \"u_probability\": 0.8745485162046076, \"m_probability_description\": \"Amongst matching record comparisons, 0.84% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.45% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.009579961335115265, \"log2_bayes_factor\": -6.70576445143028, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 3, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  104.38 times less likely to be a match\", \"probability_two_random_records_match\": 0.0002389, \"comparison_sort_order\": 4}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.VConcatChart(...)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
-   "outputs": []
+   "source": [
+    "linker_advanced.visualisations.match_weights_chart()"
+   ]
   },
   {
    "cell_type": "code",
@@ -1052,22 +3770,57 @@
    "id": "e1ee24d9-1def-4b8d-bb85-1c63b595e75e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-05-15T15:58:54.773893Z",
-     "iopub.status.busy": "2024-05-15T15:58:54.773655Z",
-     "iopub.status.idle": "2024-05-15T15:58:56.607253Z",
-     "shell.execute_reply": "2024-05-15T15:58:56.606584Z"
+     "iopub.execute_input": "2024-06-07T09:18:28.392069Z",
+     "iopub.status.busy": "2024-06-07T09:18:28.391745Z",
+     "iopub.status.idle": "2024-06-07T09:18:30.289569Z",
+     "shell.execute_reply": "2024-06-07T09:18:30.288893Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'Full name':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'date_of_birth':\n",
+      "    u values not fully trained\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Completed iteration 1, root rows count 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2    4960\n",
+       "1      80\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "predictions_adv = linker_advanced.predict()\n",
+    "predictions_adv = linker_advanced.inference.predict()\n",
     "df_predictions_adv = predictions_adv.as_pandas_dataframe()\n",
-    "clusters_adv = linker_advanced.cluster_pairwise_predictions_at_threshold(\n",
+    "clusters_adv = linker_advanced.clustering.cluster_pairwise_predictions_at_threshold(\n",
     "    predictions_adv, threshold_match_probability=0.99\n",
     ")\n",
     "df_clusters_adv = clusters_adv.as_pandas_dataframe().sort_values(\"cluster_id\")\n",
     "df_clusters_adv.groupby(\"cluster_id\").size().value_counts()"
-   ],
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
diff --git a/docs/demos/examples/duckdb/link_only.ipynb b/docs/demos/examples/duckdb/link_only.ipynb
index a1f64626f5..dba266abe1 100644
--- a/docs/demos/examples/duckdb/link_only.ipynb
+++ b/docs/demos/examples/duckdb/link_only.ipynb
@@ -1,253 +1,992 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Linking without deduplication\n",
-        "\n",
-        "A simple record linkage model using the `link_only` [link type](https://moj-analytical-services.github.io/splink/settings_dict_guide.html#link_type).\n",
-        "\n",
-        "With `link_only`, only between-dataset record comparisons are generated. No within-dataset record comparisons are created, meaning that the model does not attempt to find within-dataset duplicates.\n"
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linking without deduplication\n",
+    "\n",
+    "A simple record linkage model using the `link_only` [link type](https://moj-analytical-services.github.io/splink/settings_dict_guide.html#link_type).\n",
+    "\n",
+    "With `link_only`, only between-dataset record comparisons are generated. No within-dataset record comparisons are created, meaning that the model does not attempt to find within-dataset duplicates.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/link_only.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:42.926356Z",
+     "iopub.status.busy": "2024-06-07T09:18:42.925982Z",
+     "iopub.status.idle": "2024-06-07T09:18:42.943456Z",
+     "shell.execute_reply": "2024-06-07T09:18:42.942569Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:42.947959Z",
+     "iopub.status.busy": "2024-06-07T09:18:42.947640Z",
+     "iopub.status.idle": "2024-06-07T09:18:44.652788Z",
+     "shell.execute_reply": "2024-06-07T09:18:44.652024Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>city</th>\n",
+       "      <th>email</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>930</th>\n",
+       "      <td>930</td>\n",
+       "      <td>Luke</td>\n",
+       "      <td>Robinnso</td>\n",
+       "      <td>1981-10-18</td>\n",
+       "      <td>Coventry</td>\n",
+       "      <td>lrobinson@wolf.org</td>\n",
+       "      <td>233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>385</td>\n",
+       "      <td>Lottie</td>\n",
+       "      <td>Davis</td>\n",
+       "      <td>1972-06-12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>lottie.d7@morgan-pierce.com</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     unique_id first_name   surname         dob      city  \\\n",
+       "930        930       Luke  Robinnso  1981-10-18  Coventry   \n",
+       "385        385     Lottie     Davis  1972-06-12       NaN   \n",
+       "\n",
+       "                           email  cluster  \n",
+       "930           lrobinson@wolf.org      233  \n",
+       "385  lottie.d7@morgan-pierce.com      100  "
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import splink_datasets\n",
+    "\n",
+    "df = splink_datasets.fake_1000\n",
+    "\n",
+    "# Split a simple dataset into two, separate datasets which can be linked together.\n",
+    "df_l = df.sample(frac=0.5)\n",
+    "df_r = df.drop(df_l.index)\n",
+    "\n",
+    "df_l.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:44.695716Z",
+     "iopub.status.busy": "2024-06-07T09:18:44.695390Z",
+     "iopub.status.idle": "2024-06-07T09:18:44.942598Z",
+     "shell.execute_reply": "2024-06-07T09:18:44.942052Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import splink.comparison_library as cl\n",
+    "import splink.comparison_template_library as ctl\n",
+    "from splink import DuckDBAPI, Linker, SettingsCreator, block_on\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    link_type=\"link_only\",\n",
+    "    blocking_rules_to_generate_predictions=[\n",
+    "        block_on(\"first_name\"),\n",
+    "        block_on(\"surname\"),\n",
+    "    ],\n",
+    "    comparisons=[\n",
+    "        ctl.NameComparison(\n",
+    "            \"first_name\",\n",
+    "        ),\n",
+    "        ctl.NameComparison(\"surname\"),\n",
+    "        ctl.DateComparison(\n",
+    "            \"dob\",\n",
+    "            input_is_string=True,\n",
+    "            invalid_dates_as_null=True,\n",
+    "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
+    "            datetime_thresholds=[1, 1, 10],\n",
+    "        ),\n",
+    "        cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
+    "        ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "linker = Linker(\n",
+    "    [df_l, df_r],\n",
+    "    settings,\n",
+    "    database_api=DuckDBAPI(),\n",
+    "    input_table_aliases=[\"df_left\", \"df_right\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:44.946395Z",
+     "iopub.status.busy": "2024-06-07T09:18:44.946113Z",
+     "iopub.status.idle": "2024-06-07T09:18:45.188705Z",
+     "shell.execute_reply": "2024-06-07T09:18:45.188192Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/link_only.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-3783c1cf228d4ad4beada70e2b1aae7a.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-3783c1cf228d4ad4beada70e2b1aae7a.vega-embed details,\n",
+       "  #altair-viz-3783c1cf228d4ad4beada70e2b1aae7a.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-3783c1cf228d4ad4beada70e2b1aae7a\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-3783c1cf228d4ad4beada70e2b1aae7a\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-3783c1cf228d4ad4beada70e2b1aae7a\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"rect\", \"encoding\": {\"color\": {\"field\": \"completeness\", \"legend\": null, \"scale\": {\"scheme\": \"darkred\", \"zero\": true}, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"source_dataset\", \"title\": \"Source dataset\", \"type\": \"nominal\"}, {\"field\": \"total_rows_inc_nulls\", \"format\": \",\", \"title\": \"# of records\", \"type\": \"quantitative\"}, {\"field\": \"column_name\", \"title\": \"Column name\", \"type\": \"nominal\"}, {\"field\": \"total_null_rows\", \"format\": \",\", \"title\": \"# of nulls\", \"type\": \"quantitative\"}, {\"field\": \"completeness\", \"format\": \".1%\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"labelAngle\": 20}, \"field\": \"column_name\", \"sort\": {\"field\": \"mean_comp\", \"order\": \"descending\"}, \"title\": \"Column name\", \"type\": \"nominal\"}, \"y\": {\"field\": \"source_dataset\", \"title\": \"Source dataset\", \"type\": \"nominal\"}}, \"title\": \"Column completeness by source dataset\", \"transform\": [{\"joinaggregate\": [{\"op\": \"mean\", \"field\": \"completeness\", \"as\": \"mean_comp\"}], \"groupby\": [\"column_name\"]}]}, {\"mark\": {\"type\": \"text\"}, \"encoding\": {\"color\": {\"condition\": {\"test\": \"datum['completeness'] < 0.5\", \"value\": \"white\"}, \"value\": \"black\"}, \"text\": {\"field\": \"completeness\", \"format\": \".0%\", \"type\": \"quantitative\"}, \"x\": {\"axis\": {\"labelAngle\": 0}, \"field\": \"column_name\", \"sort\": {\"field\": \"mean_comp\", \"order\": \"descending\"}, \"type\": \"nominal\"}, \"y\": {\"field\": \"source_dataset\", \"type\": \"nominal\"}}, \"transform\": [{\"joinaggregate\": [{\"op\": \"mean\", \"field\": \"completeness\", \"as\": \"mean_comp\"}], \"groupby\": [\"column_name\"]}]}], \"data\": {\"name\": \"data-276006c9393cd1a273d1e3c29ded6ace\"}, \"height\": {\"step\": 40}, \"width\": {\"step\": 40}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-276006c9393cd1a273d1e3c29ded6ace\": [{\"source_dataset\": \"df_right\", \"column_name\": \"first_name\", \"total_null_rows\": 89, \"total_rows_inc_nulls\": 500, \"completeness\": 0.8220000267028809}, {\"source_dataset\": \"df_left\", \"column_name\": \"first_name\", \"total_null_rows\": 80, \"total_rows_inc_nulls\": 500, \"completeness\": 0.8399999737739563}, {\"source_dataset\": \"df_right\", \"column_name\": \"surname\", \"total_null_rows\": 93, \"total_rows_inc_nulls\": 500, \"completeness\": 0.8140000104904175}, {\"source_dataset\": \"df_left\", \"column_name\": \"surname\", \"total_null_rows\": 88, \"total_rows_inc_nulls\": 500, \"completeness\": 0.8240000009536743}, {\"source_dataset\": \"df_right\", \"column_name\": \"dob\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 500, \"completeness\": 1.0}, {\"source_dataset\": \"df_left\", \"column_name\": \"dob\", \"total_null_rows\": 0, \"total_rows_inc_nulls\": 500, \"completeness\": 1.0}, {\"source_dataset\": \"df_right\", \"column_name\": \"city\", \"total_null_rows\": 97, \"total_rows_inc_nulls\": 500, \"completeness\": 0.8059999942779541}, {\"source_dataset\": \"df_left\", \"column_name\": \"city\", \"total_null_rows\": 90, \"total_rows_inc_nulls\": 500, \"completeness\": 0.8199999928474426}, {\"source_dataset\": \"df_right\", \"column_name\": \"email\", \"total_null_rows\": 105, \"total_rows_inc_nulls\": 500, \"completeness\": 0.7900000214576721}, {\"source_dataset\": \"df_left\", \"column_name\": \"email\", \"total_null_rows\": 106, \"total_rows_inc_nulls\": 500, \"completeness\": 0.7879999876022339}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
       ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink.exploratory import completeness_chart\n",
+    "\n",
+    "completeness_chart(\n",
+    "    [df_l, df_r],\n",
+    "    cols=[\"first_name\", \"surname\", \"dob\", \"city\", \"email\"],\n",
+    "    db_api=DuckDBAPI(),\n",
+    "    table_names_for_chart=[\"df_left\", \"df_right\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:45.192584Z",
+     "iopub.status.busy": "2024-06-07T09:18:45.192253Z",
+     "iopub.status.idle": "2024-06-07T09:18:45.341533Z",
+     "shell.execute_reply": "2024-06-07T09:18:45.340965Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Probability two random records match is estimated to be  0.00346.\n",
+      "This means that amongst all possible pairwise record comparisons, one in 288.78 are expected to match.  With 250,000 total possible comparisons, we expect a total of around 865.71 matching pairs\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "deterministic_rules = [\n",
+    "    \"l.first_name = r.first_name and levenshtein(r.dob, l.dob) <= 1\",\n",
+    "    \"l.surname = r.surname and levenshtein(r.dob, l.dob) <= 1\",\n",
+    "    \"l.first_name = r.first_name and levenshtein(r.surname, l.surname) <= 2\",\n",
+    "    block_on(\"email\"),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:45.344512Z",
+     "iopub.status.busy": "2024-06-07T09:18:45.344289Z",
+     "iopub.status.idle": "2024-06-07T09:18:46.142225Z",
+     "shell.execute_reply": "2024-06-07T09:18:46.141712Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default value for `max_pairs`, which may be too small and thus lead to inaccurate estimates for your model's u-parameters. Consider increasing to 1e8 or 1e9, which will result in more accurate estimates, but with a longer run time.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:43.284619Z",
-          "iopub.status.busy": "2024-03-27T15:14:43.284336Z",
-          "iopub.status.idle": "2024-03-27T15:14:43.289588Z",
-          "shell.execute_reply": "2024-03-27T15:14:43.288971Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:43.293314Z",
-          "iopub.status.busy": "2024-03-27T15:14:43.293026Z",
-          "iopub.status.idle": "2024-03-27T15:14:45.144216Z",
-          "shell.execute_reply": "2024-03-27T15:14:45.143259Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink import splink_datasets\n",
-        "\n",
-        "df = splink_datasets.fake_1000\n",
-        "\n",
-        "# Split a simple dataset into two, separate datasets which can be linked together.\n",
-        "df_l = df.sample(frac=0.5)\n",
-        "df_r = df.drop(df_l.index)\n",
-        "\n",
-        "df_l.head(2)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:45.149667Z",
-          "iopub.status.busy": "2024-03-27T15:14:45.149322Z",
-          "iopub.status.idle": "2024-03-27T15:14:45.584636Z",
-          "shell.execute_reply": "2024-03-27T15:14:45.583909Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "import splink.comparison_library as cl\n",
-        "import splink.comparison_template_library as ctl\n",
-        "from splink import DuckDBAPI, Linker, SettingsCreator, block_on\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    link_type=\"link_only\",\n",
-        "    blocking_rules_to_generate_predictions=[\n",
-        "        block_on(\"first_name\"),\n",
-        "        block_on(\"surname\"),\n",
-        "    ],\n",
-        "    comparisons=[\n",
-        "        ctl.NameComparison(\n",
-        "            \"first_name\",\n",
-        "        ),\n",
-        "        ctl.NameComparison(\"surname\"),\n",
-        "        ctl.DateComparison(\n",
-        "            \"dob\",\n",
-        "            input_is_string=True,\n",
-        "            invalid_dates_as_null=True,\n",
-        "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
-        "            datetime_thresholds=[1, 1, 10],\n",
-        "        ),\n",
-        "        cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
