Group linker functions thematically

docs/charts/cluster_studio_dashboard.ipynb

@@ -1,132 +1,132 @@
 {
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "!!! warning \"Work in Progress\"\n",
-    "    This page is currently under construction. \n",
-    "\n",
-    "# `cluster_studio_dashboard`\n",
-    "\n",
-    "!!! info \"At a glance\"\n",
-    "    **Useful for:** \n",
-    "\n",
-    "    **API Documentation:** [cluster_studio_dashboard()](../linker.md#splink.linker.Linker.cluster_studio_dashboard)\n",
-    "\n",
-    "    **What is needed to generate the chart?** "
-   ]
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "!!! warning \"Work in Progress\"\n",
+        "    This page is currently under construction. \n",
+        "\n",
+        "# `cluster_studio_dashboard`\n",
+        "\n",
+        "!!! info \"At a glance\"\n",
+        "    **Useful for:** \n",
+        "\n",
+        "    **API Documentation:** [cluster_studio_dashboard()](../linker.md#splink.linker.linker.visualisations.cluster_studio_dashboard)\n",
+        "\n",
+        "    **What is needed to generate the chart?** "
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Worked Example"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from splink.duckdb.linker import DuckDBLinker\n",
+        "import splink.duckdb.comparison_library as cl\n",
+        "import splink.duckdb.comparison_template_library as ctl\n",
+        "from splink.duckdb.blocking_rule_library import block_on\n",
+        "from splink.datasets import splink_datasets\n",
+        "import logging, sys\n",
+        "logging.disable(sys.maxsize)\n",
+        "\n",
+        "df = splink_datasets.fake_1000\n",
+        "\n",
+        "settings = {\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.name_comparison(\"first_name\"),\n",
+        "        ctl.name_comparison(\"surname\"),\n",
+        "        ctl.date_comparison(\"dob\", cast_strings_to_date=True),\n",
+        "        cl.exact_match(\"city\", term_frequency_adjustments=True),\n",
+        "        ctl.email_comparison(\"email\", include_username_fuzzy_level=False),\n",
+        "    ],\n",
+        "    \"retain_intermediate_calculation_columns\": True,\n",
+        "    \"retain_matching_columns\":True,\n",
+        "}\n",
+        "\n",
+        "linker = DuckDBLinker(df, settings)\n",
+        "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)\n",
+        "\n",
+        "blocking_rule_for_training = block_on([\"first_name\", \"surname\"])\n",
+        "\n",
+        "linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)\n",
+        "\n",
+        "blocking_rule_for_training = block_on(\"dob\")\n",
+        "linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)\n",
+        "\n",
+        "df_predictions = linker.inference.predict(threshold_match_probability=0.2)\n",
+        "df_clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(df_predictions, threshold_match_probability=0.5)\n",
+        "\n",
+        "linker.visualisations.cluster_studio_dashboard(df_predictions, df_clusters, \"img/cluster_studio.html\", sampling_method=\"by_cluster_size\", overwrite=True)\n",
+        "\n",
+        "# You can view the scv.html file in your browser, or inline in a notbook as follows\n",
+        "from IPython.display import IFrame\n",
+        "IFrame(\n",
+        "    src=\"./img/cluster_studio.html\", width=\"100%\", height=1200\n",
+        ")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### What the chart shows\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### How to interpret the chart\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Actions to take as a result of the chart\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "base",
+      "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": "3.9.12"
+    },
+    "orig_nbformat": 4
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Worked Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from splink.duckdb.linker import DuckDBLinker\n",
-    "import splink.duckdb.comparison_library as cl\n",
-    "import splink.duckdb.comparison_template_library as ctl\n",
-    "from splink.duckdb.blocking_rule_library import block_on\n",
-    "from splink.datasets import splink_datasets\n",
-    "import logging, sys\n",
-    "logging.disable(sys.maxsize)\n",
-    "\n",
-    "df = splink_datasets.fake_1000\n",
-    "\n",
-    "settings = {\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.name_comparison(\"first_name\"),\n",
-    "        ctl.name_comparison(\"surname\"),\n",
-    "        ctl.date_comparison(\"dob\", cast_strings_to_date=True),\n",
-    "        cl.exact_match(\"city\", term_frequency_adjustments=True),\n",
-    "        ctl.email_comparison(\"email\", include_username_fuzzy_level=False),\n",
-    "    ],\n",
-    "    \"retain_intermediate_calculation_columns\": True,\n",
-    "    \"retain_matching_columns\":True,\n",
-    "}\n",
-    "\n",
-    "linker = DuckDBLinker(df, settings)\n",
-    "linker.estimate_u_using_random_sampling(max_pairs=1e6)\n",
-    "\n",
-    "blocking_rule_for_training = block_on([\"first_name\", \"surname\"])\n",
-    "\n",
-    "linker.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)\n",
-    "\n",
-    "blocking_rule_for_training = block_on(\"dob\")\n",
-    "linker.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)\n",
-    "\n",
-    "df_predictions = linker.predict(threshold_match_probability=0.2)\n",
-    "df_clusters = linker.cluster_pairwise_predictions_at_threshold(df_predictions, threshold_match_probability=0.5)\n",
-    "\n",
-    "linker.cluster_studio_dashboard(df_predictions, df_clusters, \"img/cluster_studio.html\", sampling_method=\"by_cluster_size\", overwrite=True)\n",
-    "\n",
-    "# You can view the scv.html file in your browser, or inline in a notbook as follows\n",
-    "from IPython.display import IFrame\n",
-    "IFrame(\n",
-    "    src=\"./img/cluster_studio.html\", width=\"100%\", height=1200\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What the chart shows\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### How to interpret the chart\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Actions to take as a result of the chart\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "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": "3.9.12"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 2
+}