" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to interpret the chart\n", + "\n", + "The most common terms (on the left of the first chart) will have a negative term frequency adjustment and the values on the chart and represent the lowest match weight for a match for the selected comparison. Conversely, the least common terms (on the right of the first chart) will have a positive term frequency adjustment and the values on the chart represent the highest match weight for a match for the selected comparison.\n", + "\n", + "Given that the first chart only shows the most and least frequently occuring values, the second chart is provided to show the distribution of final match weights (including term frequency adjustments) across all values in the dataset." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Actions to take as a result of the chart\n", + "\n", + "There are no direct actions that need to be taken as a result of this chart. It is intended to give the user an indication of the size of the impact of Term Frequency Adjustments on comparisons, as seen in the Waterfall Chart." ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" } - ], - "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\", term_frequency_adjustments = True),\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", - "}\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", - "linker.tf_adjustment_chart(\"first_name\", vals_to_include = [\"Robert\", \"Grace\"])\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the chart shows\n", - "\n", - "The `tf_adjustment_chart` shows the impact of Term Frequency Adjustments on the Match Weight of a comparison. It is made up of two charts for each selected comparison:\n", - "\n", - "- The left chart shows the match weight for two records with a matching `first_name` including a term frequency adjustment. The black horizontal line represents the base match weight (i.e. with no term frequency adjustment applied). By default this chart contains the 10 most frequent and 10 least frequent values in a comparison as well as any values assigned in the `vals_to_include` parameter.\n", - "- The right chart shows the distribution of match weights across all of the values of `first_name`." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "??? note \"What the tooltip shows\"\n", - "\n", - " #### Left chart\n", - "\n", - " \n", - "\n", - " The tooltip shows a number of statistics based on the column value of the point theat the user is hovering over, including:\n", - "\n", - " - The column value\n", - " - The base match weight (i.e. with no term frequency adjustment) for a match on the column.\n", - " - The term frequency adjustment for the column value.\n", - " - The final match weight (i.e. the combined base match weight and term frequency adjustment)\n", - "\n", - " #### Right chart\n", - "\n", - " \n", - "\n", - " The tooltip shows a number of statistics based on the bar that the user is hovering over, including:\n", - "\n", - " - The final match weight bucket (in steps of 0.5).\n", - " - The number of records with a final match weight in the final match weight bucket." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How to interpret the chart\n", - "\n", - "The most common terms (on the left of the first chart) will have a negative term frequency adjustment and the values on the chart and represent the lowest match weight for a match for the selected comparison. Conversely, the least common terms (on the right of the first chart) will have a positive term frequency adjustment and the values on the chart represent the highest match weight for a match for the selected comparison.\n", - "\n", - "Given that the first chart only shows the most and least frequently occuring values, the second chart is provided to show the distribution of final match weights (including term frequency adjustments) across all values in the dataset." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Actions to take as a result of the chart\n", - "\n", - "There are no direct actions that need to be taken as a result of this chart. It is intended to give the user an indication of the size of the impact of Term Frequency Adjustments on comparisons, as seen in the Waterfall Chart." - ] - } - ], - "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" + ], + "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 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/charts/threshold_selection_tool_from_labels_table.ipynb b/docs/charts/threshold_selection_tool_from_labels_table.ipynb index b4bedeb42b..d002c7df5f 100644 --- a/docs/charts/threshold_selection_tool_from_labels_table.ipynb +++ b/docs/charts/threshold_selection_tool_from_labels_table.ipynb @@ -1,225 +1,225 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `threshold_selection_tool_from_labels_table`\n", - "\n", - "!!! info \"At a glance\"\n", - " **Useful for:** Selecting an optimal match weight threshold for generating linked clusters.\n", - "\n", - " **API Documentation:** [accuracy_chart_from_labels_table()](../linker.md#splink.linker.Linker.accuracy_chart_from_labels_table)\n", - "\n", - " **What is needed to generate the chart?** A `linker` with some data and a corresponding labelled dataset" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Worked Example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "" + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `threshold_selection_tool_from_labels_table`\n", + "\n", + "!!! info \"At a glance\"\n", + " **Useful for:** Selecting an optimal match weight threshold for generating linked clusters.\n", + "\n", + " **API Documentation:** [accuracy_chart_from_labels_table()](../linker.md#splink.linker.Linker.accuracy_chart_from_labels_table)\n", + "\n", + " **What is needed to generate the chart?** A `linker` with some data and a corresponding labelled dataset" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Worked Example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "alt.HConcatChart(...)" + "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, splink_dataset_labels\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", + "}\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", + "\n", + "df_labels = splink_dataset_labels.fake_1000_labels\n", + "labels_table = linker.table_management.register_labels_table(df_labels)\n", + "\n", + "linker.evaluation.accuracy_analysis_from_labels_table(labels_table, add_metrics=['f1'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What the chart shows\n", + "\n", + "For a given match weight threshold, a record pair with a score above this threshold will be labelled a match and below the threshold will be labelled a non-match. Lowering the threshold to the extreme ensures many more matches are generated - this maximises the True Positives (high recall) but at the expense of some False Positives (low precision).\n", + "\n", + "You can then see the effect on the confusion matrix of raising the match threshold. As more predicted matches become non-matches at the higher threshold, True Positives become False Negatives, but False Positives become True Negatives.\n", + "\n", + "This demonstrates the trade-off between Type 1 (FP) and Type 2 (FN) errors when selecting a match threshold, or precision vs recall.\n", + "\n", + "This chart adds further context to [accuracy_chart_from_labels_table](accuracy_chart_from_labels_table.ipynb) showing:\n", + "\n", + "- the relationship between match weight and match probability\n", + "- various accuracy metrics comparing the Splink scores against clerical labels\n", + "- the confusion matrix of the predictions and the labels" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to interpret the chart\n", + "\n", + "**Precision** can be maximised by **increasing** the match threshold (reducing false positives).\n", + "\n", + "**Recall** can be maximised by **decreasing** the match threshold (reducing false negatives). \n", + "\n", + "Additional metrics can be used to find the optimal compromise between these two, looking for the threshold at which peak accuracy is achieved. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Actions to take as a result of the chart\n", + "\n", + "Having identified an optimal match weight threshold, this can be applied when generating linked clusters using [cluster_pairwise_predictions_at_thresholds()](../linker.md#splink.linker.linker.clustering.cluster_pairwise_predictions_at_thresholds)." ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } - ], - "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, splink_dataset_labels\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", - "}\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", - "\n", - "df_labels = splink_dataset_labels.fake_1000_labels\n", - "labels_table = linker.register_labels_table(df_labels)\n", - "\n", - "linker.accuracy_analysis_from_labels_table(labels_table, add_metrics=['f1'])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the chart shows\n", - "\n", - "For a given match weight threshold, a record pair with a score above this threshold will be labelled a match and below the threshold will be labelled a non-match. Lowering the threshold to the extreme ensures many more matches are generated - this maximises the True Positives (high recall) but at the expense of some False Positives (low precision).\n", - "\n", - "You can then see the effect on the confusion matrix of raising the match threshold. As more predicted matches become non-matches at the higher threshold, True Positives become False Negatives, but False Positives become True Negatives.\n", - "\n", - "This demonstrates the trade-off between Type 1 (FP) and Type 2 (FN) errors when selecting a match threshold, or precision vs recall.\n", - "\n", - "This chart adds further context to [accuracy_chart_from_labels_table](accuracy_chart_from_labels_table.ipynb) showing:\n", - "\n", - "- the relationship between match weight and match probability\n", - "- various accuracy metrics comparing the Splink scores against clerical labels\n", - "- the confusion matrix of the predictions and the labels" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How to interpret the chart\n", - "\n", - "**Precision** can be maximised by **increasing** the match threshold (reducing false positives).\n", - "\n", - "**Recall** can be maximised by **decreasing** the match threshold (reducing false negatives). \n", - "\n", - "Additional metrics can be used to find the optimal compromise between these two, looking for the threshold at which peak accuracy is achieved. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Actions to take as a result of the chart\n", - "\n", - "Having identified an optimal match weight threshold, this can be applied when generating linked clusters using [cluster_pairwise_predictions_at_thresholds()](../linker.md#splink.linker.Linker.cluster_pairwise_predictions_at_thresholds)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "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": "3.10.6" + ], + "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": "3.10.6" + }, + "orig_nbformat": 4 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/charts/unlinkables_chart.ipynb b/docs/charts/unlinkables_chart.ipynb index 048fe7b366..2ff9453c1b 100644 --- a/docs/charts/unlinkables_chart.ipynb +++ b/docs/charts/unlinkables_chart.ipynb @@ -1,251 +1,251 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `unlinkables_chart`\n", - "\n", - "!!! info \"At a glance\"\n", - " **Useful for:** Looking at how many records have insufficient information to be linked to themselves.\n", - "\n", - " **API Documentation:** [unlinkables_chart()](../linker.md#splink.linker.Linker.unlinkables_chart)\n", - "\n", - " **What is needed to generate the chart?** A trained Splink model" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Worked Example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "256d9c064596491c952798167c08db35", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `unlinkables_chart`\n", + "\n", + "!!! info \"At a glance\"\n", + " **Useful for:** Looking at how many records have insufficient information to be linked to themselves.\n", + "\n", + " **API Documentation:** [unlinkables_chart()](../linker.md#splink.linker.Linker.unlinkables_chart)\n", + "\n", + " **What is needed to generate the chart?** A trained Splink model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Worked Example" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "256d9c064596491c952798167c08db35", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "alt.LayerChart(...)" + "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", + "}\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", + "linker.evaluation.unlinkables_chart()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What the chart shows\n", + "\n", + "The `unlinkables_chart` shows the proportion of records with insufficient information to be matched to themselves at differing match thresholds.\n", + "\n", + "??? note \"What the chart tooltip shows\"\n", + "\n", + " \n", + "\n", + " This tooltip shows a number of statistics based on the match weight of the selected point of the line, including:\n", + "\n", + " - The chosen match weight and corresponding match probability.\n", + " - The proportion of records of records that cannot be linked to themselves given the chosen match weight threshold for a match." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to interpret the chart\n", + "\n", + "This chart gives an indication of both data quality and/or model predictiveness within a Splink model. If a high proportion of records are not linkable to themselves at a low match threshold (e.g. 0 match weight/50% probability) we can conclude that either/or:\n", + "\n", + "- the data quality is low enough such that a significant proportion of records are unable to be linked to themselves\n", + "- the parameters of the Splink model are such that features have not been assigned enough weight, and therefore will not perform well\n", + "\n", + "This chart also gives an indication of the number of False Negatives (i.e. missed links) at a given threshold, assuming sufficient data quality. For example:\n", + "\n", + "- we know that a record should be linked to itself, so seeing that a match weight $\\approx$ 10 gives 16% of records unable to link to themselves\n", + "- exact matches generally provide the strongest matches, therefore, we can expect that any \"fuzzy\" matches to have lower match scores. As a result, we can deduce that the propoertion of False Negatives will be higher than 16%.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Actions to take as a result of the chart\n", + "\n", + "If the level of unlinkable records is extremely high at low match weight thresholds, you have a poorly performing model. This may be an issue that can be resolved by tweaking the models comparisons, but if the poor performance is primarily down to poor data quality, there is very little that can be done to improve the model.\n", + "\n", + "When interpretted as an indicator of False Negatives, this chart can be used to establish an upper bound for match weight, depending on the propensity for False Negatives in the particular use case." ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" } - ], - "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", - "}\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", - "linker.unlinkables_chart()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the chart shows\n", - "\n", - "The `unlinkables_chart` shows the proportion of records with insufficient information to be matched to themselves at differing match thresholds.\n", - "\n", - "??? note \"What the chart tooltip shows\"\n", - "\n", - " \n", - "\n", - " This tooltip shows a number of statistics based on the match weight of the selected point of the line, including:\n", - "\n", - " - The chosen match weight and corresponding match probability.\n", - " - The proportion of records of records that cannot be linked to themselves given the chosen match weight threshold for a match." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How to interpret the chart\n", - "\n", - "This chart gives an indication of both data quality and/or model predictiveness within a Splink model. If a high proportion of records are not linkable to themselves at a low match threshold (e.g. 0 match weight/50% probability) we can conclude that either/or:\n", - "\n", - "- the data quality is low enough such that a significant proportion of records are unable to be linked to themselves\n", - "- the parameters of the Splink model are such that features have not been assigned enough weight, and therefore will not perform well\n", - "\n", - "This chart also gives an indication of the number of False Negatives (i.e. missed links) at a given threshold, assuming sufficient data quality. For example:\n", - "\n", - "- we know that a record should be linked to itself, so seeing that a match weight $\\approx$ 10 gives 16% of records unable to link to themselves\n", - "- exact matches generally provide the strongest matches, therefore, we can expect that any \"fuzzy\" matches to have lower match scores. As a result, we can deduce that the propoertion of False Negatives will be higher than 16%.\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Actions to take as a result of the chart\n", - "\n", - "If the level of unlinkable records is extremely high at low match weight thresholds, you have a poorly performing model. This may be an issue that can be resolved by tweaking the models comparisons, but if the poor performance is primarily down to poor data quality, there is very little that can be done to improve the model.\n", - "\n", - "When interpretted as an indicator of False Negatives, this chart can be used to establish an upper bound for match weight, depending on the propensity for False Negatives in the particular use case." - ] - } - ], - "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" + ], + "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 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/charts/waterfall_chart.ipynb b/docs/charts/waterfall_chart.ipynb index 75d8ea07e6..b5497c0fd3 100644 --- a/docs/charts/waterfall_chart.ipynb +++ b/docs/charts/waterfall_chart.ipynb @@ -1,268 +1,268 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `waterfall_chart`\n", - "\n", - "!!! info \"At a glance\"\n", - " **Useful for:** Looking at the breakdown of the match weight for a pair of records.\n", - "\n", - " **API Documentation:** [waterfall_chart()](../linker.md#splink.linker.Linker.waterfall_chart)\n", - "\n", - " **What is needed to generate the chart?** A trained Splink model" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Worked Example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c61587e20b704f6791a7c55073340e6d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `waterfall_chart`\n", + "\n", + "!!! info \"At a glance\"\n", + " **Useful for:** Looking at the breakdown of the match weight for a pair of records.\n", + "\n", + " **API Documentation:** [waterfall_chart()](../linker.md#splink.linker.linker.visualisations.waterfall_chart)\n", + "\n", + " **What is needed to generate the chart?** A trained Splink model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Worked Example" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c61587e20b704f6791a7c55073340e6d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "alt.LayerChart(...)" + "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\", term_frequency_adjustments=True),\n", + " ctl.name_comparison(\"surname\"),\n", + " ctl.date_comparison(\"dob\", cast_strings_to_date=True),\n", + " cl.exact_match(\"city\"),\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", + "records_to_view = df_predictions.as_record_dict(limit=5)\n", + "\n", + "linker.visualisations.waterfall_chart(records_to_view, filter_nulls=False)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What the chart shows\n", + "\n", + "The `waterfall_chart` shows the amount of evidence of a match that is provided by each comparison for a pair of records. Each bar represents a comparison and the corresponding amount of evidence (i.e. match weight) of a match for the pair of values displayed above the bar.\n", + "\n", + "??? note \"What the chart tooltip shows\"\n", + "\n", + " \n", + "\n", + " The tooltip contains information based on the bar that the user is hovering over, including:\n", + "\n", + " - The comparison column (or columns)\n", + " - The column values from the pair of records being compared\n", + " - The comparison level as a label, SQL statement and the corresponding comparison vector value\n", + " - The bayes factor (i.e. how many times more likely is a match based on this evidence)\n", + " - The match weight for the comparison level\n", + " - The cumulative match probability from the chosen comparison and all of the previous comparisons." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to interpret the chart\n", + "\n", + "The first bar (labelled \"Prior\") is the match weight if no additional knowledge of features is taken into account, and can be thought of as similar to the y-intercept in a simple regression.\n", + "\n", + "Each subsequent bar shows the match weight for a comparison. These bars can be positive or negative depending on whether the given comparison gives positive or negative evidence for the two records being a match.\n", + "\n", + "Additional bars are added for comparisons with term frequency adjustments. For example, the chart above has term frequency adjustments for `first_name` so there is an extra `tf_first_name` bar showing how the frequency of a given name impacts the amount of evidence for the two records being a match.\n", + "\n", + "The final bar represents total match weight for the pair of records. This match weight can also be translated into a final match probablility, and the corresponding match probability is shown on the right axis (note the logarithmic scale)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Actions to take as a result of the chart\n", + "\n", + "This chart is useful for spot checking pairs of records to see if the Splink model is behaving as expected.\n", + "\n", + "If a pair of records look like they are incorrectly being assigned as a match/non-match, it is a sign that the Splink model is not working optimally. If this is the case, it is worth revisiting the model training step. \n", + "\n", + "Some common scenarios include:\n", + "\n", + "- If a comparison isn't capturing a specific edge case (e.g. fuzzy match), add a comparison level to capture this case and retrain the model.\n", + "\n", + "- If the match weight for a comparison is looking unusual, refer to the [`match_weights_chart`](./match_weights_chart.ipynb) to see the match weight in context with the rest of the comparison levels within that comparison. If it is still looking unusual, you can dig deeper with the [`parameter_estimate_comparisons_chart`](./parameter_estimate_comparisons_chart.ipynb) to see if the model training runs are consistent. If there is a lot of variation between model training sessions, this can suggest some instability in the model. In this case, try some different model training rules and/or comparison levels.\n", + "\n", + "- If the \"Prior\" match weight is too small or large compared to the match weight provided by the comparisons, try some different determininstic rules and recall inputs to the [`estimate_probability_two_records_match` function](../linker.md#splink.linker.linker.training.estimate_probability_two_random_records_match).\n", + "\n", + "- If you are working with a model with term frequency adjustments and want to dig deeper into the impact of term frequency on the model as a whole (i.e. not just for a single pairwise comparison), check out the [`tf_adjustment_chart`](./tf_adjustment_chart.ipynb).\n" ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" } - ], - "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\", term_frequency_adjustments=True),\n", - " ctl.name_comparison(\"surname\"),\n", - " ctl.date_comparison(\"dob\", cast_strings_to_date=True),\n", - " cl.exact_match(\"city\"),\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", - "records_to_view = df_predictions.as_record_dict(limit=5)\n", - "\n", - "linker.waterfall_chart(records_to_view, filter_nulls=False)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the chart shows\n", - "\n", - "The `waterfall_chart` shows the amount of evidence of a match that is provided by each comparison for a pair of records. Each bar represents a comparison and the corresponding amount of evidence (i.e. match weight) of a match for the pair of values displayed above the bar.\n", - "\n", - "??? note \"What the chart tooltip shows\"\n", - "\n", - " \n", - "\n", - " The tooltip contains information based on the bar that the user is hovering over, including:\n", - "\n", - " - The comparison column (or columns)\n", - " - The column values from the pair of records being compared\n", - " - The comparison level as a label, SQL statement and the corresponding comparison vector value\n", - " - The bayes factor (i.e. how many times more likely is a match based on this evidence)\n", - " - The match weight for the comparison level\n", - " - The cumulative match probability from the chosen comparison and all of the previous comparisons." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How to interpret the chart\n", - "\n", - "The first bar (labelled \"Prior\") is the match weight if no additional knowledge of features is taken into account, and can be thought of as similar to the y-intercept in a simple regression.\n", - "\n", - "Each subsequent bar shows the match weight for a comparison. These bars can be positive or negative depending on whether the given comparison gives positive or negative evidence for the two records being a match.\n", - "\n", - "Additional bars are added for comparisons with term frequency adjustments. For example, the chart above has term frequency adjustments for `first_name` so there is an extra `tf_first_name` bar showing how the frequency of a given name impacts the amount of evidence for the two records being a match.\n", - "\n", - "The final bar represents total match weight for the pair of records. This match weight can also be translated into a final match probablility, and the corresponding match probability is shown on the right axis (note the logarithmic scale)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Actions to take as a result of the chart\n", - "\n", - "This chart is useful for spot checking pairs of records to see if the Splink model is behaving as expected.\n", - "\n", - "If a pair of records look like they are incorrectly being assigned as a match/non-match, it is a sign that the Splink model is not working optimally. If this is the case, it is worth revisiting the model training step. \n", - "\n", - "Some common scenarios include:\n", - "\n", - "- If a comparison isn't capturing a specific edge case (e.g. fuzzy match), add a comparison level to capture this case and retrain the model.\n", - "\n", - "- If the match weight for a comparison is looking unusual, refer to the [`match_weights_chart`](./match_weights_chart.ipynb) to see the match weight in context with the rest of the comparison levels within that comparison. If it is still looking unusual, you can dig deeper with the [`parameter_estimate_comparisons_chart`](./parameter_estimate_comparisons_chart.ipynb) to see if the model training runs are consistent. If there is a lot of variation between model training sessions, this can suggest some instability in the model. In this case, try some different model training rules and/or comparison levels.\n", - "\n", - "- If the \"Prior\" match weight is too small or large compared to the match weight provided by the comparisons, try some different determininstic rules and recall inputs to the [`estimate_probability_two_records_match` function](../linker.md#splink.linker.Linker.estimate_probability_two_random_records_match).\n", - "\n", - "- If you are working with a model with term frequency adjustments and want to dig deeper into the impact of term frequency on the model as a whole (i.e. not just for a single pairwise comparison), check out the [`tf_adjustment_chart`](./tf_adjustment_chart.ipynb).\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" + ], + "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 }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb b/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb index c3a953df26..3dd987d4a0 100644 --- a/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb +++ b/docs/demos/examples/athena/deduplicate_50k_synthetic.ipynb @@ -1,109096 +1,109096 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "8f18ba38-4271-490e-a3f1-7c05bcba65e7", - "metadata": {}, - "source": [ - "## Linking a dataset of real historical persons\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." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "04cc1e16-42ed-4104-8445-93980b46e42c", - "metadata": {}, - "outputs": [], - "source": [ - "from splink.athena.athena_linker import AthenaLinker\n", - "import altair as alt\n", - "alt.renderers.enable('mimetype')\n", - "\n", - "import pandas as pd\n", - "pd.options.display.max_rows = 1000\n", - "df = pd.read_parquet(\"./data/historical_figures_with_errors_50k.parquet\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8b53cd2f-c007-4997-9ecd-c27930bbcc3a", - "metadata": {}, - "source": [ - "Create a boto3 session to be used within the linker" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b77a3a3c-5c7f-4e18-8482-95c090b18b79", - "metadata": {}, - "outputs": [], - "source": [ - "import boto3\n", - "my_session = boto3.Session(region_name=\"eu-west-1\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bb51efc3-a538-488a-a525-64b7e7155f0f", - "metadata": {}, - "outputs": [], - "source": [ - "# Simple settings dictionary will be used for exploratory analysis\n", - "settings = {\n", - " \"link_type\": \"dedupe_only\",\n", - " \"blocking_rules_to_generate_predictions\": [\n", - " \"l.first_name = r.first_name and l.surname = r.surname\",\n", - " \"l.surname = r.surname and l.dob = r.dob\",\n", - " \"l.first_name = r.first_name and l.dob = r.dob\",\n", - " \"l.postcode_fake = r.postcode_fake and l.first_name = r.first_name\",\n", - " ],\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d1aa6941-e7f4-4ac3-a166-ae7357c79198", - "metadata": {}, - "source": [ - "## AthenaLinker Setup\n", - "\n", - "To work nicely with Athena, you need to outline various filepaths, buckets and the database(s) you wish to interact with.\n", - "
\n", - "\n", - "**The AthenaLinker has three required inputs:**\n", - "* input_table_or_tables - the input table to use for linking. This can either be a table in a database or a pandas dataframe\n", - "* output_database - the database to output all of your splink tables to.\n", - "* output_bucket - the s3 bucket you wish any parquet files produced by splink to be output to.\n", - "\n", - "**and two optional inputs:**\n", - "* output_filepath - the s3 filepath to output files to. This is an extension of output_bucket and dictate the full filepath your files will be output to.\n", - "* input_table_aliases - the name of your table within your database, should you choose to use a pandas df as an input." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "023f3947-9cb9-44db-a7f9-f7dab41def34", - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "application/vnd.vegalite.v4+json": { - "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json", - "config": { - "view": { - "continuousHeight": 300, - "continuousWidth": 400 - } - }, - "vconcat": [ + "attachments": {}, + "cell_type": "markdown", + "id": "8f18ba38-4271-490e-a3f1-7c05bcba65e7", + "metadata": {}, + "source": [ + "## Linking a dataset of real historical persons\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." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "04cc1e16-42ed-4104-8445-93980b46e42c", + "metadata": {}, + "outputs": [], + "source": [ + "from splink.athena.athena_linker import AthenaLinker\n", + "import altair as alt\n", + "alt.renderers.enable('mimetype')\n", + "\n", + "import pandas as pd\n", + "pd.options.display.max_rows = 1000\n", + "df = pd.read_parquet(\"./data/historical_figures_with_errors_50k.parquet\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8b53cd2f-c007-4997-9ecd-c27930bbcc3a", + "metadata": {}, + "source": [ + "Create a boto3 session to be used within the linker" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b77a3a3c-5c7f-4e18-8482-95c090b18b79", + "metadata": {}, + "outputs": [], + "source": [ + "import boto3\n", + "my_session = boto3.Session(region_name=\"eu-west-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bb51efc3-a538-488a-a525-64b7e7155f0f", + "metadata": {}, + "outputs": [], + "source": [ + "# Simple settings dictionary will be used for exploratory analysis\n", + "settings = {\n", + " \"link_type\": \"dedupe_only\",\n", + " \"blocking_rules_to_generate_predictions\": [\n", + " \"l.first_name = r.first_name and l.surname = r.surname\",\n", + " \"l.surname = r.surname and l.dob = r.dob\",\n", + " \"l.first_name = r.first_name and l.dob = r.dob\",\n", + " \"l.postcode_fake = r.postcode_fake and l.first_name = r.first_name\",\n", + " ],\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d1aa6941-e7f4-4ac3-a166-ae7357c79198", + "metadata": {}, + "source": [ + "## AthenaLinker Setup\n", + "\n", + "To work nicely with Athena, you need to outline various filepaths, buckets and the database(s) you wish to interact with.\n", + "
\n", + "\n", + "**The AthenaLinker has three required inputs:**\n", + "* input_table_or_tables - the input table to use for linking. This can either be a table in a database or a pandas dataframe\n", + "* output_database - the database to output all of your splink tables to.\n", + "* output_bucket - the s3 bucket you wish any parquet files produced by splink to be output to.\n", + "\n", + "**and two optional inputs:**\n", + "* output_filepath - the s3 filepath to output files to. This is an extension of output_bucket and dictate the full filepath your files will be output to.\n", + "* input_table_aliases - the name of your table within your database, should you choose to use a pandas df as an input." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "023f3947-9cb9-44db-a7f9-f7dab41def34", + "metadata": {}, + "outputs": [ { - "hconcat": [ - { - "data": { - "values": [ - { - "distinct_value_count": 4413, - "group_name": "first_name", - "percentile_ex_nulls": 0.9449624834194532, - "percentile_inc_nulls": 0.9450353908814109, - "sum_tokens_in_value_count_group": 2780, - "total_non_null_rows": 50511, - "total_rows_inc_nulls": 50578, - "value_count": 2780 - }, - { - "distinct_value_count": 4413, - "group_name": "first_name", - "percentile_ex_nulls": 0.8907960642236344, - "percentile_inc_nulls": 0.8909407252164973, - "sum_tokens_in_value_count_group": 2736, - "total_non_null_rows": 50511, - "total_rows_inc_nulls": 50578, - "value_count": 2736 - }, - { 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", + "text/plain": [ + "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "ad8c0de1-769a-4861-849d-8b7e6655a681",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "----- Starting EM training session -----\n",
+ "\n",
+ "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",
+ "\n",
+ "Iteration 1: Largest change in params was -0.533 in probability_two_random_records_match\n",
+ "Iteration 2: Largest change in params was -0.0419 in the m_probability of birth_place, level `All other comparisons`\n",
+ "Iteration 3: Largest change in params was -0.0154 in the m_probability of birth_place, level `All other comparisons`\n",
+ "Iteration 4: Largest change in params was 0.00489 in the m_probability of birth_place, level `Exact match`\n",
+ "\n",
+ "EM converged after 4 iterations\n",
+ "\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"
+ ]
+ }
],
- "text/plain": [
- " match_weight match_probability unique_id_l unique_id_r first_name_l \\\n",
- "0 19.465751 0.999999 Q5536981-1 Q5536981-4 george \n",
- "1 33.572592 1.000000 Q5536981-1 Q5536981-5 george \n",
- "2 33.572592 1.000000 Q5536981-1 Q5536981-6 george \n",
- "3 33.572592 1.000000 Q5536981-1 Q5536981-7 george \n",
- "4 22.025628 1.000000 Q5536981-1 Q5536981-8 george \n",
- "\n",
- " first_name_r gamma_first_name tf_first_name_l tf_first_name_r \\\n",
- "0 george 3 0.028014 0.028014 \n",
- "1 george 3 0.028014 0.028014 \n",
- "2 george 3 0.028014 0.028014 \n",
- "3 george 3 0.028014 0.028014 \n",
- "4 george 3 0.028014 0.028014 \n",
- "\n",
- " bf_first_name ... bf_birth_place bf_tf_adj_birth_place occupation_l \\\n",
- "0 48.723867 ... 162.73433 0.097709 politician \n",
- "1 48.723867 ... 162.73433 0.097709 politician \n",
- "2 48.723867 ... 162.73433 0.097709 politician \n",
- "3 48.723867 ... 162.73433 0.097709 politician \n",
- "4 48.723867 ... 162.73433 0.097709 politician \n",
- "\n",
- " occupation_r gamma_occupation tf_occupation_l tf_occupation_r \\\n",
- "0 politician 1 0.088932 0.088932 \n",
- "1 politician 1 0.088932 0.088932 \n",
- "2 politician 1 0.088932 0.088932 \n",
- "3 politician 1 0.088932 0.088932 \n",
- "4 politician 1 0.088932 0.088932 \n",
- "\n",
- " bf_occupation bf_tf_adj_occupation match_key \n",
- "0 21.983413 0.459975 0 \n",
- "1 21.983413 0.459975 0 \n",
- "2 21.983413 0.459975 0 \n",
- "3 21.983413 0.459975 0 \n",
- "4 21.983413 0.459975 0 \n",
- "\n",
- "[5 rows x 47 columns]"
+ "source": [
+ "blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n",
+ "training_session_names = linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)"
]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_predict = linker.predict()\n",
- "df_e = df_predict.as_pandas_dataframe(limit=5)\n",
- "df_e"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cf6b3c45-1031-4ab0-9398-94d731117e2c",
- "metadata": {},
- "source": [
- "You can also view rows in this dataset as a waterfall chart as follows:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "c2f47ebb-3181-4db6-89ba-1ef60df3bba7",
- "metadata": {},
- "outputs": [
+ },
{
- "data": {
- "application/vnd.vegalite.v4+json": {
- "$schema": "https://vega.github.io/schema/vega-lite/v5.2.0.json",
- "config": {
- "view": {
- "continuousHeight": 300,
- "continuousWidth": 400
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "c44fc676-e57e-4e8c-b9c6-8989e720b03a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "----- Starting EM training session -----\n",
+ "\n",
+ "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",
+ "\n",
+ "Iteration 1: Largest change in params was -0.356 in the m_probability of first_name, level `Exact match`\n",
+ "Iteration 2: Largest change in params was 0.0401 in the m_probability of first_name, level `All other comparisons`\n",
+ "Iteration 3: Largest change in params was 0.00536 in the m_probability of first_name, level `All other comparisons`\n",
+ "\n",
+ "EM converged after 3 iterations\n",
+ "\n",
+ "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
+ ]
}
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- "value_r": "| \n", - " | match_weight | \n", - "match_probability | \n", - "unique_id_l | \n", - "unique_id_r | \n", - "first_name_l | \n", - "first_name_r | \n", - "gamma_first_name | \n", - "tf_first_name_l | \n", - "tf_first_name_r | \n", - "bf_first_name | \n", - "... | \n", - "bf_birth_place | \n", - "bf_tf_adj_birth_place | \n", - "occupation_l | \n", - "occupation_r | \n", - "gamma_occupation | \n", - "tf_occupation_l | \n", - "tf_occupation_r | \n", - "bf_occupation | \n", - "bf_tf_adj_occupation | \n", - "match_key | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", - "19.465751 | \n", - "0.999999 | \n", - "Q5536981-1 | \n", - "Q5536981-4 | \n", - "george | \n", - "george | \n", - "3 | \n", - "0.028014 | \n", - "0.028014 | \n", - "48.723867 | \n", - "... | \n", - "162.73433 | \n", - "0.097709 | \n", - "politician | \n", - "politician | \n", - "1 | \n", - "0.088932 | \n", - "0.088932 | \n", - "21.983413 | \n", - "0.459975 | \n", - "0 | \n", - "
| 1 | \n", - "33.572592 | \n", - "1.000000 | \n", - "Q5536981-1 | \n", - "Q5536981-5 | \n", - "george | \n", - "george | \n", - "3 | \n", - "0.028014 | \n", - "0.028014 | \n", - "48.723867 | \n", - "... | \n", - "162.73433 | \n", - "0.097709 | \n", - "politician | \n", - "politician | \n", - "1 | \n", - "0.088932 | \n", - "0.088932 | \n", - "21.983413 | \n", - "0.459975 | \n", - "0 | \n", - "
| 2 | \n", - "33.572592 | \n", - "1.000000 | \n", - "Q5536981-1 | \n", - "Q5536981-6 | \n", - "george | \n", - "george | \n", - "3 | \n", - "0.028014 | \n", - "0.028014 | \n", - "48.723867 | \n", - "... | \n", - "162.73433 | \n", - "0.097709 | \n", - "politician | \n", - "politician | \n", - "1 | \n", - "0.088932 | \n", - "0.088932 | \n", - "21.983413 | \n", - "0.459975 | \n", - "0 | \n", - "
| 3 | \n", - "33.572592 | \n", - "1.000000 | \n", - "Q5536981-1 | \n", - "Q5536981-7 | \n", - "george | \n", - "george | \n", - "3 | \n", - "0.028014 | \n", - "0.028014 | \n", - "48.723867 | \n", - "... | \n", - "162.73433 | \n", - "0.097709 | \n", - "politician | \n", - "politician | \n", - "1 | \n", - "0.088932 | \n", - "0.088932 | \n", - "21.983413 | \n", - "0.459975 | \n", - "0 | \n", - "
| 4 | \n", - "22.025628 | \n", - "1.000000 | \n", - "Q5536981-1 | \n", - "Q5536981-8 | \n", - "george | \n", - "george | \n", - "3 | \n", - "0.028014 | \n", - "0.028014 | \n", - "48.723867 | \n", - "... | \n", - "162.73433 | \n", - "0.097709 | \n", - "politician | \n", - "politician | \n", - "1 | \n", - "0.088932 | \n", - "0.088932 | \n", - "21.983413 | \n", - "0.459975 | \n", - "0 | \n", - "
5 rows × 47 columns
\n", - "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " match_weight match_probability unique_id_l unique_id_r first_name_l \\\n",
+ "0 19.465751 0.999999 Q5536981-1 Q5536981-4 george \n",
+ "1 33.572592 1.000000 Q5536981-1 Q5536981-5 george \n",
+ "2 33.572592 1.000000 Q5536981-1 Q5536981-6 george \n",
+ "3 33.572592 1.000000 Q5536981-1 Q5536981-7 george \n",
+ "4 22.025628 1.000000 Q5536981-1 Q5536981-8 george \n",
+ "\n",
+ " first_name_r gamma_first_name tf_first_name_l tf_first_name_r \\\n",
+ "0 george 3 0.028014 0.028014 \n",
+ "1 george 3 0.028014 0.028014 \n",
+ "2 george 3 0.028014 0.028014 \n",
+ "3 george 3 0.028014 0.028014 \n",
+ "4 george 3 0.028014 0.028014 \n",
+ "\n",
+ " bf_first_name ... bf_birth_place bf_tf_adj_birth_place occupation_l \\\n",
+ "0 48.723867 ... 162.73433 0.097709 politician \n",
+ "1 48.723867 ... 162.73433 0.097709 politician \n",
+ "2 48.723867 ... 162.73433 0.097709 politician \n",
+ "3 48.723867 ... 162.73433 0.097709 politician \n",
+ "4 48.723867 ... 162.73433 0.097709 politician \n",
+ "\n",
+ " occupation_r gamma_occupation tf_occupation_l tf_occupation_r \\\n",
+ "0 politician 1 0.088932 0.088932 \n",
+ "1 politician 1 0.088932 0.088932 \n",
+ "2 politician 1 0.088932 0.088932 \n",
+ "3 politician 1 0.088932 0.088932 \n",
+ "4 politician 1 0.088932 0.088932 \n",
+ "\n",
+ " bf_occupation bf_tf_adj_occupation match_key \n",
+ "0 21.983413 0.459975 0 \n",
+ "1 21.983413 0.459975 0 \n",
+ "2 21.983413 0.459975 0 \n",
+ "3 21.983413 0.459975 0 \n",
+ "4 21.983413 0.459975 0 \n",
+ "\n",
+ "[5 rows x 47 columns]"
+ ]
},
- {
- "as": "lead",
- "field": "column_name",
- "op": "lead"
- }
- ]
- },
- {
- "as": "sum",
- "calculate": "datum.column_name === \"Final score\" ? datum.sum - datum.log2_bayes_factor : datum.sum"
- },
- {
- "as": "lead",
- "calculate": "datum.lead === null ? datum.column_name : datum.lead"
- },
- {
- "as": "previous_sum",
- "calculate": "datum.column_name === \"Final score\" || datum.column_name === \"Prior match weight\" ? 0 : datum.sum - datum.log2_bayes_factor"
- },
- {
- "as": "top_label",
- "calculate": "datum.sum > datum.previous_sum ? datum.column_name : \"\""
- },
- {
- "as": "bottom_label",
- "calculate": "datum.sum < datum.previous_sum ? datum.column_name : \"\""
- },
- {
- "as": "sum_top",
- "calculate": "datum.sum > datum.previous_sum ? datum.sum : datum.previous_sum"
- },
- {
- "as": "sum_bottom",
- "calculate": "datum.sum < datum.previous_sum ? datum.sum : datum.previous_sum"
- },
- {
- "as": "center",
- "calculate": "(datum.sum + datum.previous_sum) / 2"
- },
- {
- "as": "text_log2_bayes_factor",
- "calculate": "(datum.log2_bayes_factor > 0 ? \"+\" : \"\") + datum.log2_bayes_factor"
- },
- {
- "as": "dy",
- "calculate": "datum.sum < datum.previous_sum ? 4 : -4"
- },
- {
- "as": "baseline",
- "calculate": "datum.sum < datum.previous_sum ? \"top\" : \"bottom\""
- },
- {
- "as": "prob",
- "calculate": "1. / (1 + pow(2, -1.*datum.sum))"
- },
- {
- "as": "zero",
- "calculate": "0*datum.sum"
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
}
- ],
- "width": {
- "step": 75
- }
- },
- "image/png": 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",
- "text/plain": [
- "| \n", + " | match_weight | \n", + "match_probability | \n", + "unique_id_l | \n", + "unique_id_r | \n", + "first_name_l | \n", + "first_name_r | \n", + "gamma_first_name | \n", + "tf_first_name_l | \n", + "tf_first_name_r | \n", + "bf_first_name | \n", + "... | \n", + "bf_birth_place | \n", + "bf_tf_adj_birth_place | \n", + "occupation_l | \n", + "occupation_r | \n", + "gamma_occupation | \n", + "tf_occupation_l | \n", + "tf_occupation_r | \n", + "bf_occupation | \n", + "bf_tf_adj_occupation | \n", + "match_key | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "19.465751 | \n", + "0.999999 | \n", + "Q5536981-1 | \n", + "Q5536981-4 | \n", + "george | \n", + "george | \n", + "3 | \n", + "0.028014 | \n", + "0.028014 | \n", + "48.723867 | \n", + "... | \n", + "162.73433 | \n", + "0.097709 | \n", + "politician | \n", + "politician | \n", + "1 | \n", + "0.088932 | \n", + "0.088932 | \n", + "21.983413 | \n", + "0.459975 | \n", + "0 | \n", + "
| 1 | \n", + "33.572592 | \n", + "1.000000 | \n", + "Q5536981-1 | \n", + "Q5536981-5 | \n", + "george | \n", + "george | \n", + "3 | \n", + "0.028014 | \n", + "0.028014 | \n", + "48.723867 | \n", + "... | \n", + "162.73433 | \n", + "0.097709 | \n", + "politician | \n", + "politician | \n", + "1 | \n", + "0.088932 | \n", + "0.088932 | \n", + "21.983413 | \n", + "0.459975 | \n", + "0 | \n", + "
| 2 | \n", + "33.572592 | \n", + "1.000000 | \n", + "Q5536981-1 | \n", + "Q5536981-6 | \n", + "george | \n", + "george | \n", + "3 | \n", + "0.028014 | \n", + "0.028014 | \n", + "48.723867 | \n", + "... | \n", + "162.73433 | \n", + "0.097709 | \n", + "politician | \n", + "politician | \n", + "1 | \n", + "0.088932 | \n", + "0.088932 | \n", + "21.983413 | \n", + "0.459975 | \n", + "0 | \n", + "
| 3 | \n", + "33.572592 | \n", + "1.000000 | \n", + "Q5536981-1 | \n", + "Q5536981-7 | \n", + "george | \n", + "george | \n", + "3 | \n", + "0.028014 | \n", + "0.028014 | \n", + "48.723867 | \n", + "... | \n", + "162.73433 | \n", + "0.097709 | \n", + "politician | \n", + "politician | \n", + "1 | \n", + "0.088932 | \n", + "0.088932 | \n", + "21.983413 | \n", + "0.459975 | \n", + "0 | \n", + "
| 4 | \n", + "22.025628 | \n", + "1.000000 | \n", + "Q5536981-1 | \n", + "Q5536981-8 | \n", + "george | \n", + "george | \n", + "3 | \n", + "0.028014 | \n", + "0.028014 | \n", + "48.723867 | \n", + "... | \n", + "162.73433 | \n", + "0.097709 | \n", + "politician | \n", + "politician | \n", + "1 | \n", + "0.088932 | \n", + "0.088932 | \n", + "21.983413 | \n", + "0.459975 | \n", + "0 | \n", + "
5 rows × 47 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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)"
- ],
- "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"
+ "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",
@@ -90,20 +157,29 @@
" ],\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"
+ "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",
@@ -114,138 +190,1127 @@
" \"l.email = r.email\",\n",
"]\n",
"\n",
- "linker.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
- ],
- "outputs": []
+ "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+ ]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-27T15:10:55.970182Z",
- "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"
+ "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"
}
},
- "source": [
- "linker.estimate_u_using_random_sampling(max_pairs=1e6, seed=5)"
+ "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",
+ " - 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"
+ ]
+ }
],
- "outputs": []
+ "source": [
+ "linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=5)"
+ ]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-27T15:10:57.012299Z",
- "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"
+ "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.estimate_parameters_using_expectation_maximisation(block_on(\"dob\"))\n",
- "session_email = linker.estimate_parameters_using_expectation_maximisation(\n",
+ "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"
+ "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": [
+ "| \n", + " | unique_id | \n", + "first_name | \n", + "surname | \n", + "dob | \n", + "city | \n", + "cluster | \n", + "|
|---|---|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "Robert | \n", + "Alan | \n", + "1971-06-24 | \n", + "NaN | \n", + "robert255@smith.net | \n", + "0 | \n", + "
| 1 | \n", + "1 | \n", + "Robert | \n", + "Allen | \n", + "1971-05-24 | \n", + "NaN | \n", + "roberta25@smith.net | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\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.accuracy_analysis_from_labels_column(\n",
+ "linker.evaluation.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"
+ "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"
}
},
- "source": [
- "linker.accuracy_analysis_from_labels_column(\"cluster\", output_type=\"roc\")"
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.Chart(...)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
],
- "outputs": []
+ "source": [
+ "linker.evaluation.accuracy_analysis_from_labels_column(\"cluster\", output_type=\"roc\")"
+ ]
},
{
"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",
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+ "shell.execute_reply": "2024-06-07T09:09:22.635098Z"
}
},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.HConcatChart(...)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "linker.accuracy_analysis_from_labels_column(\n",
+ "linker.evaluation.accuracy_analysis_from_labels_column(\n",
" \"cluster\",\n",
" output_type=\"threshold_selection\",\n",
" threshold_actual=0.5,\n",
" add_metrics=[\"f1\"],\n",
")"
- ],
- "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"
+ "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": [