-        "        ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
-        "    ],\n",
-        ")\n",
-        "\n",
-        "linker = Linker(\n",
-        "    [df_l, df_r],\n",
-        "    settings,\n",
-        "    database_api=DuckDBAPI(),\n",
-        "    input_table_aliases=[\"df_left\", \"df_right\"],\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - first_name (no m values are trained).\n",
+      "    - surname (no m values are trained).\n",
+      "    - dob (no m values are trained).\n",
+      "    - city (no m values are trained).\n",
+      "    - email (no m values are trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:46.145662Z",
+     "iopub.status.busy": "2024-06-07T09:18:46.145393Z",
+     "iopub.status.idle": "2024-06-07T09:18:47.814138Z",
+     "shell.execute_reply": "2024-06-07T09:18:47.813573Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:45.588957Z",
-          "iopub.status.busy": "2024-03-27T15:14:45.588354Z",
-          "iopub.status.idle": "2024-03-27T15:14:46.120692Z",
-          "shell.execute_reply": "2024-03-27T15:14:46.119623Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink.exploratory import completeness_chart\n",
-        "\n",
-        "completeness_chart(\n",
-        "    [df_l, df_r],\n",
-        "    cols=[\"first_name\", \"surname\", \"dob\", \"city\", \"email\"],\n",
-        "    db_api=DuckDBAPI(),\n",
-        "    table_names_for_chart=[\"df_left\", \"df_right\"],\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"dob\" = r.\"dob\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - first_name\n",
+      "    - surname\n",
+      "    - city\n",
+      "    - email\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - dob\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:46.124880Z",
-          "iopub.status.busy": "2024-03-27T15:14:46.124449Z",
-          "iopub.status.idle": "2024-03-27T15:14:46.333422Z",
-          "shell.execute_reply": "2024-03-27T15:14:46.332477Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "deterministic_rules = [\n",
-        "    \"l.first_name = r.first_name and levenshtein(r.dob, l.dob) <= 1\",\n",
-        "    \"l.surname = r.surname and levenshtein(r.dob, l.dob) <= 1\",\n",
-        "    \"l.first_name = r.first_name and levenshtein(r.surname, l.surname) <= 2\",\n",
-        "    block_on(\"email\"),\n",
-        "]\n",
-        "\n",
-        "\n",
-        "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:46.337604Z",
-          "iopub.status.busy": "2024-03-27T15:14:46.337231Z",
-          "iopub.status.idle": "2024-03-27T15:14:47.729876Z",
-          "shell.execute_reply": "2024-03-27T15:14:47.728440Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.387 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:47.735185Z",
-          "iopub.status.busy": "2024-03-27T15:14:47.734598Z",
-          "iopub.status.idle": "2024-03-27T15:14:49.944190Z",
-          "shell.execute_reply": "2024-03-27T15:14:49.943452Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "session_dob = linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"dob\"))\n",
-        "session_email = linker.training.estimate_parameters_using_expectation_maximisation(\n",
-        "    block_on(\"email\")\n",
-        ")\n",
-        "session_first_name = linker.training.estimate_parameters_using_expectation_maximisation(\n",
-        "    block_on(\"first_name\")\n",
-        ")"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.113 in probability_two_random_records_match\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:49.948346Z",
-          "iopub.status.busy": "2024-03-27T15:14:49.948058Z",
-          "iopub.status.idle": "2024-03-27T15:14:50.272696Z",
-          "shell.execute_reply": "2024-03-27T15:14:50.271981Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "results = linker.inference.predict(threshold_match_probability=0.9)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 0.0347 in probability_two_random_records_match\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:50.276802Z",
-          "iopub.status.busy": "2024-03-27T15:14:50.276415Z",
-          "iopub.status.idle": "2024-03-27T15:14:50.299341Z",
-          "shell.execute_reply": "2024-03-27T15:14:50.298407Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "results.as_pandas_dataframe(limit=5)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.0122 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was 0.00504 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was 0.00226 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was 0.00105 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was 0.000497 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was 0.000237 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was 0.000114 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 11: Largest change in params was 5.46e-05 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 11 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - dob (no m values are trained).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"email\" = r.\"email\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - first_name\n",
+      "    - surname\n",
+      "    - dob\n",
+      "    - city\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - email\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.453 in the m_probability of dob, level `Exact match on dob`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was 0.0816 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 0.0173 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.00584 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was 0.00237 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was 0.00106 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was 0.000497 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was 0.000238 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was 0.000115 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was 5.6e-05 in probability_two_random_records_match\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 10 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"first_name\" = r.\"first_name\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - surname\n",
+      "    - dob\n",
+      "    - city\n",
+      "    - email\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - first_name\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was 0.182 in the m_probability of surname, level `Exact match on surname`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was -0.0082 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was -0.00119 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was -0.000228 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was -4.89e-05 in the m_probability of surname, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 5 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
+     ]
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.8"
+   ],
+   "source": [
+    "session_dob = linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"dob\"))\n",
+    "session_email = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+    "    block_on(\"email\")\n",
+    ")\n",
+    "session_first_name = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+    "    block_on(\"first_name\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:47.817058Z",
+     "iopub.status.busy": "2024-06-07T09:18:47.816828Z",
+     "iopub.status.idle": "2024-06-07T09:18:48.064527Z",
+     "shell.execute_reply": "2024-06-07T09:18:48.063844Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results = linker.inference.predict(threshold_match_probability=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:18:48.067845Z",
+     "iopub.status.busy": "2024-06-07T09:18:48.067582Z",
+     "iopub.status.idle": "2024-06-07T09:18:48.084784Z",
+     "shell.execute_reply": "2024-06-07T09:18:48.084179Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>match_weight</th>\n",
+       "      <th>match_probability</th>\n",
+       "      <th>source_dataset_l</th>\n",
+       "      <th>source_dataset_r</th>\n",
+       "      <th>unique_id_l</th>\n",
+       "      <th>unique_id_r</th>\n",
+       "      <th>first_name_l</th>\n",
+       "      <th>first_name_r</th>\n",
+       "      <th>gamma_first_name</th>\n",
+       "      <th>surname_l</th>\n",
+       "      <th>...</th>\n",
+       "      <th>dob_l</th>\n",
+       "      <th>dob_r</th>\n",
+       "      <th>gamma_dob</th>\n",
+       "      <th>city_l</th>\n",
+       "      <th>city_r</th>\n",
+       "      <th>gamma_city</th>\n",
+       "      <th>email_l</th>\n",
+       "      <th>email_r</th>\n",
+       "      <th>gamma_email</th>\n",
+       "      <th>match_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.20162</td>\n",
+       "      <td>0.90196</td>\n",
+       "      <td>df_left</td>\n",
+       "      <td>df_right</td>\n",
+       "      <td>445</td>\n",
+       "      <td>444</td>\n",
+       "      <td>Jacob</td>\n",
+       "      <td>Jacob</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Campbell</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1988-06-05</td>\n",
+       "      <td>1997-05-04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lonon</td>\n",
+       "      <td>London</td>\n",
+       "      <td>0</td>\n",
+       "      <td>j.c65@ortiz.com</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.20162</td>\n",
+       "      <td>0.90196</td>\n",
+       "      <td>df_left</td>\n",
+       "      <td>df_right</td>\n",
+       "      <td>774</td>\n",
+       "      <td>778</td>\n",
+       "      <td>Armstrong</td>\n",
+       "      <td>Armstrong</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Eva</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2027-04-21</td>\n",
+       "      <td>2017-04-23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Peterborouhg</td>\n",
+       "      <td>Peterbotrough</td>\n",
+       "      <td>0</td>\n",
+       "      <td>e.armstrong16odonnell.info</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.20162</td>\n",
+       "      <td>0.90196</td>\n",
+       "      <td>df_left</td>\n",
+       "      <td>df_right</td>\n",
+       "      <td>239</td>\n",
+       "      <td>242</td>\n",
+       "      <td>Freya</td>\n",
+       "      <td>Freya</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Shah</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1972-01-17</td>\n",
+       "      <td>1970-12-17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>London</td>\n",
+       "      <td>Lonnod</td>\n",
+       "      <td>0</td>\n",
+       "      <td>f.s@flynn.com</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.20162</td>\n",
+       "      <td>0.90196</td>\n",
+       "      <td>df_left</td>\n",
+       "      <td>df_right</td>\n",
+       "      <td>833</td>\n",
+       "      <td>834</td>\n",
+       "      <td>Mason</td>\n",
+       "      <td>Mason</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Smith</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1983-03-16</td>\n",
+       "      <td>1993-03-13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kingston-uponH-ull</td>\n",
+       "      <td>Kingston-upon-Hull</td>\n",
+       "      <td>0</td>\n",
+       "      <td>masons52@reed.com</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.20162</td>\n",
+       "      <td>0.90196</td>\n",
+       "      <td>df_left</td>\n",
+       "      <td>df_right</td>\n",
+       "      <td>439</td>\n",
+       "      <td>444</td>\n",
+       "      <td>Jacob</td>\n",
+       "      <td>Jacob</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Campbell</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1987-06-06</td>\n",
+       "      <td>1997-05-04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lonnod</td>\n",
+       "      <td>London</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   match_weight  match_probability source_dataset_l source_dataset_r  \\\n",
+       "0       3.20162            0.90196          df_left         df_right   \n",
+       "1       3.20162            0.90196          df_left         df_right   \n",
+       "2       3.20162            0.90196          df_left         df_right   \n",
+       "3       3.20162            0.90196          df_left         df_right   \n",
+       "4       3.20162            0.90196          df_left         df_right   \n",
+       "\n",
+       "   unique_id_l  unique_id_r first_name_l first_name_r  gamma_first_name  \\\n",
+       "0          445          444        Jacob        Jacob                 3   \n",
+       "1          774          778    Armstrong    Armstrong                 3   \n",
+       "2          239          242        Freya        Freya                 3   \n",
+       "3          833          834        Mason        Mason                 3   \n",
+       "4          439          444        Jacob        Jacob                 3   \n",
+       "\n",
+       "  surname_l  ...       dob_l       dob_r gamma_dob              city_l  \\\n",
+       "0  Campbell  ...  1988-06-05  1997-05-04         1               Lonon   \n",
+       "1       Eva  ...  2027-04-21  2017-04-23         1        Peterborouhg   \n",
+       "2      Shah  ...  1972-01-17  1970-12-17         1              London   \n",
+       "3     Smith  ...  1983-03-16  1993-03-13         1  Kingston-uponH-ull   \n",
+       "4  Campbell  ...  1987-06-06  1997-05-04         1              Lonnod   \n",
+       "\n",
+       "               city_r gamma_city                     email_l  email_r  \\\n",
+       "0              London          0             j.c65@ortiz.com     None   \n",
+       "1       Peterbotrough          0  e.armstrong16odonnell.info     None   \n",
+       "2              Lonnod          0               f.s@flynn.com     None   \n",
+       "3  Kingston-upon-Hull          0           masons52@reed.com     None   \n",
+       "4              London          0                        None     None   \n",
+       "\n",
+       "  gamma_email match_key  \n",
+       "0          -1         0  \n",
+       "1          -1         0  \n",
+       "2          -1         0  \n",
+       "3          -1         0  \n",
+       "4          -1         0  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "results.as_pandas_dataframe(limit=5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/demos/examples/duckdb/pairwise_labels.ipynb b/docs/demos/examples/duckdb/pairwise_labels.ipynb
index 168c09f915..f99d3a566c 100644
--- a/docs/demos/examples/duckdb/pairwise_labels.ipynb
+++ b/docs/demos/examples/duckdb/pairwise_labels.ipynb
@@ -1,390 +1,776 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Estimating m from a sample of pairwise labels\n",
-        "\n",
-        "In this example, we estimate the m probabilities of the model from a table containing pairwise record comparisons which we know are 'true' matches. For example, these may be the result of work by a clerical team who have manually labelled a sample of matches.\n",
-        "\n",
-        "The table must be in the following format:\n",
-        "\n",
-        "| source_dataset_l | unique_id_l | source_dataset_r | unique_id_r |\n",
-        "| ---------------- | ----------- | ---------------- | ----------- |\n",
-        "| df_1             | 1           | df_2             | 2           |\n",
-        "| df_1             | 1           | df_2             | 3           |\n",
-        "\n",
-        "It is assumed that every record in the table represents a certain match.\n",
-        "\n",
-        "Note that the column names above are the defaults. They should correspond to the values you've set for [`unique_id_column_name`](https://moj-analytical-services.github.io/splink/settings_dict_guide.html#unique_id_column_name) and [`source_dataset_column_name`](https://moj-analytical-services.github.io/splink/settings_dict_guide.html#source_dataset_column_name), if you've chosen custom values.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/pairwise_labels.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:53.117258Z",
-          "iopub.status.busy": "2024-03-27T15:14:53.116906Z",
-          "iopub.status.idle": "2024-03-27T15:14:53.122096Z",
-          "shell.execute_reply": "2024-03-27T15:14:53.121308Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:53.126249Z",
-          "iopub.status.busy": "2024-03-27T15:14:53.125905Z",
-          "iopub.status.idle": "2024-03-27T15:14:54.649822Z",
-          "shell.execute_reply": "2024-03-27T15:14:54.649099Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink.datasets import splink_dataset_labels\n",
-        "\n",
-        "pairwise_labels = splink_dataset_labels.fake_1000_labels\n",
-        "\n",
-        "# Choose labels indicating a match\n",
-        "pairwise_labels = pairwise_labels[pairwise_labels[\"clerical_match_score\"] == 1]\n",
-        "pairwise_labels"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We now proceed to estimate the Fellegi Sunter model:\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:54.653768Z",
-          "iopub.status.busy": "2024-03-27T15:14:54.653468Z",
-          "iopub.status.idle": "2024-03-27T15:14:54.668003Z",
-          "shell.execute_reply": "2024-03-27T15:14:54.667271Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from splink import splink_datasets\n",
-        "\n",
-        "df = splink_datasets.fake_1000\n",
-        "df.head(2)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:54.671717Z",
-          "iopub.status.busy": "2024-03-27T15:14:54.671406Z",
-          "iopub.status.idle": "2024-03-27T15:14:54.912700Z",
-          "shell.execute_reply": "2024-03-27T15:14:54.911624Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "import splink.comparison_library as cl\n",
-        "import splink.comparison_template_library as ctl\n",
-        "from splink import DuckDBAPI, Linker, SettingsCreator, block_on\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    link_type=\"dedupe_only\",\n",
-        "    blocking_rules_to_generate_predictions=[\n",
-        "        block_on(\"first_name\"),\n",
-        "        block_on(\"surname\"),\n",
-        "    ],\n",
-        "    comparisons=[\n",
-        "        ctl.NameComparison(\"first_name\"),\n",
-        "        ctl.NameComparison(\"surname\"),\n",
-        "        ctl.DateComparison(\n",
-        "            \"dob\",\n",
-        "            input_is_string=True,\n",
-        "            invalid_dates_as_null=True,\n",
-        "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
-        "            datetime_thresholds=[1, 1, 10],\n",
-        "        ),\n",
-        "        cl.LevenshteinAtThresholds(\"dob\", [2]),\n",
-        "        cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
-        "        ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
-        "    ],\n",
-        "    retain_intermediate_calculation_columns=True,\n",
-        ")"
-      ]
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimating m from a sample of pairwise labels\n",
+    "\n",
+    "In this example, we estimate the m probabilities of the model from a table containing pairwise record comparisons which we know are 'true' matches. For example, these may be the result of work by a clerical team who have manually labelled a sample of matches.\n",
+    "\n",
+    "The table must be in the following format:\n",
+    "\n",
+    "| source_dataset_l | unique_id_l | source_dataset_r | unique_id_r |\n",
+    "| ---------------- | ----------- | ---------------- | ----------- |\n",
+    "| df_1             | 1           | df_2             | 2           |\n",
+    "| df_1             | 1           | df_2             | 3           |\n",
+    "\n",
+    "It is assumed that every record in the table represents a certain match.\n",
+    "\n",
+    "Note that the column names above are the defaults. They should correspond to the values you've set for [`unique_id_column_name`](https://moj-analytical-services.github.io/splink/settings_dict_guide.html#unique_id_column_name) and [`source_dataset_column_name`](https://moj-analytical-services.github.io/splink/settings_dict_guide.html#source_dataset_column_name), if you've chosen custom values.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/pairwise_labels.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:22.461384Z",
+     "iopub.status.busy": "2024-06-07T09:20:22.461075Z",
+     "iopub.status.idle": "2024-06-07T09:20:22.466162Z",
+     "shell.execute_reply": "2024-06-07T09:20:22.465529Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:22.470034Z",
+     "iopub.status.busy": "2024-06-07T09:20:22.469740Z",
+     "iopub.status.idle": "2024-06-07T09:20:24.546756Z",
+     "shell.execute_reply": "2024-06-07T09:20:24.546033Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:54.917049Z",
-          "iopub.status.busy": "2024-03-27T15:14:54.916681Z",
-          "iopub.status.idle": "2024-03-27T15:14:55.221629Z",
-          "shell.execute_reply": "2024-03-27T15:14:55.220884Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "linker = Linker(df, settings, database_api=DuckDBAPI(), set_up_basic_logging=False)\n",
-        "deterministic_rules = [\n",
-        "    \"l.first_name = r.first_name and levenshtein(r.dob, l.dob) <= 1\",\n",
-        "    \"l.surname = r.surname and levenshtein(r.dob, l.dob) <= 1\",\n",
-        "    \"l.first_name = r.first_name and levenshtein(r.surname, l.surname) <= 2\",\n",
-        "    \"l.email = r.email\",\n",
-        "]\n",
-        "\n",
-        "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id_l</th>\n",
+       "      <th>source_dataset_l</th>\n",
+       "      <th>unique_id_r</th>\n",
+       "      <th>source_dataset_r</th>\n",
+       "      <th>clerical_match_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>1</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>1</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3171</th>\n",
+       "      <td>994</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>996</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3172</th>\n",
+       "      <td>995</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>996</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3173</th>\n",
+       "      <td>997</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>998</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3174</th>\n",
+       "      <td>997</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>999</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3175</th>\n",
+       "      <td>998</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>999</td>\n",
+       "      <td>fake_1000</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2031 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      unique_id_l source_dataset_l  unique_id_r source_dataset_r  \\\n",
+       "0               0        fake_1000            1        fake_1000   \n",
+       "1               0        fake_1000            2        fake_1000   \n",
+       "2               0        fake_1000            3        fake_1000   \n",
+       "49              1        fake_1000            2        fake_1000   \n",
+       "50              1        fake_1000            3        fake_1000   \n",
+       "...           ...              ...          ...              ...   \n",
+       "3171          994        fake_1000          996        fake_1000   \n",
+       "3172          995        fake_1000          996        fake_1000   \n",
+       "3173          997        fake_1000          998        fake_1000   \n",
+       "3174          997        fake_1000          999        fake_1000   \n",
+       "3175          998        fake_1000          999        fake_1000   \n",
+       "\n",
+       "      clerical_match_score  \n",
+       "0                      1.0  \n",
+       "1                      1.0  \n",
+       "2                      1.0  \n",
+       "49                     1.0  \n",
+       "50                     1.0  \n",
+       "...                    ...  \n",
+       "3171                   1.0  \n",
+       "3172                   1.0  \n",
+       "3173                   1.0  \n",
+       "3174                   1.0  \n",
+       "3175                   1.0  \n",
+       "\n",
+       "[2031 rows x 5 columns]"
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink.datasets import splink_dataset_labels\n",
+    "\n",
+    "pairwise_labels = splink_dataset_labels.fake_1000_labels\n",
+    "\n",
+    "# Choose labels indicating a match\n",
+    "pairwise_labels = pairwise_labels[pairwise_labels[\"clerical_match_score\"] == 1]\n",
+    "pairwise_labels"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now proceed to estimate the Fellegi Sunter model:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:24.588843Z",
+     "iopub.status.busy": "2024-06-07T09:20:24.588530Z",
+     "iopub.status.idle": "2024-06-07T09:20:24.602952Z",
+     "shell.execute_reply": "2024-06-07T09:20:24.602047Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:55.224831Z",
-          "iopub.status.busy": "2024-03-27T15:14:55.224593Z",
-          "iopub.status.idle": "2024-03-27T15:14:57.430946Z",
-          "shell.execute_reply": "2024-03-27T15:14:57.430131Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>city</th>\n",
+       "      <th>email</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Robert</td>\n",
+       "      <td>Alan</td>\n",
+       "      <td>1971-06-24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>robert255@smith.net</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Robert</td>\n",
+       "      <td>Allen</td>\n",
+       "      <td>1971-05-24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>roberta25@smith.net</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unique_id first_name surname         dob city                email  cluster\n",
+       "0          0     Robert    Alan  1971-06-24  NaN  robert255@smith.net        0\n",
+       "1          1     Robert   Allen  1971-05-24  NaN  roberta25@smith.net        0"
       ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import splink_datasets\n",
+    "\n",
+    "df = splink_datasets.fake_1000\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:24.607247Z",
+     "iopub.status.busy": "2024-06-07T09:20:24.606935Z",
+     "iopub.status.idle": "2024-06-07T09:20:24.711369Z",
+     "shell.execute_reply": "2024-06-07T09:20:24.710531Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import splink.comparison_library as cl\n",
+    "import splink.comparison_template_library as ctl\n",
+    "from splink import DuckDBAPI, Linker, SettingsCreator, block_on\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    link_type=\"dedupe_only\",\n",
+    "    blocking_rules_to_generate_predictions=[\n",
+    "        block_on(\"first_name\"),\n",
+    "        block_on(\"surname\"),\n",
+    "    ],\n",
+    "    comparisons=[\n",
+    "        ctl.NameComparison(\"first_name\"),\n",
+    "        ctl.NameComparison(\"surname\"),\n",
+    "        ctl.DateComparison(\n",
+    "            \"dob\",\n",
+    "            input_is_string=True,\n",
+    "            invalid_dates_as_null=True,\n",
+    "            datetime_metrics=[\"month\", \"year\", \"year\"],\n",
+    "            datetime_thresholds=[1, 1, 10],\n",
+    "        ),\n",
+    "        cl.LevenshteinAtThresholds(\"dob\", [2]),\n",
+    "        cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
+    "        ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
+    "    ],\n",
+    "    retain_intermediate_calculation_columns=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:24.715481Z",
+     "iopub.status.busy": "2024-06-07T09:20:24.715162Z",
+     "iopub.status.idle": "2024-06-07T09:20:25.100461Z",
+     "shell.execute_reply": "2024-06-07T09:20:25.099741Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "linker = Linker(df, settings, database_api=DuckDBAPI(), set_up_basic_logging=False)\n",
+    "deterministic_rules = [\n",
+    "    \"l.first_name = r.first_name and levenshtein(r.dob, l.dob) <= 1\",\n",
+    "    \"l.surname = r.surname and levenshtein(r.dob, l.dob) <= 1\",\n",
+    "    \"l.first_name = r.first_name and levenshtein(r.surname, l.surname) <= 2\",\n",
+    "    \"l.email = r.email\",\n",
+    "]\n",
+    "\n",
+    "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:25.104541Z",
+     "iopub.status.busy": "2024-06-07T09:20:25.104116Z",
+     "iopub.status.idle": "2024-06-07T09:20:26.866642Z",
+     "shell.execute_reply": "2024-06-07T09:20:26.866007Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:57.434534Z",
-          "iopub.status.busy": "2024-03-27T15:14:57.434260Z",
-          "iopub.status.idle": "2024-03-27T15:14:57.657154Z",
-          "shell.execute_reply": "2024-03-27T15:14:57.656336Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# Register the pairwise labels table with the database, and then use it to estimate the m values\n",
-        "labels_df = linker.table_management.register_labels_table(pairwise_labels, overwrite=True)\n",
-        "linker.estimate_m_from_pairwise_labels(labels_df)\n",
-        "\n",
-        "\n",
-        "# If the labels table already existing in the dataset you could run\n",
-        "# linker.estimate_m_from_pairwise_labels(\"labels_tablename_here\")"
-      ]
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default value for `max_pairs`, which may be too small and thus lead to inaccurate estimates for your model's u-parameters. Consider increasing to 1e8 or 1e9, which will result in more accurate estimates, but with a longer run time.\n"
+     ]
+    }
+   ],
+   "source": [
+    "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:26.871363Z",
+     "iopub.status.busy": "2024-06-07T09:20:26.871016Z",
+     "iopub.status.idle": "2024-06-07T09:20:27.051023Z",
+     "shell.execute_reply": "2024-06-07T09:20:27.050407Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Register the pairwise labels table with the database, and then use it to estimate the m values\n",
+    "labels_df = linker.table_management.register_labels_table(pairwise_labels, overwrite=True)\n",
+    "linker.training.estimate_m_from_pairwise_labels(labels_df)\n",
+    "\n",
+    "\n",
+    "# If the labels table already existing in the dataset you could run\n",
+    "# linker.training.estimate_m_from_pairwise_labels(\"labels_tablename_here\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:27.054211Z",
+     "iopub.status.busy": "2024-06-07T09:20:27.053972Z",
+     "iopub.status.idle": "2024-06-07T09:20:27.489093Z",
+     "shell.execute_reply": "2024-06-07T09:20:27.488564Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:57.662065Z",
-          "iopub.status.busy": "2024-03-27T15:14:57.661552Z",
-          "iopub.status.idle": "2024-03-27T15:14:58.144518Z",