+ "| \n", + " | truth_threshold | \n", + "match_probability | \n", + "total_clerical_labels | \n", + "p | \n", + "n | \n", + "tp | \n", + "tn | \n", + "fp | \n", + "fn | \n", + "P_rate | \n", + "... | \n", + "precision | \n", + "recall | \n", + "specificity | \n", + "npv | \n", + "accuracy | \n", + "f1 | \n", + "f2 | \n", + "f0_5 | \n", + "p4 | \n", + "phi | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "-19.3 | \n", + "0.000002 | \n", + "499500.0 | \n", + "2031.0 | \n", + "497469.0 | \n", + "1027.0 | \n", + "495147.0 | \n", + "2322.0 | \n", + "1004.0 | \n", + "0.004066 | \n", + "... | \n", + "0.306659 | \n", + "0.505662 | \n", + "0.995332 | \n", + "0.997976 | \n", + "0.993341 | \n", + "0.381784 | \n", + "0.447573 | \n", + "0.332858 | \n", + "0.552084 | \n", + "0.390667 | \n", + "
| 1 | \n", + "-19.2 | \n", + "0.000002 | \n", + "499500.0 | \n", + "2031.0 | \n", + "497469.0 | \n", + "1027.0 | \n", + "495383.0 | \n", + "2086.0 | \n", + "1004.0 | \n", + "0.004066 | \n", + "... | \n", + "0.329907 | \n", + "0.505662 | \n", + "0.995807 | \n", + "0.997977 | \n", + "0.993814 | \n", + "0.399300 | \n", + "0.456973 | \n", + "0.354554 | \n", + "0.570207 | \n", + "0.405492 | \n", + "
| 2 | \n", + "-18.0 | \n", + "0.000004 | \n", + "499500.0 | \n", + "2031.0 | \n", + "497469.0 | \n", + "1027.0 | \n", + "495584.0 | \n", + "1885.0 | \n", + "1004.0 | \n", + "0.004066 | \n", + "... | \n", + "0.352679 | \n", + "0.505662 | \n", + "0.996211 | \n", + "0.997978 | \n", + "0.994216 | \n", + "0.415537 | \n", + "0.465295 | \n", + "0.375393 | \n", + "0.586607 | \n", + "0.419506 | \n", + "
| 3 | \n", + "-17.1 | \n", + "0.000007 | \n", + "499500.0 | \n", + "2031.0 | \n", + "497469.0 | \n", + "1027.0 | \n", + "495836.0 | \n", + "1633.0 | \n", + "1004.0 | \n", + "0.004066 | \n", + "... | \n", + "0.386090 | \n", + "0.505662 | \n", + "0.996717 | \n", + "0.997979 | \n", + "0.994721 | \n", + "0.437860 | \n", + "0.476168 | \n", + "0.405256 | \n", + "0.608551 | \n", + "0.439259 | \n", + "
| 4 | \n", + "-17.0 | \n", + "0.000008 | \n", + "499500.0 | \n", + "2031.0 | \n", + "497469.0 | \n", + "1027.0 | \n", + "495957.0 | \n", + "1512.0 | \n", + "1004.0 | \n", + "0.004066 | \n", + "... | \n", + "0.404490 | \n", + "0.505662 | \n", + "0.996961 | \n", + "0.997980 | \n", + "0.994963 | \n", + "0.449453 | \n", + "0.481572 | \n", + "0.421351 | \n", + "0.619682 | \n", + "0.449767 | \n", + "
5 rows × 25 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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.prediction_errors_from_labels_column(\n",
+ "linker.evaluation.prediction_errors_from_labels_column(\n",
" \"cluster\", include_false_negatives=True, include_false_positives=True\n",
").as_pandas_dataframe(limit=5)"
- ],
- "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"
+ "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"
}
},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.LayerChart(...)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "records = linker.prediction_errors_from_labels_column(\n",
+ "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.waterfall_chart(records)"
- ],
- "outputs": []
+ "linker.visualisations.waterfall_chart(records)"
+ ]
}
],
"metadata": {
diff --git a/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb b/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb
index 639f273d0d..3e30041105 100644
--- a/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb
+++ b/docs/demos/examples/duckdb/deduplicate_50k_synthetic.ipynb
@@ -24,10 +24,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.idle": "2024-05-15T16:07:03.045834Z",
- "shell.execute_reply": "2024-05-15T16:07:03.045063Z"
+ "iopub.execute_input": "2024-06-07T09:09:25.613571Z",
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+ "shell.execute_reply": "2024-06-07T09:09:25.617985Z"
}
},
"outputs": [],
@@ -41,13 +41,228 @@
"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"
+ "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": [
+ {
+ "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"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\r",
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@@ -209,23 +424,23 @@
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+ "iopub.status.busy": "2024-06-07T09:09:28.897643Z",
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+ "shell.execute_reply": "2024-06-07T09:09:29.356107Z"
}
},
"outputs": [
@@ -308,23 +523,23 @@
"text/html": [
"\n",
"\n",
- "\n",
+ "\n",
""
],
"text/plain": [
@@ -882,7 +1090,7 @@
}
],
"source": [
- "linker.match_weights_chart()"
+ "linker.visualisations.match_weights_chart()"
]
},
{
@@ -890,10 +1098,10 @@
"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"
+ "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": [
@@ -902,23 +1110,23 @@
"text/html": [
"\n",
"\n",
- "\n",
+ "\n",
""
],
"text/plain": [
@@ -977,7 +1185,7 @@
}
],
"source": [
- "linker.unlinkables_chart()"
+ "linker.evaluation.unlinkables_chart()"
]
},
{
@@ -985,10 +1193,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-15T16:07:28.393039Z",
- "iopub.status.busy": "2024-05-15T16:07:28.392726Z",
- "iopub.status.idle": "2024-05-15T16:07:30.731337Z",
- "shell.execute_reply": "2024-05-15T16:07:30.730612Z"
+ "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": [
@@ -1039,121 +1247,121 @@
" \n",
" | \n", + " | clerical_match_score | \n", + "found_by_blocking_rules | \n", + "match_weight | \n", + "match_probability | \n", + "unique_id_l | \n", + "unique_id_r | \n", + "first_name_l | \n", + "first_name_r | \n", + "gamma_first_name | \n", + "bf_first_name | \n", + "... | \n", + "tf_city_r | \n", + "bf_city | \n", + "bf_tf_adj_city | \n", + "email_l | \n", + "email_r | \n", + "gamma_email | \n", + "bf_email | \n", + "cluster_l | \n", + "cluster_r | \n", + "match_key | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1.0 | \n", + "False | \n", + "-24.165914 | \n", + "5.312940e-08 | \n", + "417 | \n", + "418 | \n", + "Florence | \n", + "Brown | \n", + "0 | \n", + "0.213986 | \n", + "... | \n", + "0.00123 | \n", + "0.427845 | \n", + "1.0 | \n", + "fb@reose.cem | \n", + "f@b@reese.com | \n", + "0 | \n", + "0.001023 | \n", + "108 | \n", + "108 | \n", + "2 | \n", + "
| 1 | \n", + "1.0 | \n", + "False | \n", + "-21.941506 | \n", + "2.482839e-07 | \n", + "796 | \n", + "797 | \n", + "Taylor | \n", + "None | \n", + "-1 | \n", + "1.000000 | \n", + "... | \n", + "0.00738 | \n", + "0.427845 | \n", + "1.0 | \n", + "jt40o@combs.net | \n", + "jt40@cotbs.nm | \n", + "0 | \n", + "0.001023 | \n", + "201 | \n", + "201 | \n", + "2 | \n", + "
| 2 | \n", + "1.0 | \n", + "False | \n", + "-19.517277 | \n", + "1.332642e-06 | \n", + "452 | \n", + "454 | \n", + "None | \n", + "Davies | \n", + "-1 | \n", + "1.000000 | \n", + "... | \n", + "0.01599 | \n", + "0.427845 | \n", + "1.0 | \n", + "rd@lewis.com | \n", + "idlewrs.cocm | \n", + "0 | \n", + "0.001023 | \n", + "115 | \n", + "115 | \n", + "2 | \n", + "
| 3 | \n", + "1.0 | \n", + "False | \n", + "-17.978364 | \n", + "3.872323e-06 | \n", + "717 | \n", + "718 | \n", + "Mia | \n", + "Jones | \n", + "0 | \n", + "0.213986 | \n", + "... | \n", + "0.00615 | \n", + "0.427845 | \n", + "1.0 | \n", + "mia.j63@martinez.biz | \n", + "None | \n", + "-1 | \n", + "1.000000 | \n", + "182 | \n", + "182 | \n", + "2 | \n", + "
| 4 | \n", + "1.0 | \n", + "True | \n", + "-15.518690 | \n", + "2.130097e-05 | \n", + "594 | \n", + "595 | \n", + "Grace | \n", + "Grace | \n", + "3 | \n", + "85.794621 | \n", + "... | \n", + "0.00123 | \n", + "0.427845 | \n", + "1.0 | \n", + "gk@frey-robinson.org | \n", + "rgk@frey-robinon.org | \n", + "0 | \n", + "0.001023 | \n", + "146 | \n", + "146 | \n", + "0 | \n", + "
5 rows × 32 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\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",
@@ -59,8 +197,7 @@
"pd.options.display.max_rows = 1000\n",
"df = splink_datasets.historical_50k\n",
"df.head()"
- ],
- "outputs": []
+ ]
},
{
"attachments": {},
@@ -85,12 +222,92 @@
"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"
+ "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": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
@@ -108,20 +325,20 @@
" db_api=db_api,\n",
" link_type=\"dedupe_only\",\n",
")"
- ],
- "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"
+ "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",
@@ -136,8 +353,7 @@
")\n",
"\n",
"linker = Linker(df, settings, database_api=db_api)\n"
- ],
- "outputs": []
+ ]
},
{
"attachments": {},
@@ -152,17 +368,166 @@
"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"
+ "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": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | unique_id | \n", + "cluster | \n", + "full_name | \n", + "first_and_surname | \n", + "first_name | \n", + "surname | \n", + "dob | \n", + "birth_place | \n", + "postcode_fake | \n", + "gender | \n", + "occupation | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Q2296770-1 | \n", + "Q2296770 | \n", + "thomas clifford, 1st baron clifford of chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "male | \n", + "politician | \n", + "
| 1 | \n", + "Q2296770-2 | \n", + "Q2296770 | \n", + "thomas of chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "male | \n", + "politician | \n", + "
| 2 | \n", + "Q2296770-3 | \n", + "Q2296770 | \n", + "tom 1st baron clifford of chudleigh | \n", + "tom chudleigh | \n", + "tom | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "male | \n", + "politician | \n", + "
| 3 | \n", + "Q2296770-4 | \n", + "Q2296770 | \n", + "thomas 1st chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8hu | \n", + "None | \n", + "politician | \n", + "
| 4 | \n", + "Q2296770-5 | \n", + "Q2296770 | \n", + "thomas clifford, 1st baron chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "None | \n", + "politician | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\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.deterministic_link()\n",
+ "df_predict = linker.inference.deterministic_link()\n",
"df_predict.as_pandas_dataframe().head()"
- ],
- "outputs": []
+ ]
},
{
"attachments": {},
@@ -179,34 +544,205 @@
"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"
+ "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": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Completed iteration 1, root rows count 94\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Completed iteration 2, root rows count 10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Completed iteration 3, root rows count 0\n"
+ ]
+ }
+ ],
"source": [
- "clusters = linker.cluster_pairwise_predictions_at_threshold(\n",
+ "clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(\n",
" df_predict, threshold_match_probability=1\n",
")"
- ],
- "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"
+ "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": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | unique_id_l | \n", + "unique_id_r | \n", + "occupation_l | \n", + "occupation_r | \n", + "first_name_l | \n", + "first_name_r | \n", + "dob_l | \n", + "dob_r | \n", + "surname_l | \n", + "surname_r | \n", + "postcode_fake_l | \n", + "postcode_fake_r | \n", + "match_key | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Q55455287-12 | \n", + "Q55455287-2 | \n", + "None | \n", + "writer | \n", + "jaido | \n", + "jaido | \n", + "1836-01-01 | \n", + "1836-01-01 | \n", + "morata | \n", + "morata | \n", + "ta4 2ug | \n", + "ta4 2uu | \n", + "0 | \n", + "
| 1 | \n", + "Q55455287-12 | \n", + "Q55455287-3 | \n", + "None | \n", + "writer | \n", + "jaido | \n", + "jaido | \n", + "1836-01-01 | \n", + "1836-01-01 | \n", + "morata | \n", + "morata | \n", + "ta4 2ug | \n", + "ta4 2uu | \n", + "0 | \n", + "
| 2 | \n", + "Q55455287-12 | \n", + "Q55455287-4 | \n", + "None | \n", + "writer | \n", + "jaido | \n", + "jaido | \n", + "1836-01-01 | \n", + "1836-01-01 | \n", + "morata | \n", + "morata | \n", + "ta4 2ug | \n", + "ta4 2sz | \n", + "0 | \n", + "
| 3 | \n", + "Q55455287-12 | \n", + "Q55455287-5 | \n", + "None | \n", + "None | \n", + "jaido | \n", + "jaido | \n", + "1836-01-01 | \n", + "1836-01-01 | \n", + "morata | \n", + "morata | \n", + "ta4 2ug | \n", + "ta4 2ug | \n", + "0 | \n", + "
| 4 | \n", + "Q55455287-12 | \n", + "Q55455287-6 | \n", + "None | \n", + "writer | \n", + "jaido | \n", + "jaido | \n", + "1836-01-01 | \n", + "1836-01-01 | \n", + "morata | \n", + "morata | \n", + "ta4 2ug | \n", + "None | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\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)"
- ],
- "outputs": []
+ ]
},
{
"attachments": {},
@@ -221,14 +757,38 @@
"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"
+ "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": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " | \n", + " | cluster_id | \n", + "unique_id | \n", + "cluster | \n", + "full_name | \n", + "first_and_surname | \n", + "first_name | \n", + "surname | \n", + "dob | \n", + "birth_place | \n", + "postcode_fake | \n", + "gender | \n", + "occupation | \n", + "__splink_salt | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Q16025107-1 | \n", + "Q5497940-9 | \n", + "Q5497940 | \n", + "frederick hall | \n", + "frederick hall | \n", + "frederick | \n", + "hall | \n", + "1855-01-01 | \n", + "bristol, city of | \n", + "bs11 9pn | \n", + "None | \n", + "None | \n", + "0.002739 | \n", + "
| 1 | \n", + "Q1149445-1 | \n", + "Q1149445-9 | \n", + "Q1149445 | \n", + "earl egerton | \n", + "earl egerton | \n", + "earl | \n", + "egerton | \n", + "1800-01-01 | \n", + "westminster | \n", + "w1d 2hf | \n", + "None | \n", + "None | \n", + "0.991459 | \n", + "
| 2 | \n", + "Q20664532-1 | \n", + "Q21466387-2 | \n", + "Q21466387 | \n", + "harry brooker | \n", + "harry brooker | \n", + "harry | \n", + "brooker | \n", + "1848-01-01 | \n", + "plymouth | \n", + "pl4 9hx | \n", + "male | \n", + "painter | \n", + "0.506127 | \n", + "
| 3 | \n", + "Q1124636-1 | \n", + "Q1124636-12 | \n", + "Q1124636 | \n", + "tom stapleton | \n", + "tom stapleton | \n", + "tom | \n", + "stapleton | \n", + "1535-01-01 | \n", + "None | \n", + "bn6 9na | \n", + "male | \n", + "theologian | \n", + "0.612694 | \n", + "
| 4 | \n", + "Q18508292-1 | \n", + "Q21466711-4 | \n", + "Q21466711 | \n", + "harry s0ence | \n", + "harry s0ence | \n", + "harry | \n", + "s0ence | \n", + "1860-01-01 | \n", + "london | \n", + "se1 7pb | \n", + "male | \n", + "painter | \n", + "0.488917 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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",
@@ -66,10 +229,10 @@
"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"
+ "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": [],
@@ -83,10 +246,10 @@
"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"
+ "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": [],
@@ -100,10 +263,10 @@
"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"
+ "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": [],
@@ -131,13 +294,92 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.LayerChart(...)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from splink.exploratory import completeness_chart\n",
"\n",
@@ -149,13 +391,92 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.VConcatChart(...)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from splink.exploratory import profile_columns\n",
"\n",
@@ -167,13 +488,92 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
@@ -203,10 +603,10 @@
"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"
+ "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": [],
@@ -245,13 +645,22 @@
"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"
+ "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": [],
+ "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",
@@ -261,7 +670,7 @@
" \"l.given_name = r.surname and l.surname = r.given_name and l.date_of_birth = r.date_of_birth\",\n",
"]\n",
"\n",
- "linker.estimate_probability_two_random_records_match(deterministic_rules, recall=0.9)"
+ "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.9)"
]
},
{
@@ -269,15 +678,73 @@
"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"
+ "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": [],
+ "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": [
- "linker.estimate_u_using_random_sampling(max_pairs=1e6)"
+ "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
]
},
{
@@ -285,16 +752,96 @@
"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"
+ "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": [],
+ "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 -0.376 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 0.0158 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 -0.000688 in the m_probability of postcode, level `Exact match on postcode`\n"
+ ]
+ },
+ {
+ "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.estimate_parameters_using_expectation_maximisation(\n",
+ "session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
" em_blocking_rule_1\n",
")"
]
@@ -304,16 +851,137 @@
"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"
+ "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": [],
+ "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"
+ ]
+ },
+ {
+ "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.estimate_parameters_using_expectation_maximisation(\n",
+ "session_postcode = linker.training.estimate_parameters_using_expectation_maximisation(\n",
" em_blocking_rule_2\n",
")"
]
@@ -323,15 +991,94 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.VConcatChart(...)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "linker.match_weights_chart()"
+ "linker.visualisations.match_weights_chart()"
]
},
{
@@ -339,15 +1086,29 @@
"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"
+ "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": [],
+ "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.predict(threshold_match_probability=0.2)"
+ "results = linker.inference.predict(threshold_match_probability=0.2)"
]
},
{
@@ -355,15 +1116,107 @@
"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"
+ "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": [],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.Chart(...)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "linker.accuracy_analysis_from_labels_column(\n",
+ "linker.evaluation.accuracy_analysis_from_labels_column(\n",
" \"cluster\", match_weight_round_to_nearest=0.1, output_type=\"roc\"\n",
")"
]
@@ -373,15 +1226,242 @@
"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"
+ "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": [],
+ "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", + " | rec_id | \n", + "given_name | \n", + "surname | \n", + "street_number | \n", + "address_1 | \n", + "address_2 | \n", + "suburb | \n", + "postcode | \n", + "state | \n", + "date_of_birth | \n", + "soc_sec_id | \n", + "cluster | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "rec-1496-org | \n", + "mitchell | \n", + "green | \n", + "7 | \n", + "wallaby place | \n", + "delmar | \n", + "cleveland | \n", + "2119 | \n", + "sa | \n", + "19560409 | \n", + "1804974 | \n", + "rec-1496 | \n", + "
| 1 | \n", + "rec-552-dup-3 | \n", + "harley | \n", + "mccarthy | \n", + "177 | \n", + "pridhamstreet | \n", + "milton | \n", + "marsden | \n", + "3165 | \n", + "nsw | \n", + "19080419 | \n", + "6089216 | \n", + "rec-552 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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"
+ }
+ ],
"source": [
- "pred_errors_df = linker.prediction_errors_from_labels_column(\n",
+ "pred_errors_df = linker.evaluation.prediction_errors_from_labels_column(\n",
" \"cluster\"\n",
").as_pandas_dataframe()\n",
"len(pred_errors_df)\n",
@@ -393,18 +1473,110 @@
"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"
+ "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"
}
},
- "outputs": [],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.LayerChart(...)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "records = linker.prediction_errors_from_labels_column(\"cluster\").as_record_dict(\n",
+ "records = linker.evaluation.prediction_errors_from_labels_column(\"cluster\").as_record_dict(\n",
" limit=10\n",
")\n",
- "linker.waterfall_chart(records)"
+ "linker.visualisations.waterfall_chart(records)"
]
}
],
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": [
+ "| \n", + " | clerical_match_score | \n", + "found_by_blocking_rules | \n", + "match_weight | \n", + "match_probability | \n", + "rec_id_l | \n", + "rec_id_r | \n", + "given_name_l | \n", + "given_name_r | \n", + "gamma_given_name | \n", + "tf_given_name_l | \n", + "... | \n", + "postcode_l | \n", + "postcode_r | \n", + "gamma_postcode | \n", + "tf_postcode_l | \n", + "tf_postcode_r | \n", + "bf_postcode | \n", + "bf_tf_adj_postcode | \n", + "cluster_l | \n", + "cluster_r | \n", + "match_key | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1.0 | \n", + "False | \n", + "-27.448215 | \n", + "5.460897e-09 | \n", + "rec-993-dup-1 | \n", + "rec-993-dup-3 | \n", + "westbrook | \n", + "jake | \n", + "0 | \n", + "0.0004 | \n", + "... | \n", + "2704 | \n", + "2074 | \n", + "0 | \n", + "0.0002 | \n", + "0.0014 | \n", + "0.230037 | \n", + "1.0 | \n", + "rec-993 | \n", + "rec-993 | \n", + "5 | \n", + "
| 1 | \n", + "1.0 | \n", + "False | \n", + "-27.448215 | \n", + "5.460897e-09 | \n", + "rec-829-dup-0 | \n", + "rec-829-dup-2 | \n", + "wilde | \n", + "kyra | \n", + "0 | \n", + "0.0002 | \n", + "... | \n", + "3859 | \n", + "3595 | \n", + "0 | \n", + "0.0004 | \n", + "0.0006 | \n", + "0.230037 | \n", + "1.0 | \n", + "rec-829 | \n", + "rec-829 | \n", + "5 | \n", + "
| 2 | \n", + "1.0 | \n", + "False | \n", + "-19.362199 | \n", + "1.483873e-06 | \n", + "rec-829-dup-0 | \n", + "rec-829-dup-1 | \n", + "wilde | \n", + "kyra | \n", + "0 | \n", + "0.0002 | \n", + "... | \n", + "3859 | \n", + "3889 | \n", + "0 | \n", + "0.0004 | \n", + "0.0002 | \n", + "0.230037 | \n", + "1.0 | \n", + "rec-829 | \n", + "rec-829 | \n", + "5 | \n", + "
| 3 | \n", + "1.0 | \n", + "True | \n", + "-15.233122 | \n", + "2.596344e-05 | \n", + "rec-721-dup-0 | \n", + "rec-721-dup-1 | \n", + "mikhaili | \n", + "elly | \n", + "0 | \n", + "0.0008 | \n", + "... | \n", + "4806 | \n", + "4860 | \n", + "0 | \n", + "0.0008 | \n", + "0.0014 | \n", + "0.230037 | \n", + "1.0 | \n", + "rec-721 | \n", + "rec-721 | \n", + "2 | \n", + "
| 4 | \n", + "1.0 | \n", + "True | \n", + "-12.600328 | \n", + "1.610102e-04 | \n", + "rec-401-dup-1 | \n", + "rec-401-dup-3 | \n", + "whitbe | \n", + "alexa-ose | \n", + "0 | \n", + "0.0002 | \n", + "... | \n", + "3040 | \n", + "3041 | \n", + "0 | \n", + "0.0020 | \n", + "0.0004 | \n", + "0.230037 | \n", + "1.0 | \n", + "rec-401 | \n", + "rec-401 | \n", + "0 | \n", + "
5 rows × 45 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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": [
+ "| \n", + " | rec_id | \n", + "given_name | \n", + "surname | \n", + "street_number | \n", + "address_1 | \n", + "address_2 | \n", + "suburb | \n", + "postcode | \n", + "state | \n", + "date_of_birth | \n", + "soc_sec_id | \n", + "cluster | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "rec-1070-org | \n", + "michaela | \n", + "neumann | \n", + "8 | \n", + "stanley street | \n", + "miami | \n", + "winston hills | \n", + "4223 | \n", + "nsw | \n", + "19151111 | \n", + "5304218 | \n", + "rec-1070 | \n", + "
| 1 | \n", + "rec-1016-org | \n", + "courtney | \n", + "painter | \n", + "12 | \n", + "pinkerton circuit | \n", + "bega flats | \n", + "richlands | \n", + "4560 | \n", + "vic | \n", + "19161214 | \n", + "4066625 | \n", + "rec-1016 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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",
"metadata": {
"execution": {
- "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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": [
+ "| \n", + " | rec_id | \n", + "given_name | \n", + "surname | \n", + "street_number | \n", + "address_1 | \n", + "address_2 | \n", + "suburb | \n", + "postcode | \n", + "state | \n", + "date_of_birth | \n", + "soc_sec_id | \n", + "cluster | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "rec-561-dup-0 | \n", + "elton | \n", + "\n", + " | 3 | \n", + "light setreet | \n", + "pinehill | \n", + "windermere | \n", + "3212 | \n", + "vic | \n", + "19651013 | \n", + "1551941 | \n", + "rec-561 | \n", + "
| 1 | \n", + "rec-2642-dup-0 | \n", + "mitchell | \n", + "maxon | \n", + "47 | \n", + "edkins street | \n", + "lochaoair | \n", + "north ryde | \n", + "3355 | \n", + "nsw | \n", + "19390212 | \n", + "8859999 | \n", + "rec-2642 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
"metadata": {
"execution": {
- "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": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "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 1e6bb2dd85..dba266abe1 100644
--- a/docs/demos/examples/duckdb/link_only.ipynb
+++ b/docs/demos/examples/duckdb/link_only.ipynb
@@ -26,10 +26,10 @@
"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"
+ "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": [],
@@ -43,13 +43,83 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | match_weight | \n", + "match_probability | \n", + "source_dataset_l | \n", + "source_dataset_r | \n", + "rec_id_l | \n", + "rec_id_r | \n", + "given_name_l | \n", + "given_name_r | \n", + "gamma_given_name | \n", + "tf_given_name_l | \n", + "... | \n", + "gamma_postcode | \n", + "tf_postcode_l | \n", + "tf_postcode_r | \n", + "bf_postcode | \n", + "bf_tf_adj_postcode | \n", + "address_1_l | \n", + "address_1_r | \n", + "state_l | \n", + "state_r | \n", + "match_key | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "-1.825003 | \n", + "0.220115 | \n", + "__splink__input_table_0 | \n", + "__splink__input_table_1 | \n", + "rec-760-org | \n", + "rec-3951-dup-0 | \n", + "lachlan | \n", + "lachlan | \n", + "3 | \n", + "0.0113 | \n", + "... | \n", + "3 | \n", + "0.0007 | \n", + "0.0007 | \n", + "718.824003 | \n", + "1.672690 | \n", + "bushby close | \n", + "templestoew avenue | \n", + "nsw | \n", + "vic | \n", + "0 | \n", + "
| 1 | \n", + "-1.637366 | \n", + "0.243251 | \n", + "__splink__input_table_0 | \n", + "__splink__input_table_1 | \n", + "rec-4980-org | \n", + "rec-4980-dup-0 | \n", + "isabella | \n", + "ctercteko | \n", + "0 | \n", + "0.0069 | \n", + "... | \n", + "3 | \n", + "0.0004 | \n", + "0.0004 | \n", + "718.824003 | \n", + "2.927207 | \n", + "sturt avenue | \n", + "sturta venue | \n", + "vic | \n", + "vic | \n", + "2 | \n", + "
| 2 | \n", + "-1.091445 | \n", + "0.319400 | \n", + "__splink__input_table_0 | \n", + "__splink__input_table_1 | \n", + "rec-585-org | \n", + "rec-585-dup-0 | \n", + "danny | \n", + "stephenson | \n", + "0 | \n", + "0.0001 | \n", + "... | \n", + "2 | \n", + "0.0016 | \n", + "0.0012 | \n", + "11.395608 | \n", + "1.000000 | \n", + "o'shanassy street | \n", + "o'shanassy street | \n", + "tas | \n", + "tas | \n", + "1 | \n", + "
| 3 | \n", + "-0.942148 | \n", + "0.342303 | \n", + "__splink__input_table_0 | \n", + "__splink__input_table_1 | \n", + "rec-1250-org | \n", + "rec-1250-dup-0 | \n", + "luke | \n", + "gazzola | \n", + "0 | \n", + "0.0055 | \n", + "... | \n", + "2 | \n", + "0.0015 | \n", + "0.0002 | \n", + "11.395608 | \n", + "1.000000 | \n", + "newman morris circuit | \n", + "newman morr is circuit | \n", + "nsw | \n", + "nsw | \n", + "1 | \n", + "
| 4 | \n", + "-0.186499 | \n", + "0.467727 | \n", + "__splink__input_table_0 | \n", + "__splink__input_table_1 | \n", + "rec-4763-org | \n", + "rec-4763-dup-0 | \n", + "max | \n", + "alisha | \n", + "0 | \n", + "0.0021 | \n", + "... | \n", + "1 | \n", + "0.0004 | \n", + "0.0016 | \n", + "0.044469 | \n", + "1.000000 | \n", + "duffy street | \n", + "duffy s treet | \n", + "nsw | \n", + "nsw | \n", + "2 | \n", + "
5 rows × 47 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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",
@@ -67,10 +137,10 @@
"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"
+ "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": [],
@@ -115,13 +185,92 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.LayerChart(...)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from splink.exploratory import completeness_chart\n",
"\n",
@@ -138,13 +287,22 @@
"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"
+ "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": [],
+ "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",
@@ -155,7 +313,7 @@
"]\n",
"\n",
"\n",
- "linker.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+ "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
]
},
{
@@ -163,15 +321,51 @@
"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"
+ "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": [],
+ "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",
+ " - 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.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)"
+ "linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)"
]
},
{
@@ -179,19 +373,350 @@
"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"
+ "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": [],
+ "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 -0.387 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 0.113 in probability_two_random_records_match\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Iteration 3: Largest change in params was 0.0347 in probability_two_random_records_match\n"
+ ]
+ },
+ {
+ "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"
+ ]
+ }
+ ],
"source": [
- "session_dob = linker.estimate_parameters_using_expectation_maximisation(block_on(\"dob\"))\n",
- "session_email = linker.estimate_parameters_using_expectation_maximisation(\n",
+ "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.estimate_parameters_using_expectation_maximisation(\n",
+ "session_first_name = linker.training.estimate_parameters_using_expectation_maximisation(\n",
" block_on(\"first_name\")\n",
")"
]
@@ -201,15 +726,15 @@
"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"
+ "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.predict(threshold_match_probability=0.9)"
+ "results = linker.inference.predict(threshold_match_probability=0.9)"
]
},
{
@@ -217,13 +742,227 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | unique_id | \n", + "first_name | \n", + "surname | \n", + "dob | \n", + "city | \n", + "cluster | \n", + "|
|---|---|---|---|---|---|---|---|
| 930 | \n", + "930 | \n", + "Luke | \n", + "Robinnso | \n", + "1981-10-18 | \n", + "Coventry | \n", + "lrobinson@wolf.org | \n", + "233 | \n", + "
| 385 | \n", + "385 | \n", + "Lottie | \n", + "Davis | \n", + "1972-06-12 | \n", + "NaN | \n", + "lottie.d7@morgan-pierce.com | \n", + "100 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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)"
]
diff --git a/docs/demos/examples/duckdb/pairwise_labels.ipynb b/docs/demos/examples/duckdb/pairwise_labels.ipynb
index f4f9d1f513..f99d3a566c 100644
--- a/docs/demos/examples/duckdb/pairwise_labels.ipynb
+++ b/docs/demos/examples/duckdb/pairwise_labels.ipynb
@@ -35,10 +35,10 @@
"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"
+ "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": [],
@@ -52,13 +52,170 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | match_weight | \n", + "match_probability | \n", + "source_dataset_l | \n", + "source_dataset_r | \n", + "unique_id_l | \n", + "unique_id_r | \n", + "first_name_l | \n", + "first_name_r | \n", + "gamma_first_name | \n", + "surname_l | \n", + "... | \n", + "dob_l | \n", + "dob_r | \n", + "gamma_dob | \n", + "city_l | \n", + "city_r | \n", + "gamma_city | \n", + "email_l | \n", + "email_r | \n", + "gamma_email | \n", + "match_key | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "3.20162 | \n", + "0.90196 | \n", + "df_left | \n", + "df_right | \n", + "445 | \n", + "444 | \n", + "Jacob | \n", + "Jacob | \n", + "3 | \n", + "Campbell | \n", + "... | \n", + "1988-06-05 | \n", + "1997-05-04 | \n", + "1 | \n", + "Lonon | \n", + "London | \n", + "0 | \n", + "j.c65@ortiz.com | \n", + "None | \n", + "-1 | \n", + "0 | \n", + "
| 1 | \n", + "3.20162 | \n", + "0.90196 | \n", + "df_left | \n", + "df_right | \n", + "774 | \n", + "778 | \n", + "Armstrong | \n", + "Armstrong | \n", + "3 | \n", + "Eva | \n", + "... | \n", + "2027-04-21 | \n", + "2017-04-23 | \n", + "1 | \n", + "Peterborouhg | \n", + "Peterbotrough | \n", + "0 | \n", + "e.armstrong16odonnell.info | \n", + "None | \n", + "-1 | \n", + "0 | \n", + "
| 2 | \n", + "3.20162 | \n", + "0.90196 | \n", + "df_left | \n", + "df_right | \n", + "239 | \n", + "242 | \n", + "Freya | \n", + "Freya | \n", + "3 | \n", + "Shah | \n", + "... | \n", + "1972-01-17 | \n", + "1970-12-17 | \n", + "1 | \n", + "London | \n", + "Lonnod | \n", + "0 | \n", + "f.s@flynn.com | \n", + "None | \n", + "-1 | \n", + "0 | \n", + "
| 3 | \n", + "3.20162 | \n", + "0.90196 | \n", + "df_left | \n", + "df_right | \n", + "833 | \n", + "834 | \n", + "Mason | \n", + "Mason | \n", + "3 | \n", + "Smith | \n", + "... | \n", + "1983-03-16 | \n", + "1993-03-13 | \n", + "1 | \n", + "Kingston-uponH-ull | \n", + "Kingston-upon-Hull | \n", + "0 | \n", + "masons52@reed.com | \n", + "None | \n", + "-1 | \n", + "0 | \n", + "
| 4 | \n", + "3.20162 | \n", + "0.90196 | \n", + "df_left | \n", + "df_right | \n", + "439 | \n", + "444 | \n", + "Jacob | \n", + "Jacob | \n", + "3 | \n", + "Campbell | \n", + "... | \n", + "1987-06-06 | \n", + "1997-05-04 | \n", + "1 | \n", + "Lonnod | \n", + "London | \n", + "0 | \n", + "None | \n", + "None | \n", + "-1 | \n", + "0 | \n", + "
5 rows × 22 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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",
@@ -82,13 +239,79 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | unique_id_l | \n", + "source_dataset_l | \n", + "unique_id_r | \n", + "source_dataset_r | \n", + "clerical_match_score | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "fake_1000 | \n", + "1 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 1 | \n", + "0 | \n", + "fake_1000 | \n", + "2 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 2 | \n", + "0 | \n", + "fake_1000 | \n", + "3 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 49 | \n", + "1 | \n", + "fake_1000 | \n", + "2 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 50 | \n", + "1 | \n", + "fake_1000 | \n", + "3 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 3171 | \n", + "994 | \n", + "fake_1000 | \n", + "996 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 3172 | \n", + "995 | \n", + "fake_1000 | \n", + "996 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 3173 | \n", + "997 | \n", + "fake_1000 | \n", + "998 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 3174 | \n", + "997 | \n", + "fake_1000 | \n", + "999 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
| 3175 | \n", + "998 | \n", + "fake_1000 | \n", + "999 | \n", + "fake_1000 | \n", + "1.0 | \n", + "
2031 rows × 5 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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",
@@ -101,10 +324,10 @@
"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"
+ "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": [],
@@ -142,10 +365,10 @@
"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"
+ "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": [],
@@ -158,7 +381,7 @@
" \"l.email = r.email\",\n",
"]\n",
"\n",
- "linker.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+ "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
]
},
{
@@ -166,15 +389,23 @@
"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"
+ "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": [],
+ "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"
+ ]
+ }
+ ],
"source": [
- "linker.estimate_u_using_random_sampling(max_pairs=1e6)"
+ "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
]
},
{
@@ -182,21 +413,21 @@
"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"
+ "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.register_labels_table(pairwise_labels, overwrite=True)\n",
- "linker.estimate_m_from_pairwise_labels(labels_df)\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.estimate_m_from_pairwise_labels(\"labels_tablename_here\")"
+ "# linker.training.estimate_m_from_pairwise_labels(\"labels_tablename_here\")"
]
},
{
@@ -204,16 +435,27 @@
"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"
+ "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": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | unique_id | \n", + "first_name | \n", + "surname | \n", + "dob | \n", + "city | \n", + "cluster | \n", + "|
|---|---|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "Robert | \n", + "Alan | \n", + "1971-06-24 | \n", + "NaN | \n", + "robert255@smith.net | \n", + "0 | \n", + "
| 1 | \n", + "1 | \n", + "Robert | \n", + "Allen | \n", + "1971-05-24 | \n", + "NaN | \n", + "roberta25@smith.net | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\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": [
"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"
+ "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",
@@ -99,20 +237,20 @@
" 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"
+ "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",
@@ -123,57 +261,281 @@
" \"l.postcode_fake = r.postcode_fake and l.dob = r.dob\",\n",
"]\n",
"\n",
- "linker.estimate_probability_two_random_records_match(deterministic_rules, recall=0.6)"
- ],
- "outputs": []
+ "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.6)"
+ ]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-27T15:15:03.430749Z",
- "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"
+ "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.estimate_u_using_random_sampling(max_pairs=2e6)"
- ],
- "outputs": []
+ "linker.training.estimate_u_using_random_sampling(max_pairs=2e6)"
+ ]
},
{
"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"
+ "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"
}
},
- "source": [
- "results = linker.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"
+ ]
+ }
],
- "outputs": []
+ "source": [
+ "results = linker.inference.predict(threshold_match_probability=0.9)"
+ ]
},
{
"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"
+ "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": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | unique_id | \n", + "cluster | \n", + "full_name | \n", + "first_and_surname | \n", + "first_name | \n", + "surname | \n", + "dob | \n", + "birth_place | \n", + "postcode_fake | \n", + "gender | \n", + "occupation | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Q2296770-1 | \n", + "Q2296770 | \n", + "thomas clifford, 1st baron clifford of chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "male | \n", + "politician | \n", + "
| 1 | \n", + "Q2296770-2 | \n", + "Q2296770 | \n", + "thomas of chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "male | \n", + "politician | \n", + "
| 2 | \n", + "Q2296770-3 | \n", + "Q2296770 | \n", + "tom 1st baron clifford of chudleigh | \n", + "tom chudleigh | \n", + "tom | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "male | \n", + "politician | \n", + "
| 3 | \n", + "Q2296770-4 | \n", + "Q2296770 | \n", + "thomas 1st chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8hu | \n", + "None | \n", + "politician | \n", + "
| 4 | \n", + "Q2296770-5 | \n", + "Q2296770 | \n", + "thomas clifford, 1st baron chudleigh | \n", + "thomas chudleigh | \n", + "thomas | \n", + "chudleigh | \n", + "1630-08-01 | \n", + "devon | \n", + "tq13 8df | \n", + "None | \n", + "politician | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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)"
- ],
- "outputs": []
+ ]
}
],
"metadata": {
diff --git a/docs/demos/examples/duckdb/real_time_record_linkage.ipynb b/docs/demos/examples/duckdb/real_time_record_linkage.ipynb
index 516b5a045b..63bbe23d56 100644
--- a/docs/demos/examples/duckdb/real_time_record_linkage.ipynb
+++ b/docs/demos/examples/duckdb/real_time_record_linkage.ipynb
@@ -1,2488 +1,2488 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Real time linkage\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this notebook, we demonstrate splink's incremental and real time linkage capabilities - specifically:\n",
- "\n",
- "- the `linker.compare_two_records` function, that allows you to interactively explore the results of a linkage model; and\n",
- "- the `linker.find_matches_to_new_records` that allows you to incrementally find matches to a small number of new records\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " | \n", + " | match_weight | \n", + "match_probability | \n", + "unique_id_l | \n", + "unique_id_r | \n", + "full_name_l | \n", + "full_name_r | \n", + "gamma_full_name | \n", + "dob_l | \n", + "dob_r | \n", + "gamma_dob | \n", + "postcode_fake_l | \n", + "postcode_fake_r | \n", + "gamma_postcode_fake | \n", + "birth_place_l | \n", + "birth_place_r | \n", + "gamma_birth_place | \n", + "occupation_l | \n", + "occupation_r | \n", + "gamma_occupation | \n", + "match_key | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "3.172246 | \n", + "0.900145 | \n", + "Q16198727-6 | \n", + "Q16198727-8 | \n", + "henry jupp | \n", + "jupp | \n", + "0 | \n", + "1802-08-06 | \n", + "1802-08-06 | \n", + "5 | \n", + "None | \n", + "e4 9re | \n", + "-1 | \n", + "waltham forest | \n", + "waltham forest | \n", + "2 | \n", + "cricketer | \n", + "cricketer | \n", + "1 | \n", + "2 | \n", + "
| 1 | \n", + "3.172423 | \n", + "0.900156 | \n", + "Q16220644-12 | \n", + "Q16220644-7 | \n", + "1st bt. | \n", + "1st bt. | \n", + "3 | \n", + "1840-11-21 | \n", + "1810-11-21 | \n", + "4 | \n", + "None | \n", + "None | \n", + "-1 | \n", + "liverpool | \n", + "liverpool | \n", + "2 | \n", + "None | \n", + "physician | \n", + "-1 | \n", + "0 | \n", + "
| 2 | \n", + "3.173256 | \n", + "0.900208 | \n", + "Q6180874-12 | \n", + "Q6180874-19 | \n", + "richard slater | \n", + "slater | \n", + "0 | \n", + "1854-01-01 | \n", + "1854-01-01 | \n", + "5 | \n", + "al5 2ay | \n", + "al3 7rq | \n", + "0 | \n", + "st albans | \n", + "st albans | \n", + "2 | \n", + "hymnwriter | \n", + "hymnwriter | \n", + "1 | \n", + "2 | \n", + "
| 3 | \n", + "3.174182 | \n", + "0.900265 | \n", + "Q7519167-10 | \n", + "Q7519167-8 | \n", + "simeon langton | \n", + "simeon langton | \n", + "3 | \n", + "1150-01-81 | \n", + "1152-01-01 | \n", + "0 | \n", + "None | \n", + "None | \n", + "-1 | \n", + "wealden | \n", + "wealden | \n", + "2 | \n", + "priest | \n", + "None | \n", + "-1 | \n", + "0 | \n", + "
| 4 | \n", + "3.178567 | \n", + "0.900538 | \n", + "Q15980561-12 | \n", + "Q15980561-8 | \n", + "harry rosling | \n", + "henry rosling | \n", + "1 | \n", + "1828-01-11 | \n", + "1858-01-01 | \n", + "0 | \n", + "tn27 0sy | \n", + "tn27 0sy | \n", + "2 | \n", + "ashford | \n", + "ashford | \n", + "2 | \n", + "photographer | \n", + "None | \n", + "-1 | \n", + "3 | \n", + "
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-27T15:15:11.870063Z",
- "iopub.status.busy": "2024-03-27T15:15:11.869757Z",
- "iopub.status.idle": "2024-03-27T15:15:11.890661Z",
- "shell.execute_reply": "2024-03-27T15:15:11.889929Z"
- }
- },
- "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",
- "metadata": {},
- "source": [
- "### Step 1: Load a pre-trained linkage model\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-27T15:15:11.894528Z",
- "iopub.status.busy": "2024-03-27T15:15:11.894247Z",
- "iopub.status.idle": "2024-03-27T15:15:13.841789Z",
- "shell.execute_reply": "2024-03-27T15:15:13.841226Z"
- }
- },
- "source": [
- "import urllib.request\n",
- "import json\n",
- "from pathlib import Path\n",
- "from splink import Linker, DuckDBAPI, block_on, SettingsCreator, splink_datasets\n",
- "\n",
- "df = splink_datasets.fake_1000\n",
- "\n",
- "url = \"https://raw.githubusercontent.com/moj-analytical-services/splink_demos/master/demo_settings/real_time_settings.json\"\n",
- "\n",
- "with urllib.request.urlopen(url) as u:\n",
- " settings = json.loads(u.read().decode())\n",
- "\n",
- "\n",
- "linker = Linker(df, settings, database_api=DuckDBAPI())"
- ],
- "outputs": []
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-27T15:15:13.845679Z",
- "iopub.status.busy": "2024-03-27T15:15:13.845274Z",
- "iopub.status.idle": "2024-03-27T15:15:14.721033Z",
- "shell.execute_reply": "2024-03-27T15:15:14.720417Z"
- }
- },
- "source": [
- "linker.waterfall_chart(linker.predict().as_record_dict(limit=2))"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step Comparing two records\n",
- "\n",
- "It's now possible to compute a match weight for any two records using `linker.compare_two_records()`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-27T15:15:14.724585Z",
- "iopub.status.busy": "2024-03-27T15:15:14.724327Z",
- "iopub.status.idle": "2024-03-27T15:15:14.962647Z",
- "shell.execute_reply": "2024-03-27T15:15:14.961740Z"
- }
- },
- "source": [
- "record_1 = {\n",
- " \"unique_id\": 1,\n",
- " \"first_name\": \"Lucas\",\n",
- " \"surname\": \"Smith\",\n",
- " \"dob\": \"1984-01-02\",\n",
- " \"city\": \"London\",\n",
- " \"email\": \"lucas.smith@hotmail.com\",\n",
- "}\n",
- "\n",
- "record_2 = {\n",
- " \"unique_id\": 2,\n",
- " \"first_name\": \"Lucas\",\n",
- " \"surname\": \"Smith\",\n",
- " \"dob\": \"1983-02-12\",\n",
- " \"city\": \"Machester\",\n",
- " \"email\": \"lucas.smith@hotmail.com\",\n",
- "}\n",
- "\n",
- "linker._settings_obj._retain_intermediate_calculation_columns = True\n",
- "\n",
- "\n",
- "\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",
- "\n",
- "\n",
- "df_two = linker.compare_two_records(record_1, record_2)\n",
- "df_two.as_pandas_dataframe()"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step 3: Interactive comparisons\n",
- "\n",
- "One interesting applicatin of `compare_two_records` is to create a simple interface that allows the user to input two records interactively, and get real time feedback.\n",
- "\n",
- "In the following cell we use `ipywidets` for this purpose. ✨✨ Change the values in the text boxes to see the waterfall chart update in real time. ✨✨\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-27T15:15:14.968237Z",
- "iopub.status.busy": "2024-03-27T15:15:14.967899Z",
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- "shell.execute_reply": "2024-03-27T15:15:15.925656Z"
- }
- },
- "source": [
- "import ipywidgets as widgets\n",
- "from IPython.display import display\n",
- "\n",
- "\n",
- "fields = [\"unique_id\", \"first_name\", \"surname\", \"dob\", \"email\", \"city\"]\n",
- "\n",
- "left_text_boxes = []\n",
- "right_text_boxes = []\n",
- "\n",
- "inputs_to_interactive_output = {}\n",
- "\n",
- "for f in fields:\n",
- " wl = widgets.Text(description=f, value=str(record_1[f]))\n",
- " left_text_boxes.append(wl)\n",
- " inputs_to_interactive_output[f\"{f}_l\"] = wl\n",
- " wr = widgets.Text(description=f, value=str(record_2[f]))\n",
- " right_text_boxes.append(wr)\n",
- " inputs_to_interactive_output[f\"{f}_r\"] = wr\n",
- "\n",
- "b1 = widgets.VBox(left_text_boxes)\n",
- "b2 = widgets.VBox(right_text_boxes)\n",
- "ui = widgets.HBox([b1, b2])\n",
- "\n",
- "\n",
- "def myfn(**kwargs):\n",
- " my_args = dict(kwargs)\n",
- "\n",
- " record_left = {}\n",
- " record_right = {}\n",
- "\n",
- " for key, value in my_args.items():\n",
- " if value == \"\":\n",
- " value = None\n",
- " if key.endswith(\"_l\"):\n",
- " record_left[key[:-2]] = value\n",
- " elif key.endswith(\"_r\"):\n",
- " record_right[key[:-2]] = value\n",
- "\n",
- " # 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",
- "\n",
- " recs = df_two.as_pandas_dataframe().to_dict(orient=\"records\")\n",
- " from splink.charts import waterfall_chart\n",
- "\n",
- " display(linker.waterfall_chart(recs, filter_nulls=False))\n",
- "\n",
- "\n",
- "out = widgets.interactive_output(myfn, inputs_to_interactive_output)\n",
- "\n",
- "display(ui, out)"
- ],
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Finding matching records interactively\n",
- "\n",
- "It is also possible to search the records in the input dataset rapidly using the `linker.find_matches_to_new_records()` function\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-27T15:15:15.937800Z",
- "iopub.status.busy": "2024-03-27T15:15:15.935943Z",
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- "shell.execute_reply": "2024-03-27T15:15:16.474896Z"
- }
- },
- "source": [
- "record = {\n",
- " \"unique_id\": 123987,\n",
- " \"first_name\": \"Robert\",\n",
- " \"surname\": \"Alan\",\n",
- " \"dob\": \"1971-05-24\",\n",
- " \"city\": \"London\",\n",
- " \"email\": \"robert255@smith.net\",\n",
- "}\n",
- "\n",
- "\n",
- "df_inc = linker.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",
- "metadata": {},
- "source": [
- "## Interactive interface for finding records\n",
- "\n",
- "Again, we can use `ipywidgets` to build an interactive interface for the `linker.find_matches_to_new_records` function\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-27T15:15:16.486337Z",
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- "shell.execute_reply": "2024-03-27T15:15:17.548423Z"
- }
- },
- "source": [
- "@widgets.interact(\n",
- " first_name=\"Robert\",\n",
- " surname=\"Alan\",\n",
- " dob=\"1971-05-24\",\n",
- " city=\"London\",\n",
- " email=\"robert255@smith.net\",\n",
- ")\n",
- "def interactive_link(first_name, surname, dob, city, email):\n",
- "\n",
- " record = {\n",
- " \"unique_id\": 123987,\n",
- " \"first_name\": first_name,\n",
- " \"surname\": surname,\n",
- " \"dob\": dob,\n",
- " \"city\": city,\n",
- " \"email\": email,\n",
- " \"group\": 0,\n",
- " }\n",
- "\n",
- " for key in record.keys():\n",
- " if type(record[key]) == str:\n",
- " if record[key].strip() == \"\":\n",
- " record[key] = None\n",
- "\n",
- " df_inc = linker.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.waterfall_chart(recs, filter_nulls=False))"
- ],
- "outputs": []
- },
- {
- "cell_type": "code",
- "execution_count": 8,
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- "shell.execute_reply": "2024-03-27T15:15:17.884033Z"
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- "linker.match_weights_chart()"
- ],
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- }
- ],
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- "display_name": ".venv",
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+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Real time linkage\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook, we demonstrate splink's incremental and real time linkage capabilities - specifically:\n",
+ "\n",
+ "- the `linker.compare_two_records` function, that allows you to interactively explore the results of a linkage model; and\n",
+ "- the `linker.find_matches_to_new_records` that allows you to incrementally find matches to a small number of new records\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ ""
+ ]
+ },
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+ "execution_count": 1,
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+ "execution": {
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- "data": {
- "text/html": "\n\n\n",
- "text/plain": "alt.LayerChart(...)"