-          "shell.execute_reply": "2024-03-27T15:14:58.143799Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "training_blocking_rule = block_on(\"first_name\")\n",
-        "linker.training.estimate_parameters_using_expectation_maximisation(training_blocking_rule)"
+     "data": {
+      "text/plain": [
+       "<EMTrainingSession, blocking on l.\"first_name\" = r.\"first_name\", deactivating comparisons first_name>"
       ]
-    },
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_blocking_rule = block_on(\"first_name\")\n",
+    "linker.training.estimate_parameters_using_expectation_maximisation(training_blocking_rule)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:27.492742Z",
+     "iopub.status.busy": "2024-06-07T09:20:27.492510Z",
+     "iopub.status.idle": "2024-06-07T09:20:27.624619Z",
+     "shell.execute_reply": "2024-06-07T09:20:27.624114Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:14:58.149118Z",
-          "iopub.status.busy": "2024-03-27T15:14:58.148820Z",
-          "iopub.status.idle": "2024-03-27T15:14:58.295802Z",
-          "shell.execute_reply": "2024-03-27T15:14:58.294855Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "linker.parameter_estimate_comparisons_chart()"
+     "data": {
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+      "text/plain": [
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+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.visualisations.parameter_estimate_comparisons_chart()"
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+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:27.628602Z",
+     "iopub.status.busy": "2024-06-07T09:20:27.628256Z",
+     "iopub.status.idle": "2024-06-07T09:20:27.933374Z",
+     "shell.execute_reply": "2024-06-07T09:20:27.932702Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "execution": {
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-          "iopub.status.busy": "2024-03-27T15:14:58.298915Z",
-          "iopub.status.idle": "2024-03-27T15:14:58.605413Z",
-          "shell.execute_reply": "2024-03-27T15:14:58.604766Z"
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-      "outputs": [],
-      "source": [
-        "linker.visualisations.match_weights_chart()"
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+       "layout": "IPY_MODEL_8863bef4905c44fc9705add5d5165a71",
+       "max": 100,
+       "min": 0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_ce7ee37dfbeb4d26ae9171f7f3b857e7",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 100
+      }
+     },
+     "8863bef4905c44fc9705add5d5165a71": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": "auto"
+      }
+     },
+     "ce7ee37dfbeb4d26ae9171f7f3b857e7": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "bar_color": "black",
+       "description_width": ""
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/demos/examples/duckdb/quick_and_dirty_persons.ipynb b/docs/demos/examples/duckdb/quick_and_dirty_persons.ipynb
index e02245bf78..12bc1af90b 100644
--- a/docs/demos/examples/duckdb/quick_and_dirty_persons.ipynb
+++ b/docs/demos/examples/duckdb/quick_and_dirty_persons.ipynb
@@ -1,303 +1,665 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Historical people: Quick and dirty\n",
-        "\n",
-        "This example shows how to get some initial record linkage results as quickly as possible.\n",
-        "\n",
-        "There are many ways to improve the accuracy of this model. But this may be a good place to start if you just want to give Splink a try and see what it's capable of.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/quick_and_dirty_persons.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:15:01.166130Z",
-          "iopub.status.busy": "2024-03-27T15:15:01.165782Z",
-          "iopub.status.idle": "2024-03-27T15:15:01.171295Z",
-          "shell.execute_reply": "2024-03-27T15:15:01.170553Z"
-        }
-      },
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:15:01.174969Z",
-          "iopub.status.busy": "2024-03-27T15:15:01.174678Z",
-          "iopub.status.idle": "2024-03-27T15:15:02.750516Z",
-          "shell.execute_reply": "2024-03-27T15:15:02.749785Z"
-        }
-      },
-      "source": [
-        "from splink.datasets import splink_datasets\n",
-        "\n",
-        "df = splink_datasets.historical_50k\n",
-        "df.head(5)"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:15:02.754599Z",
-          "iopub.status.busy": "2024-03-27T15:15:02.754204Z",
-          "iopub.status.idle": "2024-03-27T15:15:02.762053Z",
-          "shell.execute_reply": "2024-03-27T15:15:02.761258Z"
-        }
-      },
-      "source": [
-        "from splink import block_on, SettingsCreator\n",
-        "import splink.comparison_library as cl\n",
-        "import splink.comparison_template_library as ctl\n",
-        "\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    link_type=\"dedupe_only\",\n",
-        "    blocking_rules_to_generate_predictions=[\n",
-        "        block_on(\"full_name\"),\n",
-        "        block_on(\"substr(full_name,1,6)\", \"dob\", \"birth_place\"),\n",
-        "        block_on(\"dob\", \"birth_place\"),\n",
-        "        block_on(\"postcode_fake\"),\n",
-        "    ],\n",
-        "    comparisons=[\n",
-        "        cl.JaroWinklerAtThresholds(\"full_name\", [0.9, 0.7]).configure(\n",
-        "            term_frequency_adjustments=True\n",
-        "        ),\n",
-        "        ctl.DateComparison(\n",
-        "            \"dob\",\n",
-        "            input_is_string=True,\n",
-        "            datetime_metrics=[\"day\", \"month\", \"year\"],\n",
-        "            datetime_thresholds=[5, 1, 5],\n",
-        "        ),\n",
-        "        cl.LevenshteinAtThresholds(\"postcode_fake\", 2),\n",
-        "        cl.JaroWinklerAtThresholds(\"birth_place\", 0.9).configure(\n",
-        "            term_frequency_adjustments=True\n",
-        "        ),\n",
-        "        cl.ExactMatch(\"occupation\").configure(term_frequency_adjustments=True),\n",
-        "    ],\n",
-        ")"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:15:02.766456Z",
-          "iopub.status.busy": "2024-03-27T15:15:02.766123Z",
-          "iopub.status.idle": "2024-03-27T15:15:03.425993Z",
-          "shell.execute_reply": "2024-03-27T15:15:03.424984Z"
-        }
-      },
-      "source": [
-        "from splink import Linker, DuckDBAPI\n",
-        "\n",
-        "\n",
-        "linker = Linker(df, settings, database_api=DuckDBAPI(), set_up_basic_logging=False)\n",
-        "deterministic_rules = [\n",
-        "    \"l.full_name = r.full_name\",\n",
-        "    \"l.postcode_fake = r.postcode_fake and l.dob = r.dob\",\n",
-        "]\n",
-        "\n",
-        "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.6)"
-      ],
-      "outputs": []
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Historical people: Quick and dirty\n",
+    "\n",
+    "This example shows how to get some initial record linkage results as quickly as possible.\n",
+    "\n",
+    "There are many ways to improve the accuracy of this model. But this may be a good place to start if you just want to give Splink a try and see what it's capable of.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/quick_and_dirty_persons.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:37.624889Z",
+     "iopub.status.busy": "2024-06-07T09:20:37.624517Z",
+     "iopub.status.idle": "2024-06-07T09:20:37.644289Z",
+     "shell.execute_reply": "2024-06-07T09:20:37.643404Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:37.648712Z",
+     "iopub.status.busy": "2024-06-07T09:20:37.648404Z",
+     "iopub.status.idle": "2024-06-07T09:20:39.278642Z",
+     "shell.execute_reply": "2024-06-07T09:20:39.277984Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:15:03.430749Z",
-          "iopub.status.busy": "2024-03-27T15:15:03.430387Z",
-          "iopub.status.idle": "2024-03-27T15:15:07.041399Z",
-          "shell.execute_reply": "2024-03-27T15:15:07.040743Z"
-        }
-      },
-      "source": [
-        "linker.training.estimate_u_using_random_sampling(max_pairs=2e6)"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>full_name</th>\n",
+       "      <th>first_and_surname</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>surname</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>birth_place</th>\n",
+       "      <th>postcode_fake</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>occupation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Q2296770-1</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas clifford, 1st baron clifford of chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q2296770-2</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas of chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Q2296770-3</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>tom 1st baron clifford of chudleigh</td>\n",
+       "      <td>tom chudleigh</td>\n",
+       "      <td>tom</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>male</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Q2296770-4</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas 1st chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8hu</td>\n",
+       "      <td>None</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Q2296770-5</td>\n",
+       "      <td>Q2296770</td>\n",
+       "      <td>thomas clifford, 1st baron chudleigh</td>\n",
+       "      <td>thomas chudleigh</td>\n",
+       "      <td>thomas</td>\n",
+       "      <td>chudleigh</td>\n",
+       "      <td>1630-08-01</td>\n",
+       "      <td>devon</td>\n",
+       "      <td>tq13 8df</td>\n",
+       "      <td>None</td>\n",
+       "      <td>politician</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "outputs": []
-    },
+      "text/plain": [
+       "    unique_id   cluster                                         full_name  \\\n",
+       "0  Q2296770-1  Q2296770  thomas clifford, 1st baron clifford of chudleigh   \n",
+       "1  Q2296770-2  Q2296770                               thomas of chudleigh   \n",
+       "2  Q2296770-3  Q2296770               tom 1st baron clifford of chudleigh   \n",
+       "3  Q2296770-4  Q2296770                              thomas 1st chudleigh   \n",
+       "4  Q2296770-5  Q2296770              thomas clifford, 1st baron chudleigh   \n",
+       "\n",
+       "  first_and_surname first_name    surname         dob birth_place  \\\n",
+       "0  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "1  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "2     tom chudleigh        tom  chudleigh  1630-08-01       devon   \n",
+       "3  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "4  thomas chudleigh     thomas  chudleigh  1630-08-01       devon   \n",
+       "\n",
+       "  postcode_fake gender  occupation  \n",
+       "0      tq13 8df   male  politician  \n",
+       "1      tq13 8df   male  politician  \n",
+       "2      tq13 8df   male  politician  \n",
+       "3      tq13 8hu   None  politician  \n",
+       "4      tq13 8df   None  politician  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink.datasets import splink_datasets\n",
+    "\n",
+    "df = splink_datasets.historical_50k\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:39.330739Z",
+     "iopub.status.busy": "2024-06-07T09:20:39.330384Z",
+     "iopub.status.idle": "2024-06-07T09:20:39.345331Z",
+     "shell.execute_reply": "2024-06-07T09:20:39.344598Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from splink import block_on, SettingsCreator\n",
+    "import splink.comparison_library as cl\n",
+    "import splink.comparison_template_library as ctl\n",
+    "\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    link_type=\"dedupe_only\",\n",
+    "    blocking_rules_to_generate_predictions=[\n",
+    "        block_on(\"full_name\"),\n",
+    "        block_on(\"substr(full_name,1,6)\", \"dob\", \"birth_place\"),\n",
+    "        block_on(\"dob\", \"birth_place\"),\n",
+    "        block_on(\"postcode_fake\"),\n",
+    "    ],\n",
+    "    comparisons=[\n",
+    "        cl.JaroWinklerAtThresholds(\"full_name\", [0.9, 0.7]).configure(\n",
+    "            term_frequency_adjustments=True\n",
+    "        ),\n",
+    "        ctl.DateComparison(\n",
+    "            \"dob\",\n",
+    "            input_is_string=True,\n",
+    "            datetime_metrics=[\"day\", \"month\", \"year\"],\n",
+    "            datetime_thresholds=[5, 1, 5],\n",
+    "        ),\n",
+    "        cl.LevenshteinAtThresholds(\"postcode_fake\", 2),\n",
+    "        cl.JaroWinklerAtThresholds(\"birth_place\", 0.9).configure(\n",
+    "            term_frequency_adjustments=True\n",
+    "        ),\n",
+    "        cl.ExactMatch(\"occupation\").configure(term_frequency_adjustments=True),\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:39.349123Z",
+     "iopub.status.busy": "2024-06-07T09:20:39.348832Z",
+     "iopub.status.idle": "2024-06-07T09:20:39.807802Z",
+     "shell.execute_reply": "2024-06-07T09:20:39.807089Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from splink import Linker, DuckDBAPI\n",
+    "\n",
+    "\n",
+    "linker = Linker(df, settings, database_api=DuckDBAPI(), set_up_basic_logging=False)\n",
+    "deterministic_rules = [\n",
+    "    \"l.full_name = r.full_name\",\n",
+    "    \"l.postcode_fake = r.postcode_fake and l.dob = r.dob\",\n",
+    "]\n",
+    "\n",
+    "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:39.811242Z",
+     "iopub.status.busy": "2024-06-07T09:20:39.810994Z",
+     "iopub.status.idle": "2024-06-07T09:20:42.328241Z",
+     "shell.execute_reply": "2024-06-07T09:20:42.327675Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "linker.training.estimate_u_using_random_sampling(max_pairs=2e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:42.331754Z",
+     "iopub.status.busy": "2024-06-07T09:20:42.331463Z",
+     "iopub.status.idle": "2024-06-07T09:20:44.521913Z",
+     "shell.execute_reply": "2024-06-07T09:20:44.521209Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:15:07.045995Z",
-          "iopub.status.busy": "2024-03-27T15:15:07.045404Z",
-          "iopub.status.idle": "2024-03-27T15:15:09.400752Z",
-          "shell.execute_reply": "2024-03-27T15:15:09.400029Z"
-        }
-      },
-      "source": [
-        "results = linker.inference.predict(threshold_match_probability=0.9)"
-      ],
-      "outputs": []
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " -- WARNING --\n",
+      "You have called predict(), but there are some parameter estimates which have neither been estimated or specified in your settings dictionary.  To produce predictions the following untrained trained parameters will use default values.\n",
+      "Comparison: 'full_name':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'dob':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'postcode_fake':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'birth_place':\n",
+      "    m values not fully trained\n",
+      "Comparison: 'occupation':\n",
+      "    m values not fully trained\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = linker.inference.predict(threshold_match_probability=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:20:44.525778Z",
+     "iopub.status.busy": "2024-06-07T09:20:44.525492Z",
+     "iopub.status.idle": "2024-06-07T09:20:44.543212Z",
+     "shell.execute_reply": "2024-06-07T09:20:44.542595Z"