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+ "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"
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+ "source": [
+ "### Step 1: Load a pre-trained linkage model\n"
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+ },
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+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-27T15:15:11.894528Z",
+ "iopub.status.busy": "2024-03-27T15:15:11.894247Z",
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+ },
+ "outputs": [],
+ "source": [
+ "import urllib.request\n",
+ "import json\n",
+ "from pathlib import Path\n",
+ "from splink import Linker, DuckDBAPI, block_on, SettingsCreator, splink_datasets\n",
+ "\n",
+ "df = splink_datasets.fake_1000\n",
+ "\n",
+ "url = \"https://raw.githubusercontent.com/moj-analytical-services/splink_demos/master/demo_settings/real_time_settings.json\"\n",
+ "\n",
+ "with urllib.request.urlopen(url) as u:\n",
+ " settings = json.loads(u.read().decode())\n",
+ "\n",
+ "\n",
+ "linker = Linker(df, settings, database_api=DuckDBAPI())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-27T15:15:13.845679Z",
+ "iopub.status.busy": "2024-03-27T15:15:13.845274Z",
+ "iopub.status.idle": "2024-03-27T15:15:14.721033Z",
+ "shell.execute_reply": "2024-03-27T15:15:14.720417Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "linker.visualisations.waterfall_chart(linker.inference.predict().as_record_dict(limit=2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step Comparing two records\n",
+ "\n",
+ "It's now possible to compute a match weight for any two records using `linker.compare_two_records()`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-27T15:15:14.724585Z",
+ "iopub.status.busy": "2024-03-27T15:15:14.724327Z",
+ "iopub.status.idle": "2024-03-27T15:15:14.962647Z",
+ "shell.execute_reply": "2024-03-27T15:15:14.961740Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "record_1 = {\n",
+ " \"unique_id\": 1,\n",
+ " \"first_name\": \"Lucas\",\n",
+ " \"surname\": \"Smith\",\n",
+ " \"dob\": \"1984-01-02\",\n",
+ " \"city\": \"London\",\n",
+ " \"email\": \"lucas.smith@hotmail.com\",\n",
+ "}\n",
+ "\n",
+ "record_2 = {\n",
+ " \"unique_id\": 2,\n",
+ " \"first_name\": \"Lucas\",\n",
+ " \"surname\": \"Smith\",\n",
+ " \"dob\": \"1983-02-12\",\n",
+ " \"city\": \"Machester\",\n",
+ " \"email\": \"lucas.smith@hotmail.com\",\n",
+ "}\n",
+ "\n",
+ "linker._settings_obj._retain_intermediate_calculation_columns = True\n",
+ "\n",
+ "\n",
+ "\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.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.inference.compare_two_records(record_1, record_2)\n",
+ "df_two.as_pandas_dataframe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 3: Interactive comparisons\n",
+ "\n",
+ "One interesting applicatin of `compare_two_records` is to create a simple interface that allows the user to input two records interactively, and get real time feedback.\n",
+ "\n",
+ "In the following cell we use `ipywidets` for this purpose. ✨✨ Change the values in the text boxes to see the waterfall chart update in real time. ✨✨\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-27T15:15:14.968237Z",
+ "iopub.status.busy": "2024-03-27T15:15:14.967899Z",
+ "iopub.status.idle": "2024-03-27T15:15:15.926984Z",
+ "shell.execute_reply": "2024-03-27T15:15:15.925656Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display\n",
+ "\n",
+ "\n",
+ "fields = [\"unique_id\", \"first_name\", \"surname\", \"dob\", \"email\", \"city\"]\n",
+ "\n",
+ "left_text_boxes = []\n",
+ "right_text_boxes = []\n",
+ "\n",
+ "inputs_to_interactive_output = {}\n",
+ "\n",
+ "for f in fields:\n",
+ " wl = widgets.Text(description=f, value=str(record_1[f]))\n",
+ " left_text_boxes.append(wl)\n",
+ " inputs_to_interactive_output[f\"{f}_l\"] = wl\n",
+ " wr = widgets.Text(description=f, value=str(record_2[f]))\n",
+ " right_text_boxes.append(wr)\n",
+ " inputs_to_interactive_output[f\"{f}_r\"] = wr\n",
+ "\n",
+ "b1 = widgets.VBox(left_text_boxes)\n",
+ "b2 = widgets.VBox(right_text_boxes)\n",
+ "ui = widgets.HBox([b1, b2])\n",
+ "\n",
+ "\n",
+ "def myfn(**kwargs):\n",
+ " my_args = dict(kwargs)\n",
+ "\n",
+ " record_left = {}\n",
+ " record_right = {}\n",
+ "\n",
+ " for key, value in my_args.items():\n",
+ " if value == \"\":\n",
+ " value = None\n",
+ " if key.endswith(\"_l\"):\n",
+ " record_left[key[:-2]] = value\n",
+ " elif key.endswith(\"_r\"):\n",
+ " record_right[key[:-2]] = value\n",
+ "\n",
+ " # Assuming 'linker' is defined earlier in your code\n",
+ " linker._settings_obj._retain_intermediate_calculation_columns = True\n",
+ "\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",
+ "\n",
+ " display(linker.visualisations.waterfall_chart(recs, filter_nulls=False))\n",
+ "\n",
+ "\n",
+ "out = widgets.interactive_output(myfn, inputs_to_interactive_output)\n",
+ "\n",
+ "display(ui, out)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Finding matching records interactively\n",
+ "\n",
+ "It is also possible to search the records in the input dataset rapidly using the `linker.find_matches_to_new_records()` function\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-27T15:15:15.937800Z",
+ "iopub.status.busy": "2024-03-27T15:15:15.935943Z",
+ "iopub.status.idle": "2024-03-27T15:15:16.477834Z",
+ "shell.execute_reply": "2024-03-27T15:15:16.474896Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "record = {\n",
+ " \"unique_id\": 123987,\n",
+ " \"first_name\": \"Robert\",\n",
+ " \"surname\": \"Alan\",\n",
+ " \"dob\": \"1971-05-24\",\n",
+ " \"city\": \"London\",\n",
+ " \"email\": \"robert255@smith.net\",\n",
+ "}\n",
+ "\n",
+ "\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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Interactive interface for finding records\n",
+ "\n",
+ "Again, we can use `ipywidgets` to build an interactive interface for the `linker.find_matches_to_new_records` function\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-27T15:15:16.486337Z",
+ "iopub.status.busy": "2024-03-27T15:15:16.484941Z",
+ "iopub.status.idle": "2024-03-27T15:15:17.549243Z",
+ "shell.execute_reply": "2024-03-27T15:15:17.548423Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "@widgets.interact(\n",
+ " first_name=\"Robert\",\n",
+ " surname=\"Alan\",\n",
+ " dob=\"1971-05-24\",\n",
+ " city=\"London\",\n",
+ " email=\"robert255@smith.net\",\n",
+ ")\n",
+ "def interactive_link(first_name, surname, dob, city, email):\n",
+ "\n",
+ " record = {\n",
+ " \"unique_id\": 123987,\n",
+ " \"first_name\": first_name,\n",
+ " \"surname\": surname,\n",
+ " \"dob\": dob,\n",
+ " \"city\": city,\n",
+ " \"email\": email,\n",
+ " \"group\": 0,\n",
+ " }\n",
+ "\n",
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+ " if type(record[key]) == str:\n",
+ " if record[key].strip() == \"\":\n",
+ " record[key] = None\n",
+ "\n",
+ " df_inc = linker.inference.find_matches_to_new_records(\n",
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+ " ).as_pandas_dataframe()\n",
+ " df_inc = df_inc.sort_values(\"match_weight\", ascending=False)\n",
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+ "\n",
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diff --git a/docs/demos/examples/duckdb/transactions.ipynb b/docs/demos/examples/duckdb/transactions.ipynb
index 243d162827..472e477116 100644
--- a/docs/demos/examples/duckdb/transactions.ipynb
+++ b/docs/demos/examples/duckdb/transactions.ipynb
@@ -32,29 +32,483 @@
"execution_count": 1,
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"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": []
+ ]
},
{
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}
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+ "output_type": "stream",
+ "text": [
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|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "MATTHIAS C paym | \n", + "2022-03-28 | \n", + "36.36 | \n", + "0 | \n", + "
| 1 | \n", + "1 | \n", + "M CORVINUS dona | \n", + "2022-02-14 | \n", + "221.91 | \n", + "1 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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",
@@ -63,8 +517,7 @@
"\n",
"display(df_origin.head(2))\n",
"display(df_destination.head(2))"
- ],
- "outputs": []
+ ]
},
{
"attachments": {},
@@ -83,12 +536,92 @@
"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"
+ "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",
+ "\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ "alt.VConcatChart(...)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from splink.exploratory import profile_columns\n",
"\n",
@@ -102,20 +635,99 @@
" \"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"
+ "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",
+ "\n",
+ "\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",
@@ -167,20 +779,20 @@
" db_api=db_api,\n",
" link_type=\"link_only\"\n",
")"
- ],
- "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"
+ "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",
@@ -248,20 +860,20 @@
" ],\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"
+ "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",
@@ -269,102 +881,646 @@
" 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"
+ "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"
}
},
- "source": [
- "linker.estimate_u_using_random_sampling(max_pairs=1e6)"
+ "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"
+ ]
+ }
],
- "outputs": []
+ "source": [
+ "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
+ ]
},
{
"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"
+ "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"
}
},
- "source": [
- "linker.estimate_parameters_using_expectation_maximisation(block_on(\"memo\"))"
+ "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": [
+ "| \n", + " | ground_truth | \n", + "memo | \n", + "transaction_date | \n", + "amount | \n", + "unique_id | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "MATTHIAS C payment BGC | \n", + "2022-03-29 | \n", + "36.36 | \n", + "0 | \n", + "
| 1 | \n", + "1 | \n", + "M CORVINUS BGC | \n", + "2022-02-16 | \n", + "221.91 | \n", + "1 | \n", + "
\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:29:57.518197Z",
- "iopub.status.busy": "2024-03-13T12:29:57.517750Z",
- "iopub.status.idle": "2024-03-13T12:29:57.523242Z",
- "shell.execute_reply": "2024-03-13T12:29:57.522525Z"
- }
- },
- "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\n",
- "# !pip install pyspark"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:29:57.527366Z",
- "iopub.status.busy": "2024-03-13T12:29:57.527045Z",
- "iopub.status.idle": "2024-03-13T12:30:42.348824Z",
- "shell.execute_reply": "2024-03-13T12:30:42.347900Z"
- }
- },
- "outputs": [],
- "source": [
- "from pyspark import SparkConf, SparkContext\n",
- "from pyspark.sql import SparkSession\n",
- "\n",
- "from splink.backends.spark import similarity_jar_location\n",
- "\n",
- "conf = SparkConf()\n",
- "# This parallelism setting is only suitable for a small toy example\n",
- "conf.set(\"spark.driver.memory\", \"12g\")\n",
- "conf.set(\"spark.default.parallelism\", \"16\")\n",
- "\n",
- "\n",
- "# Add custom similarity functions, which are bundled with Splink\n",
- "# documented here: https://github.com/moj-analytical-services/splink_scalaudfs\n",
- "path = similarity_jar_location()\n",
- "conf.set(\"spark.jars\", path)\n",
- "\n",
- "sc = SparkContext.getOrCreate(conf=conf)\n",
- "\n",
- "spark = SparkSession(sc)\n",
- "spark.sparkContext.setCheckpointDir(\"./tmp_checkpoints\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:30:42.353970Z",
- "iopub.status.busy": "2024-03-13T12:30:42.353260Z",
- "iopub.status.idle": "2024-03-13T12:30:42.358982Z",
- "shell.execute_reply": "2024-03-13T12:30:42.358209Z"
- }
- },
- "outputs": [],
- "source": [
- "# Disable warnings for pyspark - you don't need to include this\n",
- "import warnings\n",
- "\n",
- "spark.sparkContext.setLogLevel(\"ERROR\")\n",
- "warnings.simplefilter(\"ignore\", UserWarning)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:30:42.363648Z",
- "iopub.status.busy": "2024-03-13T12:30:42.363227Z",
- "iopub.status.idle": "2024-03-13T12:30:45.734688Z",
- "shell.execute_reply": "2024-03-13T12:30:45.733419Z"
- }
- },
- "outputs": [],
- "source": [
- "from splink import splink_datasets\n",
- "\n",
- "pandas_df = splink_datasets.fake_1000\n",
- "\n",
- "df = spark.createDataFrame(pandas_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:30:45.740685Z",
- "iopub.status.busy": "2024-03-13T12:30:45.740314Z",
- "iopub.status.idle": "2024-03-13T12:30:45.773778Z",
- "shell.execute_reply": "2024-03-13T12:30:45.772855Z"
- }
- },
- "outputs": [],
- "source": [
- "import splink.comparison_library as cl\n",
- "import splink.comparison_template_library as ctl\n",
- "from splink import Linker, SettingsCreator, SparkAPI, block_on\n",
- "\n",
- "settings = SettingsCreator(\n",
- " link_type=\"dedupe_only\",\n",
- " comparisons=[\n",
- " ctl.NameComparison(\"first_name\"),\n",
- " ctl.NameComparison(\"surname\"),\n",
- " # ctl.date_comparison(\"dob\", cast_strings_to_date=True),\n",
- " # TOD=Fix date comparison\n",
- " ctl.DateComparison(\n",
- " \"dob\",\n",
- " input_is_string=True,\n",
- " datetime_metrics=[\"month\", \"year\", \"year\"],\n",
- " datetime_thresholds=[1, 1, 10],\n",
- " datetime_format=\"%Y%m%d\",\n",
- " ),\n",
- " cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
- " ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
- " ],\n",
- " blocking_rules_to_generate_predictions=[\n",
- " block_on(\"first_name\"),\n",
- " \"l.surname = r.surname\", # alternatively, you can write BRs in their SQL form\n",
- " ],\n",
- " retain_intermediate_calculation_columns=True,\n",
- " em_convergence=0.01,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:30:45.779194Z",
- "iopub.status.busy": "2024-03-13T12:30:45.778688Z",
- "iopub.status.idle": "2024-03-13T12:30:57.746806Z",
- "shell.execute_reply": "2024-03-13T12:30:57.744480Z"
- }
- },
- "outputs": [],
- "source": [
- "linker = Linker(df, settings, database_api=SparkAPI(spark_session=spark))\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.estimate_probability_two_random_records_match(deterministic_rules, recall=0.6)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:30:57.757986Z",
- "iopub.status.busy": "2024-03-13T12:30:57.757315Z",
- "iopub.status.idle": "2024-03-13T12:31:17.080600Z",
- "shell.execute_reply": "2024-03-13T12:31:17.079503Z"
- }
- },
- "outputs": [],
- "source": [
- "linker.estimate_u_using_random_sampling(max_pairs=5e5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:31:17.085610Z",
- "iopub.status.busy": "2024-03-13T12:31:17.085246Z",
- "iopub.status.idle": "2024-03-13T12:31:36.217869Z",
- "shell.execute_reply": "2024-03-13T12:31:36.217063Z"
- }
- },
- "outputs": [],
- "source": [
- "training_blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n",
- "training_session_fname_sname = (\n",
- " linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)\n",
- ")\n",
- "\n",
- "training_blocking_rule = \"l.dob = r.dob\"\n",
- "training_session_dob = linker.estimate_parameters_using_expectation_maximisation(\n",
- " training_blocking_rule\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:31:36.223120Z",
- "iopub.status.busy": "2024-03-13T12:31:36.222561Z",
- "iopub.status.idle": "2024-03-13T12:31:44.599133Z",
- "shell.execute_reply": "2024-03-13T12:31:44.597894Z"
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Linking in Spark\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:29:57.518197Z",
+ "iopub.status.busy": "2024-03-13T12:29:57.517750Z",
+ "iopub.status.idle": "2024-03-13T12:29:57.523242Z",
+ "shell.execute_reply": "2024-03-13T12:29:57.522525Z"
+ }
+ },
+ "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\n",
+ "# !pip install pyspark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:29:57.527366Z",
+ "iopub.status.busy": "2024-03-13T12:29:57.527045Z",
+ "iopub.status.idle": "2024-03-13T12:30:42.348824Z",
+ "shell.execute_reply": "2024-03-13T12:30:42.347900Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from pyspark import SparkConf, SparkContext\n",
+ "from pyspark.sql import SparkSession\n",
+ "\n",
+ "from splink.backends.spark import similarity_jar_location\n",
+ "\n",
+ "conf = SparkConf()\n",
+ "# This parallelism setting is only suitable for a small toy example\n",
+ "conf.set(\"spark.driver.memory\", \"12g\")\n",
+ "conf.set(\"spark.default.parallelism\", \"16\")\n",
+ "\n",
+ "\n",
+ "# Add custom similarity functions, which are bundled with Splink\n",
+ "# documented here: https://github.com/moj-analytical-services/splink_scalaudfs\n",
+ "path = similarity_jar_location()\n",
+ "conf.set(\"spark.jars\", path)\n",
+ "\n",
+ "sc = SparkContext.getOrCreate(conf=conf)\n",
+ "\n",
+ "spark = SparkSession(sc)\n",
+ "spark.sparkContext.setCheckpointDir(\"./tmp_checkpoints\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:30:42.353970Z",
+ "iopub.status.busy": "2024-03-13T12:30:42.353260Z",
+ "iopub.status.idle": "2024-03-13T12:30:42.358982Z",
+ "shell.execute_reply": "2024-03-13T12:30:42.358209Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Disable warnings for pyspark - you don't need to include this\n",
+ "import warnings\n",
+ "\n",
+ "spark.sparkContext.setLogLevel(\"ERROR\")\n",
+ "warnings.simplefilter(\"ignore\", UserWarning)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:30:42.363648Z",
+ "iopub.status.busy": "2024-03-13T12:30:42.363227Z",
+ "iopub.status.idle": "2024-03-13T12:30:45.734688Z",
+ "shell.execute_reply": "2024-03-13T12:30:45.733419Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from splink import splink_datasets\n",
+ "\n",
+ "pandas_df = splink_datasets.fake_1000\n",
+ "\n",
+ "df = spark.createDataFrame(pandas_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:30:45.740685Z",
+ "iopub.status.busy": "2024-03-13T12:30:45.740314Z",
+ "iopub.status.idle": "2024-03-13T12:30:45.773778Z",
+ "shell.execute_reply": "2024-03-13T12:30:45.772855Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import splink.comparison_library as cl\n",
+ "import splink.comparison_template_library as ctl\n",
+ "from splink import Linker, SettingsCreator, SparkAPI, block_on\n",
+ "\n",
+ "settings = SettingsCreator(\n",
+ " link_type=\"dedupe_only\",\n",
+ " comparisons=[\n",
+ " ctl.NameComparison(\"first_name\"),\n",
+ " ctl.NameComparison(\"surname\"),\n",
+ " # ctl.date_comparison(\"dob\", cast_strings_to_date=True),\n",
+ " # TOD=Fix date comparison\n",
+ " ctl.DateComparison(\n",
+ " \"dob\",\n",
+ " input_is_string=True,\n",
+ " datetime_metrics=[\"month\", \"year\", \"year\"],\n",
+ " datetime_thresholds=[1, 1, 10],\n",
+ " datetime_format=\"%Y%m%d\",\n",
+ " ),\n",
+ " cl.ExactMatch(\"city\").configure(term_frequency_adjustments=True),\n",
+ " ctl.EmailComparison(\"email\", include_username_fuzzy_level=False),\n",
+ " ],\n",
+ " blocking_rules_to_generate_predictions=[\n",
+ " block_on(\"first_name\"),\n",
+ " \"l.surname = r.surname\", # alternatively, you can write BRs in their SQL form\n",
+ " ],\n",
+ " retain_intermediate_calculation_columns=True,\n",
+ " em_convergence=0.01,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:30:45.779194Z",
+ "iopub.status.busy": "2024-03-13T12:30:45.778688Z",
+ "iopub.status.idle": "2024-03-13T12:30:57.746806Z",
+ "shell.execute_reply": "2024-03-13T12:30:57.744480Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "linker = Linker(df, settings, database_api=SparkAPI(spark_session=spark))\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=0.6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:30:57.757986Z",
+ "iopub.status.busy": "2024-03-13T12:30:57.757315Z",
+ "iopub.status.idle": "2024-03-13T12:31:17.080600Z",
+ "shell.execute_reply": "2024-03-13T12:31:17.079503Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "linker.training.estimate_u_using_random_sampling(max_pairs=5e5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:31:17.085610Z",
+ "iopub.status.busy": "2024-03-13T12:31:17.085246Z",
+ "iopub.status.idle": "2024-03-13T12:31:36.217869Z",
+ "shell.execute_reply": "2024-03-13T12:31:36.217063Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "training_blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n",
+ "training_session_fname_sname = (\n",
+ " linker.training.estimate_parameters_using_expectation_maximisation(training_blocking_rule)\n",
+ ")\n",
+ "\n",
+ "training_blocking_rule = \"l.dob = r.dob\"\n",
+ "training_session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+ " training_blocking_rule\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:31:36.223120Z",
+ "iopub.status.busy": "2024-03-13T12:31:36.222561Z",
+ "iopub.status.idle": "2024-03-13T12:31:44.599133Z",
+ "shell.execute_reply": "2024-03-13T12:31:44.597894Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "results = linker.inference.predict(threshold_match_probability=0.9)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-03-13T12:31:44.605970Z",
+ "iopub.status.busy": "2024-03-13T12:31:44.605505Z",
+ "iopub.status.idle": "2024-03-13T12:31:44.750590Z",
+ "shell.execute_reply": "2024-03-13T12:31:44.749429Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "results.as_pandas_dataframe(limit=5)"
+ ]
}
- },
- "outputs": [],
- "source": [
- "results = linker.predict(threshold_match_probability=0.9)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-03-13T12:31:44.605970Z",
- "iopub.status.busy": "2024-03-13T12:31:44.605505Z",
- "iopub.status.idle": "2024-03-13T12:31:44.750590Z",
- "shell.execute_reply": "2024-03-13T12:31:44.749429Z"
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
},
- "tags": []
- },
- "outputs": [],
- "source": [
- "results.as_pandas_dataframe(limit=5)"
- ]
- }
- ],
- "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"
+ }
},
- "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
-}
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb b/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb
index ce68a7b9a6..3eea1b0b3e 100644
--- a/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb
+++ b/docs/demos/examples/sqlite/deduplicate_50k_synthetic.ipynb
@@ -1,461 +1,461 @@
{
- "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",
- "\n",
- "Note, as explained in the [backends topic guide](https://moj-analytical-services.github.io/splink/topic_guides/backends.html#sqlite), SQLite does not natively support string fuzzy matching functions such as `damareau-levenshtein` and `jaro-winkler` (as used in this example). Instead, these have been imported as python User Defined Functions (UDFs). One drawback of python UDFs is that they are considerably slower than native-SQL comparisons. As such, if you are hitting issues with large run times, consider switching to DuckDB (or some other backend).\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- " \n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:30.610213Z",
- "iopub.status.busy": "2024-05-15T18:41:30.609846Z",
- "iopub.status.idle": "2024-05-15T18:41:30.615335Z",
- "shell.execute_reply": "2024-05-15T18:41:30.614566Z"
- }
- },
- "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\n",
- "# !pip install rapidfuzz"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:30.619046Z",
- "iopub.status.busy": "2024-05-15T18:41:30.618760Z",
- "iopub.status.idle": "2024-05-15T18:41:31.933775Z",
- "shell.execute_reply": "2024-05-15T18:41:31.932989Z"
- }
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "\n",
- "from splink import splink_datasets\n",
- "\n",
- "pd.options.display.max_rows = 1000\n",
- "# reduce size of dataset to make things run faster\n",
- "df = splink_datasets.historical_50k.sample(5000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:31.938051Z",
- "iopub.status.busy": "2024-05-15T18:41:31.937677Z",
- "iopub.status.idle": "2024-05-15T18:41:32.856954Z",
- "shell.execute_reply": "2024-05-15T18:41:32.856284Z"
- }
- },
- "outputs": [],
- "source": [
- "from splink.backends.sqlite import SQLiteAPI\n",
- "from splink.exploratory import profile_columns\n",
- "\n",
- "db_api = SQLiteAPI()\n",
- "profile_columns(\n",
- " df, db_api, column_expressions=[\"first_name\", \"postcode_fake\", \"substr(dob, 1,4)\"]\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:32.900620Z",
- "iopub.status.busy": "2024-05-15T18:41:32.900280Z",
- "iopub.status.idle": "2024-05-15T18:41:33.193607Z",
- "shell.execute_reply": "2024-05-15T18:41:33.192963Z"
- }
- },
- "outputs": [],
- "source": [
- "from splink import 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 = SQLiteAPI()\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": "2024-05-15T18:41:33.197015Z",
- "iopub.status.busy": "2024-05-15T18:41:33.196743Z",
- "iopub.status.idle": "2024-05-15T18:41:33.330331Z",
- "shell.execute_reply": "2024-05-15T18:41:33.329671Z"
+ "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",
+ "\n",
+ "Note, as explained in the [backends topic guide](https://moj-analytical-services.github.io/splink/topic_guides/backends.html#sqlite), SQLite does not natively support string fuzzy matching functions such as `damareau-levenshtein` and `jaro-winkler` (as used in this example). Instead, these have been imported as python User Defined Functions (UDFs). One drawback of python UDFs is that they are considerably slower than native-SQL comparisons. As such, if you are hitting issues with large run times, consider switching to DuckDB (or some other backend).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:30.610213Z",
+ "iopub.status.busy": "2024-05-15T18:41:30.609846Z",
+ "iopub.status.idle": "2024-05-15T18:41:30.615335Z",
+ "shell.execute_reply": "2024-05-15T18:41:30.614566Z"
+ }
+ },
+ "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\n",
+ "# !pip install rapidfuzz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:30.619046Z",
+ "iopub.status.busy": "2024-05-15T18:41:30.618760Z",
+ "iopub.status.idle": "2024-05-15T18:41:31.933775Z",
+ "shell.execute_reply": "2024-05-15T18:41:31.932989Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "from splink import splink_datasets\n",
+ "\n",
+ "pd.options.display.max_rows = 1000\n",
+ "# reduce size of dataset to make things run faster\n",
+ "df = splink_datasets.historical_50k.sample(5000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:31.938051Z",
+ "iopub.status.busy": "2024-05-15T18:41:31.937677Z",
+ "iopub.status.idle": "2024-05-15T18:41:32.856954Z",
+ "shell.execute_reply": "2024-05-15T18:41:32.856284Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from splink.backends.sqlite import SQLiteAPI\n",
+ "from splink.exploratory import profile_columns\n",
+ "\n",
+ "db_api = SQLiteAPI()\n",
+ "profile_columns(\n",
+ " df, db_api, column_expressions=[\"first_name\", \"postcode_fake\", \"substr(dob, 1,4)\"]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:32.900620Z",
+ "iopub.status.busy": "2024-05-15T18:41:32.900280Z",
+ "iopub.status.idle": "2024-05-15T18:41:33.193607Z",
+ "shell.execute_reply": "2024-05-15T18:41:33.192963Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from splink import 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 = SQLiteAPI()\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": "2024-05-15T18:41:33.197015Z",
+ "iopub.status.busy": "2024-05-15T18:41:33.196743Z",
+ "iopub.status.idle": "2024-05-15T18:41:33.330331Z",
+ "shell.execute_reply": "2024-05-15T18:41:33.329671Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import splink.comparison_library as cl\n",
+ "import splink.comparison_template_library as ctl\n",
+ "from splink import Linker\n",
+ "\n",
+ "settings = {\n",
+ " \"link_type\": \"dedupe_only\",\n",
+ " \"blocking_rules_to_generate_predictions\": [\n",
+ " 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",
+ " ],\n",
+ " \"comparisons\": [\n",
+ " ctl.NameComparison(\"first_name\", fuzzy_thresholds=[0.9]).configure(\n",
+ " term_frequency_adjustments=True\n",
+ " ),\n",
+ " ctl.NameComparison(\"surname\", fuzzy_thresholds=[0.9]).configure(\n",
+ " term_frequency_adjustments=True\n",
+ " ),\n",
+ " cl.DamerauLevenshteinAtThresholds(\"dob\", [1, 2]).configure(\n",
+ " term_frequency_adjustments=True\n",
+ " ),\n",
+ " cl.DamerauLevenshteinAtThresholds(\"postcode_fake\", [1, 2]),\n",
+ " cl.ExactMatch(\"birth_place\").configure(term_frequency_adjustments=True),\n",
+ " cl.ExactMatch(\n",
+ " \"occupation\",\n",
+ " ).configure(term_frequency_adjustments=True),\n",
+ " ],\n",
+ " \"retain_matching_columns\": True,\n",
+ " \"retain_intermediate_calculation_columns\": True,\n",
+ " \"max_iterations\": 10,\n",
+ " \"em_convergence\": 0.01,\n",
+ "}\n",
+ "\n",
+ "linker = Linker(df, settings, database_api=db_api)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:33.334300Z",
+ "iopub.status.busy": "2024-05-15T18:41:33.333988Z",
+ "iopub.status.idle": "2024-05-15T18:41:33.488238Z",
+ "shell.execute_reply": "2024-05-15T18:41:33.487555Z"
+ }
+ },
+ "outputs": [],
+ "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": "2024-05-15T18:41:33.491551Z",
+ "iopub.status.busy": "2024-05-15T18:41:33.491328Z",
+ "iopub.status.idle": "2024-05-15T18:41:41.469753Z",
+ "shell.execute_reply": "2024-05-15T18:41:41.469157Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:41.473301Z",
+ "iopub.status.busy": "2024-05-15T18:41:41.473009Z",
+ "iopub.status.idle": "2024-05-15T18:41:41.683463Z",
+ "shell.execute_reply": "2024-05-15T18:41:41.682843Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "training_blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n",
+ "training_session_names = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+ " training_blocking_rule, estimate_without_term_frequencies=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:41.686951Z",
+ "iopub.status.busy": "2024-05-15T18:41:41.686683Z",
+ "iopub.status.idle": "2024-05-15T18:41:41.926273Z",
+ "shell.execute_reply": "2024-05-15T18:41:41.925689Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "training_blocking_rule = \"l.dob = r.dob\"\n",
+ "training_session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
+ " training_blocking_rule, estimate_without_term_frequencies=True\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": "2024-05-15T18:41:41.929306Z",
+ "iopub.status.busy": "2024-05-15T18:41:41.929078Z",
+ "iopub.status.idle": "2024-05-15T18:41:42.230106Z",
+ "shell.execute_reply": "2024-05-15T18:41:42.229484Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "linker.visualisations.match_weights_chart()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:42.233172Z",
+ "iopub.status.busy": "2024-05-15T18:41:42.232933Z",
+ "iopub.status.idle": "2024-05-15T18:41:42.813828Z",
+ "shell.execute_reply": "2024-05-15T18:41:42.813043Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "linker.evaluation.unlinkables_chart()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:42.817975Z",
+ "iopub.status.busy": "2024-05-15T18:41:42.817397Z",
+ "iopub.status.idle": "2024-05-15T18:41:43.292311Z",
+ "shell.execute_reply": "2024-05-15T18:41:43.291620Z"
+ }
+ },
+ "outputs": [],
+ "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": "2024-05-15T18:41:43.296030Z",
+ "iopub.status.busy": "2024-05-15T18:41:43.295753Z",
+ "iopub.status.idle": "2024-05-15T18:41:43.969119Z",
+ "shell.execute_reply": "2024-05-15T18:41:43.968521Z"
+ }
+ },
+ "outputs": [],
+ "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": "2024-05-15T18:41:43.972219Z",
+ "iopub.status.busy": "2024-05-15T18:41:43.971787Z",
+ "iopub.status.idle": "2024-05-15T18:41:44.116709Z",
+ "shell.execute_reply": "2024-05-15T18:41:44.115993Z"
+ }
+ },
+ "outputs": [],
+ "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": "2024-05-15T18:41:44.120162Z",
+ "iopub.status.busy": "2024-05-15T18:41:44.119922Z",
+ "iopub.status.idle": "2024-05-15T18:41:44.180152Z",
+ "shell.execute_reply": "2024-05-15T18:41:44.179445Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "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",
+ "from IPython.display import IFrame\n",
+ "\n",
+ "IFrame(src=\"./dashboards/50k_cluster.html\", width=\"100%\", height=1200)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:44.184020Z",
+ "iopub.status.busy": "2024-05-15T18:41:44.183710Z",
+ "iopub.status.idle": "2024-05-15T18:41:46.543532Z",
+ "shell.execute_reply": "2024-05-15T18:41:46.542614Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "linker.evaluation.accuracy_analysis_from_labels_column(\n",
+ " \"cluster\", output_type=\"roc\", match_weight_round_to_nearest=0.02\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-05-15T18:41:46.557696Z",
+ "iopub.status.busy": "2024-05-15T18:41:46.557395Z",
+ "iopub.status.idle": "2024-05-15T18:41:47.295019Z",
+ "shell.execute_reply": "2024-05-15T18:41:47.294474Z"
+ }
+ },
+ "outputs": [],
+ "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": "2024-05-15T18:41:47.298555Z",
+ "iopub.status.busy": "2024-05-15T18:41:47.298310Z",
+ "iopub.status.idle": "2024-05-15T18:41:50.039196Z",
+ "shell.execute_reply": "2024-05-15T18:41:50.038400Z"
+ }
+ },
+ "outputs": [],
+ "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)"
+ ]
}
- },
- "outputs": [],
- "source": [
- "import splink.comparison_library as cl\n",
- "import splink.comparison_template_library as ctl\n",
- "from splink import Linker\n",
- "\n",
- "settings = {\n",
- " \"link_type\": \"dedupe_only\",\n",
- " \"blocking_rules_to_generate_predictions\": [\n",
- " 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",
- " ],\n",
- " \"comparisons\": [\n",
- " ctl.NameComparison(\"first_name\", fuzzy_thresholds=[0.9]).configure(\n",
- " term_frequency_adjustments=True\n",
- " ),\n",
- " ctl.NameComparison(\"surname\", fuzzy_thresholds=[0.9]).configure(\n",
- " term_frequency_adjustments=True\n",
- " ),\n",
- " cl.DamerauLevenshteinAtThresholds(\"dob\", [1, 2]).configure(\n",
- " term_frequency_adjustments=True\n",
- " ),\n",
- " cl.DamerauLevenshteinAtThresholds(\"postcode_fake\", [1, 2]),\n",
- " cl.ExactMatch(\"birth_place\").configure(term_frequency_adjustments=True),\n",
- " cl.ExactMatch(\n",
- " \"occupation\",\n",
- " ).configure(term_frequency_adjustments=True),\n",
- " ],\n",
- " \"retain_matching_columns\": True,\n",
- " \"retain_intermediate_calculation_columns\": True,\n",
- " \"max_iterations\": 10,\n",
- " \"em_convergence\": 0.01,\n",
- "}\n",
- "\n",
- "linker = Linker(df, settings, database_api=db_api)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:33.334300Z",
- "iopub.status.busy": "2024-05-15T18:41:33.333988Z",
- "iopub.status.idle": "2024-05-15T18:41:33.488238Z",
- "shell.execute_reply": "2024-05-15T18:41:33.487555Z"
- }
- },
- "outputs": [],
- "source": [
- "linker.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": "2024-05-15T18:41:33.491551Z",
- "iopub.status.busy": "2024-05-15T18:41:33.491328Z",
- "iopub.status.idle": "2024-05-15T18:41:41.469753Z",
- "shell.execute_reply": "2024-05-15T18:41:41.469157Z"
- }
- },
- "outputs": [],
- "source": [
- "linker.estimate_u_using_random_sampling(max_pairs=1e6)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:41.473301Z",
- "iopub.status.busy": "2024-05-15T18:41:41.473009Z",
- "iopub.status.idle": "2024-05-15T18:41:41.683463Z",
- "shell.execute_reply": "2024-05-15T18:41:41.682843Z"
- }
- },
- "outputs": [],
- "source": [
- "training_blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n",
- "training_session_names = linker.estimate_parameters_using_expectation_maximisation(\n",
- " training_blocking_rule, estimate_without_term_frequencies=True\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:41.686951Z",
- "iopub.status.busy": "2024-05-15T18:41:41.686683Z",
- "iopub.status.idle": "2024-05-15T18:41:41.926273Z",
- "shell.execute_reply": "2024-05-15T18:41:41.925689Z"
- }
- },
- "outputs": [],
- "source": [
- "training_blocking_rule = \"l.dob = r.dob\"\n",
- "training_session_dob = linker.estimate_parameters_using_expectation_maximisation(\n",
- " training_blocking_rule, estimate_without_term_frequencies=True\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": "2024-05-15T18:41:41.929306Z",
- "iopub.status.busy": "2024-05-15T18:41:41.929078Z",
- "iopub.status.idle": "2024-05-15T18:41:42.230106Z",
- "shell.execute_reply": "2024-05-15T18:41:42.229484Z"
- }
- },
- "outputs": [],
- "source": [
- "linker.match_weights_chart()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:42.233172Z",
- "iopub.status.busy": "2024-05-15T18:41:42.232933Z",
- "iopub.status.idle": "2024-05-15T18:41:42.813828Z",
- "shell.execute_reply": "2024-05-15T18:41:42.813043Z"
- }
- },
- "outputs": [],
- "source": [
- "linker.unlinkables_chart()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:42.817975Z",
- "iopub.status.busy": "2024-05-15T18:41:42.817397Z",
- "iopub.status.idle": "2024-05-15T18:41:43.292311Z",
- "shell.execute_reply": "2024-05-15T18:41:43.291620Z"
- }
- },
- "outputs": [],
- "source": [
- "df_predict = linker.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": "2024-05-15T18:41:43.296030Z",
- "iopub.status.busy": "2024-05-15T18:41:43.295753Z",
- "iopub.status.idle": "2024-05-15T18:41:43.969119Z",
- "shell.execute_reply": "2024-05-15T18:41:43.968521Z"
- }
- },
- "outputs": [],
- "source": [
- "\n",
- "records_to_plot = df_e.to_dict(orient=\"records\")\n",
- "linker.waterfall_chart(records_to_plot, filter_nulls=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:43.972219Z",
- "iopub.status.busy": "2024-05-15T18:41:43.971787Z",
- "iopub.status.idle": "2024-05-15T18:41:44.116709Z",
- "shell.execute_reply": "2024-05-15T18:41:44.115993Z"
- }
- },
- "outputs": [],
- "source": [
- "clusters = linker.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": "2024-05-15T18:41:44.120162Z",
- "iopub.status.busy": "2024-05-15T18:41:44.119922Z",
- "iopub.status.idle": "2024-05-15T18:41:44.180152Z",
- "shell.execute_reply": "2024-05-15T18:41:44.179445Z"
- }
- },
- "outputs": [],
- "source": [
- "linker.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",
- "from IPython.display import IFrame\n",
- "\n",
- "IFrame(src=\"./dashboards/50k_cluster.html\", width=\"100%\", height=1200)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:44.184020Z",
- "iopub.status.busy": "2024-05-15T18:41:44.183710Z",
- "iopub.status.idle": "2024-05-15T18:41:46.543532Z",
- "shell.execute_reply": "2024-05-15T18:41:46.542614Z"
- }
- },
- "outputs": [],
- "source": [
- "linker.accuracy_analysis_from_labels_column(\n",
- " \"cluster\", output_type=\"roc\", match_weight_round_to_nearest=0.02\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:46.557696Z",
- "iopub.status.busy": "2024-05-15T18:41:46.557395Z",
- "iopub.status.idle": "2024-05-15T18:41:47.295019Z",
- "shell.execute_reply": "2024-05-15T18:41:47.294474Z"
- }
- },
- "outputs": [],
- "source": [
- "records = linker.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.waterfall_chart(records)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2024-05-15T18:41:47.298555Z",
- "iopub.status.busy": "2024-05-15T18:41:47.298310Z",
- "iopub.status.idle": "2024-05-15T18:41:50.039196Z",
- "shell.execute_reply": "2024-05-15T18:41:50.038400Z"
+ ],
+ "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"
}
- },
- "outputs": [],
- "source": [
- "# Some of the false negatives will be because they weren't detected by the blocking rules\n",
- "records = linker.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.waterfall_chart(records)"
- ]
- }
- ],
- "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"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/docs/demos/tutorials/02_Exploratory_analysis.ipynb b/docs/demos/tutorials/02_Exploratory_analysis.ipynb
index 96ca8b46ca..8639b0d256 100644
--- a/docs/demos/tutorials/02_Exploratory_analysis.ipynb
+++ b/docs/demos/tutorials/02_Exploratory_analysis.ipynb
@@ -38,10 +38,10 @@
"id": "09c3966a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:43:17.689119Z",
- "iopub.status.busy": "2024-05-20T18:43:17.688731Z",
- "iopub.status.idle": "2024-05-20T18:43:17.695621Z",
- "shell.execute_reply": "2024-05-20T18:43:17.694607Z"
+ "iopub.execute_input": "2024-06-07T09:02:36.670538Z",
+ "iopub.status.busy": "2024-06-07T09:02:36.670013Z",
+ "iopub.status.idle": "2024-06-07T09:02:36.690046Z",
+ "shell.execute_reply": "2024-06-07T09:02:36.689361Z"
}
},
"outputs": [],
@@ -56,10 +56,10 @@
"id": "ffceed65",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:43:17.700435Z",
- "iopub.status.busy": "2024-05-20T18:43:17.700005Z",
- "iopub.status.idle": "2024-05-20T18:43:21.396008Z",
- "shell.execute_reply": "2024-05-20T18:43:21.394398Z"
+ "iopub.execute_input": "2024-06-07T09:02:36.693770Z",
+ "iopub.status.busy": "2024-06-07T09:02:36.693489Z",
+ "iopub.status.idle": "2024-06-07T09:02:38.568880Z",
+ "shell.execute_reply": "2024-06-07T09:02:38.568146Z"
}
},
"outputs": [
@@ -171,7 +171,7 @@
"source": [
"### Instantiate the linker\n",
"\n",
- "Most of Splink's core functionality can be accessed as methods on a linker object. For example, to make predictions, you would call `linker.predict()`.\n",
+ "Most of Splink's core functionality can be accessed as methods on a linker object. For example, to make predictions, you would call `linker.inference.predict()`.\n",
"\n",
"We therefore begin by instantiating the linker, passing in the data we wish to deduplicate.\n"
]
@@ -182,10 +182,10 @@
"id": "8a1aa029",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:43:21.401790Z",
- "iopub.status.busy": "2024-05-20T18:43:21.401303Z",
- "iopub.status.idle": "2024-05-20T18:43:21.520430Z",
- "shell.execute_reply": "2024-05-20T18:43:21.519221Z"
+ "iopub.execute_input": "2024-06-07T09:02:38.572909Z",
+ "iopub.status.busy": "2024-06-07T09:02:38.572612Z",
+ "iopub.status.idle": "2024-06-07T09:02:38.646815Z",
+ "shell.execute_reply": "2024-06-07T09:02:38.646283Z"
}
},
"outputs": [],
@@ -220,10 +220,10 @@
"id": "6dae307c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:43:21.526661Z",
- "iopub.status.busy": "2024-05-20T18:43:21.526244Z",
- "iopub.status.idle": "2024-05-20T18:43:21.827456Z",
- "shell.execute_reply": "2024-05-20T18:43:21.826651Z"
+ "iopub.execute_input": "2024-06-07T09:02:38.650279Z",
+ "iopub.status.busy": "2024-06-07T09:02:38.650040Z",
+ "iopub.status.idle": "2024-06-07T09:02:38.861459Z",
+ "shell.execute_reply": "2024-06-07T09:02:38.860952Z"
}
},
"outputs": [
@@ -232,23 +232,23 @@
"text/html": [
"\n",
"\n",
- "\n",
+ "\n",
""
],
"text/plain": [
diff --git a/docs/demos/tutorials/03_Blocking.ipynb b/docs/demos/tutorials/03_Blocking.ipynb
index d7854f0950..2ec90b9bd2 100644
--- a/docs/demos/tutorials/03_Blocking.ipynb
+++ b/docs/demos/tutorials/03_Blocking.ipynb
@@ -124,10 +124,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:45:37.145082Z",
- "iopub.status.busy": "2024-05-20T18:45:37.144746Z",
- "iopub.status.idle": "2024-05-20T18:45:37.161836Z",
- "shell.execute_reply": "2024-05-20T18:45:37.160951Z"
+ "iopub.execute_input": "2024-06-07T09:02:41.813986Z",
+ "iopub.status.busy": "2024-06-07T09:02:41.813675Z",
+ "iopub.status.idle": "2024-06-07T09:02:41.818787Z",
+ "shell.execute_reply": "2024-06-07T09:02:41.818177Z"
}
},
"outputs": [],
@@ -141,10 +141,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:45:37.167030Z",
- "iopub.status.busy": "2024-05-20T18:45:37.166460Z",
- "iopub.status.idle": "2024-05-20T18:45:39.143681Z",
- "shell.execute_reply": "2024-05-20T18:45:39.143036Z"
+ "iopub.execute_input": "2024-06-07T09:02:41.821999Z",
+ "iopub.status.busy": "2024-06-07T09:02:41.821754Z",
+ "iopub.status.idle": "2024-06-07T09:02:43.323143Z",
+ "shell.execute_reply": "2024-06-07T09:02:43.322444Z"
},
"tags": []
},
@@ -171,10 +171,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:45:39.147367Z",
- "iopub.status.busy": "2024-05-20T18:45:39.146950Z",
- "iopub.status.idle": "2024-05-20T18:45:39.458766Z",
- "shell.execute_reply": "2024-05-20T18:45:39.457589Z"
+ "iopub.execute_input": "2024-06-07T09:02:43.327040Z",
+ "iopub.status.busy": "2024-06-07T09:02:43.326745Z",
+ "iopub.status.idle": "2024-06-07T09:02:43.595484Z",
+ "shell.execute_reply": "2024-06-07T09:02:43.594829Z"
},
"tags": []
},
@@ -247,10 +247,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T18:45:39.504502Z",
- "iopub.status.busy": "2024-05-20T18:45:39.504167Z",
- "iopub.status.idle": "2024-05-20T18:45:39.756687Z",
- "shell.execute_reply": "2024-05-20T18:45:39.756039Z"
+ "iopub.execute_input": "2024-06-07T09:02:43.600028Z",
+ "iopub.status.busy": "2024-06-07T09:02:43.599628Z",
+ "iopub.status.idle": "2024-06-07T09:02:43.828432Z",
+ "shell.execute_reply": "2024-06-07T09:02:43.827890Z"
},
"tags": []
},
@@ -260,23 +260,23 @@
"text/html": [
"\n",
"\n",
- "\n",
+ "\n",
""
],
"text/plain": [
diff --git a/docs/demos/tutorials/04_Estimating_model_parameters.ipynb b/docs/demos/tutorials/04_Estimating_model_parameters.ipynb
index 7d09bf5da9..49a92fa897 100644
--- a/docs/demos/tutorials/04_Estimating_model_parameters.ipynb
+++ b/docs/demos/tutorials/04_Estimating_model_parameters.ipynb
@@ -106,10 +106,10 @@
"id": "9ceef6f1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:05.342047Z",
- "iopub.status.busy": "2024-05-20T19:07:05.341702Z",
- "iopub.status.idle": "2024-05-20T19:07:05.361927Z",
- "shell.execute_reply": "2024-05-20T19:07:05.360830Z"
+ "iopub.execute_input": "2024-06-07T09:02:46.239817Z",
+ "iopub.status.busy": "2024-06-07T09:02:46.239448Z",
+ "iopub.status.idle": "2024-06-07T09:02:46.244716Z",
+ "shell.execute_reply": "2024-06-07T09:02:46.244029Z"
}
},
"outputs": [],
@@ -124,10 +124,10 @@
"id": "aa6a9e30",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:05.367071Z",
- "iopub.status.busy": "2024-05-20T19:07:05.366710Z",
- "iopub.status.idle": "2024-05-20T19:07:07.120254Z",
- "shell.execute_reply": "2024-05-20T19:07:07.119460Z"
+ "iopub.execute_input": "2024-06-07T09:02:46.248398Z",
+ "iopub.status.busy": "2024-06-07T09:02:46.248108Z",
+ "iopub.status.idle": "2024-06-07T09:02:47.649177Z",
+ "shell.execute_reply": "2024-06-07T09:02:47.648453Z"
},
"tags": []
},
@@ -157,10 +157,10 @@
"id": "4b7159fb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:07.124601Z",
- "iopub.status.busy": "2024-05-20T19:07:07.124275Z",
- "iopub.status.idle": "2024-05-20T19:07:07.140860Z",
- "shell.execute_reply": "2024-05-20T19:07:07.139974Z"
+ "iopub.execute_input": "2024-06-07T09:02:47.653241Z",
+ "iopub.status.busy": "2024-06-07T09:02:47.652946Z",
+ "iopub.status.idle": "2024-06-07T09:02:47.667819Z",
+ "shell.execute_reply": "2024-06-07T09:02:47.667228Z"
},
"tags": []
},
@@ -200,10 +200,10 @@
"id": "bd6143e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:07.144985Z",
- "iopub.status.busy": "2024-05-20T19:07:07.144681Z",
- "iopub.status.idle": "2024-05-20T19:07:07.175908Z",
- "shell.execute_reply": "2024-05-20T19:07:07.175137Z"
+ "iopub.execute_input": "2024-06-07T09:02:47.671318Z",
+ "iopub.status.busy": "2024-06-07T09:02:47.671046Z",
+ "iopub.status.idle": "2024-06-07T09:02:47.707750Z",
+ "shell.execute_reply": "2024-06-07T09:02:47.707104Z"
},
"tags": []
},
@@ -246,10 +246,10 @@
"id": "0fa0611a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:07.179680Z",
- "iopub.status.busy": "2024-05-20T19:07:07.179398Z",
- "iopub.status.idle": "2024-05-20T19:07:07.297459Z",
- "shell.execute_reply": "2024-05-20T19:07:07.296785Z"
+ "iopub.execute_input": "2024-06-07T09:02:47.711774Z",
+ "iopub.status.busy": "2024-06-07T09:02:47.711447Z",
+ "iopub.status.idle": "2024-06-07T09:02:47.834384Z",
+ "shell.execute_reply": "2024-06-07T09:02:47.833818Z"
},
"tags": []
},
@@ -332,10 +332,10 @@
"id": "cbf92120",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:07.301055Z",
- "iopub.status.busy": "2024-05-20T19:07:07.300814Z",
- "iopub.status.idle": "2024-05-20T19:07:07.441083Z",
- "shell.execute_reply": "2024-05-20T19:07:07.440589Z"
+ "iopub.execute_input": "2024-06-07T09:02:47.837910Z",
+ "iopub.status.busy": "2024-06-07T09:02:47.837638Z",
+ "iopub.status.idle": "2024-06-07T09:02:47.986455Z",
+ "shell.execute_reply": "2024-06-07T09:02:47.985936Z"
},
"tags": []
},
@@ -357,7 +357,7 @@
" \"l.email = r.email\",\n",
"]\n",
"\n",
- "linker.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
+ "linker.training.estimate_probability_two_random_records_match(deterministic_rules, recall=0.7)"
]
},
{
@@ -386,10 +386,10 @@
"id": "b8d49e7a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:07.444298Z",
- "iopub.status.busy": "2024-05-20T19:07:07.444048Z",
- "iopub.status.idle": "2024-05-20T19:07:07.830875Z",
- "shell.execute_reply": "2024-05-20T19:07:07.830279Z"
+ "iopub.execute_input": "2024-06-07T09:02:47.989645Z",
+ "iopub.status.busy": "2024-06-07T09:02:47.989414Z",
+ "iopub.status.idle": "2024-06-07T09:02:48.397955Z",
+ "shell.execute_reply": "2024-06-07T09:02:48.396948Z"
}
},
"outputs": [
@@ -430,7 +430,7 @@
}
],
"source": [
- "linker.estimate_u_using_random_sampling(max_pairs=1e6)"
+ "linker.training.estimate_u_using_random_sampling(max_pairs=1e6)"
]
},
{
@@ -481,10 +481,10 @@
"id": "098f0a40",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:07.833995Z",
- "iopub.status.busy": "2024-05-20T19:07:07.833766Z",
- "iopub.status.idle": "2024-05-20T19:07:08.412952Z",
- "shell.execute_reply": "2024-05-20T19:07:08.412392Z"
+ "iopub.execute_input": "2024-06-07T09:02:48.401693Z",
+ "iopub.status.busy": "2024-06-07T09:02:48.401466Z",
+ "iopub.status.idle": "2024-06-07T09:02:48.931826Z",
+ "shell.execute_reply": "2024-06-07T09:02:48.931336Z"
}
},
"outputs": [
@@ -627,7 +627,7 @@
"source": [
"training_blocking_rule = block_on(\"first_name\", \"surname\")\n",
"training_session_fname_sname = (\n",
- " linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)\n",
+ " linker.training.estimate_parameters_using_expectation_maximisation(training_blocking_rule)\n",
")"
]
},
@@ -647,10 +647,10 @@
"id": "ac8d3264",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:08.415856Z",
- "iopub.status.busy": "2024-05-20T19:07:08.415632Z",
- "iopub.status.idle": "2024-05-20T19:07:09.376153Z",
- "shell.execute_reply": "2024-05-20T19:07:09.375519Z"
+ "iopub.execute_input": "2024-06-07T09:02:48.934774Z",
+ "iopub.status.busy": "2024-06-07T09:02:48.934564Z",
+ "iopub.status.idle": "2024-06-07T09:02:49.904132Z",
+ "shell.execute_reply": "2024-06-07T09:02:49.903556Z"
}
},
"outputs": [
@@ -832,7 +832,7 @@
],
"source": [
"training_blocking_rule = block_on(\"dob\")\n",
- "training_session_dob = linker.estimate_parameters_using_expectation_maximisation(\n",
+ "training_session_dob = linker.training.estimate_parameters_using_expectation_maximisation(\n",
" training_blocking_rule\n",
")"
]
@@ -863,10 +863,10 @@
"id": "3a1e15cc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-05-20T19:07:09.379360Z",
- "iopub.status.busy": "2024-05-20T19:07:09.379104Z",
- "iopub.status.idle": "2024-05-20T19:07:09.667771Z",
- "shell.execute_reply": "2024-05-20T19:07:09.667214Z"
+ "iopub.execute_input": "2024-06-07T09:02:49.907368Z",
+ "iopub.status.busy": "2024-06-07T09:02:49.907142Z",
+ "iopub.status.idle": "2024-06-07T09:02:50.194366Z",
+ "shell.execute_reply": "2024-06-07T09:02:50.193774Z"
}
},
"outputs": [
@@ -875,23 +875,23 @@
"text/html": [
"\n",
"\n",
- "\n",
+ "\n",
""
+ ],
+ "text/plain": [
+ "alt.LayerChart(...)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"records_to_view = df_predictions.as_record_dict(limit=5)\n",
- "linker.waterfall_chart(records_to_view, filter_nulls=False)"
- ],
- "outputs": []
+ "linker.visualisations.waterfall_chart(records_to_view, filter_nulls=False)"
+ ]
},
{
"cell_type": "markdown",
@@ -138,22 +229,45 @@
"id": "da85169c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-03-27T15:09:23.291425Z",
- "iopub.status.busy": "2024-03-27T15:09:23.291204Z",
- "iopub.status.idle": "2024-03-27T15:09:23.402967Z",
- "shell.execute_reply": "2024-03-27T15:09:23.402274Z"
+ "iopub.execute_input": "2024-06-07T09:03:00.084660Z",
+ "iopub.status.busy": "2024-06-07T09:03:00.084445Z",
+ "iopub.status.idle": "2024-06-07T09:03:00.178870Z",
+ "shell.execute_reply": "2024-06-07T09:03:00.178202Z"
},
"tags": []
},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/nd/c3xr518x3txg5kcqp1h7zwc80000gp/T/ipykernel_13654/2355919473.py:39: SplinkDeprecated: target_rows is deprecated; use max_pairs\n",
+ " linker.training.estimate_u_using_random_sampling(target_rows=1e6)\n",
+ "----- Estimating u probabilities using random sampling -----\n",
+ "\n",
+ "Estimated u probabilities using random sampling\n",
+ "\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",
+ " - postcode_fake (no m values are trained).\n",
+ "\n",
+ "----- Starting EM training session -----\n",
+ "\n",
+ "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",
+ " - postcode_fake\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",
+ "\n",
+ "Iteration 1: Largest change in params was -0.352 in probability_two_random_records_match\n",
+ "Iteration 2: Largest change in params was 0.108 in the m_probability of postcode_fake, level `All other comparisons`\n",
+ "Iteration 3: Largest change in params was 0.019 in the m_probability of postcode_fake, level `All other comparisons`\n",
+ "Iteration 4: Largest change in params was 0.00276 in the m_probability of postcode_fake, level `All other comparisons`\n",
+ "Iteration 5: Largest change in params was 0.000388 in the m_probability of postcode_fake, level `All other comparisons`\n",
+ "Iteration 6: Largest change in params was 5.44e-05 in the m_probability of postcode_fake, level `All other comparisons`\n",
+ "\n",
+ "EM converged after 6 iterations\n",
+ "\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",
+ "\n",
+ "----- Starting EM training session -----\n",
+ "\n",
+ "Estimating the m probabilities of the model by blocking on:\n",
+ "(l.\"dob\" = r.\"dob\") AND (SUBSTR(l.\"postcode_fake\", 1, 3) = SUBSTR(r.\"postcode_fake\", 1, 3))\n",
+ "\n",
+ "Parameter estimates will be made for the following comparison(s):\n",
+ " - first_name\n",
+ " - surname\n",
+ "\n",
+ "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
+ " - postcode_fake\n",
+ "\n",
+ "Iteration 1: Largest change in params was 0.508 in probability_two_random_records_match\n",
+ "Iteration 2: Largest change in params was 0.0868 in probability_two_random_records_match\n",
+ "Iteration 3: Largest change in params was 0.0212 in probability_two_random_records_match\n",
+ "Iteration 4: Largest change in params was 0.00704 in probability_two_random_records_match\n",
+ "Iteration 5: Largest change in params was 0.00306 in probability_two_random_records_match\n",
+ "Iteration 6: Largest change in params was 0.00149 in probability_two_random_records_match\n",
+ "Iteration 7: Largest change in params was 0.000761 in probability_two_random_records_match\n",
+ "Iteration 8: Largest change in params was 0.000395 in probability_two_random_records_match\n",
+ "Iteration 9: Largest change in params was 0.000206 in probability_two_random_records_match\n",
+ "Iteration 10: Largest change in params was 0.000108 in probability_two_random_records_match\n",
+ "Iteration 11: Largest change in params was 5.66e-05 in probability_two_random_records_match\n",
+ "\n",
+ "EM converged after 11 iterations\n",
+ "\n",
+ "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n",
+ "Completed iteration 1, root rows count 316\n",
+ "Completed iteration 2, root rows count 63\n",
+ "Completed iteration 3, root rows count 12\n",
+ "Completed iteration 4, root rows count 0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "| \n", - " | cluster_id | \n", - "n_nodes | \n", - "n_edges | \n", - "density | \n", - "cluster_centralisation | \n", - "
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| 0 | \n", - "Q98761652-1 | \n", - "5 | \n", - "8.0 | \n", - "0.800000 | \n", - "0.333333 | \n", - "
| 1 | \n", - "Q10307857-1 | \n", - "11 | \n", - "35.0 | \n", - "0.636364 | \n", - "0.200000 | \n", - "
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+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " cluster_id n_nodes n_edges density cluster_centralisation\n",
+ "0 Q98761652-1 5 8.0 0.800000 0.333333\n",
+ "1 Q10307857-1 11 35.0 0.636364 0.200000\n",
+ "2 Q18910925-1 20 172.0 0.905263 0.105263\n",
+ "3 Q13530025-1 11 32.0 0.581818 0.266667\n",
+ "4 Q15966633-11 3 3.0 1.000000 0.000000\n",
+ "... ... ... ... ... ...\n",
+ "21530 Q5006750-7 1 0.0 NaN NaN\n",
+ "21531 Q5166888-13 1 0.0 NaN NaN\n",
+ "21532 Q5546247-8 1 0.0 NaN NaN\n",
+ "21533 Q6698372-5 1 0.0 NaN NaN\n",
+ "21534 Q7794499-6 1 0.0 NaN NaN\n",
+ "\n",
+ "[21535 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
],
- "text/plain": [
- " cluster_id n_nodes n_edges density cluster_centralisation\n",
- "0 Q98761652-1 5 8.0 0.800000 0.333333\n",
- "1 Q10307857-1 11 35.0 0.636364 0.200000\n",
- "2 Q18910925-1 20 172.0 0.905263 0.105263\n",
- "3 Q13530025-1 11 32.0 0.581818 0.266667\n",
- "4 Q15966633-11 3 3.0 1.000000 0.000000\n",
- "... ... ... ... ... ...\n",
- "21530 Q5006750-7 1 0.0 NaN NaN\n",
- "21531 Q5166888-13 1 0.0 NaN NaN\n",
- "21532 Q5546247-8 1 0.0 NaN NaN\n",
- "21533 Q6698372-5 1 0.0 NaN NaN\n",
- "21534 Q7794499-6 1 0.0 NaN NaN\n",
- "\n",
- "[21535 rows x 5 columns]"
+ "source": [
+ "import splink.duckdb.comparison_library as cl\n",
+ "from splink.datasets import splink_datasets\n",
+ "from splink.duckdb.blocking_rule_library import block_on\n",
+ "from splink.duckdb.linker import DuckDBLinker\n",
+ "\n",
+ "import ssl\n",
+ "\n",
+ "ssl._create_default_https_context = ssl._create_unverified_context\n",
+ "\n",
+ "df = splink_datasets.historical_50k\n",
+ "\n",
+ "settings_dict = {\n",
+ " \"link_type\": \"dedupe_only\",\n",
+ " \"blocking_rules_to_generate_predictions\": [\n",
+ " block_on([\"postcode_fake\", \"first_name\"]),\n",
+ " block_on([\"first_name\", \"surname\"]),\n",
+ " block_on([\"dob\", \"substr(postcode_fake,1,2)\"]),\n",
+ " block_on([\"postcode_fake\", \"substr(dob,1,3)\"]),\n",
+ " block_on([\"postcode_fake\", \"substr(dob,4,5)\"]),\n",
+ " ],\n",
+ " \"comparisons\": [\n",
+ " cl.exact_match(\n",
+ " \"first_name\",\n",
+ " term_frequency_adjustments=True,\n",
+ " ),\n",
+ " cl.jaro_winkler_at_thresholds(\n",
+ " \"surname\", distance_threshold_or_thresholds=[0.9, 0.8]\n",
+ " ),\n",
+ " cl.levenshtein_at_thresholds(\n",
+ " \"postcode_fake\", distance_threshold_or_thresholds=[1, 2]\n",
+ " ),\n",
+ " ],\n",
+ " \"retain_intermediate_calculation_columns\": True,\n",
+ "}\n",
+ "\n",
+ "\n",
+ "linker = DuckDBLinker(df, settings_dict)\n",
+ "\n",
+ "linker.training.estimate_u_using_random_sampling(target_rows=1e6)\n",
+ "\n",
+ "linker.training.estimate_parameters_using_expectation_maximisation(\n",
+ " block_on([\"first_name\", \"surname\"])\n",
+ ")\n",
+ "\n",
+ "linker.training.estimate_parameters_using_expectation_maximisation(\n",
+ " block_on([\"dob\", \"substr(postcode_fake, 1,3)\"])\n",
+ ")\n",
+ "\n",
+ "df_predict = linker.inference.predict()\n",
+ "df_clustered = linker.clustering.cluster_pairwise_predictions_at_threshold(df_predict, 0.95)\n",
+ "\n",
+ "graph_metrics = linker.compute_graph_metrics(df_predict, df_clustered)\n",
+ "\n",
+ "graph_metrics.clusters.as_pandas_dataframe()"
]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
}
- ],
- "source": [
- "import splink.duckdb.comparison_library as cl\n",
- "from splink.datasets import splink_datasets\n",
- "from splink.duckdb.blocking_rule_library import block_on\n",
- "from splink.duckdb.linker import DuckDBLinker\n",
- "\n",
- "import ssl\n",
- "\n",
- "ssl._create_default_https_context = ssl._create_unverified_context\n",
- "\n",
- "df = splink_datasets.historical_50k\n",
- "\n",
- "settings_dict = {\n",
- " \"link_type\": \"dedupe_only\",\n",
- " \"blocking_rules_to_generate_predictions\": [\n",
- " block_on([\"postcode_fake\", \"first_name\"]),\n",
- " block_on([\"first_name\", \"surname\"]),\n",
- " block_on([\"dob\", \"substr(postcode_fake,1,2)\"]),\n",
- " block_on([\"postcode_fake\", \"substr(dob,1,3)\"]),\n",
- " block_on([\"postcode_fake\", \"substr(dob,4,5)\"]),\n",
- " ],\n",
- " \"comparisons\": [\n",
- " cl.exact_match(\n",
- " \"first_name\",\n",
- " term_frequency_adjustments=True,\n",
- " ),\n",
- " cl.jaro_winkler_at_thresholds(\n",
- " \"surname\", distance_threshold_or_thresholds=[0.9, 0.8]\n",
- " ),\n",
- " cl.levenshtein_at_thresholds(\n",
- " \"postcode_fake\", distance_threshold_or_thresholds=[1, 2]\n",
- " ),\n",
- " ],\n",
- " \"retain_intermediate_calculation_columns\": True,\n",
- "}\n",
- "\n",
- "\n",
- "linker = DuckDBLinker(df, settings_dict)\n",
- "\n",
- "linker.estimate_u_using_random_sampling(target_rows=1e6)\n",
- "\n",
- "linker.estimate_parameters_using_expectation_maximisation(\n",
- " block_on([\"first_name\", \"surname\"])\n",
- ")\n",
- "\n",
- "linker.estimate_parameters_using_expectation_maximisation(\n",
- " block_on([\"dob\", \"substr(postcode_fake, 1,3)\"])\n",
- ")\n",
- "\n",
- "df_predict = linker.predict()\n",
- "df_clustered = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.95)\n",
- "\n",
- "graph_metrics = linker.compute_graph_metrics(df_predict, df_clustered)\n",
- "\n",
- "graph_metrics.clusters.as_pandas_dataframe()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "splink-bxsLLt4m",
- "language": "python",
- "name": "python3"
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "splink-bxsLLt4m",
+ "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": "3.9.6"
+ }
},
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/splink/internals/accuracy.py b/splink/internals/accuracy.py
index ab43e2f4e7..96d36d7f4f 100644
--- a/splink/internals/accuracy.py
+++ b/splink/internals/accuracy.py
@@ -1,7 +1,7 @@
from __future__ import annotations
from copy import deepcopy
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
from splink.internals.block_from_labels import block_from_labels
from splink.internals.blocking import BlockingRule
@@ -307,8 +307,11 @@ def _select_found_by_blocking_rules(linker: "Linker") -> str:
def truth_space_table_from_labels_table(
- linker, labels_tablename, threshold_actual=0.5, match_weight_round_to_nearest=None
-):
+ linker: Linker,
+ labels_tablename: str,
+ threshold_actual: float = 0.5,
+ match_weight_round_to_nearest: Optional[float] = None,
+) -> SplinkDataFrame:
pipeline = CTEPipeline()
nodes_with_tf = compute_df_concat_with_tf(linker, pipeline)
@@ -323,7 +326,7 @@ def truth_space_table_from_labels_table(
)
pipeline.enqueue_list_of_sqls(sqls)
- df_truth_space_table = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_truth_space_table = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_truth_space_table
@@ -356,7 +359,7 @@ def truth_space_table_from_labels_column(
"""
pipeline.enqueue_sql(sql, "__splink__cartesian_product")
- cartesian_count = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ cartesian_count = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
row_count_df = cartesian_count.as_record_dict()
cartesian_count.drop_table_from_database_and_remove_from_cache()
@@ -393,7 +396,7 @@ def truth_space_table_from_labels_column(
)
pipeline.enqueue_list_of_sqls(sqls)
- df_truth_space_table = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_truth_space_table = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_truth_space_table
@@ -439,12 +442,12 @@ def predictions_from_sample_of_pairwise_labels_sql(linker, labels_tablename):
def prediction_errors_from_labels_table(
- linker,
- labels_tablename,
- include_false_positives=True,
- include_false_negatives=True,
- threshold=0.5,
-):
+ linker: Linker,
+ labels_tablename: str,
+ include_false_positives: bool = True,
+ include_false_negatives: bool = True,
+ threshold: float = 0.5,
+) -> SplinkDataFrame:
pipeline = CTEPipeline()
nodes_with_tf = compute_df_concat_with_tf(linker, pipeline)
pipeline = CTEPipeline([nodes_with_tf])
@@ -486,7 +489,7 @@ def prediction_errors_from_labels_table(
pipeline.enqueue_sql(sql, "__splink__labels_with_fp_fn_status")
- return linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ return linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
def _predict_from_label_column_sql(linker, label_colname):
@@ -509,18 +512,18 @@ def _predict_from_label_column_sql(linker, label_colname):
settings._additional_column_names_to_retain.append(label_colname)
# Now we want to create predictions
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
return df_predict
def prediction_errors_from_label_column(
- linker,
- label_colname,
- include_false_positives=True,
- include_false_negatives=True,
- threshold=0.5,
-):
+ linker: Linker,
+ label_colname: str,
+ include_false_positives: bool = True,
+ include_false_negatives: bool = True,
+ threshold: float = 0.5,
+) -> SplinkDataFrame:
df_predict = _predict_from_label_column_sql(
linker,
label_colname,
@@ -577,6 +580,6 @@ def prediction_errors_from_label_column(
pipeline.enqueue_sql(sql, "__splink__predictions_from_label_column_fp_fn_only")
- predictions = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ predictions = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return predictions
diff --git a/splink/internals/cluster_studio.py b/splink/internals/cluster_studio.py
index b53b114b69..76d4da678f 100644
--- a/splink/internals/cluster_studio.py
+++ b/splink/internals/cluster_studio.py
@@ -63,7 +63,7 @@ def df_clusters_as_records(
sql = _clusters_sql(df_clustered_nodes, cluster_ids)
pipeline = CTEPipeline()
pipeline.enqueue_sql(sql, "__splink__scs_clusters")
- df_clusters = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_clusters = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_clusters.as_record_dict()
@@ -107,7 +107,7 @@ def create_df_nodes(
pipeline = CTEPipeline()
sql = _nodes_sql(df_clustered_nodes, cluster_ids)
pipeline.enqueue_sql(sql, "__splink__scs_nodes")
- df_nodes = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_nodes = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_nodes
@@ -151,7 +151,7 @@ def df_edges_as_records(
sql = _edges_sql(linker, df_predicted_edges, df_nodes)
pipeline = CTEPipeline()
pipeline.enqueue_sql(sql, "__splink__scs_edges")
- df_edges = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_edges = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_edges.as_record_dict()
@@ -168,7 +168,7 @@ def _get_random_cluster_ids(
"""
pipeline = CTEPipeline()
pipeline.enqueue_sql(sql, "__splink__cluster_count")
- df_cluster_count = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_cluster_count = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
cluster_count = df_cluster_count.as_record_dict()[0]["count"]
df_cluster_count.drop_table_from_database_and_remove_from_cache()
@@ -192,7 +192,7 @@ def _get_random_cluster_ids(
"""
pipeline = CTEPipeline()
pipeline.enqueue_sql(sql, "__splink__df_concat_with_tf_sample")
- df_sample = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_sample = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return [r["cluster_id"] for r in df_sample.as_record_dict()]
@@ -234,7 +234,7 @@ def _get_cluster_id_of_each_size(
"""
pipeline.enqueue_sql(sql, "__splink__cluster_count_row_numbered")
- df_cluster_sample_with_size = linker.db_api.sql_pipeline_to_splink_dataframe(
+ df_cluster_sample_with_size = linker._db_api.sql_pipeline_to_splink_dataframe(
pipeline
)
@@ -285,7 +285,7 @@ def _get_lowest_density_clusters(
"""
pipeline.enqueue_sql(sql, "__splink__lowest_density_clusters")
- df_lowest_density_clusters = linker.db_api.sql_pipeline_to_splink_dataframe(
+ df_lowest_density_clusters = linker._db_api.sql_pipeline_to_splink_dataframe(
pipeline
)
diff --git a/splink/internals/connected_components.py b/splink/internals/connected_components.py
index 69a3bae3a0..50319418ce 100644
--- a/splink/internals/connected_components.py
+++ b/splink/internals/connected_components.py
@@ -355,7 +355,7 @@ def _cc_create_unique_id_cols(
"""
pipeline = CTEPipeline()
pipeline.enqueue_sql(sql, "__splink__df_connected_components_df")
- return linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ return linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
def _exit_query(
@@ -453,7 +453,7 @@ def solve_connected_components(
pipeline.enqueue_sql(sql, "nodes")
sql = _cc_generate_neighbours_representation()
pipeline.enqueue_sql(sql, "__splink__df_neighbours")
- neighbours = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ neighbours = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
# Create our initial representatives table
pipeline = CTEPipeline([neighbours])
@@ -465,7 +465,7 @@ def solve_connected_components(
# Execute if we have no batching, otherwise add it to our batched process
pipeline.enqueue_sql(sql, "__splink__df_representatives")
- representatives = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ representatives = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
prev_representatives_table = representatives
# Loop while our representative table still has unsettled nodes
@@ -500,7 +500,7 @@ def solve_connected_components(
repr_name,
)
- representatives = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ representatives = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
pipeline = CTEPipeline()
# Update table reference
@@ -512,7 +512,7 @@ def solve_connected_components(
pipeline.enqueue_sql(sql, "__splink__df_root_rows")
- root_rows_df = linker.db_api.sql_pipeline_to_splink_dataframe(
+ root_rows_df = linker._db_api.sql_pipeline_to_splink_dataframe(
pipeline, use_cache=False
)
@@ -540,6 +540,6 @@ def solve_connected_components(
)
pipeline = CTEPipeline([representatives])
pipeline.enqueue_sql(exit_query, "__splink__df_representatives")
- representatives = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ representatives = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return representatives
diff --git a/splink/internals/edge_metrics.py b/splink/internals/edge_metrics.py
index da9ee248c9..34f97707b4 100644
--- a/splink/internals/edge_metrics.py
+++ b/splink/internals/edge_metrics.py
@@ -68,7 +68,7 @@ def compute_basic_edge_metrics(
)
pipeline.enqueue_sql(**sql_info)
- df_truncated_edges = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_truncated_edges = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_truncated_edges
@@ -96,13 +96,13 @@ def compute_igraph_metrics(
# this is how igraph deals with nodes
sql_infos = _node_mapping_table_sql(df_node_metrics)
pipeline.enqueue_list_of_sqls(sql_infos)
- df_node_mappings = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_node_mappings = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
# we keep only edges at or above relevant threshold
pipeline = CTEPipeline()
sql_info = _truncated_edges_sql(df_predict, threshold_match_probability)
pipeline.enqueue_sql(**sql_info)
- df_truncated_edges = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_truncated_edges = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
# we map the truncated edges to the integer encoding for nodes above,
# keeping only the list of endpoints
@@ -114,7 +114,7 @@ def compute_igraph_metrics(
composite_uid_edges_r,
)
pipeline.enqueue_sql(**sql_info)
- edges_for_igraph = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ edges_for_igraph = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
# we will need to manually register a table, so we use the hash from this table
igraph_edges_hash = edges_for_igraph.physical_name[-9:]
# NB: for large data we may have to revise this and process in chunks
@@ -124,7 +124,7 @@ def compute_igraph_metrics(
igraph_df = ig.Graph.DataFrame(df_edges_for_igraph, directed=False)
bridges_indices = igraph_df.bridges()
df_bridges_pd = df_edges_for_igraph.iloc[bridges_indices, :]
- df_bridges = linker.register_table(
+ df_bridges = linker.table_management.register_table(
df_bridges_pd, f"__splink__bridges_{igraph_edges_hash}"
)
# map our bridge edges back to the original node labelling
@@ -139,5 +139,5 @@ def compute_igraph_metrics(
composite_uid_edges_r,
)
pipeline.enqueue_sql(**sql_info)
- df_edge_metrics = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_edge_metrics = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_edge_metrics
diff --git a/splink/internals/estimate_u.py b/splink/internals/estimate_u.py
index 50b3391131..4d24235ac2 100644
--- a/splink/internals/estimate_u.py
+++ b/splink/internals/estimate_u.py
@@ -74,7 +74,7 @@ def estimate_u_values(linker: Linker, max_pairs: float, seed: int = None) -> Non
settings_obj._retain_matching_columns = False
settings_obj._retain_intermediate_calculation_columns = False
- db_api = training_linker.db_api
+ db_api = training_linker._db_api
for cc in settings_obj.comparisons:
for cl in cc.comparison_levels:
@@ -211,6 +211,7 @@ def estimate_u_values(linker: Linker, max_pairs: float, seed: int = None) -> Non
]
m_u_records_lookup = m_u_records_to_lookup_dict(m_u_records)
+
for c in original_settings_obj.comparisons:
for cl in c._comparison_levels_excluding_null:
append_u_probability_to_comparison_level_trained_probabilities(
diff --git a/splink/internals/find_brs_with_comparison_counts_below_threshold.py b/splink/internals/find_brs_with_comparison_counts_below_threshold.py
index b49bf76aee..f7594705de 100644
--- a/splink/internals/find_brs_with_comparison_counts_below_threshold.py
+++ b/splink/internals/find_brs_with_comparison_counts_below_threshold.py
@@ -158,13 +158,13 @@ def _search_tree_for_blocking_rules_below_threshold_count(
if len(current_combination) == len(all_columns):
return results # All fields included, meaning we're at a leaf so exit recursion
- br = _generate_blocking_rule(linker.db_api, current_combination)
+ br = _generate_blocking_rule(linker._db_api, current_combination)
comparison_count = _count_comparisons_generated_from_blocking_rule(
splink_df_dict=linker._input_tables_dict,
blocking_rule=br,
link_type=linker._settings_obj._link_type,
- db_api=linker.db_api,
+ db_api=linker._db_api,
compute_post_filter_count=False,
source_dataset_input_column=linker._settings_obj.column_info_settings.source_dataset_input_column,
unique_id_input_column=linker._settings_obj.column_info_settings.unique_id_input_column,
diff --git a/splink/internals/labelling_tool.py b/splink/internals/labelling_tool.py
index 9113e1a270..e0f46b5d70 100644
--- a/splink/internals/labelling_tool.py
+++ b/splink/internals/labelling_tool.py
@@ -50,9 +50,9 @@ def generate_labelling_tool_comparisons(
"""
pipeline.enqueue_sql(sql, "__splink__df_labelling_tool_record")
- splink_df = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ splink_df = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
- matches = linker.find_matches_to_new_records(
+ matches = linker.inference.find_matches_to_new_records(
splink_df.physical_name, match_weight_threshold=match_weight_threshold
)
diff --git a/splink/internals/linker.py b/splink/internals/linker.py
index 79bc2fd3bb..cfb50a0088 100644
--- a/splink/internals/linker.py
+++ b/splink/internals/linker.py
@@ -1,88 +1,37 @@
from __future__ import annotations
-import json
import logging
-import os
from copy import copy, deepcopy
from pathlib import Path
from statistics import median
-from typing import Any, Dict, List, Literal, Optional, Sequence, Union
+from typing import Any, Dict, List, Optional, Sequence
-from splink.internals.accuracy import (
- prediction_errors_from_label_column,
- prediction_errors_from_labels_table,
- truth_space_table_from_labels_column,
- truth_space_table_from_labels_table,
-)
from splink.internals.blocking import (
BlockingRule,
- SaltedBlockingRule,
block_using_rules_sqls,
- blocking_rule_to_obj,
- materialise_exploded_id_tables,
-)
-from splink.internals.blocking_analysis import (
- _cumulative_comparisons_to_be_scored_from_blocking_rules,
)
-from splink.internals.blocking_rule_creator import BlockingRuleCreator
-from splink.internals.blocking_rule_creator_utils import to_blocking_rule_creator
from splink.internals.cache_dict_with_logging import CacheDictWithLogging
-from splink.internals.charts import (
- ChartReturnType,
- accuracy_chart,
- match_weights_histogram,
- parameter_estimate_comparisons,
- precision_recall_chart,
- roc_chart,
- threshold_selection_tool,
- unlinkables_chart,
- waterfall_chart,
-)
-from splink.internals.cluster_studio import (
- SamplingMethods,
- render_splink_cluster_studio_html,
-)
-from splink.internals.comparison import Comparison
-from splink.internals.comparison_level import ComparisonLevel
-from splink.internals.comparison_vector_distribution import (
- comparison_vector_distribution_sql,
-)
from splink.internals.comparison_vector_values import (
compute_comparison_vector_values_sql,
)
-from splink.internals.connected_components import (
- _cc_create_unique_id_cols,
- solve_connected_components,
-)
from splink.internals.database_api import AcceptableInputTableType, DatabaseAPISubClass
from splink.internals.dialects import SplinkDialect
-from splink.internals.edge_metrics import compute_edge_metrics
from splink.internals.em_training_session import EMTrainingSession
-from splink.internals.estimate_u import estimate_u_values
from splink.internals.exceptions import SplinkException
from splink.internals.find_brs_with_comparison_counts_below_threshold import (
find_blocking_rules_below_threshold_comparison_count,
)
-from splink.internals.find_matches_to_new_records import (
- add_unique_id_and_source_dataset_cols_if_needed,
-)
-from splink.internals.graph_metrics import (
- GraphMetricsResults,
- _node_degree_sql,
- _size_density_centralisation_sql,
-)
from splink.internals.input_column import InputColumn
-from splink.internals.labelling_tool import (
- generate_labelling_tool_comparisons,
- render_labelling_tool_html,
-)
-from splink.internals.m_from_labels import estimate_m_from_pairwise_labels
-from splink.internals.m_training import estimate_m_values_from_label_column
-from splink.internals.match_weights_histogram import histogram_data
+from splink.internals.linker_components.clustering import LinkerClustering
+from splink.internals.linker_components.evaluation import LinkerEvalution
+from splink.internals.linker_components.inference import LinkerInference
+from splink.internals.linker_components.misc import LinkerMisc
+from splink.internals.linker_components.table_management import LinkerTableManagement
+from splink.internals.linker_components.training import LinkerTraining
+from splink.internals.linker_components.visualisations import LinkerVisualisations
from splink.internals.misc import (
ascii_uid,
bayes_factor_to_prob,
- ensure_is_iterable,
ensure_is_list,
prob_to_bayes_factor,
)
@@ -90,7 +39,6 @@
from splink.internals.pipeline import CTEPipeline
from splink.internals.predict import (
predict_from_comparison_vectors_sqls,
- predict_from_comparison_vectors_sqls_using_settings,
)
from splink.internals.settings_creator import SettingsCreator
from splink.internals.settings_validation.log_invalid_columns import (
@@ -100,27 +48,12 @@
from splink.internals.settings_validation.valid_types import (
_validate_dialect,
)
-from splink.internals.splink_comparison_viewer import (
- comparison_viewer_table_sqls,
- render_splink_comparison_viewer_html,
-)
from splink.internals.splink_dataframe import SplinkDataFrame
-from splink.internals.term_frequencies import (
- _join_new_table_to_df_concat_with_tf_sql,
- colname_to_tf_tablename,
- term_frequencies_for_single_column_sql,
- tf_adjustment_chart,
-)
from splink.internals.unique_id_concat import (
_composite_unique_id_from_edges_sql,
- _composite_unique_id_from_nodes_sql,
)
-from splink.internals.unlinkables import unlinkables_data
from splink.internals.vertically_concatenate import (
compute_df_concat_with_tf,
- enqueue_df_concat,
- enqueue_df_concat_with_tf,
- split_df_concat_with_tf_into_two_tables_sqls,
)
logger = logging.getLogger(__name__)
@@ -200,11 +133,11 @@ def __init__(
splink_logger = logging.getLogger("splink")
splink_logger.setLevel(logging.INFO)
- self.db_api = database_api
+ self._db_api = database_api
# TODO: temp hack for compat
self._intermediate_table_cache: CacheDictWithLogging = (
- self.db_api._intermediate_table_cache
+ self._db_api._intermediate_table_cache
)
# Turn into a creator
@@ -237,7 +170,15 @@ def __init__(
self._validate_settings(validate_settings)
self._em_training_sessions: list[EMTrainingSession] = []
- self.debug_mode = False
+ self._debug_mode = False
+
+ self.clustering: "LinkerClustering" = LinkerClustering(self)
+ self.evaluation: "LinkerEvalution" = LinkerEvalution(self)
+ self.inference: "LinkerInference" = LinkerInference(self)
+ self.misc: "LinkerMisc" = LinkerMisc(self)
+ self.table_management: "LinkerTableManagement" = LinkerTableManagement(self)
+ self.training: "LinkerTraining" = LinkerTraining(self)
+ self.visualisations: "LinkerVisualisations" = LinkerVisualisations(self)
def _input_columns(
self,
@@ -333,21 +274,21 @@ def _two_dataset_link_only(self):
# convenience wrappers:
@property
- def debug_mode(self) -> bool:
- return self.db_api.debug_mode
+ def _debug_mode(self) -> bool:
+ return self._db_api.debug_mode
- @debug_mode.setter
- def debug_mode(self, value: bool) -> None:
- self.db_api.debug_mode = value
+ @_debug_mode.setter
+ def _debug_mode(self, value: bool) -> None:
+ self._db_api.debug_mode = value
# TODO: rename these!
@property
def _sql_dialect(self) -> str:
- return self.db_api.sql_dialect.name
+ return self._db_api.sql_dialect.name
@property
def _sql_dialect_object(self) -> SplinkDialect:
- return self.db_api.sql_dialect
+ return self._db_api.sql_dialect
@property
def _infinity_expression(self):
@@ -374,7 +315,7 @@ def _register_input_tables(
input_table_aliases = ensure_is_list(input_aliases)
overwrite = False
- return self.db_api.register_multiple_tables(
+ return self._db_api.register_multiple_tables(
input_tables, input_table_aliases, overwrite
)
@@ -413,80 +354,7 @@ def _table_to_splink_dataframe(
templated_name (str): The purpose of the table to Splink
physical_name (str): The name of the table in the underlying databse
"""
- return self.db_api.table_to_splink_dataframe(templated_name, physical_name)
-
- def register_table(
- self,
- input_table: AcceptableInputTableType,
- table_name: str,
- overwrite: bool = False,
- ) -> SplinkDataFrame:
- """
- Register a table to your backend database, to be used in one of the
- splink methods, or simply to allow querying.
-
- Tables can be of type: dictionary, record level dictionary,
- pandas dataframe, pyarrow table and in the spark case, a spark df.
-
- Examples:
- ```py
- test_dict = {"a": [666,777,888],"b": [4,5,6]}
- linker.register_table(test_dict, "test_dict")
- linker.query_sql("select * from test_dict")
- ```
-
- Args:
- input: The data you wish to register. This can be either a dictionary,
- pandas dataframe, pyarrow table or a spark dataframe.
- table_name (str): The name you wish to assign to the table.
- overwrite (bool): Overwrite the table in the underlying database if it
- exists
-
- Returns:
- SplinkDataFrame: An abstraction representing the table created by the sql
- pipeline
- """
-
- return self.db_api.register_table(input_table, table_name, overwrite)
-
- def query_sql(self, sql, output_type="pandas"):
- """
- Run a SQL query against your backend database and return
- the resulting output.
-
- Examples:
- ```py
- linker = Linker(df, settings, db_api)
- df_predict = linker.predict()
- linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
- ```
-
- Args:
- sql (str): The SQL to be queried.
- output_type (str): One of splink_df/splinkdf or pandas.
- This determines the type of table that your results are output in.
- """
-
- output_tablename_templated = "__splink__df_sql_query"
-
- pipeline = CTEPipeline()
- pipeline.enqueue_sql(sql, output_tablename_templated)
- splink_dataframe = self.db_api.sql_pipeline_to_splink_dataframe(
- pipeline, use_cache=False
- )
-
- if output_type in ("splink_df", "splinkdf"):
- return splink_dataframe
- elif output_type == "pandas":
- out = splink_dataframe.as_pandas_dataframe()
- # If pandas, drop the table to cleanup the db
- splink_dataframe.drop_table_from_database_and_remove_from_cache()
- return out
- else:
- raise ValueError(
- f"output_type '{output_type}' is not supported.",
- "Must be one of 'splink_df'/'splinkdf' or 'pandas'",
- )
+ return self._db_api.table_to_splink_dataframe(templated_name, physical_name)
def __deepcopy__(self, memo):
"""When we do EM training, we need a copy of the linker which is independent
@@ -495,8 +363,18 @@ def __deepcopy__(self, memo):
"""
new_linker = copy(self)
new_linker._em_training_sessions = []
+
new_settings = deepcopy(self._settings_obj)
new_linker._settings_obj = new_settings
+
+ new_linker.clustering = LinkerClustering(new_linker)
+ new_linker.evaluation = LinkerEvalution(new_linker)
+ new_linker.inference = LinkerInference(new_linker)
+ new_linker.misc = LinkerMisc(new_linker)
+ new_linker.table_management = LinkerTableManagement(new_linker)
+ new_linker.training = LinkerTraining(new_linker)
+ new_linker.visualisations = LinkerVisualisations(new_linker)
+
return new_linker
def _predict_warning(self):
@@ -616,9 +494,6 @@ def _populate_m_u_from_trained_values(self):
if cl._has_estimated_m_values:
cl.m_probability = cl._trained_m_median
- def delete_tables_created_by_splink_from_db(self):
- self.db_api.delete_tables_created_by_splink_from_db()
-
def _raise_error_if_necessary_waterfall_columns_not_computed(self):
ricc = self._settings_obj._retain_intermediate_calculation_columns
rmc = self._settings_obj._retain_matching_columns
@@ -643,1094 +518,69 @@ def _raise_error_if_necessary_accuracy_columns_not_computed(self):
"Please re-run your linkage with it set to True."
)
- def compute_tf_table(self, column_name: str) -> SplinkDataFrame:
- """Compute a term frequency table for a given column and persist to the database
-
- This method is useful if you want to pre-compute term frequency tables e.g.
- so that real time linkage executes faster, or so that you can estimate
- various models without having to recompute term frequency tables each time
-
- Examples:
-
- Real time linkage
- ```py
- linker = Linker(df, db_api)
- linker.load_settings("saved_settings.json")
- linker.compute_tf_table("surname")
- linker.compare_two_records(record_left, record_right)
- ```
- Pre-computed term frequency tables
- ```py
- linker = Linker(df, db_api)
- df_first_name_tf = linker.compute_tf_table("first_name")
- df_first_name_tf.write.parquet("folder/first_name_tf")
- >>>
- # On subsequent data linking job, read this table rather than recompute
- df_first_name_tf = pd.read_parquet("folder/first_name_tf")
- df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")
- ```
-
-
- Args:
- column_name (str): The column name in the input table
-
- Returns:
- SplinkDataFrame: The resultant table as a splink data frame
- """
-
- input_col = InputColumn(
- column_name,
- column_info_settings=self._settings_obj.column_info_settings,
- sql_dialect=self._settings_obj._sql_dialect,
- )
- tf_tablename = colname_to_tf_tablename(input_col)
- cache = self._intermediate_table_cache
-
- if tf_tablename in cache:
- tf_df = cache.get_with_logging(tf_tablename)
- else:
- pipeline = CTEPipeline()
- pipeline = enqueue_df_concat(self, pipeline)
- sql = term_frequencies_for_single_column_sql(input_col)
- pipeline.enqueue_sql(sql, tf_tablename)
- tf_df = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
- self._intermediate_table_cache[tf_tablename] = tf_df
-
- return tf_df
-
- def deterministic_link(self) -> SplinkDataFrame:
- """Uses the blocking rules specified by
- `blocking_rules_to_generate_predictions` in the settings dictionary to
- generate pairwise record comparisons.
-
- For deterministic linkage, this should be a list of blocking rules which
- are strict enough to generate only true links.
-
- Deterministic linkage, however, is likely to result in missed links
- (false negatives).
-
- Examples:
-
- ```py
- from splink.linker import Linker
- from splink.duckdb.database_api import DuckDBAPI
-
- db_api = DuckDBAPI()
-
- settings = {
- "link_type": "dedupe_only",
- "blocking_rules_to_generate_predictions": [
- "l.first_name = r.first_name",
- "l.surname = r.surname",
- ],
- "comparisons": []
- }
- >>>
- linker = Linker(df, settings, db_api)
- df = linker.deterministic_link()
- ```
-
-
- Returns:
- SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons. This
- represents a table materialised in the database. Methods on the
- SplinkDataFrame allow you to access the underlying data.
- """
- pipeline = CTEPipeline()
- # Allows clustering during a deterministic linkage.
- # This is used in `cluster_pairwise_predictions_at_threshold`
- # to set the cluster threshold to 1
-
- df_concat_with_tf = compute_df_concat_with_tf(self, pipeline)
- pipeline = CTEPipeline([df_concat_with_tf])
- link_type = self._settings_obj._link_type
-
- blocking_input_tablename_l = "__splink__df_concat_with_tf"
- blocking_input_tablename_r = "__splink__df_concat_with_tf"
-
- link_type = self._settings_obj._link_type
- if (
- len(self._input_tables_dict) == 2
- and self._settings_obj._link_type == "link_only"
- ):
- sqls = split_df_concat_with_tf_into_two_tables_sqls(
- "__splink__df_concat_with_tf",
- self._settings_obj.column_info_settings.source_dataset_column_name,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- blocking_input_tablename_l = "__splink__df_concat_with_tf_left"
- blocking_input_tablename_r = "__splink__df_concat_with_tf_right"
- link_type = "two_dataset_link_only"
-
- exploding_br_with_id_tables = materialise_exploded_id_tables(
- link_type=link_type,
- blocking_rules=self._settings_obj._blocking_rules_to_generate_predictions,
- db_api=self.db_api,
- splink_df_dict=self._input_tables_dict,
- source_dataset_input_column=self._settings_obj.column_info_settings.source_dataset_input_column,
- unique_id_input_column=self._settings_obj.column_info_settings.unique_id_input_column,
- )
-
- columns_to_select = self._settings_obj._columns_to_select_for_blocking
- sql_select_expr = ", ".join(columns_to_select)
-
- sqls = block_using_rules_sqls(
- input_tablename_l=blocking_input_tablename_l,
- input_tablename_r=blocking_input_tablename_r,
- blocking_rules=self._settings_obj._blocking_rules_to_generate_predictions,
- link_type=link_type,
- columns_to_select_sql=sql_select_expr,
- source_dataset_input_column=self._settings_obj.column_info_settings.source_dataset_input_column,
- unique_id_input_column=self._settings_obj.column_info_settings.unique_id_input_column,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- deterministic_link_df = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
- deterministic_link_df.metadata["is_deterministic_link"] = True
-
- [b.drop_materialised_id_pairs_dataframe() for b in exploding_br_with_id_tables]
-
- return deterministic_link_df
-
- def estimate_u_using_random_sampling(
- self, max_pairs: float = 1e6, seed: int = None
- ) -> None:
- """Estimate the u parameters of the linkage model using random sampling.
-
- The u parameters represent the proportion of record comparisons that fall
- into each comparison level amongst truly non-matching records.
-
- This procedure takes a sample of the data and generates the cartesian
- product of pairwise record comparisons amongst the sampled records.
- The validity of the u values rests on the assumption that the resultant
- pairwise comparisons are non-matches (or at least, they are very unlikely to be
- matches). For large datasets, this is typically true.
-
- The results of estimate_u_using_random_sampling, and therefore an entire splink
- model, can be made reproducible by setting the seed parameter. Setting the seed
- will have performance implications as additional processing is required.
-
- Args:
- max_pairs (int): The maximum number of pairwise record comparisons to
- sample. Larger will give more accurate estimates
- but lead to longer runtimes. In our experience at least 1e9 (one billion)
- gives best results but can take a long time to compute. 1e7 (ten million)
- is often adequate whilst testing different model specifications, before
- the final model is estimated.
- seed (int): Seed for random sampling. Assign to get reproducible u
- probabilities. Note, seed for random sampling is only supported for
- DuckDB and Spark, for Athena and SQLite set to None.
-
- Examples:
- ```py
- linker.estimate_u_using_random_sampling(1e8)
- ```
-
- Returns:
- None: Updates the estimated u parameters within the linker object
- and returns nothing.
- """
- if max_pairs == 1e6:
- # keep default value small so as not to take too long, but warn users
- logger.warning(
- "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."
- )
- estimate_u_values(self, max_pairs, seed)
- self._populate_m_u_from_trained_values()
-
- self._settings_obj._columns_without_estimated_parameters_message()
-
- def estimate_m_from_label_column(self, label_colname: str) -> None:
- """Estimate the m parameters of the linkage model from a label (ground truth)
- column in the input dataframe(s).
-
- The m parameters represent the proportion of record comparisons that fall
- into each comparison level amongst truly matching records.
-
- The ground truth column is used to generate pairwise record comparisons
- which are then assumed to be matches.
-
- For example, if the entity being matched is persons, and your input dataset(s)
- contain social security number, this could be used to estimate the m values
- for the model.
-
- Note that this column does not need to be fully populated. A common case is
- where a unique identifier such as social security number is only partially
- populated.
-
- Args:
- label_colname (str): The name of the column containing the ground truth
- label in the input data.
-
- Examples:
- ```py
- linker.estimate_m_from_label_column("social_security_number")
- ```
-
- Returns:
- Updates the estimated m parameters within the linker object
- and returns nothing.
- """
-
- # Ensure this has been run on the main linker so that it can be used by
- # training linker when it checks the cache
- pipeline = CTEPipeline()
- compute_df_concat_with_tf(self, pipeline)
-
- estimate_m_values_from_label_column(
- self,
- self._input_tables_dict,
- label_colname,
- )
- self._populate_m_u_from_trained_values()
-
- self._settings_obj._columns_without_estimated_parameters_message()
-
- def estimate_parameters_using_expectation_maximisation(
- self,
- blocking_rule: Union[str, BlockingRuleCreator],
- comparisons_to_deactivate: list[Comparison] = None,
- comparison_levels_to_reverse_blocking_rule: list[ComparisonLevel] = None,
- estimate_without_term_frequencies: bool = False,
- fix_probability_two_random_records_match: bool = False,
- fix_m_probabilities: bool = False,
- fix_u_probabilities: bool = True,
- populate_probability_two_random_records_match_from_trained_values: bool = False,
- ) -> EMTrainingSession:
- """Estimate the parameters of the linkage model using expectation maximisation.
-
- By default, the m probabilities are estimated, but not the u probabilities,
- because good estimates for the u probabilities can be obtained from
- `linker.estimate_u_using_random_sampling()`. You can change this by setting
- `fix_u_probabilities` to False.
-
- The blocking rule provided is used to generate pairwise record comparisons.
- Usually, this should be a blocking rule that results in a dataframe where
- matches are between about 1% and 99% of the comparisons.
-
- By default, m parameters are estimated for all comparisons except those which
- are included in the blocking rule.
-
- For example, if the blocking rule is `l.first_name = r.first_name`, then
- parameter esimates will be made for all comparison except those which use
- `first_name` in their sql_condition
-
- By default, the probability two random records match is estimated for the
- blocked data, and then the m and u parameters for the columns specified in the
- blocking rules are used to estiamte the global probability two random records
- match.
-
- To control which comparisons should have their parameter estimated, and the
- process of 'reversing out' the global probability two random records match, the
- user may specify `comparisons_to_deactivate` and
- `comparison_levels_to_reverse_blocking_rule`. This is useful, for example
- if you block on the dmetaphone of a column but match on the original column.
-
- Examples:
- Default behaviour
- ```py
- br_training = "l.first_name = r.first_name and l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(br_training)
- ```
- Specify which comparisons to deactivate
- ```py
- br_training = "l.dmeta_first_name = r.dmeta_first_name"
- settings_obj = linker._settings_obj
- comp = settings_obj._get_comparison_by_output_column_name("first_name")
- dmeta_level = comp._get_comparison_level_by_comparison_vector_value(1)
- linker.estimate_parameters_using_expectation_maximisation(
- br_training,
- comparisons_to_deactivate=["first_name"],
- comparison_levels_to_reverse_blocking_rule=[dmeta_level],
- )
- ```
-
- Args:
- blocking_rule (BlockingRuleCreator | str): The blocking rule used to
- generate pairwise record comparisons.
- comparisons_to_deactivate (list, optional): By default, splink will
- analyse the blocking rule provided and estimate the m parameters for
- all comaprisons except those included in the blocking rule. If
- comparisons_to_deactivate are provided, spink will instead
- estimate m parameters for all comparison except those specified
- in the comparisons_to_deactivate list. This list can either contain
- the output_column_name of the Comparison as a string, or Comparison
- objects. Defaults to None.
- comparison_levels_to_reverse_blocking_rule (list, optional): By default,
- splink will analyse the blocking rule provided and adjust the
- global probability two random records match to account for the matches
- specified in the blocking rule. If provided, this argument will overrule
- this default behaviour. The user must provide a list of ComparisonLevel
- objects. Defaults to None.
- estimate_without_term_frequencies (bool, optional): If True, the iterations
- of the EM algorithm ignore any term frequency adjustments and only
- depend on the comparison vectors. This allows the EM algorithm to run
- much faster, but the estimation of the parameters will change slightly.
- fix_probability_two_random_records_match (bool, optional): If True, do not
- update the probability two random records match after each iteration.