+    }
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-03-27T15:15:09.404703Z",
-          "iopub.status.busy": "2024-03-27T15:15:09.404377Z",
-          "iopub.status.idle": "2024-03-27T15:15:09.428537Z",
-          "shell.execute_reply": "2024-03-27T15:15:09.427244Z"
-        }
-      },
-      "source": [
-        "results.as_pandas_dataframe(limit=5)"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>match_weight</th>\n",
+       "      <th>match_probability</th>\n",
+       "      <th>unique_id_l</th>\n",
+       "      <th>unique_id_r</th>\n",
+       "      <th>full_name_l</th>\n",
+       "      <th>full_name_r</th>\n",
+       "      <th>gamma_full_name</th>\n",
+       "      <th>dob_l</th>\n",
+       "      <th>dob_r</th>\n",
+       "      <th>gamma_dob</th>\n",
+       "      <th>postcode_fake_l</th>\n",
+       "      <th>postcode_fake_r</th>\n",
+       "      <th>gamma_postcode_fake</th>\n",
+       "      <th>birth_place_l</th>\n",
+       "      <th>birth_place_r</th>\n",
+       "      <th>gamma_birth_place</th>\n",
+       "      <th>occupation_l</th>\n",
+       "      <th>occupation_r</th>\n",
+       "      <th>gamma_occupation</th>\n",
+       "      <th>match_key</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.172246</td>\n",
+       "      <td>0.900145</td>\n",
+       "      <td>Q16198727-6</td>\n",
+       "      <td>Q16198727-8</td>\n",
+       "      <td>henry jupp</td>\n",
+       "      <td>jupp</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1802-08-06</td>\n",
+       "      <td>1802-08-06</td>\n",
+       "      <td>5</td>\n",
+       "      <td>None</td>\n",
+       "      <td>e4 9re</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>waltham forest</td>\n",
+       "      <td>waltham forest</td>\n",
+       "      <td>2</td>\n",
+       "      <td>cricketer</td>\n",
+       "      <td>cricketer</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.172423</td>\n",
+       "      <td>0.900156</td>\n",
+       "      <td>Q16220644-12</td>\n",
+       "      <td>Q16220644-7</td>\n",
+       "      <td>1st bt.</td>\n",
+       "      <td>1st bt.</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1840-11-21</td>\n",
+       "      <td>1810-11-21</td>\n",
+       "      <td>4</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>liverpool</td>\n",
+       "      <td>liverpool</td>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>physician</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.173256</td>\n",
+       "      <td>0.900208</td>\n",
+       "      <td>Q6180874-12</td>\n",
+       "      <td>Q6180874-19</td>\n",
+       "      <td>richard slater</td>\n",
+       "      <td>slater</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1854-01-01</td>\n",
+       "      <td>1854-01-01</td>\n",
+       "      <td>5</td>\n",
+       "      <td>al5 2ay</td>\n",
+       "      <td>al3 7rq</td>\n",
+       "      <td>0</td>\n",
+       "      <td>st albans</td>\n",
+       "      <td>st albans</td>\n",
+       "      <td>2</td>\n",
+       "      <td>hymnwriter</td>\n",
+       "      <td>hymnwriter</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.174182</td>\n",
+       "      <td>0.900265</td>\n",
+       "      <td>Q7519167-10</td>\n",
+       "      <td>Q7519167-8</td>\n",
+       "      <td>simeon langton</td>\n",
+       "      <td>simeon langton</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1150-01-81</td>\n",
+       "      <td>1152-01-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>wealden</td>\n",
+       "      <td>wealden</td>\n",
+       "      <td>2</td>\n",
+       "      <td>priest</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.178567</td>\n",
+       "      <td>0.900538</td>\n",
+       "      <td>Q15980561-12</td>\n",
+       "      <td>Q15980561-8</td>\n",
+       "      <td>harry rosling</td>\n",
+       "      <td>henry rosling</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1828-01-11</td>\n",
+       "      <td>1858-01-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>tn27 0sy</td>\n",
+       "      <td>tn27 0sy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ashford</td>\n",
+       "      <td>ashford</td>\n",
+       "      <td>2</td>\n",
+       "      <td>photographer</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "outputs": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
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-              "description_width": ""
-            }
-          }
-        },
-        "version_major": 2,
-        "version_minor": 0
-      }
+      "text/plain": [
+       "   match_weight  match_probability   unique_id_l  unique_id_r     full_name_l  \\\n",
+       "0      3.172246           0.900145   Q16198727-6  Q16198727-8      henry jupp   \n",
+       "1      3.172423           0.900156  Q16220644-12  Q16220644-7         1st bt.   \n",
+       "2      3.173256           0.900208   Q6180874-12  Q6180874-19  richard slater   \n",
+       "3      3.174182           0.900265   Q7519167-10   Q7519167-8  simeon langton   \n",
+       "4      3.178567           0.900538  Q15980561-12  Q15980561-8   harry rosling   \n",
+       "\n",
+       "      full_name_r  gamma_full_name       dob_l       dob_r  gamma_dob  \\\n",
+       "0            jupp                0  1802-08-06  1802-08-06          5   \n",
+       "1         1st bt.                3  1840-11-21  1810-11-21          4   \n",
+       "2          slater                0  1854-01-01  1854-01-01          5   \n",
+       "3  simeon langton                3  1150-01-81  1152-01-01          0   \n",
+       "4   henry rosling                1  1828-01-11  1858-01-01          0   \n",
+       "\n",
+       "  postcode_fake_l postcode_fake_r  gamma_postcode_fake   birth_place_l  \\\n",
+       "0            None          e4 9re                   -1  waltham forest   \n",
+       "1            None            None                   -1       liverpool   \n",
+       "2         al5 2ay         al3 7rq                    0       st albans   \n",
+       "3            None            None                   -1         wealden   \n",
+       "4        tn27 0sy        tn27 0sy                    2         ashford   \n",
+       "\n",
+       "    birth_place_r  gamma_birth_place  occupation_l occupation_r  \\\n",
+       "0  waltham forest                  2     cricketer    cricketer   \n",
+       "1       liverpool                  2          None    physician   \n",
+       "2       st albans                  2    hymnwriter   hymnwriter   \n",
+       "3         wealden                  2        priest         None   \n",
+       "4         ashford                  2  photographer         None   \n",
+       "\n",
+       "   gamma_occupation match_key  \n",
+       "0                 1         2  \n",
+       "1                -1         0  \n",
+       "2                 1         2  \n",
+       "3                -1         0  \n",
+       "4                -1         3  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "results.as_pandas_dataframe(limit=5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
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+   "name": "python",
+   "nbconvert_exporter": "python",
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   },
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\ No newline at end of file
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+}
diff --git a/docs/demos/examples/duckdb/real_time_record_linkage.ipynb b/docs/demos/examples/duckdb/real_time_record_linkage.ipynb
index 1f6adf946f..63bbe23d56 100644
--- a/docs/demos/examples/duckdb/real_time_record_linkage.ipynb
+++ b/docs/demos/examples/duckdb/real_time_record_linkage.ipynb
@@ -37,13 +37,13 @@
           "shell.execute_reply": "2024-03-27T15:15:11.889929Z"
         }
       },
+      "outputs": [],
       "source": [
         "# Uncomment and run this cell if you're running in Google Colab.\n",
         "# !pip install ipywidgets\n",
         "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev\n",
         "# !jupyter nbextension enable --py widgetsnbextension"
-      ],
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -63,6 +63,7 @@
           "shell.execute_reply": "2024-03-27T15:15:13.841226Z"
         }
       },
+      "outputs": [],
       "source": [
         "import urllib.request\n",
         "import json\n",
@@ -78,8 +79,7 @@
         "\n",
         "\n",
         "linker = Linker(df, settings, database_api=DuckDBAPI())"
-      ],
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
@@ -92,10 +92,10 @@
           "shell.execute_reply": "2024-03-27T15:15:14.720417Z"
         }
       },
+      "outputs": [],
       "source": [
         "linker.visualisations.waterfall_chart(linker.inference.predict().as_record_dict(limit=2))"
-      ],
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -117,6 +117,7 @@
           "shell.execute_reply": "2024-03-27T15:15:14.961740Z"
         }
       },
+      "outputs": [],
       "source": [
         "record_1 = {\n",
         "    \"unique_id\": 1,\n",
@@ -142,17 +143,16 @@
         "\n",
         "# To `compare_two_records` the linker needs to compute term frequency tables\n",
         "# If you have precomputed tables, you can linker.register_term_frequency_lookup()\n",
-        "linker.compute_tf_table(\"first_name\")\n",
-        "linker.compute_tf_table(\"surname\")\n",
-        "linker.compute_tf_table(\"dob\")\n",
-        "linker.compute_tf_table(\"city\")\n",
-        "linker.compute_tf_table(\"email\")\n",
+        "linker.table_management.compute_tf_table(\"first_name\")\n",
+        "linker.table_management.compute_tf_table(\"surname\")\n",
+        "linker.table_management.compute_tf_table(\"dob\")\n",
+        "linker.table_management.compute_tf_table(\"city\")\n",
+        "linker.table_management.compute_tf_table(\"email\")\n",
         "\n",
         "\n",
-        "df_two = linker.compare_two_records(record_1, record_2)\n",
+        "df_two = linker.inference.compare_two_records(record_1, record_2)\n",
         "df_two.as_pandas_dataframe()"
-      ],
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -176,6 +176,7 @@
           "shell.execute_reply": "2024-03-27T15:15:15.925656Z"
         }
       },
+      "outputs": [],
       "source": [
         "import ipywidgets as widgets\n",
         "from IPython.display import display\n",
@@ -218,7 +219,7 @@
         "    # Assuming 'linker' is defined earlier in your code\n",
         "    linker._settings_obj._retain_intermediate_calculation_columns = True\n",
         "\n",
-        "    df_two = linker.compare_two_records(record_left, record_right)\n",
+        "    df_two = linker.inference.compare_two_records(record_left, record_right)\n",
         "\n",
         "    recs = df_two.as_pandas_dataframe().to_dict(orient=\"records\")\n",
         "    from splink.charts import waterfall_chart\n",
@@ -229,8 +230,7 @@
         "out = widgets.interactive_output(myfn, inputs_to_interactive_output)\n",
         "\n",
         "display(ui, out)"
-      ],
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -252,6 +252,7 @@
           "shell.execute_reply": "2024-03-27T15:15:16.474896Z"
         }
       },
+      "outputs": [],
       "source": [
         "record = {\n",
         "    \"unique_id\": 123987,\n",
@@ -263,12 +264,11 @@
         "}\n",
         "\n",
         "\n",
-        "df_inc = linker.find_matches_to_new_records(\n",
+        "df_inc = linker.inference.find_matches_to_new_records(\n",
         "    [record], blocking_rules=[]\n",
         ").as_pandas_dataframe()\n",
         "df_inc.sort_values(\"match_weight\", ascending=False)"
-      ],
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -290,6 +290,7 @@
           "shell.execute_reply": "2024-03-27T15:15:17.548423Z"
         }
       },
+      "outputs": [],
       "source": [
         "@widgets.interact(\n",
         "    first_name=\"Robert\",\n",
@@ -315,15 +316,14 @@
         "            if record[key].strip() == \"\":\n",
         "                record[key] = None\n",
         "\n",
-        "    df_inc = linker.find_matches_to_new_records(\n",
+        "    df_inc = linker.inference.find_matches_to_new_records(\n",
         "        [record], blocking_rules=[f\"(true)\"]\n",
         "    ).as_pandas_dataframe()\n",
         "    df_inc = df_inc.sort_values(\"match_weight\", ascending=False)\n",
         "    recs = df_inc.to_dict(orient=\"records\")\n",
         "\n",
         "    display(linker.visualisations.waterfall_chart(recs, filter_nulls=False))"
-      ],
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
@@ -336,10 +336,10 @@
           "shell.execute_reply": "2024-03-27T15:15:17.884033Z"
         }
       },
+      "outputs": [],
       "source": [
         "linker.visualisations.match_weights_chart()"
-      ],
-      "outputs": []
+      ]
     }
   ],
   "metadata": {
@@ -358,7 +358,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.8"
+      "version": "3.10.8"
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
@@ -2485,4 +2485,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/docs/demos/examples/duckdb/transactions.ipynb b/docs/demos/examples/duckdb/transactions.ipynb
index d34119f9de..472e477116 100644
--- a/docs/demos/examples/duckdb/transactions.ipynb
+++ b/docs/demos/examples/duckdb/transactions.ipynb
@@ -1,731 +1,2044 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Linking banking transactions\n",
-        "\n",
-        "This example shows how to perform a one-to-one link on banking transactions.\n",
-        "\n",
-        "The data is fake data, and was generated has the following features:\n",
-        "\n",
-        "- Money shows up in the destination account with some time delay\n",
-        "- The amount sent and the amount received are not always the same - there are hidden fees and foreign exchange effects\n",
-        "- The memo is sometimes truncated and content is sometimes missing\n",
-        "\n",
-        "Since each origin payment should end up in the destination account, the `probability_two_random_records_match` of the model is known.\n"
-      ]
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linking banking transactions\n",
+    "\n",
+    "This example shows how to perform a one-to-one link on banking transactions.\n",
+    "\n",
+    "The data is fake data, and was generated has the following features:\n",
+    "\n",
+    "- Money shows up in the destination account with some time delay\n",
+    "- The amount sent and the amount received are not always the same - there are hidden fees and foreign exchange effects\n",
+    "- The memo is sometimes truncated and content is sometimes missing\n",
+    "\n",
+    "Since each origin payment should end up in the destination account, the `probability_two_random_records_match` of the model is known.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/transactions.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:27.648457Z",
+     "iopub.status.busy": "2024-06-07T09:22:27.648128Z",
+     "iopub.status.idle": "2024-06-07T09:22:27.653498Z",
+     "shell.execute_reply": "2024-06-07T09:22:27.652626Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment and run this cell if you're running in Google Colab.\n",
+    "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:27.657230Z",
+     "iopub.status.busy": "2024-06-07T09:22:27.656926Z",