- Defaults to False.
- fix_m_probabilities (bool, optional): If True, do not update the m
- probabilities after each iteration. Defaults to False.
- fix_u_probabilities (bool, optional): If True, do not update the u
- probabilities after each iteration. Defaults to True.
- populate_probability_two_random_records_match_from_trained_values
- (bool, optional): If True, derive this parameter from
- the blocked value. Defaults to False.
-
- Examples:
- ```py
- blocking_rule = "l.first_name = r.first_name and l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
- ```
- or using pre-built rules
- ```py
- from splink.duckdb.blocking_rule_library import block_on
- blocking_rule = block_on(["first_name", "surname"])
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
- ```
+ def _self_link(self) -> SplinkDataFrame:
+ """Use the linkage model to compare and score all records in our input df with
+ themselves.
Returns:
- EMTrainingSession: An object containing information about the training
- session such as how parameters changed during the iteration history
-
+ SplinkDataFrame: Scored pairwise comparisons of the input records to
+ themselves.
"""
- # Ensure this has been run on the main linker so that it's in the cache
- # to be used by the training linkers
- pipeline = CTEPipeline()
- compute_df_concat_with_tf(self, pipeline)
-
- blocking_rule_obj = to_blocking_rule_creator(blocking_rule).get_blocking_rule(
- self._sql_dialect
- )
-
- if type(blocking_rule_obj) not in (BlockingRule, SaltedBlockingRule):
- # TODO: seems a mismatch between message and type re: SaltedBlockingRule
- raise TypeError(
- "EM blocking rules must be plain blocking rules, not "
- "salted or exploding blocking rules"
- )
- if comparisons_to_deactivate:
- # If user provided a string, convert to Comparison object
- comparisons_to_deactivate = [
- (
- self._settings_obj._get_comparison_by_output_column_name(n)
- if isinstance(n, str)
- else n
- )
- for n in comparisons_to_deactivate
- ]
- if comparison_levels_to_reverse_blocking_rule is None:
- logger.warning(
- "\nWARNING: \n"
- "You have provided comparisons_to_deactivate but not "
- "comparison_levels_to_reverse_blocking_rule.\n"
- "If comparisons_to_deactivate is provided, then "
- "you usually need to provide corresponding "
- "comparison_levels_to_reverse_blocking_rule "
- "because each comparison to deactivate is effectively treated "
- "as an exact match."
- )
+ # Block on uid i.e. create pairwise record comparisons where the uid matches
+ settings = self._settings_obj
+ uid_cols = settings.column_info_settings.unique_id_input_columns
+ uid_l = _composite_unique_id_from_edges_sql(uid_cols, None, "l")
+ uid_r = _composite_unique_id_from_edges_sql(uid_cols, None, "r")
- em_training_session = EMTrainingSession(
- self,
- db_api=self.db_api,
- blocking_rule_for_training=blocking_rule_obj,
- core_model_settings=self._settings_obj.core_model_settings,
- training_settings=self._settings_obj.training_settings,
- unique_id_input_columns=self._settings_obj.column_info_settings.unique_id_input_columns,
- fix_u_probabilities=fix_u_probabilities,
- fix_m_probabilities=fix_m_probabilities,
- fix_probability_two_random_records_match=fix_probability_two_random_records_match, # noqa 501
- comparisons_to_deactivate=comparisons_to_deactivate,
- comparison_levels_to_reverse_blocking_rule=comparison_levels_to_reverse_blocking_rule, # noqa 501
- estimate_without_term_frequencies=estimate_without_term_frequencies,
+ blocking_rule = BlockingRule(
+ f"{uid_l} = {uid_r}", sqlglot_dialect=self._sql_dialect
)
- core_model_settings = em_training_session._train()
- # overwrite with the newly trained values in our linker settings
- self._settings_obj.core_model_settings = core_model_settings
- self._em_training_sessions.append(em_training_session)
-
- self._populate_m_u_from_trained_values()
-
- if populate_probability_two_random_records_match_from_trained_values:
- self._populate_probability_two_random_records_match_from_trained_values()
-
- self._settings_obj._columns_without_estimated_parameters_message()
-
- return em_training_session
-
- def predict(
- self,
- threshold_match_probability: float = None,
- threshold_match_weight: float = None,
- materialise_after_computing_term_frequencies: bool = True,
- ) -> SplinkDataFrame:
- """Create a dataframe of scored pairwise comparisons using the parameters
- of the linkage model.
-
- Uses the blocking rules specified in the
- `blocking_rules_to_generate_predictions` of the settings dictionary to
- generate the pairwise comparisons.
-
- Args:
- threshold_match_probability (float, optional): If specified,
- filter the results to include only pairwise comparisons with a
- match_probability above this threshold. Defaults to None.
- threshold_match_weight (float, optional): If specified,
- filter the results to include only pairwise comparisons with a
- match_weight above this threshold. Defaults to None.
- materialise_after_computing_term_frequencies (bool): If true, Splink
- will materialise the table containing the input nodes (rows)
- joined to any term frequencies which have been asked
- for in the settings object. If False, this will be
- computed as part of one possibly gigantic CTE
- pipeline. Defaults to True
-
- Examples:
- ```py
- linker = DuckDBLinker(df)
- linker.load_settings("saved_settings.json")
- df = linker.predict(threshold_match_probability=0.95)
- df.as_pandas_dataframe(limit=5)
- ```
- Returns:
- SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons. This
- represents a table materialised in the database. Methods on the
- SplinkDataFrame allow you to access the underlying data.
-
- """
-
pipeline = CTEPipeline()
+ nodes_with_tf = compute_df_concat_with_tf(self, pipeline)
- # If materialise_after_computing_term_frequencies=False and the user only
- # calls predict, it runs as a single pipeline with no materialisation
- # of anything.
-
- # In duckdb, calls to random() in a CTE pipeline cause problems:
- # https://gist.github.com/RobinL/d329e7004998503ce91b68479aa41139
- if (
- materialise_after_computing_term_frequencies
- or self._sql_dialect == "duckdb"
- ):
- df_concat_with_tf = compute_df_concat_with_tf(self, pipeline)
- pipeline = CTEPipeline([df_concat_with_tf])
- else:
- pipeline = enqueue_df_concat_with_tf(self, pipeline)
-
- blocking_input_tablename_l = "__splink__df_concat_with_tf"
- blocking_input_tablename_r = "__splink__df_concat_with_tf"
-
- link_type = self._settings_obj._link_type
- if (
- len(self._input_tables_dict) == 2
- and self._settings_obj._link_type == "link_only"
- ):
- sqls = split_df_concat_with_tf_into_two_tables_sqls(
- "__splink__df_concat_with_tf",
- self._settings_obj.column_info_settings.source_dataset_column_name,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- blocking_input_tablename_l = "__splink__df_concat_with_tf_left"
- blocking_input_tablename_r = "__splink__df_concat_with_tf_right"
- link_type = "two_dataset_link_only"
-
- # If exploded blocking rules exist, we need to materialise
- # the tables of ID pairs
-
- exploding_br_with_id_tables = materialise_exploded_id_tables(
- link_type=link_type,
- blocking_rules=self._settings_obj._blocking_rules_to_generate_predictions,
- db_api=self.db_api,
- splink_df_dict=self._input_tables_dict,
- source_dataset_input_column=self._settings_obj.column_info_settings.source_dataset_input_column,
- unique_id_input_column=self._settings_obj.column_info_settings.unique_id_input_column,
- )
-
- columns_to_select = self._settings_obj._columns_to_select_for_blocking
- sql_select_expr = ", ".join(columns_to_select)
+ pipeline = CTEPipeline([nodes_with_tf])
sqls = block_using_rules_sqls(
- input_tablename_l=blocking_input_tablename_l,
- input_tablename_r=blocking_input_tablename_r,
- blocking_rules=self._settings_obj._blocking_rules_to_generate_predictions,
- link_type=link_type,
- columns_to_select_sql=sql_select_expr,
- source_dataset_input_column=self._settings_obj.column_info_settings.source_dataset_input_column,
- unique_id_input_column=self._settings_obj.column_info_settings.unique_id_input_column,
+ input_tablename_l="__splink__df_concat_with_tf",
+ input_tablename_r="__splink__df_concat_with_tf",
+ blocking_rules=[blocking_rule],
+ link_type="self_link",
+ columns_to_select_sql=", ".join(settings._columns_to_select_for_blocking),
+ source_dataset_input_column=settings.column_info_settings.source_dataset_input_column,
+ unique_id_input_column=settings.column_info_settings.unique_id_input_column,
)
-
pipeline.enqueue_list_of_sqls(sqls)
- repartition_after_blocking = getattr(self, "repartition_after_blocking", False)
-
- # repartition after blocking only exists on the SparkLinker
- if repartition_after_blocking:
- pipeline = pipeline.break_lineage(self.db_api)
-
sql = compute_comparison_vector_values_sql(
self._settings_obj._columns_to_select_for_comparison_vector_values
)
+
pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
- sqls = predict_from_comparison_vectors_sqls_using_settings(
- self._settings_obj,
- threshold_match_probability,
- threshold_match_weight,
+ sql_infos = predict_from_comparison_vectors_sqls(
+ unique_id_input_columns=uid_cols,
+ core_model_settings=self._settings_obj.core_model_settings,
+ sql_dialect=self._sql_dialect,
sql_infinity_expression=self._infinity_expression,
)
- pipeline.enqueue_list_of_sqls(sqls)
-
- predictions = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
- self._predict_warning()
+ for sql_info in sql_infos:
+ output_table_name = sql_info["output_table_name"]
+ output_table_name = output_table_name.replace("predict", "self_link")
+ pipeline.enqueue_sql(sql_info["sql"], output_table_name)
- [b.drop_materialised_id_pairs_dataframe() for b in exploding_br_with_id_tables]
+ predictions = self._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return predictions
- def find_matches_to_new_records(
- self,
- records_or_tablename: AcceptableInputTableType | str,
- blocking_rules: list[BlockingRuleCreator | dict[str, Any] | str]
- | BlockingRuleCreator
- | dict[str, Any]
- | str = [],
- match_weight_threshold: float = -4,
- ) -> SplinkDataFrame:
- """Given one or more records, find records in the input dataset(s) which match
- and return in order of the Splink prediction score.
-
- This effectively provides a way of searching the input datasets
- for given record(s)
-
- Args:
- records_or_tablename (List[dict]): Input search record(s) as list of dict,
- or a table registered to the database.
- blocking_rules (list, optional): Blocking rules to select
- which records to find and score. If [], do not use a blocking
- rule - meaning the input records will be compared to all records
- provided to the linker when it was instantiated. Defaults to [].
- match_weight_threshold (int, optional): Return matches with a match weight
- above this threshold. Defaults to -4.
-
- Examples:
- ```py
- linker = DuckDBLinker(df)
- linker.load_settings("saved_settings.json")
- # Pre-compute tf tables for any tables with
- # term frequency adjustments
- linker.compute_tf_table("first_name")
- record = {'unique_id': 1,
- 'first_name': "John",
- 'surname': "Smith",
- 'dob': "1971-05-24",
- 'city': "London",
- 'email': "john@smith.net"
- }
- df = linker.find_matches_to_new_records([record], blocking_rules=[])
- ```
-
- Returns:
- SplinkDataFrame: The pairwise comparisons.
- """
-
- original_blocking_rules = (
- self._settings_obj._blocking_rules_to_generate_predictions
- )
- original_link_type = self._settings_obj._link_type
-
- blocking_rule_list = ensure_is_list(blocking_rules)
-
- if not isinstance(records_or_tablename, str):
- uid = ascii_uid(8)
- new_records_tablename = f"__splink__df_new_records_{uid}"
- self.register_table(
- records_or_tablename, new_records_tablename, overwrite=True
- )
-
- else:
- new_records_tablename = records_or_tablename
-
- new_records_df = self.db_api.table_to_splink_dataframe(
- "__splink__df_new_records", new_records_tablename
- )
-
- pipeline = CTEPipeline()
- nodes_with_tf = compute_df_concat_with_tf(self, pipeline)
-
- pipeline = CTEPipeline([nodes_with_tf, new_records_df])
- if len(blocking_rule_list) == 0:
- blocking_rule_list = [BlockingRule("1=1")]
- blocking_rule_list = [blocking_rule_to_obj(br) for br in blocking_rule_list]
- for n, br in enumerate(blocking_rule_list):
- br.add_preceding_rules(blocking_rule_list[:n])
-
- self._settings_obj._blocking_rules_to_generate_predictions = blocking_rule_list
-
- for tf_col in self._settings_obj._term_frequency_columns:
- tf_table_name = colname_to_tf_tablename(tf_col)
- if tf_table_name in self._intermediate_table_cache:
- tf_table = self._intermediate_table_cache.get_with_logging(
- tf_table_name
- )
- pipeline.append_input_dataframe(tf_table)
-
- sql = _join_new_table_to_df_concat_with_tf_sql(self, "__splink__df_new_records")
- pipeline.enqueue_sql(sql, "__splink__df_new_records_with_tf_before_uid_fix")
-
- pipeline = add_unique_id_and_source_dataset_cols_if_needed(
- self, new_records_df, pipeline
- )
- settings = self._settings_obj
- sqls = block_using_rules_sqls(
- input_tablename_l="__splink__df_concat_with_tf",
- input_tablename_r="__splink__df_new_records_with_tf",
- blocking_rules=blocking_rule_list,
- link_type="two_dataset_link_only",
- columns_to_select_sql=", ".join(settings._columns_to_select_for_blocking),
- source_dataset_input_column=settings.column_info_settings.source_dataset_input_column,
- unique_id_input_column=settings.column_info_settings.unique_id_input_column,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- sql = compute_comparison_vector_values_sql(
- self._settings_obj._columns_to_select_for_comparison_vector_values
- )
- pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
-
- sqls = predict_from_comparison_vectors_sqls_using_settings(
- self._settings_obj,
- sql_infinity_expression=self._infinity_expression,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- sql = f"""
- select * from __splink__df_predict
- where match_weight > {match_weight_threshold}
- """
-
- pipeline.enqueue_sql(sql, "__splink__find_matches_predictions")
-
- predictions = self.db_api.sql_pipeline_to_splink_dataframe(
- pipeline, use_cache=False
- )
-
- self._settings_obj._blocking_rules_to_generate_predictions = (
- original_blocking_rules
- )
- self._settings_obj._link_type = original_link_type
-
- return predictions
-
- def compare_two_records(
- self, record_1: dict[str, Any], record_2: dict[str, Any]
- ) -> SplinkDataFrame:
- """Use the linkage model to compare and score a pairwise record comparison
- based on the two input records provided
-
- Args:
- record_1 (dict): dictionary representing the first record. Columns names
- and data types must be the same as the columns in the settings object
- record_2 (dict): dictionary representing the second record. Columns names
- and data types must be the same as the columns in the settings object
-
- Examples:
- ```py
- linker = DuckDBLinker(df)
- linker.load_settings("saved_settings.json")
- linker.compare_two_records(record_left, record_right)
- ```
-
- Returns:
- SplinkDataFrame: Pairwise comparison with scored prediction
- """
-
- cache = self._intermediate_table_cache
-
- uid = ascii_uid(8)
- df_records_left = self.register_table(
- [record_1], f"__splink__compare_two_records_left_{uid}", overwrite=True
- )
- df_records_left.templated_name = "__splink__compare_two_records_left"
-
- df_records_right = self.register_table(
- [record_2], f"__splink__compare_two_records_right_{uid}", overwrite=True
- )
- df_records_right.templated_name = "__splink__compare_two_records_right"
-
- pipeline = CTEPipeline([df_records_left, df_records_right])
-
- if "__splink__df_concat_with_tf" in cache:
- nodes_with_tf = cache.get_with_logging("__splink__df_concat_with_tf")
- pipeline.append_input_dataframe(nodes_with_tf)
-
- for tf_col in self._settings_obj._term_frequency_columns:
- tf_table_name = colname_to_tf_tablename(tf_col)
- if tf_table_name in cache:
- tf_table = cache.get_with_logging(tf_table_name)
- pipeline.append_input_dataframe(tf_table)
- else:
- if "__splink__df_concat_with_tf" not in cache:
- logger.warning(
- f"No term frequencies found for column {tf_col.name}.\n"
- "To apply term frequency adjustments, you need to register"
- " a lookup using `linker.register_term_frequency_lookup`."
- )
-
- sql_join_tf = _join_new_table_to_df_concat_with_tf_sql(
- self, "__splink__compare_two_records_left"
- )
-
- pipeline.enqueue_sql(sql_join_tf, "__splink__compare_two_records_left_with_tf")
-
- sql_join_tf = _join_new_table_to_df_concat_with_tf_sql(
- self, "__splink__compare_two_records_right"
- )
-
- pipeline.enqueue_sql(sql_join_tf, "__splink__compare_two_records_right_with_tf")
-
- sqls = block_using_rules_sqls(
- input_tablename_l="__splink__compare_two_records_left_with_tf",
- input_tablename_r="__splink__compare_two_records_right_with_tf",
- blocking_rules=[BlockingRule("1=1")],
- link_type=self._settings_obj._link_type,
- columns_to_select_sql=", ".join(
- self._settings_obj._columns_to_select_for_blocking
- ),
- source_dataset_input_column=self._settings_obj.column_info_settings.source_dataset_input_column,
- unique_id_input_column=self._settings_obj.column_info_settings.unique_id_input_column,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- sql = compute_comparison_vector_values_sql(
- self._settings_obj._columns_to_select_for_comparison_vector_values
- )
- pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
-
- sqls = predict_from_comparison_vectors_sqls_using_settings(
- self._settings_obj,
- sql_infinity_expression=self._infinity_expression,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- predictions = self.db_api.sql_pipeline_to_splink_dataframe(
- pipeline, use_cache=False
- )
-
- return predictions
-
- def _self_link(self) -> SplinkDataFrame:
- """Use the linkage model to compare and score all records in our input df with
- themselves.
-
- Returns:
- SplinkDataFrame: Scored pairwise comparisons of the input records to
- themselves.
- """
-
- # Block on uid i.e. create pairwise record comparisons where the uid matches
- settings = self._settings_obj
- uid_cols = settings.column_info_settings.unique_id_input_columns
- uid_l = _composite_unique_id_from_edges_sql(uid_cols, None, "l")
- uid_r = _composite_unique_id_from_edges_sql(uid_cols, None, "r")
-
- blocking_rule = BlockingRule(
- f"{uid_l} = {uid_r}", sqlglot_dialect=self._sql_dialect
- )
-
- pipeline = CTEPipeline()
- nodes_with_tf = compute_df_concat_with_tf(self, pipeline)
-
- pipeline = CTEPipeline([nodes_with_tf])
-
- sqls = block_using_rules_sqls(
- input_tablename_l="__splink__df_concat_with_tf",
- input_tablename_r="__splink__df_concat_with_tf",
- blocking_rules=[blocking_rule],
- link_type="self_link",
- columns_to_select_sql=", ".join(settings._columns_to_select_for_blocking),
- source_dataset_input_column=settings.column_info_settings.source_dataset_input_column,
- unique_id_input_column=settings.column_info_settings.unique_id_input_column,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- sql = compute_comparison_vector_values_sql(
- self._settings_obj._columns_to_select_for_comparison_vector_values
- )
-
- pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
-
- sql_infos = predict_from_comparison_vectors_sqls(
- unique_id_input_columns=uid_cols,
- core_model_settings=self._settings_obj.core_model_settings,
- sql_dialect=self._sql_dialect,
- sql_infinity_expression=self._infinity_expression,
- )
- for sql_info in sql_infos:
- output_table_name = sql_info["output_table_name"]
- output_table_name = output_table_name.replace("predict", "self_link")
- pipeline.enqueue_sql(sql_info["sql"], output_table_name)
-
- predictions = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
-
- return predictions
-
- def cluster_pairwise_predictions_at_threshold(
- self,
- df_predict: SplinkDataFrame,
- threshold_match_probability: Optional[float] = None,
- pairwise_formatting: bool = False,
- filter_pairwise_format_for_clusters: bool = True,
- ) -> SplinkDataFrame:
- """Clusters the pairwise match predictions that result from `linker.predict()`
- into groups of connected record using the connected components graph clustering
- algorithm
-
- Records with an estimated `match_probability` at or above
- `threshold_match_probability` are considered to be a match (i.e. they represent
- the same entity).
-
- Args:
- df_predict (SplinkDataFrame): The results of `linker.predict()`
- threshold_match_probability (float): Filter the pairwise match predictions
- to include only pairwise comparisons with a match_probability at or
- above this threshold. This dataframe is then fed into the clustering
- algorithm.
- pairwise_formatting (bool): Whether to output the pairwise match predictions
- from linker.predict() with cluster IDs.
- If this is set to false, the output will be a list of all IDs, clustered
- into groups based on the desired match threshold.
- filter_pairwise_format_for_clusters (bool): If pairwise formatting has been
- selected, whether to output all columns found within linker.predict(),
- or just return clusters.
-
- Returns:
- SplinkDataFrame: A SplinkDataFrame containing a list of all IDs, clustered
- into groups based on the desired match threshold.
-
- """
-
- # Feeding in df_predict forces materiailisation, if it exists in your database
- pipeline = CTEPipeline()
- nodes_with_tf = compute_df_concat_with_tf(self, pipeline)
-
- edges_table = _cc_create_unique_id_cols(
- self,
- nodes_with_tf.physical_name,
- df_predict,
- threshold_match_probability,
- )
-
- cc = solve_connected_components(
- self,
- edges_table,
- df_predict,
- nodes_with_tf,
- pairwise_formatting,
- filter_pairwise_format_for_clusters,
- )
- cc.metadata["threshold_match_probability"] = threshold_match_probability
-
- return cc
-
- def _compute_metrics_nodes(
- self,
- df_predict: SplinkDataFrame,
- df_clustered: SplinkDataFrame,
- threshold_match_probability: float,
- ) -> SplinkDataFrame:
- """
- Internal function for computing node-level metrics.
-
- Accepts outputs of `linker.predict()` and
- `linker.cluster_pairwise_at_threshold()`, along with the clustering threshold
- and produces a table of node metrics.
-
- Node metrics produced:
- * node_degree (absolute number of neighbouring nodes)
-
- Output table has a single row per input node, along with the cluster id (as
- assigned in `linker.cluster_pairwise_at_threshold()`) and the metric
- node_degree:
- |-------------------------------------------------|
- | composite_unique_id | cluster_id | node_degree |
- |---------------------|-------------|-------------|
- | s1-__-10001 | s1-__-10001 | 6 |
- | s1-__-10002 | s1-__-10001 | 4 |
- | s1-__-10003 | s1-__-10003 | 2 |
- ...
- """
- uid_cols = self._settings_obj.column_info_settings.unique_id_input_columns
- # need composite unique ids
- composite_uid_edges_l = _composite_unique_id_from_edges_sql(uid_cols, "l")
- composite_uid_edges_r = _composite_unique_id_from_edges_sql(uid_cols, "r")
- composite_uid_clusters = _composite_unique_id_from_nodes_sql(uid_cols)
-
- pipeline = CTEPipeline()
- sqls = _node_degree_sql(
- df_predict,
- df_clustered,
- composite_uid_edges_l,
- composite_uid_edges_r,
- composite_uid_clusters,
- threshold_match_probability,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- df_node_metrics = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
-
- df_node_metrics.metadata["threshold_match_probability"] = (
- threshold_match_probability
- )
- return df_node_metrics
-
- def _compute_metrics_edges(
- self,
- df_node_metrics: SplinkDataFrame,
- df_predict: SplinkDataFrame,
- df_clustered: SplinkDataFrame,
- threshold_match_probability: float,
- ) -> SplinkDataFrame:
- """
- Internal function for computing edge-level metrics.
-
- Accepts outputs of `linker._compute_node_metrics()`, `linker.predict()` and
- `linker.cluster_pairwise_at_threshold()`, along with the clustering threshold
- and produces a table of edge metrics.
-
- Uses `igraph` under-the-hood for calculations
-
- Edge metrics produced:
- * is_bridge (is the edge a bridge?)
-
- Output table has a single row per edge, and the metric is_bridge:
- |-------------------------------------------------------------|
- | composite_unique_id_l | composite_unique_id_r | is_bridge |
- |-----------------------|-------------------------|-----------|
- | s1-__-10001 | s1-__-10003 | True |
- | s1-__-10001 | s1-__-10005 | False |
- | s1-__-10005 | s1-__-10009 | False |
- | s1-__-10021 | s1-__-10024 | True |
- ...
- """
- df_edge_metrics = compute_edge_metrics(
- self, df_node_metrics, df_predict, df_clustered, threshold_match_probability
- )
- df_edge_metrics.metadata["threshold_match_probability"] = (
- threshold_match_probability
- )
- return df_edge_metrics
-
- def _compute_metrics_clusters(
- self,
- df_node_metrics: SplinkDataFrame,
- ) -> SplinkDataFrame:
- """
- Internal function for computing cluster-level metrics.
-
- Accepts output of `linker._compute_node_metrics()` (which has the relevant
- information from `linker.predict() and
- `linker.cluster_pairwise_at_threshold()`), produces a table of cluster metrics.
-
- Cluster metrics produced:
- * n_nodes (aka cluster size, number of nodes in cluster)
- * n_edges (number of edges in cluster)
- * density (number of edges normalised wrt maximum possible number)
- * cluster_centralisation (average absolute deviation from maximum node_degree
- normalised wrt maximum possible value)
-
- Output table has a single row per cluster, along with the cluster metrics
- listed above
- |--------------------------------------------------------------------|
- | cluster_id | n_nodes | n_edges | density | cluster_centralisation |
- |-------------|---------|---------|---------|------------------------|
- | s1-__-10006 | 4 | 4 | 0.66667 | 0.6666 |
- | s1-__-10008 | 6 | 5 | 0.33333 | 0.4 |
- | s1-__-10013 | 11 | 19 | 0.34545 | 0.3111 |
- ...
- """
- pipeline = CTEPipeline()
- sqls = _size_density_centralisation_sql(
- df_node_metrics,
- )
- pipeline.enqueue_list_of_sqls(sqls)
-
- df_cluster_metrics = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
- df_cluster_metrics.metadata["threshold_match_probability"] = (
- df_node_metrics.metadata["threshold_match_probability"]
- )
- return df_cluster_metrics
-
- def compute_graph_metrics(
- self,
- df_predict: SplinkDataFrame,
- df_clustered: SplinkDataFrame,
- *,
- threshold_match_probability: float = None,
- ) -> GraphMetricsResults:
- """
- Generates tables containing graph metrics (for nodes, edges and clusters),
- and returns a data class of Splink dataframes
-
- Args:
- df_predict (SplinkDataFrame): The results of `linker.predict()`
- df_clustered (SplinkDataFrame): The outputs of
- `linker.cluster_pairwise_predictions_at_threshold()`
- threshold_match_probability (float, optional): Filter the pairwise match
- predictions to include only pairwise comparisons with a
- match_probability at or above this threshold. If not provided, the value
- will be taken from metadata on `df_clustered`. If no such metadata is
- available, this value _must_ be provided.
-
- Returns:
- GraphMetricsResult: A data class containing SplinkDataFrames
- of cluster IDs and selected node, edge or cluster metrics.
- attribute "nodes" for nodes metrics table
- attribute "edges" for edge metrics table
- attribute "clusters" for cluster metrics table
-
- """
- if threshold_match_probability is None:
- threshold_match_probability = df_clustered.metadata.get(
- "threshold_match_probability", None
- )
- # we may not have metadata if clusters have been manually registered, or
- # read in from a format that does not include it
- if threshold_match_probability is None:
- raise TypeError(
- "As `df_clustered` has no threshold metadata associated to it, "
- "to compute graph metrics you must provide "
- "`threshold_match_probability` manually"
- )
- df_node_metrics = self._compute_metrics_nodes(
- df_predict, df_clustered, threshold_match_probability
- )
- df_edge_metrics = self._compute_metrics_edges(
- df_node_metrics,
- df_predict,
- df_clustered,
- threshold_match_probability,
- )
- # don't need edges as information is baked into node metrics
- df_cluster_metrics = self._compute_metrics_clusters(df_node_metrics)
-
- return GraphMetricsResults(
- nodes=df_node_metrics, edges=df_edge_metrics, clusters=df_cluster_metrics
- )
-
- def _get_labels_tablename_from_input(
- self, labels_splinkdataframe_or_table_name: str | SplinkDataFrame
- ) -> str:
- if isinstance(labels_splinkdataframe_or_table_name, SplinkDataFrame):
- labels_tablename = labels_splinkdataframe_or_table_name.physical_name
- elif isinstance(labels_splinkdataframe_or_table_name, str):
- labels_tablename = labels_splinkdataframe_or_table_name
+ def _get_labels_tablename_from_input(
+ self, labels_splinkdataframe_or_table_name: str | SplinkDataFrame
+ ) -> str:
+ if isinstance(labels_splinkdataframe_or_table_name, SplinkDataFrame):
+ labels_tablename = labels_splinkdataframe_or_table_name.physical_name
+ elif isinstance(labels_splinkdataframe_or_table_name, str):
+ labels_tablename = labels_splinkdataframe_or_table_name
else:
raise ValueError(
"The 'labels_splinkdataframe_or_table_name' argument"
@@ -1739,932 +589,6 @@ def _get_labels_tablename_from_input(
)
return labels_tablename
- def estimate_m_from_pairwise_labels(self, labels_splinkdataframe_or_table_name):
- """Estimate the m parameters of the linkage model from a dataframe of pairwise
- labels.
-
- The table of labels should be in the following format, and should
- be registered with your database:
- |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|
- |----------------|-----------|----------------|-----------|
- |df_1 |1 |df_2 |2 |
- |df_1 |1 |df_2 |3 |
-
- Note that `source_dataset` and `unique_id` should correspond to the
- values specified in the settings dict, and the `input_table_aliases`
- passed to the `linker` object. Note that at the moment, this method does
- not respect values in a `clerical_match_score` column. If provided, these
- are ignored and it is assumed that every row in the table of labels is a score
- of 1, i.e. a perfect match.
-
- Args:
- labels_splinkdataframe_or_table_name (str): Name of table containing labels
- in the database or SplinkDataframe
-
- Examples:
- ```py
- pairwise_labels = pd.read_csv("./data/pairwise_labels_to_estimate_m.csv")
- linker.register_table(pairwise_labels, "labels", overwrite=True)
- linker.estimate_m_from_pairwise_labels("labels")
- ```
- """
- labels_tablename = self._get_labels_tablename_from_input(
- labels_splinkdataframe_or_table_name
- )
- estimate_m_from_pairwise_labels(self, labels_tablename)
-
- def prediction_errors_from_labels_table(
- self,
- labels_splinkdataframe_or_table_name,
- include_false_positives=True,
- include_false_negatives=True,
- threshold=0.5,
- ):
- """Generate a dataframe containing false positives and false negatives
- based on the comparison between the clerical_match_score in the labels
- table compared with the splink predicted match probability
-
- Args:
- labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
- containing labels in the database
- include_false_positives (bool, optional): Defaults to True.
- include_false_negatives (bool, optional): Defaults to True.
- threshold (float, optional): Threshold above which a score is considered
- to be a match. Defaults to 0.5.
-
- Returns:
- SplinkDataFrame: Table containing false positives and negatives
- """
- labels_tablename = self._get_labels_tablename_from_input(
- labels_splinkdataframe_or_table_name
- )
- return prediction_errors_from_labels_table(
- self,
- labels_tablename,
- include_false_positives,
- include_false_negatives,
- threshold,
- )
-
- def accuracy_analysis_from_labels_column(
- self,
- labels_column_name: str,
- *,
- threshold_actual: float = 0.5,
- match_weight_round_to_nearest: float = 0.1,
- output_type: Literal[
- "threshold_selection", "roc", "precision_recall", "table", "accuracy"
- ] = "threshold_selection",
- add_metrics: List[
- Literal[
- "specificity",
- "npv",
- "accuracy",
- "f1",
- "f2",
- "f0_5",
- "p4",
- "phi",
- ]
- ] = [],
- positives_not_captured_by_blocking_rules_scored_as_zero: bool = True,
- ) -> Union[ChartReturnType, SplinkDataFrame]:
- """Generate an accuracy chart or table from ground truth data, where the ground
- truth is in a column in the input dataset called `labels_column_name`
-
- Args:
- labels_column_name (str): Column name containing labels in the input table
- threshold_actual (float, optional): Where the `clerical_match_score`
- provided by the user is a probability rather than binary, this value
- is used as the threshold to classify `clerical_match_score`s as binary
- matches or non matches. Defaults to 0.5.
- match_weight_round_to_nearest (float, optional): When provided, thresholds
- are rounded. When large numbers of labels are provided, this is
- sometimes necessary to reduce the size of the ROC table, and therefore
- the number of points plotted on the chart. Defaults to None.
- add_metrics (list(str), optional): Precision and recall metrics are always
- included. Where provided, `add_metrics` specifies additional metrics
- to show, with the following options:
-
- - `"specificity"`: specificity, selectivity, true negative rate (TNR)
- - `"npv"`: negative predictive value (NPV)
- - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
- - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03b2=1 (balanced), \u03b2=2
- (emphasis on recall) and \u03b2=0.5 (emphasis on precision)
- - `"p4"` - an extended F1 score with specificity and NPV included
- - `"phi"` - \u03c6 coefficient or Matthews correlation coefficient (MCC)
- Examples:
- ```py
- linker.accuracy_analysis_from_labels_column("ground_truth", add_metrics=["f1"])
- ```
-
- Returns:
- altair.Chart: An altair chart
- """ # noqa: E501
-
- allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]
-
- if not isinstance(add_metrics, list):
- raise Exception(
- "add_metrics must be a list containing one or more of the following:",
- allowed,
- )
-
- if not all(metric in allowed for metric in add_metrics):
- raise ValueError(
- "Invalid metric. " f"Allowed metrics are: {', '.join(allowed)}."
- )
-
- df_truth_space = truth_space_table_from_labels_column(
- self,
- labels_column_name,
- threshold_actual=threshold_actual,
- match_weight_round_to_nearest=match_weight_round_to_nearest,
- positives_not_captured_by_blocking_rules_scored_as_zero=positives_not_captured_by_blocking_rules_scored_as_zero,
- )
- recs = df_truth_space.as_record_dict()
-
- if output_type == "threshold_selection":
- return threshold_selection_tool(recs, add_metrics=add_metrics)
- elif output_type == "accuracy":
- return accuracy_chart(recs, add_metrics=add_metrics)
- elif output_type == "roc":
- return roc_chart(recs)
- elif output_type == "precision_recall":
- return precision_recall_chart(recs)
- elif output_type == "table":
- return df_truth_space
- else:
- raise ValueError(
- "Invalid chart_type. Allowed chart types are: "
- "'threshold_selection', 'roc', 'precision_recall', 'accuracy."
- )
-
- def accuracy_analysis_from_labels_table(
- self,
- labels_splinkdataframe_or_table_name: str | SplinkDataFrame,
- *,
- threshold_actual: float = 0.5,
- match_weight_round_to_nearest: float = 0.1,
- output_type: Literal[
- "threshold_selection", "roc", "precision_recall", "table", "accuracy"
- ] = "threshold_selection",
- add_metrics: List[
- Literal[
- "specificity",
- "npv",
- "accuracy",
- "f1",
- "f2",
- "f0_5",
- "p4",
- "phi",
- ]
- ] = [],
- ) -> Union[ChartReturnType, SplinkDataFrame]:
- """Generate an accuracy chart or table from labelled (ground truth) data.
-
- The table of labels should be in the following format, and should be registered
- as a table with your database using
- `labels_table = linker.register_labels_table(my_df)`
-
- |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
- |----------------|-----------|----------------|-----------|--------------------|
- |df_1 |1 |df_2 |2 |0.99 |
- |df_1 |1 |df_2 |3 |0.2 |
-
- Note that `source_dataset` and `unique_id` should correspond to the values
- specified in the settings dict, and the `input_table_aliases` passed to the
- `linker` object.
-
- For `dedupe_only` links, the `source_dataset` columns can be ommitted.
-
- Args:
- labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
- containing labels in the database
- threshold_actual (float, optional): Where the `clerical_match_score`
- provided by the user is a probability rather than binary, this value
- is used as the threshold to classify `clerical_match_score`s as binary
- matches or non matches. Defaults to 0.5.
- match_weight_round_to_nearest (float, optional): When provided, thresholds
- are rounded. When large numbers of labels are provided, this is
- sometimes necessary to reduce the size of the ROC table, and therefore
- the number of points plotted on the chart. Defaults to None.
- add_metrics (list(str), optional): Precision and recall metrics are always
- included. Where provided, `add_metrics` specifies additional metrics
- to show, with the following options:
-
- - `"specificity"`: specificity, selectivity, true negative rate (TNR)
- - `"npv"`: negative predictive value (NPV)
- - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
- - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03b2=1 (balanced), \u03b2=2
- (emphasis on recall) and \u03b2=0.5 (emphasis on precision)
- - `"p4"` - an extended F1 score with specificity and NPV included
- - `"phi"` - \u03c6 coefficient or Matthews correlation coefficient (MCC)
- Examples:
- ```py
- linker.accuracy_analysis_from_labels_table("ground_truth", add_metrics=["f1"])
- ```
-
- Returns:
- altair.Chart: An altair chart
- """ # noqa: E501
-
- allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]
-
- if not isinstance(add_metrics, list):
- raise Exception(
- "add_metrics must be a list containing one or more of the following:",
- allowed,
- )
-
- if not all(metric in allowed for metric in add_metrics):
- raise ValueError(
- f"Invalid metric. Allowed metrics are: {', '.join(allowed)}."
- )
-
- labels_tablename = self._get_labels_tablename_from_input(
- labels_splinkdataframe_or_table_name
- )
- self._raise_error_if_necessary_accuracy_columns_not_computed()
- df_truth_space = truth_space_table_from_labels_table(
- self,
- labels_tablename,
- threshold_actual=threshold_actual,
- match_weight_round_to_nearest=match_weight_round_to_nearest,
- )
- recs = df_truth_space.as_record_dict()
-
- if output_type == "threshold_selection":
- return threshold_selection_tool(recs, add_metrics=add_metrics)
- elif output_type == "accuracy":
- return accuracy_chart(recs, add_metrics=add_metrics)
- elif output_type == "roc":
- return roc_chart(recs)
- elif output_type == "precision_recall":
- return precision_recall_chart(recs)
- elif output_type == "table":
- return df_truth_space
- else:
- raise ValueError(
- "Invalid chart_type. Allowed chart types are: "
- "'threshold_selection', 'roc', 'precision_recall', 'accuracy."
- )
-
- def prediction_errors_from_labels_column(
- self,
- label_colname,
- include_false_positives=True,
- include_false_negatives=True,
- threshold=0.5,
- ):
- """Generate a dataframe containing false positives and false negatives
- based on the comparison between the splink match probability and the
- labels column. A label column is a column in the input dataset that contains
- the 'ground truth' cluster to which the record belongs
-
- Args:
- label_colname (str): Name of labels column in input data
- include_false_positives (bool, optional): Defaults to True.
- include_false_negatives (bool, optional): Defaults to True.
- threshold (float, optional): Threshold above which a score is considered
- to be a match. Defaults to 0.5.
-
- Returns:
- SplinkDataFrame: Table containing false positives and negatives
- """
- return prediction_errors_from_label_column(
- self,
- label_colname,
- include_false_positives,
- include_false_negatives,
- threshold,
- )
-
- def match_weights_histogram(
- self,
- df_predict: SplinkDataFrame,
- target_bins: int = 30,
- width: int = 600,
- height: int = 250,
- ) -> ChartReturnType:
- """Generate a histogram that shows the distribution of match weights in
- `df_predict`
-
- Args:
- df_predict (SplinkDataFrame): Output of `linker.predict()`
- target_bins (int, optional): Target number of bins in histogram. Defaults to
- 30.
- width (int, optional): Width of output. Defaults to 600.
- height (int, optional): Height of output chart. Defaults to 250.
-
-
- Returns:
- altair.Chart: An altair chart
-
- """
- df = histogram_data(self, df_predict, target_bins)
- recs = df.as_record_dict()
- return match_weights_histogram(recs, width=width, height=height)
-
- def waterfall_chart(
- self,
- records: list[dict[str, Any]],
- filter_nulls: bool = True,
- remove_sensitive_data: bool = False,
- ) -> ChartReturnType:
- """Visualise how the final match weight is computed for the provided pairwise
- record comparisons.
-
- Records must be provided as a list of dictionaries. This would usually be
- obtained from `df.as_record_dict(limit=n)` where `df` is a SplinkDataFrame.
-
- Examples:
- ```py
- df = linker.predict(threshold_match_weight=2)
- records = df.as_record_dict(limit=10)
- linker.waterfall_chart(records)
- ```
-
- Args:
- records (List[dict]): Usually be obtained from `df.as_record_dict(limit=n)`
- where `df` is a SplinkDataFrame.
- filter_nulls (bool, optional): Whether the visualiation shows null
- comparisons, which have no effect on final match weight. Defaults to
- True.
- remove_sensitive_data (bool, optional): When True, The waterfall chart will
- contain match weights only, and all of the (potentially sensitive) data
- from the input tables will be removed prior to the chart being created.
-
-
- Returns:
- altair.Chart: An altair chart
-
- """
- self._raise_error_if_necessary_waterfall_columns_not_computed()
-
- return waterfall_chart(
- records, self._settings_obj, filter_nulls, remove_sensitive_data
- )
-
- def unlinkables_chart(
- self,
- x_col: str = "match_weight",
- name_of_data_in_title: str | None = None,
- as_dict: bool = False,
- ) -> ChartReturnType:
- """Generate an interactive chart displaying the proportion of records that
- are "unlinkable" for a given splink score threshold and model parameters.
-
- Unlinkable records are those that, even when compared with themselves, do not
- contain enough information to confirm a match.
-
- Args:
- x_col (str, optional): Column to use for the x-axis.
- Defaults to "match_weight".
- name_of_data_in_title (str, optional): Name of the source dataset to use for
- the title of the output chart.
- as_dict (bool, optional): If True, return a dict version of the chart.
-
- Examples:
- For the simplest code pipeline, load a pre-trained model
- and run this against the test data.
- ```py
- from splink.datasets import splink_datasets
- df = splink_datasets.fake_1000
- linker = DuckDBLinker(df)
- linker.load_settings("saved_settings.json")
- linker.unlinkables_chart()
- ```
- For more complex code pipelines, you can run an entire pipeline
- that estimates your m and u values, before `unlinkables_chart().
-
- Returns:
- altair.Chart: An altair chart
- """
-
- # Link our initial df on itself and calculate the % of unlinkable entries
- records = unlinkables_data(self)
- return unlinkables_chart(records, x_col, name_of_data_in_title, as_dict)
-
- def comparison_viewer_dashboard(
- self,
- df_predict: SplinkDataFrame,
- out_path: str,
- overwrite: bool = False,
- num_example_rows: int = 2,
- return_html_as_string: bool = False,
- ) -> str | None:
- """Generate an interactive html visualization of the linker's predictions and
- save to `out_path`. For more information see
- [this video](https://www.youtube.com/watch?v=DNvCMqjipis)
-
-
- Args:
- df_predict (SplinkDataFrame): The outputs of `linker.predict()`
- out_path (str): The path (including filename) to save the html file to.
- overwrite (bool, optional): Overwrite the html file if it already exists?
- Defaults to False.
- num_example_rows (int, optional): Number of example rows per comparison
- vector. Defaults to 2.
- return_html_as_string: If True, return the html as a string
-
- Examples:
- ```py
- df_predictions = linker.predict()
- linker.comparison_viewer_dashboard(df_predictions, "scv.html", True, 2)
- ```
-
- Optionally, in Jupyter, you can display the results inline
- Otherwise you can just load the html file in your browser
- ```py
- from IPython.display import IFrame
- IFrame(src="./scv.html", width="100%", height=1200)
- ```
-
- """
- self._raise_error_if_necessary_waterfall_columns_not_computed()
- pipeline = CTEPipeline([df_predict])
- sql = comparison_vector_distribution_sql(self)
- pipeline.enqueue_sql(sql, "__splink__df_comparison_vector_distribution")
-
- sqls = comparison_viewer_table_sqls(self, num_example_rows)
- pipeline.enqueue_list_of_sqls(sqls)
-
- df = self.db_api.sql_pipeline_to_splink_dataframe(pipeline)
-
- rendered = render_splink_comparison_viewer_html(
- df.as_record_dict(),
- self._settings_obj._as_completed_dict(),
- out_path,
- overwrite,
- )
- if return_html_as_string:
- return rendered
- return None
-
- def parameter_estimate_comparisons_chart(
- self, include_m: bool = True, include_u: bool = False
- ) -> ChartReturnType:
- """Show a chart that shows how parameter estimates have differed across
- the different estimation methods you have used.
-
- For example, if you have run two EM estimation sessions, blocking on
- different variables, and both result in parameter estimates for
- first_name, this chart will enable easy comparison of the different
- estimates
-
- Args:
- include_m (bool, optional): Show different estimates of m values. Defaults
- to True.
- include_u (bool, optional): Show different estimates of u values. Defaults
- to False.
-
- """
- records = self._settings_obj._parameter_estimates_as_records
-
- to_retain = []
- if include_m:
- to_retain.append("m")
- if include_u:
- to_retain.append("u")
-
- records = [r for r in records if r["m_or_u"] in to_retain]
-
- return parameter_estimate_comparisons(records)
-
- def match_weights_chart(self):
- """Display a chart of the (partial) match weights of the linkage model
-
- Examples:
- ```py
- linker.match_weights_chart()
- ```
- To view offline (if you don't have an internet connection):
- ```py
- from splink.charts import save_offline_chart
- c = linker.match_weights_chart()
- save_offline_chart(c.to_dict(), "test_chart.html")
- ```
- View resultant html file in Jupyter (or just load it in your browser)
- ```py
- from IPython.display import IFrame
- IFrame(src="./test_chart.html", width=1000, height=500)
- ```
-
- Returns:
- altair.Chart: An altair chart
- """
- return self._settings_obj.match_weights_chart()
-
- def tf_adjustment_chart(
- self,
- output_column_name: str,
- n_most_freq: int = 10,
- n_least_freq: int = 10,
- vals_to_include: str | list[str] | None = None,
- as_dict: bool = False,
- ) -> ChartReturnType:
- """Display a chart showing the impact of term frequency adjustments on a
- specific comparison level.
- Each value
-
- Args:
- output_column_name (str): Name of an output column for which term frequency
- adjustment has been applied.
- n_most_freq (int, optional): Number of most frequent values to show. If this
- or `n_least_freq` set to None, all values will be shown.
- Default to 10.
- n_least_freq (int, optional): Number of least frequent values to show. If
- this or `n_most_freq` set to None, all values will be shown.
- Default to 10.
- vals_to_include (list, optional): Specific values for which to show term
- sfrequency adjustments.
- Defaults to None.
-
- Returns:
- altair.Chart: An altair chart
- """
-
- # Comparisons with TF adjustments
- tf_comparisons = [
- c.output_column_name
- for c in self._settings_obj.comparisons
- if any([cl._has_tf_adjustments for cl in c.comparison_levels])
- ]
- if output_column_name not in tf_comparisons:
- raise ValueError(
- f"{output_column_name} is not a valid comparison column, or does not"
- f" have term frequency adjustment activated"
- )
-
- vals_to_include = (
- [] if vals_to_include is None else ensure_is_list(vals_to_include)
- )
-
- return tf_adjustment_chart(
- self,
- output_column_name,
- n_most_freq,
- n_least_freq,
- vals_to_include,
- as_dict,
- )
-
- def m_u_parameters_chart(self):
- """Display a chart of the m and u parameters of the linkage model
-
- Examples:
- ```py
- linker.m_u_parameters_chart()
- ```
- To view offline (if you don't have an internet connection):
- ```py
- from splink.charts import save_offline_chart
- c = linker.match_weights_chart()
- save_offline_chart(c.to_dict(), "test_chart.html")
- ```
- View resultant html file in Jupyter (or just load it in your browser)
- ```py
- from IPython.display import IFrame
- IFrame(src="./test_chart.html", width=1000, height=500)
- ```
-
- Returns:
- altair.Chart: An altair chart
- """
-
- return self._settings_obj.m_u_parameters_chart()
-
- def cluster_studio_dashboard(
- self,
- df_predict: SplinkDataFrame,
- df_clustered: SplinkDataFrame,
- out_path: str,
- sampling_method: SamplingMethods = "random",
- sample_size: int = 10,
- cluster_ids: list[str] = None,
- cluster_names: list[str] = None,
- overwrite: bool = False,
- return_html_as_string: bool = False,
- _df_cluster_metrics: SplinkDataFrame = None,
- ) -> str | None:
- """Generate an interactive html visualization of the predicted cluster and
- save to `out_path`.
-
- Args:
- df_predict (SplinkDataFrame): The outputs of `linker.predict()`
- df_clustered (SplinkDataFrame): The outputs of
- `linker.cluster_pairwise_predictions_at_threshold()`
- out_path (str): The path (including filename) to save the html file to.
- sampling_method (str, optional): `random`, `by_cluster_size` or
- `lowest_density_clusters`. Defaults to `random`.
- sample_size (int, optional): Number of clusters to show in the dahboard.
- Defaults to 10.
- cluster_ids (list): The IDs of the clusters that will be displayed in the
- dashboard. If provided, ignore the `sampling_method` and `sample_size`
- arguments. Defaults to None.
- overwrite (bool, optional): Overwrite the html file if it already exists?
- Defaults to False.
- cluster_names (list, optional): If provided, the dashboard will display
- these names in the selection box. Ony works in conjunction with
- `cluster_ids`. Defaults to None.
- return_html_as_string: If True, return the html as a string
-
- Examples:
- ```py
- df_p = linker.predict()
- df_c = linker.cluster_pairwise_predictions_at_threshold(df_p, 0.5)
- linker.cluster_studio_dashboard(
- df_p, df_c, [0, 4, 7], "cluster_studio.html"
- )
- ```
- Optionally, in Jupyter, you can display the results inline
- Otherwise you can just load the html file in your browser
- ```py
- from IPython.display import IFrame
- IFrame(src="./cluster_studio.html", width="100%", height=1200)
- ```
- """
- self._raise_error_if_necessary_waterfall_columns_not_computed()
-
- rendered = render_splink_cluster_studio_html(
- self,
- df_predict,
- df_clustered,
- out_path,
- sampling_method=sampling_method,
- sample_size=sample_size,
- cluster_ids=cluster_ids,
- overwrite=overwrite,
- cluster_names=cluster_names,
- _df_cluster_metrics=_df_cluster_metrics,
- )
-
- if return_html_as_string:
- return rendered
- return None
-
- def save_model_to_json(
- self, out_path: str | None = None, overwrite: bool = False
- ) -> dict[str, Any]:
- """Save the configuration and parameters of the linkage model to a `.json` file.
-
- The model can later be loaded back in using `linker.load_model()`.
- The settings dict is also returned in case you want to save it a different way.
-
- Examples:
- ```py
- linker.save_model_to_json("my_settings.json", overwrite=True)
- ```
- Args:
- out_path (str, optional): File path for json file. If None, don't save to
- file. Defaults to None.
- overwrite (bool, optional): Overwrite if already exists? Defaults to False.
-
- Returns:
- dict: The settings as a dictionary.
- """
- model_dict = self._settings_obj.as_dict()
- if out_path:
- if os.path.isfile(out_path) and not overwrite:
- raise ValueError(
- f"The path {out_path} already exists. Please provide a different "
- "path or set overwrite=True"
- )
- with open(out_path, "w", encoding="utf-8") as f:
- json.dump(model_dict, f, indent=4)
- return model_dict
-
- def estimate_probability_two_random_records_match(
- self,
- deterministic_matching_rules: List[Union[str, BlockingRuleCreator]],
- recall: float,
- max_rows_limit: int = int(1e9),
- ) -> None:
- """Estimate the model parameter `probability_two_random_records_match` using
- a direct estimation approach.
-
- See [here](https://github.com/moj-analytical-services/splink/issues/462)
- for discussion of methodology
-
- Args:
- deterministic_matching_rules (list): A list of deterministic matching
- rules that should be designed to admit very few (none if possible)
- false positives
- recall (float): A guess at the recall the deterministic matching rules
- will attain. i.e. what proportion of true matches will be recovered
- by these deterministic rules
- """
-
- if (recall > 1) or (recall <= 0):
- raise ValueError(
- f"Estimated recall must be greater than 0 "
- f"and no more than 1. Supplied value {recall}."
- ) from None
-
- deterministic_matching_rules = ensure_is_iterable(deterministic_matching_rules)
- blocking_rules: List[BlockingRule] = []
- for br in deterministic_matching_rules:
- blocking_rules.append(
- to_blocking_rule_creator(br).get_blocking_rule(
- self.db_api.sql_dialect.name
- )
- )
-
- pd_df = _cumulative_comparisons_to_be_scored_from_blocking_rules(
- splink_df_dict=self._input_tables_dict,
- blocking_rules=blocking_rules,
- link_type=self._settings_obj._link_type,
- db_api=self.db_api,
- max_rows_limit=max_rows_limit,
- unique_id_input_column=self._settings_obj.column_info_settings.unique_id_input_column,
- source_dataset_input_column=self._settings_obj.column_info_settings.source_dataset_input_column,
- )
-
- records = pd_df.to_dict(orient="records")
-
- summary_record = records[-1]
- num_observed_matches = summary_record["cumulative_rows"]
- num_total_comparisons = summary_record["cartesian"]
-
- if num_observed_matches > num_total_comparisons * recall:
- raise ValueError(
- f"Deterministic matching rules led to more "
- f"observed matches than is consistent with supplied recall. "
- f"With these rules, recall must be at least "
- f"{num_observed_matches/num_total_comparisons:,.2f}."
- )
-
- num_expected_matches = num_observed_matches / recall
- prob = num_expected_matches / num_total_comparisons
-
- # warn about boundary values, as these will usually be in error
- if num_observed_matches == 0:
- logger.warning(
- f"WARNING: Deterministic matching rules led to no observed matches! "
- f"This means that no possible record pairs are matches, "
- f"and no records are linked to one another.\n"
- f"If this is truly the case then you do not need "
- f"to run the linkage model.\n"
- f"However this is usually in error; "
- f"expected rules to have recall of {100*recall:,.0f}%. "
- f"Consider revising rules as they may have an error."
- )
- if prob == 1:
- logger.warning(
- "WARNING: Probability two random records match is estimated to be 1.\n"
- "This means that all possible record pairs are matches, "
- "and all records are linked to one another.\n"
- "If this is truly the case then you do not need "
- "to run the linkage model.\n"
- "However, it is more likely that this estimate is faulty. "
- "Perhaps your deterministic matching rules include "
- "too many false positives?"
- )
-
- self._settings_obj._probability_two_random_records_match = prob
-
- reciprocal_prob = "Infinity" if prob == 0 else f"{1/prob:,.2f}"
- logger.info(
- f"Probability two random records match is estimated to be {prob:.3g}.\n"
- f"This means that amongst all possible pairwise record comparisons, one in "
- f"{reciprocal_prob} are expected to match. "
- f"With {num_total_comparisons:,.0f} total"
- " possible comparisons, we expect a total of around "
- f"{num_expected_matches:,.2f} matching pairs"
- )
-
- def invalidate_cache(self):
- """Invalidate the Splink cache. Any previously-computed tables
- will be recomputed.
- This is useful, for example, if the input data tables have changed.
- """
-
- # Nothing to delete
- if len(self._intermediate_table_cache) == 0:
- return
-
- # Before Splink executes a SQL command, it checks the cache to see
- # whether a table already exists with the name of the output table
-
- # This function has the effect of changing the names of the output tables
- # to include a different unique id
-
- # As a result, any previously cached tables will not be found
- self._cache_uid = ascii_uid(8)
-
- # Drop any existing splink tables from the database
- # Note, this is not actually necessary, it's just good housekeeping
- self.delete_tables_created_by_splink_from_db()
-
- # As a result, any previously cached tables will not be found
- self._intermediate_table_cache.invalidate_cache()
-
- def register_table_input_nodes_concat_with_tf(self, input_data, overwrite=False):
- """Register a pre-computed version of the input_nodes_concat_with_tf table that
- you want to re-use e.g. that you created in a previous run
-
- This method allowed you to register this table in the Splink cache
- so it will be used rather than Splink computing this table anew.
-
- Args:
- input_data: The data you wish to register. This can be either a dictionary,
- pandas dataframe, pyarrow table or a spark dataframe.
- overwrite (bool): Overwrite the table in the underlying database if it
- exists
- """
-
- table_name_physical = "__splink__df_concat_with_tf_" + self._cache_uid
- splink_dataframe = self.register_table(
- input_data, table_name_physical, overwrite=overwrite
- )
- splink_dataframe.templated_name = "__splink__df_concat_with_tf"
-
- self._intermediate_table_cache["__splink__df_concat_with_tf"] = splink_dataframe
- return splink_dataframe
-
- def register_table_predict(self, input_data, overwrite=False):
- table_name_physical = "__splink__df_predict_" + self._cache_uid
- splink_dataframe = self.register_table(
- input_data, table_name_physical, overwrite=overwrite
- )
- self._intermediate_table_cache["__splink__df_predict"] = splink_dataframe
- splink_dataframe.templated_name = "__splink__df_predict"
- return splink_dataframe
-
- def register_term_frequency_lookup(self, input_data, col_name, overwrite=False):
- input_col = InputColumn(
- col_name,
- column_info_settings=self._settings_obj.column_info_settings,
- sql_dialect=self._settings_obj._sql_dialect,
- )
- table_name_templated = colname_to_tf_tablename(input_col)
- table_name_physical = f"{table_name_templated}_{self._cache_uid}"
- splink_dataframe = self.register_table(
- input_data, table_name_physical, overwrite=overwrite
- )
- self._intermediate_table_cache[table_name_templated] = splink_dataframe
- splink_dataframe.templated_name = table_name_templated
- return splink_dataframe
-
- def register_labels_table(self, input_data, overwrite=False):
- table_name_physical = "__splink__df_labels_" + ascii_uid(8)
- splink_dataframe = self.register_table(
- input_data, table_name_physical, overwrite=overwrite
- )
- splink_dataframe.templated_name = "__splink__df_labels"
- return splink_dataframe
-
- def labelling_tool_for_specific_record(
- self,
- unique_id,
- source_dataset=None,
- out_path="labelling_tool.html",
- overwrite=False,
- match_weight_threshold=-4,
- view_in_jupyter=False,
- show_splink_predictions_in_interface=True,
- ):
- """Create a standalone, offline labelling dashboard for a specific record
- as identified by its unique id
-
- Args:
- unique_id (str): The unique id of the record for which to create the
- labelling tool
- source_dataset (str, optional): If there are multiple datasets, to
- identify the record you must also specify the source_dataset. Defaults
- to None.
- out_path (str, optional): The output path for the labelling tool. Defaults
- to "labelling_tool.html".
- overwrite (bool, optional): If true, overwrite files at the output
- path if they exist. Defaults to False.
- match_weight_threshold (int, optional): Include possible matches in the
- output which score above this threshold. Defaults to -4.
- view_in_jupyter (bool, optional): If you're viewing in the Jupyter
- html viewer, set this to True to extract your labels. Defaults to False.
- show_splink_predictions_in_interface (bool, optional): Whether to
- show information about the Splink model's predictions that could
- potentially bias the decision of the clerical labeller. Defaults to
- True.
- """
-
- df_comparisons = generate_labelling_tool_comparisons(
- self,
- unique_id,
- source_dataset,
- match_weight_threshold=match_weight_threshold,
- )
-
- render_labelling_tool_html(
- self,
- df_comparisons,
- show_splink_predictions_in_interface=show_splink_predictions_in_interface,
- out_path=out_path,
- view_in_jupyter=view_in_jupyter,
- overwrite=overwrite,
- )
-
def _find_blocking_rules_below_threshold(
self, max_comparisons_per_rule, blocking_expressions=None
):
diff --git a/splink/internals/linker_components/clustering.py b/splink/internals/linker_components/clustering.py
new file mode 100644
index 0000000000..f4802030fd
--- /dev/null
+++ b/splink/internals/linker_components/clustering.py
@@ -0,0 +1,284 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Optional
+
+from splink.internals.connected_components import (
+ _cc_create_unique_id_cols,
+ solve_connected_components,
+)
+from splink.internals.edge_metrics import compute_edge_metrics
+from splink.internals.graph_metrics import (
+ GraphMetricsResults,
+ _node_degree_sql,
+ _size_density_centralisation_sql,
+)
+from splink.internals.pipeline import CTEPipeline
+from splink.internals.splink_dataframe import SplinkDataFrame
+from splink.internals.unique_id_concat import (
+ _composite_unique_id_from_edges_sql,
+ _composite_unique_id_from_nodes_sql,
+)
+from splink.internals.vertically_concatenate import (
+ compute_df_concat_with_tf,
+)
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
+
+
+class LinkerClustering:
+ def __init__(self, linker: Linker):
+ self._linker = linker
+
+ def cluster_pairwise_predictions_at_threshold(
+ self,
+ df_predict: SplinkDataFrame,
+ threshold_match_probability: Optional[float] = None,
+ pairwise_formatting: bool = False,
+ filter_pairwise_format_for_clusters: bool = True,
+ ) -> SplinkDataFrame:
+ """Clusters the pairwise match predictions that result from `linker.predict()`
+ into groups of connected record using the connected components graph clustering
+ algorithm
+
+ Records with an estimated `match_probability` at or above
+ `threshold_match_probability` are considered to be a match (i.e. they represent
+ the same entity).
+
+ Args:
+ df_predict (SplinkDataFrame): The results of `linker.predict()`
+ threshold_match_probability (float): Filter the pairwise match predictions
+ to include only pairwise comparisons with a match_probability at or
+ above this threshold. This dataframe is then fed into the clustering
+ algorithm.
+ pairwise_formatting (bool): Whether to output the pairwise match predictions
+ from linker.predict() with cluster IDs.
+ If this is set to false, the output will be a list of all IDs, clustered
+ into groups based on the desired match threshold.
+ filter_pairwise_format_for_clusters (bool): If pairwise formatting has been
+ selected, whether to output all columns found within linker.predict(),
+ or just return clusters.
+
+ Returns:
+ SplinkDataFrame: A SplinkDataFrame containing a list of all IDs, clustered
+ into groups based on the desired match threshold.
+
+ """
+
+ # Feeding in df_predict forces materiailisation, if it exists in your database
+ pipeline = CTEPipeline()
+ nodes_with_tf = compute_df_concat_with_tf(self._linker, pipeline)
+
+ edges_table = _cc_create_unique_id_cols(
+ self._linker,
+ nodes_with_tf.physical_name,
+ df_predict,
+ threshold_match_probability,
+ )
+
+ cc = solve_connected_components(
+ self._linker,
+ edges_table,
+ df_predict,
+ nodes_with_tf,
+ pairwise_formatting,
+ filter_pairwise_format_for_clusters,
+ )
+ cc.metadata["threshold_match_probability"] = threshold_match_probability
+
+ return cc
+
+ def _compute_metrics_nodes(
+ self,
+ df_predict: SplinkDataFrame,
+ df_clustered: SplinkDataFrame,
+ threshold_match_probability: float,
+ ) -> SplinkDataFrame:
+ """
+ Internal function for computing node-level metrics.
+
+ Accepts outputs of `linker.predict()` and
+ `linker.cluster_pairwise_at_threshold()`, along with the clustering threshold
+ and produces a table of node metrics.
+
+ Node metrics produced:
+ * node_degree (absolute number of neighbouring nodes)
+
+ Output table has a single row per input node, along with the cluster id (as
+ assigned in `linker.cluster_pairwise_at_threshold()`) and the metric
+ node_degree:
+ |-------------------------------------------------|
+ | composite_unique_id | cluster_id | node_degree |
+ |---------------------|-------------|-------------|
+ | s1-__-10001 | s1-__-10001 | 6 |
+ | s1-__-10002 | s1-__-10001 | 4 |
+ | s1-__-10003 | s1-__-10003 | 2 |
+ ...
+ """
+ uid_cols = (
+ self._linker._settings_obj.column_info_settings.unique_id_input_columns
+ )
+ # need composite unique ids
+ composite_uid_edges_l = _composite_unique_id_from_edges_sql(uid_cols, "l")
+ composite_uid_edges_r = _composite_unique_id_from_edges_sql(uid_cols, "r")
+ composite_uid_clusters = _composite_unique_id_from_nodes_sql(uid_cols)
+
+ pipeline = CTEPipeline()
+ sqls = _node_degree_sql(
+ df_predict,
+ df_clustered,
+ composite_uid_edges_l,
+ composite_uid_edges_r,
+ composite_uid_clusters,
+ threshold_match_probability,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ df_node_metrics = self._linker._db_api.sql_pipeline_to_splink_dataframe(
+ pipeline
+ )
+
+ df_node_metrics.metadata["threshold_match_probability"] = (
+ threshold_match_probability
+ )
+ return df_node_metrics
+
+ def _compute_metrics_edges(
+ self,
+ df_node_metrics: SplinkDataFrame,
+ df_predict: SplinkDataFrame,
+ df_clustered: SplinkDataFrame,
+ threshold_match_probability: float,
+ ) -> SplinkDataFrame:
+ """
+ Internal function for computing edge-level metrics.
+
+ Accepts outputs of `linker._compute_node_metrics()`, `linker.predict()` and
+ `linker.cluster_pairwise_at_threshold()`, along with the clustering threshold
+ and produces a table of edge metrics.
+
+ Uses `igraph` under-the-hood for calculations
+
+ Edge metrics produced:
+ * is_bridge (is the edge a bridge?)
+
+ Output table has a single row per edge, and the metric is_bridge:
+ |-------------------------------------------------------------|
+ | composite_unique_id_l | composite_unique_id_r | is_bridge |
+ |-----------------------|-------------------------|-----------|
+ | s1-__-10001 | s1-__-10003 | True |
+ | s1-__-10001 | s1-__-10005 | False |
+ | s1-__-10005 | s1-__-10009 | False |
+ | s1-__-10021 | s1-__-10024 | True |
+ ...
+ """
+ df_edge_metrics = compute_edge_metrics(
+ self._linker,
+ df_node_metrics,
+ df_predict,
+ df_clustered,
+ threshold_match_probability,
+ )
+ df_edge_metrics.metadata["threshold_match_probability"] = (
+ threshold_match_probability
+ )
+ return df_edge_metrics
+
+ def _compute_metrics_clusters(
+ self,
+ df_node_metrics: SplinkDataFrame,
+ ) -> SplinkDataFrame:
+ """
+ Internal function for computing cluster-level metrics.
+
+ Accepts output of `linker._compute_node_metrics()` (which has the relevant
+ information from `linker.predict() and
+ `linker.cluster_pairwise_at_threshold()`), produces a table of cluster metrics.
+
+ Cluster metrics produced:
+ * n_nodes (aka cluster size, number of nodes in cluster)
+ * n_edges (number of edges in cluster)
+ * density (number of edges normalised wrt maximum possible number)
+ * cluster_centralisation (average absolute deviation from maximum node_degree
+ normalised wrt maximum possible value)
+
+ Output table has a single row per cluster, along with the cluster metrics
+ listed above
+ |--------------------------------------------------------------------|
+ | cluster_id | n_nodes | n_edges | density | cluster_centralisation |
+ |-------------|---------|---------|---------|------------------------|
+ | s1-__-10006 | 4 | 4 | 0.66667 | 0.6666 |
+ | s1-__-10008 | 6 | 5 | 0.33333 | 0.4 |
+ | s1-__-10013 | 11 | 19 | 0.34545 | 0.3111 |
+ ...
+ """
+ pipeline = CTEPipeline()
+ sqls = _size_density_centralisation_sql(
+ df_node_metrics,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ df_cluster_metrics = self._linker._db_api.sql_pipeline_to_splink_dataframe(
+ pipeline
+ )
+ df_cluster_metrics.metadata["threshold_match_probability"] = (
+ df_node_metrics.metadata["threshold_match_probability"]
+ )
+ return df_cluster_metrics
+
+ def compute_graph_metrics(
+ self,
+ df_predict: SplinkDataFrame,
+ df_clustered: SplinkDataFrame,
+ *,
+ threshold_match_probability: float = None,
+ ) -> GraphMetricsResults:
+ """
+ Generates tables containing graph metrics (for nodes, edges and clusters),
+ and returns a data class of Splink dataframes
+
+ Args:
+ df_predict (SplinkDataFrame): The results of `linker.predict()`
+ df_clustered (SplinkDataFrame): The outputs of
+ `linker.cluster_pairwise_predictions_at_threshold()`
+ threshold_match_probability (float, optional): Filter the pairwise match
+ predictions to include only pairwise comparisons with a
+ match_probability at or above this threshold. If not provided, the value
+ will be taken from metadata on `df_clustered`. If no such metadata is
+ available, this value _must_ be provided.
+
+ Returns:
+ GraphMetricsResult: A data class containing SplinkDataFrames
+ of cluster IDs and selected node, edge or cluster metrics.
+ attribute "nodes" for nodes metrics table
+ attribute "edges" for edge metrics table
+ attribute "clusters" for cluster metrics table
+
+ """
+ if threshold_match_probability is None:
+ threshold_match_probability = df_clustered.metadata.get(
+ "threshold_match_probability", None
+ )
+ # we may not have metadata if clusters have been manually registered, or
+ # read in from a format that does not include it
+ if threshold_match_probability is None:
+ raise TypeError(
+ "As `df_clustered` has no threshold metadata associated to it, "
+ "to compute graph metrics you must provide "
+ "`threshold_match_probability` manually"
+ )
+ df_node_metrics = self._compute_metrics_nodes(
+ df_predict, df_clustered, threshold_match_probability
+ )
+ df_edge_metrics = self._compute_metrics_edges(
+ df_node_metrics,
+ df_predict,
+ df_clustered,
+ threshold_match_probability,
+ )
+ # don't need edges as information is baked into node metrics
+ df_cluster_metrics = self._compute_metrics_clusters(df_node_metrics)
+
+ return GraphMetricsResults(
+ nodes=df_node_metrics, edges=df_edge_metrics, clusters=df_cluster_metrics
+ )
diff --git a/splink/internals/linker_components/evaluation.py b/splink/internals/linker_components/evaluation.py
new file mode 100644
index 0000000000..2018a30ffa
--- /dev/null
+++ b/splink/internals/linker_components/evaluation.py
@@ -0,0 +1,389 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, List, Literal, Union
+
+from splink.internals.accuracy import (
+ prediction_errors_from_label_column,
+ prediction_errors_from_labels_table,
+ truth_space_table_from_labels_column,
+ truth_space_table_from_labels_table,
+)
+from splink.internals.charts import (
+ ChartReturnType,
+ accuracy_chart,
+ precision_recall_chart,
+ roc_chart,
+ threshold_selection_tool,
+ unlinkables_chart,
+)
+from splink.internals.labelling_tool import (
+ generate_labelling_tool_comparisons,
+ render_labelling_tool_html,
+)
+from splink.internals.splink_dataframe import SplinkDataFrame
+from splink.internals.unlinkables import unlinkables_data
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
+
+
+class LinkerEvalution:
+ def __init__(self, linker: Linker):
+ self._linker = linker
+
+ def prediction_errors_from_labels_table(
+ self,
+ labels_splinkdataframe_or_table_name,
+ include_false_positives=True,
+ include_false_negatives=True,
+ threshold=0.5,
+ ):
+ """Generate a dataframe containing false positives and false negatives
+ based on the comparison between the clerical_match_score in the labels
+ table compared with the splink predicted match probability
+
+ Args:
+ labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
+ containing labels in the database
+ include_false_positives (bool, optional): Defaults to True.
+ include_false_negatives (bool, optional): Defaults to True.
+ threshold (float, optional): Threshold above which a score is considered
+ to be a match. Defaults to 0.5.
+
+ Returns:
+ SplinkDataFrame: Table containing false positives and negatives
+ """
+ labels_tablename = self._linker._get_labels_tablename_from_input(
+ labels_splinkdataframe_or_table_name
+ )
+ return prediction_errors_from_labels_table(
+ self._linker,
+ labels_tablename,
+ include_false_positives,
+ include_false_negatives,
+ threshold,
+ )
+
+ def accuracy_analysis_from_labels_column(
+ self,
+ labels_column_name: str,
+ *,
+ threshold_actual: float = 0.5,
+ match_weight_round_to_nearest: float = 0.1,
+ output_type: Literal[
+ "threshold_selection", "roc", "precision_recall", "table", "accuracy"
+ ] = "threshold_selection",
+ add_metrics: List[
+ Literal[
+ "specificity",
+ "npv",
+ "accuracy",
+ "f1",
+ "f2",
+ "f0_5",
+ "p4",
+ "phi",
+ ]
+ ] = [],
+ positives_not_captured_by_blocking_rules_scored_as_zero: bool = True,
+ ) -> Union[ChartReturnType, SplinkDataFrame]:
+ """Generate an accuracy chart or table from ground truth data, where the ground
+ truth is in a column in the input dataset called `labels_column_name`
+
+ Args:
+ labels_column_name (str): Column name containing labels in the input table
+ threshold_actual (float, optional): Where the `clerical_match_score`
+ provided by the user is a probability rather than binary, this value
+ is used as the threshold to classify `clerical_match_score`s as binary
+ matches or non matches. Defaults to 0.5.
+ match_weight_round_to_nearest (float, optional): When provided, thresholds
+ are rounded. When large numbers of labels are provided, this is
+ sometimes necessary to reduce the size of the ROC table, and therefore
+ the number of points plotted on the chart. Defaults to None.
+ add_metrics (list(str), optional): Precision and recall metrics are always
+ included. Where provided, `add_metrics` specifies additional metrics
+ to show, with the following options:
+
+ - `"specificity"`: specificity, selectivity, true negative rate (TNR)
+ - `"npv"`: negative predictive value (NPV)
+ - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
+ - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03b2=1 (balanced), \u03b2=2
+ (emphasis on recall) and \u03b2=0.5 (emphasis on precision)
+ - `"p4"` - an extended F1 score with specificity and NPV included
+ - `"phi"` - \u03c6 coefficient or Matthews correlation coefficient (MCC)
+ Examples:
+ ```py
+ linker.evaluation.accuracy_analysis_from_labels_column("ground_truth", add_metrics=["f1"])
+ ```
+
+ Returns:
+ altair.Chart: An altair chart
+ """ # noqa: E501
+
+ allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]
+
+ if not isinstance(add_metrics, list):
+ raise Exception(
+ "add_metrics must be a list containing one or more of the following:",
+ allowed,
+ )
+
+ if not all(metric in allowed for metric in add_metrics):
+ raise ValueError(
+ "Invalid metric. " f"Allowed metrics are: {', '.join(allowed)}."
+ )
+
+ df_truth_space = truth_space_table_from_labels_column(
+ self._linker,
+ labels_column_name,
+ threshold_actual=threshold_actual,
+ match_weight_round_to_nearest=match_weight_round_to_nearest,
+ positives_not_captured_by_blocking_rules_scored_as_zero=positives_not_captured_by_blocking_rules_scored_as_zero,
+ )
+ recs = df_truth_space.as_record_dict()
+
+ if output_type == "threshold_selection":
+ return threshold_selection_tool(recs, add_metrics=add_metrics)
+ elif output_type == "accuracy":
+ return accuracy_chart(recs, add_metrics=add_metrics)
+ elif output_type == "roc":
+ return roc_chart(recs)
+ elif output_type == "precision_recall":
+ return precision_recall_chart(recs)
+ elif output_type == "table":
+ return df_truth_space
+ else:
+ raise ValueError(
+ "Invalid chart_type. Allowed chart types are: "
+ "'threshold_selection', 'roc', 'precision_recall', 'accuracy."
+ )
+
+ def accuracy_analysis_from_labels_table(
+ self,
+ labels_splinkdataframe_or_table_name: str | SplinkDataFrame,
+ *,
+ threshold_actual: float = 0.5,
+ match_weight_round_to_nearest: float = 0.1,
+ output_type: Literal[
+ "threshold_selection", "roc", "precision_recall", "table", "accuracy"
+ ] = "threshold_selection",
+ add_metrics: List[
+ Literal[
+ "specificity",
+ "npv",
+ "accuracy",
+ "f1",
+ "f2",
+ "f0_5",
+ "p4",
+ "phi",
+ ]
+ ] = [],
+ ) -> Union[ChartReturnType, SplinkDataFrame]:
+ """Generate an accuracy chart or table from labelled (ground truth) data.
+
+ The table of labels should be in the following format, and should be registered
+ as a table with your database using
+ `labels_table = linker.register_labels_table(my_df)`
+
+ |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|clerical_match_score|
+ |----------------|-----------|----------------|-----------|--------------------|
+ |df_1 |1 |df_2 |2 |0.99 |
+ |df_1 |1 |df_2 |3 |0.2 |
+
+ Note that `source_dataset` and `unique_id` should correspond to the values
+ specified in the settings dict, and the `input_table_aliases` passed to the
+ `linker` object.
+
+ For `dedupe_only` links, the `source_dataset` columns can be ommitted.
+
+ Args:
+ labels_splinkdataframe_or_table_name (str | SplinkDataFrame): Name of table
+ containing labels in the database
+ threshold_actual (float, optional): Where the `clerical_match_score`
+ provided by the user is a probability rather than binary, this value
+ is used as the threshold to classify `clerical_match_score`s as binary
+ matches or non matches. Defaults to 0.5.
+ match_weight_round_to_nearest (float, optional): When provided, thresholds
+ are rounded. When large numbers of labels are provided, this is
+ sometimes necessary to reduce the size of the ROC table, and therefore
+ the number of points plotted on the chart. Defaults to None.
+ add_metrics (list(str), optional): Precision and recall metrics are always
+ included. Where provided, `add_metrics` specifies additional metrics
+ to show, with the following options:
+
+ - `"specificity"`: specificity, selectivity, true negative rate (TNR)
+ - `"npv"`: negative predictive value (NPV)
+ - `"accuracy"`: overall accuracy (TP+TN)/(P+N)
+ - `"f1"`/`"f2"`/`"f0_5"`: F-scores for \u03b2=1 (balanced), \u03b2=2
+ (emphasis on recall) and \u03b2=0.5 (emphasis on precision)
+ - `"p4"` - an extended F1 score with specificity and NPV included
+ - `"phi"` - \u03c6 coefficient or Matthews correlation coefficient (MCC)
+ Examples:
+ ```py
+ linker.accuracy_analysis_from_labels_table("ground_truth", add_metrics=["f1"])
+ ```
+
+ Returns:
+ altair.Chart: An altair chart
+ """ # noqa: E501
+
+ allowed = ["specificity", "npv", "accuracy", "f1", "f2", "f0_5", "p4", "phi"]
+
+ if not isinstance(add_metrics, list):
+ raise Exception(
+ "add_metrics must be a list containing one or more of the following:",
+ allowed,
+ )
+
+ if not all(metric in allowed for metric in add_metrics):
+ raise ValueError(
+ f"Invalid metric. Allowed metrics are: {', '.join(allowed)}."
+ )
+
+ labels_tablename = self._linker._get_labels_tablename_from_input(
+ labels_splinkdataframe_or_table_name
+ )
+ self._linker._raise_error_if_necessary_accuracy_columns_not_computed()
+ df_truth_space = truth_space_table_from_labels_table(
+ self._linker,
+ labels_tablename,
+ threshold_actual=threshold_actual,
+ match_weight_round_to_nearest=match_weight_round_to_nearest,
+ )
+ recs = df_truth_space.as_record_dict()
+
+ if output_type == "threshold_selection":
+ return threshold_selection_tool(recs, add_metrics=add_metrics)
+ elif output_type == "accuracy":
+ return accuracy_chart(recs, add_metrics=add_metrics)
+ elif output_type == "roc":
+ return roc_chart(recs)
+ elif output_type == "precision_recall":
+ return precision_recall_chart(recs)
+ elif output_type == "table":
+ return df_truth_space
+ else:
+ raise ValueError(
+ "Invalid chart_type. Allowed chart types are: "
+ "'threshold_selection', 'roc', 'precision_recall', 'accuracy."
+ )
+
+ def prediction_errors_from_labels_column(
+ self,
+ label_colname,
+ include_false_positives=True,
+ include_false_negatives=True,
+ threshold=0.5,
+ ):
+ """Generate a dataframe containing false positives and false negatives
+ based on the comparison between the splink match probability and the
+ labels column. A label column is a column in the input dataset that contains
+ the 'ground truth' cluster to which the record belongs
+
+ Args:
+ label_colname (str): Name of labels column in input data
+ include_false_positives (bool, optional): Defaults to True.
+ include_false_negatives (bool, optional): Defaults to True.
+ threshold (float, optional): Threshold above which a score is considered
+ to be a match. Defaults to 0.5.
+
+ Returns:
+ SplinkDataFrame: Table containing false positives and negatives
+ """
+ return prediction_errors_from_label_column(
+ self._linker,
+ label_colname,
+ include_false_positives,
+ include_false_negatives,
+ threshold,
+ )
+
+ def unlinkables_chart(
+ self,
+ x_col: str = "match_weight",
+ name_of_data_in_title: str | None = None,
+ as_dict: bool = False,
+ ) -> ChartReturnType:
+ """Generate an interactive chart displaying the proportion of records that
+ are "unlinkable" for a given splink score threshold and model parameters.
+
+ Unlinkable records are those that, even when compared with themselves, do not
+ contain enough information to confirm a match.
+
+ Args:
+ x_col (str, optional): Column to use for the x-axis.
+ Defaults to "match_weight".
+ name_of_data_in_title (str, optional): Name of the source dataset to use for
+ the title of the output chart.
+ as_dict (bool, optional): If True, return a dict version of the chart.
+
+ Examples:
+ For the simplest code pipeline, load a pre-trained model
+ and run this against the test data.
+ ```py
+ from splink.datasets import splink_datasets
+ df = splink_datasets.fake_1000
+ linker = DuckDBLinker(df)
+ linker.load_settings("saved_settings.json")
+ linker.unlinkables_chart()
+ ```
+ For more complex code pipelines, you can run an entire pipeline
+ that estimates your m and u values, before `unlinkables_chart().
+
+ Returns:
+ altair.Chart: An altair chart
+ """
+
+ # Link our initial df on itself and calculate the % of unlinkable entries
+ records = unlinkables_data(self._linker)
+ return unlinkables_chart(records, x_col, name_of_data_in_title, as_dict)
+
+ def labelling_tool_for_specific_record(
+ self,
+ unique_id,
+ source_dataset=None,
+ out_path="labelling_tool.html",
+ overwrite=False,
+ match_weight_threshold=-4,
+ view_in_jupyter=False,
+ show_splink_predictions_in_interface=True,
+ ):
+ """Create a standalone, offline labelling dashboard for a specific record
+ as identified by its unique id
+
+ Args:
+ unique_id (str): The unique id of the record for which to create the
+ labelling tool
+ source_dataset (str, optional): If there are multiple datasets, to
+ identify the record you must also specify the source_dataset. Defaults
+ to None.
+ out_path (str, optional): The output path for the labelling tool. Defaults
+ to "labelling_tool.html".
+ overwrite (bool, optional): If true, overwrite files at the output
+ path if they exist. Defaults to False.
+ match_weight_threshold (int, optional): Include possible matches in the
+ output which score above this threshold. Defaults to -4.
+ view_in_jupyter (bool, optional): If you're viewing in the Jupyter
+ html viewer, set this to True to extract your labels. Defaults to False.
+ show_splink_predictions_in_interface (bool, optional): Whether to
+ show information about the Splink model's predictions that could
+ potentially bias the decision of the clerical labeller. Defaults to
+ True.
+ """
+
+ df_comparisons = generate_labelling_tool_comparisons(
+ self._linker,
+ unique_id,
+ source_dataset,
+ match_weight_threshold=match_weight_threshold,
+ )
+
+ render_labelling_tool_html(
+ self._linker,
+ df_comparisons,
+ show_splink_predictions_in_interface=show_splink_predictions_in_interface,
+ out_path=out_path,
+ view_in_jupyter=view_in_jupyter,
+ overwrite=overwrite,
+ )
diff --git a/splink/internals/linker_components/inference.py b/splink/internals/linker_components/inference.py
new file mode 100644
index 0000000000..16c2ca7899
--- /dev/null
+++ b/splink/internals/linker_components/inference.py
@@ -0,0 +1,513 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any
+
+from splink.internals.blocking import (
+ BlockingRule,
+ block_using_rules_sqls,
+ blocking_rule_to_obj,
+ materialise_exploded_id_tables,
+)
+from splink.internals.blocking_rule_creator import BlockingRuleCreator
+from splink.internals.comparison_vector_values import (
+ compute_comparison_vector_values_sql,
+)
+from splink.internals.database_api import AcceptableInputTableType
+from splink.internals.find_matches_to_new_records import (
+ add_unique_id_and_source_dataset_cols_if_needed,
+)
+from splink.internals.misc import (
+ ascii_uid,
+ ensure_is_list,
+)
+from splink.internals.pipeline import CTEPipeline
+from splink.internals.predict import (
+ predict_from_comparison_vectors_sqls_using_settings,
+)
+from splink.internals.splink_dataframe import SplinkDataFrame
+from splink.internals.term_frequencies import (
+ _join_new_table_to_df_concat_with_tf_sql,
+ colname_to_tf_tablename,
+)
+from splink.internals.vertically_concatenate import (
+ compute_df_concat_with_tf,
+ enqueue_df_concat_with_tf,
+ split_df_concat_with_tf_into_two_tables_sqls,
+)
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
+
+logger = logging.getLogger(__name__)
+
+
+class LinkerInference:
+ def __init__(self, linker: Linker):
+ self._linker = linker
+
+ def deterministic_link(self) -> SplinkDataFrame:
+ """Uses the blocking rules specified by
+ `blocking_rules_to_generate_predictions` in the settings dictionary to
+ generate pairwise record comparisons.
+
+ For deterministic linkage, this should be a list of blocking rules which
+ are strict enough to generate only true links.
+
+ Deterministic linkage, however, is likely to result in missed links
+ (false negatives).
+
+ Examples:
+
+ ```py
+ from splink.linker import Linker
+ from splink.duckdb.database_api import DuckDBAPI
+
+ db_api = DuckDBAPI()
+
+ settings = {
+ "link_type": "dedupe_only",
+ "blocking_rules_to_generate_predictions": [
+ "l.first_name = r.first_name",
+ "l.surname = r.surname",
+ ],
+ "comparisons": []
+ }
+ >>>
+ linker = Linker(df, settings, db_api)
+ df = linker.deterministic_link()
+ ```
+
+
+ Returns:
+ SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons. This
+ represents a table materialised in the database. Methods on the
+ SplinkDataFrame allow you to access the underlying data.
+ """
+ pipeline = CTEPipeline()
+ # Allows clustering during a deterministic linkage.
+ # This is used in `cluster_pairwise_predictions_at_threshold`
+ # to set the cluster threshold to 1
+
+ df_concat_with_tf = compute_df_concat_with_tf(self._linker, pipeline)
+ pipeline = CTEPipeline([df_concat_with_tf])
+ link_type = self._linker._settings_obj._link_type
+
+ blocking_input_tablename_l = "__splink__df_concat_with_tf"
+ blocking_input_tablename_r = "__splink__df_concat_with_tf"
+
+ link_type = self._linker._settings_obj._link_type
+ if (
+ len(self._linker._input_tables_dict) == 2
+ and self._linker._settings_obj._link_type == "link_only"
+ ):
+ sqls = split_df_concat_with_tf_into_two_tables_sqls(
+ "__splink__df_concat_with_tf",
+ self._linker._settings_obj.column_info_settings.source_dataset_column_name,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ blocking_input_tablename_l = "__splink__df_concat_with_tf_left"
+ blocking_input_tablename_r = "__splink__df_concat_with_tf_right"
+ link_type = "two_dataset_link_only"
+
+ exploding_br_with_id_tables = materialise_exploded_id_tables(
+ link_type=link_type,
+ blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
+ db_api=self._linker._db_api,
+ splink_df_dict=self._linker._input_tables_dict,
+ source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
+ unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
+ )
+
+ columns_to_select = self._linker._settings_obj._columns_to_select_for_blocking
+ sql_select_expr = ", ".join(columns_to_select)
+
+ sqls = block_using_rules_sqls(
+ input_tablename_l=blocking_input_tablename_l,
+ input_tablename_r=blocking_input_tablename_r,
+ blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
+ link_type=link_type,
+ columns_to_select_sql=sql_select_expr,
+ source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
+ unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ deterministic_link_df = self._linker._db_api.sql_pipeline_to_splink_dataframe(
+ pipeline
+ )
+ deterministic_link_df.metadata["is_deterministic_link"] = True
+
+ [b.drop_materialised_id_pairs_dataframe() for b in exploding_br_with_id_tables]
+
+ return deterministic_link_df
+
+ def predict(
+ self,
+ threshold_match_probability: float = None,
+ threshold_match_weight: float = None,
+ materialise_after_computing_term_frequencies: bool = True,
+ ) -> SplinkDataFrame:
+ """Create a dataframe of scored pairwise comparisons using the parameters
+ of the linkage model.
+
+ Uses the blocking rules specified in the
+ `blocking_rules_to_generate_predictions` of the settings dictionary to
+ generate the pairwise comparisons.
+
+ Args:
+ threshold_match_probability (float, optional): If specified,
+ filter the results to include only pairwise comparisons with a
+ match_probability above this threshold. Defaults to None.
+ threshold_match_weight (float, optional): If specified,
+ filter the results to include only pairwise comparisons with a
+ match_weight above this threshold. Defaults to None.
+ materialise_after_computing_term_frequencies (bool): If true, Splink
+ will materialise the table containing the input nodes (rows)
+ joined to any term frequencies which have been asked
+ for in the settings object. If False, this will be
+ computed as part of one possibly gigantic CTE
+ pipeline. Defaults to True
+
+ Examples:
+ ```py
+ linker = DuckDBLinker(df)
+ linker.load_settings("saved_settings.json")
+ df = linker.predict(threshold_match_probability=0.95)
+ df.as_pandas_dataframe(limit=5)
+ ```
+ Returns:
+ SplinkDataFrame: A SplinkDataFrame of the pairwise comparisons. This
+ represents a table materialised in the database. Methods on the
+ SplinkDataFrame allow you to access the underlying data.
+
+ """
+
+ pipeline = CTEPipeline()
+
+ # If materialise_after_computing_term_frequencies=False and the user only
+ # calls predict, it runs as a single pipeline with no materialisation
+ # of anything.
+
+ # In duckdb, calls to random() in a CTE pipeline cause problems:
+ # https://gist.github.com/RobinL/d329e7004998503ce91b68479aa41139
+ if (
+ materialise_after_computing_term_frequencies
+ or self._linker._sql_dialect == "duckdb"
+ ):
+ df_concat_with_tf = compute_df_concat_with_tf(self._linker, pipeline)
+ pipeline = CTEPipeline([df_concat_with_tf])
+ else:
+ pipeline = enqueue_df_concat_with_tf(self._linker, pipeline)
+
+ blocking_input_tablename_l = "__splink__df_concat_with_tf"
+ blocking_input_tablename_r = "__splink__df_concat_with_tf"
+
+ link_type = self._linker._settings_obj._link_type
+ if (
+ len(self._linker._input_tables_dict) == 2
+ and self._linker._settings_obj._link_type == "link_only"
+ ):
+ sqls = split_df_concat_with_tf_into_two_tables_sqls(
+ "__splink__df_concat_with_tf",
+ self._linker._settings_obj.column_info_settings.source_dataset_column_name,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ blocking_input_tablename_l = "__splink__df_concat_with_tf_left"
+ blocking_input_tablename_r = "__splink__df_concat_with_tf_right"
+ link_type = "two_dataset_link_only"
+
+ # If exploded blocking rules exist, we need to materialise
+ # the tables of ID pairs
+
+ exploding_br_with_id_tables = materialise_exploded_id_tables(
+ link_type=link_type,
+ blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
+ db_api=self._linker._db_api,
+ splink_df_dict=self._linker._input_tables_dict,
+ source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
+ unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
+ )
+
+ columns_to_select = self._linker._settings_obj._columns_to_select_for_blocking
+ sql_select_expr = ", ".join(columns_to_select)
+
+ sqls = block_using_rules_sqls(
+ input_tablename_l=blocking_input_tablename_l,
+ input_tablename_r=blocking_input_tablename_r,
+ blocking_rules=self._linker._settings_obj._blocking_rules_to_generate_predictions,
+ link_type=link_type,
+ columns_to_select_sql=sql_select_expr,
+ source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
+ unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
+ )
+
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ repartition_after_blocking = getattr(
+ self._linker, "repartition_after_blocking", False
+ )
+
+ # repartition after blocking only exists on the SparkLinker
+ if repartition_after_blocking:
+ pipeline = pipeline.break_lineage(self._linker._db_api)
+
+ sql = compute_comparison_vector_values_sql(
+ self._linker._settings_obj._columns_to_select_for_comparison_vector_values
+ )
+ pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
+
+ sqls = predict_from_comparison_vectors_sqls_using_settings(
+ self._linker._settings_obj,
+ threshold_match_probability,
+ threshold_match_weight,
+ sql_infinity_expression=self._linker._infinity_expression,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ predictions = self._linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ self._linker._predict_warning()
+
+ [b.drop_materialised_id_pairs_dataframe() for b in exploding_br_with_id_tables]
+
+ return predictions
+
+ def find_matches_to_new_records(
+ self,
+ records_or_tablename: AcceptableInputTableType | str,
+ blocking_rules: list[BlockingRuleCreator | dict[str, Any] | str]
+ | BlockingRuleCreator
+ | dict[str, Any]
+ | str = [],
+ match_weight_threshold: float = -4,
+ ) -> SplinkDataFrame:
+ """Given one or more records, find records in the input dataset(s) which match
+ and return in order of the Splink prediction score.
+
+ This effectively provides a way of searching the input datasets
+ for given record(s)
+
+ Args:
+ records_or_tablename (List[dict]): Input search record(s) as list of dict,
+ or a table registered to the database.
+ blocking_rules (list, optional): Blocking rules to select
+ which records to find and score. If [], do not use a blocking
+ rule - meaning the input records will be compared to all records
+ provided to the linker when it was instantiated. Defaults to [].
+ match_weight_threshold (int, optional): Return matches with a match weight
+ above this threshold. Defaults to -4.
+
+ Examples:
+ ```py
+ linker = DuckDBLinker(df)
+ linker.load_settings("saved_settings.json")
+ # Pre-compute tf tables for any tables with
+ # term frequency adjustments
+ linker.table_management.compute_tf_table("first_name")
+ record = {'unique_id': 1,
+ 'first_name': "John",
+ 'surname': "Smith",
+ 'dob': "1971-05-24",
+ 'city': "London",
+ 'email': "john@smith.net"
+ }
+ df = linker.inference.find_matches_to_new_records(
+ [record], blocking_rules=[]
+ )
+ ```
+
+ Returns:
+ SplinkDataFrame: The pairwise comparisons.
+ """
+
+ original_blocking_rules = (
+ self._linker._settings_obj._blocking_rules_to_generate_predictions
+ )
+ original_link_type = self._linker._settings_obj._link_type
+
+ blocking_rule_list = ensure_is_list(blocking_rules)
+
+ if not isinstance(records_or_tablename, str):
+ uid = ascii_uid(8)
+ new_records_tablename = f"__splink__df_new_records_{uid}"
+ self._linker.table_management.register_table(
+ records_or_tablename, new_records_tablename, overwrite=True
+ )
+
+ else:
+ new_records_tablename = records_or_tablename
+
+ new_records_df = self._linker._db_api.table_to_splink_dataframe(
+ "__splink__df_new_records", new_records_tablename
+ )
+
+ pipeline = CTEPipeline()
+ nodes_with_tf = compute_df_concat_with_tf(self._linker, pipeline)
+
+ pipeline = CTEPipeline([nodes_with_tf, new_records_df])
+ if len(blocking_rule_list) == 0:
+ blocking_rule_list = [BlockingRule("1=1")]
+ blocking_rule_list = [blocking_rule_to_obj(br) for br in blocking_rule_list]
+ for n, br in enumerate(blocking_rule_list):
+ br.add_preceding_rules(blocking_rule_list[:n])
+
+ self._linker._settings_obj._blocking_rules_to_generate_predictions = (
+ blocking_rule_list
+ )
+
+ for tf_col in self._linker._settings_obj._term_frequency_columns:
+ tf_table_name = colname_to_tf_tablename(tf_col)
+ if tf_table_name in self._linker._intermediate_table_cache:
+ tf_table = self._linker._intermediate_table_cache.get_with_logging(
+ tf_table_name
+ )
+ pipeline.append_input_dataframe(tf_table)
+
+ sql = _join_new_table_to_df_concat_with_tf_sql(
+ self._linker, "__splink__df_new_records"
+ )
+ pipeline.enqueue_sql(sql, "__splink__df_new_records_with_tf_before_uid_fix")
+
+ pipeline = add_unique_id_and_source_dataset_cols_if_needed(
+ self._linker, new_records_df, pipeline
+ )
+ settings = self._linker._settings_obj
+ sqls = block_using_rules_sqls(
+ input_tablename_l="__splink__df_concat_with_tf",
+ input_tablename_r="__splink__df_new_records_with_tf",
+ blocking_rules=blocking_rule_list,
+ link_type="two_dataset_link_only",
+ columns_to_select_sql=", ".join(settings._columns_to_select_for_blocking),
+ source_dataset_input_column=settings.column_info_settings.source_dataset_input_column,
+ unique_id_input_column=settings.column_info_settings.unique_id_input_column,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ sql = compute_comparison_vector_values_sql(
+ self._linker._settings_obj._columns_to_select_for_comparison_vector_values
+ )
+ pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
+
+ sqls = predict_from_comparison_vectors_sqls_using_settings(
+ self._linker._settings_obj,
+ sql_infinity_expression=self._linker._infinity_expression,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ sql = f"""
+ select * from __splink__df_predict
+ where match_weight > {match_weight_threshold}
+ """
+
+ pipeline.enqueue_sql(sql, "__splink__find_matches_predictions")
+
+ predictions = self._linker._db_api.sql_pipeline_to_splink_dataframe(
+ pipeline, use_cache=False
+ )
+
+ self._linker._settings_obj._blocking_rules_to_generate_predictions = (
+ original_blocking_rules
+ )
+ self._linker._settings_obj._link_type = original_link_type
+
+ return predictions
+
+ def compare_two_records(
+ self, record_1: dict[str, Any], record_2: dict[str, Any]
+ ) -> SplinkDataFrame:
+ """Use the linkage model to compare and score a pairwise record comparison
+ based on the two input records provided
+
+ Args:
+ record_1 (dict): dictionary representing the first record. Columns names
+ and data types must be the same as the columns in the settings object
+ record_2 (dict): dictionary representing the second record. Columns names
+ and data types must be the same as the columns in the settings object
+
+ Examples:
+ ```py
+ linker = DuckDBLinker(df)
+ linker.load_settings("saved_settings.json")
+ linker.compare_two_records(record_left, record_right)
+ ```
+
+ Returns:
+ SplinkDataFrame: Pairwise comparison with scored prediction
+ """
+
+ cache = self._linker._intermediate_table_cache
+
+ uid = ascii_uid(8)
+ df_records_left = self._linker.table_management.register_table(
+ [record_1], f"__splink__compare_two_records_left_{uid}", overwrite=True
+ )
+ df_records_left.templated_name = "__splink__compare_two_records_left"
+
+ df_records_right = self._linker.table_management.register_table(
+ [record_2], f"__splink__compare_two_records_right_{uid}", overwrite=True
+ )
+ df_records_right.templated_name = "__splink__compare_two_records_right"
+
+ pipeline = CTEPipeline([df_records_left, df_records_right])
+
+ if "__splink__df_concat_with_tf" in cache:
+ nodes_with_tf = cache.get_with_logging("__splink__df_concat_with_tf")
+ pipeline.append_input_dataframe(nodes_with_tf)
+
+ for tf_col in self._linker._settings_obj._term_frequency_columns:
+ tf_table_name = colname_to_tf_tablename(tf_col)
+ if tf_table_name in cache:
+ tf_table = cache.get_with_logging(tf_table_name)
+ pipeline.append_input_dataframe(tf_table)
+ else:
+ if "__splink__df_concat_with_tf" not in cache:
+ logger.warning(
+ f"No term frequencies found for column {tf_col.name}.\n"
+ "To apply term frequency adjustments, you need to register"
+ " a lookup using "
+ "`linker.table_management.register_term_frequency_lookup`."