+     "iopub.status.idle": "2024-06-07T09:22:31.983888Z",
+     "shell.execute_reply": "2024-06-07T09:22:31.983040Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloading: https://raw.githubusercontent.com/moj-analytical-services/splink_datasets/master/data/transactions_origin.parquet\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/moj-analytical-services/splink/blob/splink4_dev/docs/demos/examples/duckdb/transactions.ipynb\">\n",
-        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-        "</a>\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
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+      "downloading: https://raw.githubusercontent.com/moj-analytical-services/splink_datasets/master/data/transactions_destination.parquet\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:14.252200Z",
-          "iopub.status.busy": "2024-05-16T12:13:14.251497Z",
-          "iopub.status.idle": "2024-05-16T12:13:14.257616Z",
-          "shell.execute_reply": "2024-05-16T12:13:14.256908Z"
-        }
-      },
-      "source": [
-        "# Uncomment and run this cell if you're running in Google Colab.\n",
-        "# !pip install git+https://github.com/moj-analytical-services/splink.git@splink4_dev"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:14.261383Z",
-          "iopub.status.busy": "2024-05-16T12:13:14.261079Z",
-          "iopub.status.idle": "2024-05-16T12:13:16.084252Z",
-          "shell.execute_reply": "2024-05-16T12:13:16.083429Z"
-        }
-      },
-      "source": [
-        "from splink import DuckDBAPI, Linker, SettingsCreator, block_on, splink_datasets\n",
-        "\n",
-        "df_origin = splink_datasets.transactions_origin\n",
-        "df_destination = splink_datasets.transactions_destination\n",
-        "\n",
-        "display(df_origin.head(2))\n",
-        "display(df_destination.head(2))"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
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+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ground_truth</th>\n",
+       "      <th>memo</th>\n",
+       "      <th>transaction_date</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>unique_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>MATTHIAS C paym</td>\n",
+       "      <td>2022-03-28</td>\n",
+       "      <td>36.36</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>M CORVINUS dona</td>\n",
+       "      <td>2022-02-14</td>\n",
+       "      <td>221.91</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "outputs": []
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the following chart, we can see this is a challenging dataset to link:\n",
-        "\n",
-        "- There are only 151 distinct transaction dates, with strong skew\n",
-        "- Some 'memos' are used multiple times (up to 48 times)\n",
-        "- There is strong skew in the 'amount' column, with 1,400 transactions of around 60.00\n"
+      "text/plain": [
+       "   ground_truth             memo transaction_date  amount  unique_id\n",
+       "0             0  MATTHIAS C paym       2022-03-28   36.36          0\n",
+       "1             1  M CORVINUS dona       2022-02-14  221.91          1"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:16.143823Z",
-          "iopub.status.busy": "2024-05-16T12:13:16.143431Z",
-          "iopub.status.idle": "2024-05-16T12:13:16.849535Z",
-          "shell.execute_reply": "2024-05-16T12:13:16.848871Z"
-        }
-      },
-      "source": [
-        "from splink.exploratory import profile_columns\n",
-        "\n",
-        "db_api = DuckDBAPI()\n",
-        "profile_columns(\n",
-        "    [df_origin, df_destination],\n",
-        "    db_api=db_api,\n",
-        "    column_expressions=[\n",
-        "        \"memo\",\n",
-        "        \"transaction_date\",\n",
-        "        \"amount\",\n",
-        "    ],\n",
-        ")"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:16.852855Z",
-          "iopub.status.busy": "2024-05-16T12:13:16.852594Z",
-          "iopub.status.idle": "2024-05-16T12:13:18.407824Z",
-          "shell.execute_reply": "2024-05-16T12:13:18.407265Z"
-        }
-      },
-      "source": [
-        "from splink import DuckDBAPI, block_on\n",
-        "from splink.blocking_analysis import (\n",
-        "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
-        ")\n",
-        "\n",
-        "# Design blocking rules that allow for differences in transaction date and amounts\n",
-        "blocking_rule_date_1 = \"\"\"\n",
-        "    strftime(l.transaction_date, '%Y%m') = strftime(r.transaction_date, '%Y%m')\n",
-        "    and substr(l.memo, 1,3) = substr(r.memo,1,3)\n",
-        "    and l.amount/r.amount > 0.7   and l.amount/r.amount < 1.3\n",
-        "\"\"\"\n",
-        "\n",
-        "# Offset by half a month to ensure we capture case when the dates are e.g. 31st Jan and 1st Feb\n",
-        "blocking_rule_date_2 = \"\"\"\n",
-        "    strftime(l.transaction_date+15, '%Y%m') = strftime(r.transaction_date, '%Y%m')\n",
-        "    and substr(l.memo, 1,3) = substr(r.memo,1,3)\n",
-        "    and l.amount/r.amount > 0.7   and l.amount/r.amount < 1.3\n",
-        "\"\"\"\n",
-        "\n",
-        "blocking_rule_memo = block_on(\"substr(memo,1,9)\")\n",
-        "\n",
-        "blocking_rule_amount_1 = \"\"\"\n",
-        "round(l.amount/2,0)*2 = round(r.amount/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date)\n",
-        "\"\"\"\n",
-        "\n",
-        "blocking_rule_amount_2 = \"\"\"\n",
-        "round(l.amount/2,0)*2 = round((r.amount+1)/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date + 4)\n",
-        "\"\"\"\n",
-        "\n",
-        "blocking_rule_cheat = block_on(\"unique_id\")\n",
-        "\n",
-        "\n",
-        "brs = [\n",
-        "    blocking_rule_date_1,\n",
-        "    blocking_rule_date_2,\n",
-        "    blocking_rule_memo,\n",
-        "    blocking_rule_amount_1,\n",
-        "    blocking_rule_amount_2,\n",
-        "    blocking_rule_cheat,\n",
-        "]\n",
-        "\n",
-        "\n",
-        "db_api = DuckDBAPI()\n",
-        "\n",
-        "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
-        "    table_or_tables=[df_origin, df_destination],\n",
-        "    blocking_rules=brs,\n",
-        "    db_api=db_api,\n",
-        "    link_type=\"link_only\"\n",
-        ")"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ground_truth</th>\n",
+       "      <th>memo</th>\n",
+       "      <th>transaction_date</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>unique_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>MATTHIAS C payment BGC</td>\n",
+       "      <td>2022-03-29</td>\n",
+       "      <td>36.36</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>M CORVINUS BGC</td>\n",
+       "      <td>2022-02-16</td>\n",
+       "      <td>221.91</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:18.411066Z",
-          "iopub.status.busy": "2024-05-16T12:13:18.410832Z",
-          "iopub.status.idle": "2024-05-16T12:13:18.418094Z",
-          "shell.execute_reply": "2024-05-16T12:13:18.416984Z"
-        }
-      },
-      "source": [
-        "# Full settings for linking model\n",
-        "import splink.comparison_level_library as cll\n",
-        "import splink.comparison_library as cl\n",
-        "\n",
-        "comparison_amount = {\n",
-        "    \"output_column_name\": \"amount\",\n",
-        "    \"comparison_levels\": [\n",
-        "        cll.NullLevel(\"amount\"),\n",
-        "        cll.ExactMatchLevel(\"amount\"),\n",
-        "        cll.PercentageDifferenceLevel(\"amount\", 0.01),\n",
-        "        cll.PercentageDifferenceLevel(\"amount\", 0.03),\n",
-        "        cll.PercentageDifferenceLevel(\"amount\", 0.1),\n",
-        "        cll.PercentageDifferenceLevel(\"amount\", 0.3),\n",
-        "        cll.ElseLevel(),\n",
-        "    ],\n",
-        "    \"comparison_description\": \"Amount percentage difference\",\n",
-        "}\n",
-        "\n",
-        "# The date distance is one sided becaause transactions should only arrive after they've left\n",
-        "# As a result, the comparison_template_library date difference functions are not appropriate\n",
-        "within_n_days_template = \"transaction_date_r - transaction_date_l <= {n} and transaction_date_r >= transaction_date_l\"\n",
-        "\n",
-        "comparison_date = {\n",
-        "    \"output_column_name\": \"transaction_date\",\n",
-        "    \"comparison_levels\": [\n",
-        "        cll.NullLevel(\"transaction_date\"),\n",
-        "        {\n",
-        "            \"sql_condition\": within_n_days_template.format(n=1),\n",
-        "            \"label_for_charts\": \"1 day\",\n",
-        "        },\n",
-        "        {\n",
-        "            \"sql_condition\": within_n_days_template.format(n=4),\n",
-        "            \"label_for_charts\": \"<=4 days\",\n",
-        "        },\n",
-        "        {\n",
-        "            \"sql_condition\": within_n_days_template.format(n=10),\n",
-        "            \"label_for_charts\": \"<=10 days\",\n",
-        "        },\n",
-        "        {\n",
-        "            \"sql_condition\": within_n_days_template.format(n=30),\n",
-        "            \"label_for_charts\": \"<=30 days\",\n",
-        "        },\n",
-        "        cll.ElseLevel(),\n",
-        "    ],\n",
-        "    \"comparison_description\": \"Transaction date days apart\",\n",
-        "}\n",
-        "\n",
-        "\n",
-        "settings = SettingsCreator(\n",
-        "    link_type=\"link_only\",\n",
-        "    probability_two_random_records_match=1 / len(df_origin),\n",
-        "    blocking_rules_to_generate_predictions=[\n",
-        "        blocking_rule_date_1,\n",
-        "        blocking_rule_date_2,\n",
-        "        blocking_rule_memo,\n",
-        "        blocking_rule_amount_1,\n",
-        "        blocking_rule_amount_2,\n",
-        "        blocking_rule_cheat,\n",
-        "    ],\n",
-        "    comparisons=[\n",
-        "        comparison_amount,\n",
-        "        cl.LevenshteinAtThresholds(\"memo\", [2, 6, 10]),\n",
-        "        comparison_date,\n",
-        "    ],\n",
-        "    retain_intermediate_calculation_columns=True,\n",
-        ")"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:18.421517Z",
-          "iopub.status.busy": "2024-05-16T12:13:18.421286Z",
-          "iopub.status.idle": "2024-05-16T12:13:18.552970Z",
-          "shell.execute_reply": "2024-05-16T12:13:18.552184Z"
-        }
-      },
-      "source": [
-        "linker = Linker(\n",
-        "    [df_origin, df_destination],\n",
-        "    settings,\n",
-        "    input_table_aliases=[\"__ori\", \"_dest\"],\n",
-        "    database_api=db_api,\n",
-        ")"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:18.556284Z",
-          "iopub.status.busy": "2024-05-16T12:13:18.556053Z",
-          "iopub.status.idle": "2024-05-16T12:13:20.529952Z",
-          "shell.execute_reply": "2024-05-16T12:13:20.529065Z"
-        }
-      },
-      "source": [
-        "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:20.532832Z",
-          "iopub.status.busy": "2024-05-16T12:13:20.532606Z",
-          "iopub.status.idle": "2024-05-16T12:13:21.867808Z",
-          "shell.execute_reply": "2024-05-16T12:13:21.867084Z"
-        }
-      },
-      "source": [
-        "linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"memo\"))"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:21.871283Z",
-          "iopub.status.busy": "2024-05-16T12:13:21.871004Z",
-          "iopub.status.idle": "2024-05-16T12:13:23.094606Z",
-          "shell.execute_reply": "2024-05-16T12:13:23.093838Z"
-        }
-      },
-      "source": [
-        "session = linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"amount\"))"
-      ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:23.097922Z",
-          "iopub.status.busy": "2024-05-16T12:13:23.097670Z",
-          "iopub.status.idle": "2024-05-16T12:13:23.382589Z",
-          "shell.execute_reply": "2024-05-16T12:13:23.382014Z"
-        }
-      },
-      "source": [
-        "linker.visualisations.match_weights_chart()"
+      "text/plain": [
+       "   ground_truth                    memo transaction_date  amount  unique_id\n",
+       "0             0  MATTHIAS C payment BGC       2022-03-29   36.36          0\n",
+       "1             1          M CORVINUS BGC       2022-02-16  221.91          1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from splink import DuckDBAPI, Linker, SettingsCreator, block_on, splink_datasets\n",
+    "\n",
+    "df_origin = splink_datasets.transactions_origin\n",
+    "df_destination = splink_datasets.transactions_destination\n",
+    "\n",
+    "display(df_origin.head(2))\n",
+    "display(df_destination.head(2))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following chart, we can see this is a challenging dataset to link:\n",
+    "\n",
+    "- There are only 151 distinct transaction dates, with strong skew\n",
+    "- Some 'memos' are used multiple times (up to 48 times)\n",
+    "- There is strong skew in the 'amount' column, with 1,400 transactions of around 60.00\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:31.987843Z",
+     "iopub.status.busy": "2024-06-07T09:22:31.987459Z",
+     "iopub.status.idle": "2024-06-07T09:22:32.720064Z",
+     "shell.execute_reply": "2024-06-07T09:22:32.719389Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-745ded07550942e39f6a00e1e7ffe84d.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-745ded07550942e39f6a00e1e7ffe84d.vega-embed details,\n",
+       "  #altair-viz-745ded07550942e39f6a00e1e7ffe84d.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
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\"total_rows_inc_nulls\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"percentile_ex_nulls\", \"sort\": \"descending\", \"title\": \"Percentile\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"value_count\", \"title\": \"Count of values\", \"type\": \"quantitative\"}}, \"title\": {\"text\": \"Distribution of counts of values in column transaction_date\", \"subtitle\": \"In this col, 0 values (0.0%) are null and there are 151 distinct values\"}}, {\"mark\": \"bar\", \"data\": {\"values\": [{\"value_count\": 1389, \"group_name\": \"transaction_date\", \"value\": \"2022-05-07\", \"total_non_null_rows\": 90652, \"total_rows_inc_nulls\": 90652, \"distinct_value_count\": 151}, {\"value_count\": 1372, \"group_name\": \"transaction_date\", \"value\": \"2022-05-09\", \"total_non_null_rows\": 90652, \"total_rows_inc_nulls\": 90652, \"distinct_value_count\": 151}, {\"value_count\": 1338, \"group_name\": \"transaction_date\", \"value\": \"2022-05-10\", \"total_non_null_rows\": 90652, 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\"value_count\", \"type\": \"quantitative\"}, {\"field\": \"total_non_null_rows\", \"type\": \"quantitative\"}, {\"field\": \"total_rows_inc_nulls\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"value\", \"sort\": \"-y\", \"title\": null, \"type\": \"nominal\"}, \"y\": {\"field\": \"value_count\", \"scale\": {\"domain\": [0, 14]}, \"title\": \"Value count\", \"type\": \"quantitative\"}}, \"title\": \"Bottom 10 values by value count\"}]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:23.385651Z",
-          "iopub.status.busy": "2024-05-16T12:13:23.385430Z",
-          "iopub.status.idle": "2024-05-16T12:13:47.966948Z",
-          "shell.execute_reply": "2024-05-16T12:13:47.966113Z"
-        }
-      },
-      "source": [
-        "df_predict = linker.inference.predict(threshold_match_probability=0.001)"
+      "text/plain": [
+       "alt.VConcatChart(...)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink.exploratory import profile_columns\n",
+    "\n",
+    "db_api = DuckDBAPI()\n",
+    "profile_columns(\n",
+    "    [df_origin, df_destination],\n",
+    "    db_api=db_api,\n",
+    "    column_expressions=[\n",
+    "        \"memo\",\n",
+    "        \"transaction_date\",\n",
+    "        \"amount\",\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:32.724189Z",
+     "iopub.status.busy": "2024-06-07T09:22:32.723901Z",
+     "iopub.status.idle": "2024-06-07T09:22:33.500975Z",
+     "shell.execute_reply": "2024-06-07T09:22:33.500399Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-5e698cc8096f4d55823229a14567e05d.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-5e698cc8096f4d55823229a14567e05d.vega-embed details,\n",
+       "  #altair-viz-5e698cc8096f4d55823229a14567e05d.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-5e698cc8096f4d55823229a14567e05d\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-5e698cc8096f4d55823229a14567e05d\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-5e698cc8096f4d55823229a14567e05d\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-a1d71cae6ee65fb38b878f1840953fd5\"}, \"mark\": \"bar\", \"encoding\": {\"order\": {\"field\": \"cumulative_rows\"}, \"tooltip\": [{\"field\": \"blocking_rule\", \"title\": \"SQL Condition\", \"type\": \"nominal\"}, {\"field\": \"row_count\", \"format\": \",\", \"title\": \"Comparisons Generated\", \"type\": \"quantitative\"}, {\"field\": \"cumulative_rows\", \"format\": \",\", \"title\": \"Cumulative Comparisons\", \"type\": \"quantitative\"}, {\"field\": \"cartesian\", \"format\": \",\", \"title\": \"Total comparisons in Cartesian product\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"start\", \"title\": \"Comparisons Generated by Rule(s)\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"cumulative_rows\"}, \"y\": {\"field\": \"blocking_rule\", \"sort\": [\"-x2\"], \"title\": \"SQL Blocking Rule\"}}, \"height\": {\"step\": 20}, \"title\": {\"text\": \"Count of Additional Comparisons Generated by Each Blocking Rule\", \"subtitle\": \"(Counts exclude comparisons already generated by previous rules)\"}, \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.9.3.json\", \"datasets\": {\"data-a1d71cae6ee65fb38b878f1840953fd5\": [{\"blocking_rule\": \"\\n    strftime(l.transaction_date, '%Y%m') = strftime(r.transaction_date, '%Y%m')\\n    and substr(l.memo, 1,3) = substr(r.memo,1,3)\\n    and l.amount/r.amount > 0.7   and l.amount/r.amount < 1.3\\n\", \"row_count\": 301614, \"cumulative_rows\": 301614, \"cartesian\": 2054446276, \"match_key\": \"0\", \"start\": 0}, {\"blocking_rule\": \"\\n    strftime(l.transaction_date+15, '%Y%m') = strftime(r.transaction_date, '%Y%m')\\n    and substr(l.memo, 1,3) = substr(r.memo,1,3)\\n    and l.amount/r.amount > 0.7   and l.amount/r.amount < 1.3\\n\", \"row_count\": 126636, \"cumulative_rows\": 428250, \"cartesian\": 2054446276, \"match_key\": \"1\", \"start\": 301614}, {\"blocking_rule\": \"SUBSTR(l.memo, 1, 9) = SUBSTR(r.memo, 1, 9)\", \"row_count\": 281940, \"cumulative_rows\": 710190, \"cartesian\": 2054446276, \"match_key\": \"2\", \"start\": 428250}, {\"blocking_rule\": \"\\nround(l.amount/2,0)*2 = round(r.amount/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date)\\n\", \"row_count\": 341182, \"cumulative_rows\": 1051372, \"cartesian\": 2054446276, \"match_key\": \"3\", \"start\": 710190}, {\"blocking_rule\": \"\\nround(l.amount/2,0)*2 = round((r.amount+1)/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date + 4)\\n\", \"row_count\": 270166, \"cumulative_rows\": 1321538, \"cartesian\": 2054446276, \"match_key\": \"4\", \"start\": 1051372}, {\"blocking_rule\": \"l.\\\"unique_id\\\" = r.\\\"unique_id\\\"\", \"row_count\": 27, \"cumulative_rows\": 1321565, \"cartesian\": 2054446276, \"match_key\": \"5\", \"start\": 1321538}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:47.970901Z",
-          "iopub.status.busy": "2024-05-16T12:13:47.970603Z",
-          "iopub.status.idle": "2024-05-16T12:13:48.365220Z",
-          "shell.execute_reply": "2024-05-16T12:13:48.364442Z"
-        }
-      },
-      "source": [
-        "linker.visualisations.comparison_viewer_dashboard(\n",
-        "    df_predict, \"dashboards/comparison_viewer_transactions.html\", overwrite=True\n",
-        ")\n",
-        "from IPython.display import IFrame\n",
-        "\n",
-        "IFrame(\n",
-        "    src=\"./dashboards/comparison_viewer_transactions.html\", width=\"100%\", height=1200\n",
-        ")"
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from splink import DuckDBAPI, block_on\n",
+    "from splink.blocking_analysis import (\n",
+    "    cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n",
+    ")\n",
+    "\n",
+    "# Design blocking rules that allow for differences in transaction date and amounts\n",
+    "blocking_rule_date_1 = \"\"\"\n",
+    "    strftime(l.transaction_date, '%Y%m') = strftime(r.transaction_date, '%Y%m')\n",
+    "    and substr(l.memo, 1,3) = substr(r.memo,1,3)\n",
+    "    and l.amount/r.amount > 0.7   and l.amount/r.amount < 1.3\n",
+    "\"\"\"\n",
+    "\n",
+    "# Offset by half a month to ensure we capture case when the dates are e.g. 31st Jan and 1st Feb\n",
+    "blocking_rule_date_2 = \"\"\"\n",
+    "    strftime(l.transaction_date+15, '%Y%m') = strftime(r.transaction_date, '%Y%m')\n",
+    "    and substr(l.memo, 1,3) = substr(r.memo,1,3)\n",
+    "    and l.amount/r.amount > 0.7   and l.amount/r.amount < 1.3\n",
+    "\"\"\"\n",
+    "\n",
+    "blocking_rule_memo = block_on(\"substr(memo,1,9)\")\n",
+    "\n",
+    "blocking_rule_amount_1 = \"\"\"\n",
+    "round(l.amount/2,0)*2 = round(r.amount/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date)\n",
+    "\"\"\"\n",
+    "\n",
+    "blocking_rule_amount_2 = \"\"\"\n",
+    "round(l.amount/2,0)*2 = round((r.amount+1)/2,0)*2 and yearweek(r.transaction_date) = yearweek(l.transaction_date + 4)\n",
+    "\"\"\"\n",
+    "\n",
+    "blocking_rule_cheat = block_on(\"unique_id\")\n",
+    "\n",
+    "\n",
+    "brs = [\n",
+    "    blocking_rule_date_1,\n",
+    "    blocking_rule_date_2,\n",
+    "    blocking_rule_memo,\n",
+    "    blocking_rule_amount_1,\n",
+    "    blocking_rule_amount_2,\n",
+    "    blocking_rule_cheat,\n",
+    "]\n",
+    "\n",
+    "\n",
+    "db_api = DuckDBAPI()\n",
+    "\n",
+    "cumulative_comparisons_to_be_scored_from_blocking_rules_chart(\n",
+    "    table_or_tables=[df_origin, df_destination],\n",
+    "    blocking_rules=brs,\n",
+    "    db_api=db_api,\n",
+    "    link_type=\"link_only\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:33.504001Z",
+     "iopub.status.busy": "2024-06-07T09:22:33.503779Z",
+     "iopub.status.idle": "2024-06-07T09:22:33.511675Z",
+     "shell.execute_reply": "2024-06-07T09:22:33.511212Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Full settings for linking model\n",
+    "import splink.comparison_level_library as cll\n",
+    "import splink.comparison_library as cl\n",
+    "\n",
+    "comparison_amount = {\n",
+    "    \"output_column_name\": \"amount\",\n",
+    "    \"comparison_levels\": [\n",
+    "        cll.NullLevel(\"amount\"),\n",
+    "        cll.ExactMatchLevel(\"amount\"),\n",
+    "        cll.PercentageDifferenceLevel(\"amount\", 0.01),\n",
+    "        cll.PercentageDifferenceLevel(\"amount\", 0.03),\n",
+    "        cll.PercentageDifferenceLevel(\"amount\", 0.1),\n",
+    "        cll.PercentageDifferenceLevel(\"amount\", 0.3),\n",
+    "        cll.ElseLevel(),\n",
+    "    ],\n",
+    "    \"comparison_description\": \"Amount percentage difference\",\n",
+    "}\n",
+    "\n",
+    "# The date distance is one sided becaause transactions should only arrive after they've left\n",
+    "# As a result, the comparison_template_library date difference functions are not appropriate\n",
+    "within_n_days_template = \"transaction_date_r - transaction_date_l <= {n} and transaction_date_r >= transaction_date_l\"\n",
+    "\n",
+    "comparison_date = {\n",
+    "    \"output_column_name\": \"transaction_date\",\n",
+    "    \"comparison_levels\": [\n",
+    "        cll.NullLevel(\"transaction_date\"),\n",
+    "        {\n",
+    "            \"sql_condition\": within_n_days_template.format(n=1),\n",
+    "            \"label_for_charts\": \"1 day\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"sql_condition\": within_n_days_template.format(n=4),\n",
+    "            \"label_for_charts\": \"<=4 days\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"sql_condition\": within_n_days_template.format(n=10),\n",
+    "            \"label_for_charts\": \"<=10 days\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"sql_condition\": within_n_days_template.format(n=30),\n",
+    "            \"label_for_charts\": \"<=30 days\",\n",
+    "        },\n",
+    "        cll.ElseLevel(),\n",
+    "    ],\n",
+    "    \"comparison_description\": \"Transaction date days apart\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "settings = SettingsCreator(\n",
+    "    link_type=\"link_only\",\n",
+    "    probability_two_random_records_match=1 / len(df_origin),\n",
+    "    blocking_rules_to_generate_predictions=[\n",
+    "        blocking_rule_date_1,\n",
+    "        blocking_rule_date_2,\n",
+    "        blocking_rule_memo,\n",
+    "        blocking_rule_amount_1,\n",
+    "        blocking_rule_amount_2,\n",
+    "        blocking_rule_cheat,\n",
+    "    ],\n",
+    "    comparisons=[\n",
+    "        comparison_amount,\n",
+    "        cl.LevenshteinAtThresholds(\"memo\", [2, 6, 10]),\n",
+    "        comparison_date,\n",
+    "    ],\n",
+    "    retain_intermediate_calculation_columns=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:33.514381Z",
+     "iopub.status.busy": "2024-06-07T09:22:33.514150Z",
+     "iopub.status.idle": "2024-06-07T09:22:33.621746Z",
+     "shell.execute_reply": "2024-06-07T09:22:33.621038Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "linker = Linker(\n",
+    "    [df_origin, df_destination],\n",
+    "    settings,\n",
+    "    input_table_aliases=[\"__ori\", \"_dest\"],\n",
+    "    database_api=db_api,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:33.625044Z",
+     "iopub.status.busy": "2024-06-07T09:22:33.624807Z",
+     "iopub.status.idle": "2024-06-07T09:22:35.145751Z",
+     "shell.execute_reply": "2024-06-07T09:22:35.145280Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You are using the default value for `max_pairs`, which may be too small and thus lead to inaccurate estimates for your model's u-parameters. Consider increasing to 1e8 or 1e9, which will result in more accurate estimates, but with a longer run time.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "----- Estimating u probabilities using random sampling -----\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Estimated u probabilities using random sampling\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - amount (no m values are trained).\n",
+      "    - memo (no m values are trained).\n",
+      "    - transaction_date (no m values are trained).\n"
+     ]
+    }
+   ],
+   "source": [
+    "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:35.148614Z",
+     "iopub.status.busy": "2024-06-07T09:22:35.148331Z",
+     "iopub.status.idle": "2024-06-07T09:22:36.323460Z",
+     "shell.execute_reply": "2024-06-07T09:22:36.322736Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"memo\" = r.\"memo\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - amount\n",
+      "    - transaction_date\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - memo\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.596 in the m_probability of amount, level `Exact match on amount`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was -0.167 in the m_probability of transaction_date, level `1 day`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 0.00961 in the m_probability of amount, level `Percentage difference of 'amount' within 10.00%`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was 0.00211 in the m_probability of transaction_date, level `<=30 days`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was 0.000367 in the m_probability of transaction_date, level `<=30 days`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was -0.000315 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was -0.000282 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was -0.000254 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was -0.00023 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was -0.000209 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 11: Largest change in params was -0.00019 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 12: Largest change in params was -0.000174 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 13: Largest change in params was -0.000159 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 14: Largest change in params was -0.000147 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 15: Largest change in params was -0.000135 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 16: Largest change in params was -0.000125 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 17: Largest change in params was -0.000116 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 18: Largest change in params was -0.000108 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 19: Largest change in params was -0.0001 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 20: Largest change in params was -9.33e-05 in the m_probability of amount, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 20 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is not yet fully trained. Missing estimates for:\n",
+      "    - memo (no m values are trained).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<EMTrainingSession, blocking on l.\"memo\" = r.\"memo\", deactivating comparisons memo>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"memo\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:36.326561Z",
+     "iopub.status.busy": "2024-06-07T09:22:36.326344Z",
+     "iopub.status.idle": "2024-06-07T09:22:37.563023Z",
+     "shell.execute_reply": "2024-06-07T09:22:37.562461Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----- Starting EM training session -----\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Estimating the m probabilities of the model by blocking on:\n",
+      "l.\"amount\" = r.\"amount\"\n",
+      "\n",
+      "Parameter estimates will be made for the following comparison(s):\n",
+      "    - memo\n",
+      "    - transaction_date\n",
+      "\n",
+      "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+      "    - amount\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Largest change in params was -0.378 in the m_probability of memo, level `Exact match on memo`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 2: Largest change in params was -0.104 in the m_probability of memo, level `Exact match on memo`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: Largest change in params was 0.0215 in the m_probability of memo, level `Levenshtein distance of memo <= 10`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 4: Largest change in params was -0.00538 in the m_probability of memo, level `Exact match on memo`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 5: Largest change in params was 0.00474 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 6: Largest change in params was 0.00502 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 7: Largest change in params was 0.00499 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 8: Largest change in params was 0.00466 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 9: Largest change in params was 0.00413 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 10: Largest change in params was 0.00348 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 11: Largest change in params was 0.00283 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 12: Largest change in params was 0.00223 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 13: Largest change in params was 0.00171 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 14: Largest change in params was 0.00129 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 15: Largest change in params was 0.000959 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 16: Largest change in params was 0.000706 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 17: Largest change in params was 0.000516 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 18: Largest change in params was 0.000375 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 19: Largest change in params was 0.000272 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 20: Largest change in params was 0.000196 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 21: Largest change in params was 0.000141 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 22: Largest change in params was 0.000102 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Iteration 23: Largest change in params was 7.32e-05 in the m_probability of memo, level `All other comparisons`\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "EM converged after 23 iterations\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
+     ]
+    }
+   ],
+   "source": [
+    "session = linker.training.estimate_parameters_using_expectation_maximisation(block_on(\"amount\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:37.565956Z",
+     "iopub.status.busy": "2024-06-07T09:22:37.565738Z",
+     "iopub.status.idle": "2024-06-07T09:22:37.832159Z",
+     "shell.execute_reply": "2024-06-07T09:22:37.831506Z"
+    }
+   },
+   "outputs": [
+    {
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levenshtein distance of memo <= 2 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 0.18% of records are in the levenshtein distance of memo <= 2 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 60.14988763570764, \"log2_bayes_factor\": 5.9104901372337055, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `levenshtein distance of memo <= 2` then comparison is 60.15 times more likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 1}, {\"comparison_name\": \"memo\", \"sql_condition\": \"levenshtein(\\\"memo_l\\\", \\\"memo_r\\\") <= 6\", \"label_for_charts\": \"Levenshtein distance of memo <= 6\", \"m_probability\": 0.2606413727512134, \"u_probability\": 0.023608648648648648, \"m_probability_description\": \"Amongst matching record comparisons, 26.06% of records are in the levenshtein distance of memo <= 6 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 2.36% of records are in the levenshtein distance of memo <= 6 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 11.040080126150398, \"log2_bayes_factor\": 3.4646787377625325, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `levenshtein distance of memo <= 6` then comparison is 11.04 times more likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 1}, {\"comparison_name\": \"memo\", \"sql_condition\": \"levenshtein(\\\"memo_l\\\", \\\"memo_r\\\") <= 10\", \"label_for_charts\": \"Levenshtein distance of memo <= 10\", \"m_probability\": 0.14681583981923468, \"u_probability\": 0.10217621621621621, \"m_probability_description\": \"Amongst matching record comparisons, 14.68% of records are in the levenshtein distance of memo <= 10 comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 10.22% of records are in the levenshtein distance of memo <= 10 comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 1.4368885955666637, \"log2_bayes_factor\": 0.5229482115141803, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `levenshtein distance of memo <= 10` then comparison is 1.44 times more likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 1}, {\"comparison_name\": \"memo\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 0.05809789657601779, \"u_probability\": 0.8723956756756757, \"m_probability_description\": \"Amongst matching record comparisons, 5.81% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 87.24% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.06659580990130495, \"log2_bayes_factor\": -3.908424781618968, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  15.02 times less likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 1}, {\"comparison_name\": \"transaction_date\", \"sql_condition\": \"transaction_date_r - transaction_date_l <= 1 and transaction_date_r >= transaction_date_l\", \"label_for_charts\": \"1 day\", \"m_probability\": 0.38866128934161964, \"u_probability\": 0.019468108108108107, \"m_probability_description\": \"Amongst matching record comparisons, 38.87% of records are in the 1 day comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 1.95% of records are in the 1 day comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 19.963998924977687, \"log2_bayes_factor\": 4.319328826153099, \"comparison_vector_value\": 4, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `1 day` then comparison is 19.96 times more likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 2}, {\"comparison_name\": \"transaction_date\", \"sql_condition\": \"transaction_date_r - transaction_date_l <= 4 and transaction_date_r >= transaction_date_l\", \"label_for_charts\": \"<=4 days\", \"m_probability\": 0.4657756383435946, \"u_probability\": 0.028956756756756758, \"m_probability_description\": \"Amongst matching record comparisons, 46.58% of records are in the <=4 days comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 2.90% of records are in the <=4 days comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 16.085214316513905, \"log2_bayes_factor\": 4.007663253216339, \"comparison_vector_value\": 3, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `<=4 days` then comparison is 16.09 times more likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 2}, {\"comparison_name\": \"transaction_date\", \"sql_condition\": \"transaction_date_r - transaction_date_l <= 10 and transaction_date_r >= transaction_date_l\", \"label_for_charts\": \"<=10 days\", \"m_probability\": 0.09632534163977555, \"u_probability\": 0.055486486486486485, \"m_probability_description\": \"Amongst matching record comparisons, 9.63% of records are in the <=10 days comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 5.55% of records are in the <=10 days comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 1.7360144377358477, \"log2_bayes_factor\": 0.7957789461483454, \"comparison_vector_value\": 2, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `<=10 days` then comparison is 1.74 times more likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 2}, {\"comparison_name\": \"transaction_date\", \"sql_condition\": \"transaction_date_r - transaction_date_l <= 30 and transaction_date_r >= transaction_date_l\", \"label_for_charts\": \"<=30 days\", \"m_probability\": 0.04914957410979566, \"u_probability\": 0.16167027027027028, \"m_probability_description\": \"Amongst matching record comparisons, 4.91% of records are in the <=30 days comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 16.17% of records are in the <=30 days comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.3040112076736834, \"log2_bayes_factor\": -1.717803583859319, \"comparison_vector_value\": 1, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `<=30 days` then comparison is  3.29 times less likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 2}, {\"comparison_name\": \"transaction_date\", \"sql_condition\": \"ELSE\", \"label_for_charts\": \"All other comparisons\", \"m_probability\": 8.815656521464847e-05, \"u_probability\": 0.7344183783783784, \"m_probability_description\": \"Amongst matching record comparisons, 0.01% of records are in the all other comparisons comparison level\", \"u_probability_description\": \"Amongst non-matching record comparisons, 73.44% of records are in the all other comparisons comparison level\", \"has_tf_adjustments\": false, \"tf_adjustment_column\": null, \"tf_adjustment_weight\": 1.0, \"is_null_level\": false, \"bayes_factor\": 0.00012003589208824167, \"log2_bayes_factor\": -13.02424652708783, \"comparison_vector_value\": 0, \"max_comparison_vector_value\": 4, \"bayes_factor_description\": \"If comparison level is `all other comparisons` then comparison is  8,330.84 times less likely to be a match\", \"probability_two_random_records_match\": 2.2062392445836827e-05, \"comparison_sort_order\": 2}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:48.369330Z",
-          "iopub.status.busy": "2024-05-16T12:13:48.369001Z",
-          "iopub.status.idle": "2024-05-16T12:13:54.043730Z",
-          "shell.execute_reply": "2024-05-16T12:13:54.043073Z"
-        }
-      },
-      "source": [
-        "pred_errors = linker.inference.prediction_errors_from_labels_column(\n",
-        "    \"ground_truth\", include_false_positives=True, include_false_negatives=False\n",
-        ")\n",
-        "linker.visualisations.waterfall_chart(pred_errors.as_record_dict(limit=5))"
+      "text/plain": [
+       "alt.VConcatChart(...)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.visualisations.match_weights_chart()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:37.835082Z",
+     "iopub.status.busy": "2024-06-07T09:22:37.834871Z",
+     "iopub.status.idle": "2024-06-07T09:22:58.616771Z",
+     "shell.execute_reply": "2024-06-07T09:22:58.615862Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_predict = linker.inference.predict(threshold_match_probability=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:58.620828Z",
+     "iopub.status.busy": "2024-06-07T09:22:58.620523Z",
+     "iopub.status.idle": "2024-06-07T09:22:59.018555Z",
+     "shell.execute_reply": "2024-06-07T09:22:59.017917Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"1200\"\n",
+       "            src=\"./dashboards/comparison_viewer_transactions.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
       ],
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "execution": {
-          "iopub.execute_input": "2024-05-16T12:13:54.047308Z",
-          "iopub.status.busy": "2024-05-16T12:13:54.047030Z",
-          "iopub.status.idle": "2024-05-16T12:13:54.884355Z",
-          "shell.execute_reply": "2024-05-16T12:13:54.883814Z"
-        }
-      },
-      "source": [
-        "pred_errors = linker.inference.prediction_errors_from_labels_column(\n",
-        "    \"ground_truth\", include_false_positives=False, include_false_negatives=True\n",
-        ")\n",
-        "linker.visualisations.waterfall_chart(pred_errors.as_record_dict(limit=5))"
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x1237d7f10>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linker.visualisations.comparison_viewer_dashboard(\n",
+    "    df_predict, \"dashboards/comparison_viewer_transactions.html\", overwrite=True\n",
+    ")\n",
+    "from IPython.display import IFrame\n",
+    "\n",
+    "IFrame(\n",
+    "    src=\"./dashboards/comparison_viewer_transactions.html\", width=\"100%\", height=1200\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:22:59.022067Z",
+     "iopub.status.busy": "2024-06-07T09:22:59.021794Z",
+     "iopub.status.idle": "2024-06-07T09:23:04.254280Z",
+     "shell.execute_reply": "2024-06-07T09:23:04.253648Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "  #altair-viz-45b2f5deaca44310a2c9da9424f25673.vega-embed {\n",
+       "    width: 100%;\n",
+       "    display: flex;\n",
+       "  }\n",
+       "\n",
+       "  #altair-viz-45b2f5deaca44310a2c9da9424f25673.vega-embed details,\n",
+       "  #altair-viz-45b2f5deaca44310a2c9da9424f25673.vega-embed details summary {\n",
+       "    position: relative;\n",
+       "  }\n",
+       "</style>\n",
+       "<div id=\"altair-viz-45b2f5deaca44310a2c9da9424f25673\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-45b2f5deaca44310a2c9da9424f25673\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-45b2f5deaca44310a2c9da9424f25673\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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+   ],
+   "source": [
+    "pred_errors = linker.evaluation.prediction_errors_from_labels_column(\n",
+    "    \"ground_truth\", include_false_positives=True, include_false_negatives=False\n",
+    ")\n",
+    "linker.visualisations.waterfall_chart(pred_errors.as_record_dict(limit=5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-07T09:23:04.257242Z",
+     "iopub.status.busy": "2024-06-07T09:23:04.257017Z",
+     "iopub.status.idle": "2024-06-07T09:23:05.029715Z",
+     "shell.execute_reply": "2024-06-07T09:23:05.029153Z"
+    }
+   },
+   "outputs": [
+    {
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
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+   ],
+   "source": [
+    "pred_errors = linker.evaluation.prediction_errors_from_labels_column(\n",
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+    ")\n",
+    "linker.visualisations.waterfall_chart(pred_errors.as_record_dict(limit=5))"
+   ]
+  }
+ ],
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+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": "auto"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb b/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb
index ff1369e20a..3eea1b0b3e 100644
--- a/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb
+++ b/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb
@@ -385,7 +385,7 @@
       },
       "outputs": [],
       "source": [
-        "linker.accuracy_analysis_from_labels_column(\n",
+        "linker.evaluation.accuracy_analysis_from_labels_column(\n",
         "    \"cluster\", output_type=\"roc\", match_weight_round_to_nearest=0.02\n",
         ")"
       ]
@@ -403,7 +403,7 @@
       },
       "outputs": [],
       "source": [
-        "records = linker.inference.prediction_errors_from_labels_column(\n",
+        "records = linker.evaluation.prediction_errors_from_labels_column(\n",
         "    \"cluster\",\n",
         "    threshold=0.999,\n",
         "    include_false_negatives=False,\n",
@@ -426,7 +426,7 @@
       "outputs": [],
       "source": [
         "# Some of the false negatives will be because they weren't detected by the blocking rules\n",
-        "records = linker.inference.prediction_errors_from_labels_column(\n",
+        "records = linker.evaluation.prediction_errors_from_labels_column(\n",
         "    \"cluster\",\n",
         "    threshold=0.5,\n",
         "    include_false_negatives=True,\n",