+ )
+
+ sql_join_tf = _join_new_table_to_df_concat_with_tf_sql(
+ self._linker, "__splink__compare_two_records_left"
+ )
+
+ pipeline.enqueue_sql(sql_join_tf, "__splink__compare_two_records_left_with_tf")
+
+ sql_join_tf = _join_new_table_to_df_concat_with_tf_sql(
+ self._linker, "__splink__compare_two_records_right"
+ )
+
+ pipeline.enqueue_sql(sql_join_tf, "__splink__compare_two_records_right_with_tf")
+
+ sqls = block_using_rules_sqls(
+ input_tablename_l="__splink__compare_two_records_left_with_tf",
+ input_tablename_r="__splink__compare_two_records_right_with_tf",
+ blocking_rules=[BlockingRule("1=1")],
+ link_type=self._linker._settings_obj._link_type,
+ columns_to_select_sql=", ".join(
+ self._linker._settings_obj._columns_to_select_for_blocking
+ ),
+ source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
+ unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ sql = compute_comparison_vector_values_sql(
+ self._linker._settings_obj._columns_to_select_for_comparison_vector_values
+ )
+ pipeline.enqueue_sql(sql, "__splink__df_comparison_vectors")
+
+ sqls = predict_from_comparison_vectors_sqls_using_settings(
+ self._linker._settings_obj,
+ sql_infinity_expression=self._linker._infinity_expression,
+ )
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ predictions = self._linker._db_api.sql_pipeline_to_splink_dataframe(
+ pipeline, use_cache=False
+ )
+
+ return predictions
diff --git a/splink/internals/linker_components/misc.py b/splink/internals/linker_components/misc.py
new file mode 100644
index 0000000000..7cda97c9c3
--- /dev/null
+++ b/splink/internals/linker_components/misc.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import json
+import os
+from typing import TYPE_CHECKING, Any
+
+from splink.internals.pipeline import CTEPipeline
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
+
+
+class LinkerMisc:
+ def __init__(self, linker: Linker):
+ self._linker = linker
+
+ def save_model_to_json(
+ self, out_path: str | None = None, overwrite: bool = False
+ ) -> dict[str, Any]:
+ """Save the configuration and parameters of the linkage model to a `.json` file.
+
+ The model can later be loaded back in using `linker.load_model()`.
+ The settings dict is also returned in case you want to save it a different way.
+
+ Examples:
+ ```py
+ linker.save_model_to_json("my_settings.json", overwrite=True)
+ ```
+ Args:
+ out_path (str, optional): File path for json file. If None, don't save to
+ file. Defaults to None.
+ overwrite (bool, optional): Overwrite if already exists? Defaults to False.
+
+ Returns:
+ dict: The settings as a dictionary.
+ """
+ model_dict = self._linker._settings_obj.as_dict()
+ if out_path:
+ if os.path.isfile(out_path) and not overwrite:
+ raise ValueError(
+ f"The path {out_path} already exists. Please provide a different "
+ "path or set overwrite=True"
+ )
+ with open(out_path, "w", encoding="utf-8") as f:
+ json.dump(model_dict, f, indent=4)
+ return model_dict
+
+ def query_sql(self, sql, output_type="pandas"):
+ """
+ Run a SQL query against your backend database and return
+ the resulting output.
+
+ Examples:
+ ```py
+ linker = Linker(df, settings, db_api)
+ df_predict = linker.predict()
+ linker.query_sql(f"select * from {df_predict.physical_name} limit 10")
+ ```
+
+ Args:
+ sql (str): The SQL to be queried.
+ output_type (str): One of splink_df/splinkdf or pandas.
+ This determines the type of table that your results are output in.
+ """
+
+ output_tablename_templated = "__splink__df_sql_query"
+
+ pipeline = CTEPipeline()
+ pipeline.enqueue_sql(sql, output_tablename_templated)
+ splink_dataframe = self._linker._db_api.sql_pipeline_to_splink_dataframe(
+ pipeline, use_cache=False
+ )
+
+ if output_type in ("splink_df", "splinkdf"):
+ return splink_dataframe
+ elif output_type == "pandas":
+ out = splink_dataframe.as_pandas_dataframe()
+ # If pandas, drop the table to cleanup the db
+ splink_dataframe.drop_table_from_database_and_remove_from_cache()
+ return out
+ else:
+ raise ValueError(
+ f"output_type '{output_type}' is not supported.",
+ "Must be one of 'splink_df'/'splinkdf' or 'pandas'",
+ )
diff --git a/splink/internals/linker_components/table_management.py b/splink/internals/linker_components/table_management.py
new file mode 100644
index 0000000000..5e5dcdb553
--- /dev/null
+++ b/splink/internals/linker_components/table_management.py
@@ -0,0 +1,206 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING
+
+from splink.internals.database_api import AcceptableInputTableType
+from splink.internals.input_column import InputColumn
+from splink.internals.misc import (
+ ascii_uid,
+)
+from splink.internals.pipeline import CTEPipeline
+from splink.internals.splink_dataframe import SplinkDataFrame
+from splink.internals.term_frequencies import (
+ colname_to_tf_tablename,
+ term_frequencies_for_single_column_sql,
+)
+from splink.internals.vertically_concatenate import (
+ enqueue_df_concat,
+)
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
+
+logger = logging.getLogger(__name__)
+
+
+class LinkerTableManagement:
+ def __init__(self, linker: Linker):
+ self._linker = linker
+
+ def compute_tf_table(self, column_name: str) -> SplinkDataFrame:
+ """Compute a term frequency table for a given column and persist to the database
+
+ This method is useful if you want to pre-compute term frequency tables e.g.
+ so that real time linkage executes faster, or so that you can estimate
+ various models without having to recompute term frequency tables each time
+
+ Examples:
+
+ Real time linkage
+ ```py
+ linker = Linker(df, db_api)
+ linker.load_settings("saved_settings.json")
+ linker.table_management.compute_tf_table("surname")
+ linker.compare_two_records(record_left, record_right)
+ ```
+ Pre-computed term frequency tables
+ ```py
+ linker = Linker(df, db_api)
+ df_first_name_tf = linker.table_management.compute_tf_table("first_name")
+ df_first_name_tf.write.parquet("folder/first_name_tf")
+ >>>
+ # On subsequent data linking job, read this table rather than recompute
+ df_first_name_tf = pd.read_parquet("folder/first_name_tf")
+ df_first_name_tf.createOrReplaceTempView("__splink__df_tf_first_name")
+ ```
+
+
+ Args:
+ column_name (str): The column name in the input table
+
+ Returns:
+ SplinkDataFrame: The resultant table as a splink data frame
+ """
+
+ input_col = InputColumn(
+ column_name,
+ column_info_settings=self._linker._settings_obj.column_info_settings,
+ sql_dialect=self._linker._settings_obj._sql_dialect,
+ )
+ tf_tablename = colname_to_tf_tablename(input_col)
+ cache = self._linker._intermediate_table_cache
+
+ if tf_tablename in cache:
+ tf_df = cache.get_with_logging(tf_tablename)
+ else:
+ pipeline = CTEPipeline()
+ pipeline = enqueue_df_concat(self._linker, pipeline)
+ sql = term_frequencies_for_single_column_sql(input_col)
+ pipeline.enqueue_sql(sql, tf_tablename)
+ tf_df = self._linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ self._linker._intermediate_table_cache[tf_tablename] = tf_df
+
+ return tf_df
+
+ def invalidate_cache(self):
+ """Invalidate the Splink cache. Any previously-computed tables
+ will be recomputed.
+ This is useful, for example, if the input data tables have changed.
+ """
+
+ # Nothing to delete
+ if len(self._linker._intermediate_table_cache) == 0:
+ return
+
+ # Before Splink executes a SQL command, it checks the cache to see
+ # whether a table already exists with the name of the output table
+
+ # This function has the effect of changing the names of the output tables
+ # to include a different unique id
+
+ # As a result, any previously cached tables will not be found
+ self._linker._cache_uid = ascii_uid(8)
+
+ # Drop any existing splink tables from the database
+ # Note, this is not actually necessary, it's just good housekeeping
+ self.delete_tables_created_by_splink_from_db()
+
+ # As a result, any previously cached tables will not be found
+ self._linker._intermediate_table_cache.invalidate_cache()
+
+ def register_table_input_nodes_concat_with_tf(self, input_data, overwrite=False):
+ """Register a pre-computed version of the input_nodes_concat_with_tf table that
+ you want to re-use e.g. that you created in a previous run
+
+ This method allowed you to register this table in the Splink cache
+ so it will be used rather than Splink computing this table anew.
+
+ Args:
+ input_data: The data you wish to register. This can be either a dictionary,
+ pandas dataframe, pyarrow table or a spark dataframe.
+ overwrite (bool): Overwrite the table in the underlying database if it
+ exists
+ """
+
+ table_name_physical = "__splink__df_concat_with_tf_" + self._linker._cache_uid
+ splink_dataframe = self.register_table(
+ input_data, table_name_physical, overwrite=overwrite
+ )
+ splink_dataframe.templated_name = "__splink__df_concat_with_tf"
+
+ self._linker._intermediate_table_cache["__splink__df_concat_with_tf"] = (
+ splink_dataframe
+ )
+ return splink_dataframe
+
+ def register_table_predict(self, input_data, overwrite=False):
+ table_name_physical = "__splink__df_predict_" + self._linker._cache_uid
+ splink_dataframe = self.register_table(
+ input_data, table_name_physical, overwrite=overwrite
+ )
+ self._linker._intermediate_table_cache["__splink__df_predict"] = (
+ splink_dataframe
+ )
+ splink_dataframe.templated_name = "__splink__df_predict"
+ return splink_dataframe
+
+ def register_term_frequency_lookup(self, input_data, col_name, overwrite=False):
+ input_col = InputColumn(
+ col_name,
+ column_info_settings=self._linker._settings_obj.column_info_settings,
+ sql_dialect=self._linker._settings_obj._sql_dialect,
+ )
+ table_name_templated = colname_to_tf_tablename(input_col)
+ table_name_physical = f"{table_name_templated}_{self._linker._cache_uid}"
+ splink_dataframe = self.register_table(
+ input_data, table_name_physical, overwrite=overwrite
+ )
+ self._linker._intermediate_table_cache[table_name_templated] = splink_dataframe
+ splink_dataframe.templated_name = table_name_templated
+ return splink_dataframe
+
+ def register_labels_table(self, input_data, overwrite=False):
+ table_name_physical = "__splink__df_labels_" + ascii_uid(8)
+ splink_dataframe = self.register_table(
+ input_data, table_name_physical, overwrite=overwrite
+ )
+ splink_dataframe.templated_name = "__splink__df_labels"
+ return splink_dataframe
+
+ def delete_tables_created_by_splink_from_db(self):
+ self._linker._db_api.delete_tables_created_by_splink_from_db()
+
+ def register_table(
+ self,
+ input_table: AcceptableInputTableType,
+ table_name: str,
+ overwrite: bool = False,
+ ) -> SplinkDataFrame:
+ """
+ Register a table to your backend database, to be used in one of the
+ splink methods, or simply to allow querying.
+
+ Tables can be of type: dictionary, record level dictionary,
+ pandas dataframe, pyarrow table and in the spark case, a spark df.
+
+ Examples:
+ ```py
+ test_dict = {"a": [666,777,888],"b": [4,5,6]}
+ linker.table_management.register_table(test_dict, "test_dict")
+ linker.query_sql("select * from test_dict")
+ ```
+
+ Args:
+ input: The data you wish to register. This can be either a dictionary,
+ pandas dataframe, pyarrow table or a spark dataframe.
+ table_name (str): The name you wish to assign to the table.
+ overwrite (bool): Overwrite the table in the underlying database if it
+ exists
+
+ Returns:
+ SplinkDataFrame: An abstraction representing the table created by the sql
+ pipeline
+ """
+
+ return self._linker._db_api.register_table(input_table, table_name, overwrite)
diff --git a/splink/internals/linker_components/training.py b/splink/internals/linker_components/training.py
new file mode 100644
index 0000000000..069450d52c
--- /dev/null
+++ b/splink/internals/linker_components/training.py
@@ -0,0 +1,444 @@
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, List, Union
+
+from splink.internals.blocking import (
+ BlockingRule,
+ SaltedBlockingRule,
+)
+from splink.internals.blocking_analysis import (
+ _cumulative_comparisons_to_be_scored_from_blocking_rules,
+)
+from splink.internals.blocking_rule_creator import BlockingRuleCreator
+from splink.internals.blocking_rule_creator_utils import to_blocking_rule_creator
+from splink.internals.comparison import Comparison
+from splink.internals.comparison_level import ComparisonLevel
+from splink.internals.em_training_session import EMTrainingSession
+from splink.internals.estimate_u import estimate_u_values
+from splink.internals.m_from_labels import estimate_m_from_pairwise_labels
+from splink.internals.m_training import estimate_m_values_from_label_column
+from splink.internals.misc import (
+ ensure_is_iterable,
+)
+from splink.internals.pipeline import CTEPipeline
+from splink.internals.vertically_concatenate import (
+ compute_df_concat_with_tf,
+)
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
+
+logger = logging.getLogger(__name__)
+
+
+class LinkerTraining:
+ def __init__(self, linker: Linker):
+ self._linker = linker
+
+ def estimate_probability_two_random_records_match(
+ self,
+ deterministic_matching_rules: List[Union[str, BlockingRuleCreator]],
+ recall: float,
+ max_rows_limit: int = int(1e9),
+ ) -> None:
+ """Estimate the model parameter `probability_two_random_records_match` using
+ a direct estimation approach.
+
+ See [here](https://github.com/moj-analytical-services/splink/issues/462)
+ for discussion of methodology
+
+ Args:
+ deterministic_matching_rules (list): A list of deterministic matching
+ rules that should be designed to admit very few (none if possible)
+ false positives
+ recall (float): A guess at the recall the deterministic matching rules
+ will attain. i.e. what proportion of true matches will be recovered
+ by these deterministic rules
+ """
+
+ if (recall > 1) or (recall <= 0):
+ raise ValueError(
+ f"Estimated recall must be greater than 0 "
+ f"and no more than 1. Supplied value {recall}."
+ ) from None
+
+ deterministic_matching_rules = ensure_is_iterable(deterministic_matching_rules)
+ blocking_rules: List[BlockingRule] = []
+ for br in deterministic_matching_rules:
+ blocking_rules.append(
+ to_blocking_rule_creator(br).get_blocking_rule(
+ self._linker._db_api.sql_dialect.name
+ )
+ )
+
+ pd_df = _cumulative_comparisons_to_be_scored_from_blocking_rules(
+ splink_df_dict=self._linker._input_tables_dict,
+ blocking_rules=blocking_rules,
+ link_type=self._linker._settings_obj._link_type,
+ db_api=self._linker._db_api,
+ max_rows_limit=max_rows_limit,
+ unique_id_input_column=self._linker._settings_obj.column_info_settings.unique_id_input_column,
+ source_dataset_input_column=self._linker._settings_obj.column_info_settings.source_dataset_input_column,
+ )
+
+ records = pd_df.to_dict(orient="records")
+
+ summary_record = records[-1]
+ num_observed_matches = summary_record["cumulative_rows"]
+ num_total_comparisons = summary_record["cartesian"]
+
+ if num_observed_matches > num_total_comparisons * recall:
+ raise ValueError(
+ f"Deterministic matching rules led to more "
+ f"observed matches than is consistent with supplied recall. "
+ f"With these rules, recall must be at least "
+ f"{num_observed_matches/num_total_comparisons:,.2f}."
+ )
+
+ num_expected_matches = num_observed_matches / recall
+ prob = num_expected_matches / num_total_comparisons
+
+ # warn about boundary values, as these will usually be in error
+ if num_observed_matches == 0:
+ logger.warning(
+ f"WARNING: Deterministic matching rules led to no observed matches! "
+ f"This means that no possible record pairs are matches, "
+ f"and no records are linked to one another.\n"
+ f"If this is truly the case then you do not need "
+ f"to run the linkage model.\n"
+ f"However this is usually in error; "
+ f"expected rules to have recall of {100*recall:,.0f}%. "
+ f"Consider revising rules as they may have an error."
+ )
+ if prob == 1:
+ logger.warning(
+ "WARNING: Probability two random records match is estimated to be 1.\n"
+ "This means that all possible record pairs are matches, "
+ "and all records are linked to one another.\n"
+ "If this is truly the case then you do not need "
+ "to run the linkage model.\n"
+ "However, it is more likely that this estimate is faulty. "
+ "Perhaps your deterministic matching rules include "
+ "too many false positives?"
+ )
+
+ self._linker._settings_obj._probability_two_random_records_match = prob
+
+ reciprocal_prob = "Infinity" if prob == 0 else f"{1/prob:,.2f}"
+ logger.info(
+ f"Probability two random records match is estimated to be {prob:.3g}.\n"
+ f"This means that amongst all possible pairwise record comparisons, one in "
+ f"{reciprocal_prob} are expected to match. "
+ f"With {num_total_comparisons:,.0f} total"
+ " possible comparisons, we expect a total of around "
+ f"{num_expected_matches:,.2f} matching pairs"
+ )
+
+ def estimate_u_using_random_sampling(
+ self, max_pairs: float = 1e6, seed: int = None
+ ) -> None:
+ """Estimate the u parameters of the linkage model using random sampling.
+
+ The u parameters represent the proportion of record comparisons that fall
+ into each comparison level amongst truly non-matching records.
+
+ This procedure takes a sample of the data and generates the cartesian
+ product of pairwise record comparisons amongst the sampled records.
+ The validity of the u values rests on the assumption that the resultant
+ pairwise comparisons are non-matches (or at least, they are very unlikely to be
+ matches). For large datasets, this is typically true.
+
+ The results of estimate_u_using_random_sampling, and therefore an entire splink
+ model, can be made reproducible by setting the seed parameter. Setting the seed
+ will have performance implications as additional processing is required.
+
+ Args:
+ max_pairs (int): The maximum number of pairwise record comparisons to
+ sample. Larger will give more accurate estimates
+ but lead to longer runtimes. In our experience at least 1e9 (one billion)
+ gives best results but can take a long time to compute. 1e7 (ten million)
+ is often adequate whilst testing different model specifications, before
+ the final model is estimated.
+ seed (int): Seed for random sampling. Assign to get reproducible u
+ probabilities. Note, seed for random sampling is only supported for
+ DuckDB and Spark, for Athena and SQLite set to None.
+
+ Examples:
+ ```py
+ linker.estimate_u_using_random_sampling(1e8)
+ ```
+
+ Returns:
+ None: Updates the estimated u parameters within the linker object
+ and returns nothing.
+ """
+ if max_pairs == 1e6:
+ # keep default value small so as not to take too long, but warn users
+ logger.warning(
+ "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."
+ )
+
+ estimate_u_values(self._linker, max_pairs, seed)
+ self._linker._populate_m_u_from_trained_values()
+
+ self._linker._settings_obj._columns_without_estimated_parameters_message()
+
+ def estimate_parameters_using_expectation_maximisation(
+ self,
+ blocking_rule: Union[str, BlockingRuleCreator],
+ comparisons_to_deactivate: list[Comparison] = None,
+ comparison_levels_to_reverse_blocking_rule: list[ComparisonLevel] = None,
+ estimate_without_term_frequencies: bool = False,
+ fix_probability_two_random_records_match: bool = False,
+ fix_m_probabilities: bool = False,
+ fix_u_probabilities: bool = True,
+ populate_probability_two_random_records_match_from_trained_values: bool = False,
+ ) -> EMTrainingSession:
+ """Estimate the parameters of the linkage model using expectation maximisation.
+
+ By default, the m probabilities are estimated, but not the u probabilities,
+ because good estimates for the u probabilities can be obtained from
+ `linker.estimate_u_using_random_sampling()`. You can change this by setting
+ `fix_u_probabilities` to False.
+
+ The blocking rule provided is used to generate pairwise record comparisons.
+ Usually, this should be a blocking rule that results in a dataframe where
+ matches are between about 1% and 99% of the comparisons.
+
+ By default, m parameters are estimated for all comparisons except those which
+ are included in the blocking rule.
+
+ For example, if the blocking rule is `l.first_name = r.first_name`, then
+ parameter esimates will be made for all comparison except those which use
+ `first_name` in their sql_condition
+
+ By default, the probability two random records match is estimated for the
+ blocked data, and then the m and u parameters for the columns specified in the
+ blocking rules are used to estiamte the global probability two random records
+ match.
+
+ To control which comparisons should have their parameter estimated, and the
+ process of 'reversing out' the global probability two random records match, the
+ user may specify `comparisons_to_deactivate` and
+ `comparison_levels_to_reverse_blocking_rule`. This is useful, for example
+ if you block on the dmetaphone of a column but match on the original column.
+
+ Examples:
+ Default behaviour
+ ```py
+ br_training = "l.first_name = r.first_name and l.dob = r.dob"
+ linker.training.estimate_parameters_using_expectation_maximisation(br_training)
+ ```
+ Specify which comparisons to deactivate
+ ```py
+ br_training = "l.dmeta_first_name = r.dmeta_first_name"
+ settings_obj = linker._settings_obj
+ comp = settings_obj._get_comparison_by_output_column_name("first_name")
+ dmeta_level = comp._get_comparison_level_by_comparison_vector_value(1)
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ br_training,
+ comparisons_to_deactivate=["first_name"],
+ comparison_levels_to_reverse_blocking_rule=[dmeta_level],
+ )
+ ```
+
+ Args:
+ blocking_rule (BlockingRuleCreator | str): The blocking rule used to
+ generate pairwise record comparisons.
+ comparisons_to_deactivate (list, optional): By default, splink will
+ analyse the blocking rule provided and estimate the m parameters for
+ all comaprisons except those included in the blocking rule. If
+ comparisons_to_deactivate are provided, spink will instead
+ estimate m parameters for all comparison except those specified
+ in the comparisons_to_deactivate list. This list can either contain
+ the output_column_name of the Comparison as a string, or Comparison
+ objects. Defaults to None.
+ comparison_levels_to_reverse_blocking_rule (list, optional): By default,
+ splink will analyse the blocking rule provided and adjust the
+ global probability two random records match to account for the matches
+ specified in the blocking rule. If provided, this argument will overrule
+ this default behaviour. The user must provide a list of ComparisonLevel
+ objects. Defaults to None.
+ estimate_without_term_frequencies (bool, optional): If True, the iterations
+ of the EM algorithm ignore any term frequency adjustments and only
+ depend on the comparison vectors. This allows the EM algorithm to run
+ much faster, but the estimation of the parameters will change slightly.
+ fix_probability_two_random_records_match (bool, optional): If True, do not
+ update the probability two random records match after each iteration.
+ Defaults to False.
+ fix_m_probabilities (bool, optional): If True, do not update the m
+ probabilities after each iteration. Defaults to False.
+ fix_u_probabilities (bool, optional): If True, do not update the u
+ probabilities after each iteration. Defaults to True.
+ populate_probability_two_random_records_match_from_trained_values
+ (bool, optional): If True, derive this parameter from
+ the blocked value. Defaults to False.
+
+ Examples:
+ ```py
+ blocking_rule = "l.first_name = r.first_name and l.dob = r.dob"
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ ```
+ or using pre-built rules
+ ```py
+ from splink.duckdb.blocking_rule_library import block_on
+ blocking_rule = block_on(["first_name", "surname"])
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ ```
+
+ Returns:
+ EMTrainingSession: An object containing information about the training
+ session such as how parameters changed during the iteration history
+
+ """
+ # Ensure this has been run on the main linker so that it's in the cache
+ # to be used by the training linkers
+ pipeline = CTEPipeline()
+ compute_df_concat_with_tf(self._linker, pipeline)
+
+ blocking_rule_obj = to_blocking_rule_creator(blocking_rule).get_blocking_rule(
+ self._linker._sql_dialect
+ )
+
+ if type(blocking_rule_obj) not in (BlockingRule, SaltedBlockingRule):
+ # TODO: seems a mismatch between message and type re: SaltedBlockingRule
+ raise TypeError(
+ "EM blocking rules must be plain blocking rules, not "
+ "salted or exploding blocking rules"
+ )
+
+ if comparisons_to_deactivate:
+ # If user provided a string, convert to Comparison object
+ comparisons_to_deactivate = [
+ (
+ self._linker._settings_obj._get_comparison_by_output_column_name(n)
+ if isinstance(n, str)
+ else n
+ )
+ for n in comparisons_to_deactivate
+ ]
+ if comparison_levels_to_reverse_blocking_rule is None:
+ logger.warning(
+ "\nWARNING: \n"
+ "You have provided comparisons_to_deactivate but not "
+ "comparison_levels_to_reverse_blocking_rule.\n"
+ "If comparisons_to_deactivate is provided, then "
+ "you usually need to provide corresponding "
+ "comparison_levels_to_reverse_blocking_rule "
+ "because each comparison to deactivate is effectively treated "
+ "as an exact match."
+ )
+
+ em_training_session = EMTrainingSession(
+ self._linker,
+ db_api=self._linker._db_api,
+ blocking_rule_for_training=blocking_rule_obj,
+ core_model_settings=self._linker._settings_obj.core_model_settings,
+ training_settings=self._linker._settings_obj.training_settings,
+ unique_id_input_columns=self._linker._settings_obj.column_info_settings.unique_id_input_columns,
+ fix_u_probabilities=fix_u_probabilities,
+ fix_m_probabilities=fix_m_probabilities,
+ fix_probability_two_random_records_match=fix_probability_two_random_records_match, # noqa 501
+ comparisons_to_deactivate=comparisons_to_deactivate,
+ comparison_levels_to_reverse_blocking_rule=comparison_levels_to_reverse_blocking_rule, # noqa 501
+ estimate_without_term_frequencies=estimate_without_term_frequencies,
+ )
+
+ core_model_settings = em_training_session._train()
+ # overwrite with the newly trained values in our linker settings
+ self._linker._settings_obj.core_model_settings = core_model_settings
+ self._linker._em_training_sessions.append(em_training_session)
+
+ self._linker._populate_m_u_from_trained_values()
+
+ if populate_probability_two_random_records_match_from_trained_values:
+ self._linker._populate_probability_two_random_records_match_from_trained_values()
+
+ self._linker._settings_obj._columns_without_estimated_parameters_message()
+
+ return em_training_session
+
+ def estimate_m_from_pairwise_labels(self, labels_splinkdataframe_or_table_name):
+ """Estimate the m parameters of the linkage model from a dataframe of pairwise
+ labels.
+
+ The table of labels should be in the following format, and should
+ be registered with your database:
+ |source_dataset_l|unique_id_l|source_dataset_r|unique_id_r|
+ |----------------|-----------|----------------|-----------|
+ |df_1 |1 |df_2 |2 |
+ |df_1 |1 |df_2 |3 |
+
+ Note that `source_dataset` and `unique_id` should correspond to the
+ values specified in the settings dict, and the `input_table_aliases`
+ passed to the `linker` object. Note that at the moment, this method does
+ not respect values in a `clerical_match_score` column. If provided, these
+ are ignored and it is assumed that every row in the table of labels is a score
+ of 1, i.e. a perfect match.
+
+ Args:
+ labels_splinkdataframe_or_table_name (str): Name of table containing labels
+ in the database or SplinkDataframe
+
+ Examples:
+ ```py
+ pairwise_labels = pd.read_csv("./data/pairwise_labels_to_estimate_m.csv")
+ linker.table_management.register_table(
+ pairwise_labels, "labels", overwrite=True
+ )
+ linker.estimate_m_from_pairwise_labels("labels")
+ ```
+ """
+ labels_tablename = self._linker._get_labels_tablename_from_input(
+ labels_splinkdataframe_or_table_name
+ )
+ estimate_m_from_pairwise_labels(self._linker, labels_tablename)
+
+ def estimate_m_from_label_column(self, label_colname: str) -> None:
+ """Estimate the m parameters of the linkage model from a label (ground truth)
+ column in the input dataframe(s).
+
+ The m parameters represent the proportion of record comparisons that fall
+ into each comparison level amongst truly matching records.
+
+ The ground truth column is used to generate pairwise record comparisons
+ which are then assumed to be matches.
+
+ For example, if the entity being matched is persons, and your input dataset(s)
+ contain social security number, this could be used to estimate the m values
+ for the model.
+
+ Note that this column does not need to be fully populated. A common case is
+ where a unique identifier such as social security number is only partially
+ populated.
+
+ Args:
+ label_colname (str): The name of the column containing the ground truth
+ label in the input data.
+
+ Examples:
+ ```py
+ linker.training.estimate_m_from_label_column("social_security_number")
+ ```
+
+ Returns:
+ Updates the estimated m parameters within the linker object
+ and returns nothing.
+ """
+
+ # Ensure this has been run on the main linker so that it can be used by
+ # training linker when it checks the cache
+ pipeline = CTEPipeline()
+ compute_df_concat_with_tf(self._linker, pipeline)
+
+ estimate_m_values_from_label_column(
+ self._linker,
+ label_colname,
+ )
+ self._linker._populate_m_u_from_trained_values()
+
+ self._linker._settings_obj._columns_without_estimated_parameters_message()
diff --git a/splink/internals/linker_components/visualisations.py b/splink/internals/linker_components/visualisations.py
new file mode 100644
index 0000000000..1ea2604f6d
--- /dev/null
+++ b/splink/internals/linker_components/visualisations.py
@@ -0,0 +1,360 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from splink.internals.charts import (
+ ChartReturnType,
+ match_weights_histogram,
+ parameter_estimate_comparisons,
+ waterfall_chart,
+)
+from splink.internals.cluster_studio import (
+ SamplingMethods,
+ render_splink_cluster_studio_html,
+)
+from splink.internals.comparison_vector_distribution import (
+ comparison_vector_distribution_sql,
+)
+from splink.internals.match_weights_histogram import histogram_data
+from splink.internals.misc import ensure_is_list
+from splink.internals.pipeline import CTEPipeline
+from splink.internals.splink_comparison_viewer import (
+ comparison_viewer_table_sqls,
+ render_splink_comparison_viewer_html,
+)
+from splink.internals.splink_dataframe import SplinkDataFrame
+from splink.internals.term_frequencies import (
+ tf_adjustment_chart,
+)
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
+
+
+class LinkerVisualisations:
+ def __init__(self, linker: Linker):
+ self._linker = linker
+
+ def match_weights_chart(self):
+ """Display a chart of the (partial) match weights of the linkage model
+
+ Examples:
+ ```py
+ linker.match_weights_chart()
+ ```
+ To view offline (if you don't have an internet connection):
+ ```py
+ from splink.charts import save_offline_chart
+ c = linker.match_weights_chart()
+ save_offline_chart(c.to_dict(), "test_chart.html")
+ ```
+ View resultant html file in Jupyter (or just load it in your browser)
+ ```py
+ from IPython.display import IFrame
+ IFrame(src="./test_chart.html", width=1000, height=500)
+ ```
+
+ Returns:
+ altair.Chart: An altair chart
+ """
+ return self._linker._settings_obj.match_weights_chart()
+
+ def m_u_parameters_chart(self):
+ """Display a chart of the m and u parameters of the linkage model
+
+ Examples:
+ ```py
+ linker.m_u_parameters_chart()
+ ```
+ To view offline (if you don't have an internet connection):
+ ```py
+ from splink.charts import save_offline_chart
+ c = linker.match_weights_chart()
+ save_offline_chart(c.to_dict(), "test_chart.html")
+ ```
+ View resultant html file in Jupyter (or just load it in your browser)
+ ```py
+ from IPython.display import IFrame
+ IFrame(src="./test_chart.html", width=1000, height=500)
+ ```
+
+ Returns:
+ altair.Chart: An altair chart
+ """
+
+ return self._linker._settings_obj.m_u_parameters_chart()
+
+ def match_weights_histogram(
+ self,
+ df_predict: SplinkDataFrame,
+ target_bins: int = 30,
+ width: int = 600,
+ height: int = 250,
+ ) -> ChartReturnType:
+ """Generate a histogram that shows the distribution of match weights in
+ `df_predict`
+
+ Args:
+ df_predict (SplinkDataFrame): Output of `linker.predict()`
+ target_bins (int, optional): Target number of bins in histogram. Defaults to
+ 30.
+ width (int, optional): Width of output. Defaults to 600.
+ height (int, optional): Height of output chart. Defaults to 250.
+
+
+ Returns:
+ altair.Chart: An altair chart
+
+ """
+ df = histogram_data(self._linker, df_predict, target_bins)
+ recs = df.as_record_dict()
+ return match_weights_histogram(recs, width=width, height=height)
+
+ def parameter_estimate_comparisons_chart(
+ self, include_m: bool = True, include_u: bool = False
+ ) -> ChartReturnType:
+ """Show a chart that shows how parameter estimates have differed across
+ the different estimation methods you have used.
+
+ For example, if you have run two EM estimation sessions, blocking on
+ different variables, and both result in parameter estimates for
+ first_name, this chart will enable easy comparison of the different
+ estimates
+
+ Args:
+ include_m (bool, optional): Show different estimates of m values. Defaults
+ to True.
+ include_u (bool, optional): Show different estimates of u values. Defaults
+ to False.
+
+ """
+ records = self._linker._settings_obj._parameter_estimates_as_records
+
+ to_retain = []
+ if include_m:
+ to_retain.append("m")
+ if include_u:
+ to_retain.append("u")
+
+ records = [r for r in records if r["m_or_u"] in to_retain]
+
+ return parameter_estimate_comparisons(records)
+
+ def tf_adjustment_chart(
+ self,
+ output_column_name: str,
+ n_most_freq: int = 10,
+ n_least_freq: int = 10,
+ vals_to_include: str | list[str] | None = None,
+ as_dict: bool = False,
+ ) -> ChartReturnType:
+ """Display a chart showing the impact of term frequency adjustments on a
+ specific comparison level.
+ Each value
+
+ Args:
+ output_column_name (str): Name of an output column for which term frequency
+ adjustment has been applied.
+ n_most_freq (int, optional): Number of most frequent values to show. If this
+ or `n_least_freq` set to None, all values will be shown.
+ Default to 10.
+ n_least_freq (int, optional): Number of least frequent values to show. If
+ this or `n_most_freq` set to None, all values will be shown.
+ Default to 10.
+ vals_to_include (list, optional): Specific values for which to show term
+ sfrequency adjustments.
+ Defaults to None.
+
+ Returns:
+ altair.Chart: An altair chart
+ """
+
+ # Comparisons with TF adjustments
+ tf_comparisons = [
+ c.output_column_name
+ for c in self._linker._settings_obj.comparisons
+ if any([cl._has_tf_adjustments for cl in c.comparison_levels])
+ ]
+ if output_column_name not in tf_comparisons:
+ raise ValueError(
+ f"{output_column_name} is not a valid comparison column, or does not"
+ f" have term frequency adjustment activated"
+ )
+
+ vals_to_include = (
+ [] if vals_to_include is None else ensure_is_list(vals_to_include)
+ )
+
+ return tf_adjustment_chart(
+ self._linker,
+ output_column_name,
+ n_most_freq,
+ n_least_freq,
+ vals_to_include,
+ as_dict,
+ )
+
+ def waterfall_chart(
+ self,
+ records: list[dict[str, Any]],
+ filter_nulls: bool = True,
+ remove_sensitive_data: bool = False,
+ ) -> ChartReturnType:
+ """Visualise how the final match weight is computed for the provided pairwise
+ record comparisons.
+
+ Records must be provided as a list of dictionaries. This would usually be
+ obtained from `df.as_record_dict(limit=n)` where `df` is a SplinkDataFrame.
+
+ Examples:
+ ```py
+ df = linker.predict(threshold_match_weight=2)
+ records = df.as_record_dict(limit=10)
+ linker.waterfall_chart(records)
+ ```
+
+ Args:
+ records (List[dict]): Usually be obtained from `df.as_record_dict(limit=n)`
+ where `df` is a SplinkDataFrame.
+ filter_nulls (bool, optional): Whether the visualiation shows null
+ comparisons, which have no effect on final match weight. Defaults to
+ True.
+ remove_sensitive_data (bool, optional): When True, The waterfall chart will
+ contain match weights only, and all of the (potentially sensitive) data
+ from the input tables will be removed prior to the chart being created.
+
+
+ Returns:
+ altair.Chart: An altair chart
+
+ """
+ self._linker._raise_error_if_necessary_waterfall_columns_not_computed()
+
+ return waterfall_chart(
+ records, self._linker._settings_obj, filter_nulls, remove_sensitive_data
+ )
+
+ def comparison_viewer_dashboard(
+ self,
+ df_predict: SplinkDataFrame,
+ out_path: str,
+ overwrite: bool = False,
+ num_example_rows: int = 2,
+ return_html_as_string: bool = False,
+ ) -> str | None:
+ """Generate an interactive html visualization of the linker's predictions and
+ save to `out_path`. For more information see
+ [this video](https://www.youtube.com/watch?v=DNvCMqjipis)
+
+
+ Args:
+ df_predict (SplinkDataFrame): The outputs of `linker.predict()`
+ out_path (str): The path (including filename) to save the html file to.
+ overwrite (bool, optional): Overwrite the html file if it already exists?
+ Defaults to False.
+ num_example_rows (int, optional): Number of example rows per comparison
+ vector. Defaults to 2.
+ return_html_as_string: If True, return the html as a string
+
+ Examples:
+ ```py
+ df_predictions = linker.predict()
+ linker.comparison_viewer_dashboard(df_predictions, "scv.html", True, 2)
+ ```
+
+ Optionally, in Jupyter, you can display the results inline
+ Otherwise you can just load the html file in your browser
+ ```py
+ from IPython.display import IFrame
+ IFrame(src="./scv.html", width="100%", height=1200)
+ ```
+
+ """
+ self._linker._raise_error_if_necessary_waterfall_columns_not_computed()
+ pipeline = CTEPipeline([df_predict])
+ sql = comparison_vector_distribution_sql(self._linker)
+ pipeline.enqueue_sql(sql, "__splink__df_comparison_vector_distribution")
+
+ sqls = comparison_viewer_table_sqls(self._linker, num_example_rows)
+ pipeline.enqueue_list_of_sqls(sqls)
+
+ df = self._linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
+
+ rendered = render_splink_comparison_viewer_html(
+ df.as_record_dict(),
+ self._linker._settings_obj._as_completed_dict(),
+ out_path,
+ overwrite,
+ )
+ if return_html_as_string:
+ return rendered
+ return None
+
+ def cluster_studio_dashboard(
+ self,
+ df_predict: SplinkDataFrame,
+ df_clustered: SplinkDataFrame,
+ out_path: str,
+ sampling_method: SamplingMethods = "random",
+ sample_size: int = 10,
+ cluster_ids: list[str] = None,
+ cluster_names: list[str] = None,
+ overwrite: bool = False,
+ return_html_as_string: bool = False,
+ _df_cluster_metrics: SplinkDataFrame = None,
+ ) -> str | None:
+ """Generate an interactive html visualization of the predicted cluster and
+ save to `out_path`.
+
+ Args:
+ df_predict (SplinkDataFrame): The outputs of `linker.predict()`
+ df_clustered (SplinkDataFrame): The outputs of
+ `linker.cluster_pairwise_predictions_at_threshold()`
+ out_path (str): The path (including filename) to save the html file to.
+ sampling_method (str, optional): `random`, `by_cluster_size` or
+ `lowest_density_clusters`. Defaults to `random`.
+ sample_size (int, optional): Number of clusters to show in the dahboard.
+ Defaults to 10.
+ cluster_ids (list): The IDs of the clusters that will be displayed in the
+ dashboard. If provided, ignore the `sampling_method` and `sample_size`
+ arguments. Defaults to None.
+ overwrite (bool, optional): Overwrite the html file if it already exists?
+ Defaults to False.
+ cluster_names (list, optional): If provided, the dashboard will display
+ these names in the selection box. Ony works in conjunction with
+ `cluster_ids`. Defaults to None.
+ return_html_as_string: If True, return the html as a string
+
+ Examples:
+ ```py
+ df_p = linker.predict()
+ df_c = linker.cluster_pairwise_predictions_at_threshold(df_p, 0.5)
+ linker.cluster_studio_dashboard(
+ df_p, df_c, [0, 4, 7], "cluster_studio.html"
+ )
+ ```
+ Optionally, in Jupyter, you can display the results inline
+ Otherwise you can just load the html file in your browser
+ ```py
+ from IPython.display import IFrame
+ IFrame(src="./cluster_studio.html", width="100%", height=1200)
+ ```
+ """
+ self._linker._raise_error_if_necessary_waterfall_columns_not_computed()
+
+ rendered = render_splink_cluster_studio_html(
+ self._linker,
+ df_predict,
+ df_clustered,
+ out_path,
+ sampling_method=sampling_method,
+ sample_size=sample_size,
+ cluster_ids=cluster_ids,
+ overwrite=overwrite,
+ cluster_names=cluster_names,
+ _df_cluster_metrics=_df_cluster_metrics,
+ )
+
+ if return_html_as_string:
+ return rendered
+ return None
diff --git a/splink/internals/m_from_labels.py b/splink/internals/m_from_labels.py
index 1454974ac0..aadd816d85 100644
--- a/splink/internals/m_from_labels.py
+++ b/splink/internals/m_from_labels.py
@@ -1,4 +1,5 @@
import logging
+from typing import TYPE_CHECKING
from splink.internals.block_from_labels import block_from_labels
from splink.internals.comparison_vector_values import (
@@ -16,10 +17,12 @@
m_u_records_to_lookup_dict,
)
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
logger = logging.getLogger(__name__)
-def estimate_m_from_pairwise_labels(linker, table_name):
+def estimate_m_from_pairwise_labels(linker: "Linker", table_name: str) -> None:
pipeline = CTEPipeline()
nodes_with_tf = compute_df_concat_with_tf(linker, pipeline)
pipeline = CTEPipeline([nodes_with_tf])
@@ -45,7 +48,7 @@ def estimate_m_from_pairwise_labels(linker, table_name):
)
pipeline.enqueue_sql(sql, "__splink__m_u_counts")
- df_params = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_params = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
param_records = df_params.as_pandas_dataframe()
param_records = compute_proportions_for_new_parameters(param_records)
diff --git a/splink/internals/m_training.py b/splink/internals/m_training.py
index e1ce5a41f4..1c07a62a88 100644
--- a/splink/internals/m_training.py
+++ b/splink/internals/m_training.py
@@ -1,5 +1,6 @@
import logging
from copy import deepcopy
+from typing import TYPE_CHECKING
from splink.internals.blocking import BlockingRule, block_using_rules_sqls
from splink.internals.comparison_vector_values import (
@@ -17,10 +18,12 @@
m_u_records_to_lookup_dict,
)
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
logger = logging.getLogger(__name__)
-def estimate_m_values_from_label_column(linker, df_dict, label_colname):
+def estimate_m_values_from_label_column(linker: "Linker", label_colname: str) -> None:
msg = f" Estimating m probabilities using from column {label_colname} "
logger.info(f"{msg:-^70}")
@@ -68,7 +71,7 @@ def estimate_m_values_from_label_column(linker, df_dict, label_colname):
)
pipeline.enqueue_sql(sql, "__splink__m_u_counts")
- df_params = training_linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_params = training_linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
param_records = df_params.as_pandas_dataframe()
param_records = compute_proportions_for_new_parameters(param_records)
diff --git a/splink/internals/match_weights_histogram.py b/splink/internals/match_weights_histogram.py
index 43993f790e..6198ffebbc 100644
--- a/splink/internals/match_weights_histogram.py
+++ b/splink/internals/match_weights_histogram.py
@@ -1,6 +1,11 @@
from math import floor
+from typing import TYPE_CHECKING
from splink.internals.pipeline import CTEPipeline
+from splink.internals.splink_dataframe import SplinkDataFrame
+
+if TYPE_CHECKING:
+ from splink.internals.linker import Linker
def _bins(min, max, num_bins):
@@ -58,7 +63,9 @@ def _hist_sql(bin_width):
return sqls
-def histogram_data(linker, df_predict, num_bins=100):
+def histogram_data(
+ linker: "Linker", df_predict: SplinkDataFrame, num_bins: int = 100
+) -> SplinkDataFrame:
sql = """
select min(match_weight) as min_weight, max(match_weight) as max_weight from
__splink__df_predict
@@ -66,7 +73,7 @@ def histogram_data(linker, df_predict, num_bins=100):
pipeline = CTEPipeline([df_predict])
pipeline.enqueue_sql(sql, "__splink__df_min_max")
- df_min_max = linker.db_api.sql_pipeline_to_splink_dataframe(
+ df_min_max = linker._db_api.sql_pipeline_to_splink_dataframe(
pipeline
).as_record_dict()
@@ -79,6 +86,6 @@ def histogram_data(linker, df_predict, num_bins=100):
sqls = _hist_sql(binwidth)
pipeline.enqueue_list_of_sqls(sqls)
- df_hist = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_hist = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
return df_hist
diff --git a/splink/internals/optimise_cost_of_brs.py b/splink/internals/optimise_cost_of_brs.py
index 0e842fd6fd..65e89a4651 100644
--- a/splink/internals/optimise_cost_of_brs.py
+++ b/splink/internals/optimise_cost_of_brs.py
@@ -113,9 +113,8 @@ def get_em_training_string(br_rows):
training_statements = []
for block_on_str in block_on_strings:
- statement = (
- f"linker.estimate_parameters_using_expectation_maximisation({block_on_str})"
- )
+ m_name = "linker.training.estimate_parameters_using_expectation_maximisation"
+ statement = f"{m_name}({block_on_str})"
training_statements.append(statement)
return " \n".join(training_statements)
diff --git a/splink/internals/term_frequencies.py b/splink/internals/term_frequencies.py
index 50987f9b45..973c0e7965 100644
--- a/splink/internals/term_frequencies.py
+++ b/splink/internals/term_frequencies.py
@@ -84,7 +84,7 @@ def _join_new_table_to_df_concat_with_tf_sql(linker: Linker, new_tablename: str)
Joins any required tf columns onto new_tablename
This is needed e.g. when using linker.compare_two_records
- or linker.find_matches_to_new_records in which the user provides
+ or linker.inference.find_matches_to_new_records in which the user provides
new records which need tf adjustments computed
"""
@@ -241,7 +241,7 @@ def tf_adjustment_chart(
dict(
cl,
**{
- "df_tf": linker.compute_tf_table(
+ "df_tf": linker.table_management.compute_tf_table(
cl["tf_adjustment_column"]
).as_pandas_dataframe()
},
diff --git a/splink/internals/unlinkables.py b/splink/internals/unlinkables.py
index 48c1d6975c..f2c500a693 100644
--- a/splink/internals/unlinkables.py
+++ b/splink/internals/unlinkables.py
@@ -51,7 +51,7 @@ def unlinkables_data(linker: Linker) -> dict[str, Any]:
where match_probability < 1
"""
pipeline.enqueue_sql(sql, "__splink__df_unlinkables_proportions_cumulative")
- data = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline, use_cache=False)
+ data = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline, use_cache=False)
unlinkables_dict = data.as_record_dict()
data.drop_table_from_database_and_remove_from_cache()
diff --git a/splink/internals/vertically_concatenate.py b/splink/internals/vertically_concatenate.py
index 7577c35e22..c2fcfd0d34 100644
--- a/splink/internals/vertically_concatenate.py
+++ b/splink/internals/vertically_concatenate.py
@@ -106,7 +106,7 @@ def enqueue_df_concat_with_tf(linker: Linker, pipeline: CTEPipeline) -> CTEPipel
def compute_df_concat_with_tf(linker: Linker, pipeline: CTEPipeline) -> SplinkDataFrame:
cache = linker._intermediate_table_cache
- db_api = linker.db_api
+ db_api = linker._db_api
if "__splink__df_concat_with_tf" in cache:
return cache.get_with_logging("__splink__df_concat_with_tf")
@@ -158,7 +158,7 @@ def enqueue_df_concat(linker: Linker, pipeline: CTEPipeline) -> CTEPipeline:
def compute_df_concat(linker: Linker, pipeline: CTEPipeline) -> SplinkDataFrame:
cache = linker._intermediate_table_cache
- db_api = linker.db_api
+ db_api = linker._db_api
if "__splink__df_concat" in cache:
return cache.get_with_logging("__splink__df_concat")
diff --git a/tests/cc_testing_utils.py b/tests/cc_testing_utils.py
index 30009ea623..beab85a622 100644
--- a/tests/cc_testing_utils.py
+++ b/tests/cc_testing_utils.py
@@ -42,10 +42,10 @@ def register_cc_df(G):
)
# re-register under our required name to run the CC function
- linker.register_table(df_concat, table_name, overwrite=True)
+ linker.table_management.register_table(df_concat, table_name, overwrite=True)
df_nodes = pd.DataFrame({"unique_id": G.nodes})
- linker.register_table_input_nodes_concat_with_tf(df_nodes)
+ linker.table_management.register_table_input_nodes_concat_with_tf(df_nodes)
# add our prediction df to our list of created tables
predict_df = DuckDBDataFrame(table_name, table_name, db_api)
diff --git a/tests/helpers.py b/tests/helpers.py
index d5f3d7bd20..38dedf172b 100644
--- a/tests/helpers.py
+++ b/tests/helpers.py
@@ -15,7 +15,7 @@
class TestHelper(ABC):
@property
- def Linker(self):
+ def Linker(self) -> Linker:
return Linker
@property
diff --git a/tests/linker_utils.py b/tests/linker_utils.py
index 2aa21497a8..8d0fa7ad00 100644
--- a/tests/linker_utils.py
+++ b/tests/linker_utils.py
@@ -12,22 +12,26 @@ def _test_table_registration(
# Standard pandas df...
a = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
- linker.register_table(a, "__splink__df_pd")
- pd_df = linker.query_sql("select * from __splink__df_pd", output_type="splinkdf")
+ linker.table_management.register_table(a, "__splink__df_pd")
+ pd_df = linker.misc.query_sql(
+ "select * from __splink__df_pd", output_type="splinkdf"
+ )
assert sum(pd_df.as_pandas_dataframe().a) == sum(a.a)
# Standard dictionary
test_dict = {"a": [666, 777, 888], "b": [4, 5, 6]}
- t_dict = linker.register_table(test_dict, "__splink__df_test_dict")
+ t_dict = linker.table_management.register_table(test_dict, "__splink__df_test_dict")
test_dict_df = pd.DataFrame(test_dict)
assert sum(t_dict.as_pandas_dataframe().b) == sum(test_dict_df.b)
# Duplicate table name (check for error)
with pytest.raises(ValueError):
- linker.register_table(test_dict, "__splink__df_pd")
+ linker.table_management.register_table(test_dict, "__splink__df_pd")
# Test overwriting works
- linker.register_table(test_dict_df, "__splink__df_pd", overwrite=True)
- out = linker.query_sql("select * from __splink__df_pd", output_type="pandas")
+ linker.table_management.register_table(
+ test_dict_df, "__splink__df_pd", overwrite=True
+ )
+ out = linker.misc.query_sql("select * from __splink__df_pd", output_type="pandas")
assert sum(out.a) == sum(test_dict_df.a)
# Record level dictionary
@@ -37,20 +41,22 @@ def _test_table_registration(
{"a": 3, "b": 44, "c": 555},
]
- linker.register_table(b, "__splink__df_record_df")
- record_df = linker.query_sql(
+ linker.table_management.register_table(b, "__splink__df_record_df")
+ record_df = linker.misc.query_sql(
"select * from __splink__df_record_df", output_type="pandas"
)
assert sum(record_df.b) == sum(record["b"] for record in b)
with pytest.raises(ValueError):
- linker.query_sql("select * from __splink__df_test_dict", output_type="testing")
- df = linker.query_sql(
+ linker.misc.query_sql(
+ "select * from __splink__df_test_dict", output_type="testing"
+ )
+ df = linker.misc.query_sql(
"select * from __splink__df_test_dict", output_type="splinkdf"
).as_pandas_dataframe()
assert sum(df.b) == sum(test_dict_df.b)
- r_dict = linker.query_sql(
+ r_dict = linker.misc.query_sql(
"select * from __splink__df_record_df", output_type="splinkdf"
).as_record_dict()
assert sum(pd.DataFrame.from_records(r_dict).a) == sum(record["a"] for record in b)
@@ -58,7 +64,7 @@ def _test_table_registration(
# Test registration on additional data types for specific linkers
if additional_tables_to_register:
for table in additional_tables_to_register:
- linker.register_table(table, "test_table", overwrite=True)
+ linker.table_management.register_table(table, "test_table", overwrite=True)
def register_roc_data(linker):
@@ -83,7 +89,7 @@ def register_roc_data(linker):
axis=1,
)
- linker.register_table(df_labels, "labels")
+ linker.table_management.register_table(df_labels, "labels")
def _test_write_functionality(linker, read_csv_func):
@@ -93,18 +99,18 @@ def _test_write_functionality(linker, read_csv_func):
shutil.rmtree(root)
parquet_f = f"{root}/tmp_files/test.parquet"
- linker.predict().to_parquet(parquet_f)
+ linker.inference.predict().to_parquet(parquet_f)
assert len(pd.read_parquet(parquet_f)) == 3167
# Duplicate table name (check for error)
with pytest.raises(FileExistsError):
- linker.predict().to_parquet(parquet_f)
+ linker.inference.predict().to_parquet(parquet_f)
csv_f = f"{root}/tmp_files/test.csv"
- linker.predict().to_csv(csv_f)
+ linker.inference.predict().to_csv(csv_f)
assert len(read_csv_func(csv_f)) == 3167
# Duplicate table name (check for error)
with pytest.raises(FileExistsError):
- linker.predict().to_csv(csv_f)
+ linker.inference.predict().to_csv(csv_f)
# delete the folder and its contents
shutil.rmtree(root)
diff --git a/tests/test_accuracy.py b/tests/test_accuracy.py
index 61f22f9950..e9fb9025c0 100644
--- a/tests/test_accuracy.py
+++ b/tests/test_accuracy.py
@@ -56,12 +56,12 @@ def test_scored_labels_table():
concat_with_tf = compute_df_concat_with_tf(linker, pipeline)
pipeline = CTEPipeline([concat_with_tf])
- linker.register_table(df_labels, "labels")
+ linker.table_management.register_table(df_labels, "labels")
sqls = predictions_from_sample_of_pairwise_labels_sql(linker, "labels")
pipeline.enqueue_list_of_sqls(sqls)
- df_scores_labels = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_scores_labels = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
df_scores_labels = df_scores_labels.as_pandas_dataframe()
df_scores_labels.sort_values(["unique_id_l", "unique_id_r"], inplace=True)
@@ -69,7 +69,7 @@ def test_scored_labels_table():
assert len(df_scores_labels) == 6
# Check predictions are the same as the labels
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
f1 = df_predict["unique_id_l"] == 1
f2 = df_predict["unique_id_r"] == 2
@@ -139,12 +139,14 @@ def test_truth_space_table():
]
labels_with_predictions = pd.DataFrame(labels_with_predictions)
- linker.register_table(labels_with_predictions, "__splink__labels_with_predictions")
+ linker.table_management.register_table(
+ labels_with_predictions, "__splink__labels_with_predictions"
+ )
pipeline = CTEPipeline()
sqls = truth_space_table_from_labels_with_predictions_sqls(0.5)
pipeline.enqueue_list_of_sqls(sqls)
- df_roc = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ df_roc = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
df_roc = df_roc.as_pandas_dataframe()
@@ -195,9 +197,9 @@ def test_roc_chart_dedupe_only():
linker = Linker(df, settings_dict, database_api=db_api)
- labels_sdf = linker.register_table(df_labels, "labels")
+ labels_sdf = linker.table_management.register_table(df_labels, "labels")
- linker.accuracy_analysis_from_labels_table(labels_sdf, output_type="roc")
+ linker.evaluation.accuracy_analysis_from_labels_table(labels_sdf, output_type="roc")
def test_roc_chart_link_and_dedupe():
@@ -228,9 +230,9 @@ def test_roc_chart_link_and_dedupe():
df, settings_dict, input_table_aliases="fake_data_1", database_api=db_api
)
- labels_sdf = linker.register_table(df_labels, "labels")
+ labels_sdf = linker.table_management.register_table(df_labels, "labels")
- linker.accuracy_analysis_from_labels_table(labels_sdf, output_type="roc")
+ linker.evaluation.accuracy_analysis_from_labels_table(labels_sdf, output_type="roc")
def test_prediction_errors_from_labels_table():
@@ -290,12 +292,14 @@ def test_prediction_errors_from_labels_table():
linker = Linker(df, settings, database_api=db_api)
- linker.register_table(df_labels, "labels")
+ linker.table_management.register_table(df_labels, "labels")
pipeline = CTEPipeline()
compute_df_concat_with_tf(linker, pipeline)
- df_res = linker.prediction_errors_from_labels_table("labels").as_pandas_dataframe()
+ df_res = linker.evaluation.prediction_errors_from_labels_table(
+ "labels"
+ ).as_pandas_dataframe()
df_res = df_res[["unique_id_l", "unique_id_r"]]
records = list(df_res.to_records(index=False))
records = [tuple(p) for p in records]
@@ -309,12 +313,12 @@ def test_prediction_errors_from_labels_table():
linker = Linker(df, settings, database_api=db_api)
- linker.register_table(df_labels, "labels")
+ linker.table_management.register_table(df_labels, "labels")
pipeline = CTEPipeline()
compute_df_concat_with_tf(linker, pipeline)
- df_res = linker.prediction_errors_from_labels_table(
+ df_res = linker.evaluation.prediction_errors_from_labels_table(
"labels", include_false_negatives=False
).as_pandas_dataframe()
df_res = df_res[["unique_id_l", "unique_id_r"]]
@@ -329,12 +333,12 @@ def test_prediction_errors_from_labels_table():
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.register_table(df_labels, "labels")
+ linker.table_management.register_table(df_labels, "labels")
pipeline = CTEPipeline()
compute_df_concat_with_tf(linker, pipeline)
- df_res = linker.prediction_errors_from_labels_table(
+ df_res = linker.evaluation.prediction_errors_from_labels_table(
"labels", include_false_positives=False
).as_pandas_dataframe()
df_res = df_res[["unique_id_l", "unique_id_r"]]
@@ -393,7 +397,7 @@ def test_prediction_errors_from_labels_column():
linker = Linker(df, settings, database_api=db_api)
- df_res = linker.prediction_errors_from_labels_column(
+ df_res = linker.evaluation.prediction_errors_from_labels_column(
"cluster"
).as_pandas_dataframe()
df_res = df_res[["unique_id_l", "unique_id_r"]]
@@ -410,7 +414,7 @@ def test_prediction_errors_from_labels_column():
linker = Linker(df, settings, database_api=db_api)
- df_res = linker.prediction_errors_from_labels_column(
+ df_res = linker.evaluation.prediction_errors_from_labels_column(
"cluster", include_false_positives=False
).as_pandas_dataframe()
df_res = df_res[["unique_id_l", "unique_id_r"]]
@@ -427,7 +431,7 @@ def test_prediction_errors_from_labels_column():
linker = Linker(df, settings, database_api=db_api)
- df_res = linker.prediction_errors_from_labels_column(
+ df_res = linker.evaluation.prediction_errors_from_labels_column(
"cluster", include_false_negatives=False
).as_pandas_dataframe()
df_res = df_res[["unique_id_l", "unique_id_r"]]
@@ -489,7 +493,7 @@ def test_truth_space_table_from_labels_column_dedupe_only():
linker = Linker(df, settings, database_api=db_api)
- tt = linker.accuracy_analysis_from_labels_column(
+ tt = linker.evaluation.accuracy_analysis_from_labels_column(
"cluster", output_type="table"
).as_record_dict()
# Truth threshold -3.17, meaning all comparisons get classified as positive
@@ -560,7 +564,7 @@ def test_truth_space_table_from_labels_column_link_only():
linker = Linker([df_left, df_right], settings, database_api=db_api)
- tt = linker.accuracy_analysis_from_labels_column(
+ tt = linker.evaluation.accuracy_analysis_from_labels_column(
"ground_truth", output_type="table"
).as_record_dict()
# Truth threshold -3.17, meaning all comparisons get classified as positive
@@ -609,7 +613,7 @@ def test_truth_space_table_from_column_vs_pandas_implementaiton_inc_unblocked():
)
linker_for_predictions = Linker(df, settings, database_api=DuckDBAPI())
- df_predictions_raw = linker_for_predictions.predict()
+ df_predictions_raw = linker_for_predictions.inference.predict()
# Score all of the positive labels even if not captured by the blocking rules
# but not score any negative pairwise comaprisons not captured by the blocking rules
@@ -630,7 +634,7 @@ def test_truth_space_table_from_column_vs_pandas_implementaiton_inc_unblocked():
match_key
from {df_predictions_raw.physical_name}
"""
- df_predictions = linker_for_predictions.query_sql(sql)
+ df_predictions = linker_for_predictions.misc.query_sql(sql)
settings = SettingsCreator(
link_type="dedupe_only",
@@ -644,11 +648,13 @@ def test_truth_space_table_from_column_vs_pandas_implementaiton_inc_unblocked():
)
linker_for_splink_answer = Linker(df, settings, database_api=DuckDBAPI())
- df_from_splink = linker_for_splink_answer.accuracy_analysis_from_labels_column(
- "cluster",
- output_type="table",
- positives_not_captured_by_blocking_rules_scored_as_zero=False,
- ).as_pandas_dataframe()
+ df_from_splink = (
+ linker_for_splink_answer.evaluation.accuracy_analysis_from_labels_column(
+ "cluster",
+ output_type="table",
+ positives_not_captured_by_blocking_rules_scored_as_zero=False,
+ ).as_pandas_dataframe()
+ )
for _, splink_row in df_from_splink.iterrows():
threshold = splink_row["truth_threshold"]
@@ -683,7 +689,7 @@ def test_truth_space_table_from_column_vs_pandas_implementaiton_ex_unblocked():
)
linker_for_predictions = Linker([df_1, df_2], settings, database_api=DuckDBAPI())
- df_predictions_raw = linker_for_predictions.predict()
+ df_predictions_raw = linker_for_predictions.inference.predict()
# When match_key = 1, the record is not really recovered by the blocking rules
# so its score must be zero. Want
@@ -698,7 +704,7 @@ def test_truth_space_table_from_column_vs_pandas_implementaiton_ex_unblocked():
match_key
from {df_predictions_raw.physical_name}
"""
- df_predictions = linker_for_predictions.query_sql(sql)
+ df_predictions = linker_for_predictions.misc.query_sql(sql)
settings = SettingsCreator(
link_type="link_only",
@@ -713,11 +719,13 @@ def test_truth_space_table_from_column_vs_pandas_implementaiton_ex_unblocked():
)
linker_for_splink_answer = Linker([df_1, df_2], settings, database_api=DuckDBAPI())
- df_from_splink = linker_for_splink_answer.accuracy_analysis_from_labels_column(
- "cluster",
- output_type="table",
- positives_not_captured_by_blocking_rules_scored_as_zero=True,
- ).as_pandas_dataframe()
+ df_from_splink = (
+ linker_for_splink_answer.evaluation.accuracy_analysis_from_labels_column(
+ "cluster",
+ output_type="table",
+ positives_not_captured_by_blocking_rules_scored_as_zero=True,
+ ).as_pandas_dataframe()
+ )
for _, splink_row in df_from_splink.iterrows():
threshold = splink_row["truth_threshold"]
@@ -759,13 +767,17 @@ def test_truth_space_table_from_table_vs_pandas_cartesian():
)
linker_for_predictions = Linker(df_first_50, settings, database_api=DuckDBAPI())
- df_predictions = linker_for_predictions.predict().as_pandas_dataframe()
+ df_predictions = linker_for_predictions.inference.predict().as_pandas_dataframe()
linker_for_splink_answer = Linker(df, settings, database_api=DuckDBAPI())
- labels_input = linker_for_splink_answer.register_labels_table(labels_table)
- df_from_splink = linker_for_splink_answer.accuracy_analysis_from_labels_table(
- labels_input, output_type="table"
- ).as_pandas_dataframe()
+ labels_input = linker_for_splink_answer.table_management.register_labels_table(
+ labels_table
+ )
+ df_from_splink = (
+ linker_for_splink_answer.evaluation.accuracy_analysis_from_labels_table(
+ labels_input, output_type="table"
+ ).as_pandas_dataframe()
+ )
for _, splink_row in df_from_splink.iterrows():
threshold = splink_row["truth_threshold"]
@@ -816,7 +828,7 @@ def test_truth_space_table_from_table_vs_pandas_with_blocking():
linker_for_predictions = Linker(
[df_1_first_50, df_2_first_50], settings, database_api=DuckDBAPI()
)
- df_predictions_raw = linker_for_predictions.predict()
+ df_predictions_raw = linker_for_predictions.inference.predict()
df_predictions_raw.as_pandas_dataframe()
sql = f"""
select
@@ -830,7 +842,7 @@ def test_truth_space_table_from_table_vs_pandas_with_blocking():
cluster_r,
from {df_predictions_raw.physical_name}
"""
- df_predictions = linker_for_predictions.query_sql(sql)
+ df_predictions = linker_for_predictions.misc.query_sql(sql)
settings = SettingsCreator(
link_type="link_only",
@@ -844,10 +856,14 @@ def test_truth_space_table_from_table_vs_pandas_with_blocking():
)
linker_for_splink_answer = Linker([df_1, df_2], settings, database_api=DuckDBAPI())
- labels_input = linker_for_splink_answer.register_labels_table(labels_table)
- df_from_splink = linker_for_splink_answer.accuracy_analysis_from_labels_table(
- labels_input, output_type="table"
- ).as_pandas_dataframe()
+ labels_input = linker_for_splink_answer.table_management.register_labels_table(
+ labels_table
+ )
+ df_from_splink = (
+ linker_for_splink_answer.evaluation.accuracy_analysis_from_labels_table(
+ labels_input, output_type="table"
+ ).as_pandas_dataframe()
+ )
for _, splink_row in df_from_splink.iterrows():
threshold = splink_row["truth_threshold"]
diff --git a/tests/test_array_based_blocking.py b/tests/test_array_based_blocking.py
index 3d0c728b03..d22fa1d28b 100644
--- a/tests/test_array_based_blocking.py
+++ b/tests/test_array_based_blocking.py
@@ -38,7 +38,7 @@ def test_simple_example_link_only(test_helpers, dialect):
## the additional pairs returned by the second blocking rule are (1,4),(3,5)
linker = helper.Linker([data_l, data_r], settings, **helper.extra_linker_args())
linker.debug_mode = False
- returned_triples = linker.predict().as_pandas_dataframe()[
+ returned_triples = linker.inference.predict().as_pandas_dataframe()[
["unique_id_l", "unique_id_r", "match_key"]
]
returned_triples = {
@@ -110,7 +110,7 @@ def test_array_based_blocking_with_random_data_dedupe(test_helpers, dialect):
}
linker = helper.Linker(input_data, settings, **helper.extra_linker_args())
linker.debug_mode = False
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
## check that there are no duplicates in the output
assert (
df_predict.drop_duplicates(["unique_id_l", "unique_id_r"]).shape[0]
@@ -159,7 +159,7 @@ def test_array_based_blocking_with_random_data_link_only(test_helpers, dialect):
[input_data_l, input_data_r], settings, **helper.extra_linker_args()
)
linker.debug_mode = False
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
## check that we get no within-dataset links
within_dataset_links = df_predict[
@@ -232,7 +232,7 @@ def test_link_only_unique_id_ambiguity(test_helpers, dialect):
input_table_aliases=["a_", "b_", "c_"],
**helper.extra_linker_args(),
)
- returned_triples = linker.predict().as_pandas_dataframe()[
+ returned_triples = linker.inference.predict().as_pandas_dataframe()[
[
"source_dataset_l",
"unique_id_l",
diff --git a/tests/test_blocking.py b/tests/test_blocking.py
index c497493d7f..aac6fa2c69 100644
--- a/tests/test_blocking.py
+++ b/tests/test_blocking.py
@@ -65,11 +65,11 @@ def test_simple_end_to_end(test_helpers, dialect):
linker = Linker(df, settings, **helper.extra_linker_args())
- linker.estimate_u_using_random_sampling(max_pairs=1e3)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e3)
blocking_rule = block_on("first_name", "surname")
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
- linker.estimate_parameters_using_expectation_maximisation(block_on("dob"))
+ linker.training.estimate_parameters_using_expectation_maximisation(block_on("dob"))
- linker.predict()
+ linker.inference.predict()
diff --git a/tests/test_caching.py b/tests/test_caching.py
index a9a5933076..ded0f702c0 100644
--- a/tests/test_caching.py
+++ b/tests/test_caching.py
@@ -38,7 +38,7 @@ def test_cache_id(tmp_path):
prior = linker._settings_obj._cache_uid
path = os.path.join(tmp_path, "model.json")
- linker.save_model_to_json(path, overwrite=True)
+ linker.misc.save_model_to_json(path, overwrite=True)
db_api = DuckDBAPI()
@@ -83,7 +83,7 @@ def test_cache_access_df_concat(debug_mode):
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.debug_mode = debug_mode
+ linker._debug_mode = debug_mode
with patch.object(
db_api, "_sql_to_splink_dataframe", new=make_mock_execute(db_api)
) as mockexecute_sql_pipeline:
@@ -115,16 +115,16 @@ def test_cache_access_compute_tf_table(debug_mode):
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.debug_mode = debug_mode
+ linker._debug_mode = debug_mode
with patch.object(
db_api, "_sql_to_splink_dataframe", new=make_mock_execute(db_api)
) as mockexecute_sql_pipeline:
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("first_name")
mockexecute_sql_pipeline.assert_called()
# reset the call counter on the mock
mockexecute_sql_pipeline.reset_mock()
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("first_name")
mockexecute_sql_pipeline.assert_not_called()
@@ -135,7 +135,7 @@ def test_invalidate_cache(debug_mode):
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.debug_mode = debug_mode
+ linker._debug_mode = debug_mode
with patch.object(
db_api, "_sql_to_splink_dataframe", new=make_mock_execute(db_api)
@@ -151,14 +151,14 @@ def test_invalidate_cache(debug_mode):
mockexecute_sql_pipeline.assert_not_called()
# create this:
- linker.compute_tf_table("surname")
+ linker.table_management.compute_tf_table("surname")
mockexecute_sql_pipeline.assert_called()
mockexecute_sql_pipeline.reset_mock()
# then check the cache
- linker.compute_tf_table("surname")
+ linker.table_management.compute_tf_table("surname")
mockexecute_sql_pipeline.assert_not_called()
- linker.invalidate_cache()
+ linker.table_management.invalidate_cache()
# now we _SHOULD_ compute afresh:
pipeline = CTEPipeline()
@@ -170,11 +170,11 @@ def test_invalidate_cache(debug_mode):
compute_df_concat_with_tf(linker, pipeline)
mockexecute_sql_pipeline.assert_not_called()
# and should compute this again:
- linker.compute_tf_table("surname")
+ linker.table_management.compute_tf_table("surname")
mockexecute_sql_pipeline.assert_called()
mockexecute_sql_pipeline.reset_mock()
# then check the cache
- linker.compute_tf_table("surname")
+ linker.table_management.compute_tf_table("surname")
mockexecute_sql_pipeline.assert_not_called()
@@ -185,7 +185,7 @@ def test_cache_invalidates_with_new_linker(debug_mode):
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.debug_mode = debug_mode
+ linker._debug_mode = debug_mode
with patch.object(
db_api, "_sql_to_splink_dataframe", new=make_mock_execute(db_api)
) as mockexecute_sql_pipeline:
@@ -202,7 +202,7 @@ def test_cache_invalidates_with_new_linker(debug_mode):
db_api = DuckDBAPI()
new_linker = Linker(df, settings, database_api=db_api)
- new_linker.debug_mode = debug_mode
+ new_linker._debug_mode = debug_mode
with patch.object(
db_api, "_sql_to_splink_dataframe", new=make_mock_execute(db_api)
) as mockexecute_sql_pipeline:
@@ -233,14 +233,14 @@ def test_cache_register_compute_concat_with_tf_table(debug_mode):
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.debug_mode = debug_mode
+ linker._debug_mode = debug_mode
with patch.object(
db_api, "_sql_to_splink_dataframe", new=make_mock_execute(db_api)
) as mockexecute_sql_pipeline:
# can actually register frame, as that part not cached
# don't need function so use any frame
- linker.register_table_input_nodes_concat_with_tf(df)
+ linker.table_management.register_table_input_nodes_concat_with_tf(df)
# now this should be cached, as I have manually registered
pipeline = CTEPipeline()
compute_df_concat_with_tf(linker, pipeline)
@@ -254,14 +254,14 @@ def test_cache_register_compute_tf_table(debug_mode):
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.debug_mode = debug_mode
+ linker._debug_mode = debug_mode
with patch.object(
db_api, "_sql_to_splink_dataframe", new=make_mock_execute(db_api)
) as mockexecute_sql_pipeline:
# can actually register frame, as that part not cached
# don't need function so use any frame
- linker.register_term_frequency_lookup(df, "first_name")
+ linker.table_management.register_term_frequency_lookup(df, "first_name")
# now this should be cached, as I have manually registered
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("first_name")
mockexecute_sql_pipeline.assert_not_called()
diff --git a/tests/test_caching_tables.py b/tests/test_caching_tables.py
index f7b8b35db3..66a5cba21d 100644
--- a/tests/test_caching_tables.py
+++ b/tests/test_caching_tables.py
@@ -32,11 +32,11 @@ def test_cache_tracking_works():
cache = linker._intermediate_table_cache
assert cache.is_in_executed_queries("__splink__df_concat_with_tf") is False
- linker.predict()
+ linker.inference.predict()
assert cache.is_in_executed_queries("__splink__df_concat_with_tf") is True
- linker.predict()
+ linker.inference.predict()
assert (
cache.is_in_queries_retrieved_from_cache("__splink__df_concat_with_tf") is True
)
@@ -48,18 +48,18 @@ def test_cache_tracking_works():
assert (
cache.is_in_queries_retrieved_from_cache("__splink__df_concat_with_tf") is False
)
- linker.predict()
+ linker.inference.predict()
assert cache.is_in_executed_queries("__splink__df_concat_with_tf") is False
assert (
cache.is_in_queries_retrieved_from_cache("__splink__df_concat_with_tf") is True
)
- linker.invalidate_cache()
+ linker.table_management.invalidate_cache()
cache.reset_executed_queries_tracker()
cache.reset_queries_retrieved_from_cache_tracker()
- linker.predict()
+ linker.inference.predict()
# Triggers adding to queries retrieved from cache
- linker.predict()
+ linker.inference.predict()
assert cache.is_in_executed_queries("__splink__df_concat_with_tf") is True
assert (
cache.is_in_queries_retrieved_from_cache("__splink__df_concat_with_tf") is True
@@ -95,8 +95,10 @@ def test_cache_used_when_registering_nodes_table():
linker = Linker(df, settings, database_api=db_api)
cache = linker._intermediate_table_cache
- linker.register_table_input_nodes_concat_with_tf(splink__df_concat_with_tf)
- linker.predict()
+ linker.table_management.register_table_input_nodes_concat_with_tf(
+ splink__df_concat_with_tf
+ )
+ linker.inference.predict()
assert cache.is_in_executed_queries("__splink__df_concat_with_tf") is False
assert (
cache.is_in_queries_retrieved_from_cache("__splink__df_concat_with_tf") is True
@@ -147,7 +149,7 @@ def test_cache_used_when_registering_tf_tables():
linker = Linker(df, settings, database_api=db_api)
cache = linker._intermediate_table_cache
- linker.predict()
+ linker.inference.predict()
assert not cache.is_in_queries_retrieved_from_cache("__splink__df_tf_first_name")
assert not cache.is_in_queries_retrieved_from_cache("__splink__df_tf_surname")
@@ -157,8 +159,8 @@ def test_cache_used_when_registering_tf_tables():
linker = Linker(df, settings, database_api=db_api)
cache = linker._intermediate_table_cache
- linker.register_term_frequency_lookup(surname_tf_table, "surname")
- linker.predict()
+ linker.table_management.register_term_frequency_lookup(surname_tf_table, "surname")
+ linker.inference.predict()
assert not cache.is_in_queries_retrieved_from_cache("__splink__df_tf_first_name")
assert cache.is_in_queries_retrieved_from_cache("__splink__df_tf_surname")
@@ -168,9 +170,11 @@ def test_cache_used_when_registering_tf_tables():
linker = Linker(df, settings, database_api=db_api)
cache = linker._intermediate_table_cache
- linker.register_term_frequency_lookup(surname_tf_table, "surname")
- linker.register_term_frequency_lookup(first_name_tf_table, "first_name")
- linker.predict()
+ linker.table_management.register_term_frequency_lookup(surname_tf_table, "surname")
+ linker.table_management.register_term_frequency_lookup(
+ first_name_tf_table, "first_name"
+ )
+ linker.inference.predict()
assert cache.is_in_queries_retrieved_from_cache("__splink__df_tf_first_name")
assert cache.is_in_queries_retrieved_from_cache("__splink__df_tf_surname")
@@ -195,9 +199,9 @@ def test_cache_invalidation():
linker = Linker(df, settings, database_api=db_api)
cache = linker._intermediate_table_cache
- linker.compute_tf_table("name")
+ linker.table_management.compute_tf_table("name")
len_before = len(cache.executed_queries)
- linker.compute_tf_table("name")
+ linker.table_management.compute_tf_table("name")
len_after = len(cache.executed_queries)
# If cache not invalidated, cache should be used
@@ -209,10 +213,10 @@ def test_cache_invalidation():
linker = Linker(df, settings, database_api=db_api)
cache = linker._intermediate_table_cache
- linker.compute_tf_table("name")
+ linker.table_management.compute_tf_table("name")
len_before = len(cache.executed_queries)
- linker.invalidate_cache()
- linker.compute_tf_table("name")
+ linker.table_management.invalidate_cache()
+ linker.table_management.compute_tf_table("name")
len_after = len(cache.executed_queries)
# If cache is invalidated, an additional query should have been executed
assert len_before + 1 == len_after
@@ -243,11 +247,11 @@ def test_table_deletions():
table_names_before = set(get_duckdb_table_names_as_list(db_api._con))
- linker.compute_tf_table("name")
- linker.estimate_u_using_random_sampling(max_pairs=1e4)
+ linker.table_management.compute_tf_table("name")
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e4)
# # The database should be empty except for the original non-splink table
- linker.delete_tables_created_by_splink_from_db()
+ linker.table_management.delete_tables_created_by_splink_from_db()
table_names_after = set(get_duckdb_table_names_as_list(db_api._con))
assert table_names_before == table_names_after
@@ -290,18 +294,20 @@ def test_table_deletions_with_preregistered():
db_api = DuckDBAPI(connection=con)
linker = Linker("my_data_table", settings, database_api=db_api)
- linker.register_table_input_nodes_concat_with_tf("my_nodes_with_tf_table")
+ linker.table_management.register_table_input_nodes_concat_with_tf(
+ "my_nodes_with_tf_table"
+ )
table_names_before = set(get_duckdb_table_names_as_list(db_api._con))
- linker.compute_tf_table("name")
- linker.estimate_u_using_random_sampling(max_pairs=1e4)
+ linker.table_management.compute_tf_table("name")
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e4)
# Note we shouldn't have executed a __splink__df_concat_with_tf query
cache = linker._intermediate_table_cache
assert not cache.is_in_executed_queries("__splink__df_concat_with_tf")
- linker.delete_tables_created_by_splink_from_db()
+ linker.table_management.delete_tables_created_by_splink_from_db()
table_names_after = set(get_duckdb_table_names_as_list(db_api._con))
@@ -327,7 +333,7 @@ def test_single_deletion():
linker = Linker(df, settings, database_api=db_api)
cache = linker._intermediate_table_cache
- tf_table = linker.compute_tf_table("name")
+ tf_table = linker.table_management.compute_tf_table("name")
table_name = tf_table.physical_name
# Check it is in the cache and database
assert table_name in get_duckdb_table_names_as_list(db_api._con)
diff --git a/tests/test_charts.py b/tests/test_charts.py
index e919480065..8bc79b2a92 100644
--- a/tests/test_charts.py
+++ b/tests/test_charts.py
@@ -132,11 +132,11 @@ def test_m_u_charts():
linker = Linker(df, settings, database_api=db_api)
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.true_match_id = r.true_match_id"], recall=1.0
)
- linker.estimate_parameters_using_expectation_maximisation(
+ linker.training.estimate_parameters_using_expectation_maximisation(
"l.surname = r.surname",
fix_u_probabilities=False,
fix_probability_two_random_records_match=True,
@@ -144,7 +144,7 @@ def test_m_u_charts():
assert linker._settings_obj.comparisons[1].comparison_levels[1].u_probability == 0.0
- linker.match_weights_chart()
+ linker.visualisations.match_weights_chart()
def test_parameter_estimate_charts():
@@ -160,16 +160,16 @@ def test_parameter_estimate_charts():
linker = Linker(df, settings, database_api=db_api)
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.true_match_id = r.true_match_id"], recall=1.0
)
- linker.estimate_parameters_using_expectation_maximisation(
+ linker.training.estimate_parameters_using_expectation_maximisation(
"l.surname = r.surname",
fix_u_probabilities=False,
fix_probability_two_random_records_match=True,
)
- linker.estimate_parameters_using_expectation_maximisation(
+ linker.training.estimate_parameters_using_expectation_maximisation(
"l.first_name = r.first_name",
fix_u_probabilities=False,
fix_probability_two_random_records_match=True,
@@ -183,7 +183,7 @@ def test_parameter_estimate_charts():
]
assert 1.0 in exact_gender_m_estimates
- linker.parameter_estimate_comparisons_chart()
+ linker.visualisations.parameter_estimate_comparisons_chart()
settings = {
"link_type": "dedupe_only",
@@ -196,9 +196,9 @@ def test_parameter_estimate_charts():
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.estimate_u_using_random_sampling(1e6)
+ linker.training.estimate_u_using_random_sampling(1e6)
- linker.parameter_estimate_comparisons_chart()
+ linker.visualisations.parameter_estimate_comparisons_chart()
def test_tf_adjustment_chart():
@@ -215,8 +215,8 @@ def test_tf_adjustment_chart():
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.tf_adjustment_chart("gender")
- linker.tf_adjustment_chart("first_name")
+ linker.visualisations.tf_adjustment_chart("gender")
+ linker.visualisations.tf_adjustment_chart("first_name")
with pytest.raises(ValueError):
- linker.tf_adjustment_chart("surname")
+ linker.visualisations.tf_adjustment_chart("surname")
diff --git a/tests/test_cluster_studio.py b/tests/test_cluster_studio.py
index e906b4b8de..b115345ce3 100644
--- a/tests/test_cluster_studio.py
+++ b/tests/test_cluster_studio.py
@@ -34,7 +34,7 @@ def test_density_sample():
)
# Convert to Splink dataframe
- df_cluster_metrics = linker.register_table(
+ df_cluster_metrics = linker.table_management.register_table(
pd_metrics, "df_cluster_metrics", overwrite=True
)
result = _get_lowest_density_clusters(
diff --git a/tests/test_columns_selected.py b/tests/test_columns_selected.py
index 88297dbc4a..d0a612f10f 100644
--- a/tests/test_columns_selected.py
+++ b/tests/test_columns_selected.py
@@ -64,7 +64,7 @@ def test_regression(tmp_path):
linker = Linker(df.copy(), settings_dict, database_api=db_api)
- linker.predict()
+ linker.inference.predict()
def test_discussion_example(tmp_path):
@@ -125,4 +125,4 @@ def test_discussion_example(tmp_path):
linker = Linker(df.copy(), settings_dict, database_api=db_api)
- linker.predict()
+ linker.inference.predict()
diff --git a/tests/test_compare_splink2.py b/tests/test_compare_splink2.py
index 15a2c1437c..eea7e40469 100644
--- a/tests/test_compare_splink2.py
+++ b/tests/test_compare_splink2.py
@@ -19,7 +19,7 @@ def test_splink_2_predict():
expected_record = pd.read_csv("tests/datasets/splink2_479_vs_481.csv")
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
f1 = df_e["unique_id_l"] == 479
f2 = df_e["unique_id_r"] == 481
@@ -37,7 +37,7 @@ def test_splink_2_predict_spark(df_spark, spark_api):
settings_dict = get_settings_dict()
linker = Linker(df_spark, settings_dict, spark_api)
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
f1 = df_e["unique_id_l"] == "479"
f2 = df_e["unique_id_r"] == "481"
actual_record = df_e[f1 & f2]
@@ -64,7 +64,7 @@ def test_splink_2_predict_sqlite():
db_api = SQLiteAPI(con)
linker = Linker("fake_data_1", settings_dict, database_api=db_api)
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
f1 = df_e["unique_id_l"] == 479
f2 = df_e["unique_id_r"] == 481
@@ -76,7 +76,7 @@ def test_splink_2_predict_sqlite():
assert expected_match_weight == pytest.approx(actual_match_weight)
- linker.estimate_parameters_using_expectation_maximisation("l.dob=r.dob")
+ linker.training.estimate_parameters_using_expectation_maximisation("l.dob=r.dob")
def test_splink_2_em_fixed_u():
@@ -91,8 +91,10 @@ def test_splink_2_em_fixed_u():
"tests/datasets/splink2_proportion_of_matches_history_fixed_u.csv"
)
- training_session = linker.estimate_parameters_using_expectation_maximisation(
- "l.surname = r.surname"
+ training_session = (
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.surname = r.surname"
+ )
)
actual_prop_history = pd.DataFrame(training_session._lambda_history_records)
@@ -137,8 +139,10 @@ def test_splink_2_em_no_fix():
"tests/datasets/splink2_proportion_of_matches_history_no_fix.csv"
)
- training_session = linker.estimate_parameters_using_expectation_maximisation(
- "l.surname = r.surname", fix_u_probabilities=False
+ training_session = (
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.surname = r.surname", fix_u_probabilities=False
+ )
)
actual_prop_history = pd.DataFrame(training_session._lambda_history_records)
@@ -188,14 +192,16 @@ def test_lambda():
linker = Linker(df, settings_dict, database_api=db_api)
- ma = linker.predict().as_pandas_dataframe()
+ ma = linker.inference.predict().as_pandas_dataframe()
f1 = ma["unique_id_l"] == 924
f2 = ma["unique_id_r"] == 925
ma[f1 & f2]
# actual_record
ma["match_probability"].mean()
- training_session = linker.estimate_parameters_using_expectation_maximisation(
- "l.dob = r.dob", fix_u_probabilities=False
+ training_session = (
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.dob = r.dob", fix_u_probabilities=False
+ )
)
pd.DataFrame(training_session._lambda_history_records)
@@ -229,10 +235,12 @@ def test_lambda():
linker._settings_obj._probability_two_random_records_match = glo
- training_session = linker.estimate_parameters_using_expectation_maximisation(
- "l.first_name = r.first_name and l.surname = r.surname",
- fix_u_probabilities=False,
- populate_probability_two_random_records_match_from_trained_values=True,
+ training_session = (
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.first_name = r.first_name and l.surname = r.surname",
+ fix_u_probabilities=False,
+ populate_probability_two_random_records_match_from_trained_values=True,
+ )
)
# linker._settings_obj.match_weights_chart()
diff --git a/tests/test_comparison_level_composition.py b/tests/test_comparison_level_composition.py
index acddee31af..10dfcce978 100644
--- a/tests/test_comparison_level_composition.py
+++ b/tests/test_comparison_level_composition.py
@@ -190,7 +190,7 @@ def test_composition_outputs(test_helpers, dialect):
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- pred = linker.predict()
+ pred = linker.inference.predict()
out = pred.as_pandas_dataframe().sort_values(by=["unique_id_l", "unique_id_r"])
# Check individual IDs are assigned to the correct gamma values
diff --git a/tests/test_comparison_level_lib.py b/tests/test_comparison_level_lib.py
index 60c2a4c09e..b991e28ed4 100644
--- a/tests/test_comparison_level_lib.py
+++ b/tests/test_comparison_level_lib.py
@@ -39,7 +39,7 @@ def test_column_reversal(test_helpers, dialect):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
row = dict(df_e.query("id_l == 1 and id_r == 2").iloc[0])
assert row["gamma_full_name"] == 1
@@ -85,7 +85,7 @@ def test_perc_difference(test_helpers, dialect):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
row = dict(df_e.query("id_l == 1 and id_r == 2").iloc[0]) # 16.66%
assert row["gamma_amount"] == 3
@@ -168,7 +168,7 @@ def gamma_lev_from_distance(dist):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
for id_r, lev_dist in id_distance_from_1.items():
expected_gamma_lev = gamma_lev_from_distance(lev_dist)
@@ -248,7 +248,7 @@ def gamma_lev_from_distance(dist):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
for id_r, lev_dist in id_distance_from_1.items():
expected_gamma_lev = gamma_lev_from_distance(lev_dist)
diff --git a/tests/test_comparison_lib.py b/tests/test_comparison_lib.py
index c91c21d7ed..3ae143d606 100644
--- a/tests/test_comparison_lib.py
+++ b/tests/test_comparison_lib.py
@@ -33,7 +33,7 @@ def test_distance_function_comparison():
linker = Linker(df, settings, database_api=db_api)
- df_pred = linker.predict().as_pandas_dataframe()
+ df_pred = linker.inference.predict().as_pandas_dataframe()
expected_gamma_counts = {
"forename": {
@@ -85,7 +85,7 @@ def test_set_to_lowercase():
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
row = dict(df_e.query("id_l == 1 and id_r == 2").iloc[0])
assert row["gamma_forename"] == 1
diff --git a/tests/test_comparison_template_lib.py b/tests/test_comparison_template_lib.py
index 69e297b78b..623a5c9ffa 100644
--- a/tests/test_comparison_template_lib.py
+++ b/tests/test_comparison_template_lib.py
@@ -68,7 +68,7 @@ def test_name_comparison_levels(dialect, test_helpers):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- linker_output = linker.predict().as_pandas_dataframe()
+ linker_output = linker.inference.predict().as_pandas_dataframe()
# # Dict key: {gamma_level value: size}
size_gamma_lookup = {0: 6, 1: 6, 2: 0, 3: 2, 4: 1}
@@ -161,7 +161,7 @@ def test_forename_surname_comparison_levels(dialect, test_helpers):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- linker_output = linker.predict().as_pandas_dataframe()
+ linker_output = linker.inference.predict().as_pandas_dataframe()
# # Dict key: {gamma_level value: size}
size_gamma_lookup = {0: 8, 1: 3, 2: 3, 3: 2, 4: 2, 5: 2, 6: 1}
@@ -273,7 +273,7 @@ def test_postcode_comparison_levels(dialect, test_helpers, test_gamma_assert):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- linker_output = linker.predict().as_pandas_dataframe()
+ linker_output = linker.inference.predict().as_pandas_dataframe()
# Check individual IDs are assigned to the correct gamma values
# Dict key: {gamma_level: tuple of ID pairs}
@@ -326,7 +326,7 @@ def test_email_comparison_levels(dialect, test_helpers, test_gamma_assert):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- linker_output = linker.predict().as_pandas_dataframe()
+ linker_output = linker.inference.predict().as_pandas_dataframe()
# Check individual IDs are assigned to the correct gamma values
# Dict key: {gamma_level: tuple of ID pairs}
diff --git a/tests/test_compound_comparison_levels.py b/tests/test_compound_comparison_levels.py
index e487e91126..60772e5e2f 100644
--- a/tests/test_compound_comparison_levels.py
+++ b/tests/test_compound_comparison_levels.py
@@ -129,7 +129,9 @@ def col_is_null(col):
"surname",
}
- linker.estimate_parameters_using_expectation_maximisation("l.city = r.city")
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.city = r.city"
+ )
def test_complex_compound_comparison_level():
@@ -218,4 +220,4 @@ def test_complex_compound_comparison_level():
linker = Linker(df, settings, database_api=db_api)
- linker.estimate_parameters_using_expectation_maximisation("1=1")
+ linker.training.estimate_parameters_using_expectation_maximisation("1=1")
diff --git a/tests/test_correctness_of_convergence.py b/tests/test_correctness_of_convergence.py
index db165d8cf6..57839ebffc 100644
--- a/tests/test_correctness_of_convergence.py
+++ b/tests/test_correctness_of_convergence.py
@@ -105,7 +105,7 @@ def test_splink_converges_to_known_params():
linker._populate_probability_two_random_records_match_from_trained_values()
- linker.match_weights_chart()
+ linker.visualisations.match_weights_chart()
cv = DuckDBDataFrame(
"__splink__df_comparison_vectors",
@@ -120,7 +120,7 @@ def test_splink_converges_to_known_params():
)
pipeline.enqueue_list_of_sqls(sqls)
- predictions = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ predictions = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
predictions_df = predictions.as_pandas_dataframe()
from pandas.testing import assert_series_equal
diff --git a/tests/test_disable_tf_exact_match_detection.py b/tests/test_disable_tf_exact_match_detection.py
index 5b4ab37f9c..7222c9bbee 100644
--- a/tests/test_disable_tf_exact_match_detection.py
+++ b/tests/test_disable_tf_exact_match_detection.py
@@ -150,16 +150,16 @@ def get_settings(disable_tf_exact_match_detection, tf_minimum_u_value=None):
{"surname": "Taylor", "tf_surname": 0.4},
{"surname": "Kirk", "tf_surname": 0.2},
]
- linker.register_term_frequency_lookup(tf_lookup, "surname")
+ linker.table_management.register_term_frequency_lookup(tf_lookup, "surname")
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
sql = f"""
select * from {df_predict.physical_name}
where unique_id_l = 835
and unique_id_r = 836
"""
- res = linker.query_sql(sql).to_dict(orient="records")[0]
+ res = linker.misc.query_sql(sql).to_dict(orient="records")[0]
# Exact match, normal tf adjustement, Kirk
assert res["bf_surname"] == pytest.approx(8.0)
@@ -172,7 +172,7 @@ def get_settings(disable_tf_exact_match_detection, tf_minimum_u_value=None):
where unique_id_l = 147
and unique_id_r = 975
"""
- res = linker.query_sql(sql).to_dict(orient="records")[0]
+ res = linker.misc.query_sql(sql).to_dict(orient="records")[0]
# Levenshtein match, normal tf adustments, Taylor
# Splink makes the tf adjustment based on on the exact match level
# Lev match level has bf of 0.9/0.3
@@ -192,16 +192,16 @@ def get_settings(disable_tf_exact_match_detection, tf_minimum_u_value=None):
{"surname": "Taylor", "tf_surname": 0.4},
{"surname": "Kirk", "tf_surname": 0.2},
]
- linker.register_term_frequency_lookup(tf_lookup, "surname")
+ linker.table_management.register_term_frequency_lookup(tf_lookup, "surname")
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
sql = f"""
select * from {df_predict.physical_name}
where unique_id_l = 835
and unique_id_r = 836
"""
- res = linker.query_sql(sql).to_dict(orient="records")[0]
+ res = linker.misc.query_sql(sql).to_dict(orient="records")[0]
# Exact match, normal tf adjustement, Kirk
assert res["bf_surname"] == pytest.approx(8.0)
# Overall BF should be m/u = 0.8/0.2 = 4
@@ -212,7 +212,7 @@ def get_settings(disable_tf_exact_match_detection, tf_minimum_u_value=None):
where unique_id_l = 147
and unique_id_r = 975
"""
- res = linker.query_sql(sql).to_dict(orient="records")[0]
+ res = linker.misc.query_sql(sql).to_dict(orient="records")[0]
# Levenshtein match, tf exact match detection disabled, Taylor
# Splink makes the tf adjustment based on on the exact match level
# Lev match level has bf of 0.9/0.3
@@ -231,7 +231,10 @@ def get_settings(disable_tf_exact_match_detection, tf_minimum_u_value=None):
)
linker_base = Linker(df, settings_disabled_with_min_tf, DuckDBAPI())
- linkers = [linker_base, Linker(df, linker_base.save_model_to_json(), DuckDBAPI())]
+ linkers = [
+ linker_base,
+ Linker(df, linker_base.misc.save_model_to_json(), DuckDBAPI()),
+ ]
# This ensures we're checking that serialisation and deserialisation
# works on the disable_tf_exact_match_detection and tf_minimum_u_value settings
@@ -240,16 +243,16 @@ def get_settings(disable_tf_exact_match_detection, tf_minimum_u_value=None):
{"surname": "Taylor", "tf_surname": 0.001},
{"surname": "Kirk", "tf_surname": 0.2},
]
- linker.register_term_frequency_lookup(tf_lookup, "surname")
+ linker.table_management.register_term_frequency_lookup(tf_lookup, "surname")
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
sql = f"""
select * from {df_predict.physical_name}
where unique_id_l = 835
and unique_id_r = 836
"""
- res = linker.query_sql(sql).to_dict(orient="records")[0]
+ res = linker.misc.query_sql(sql).to_dict(orient="records")[0]
# Exact match, normal tf adjustement, Kirk
assert res["bf_surname"] == pytest.approx(8.0)
# Overall BF should be m/u = 0.8/0.2 = 4
@@ -260,7 +263,7 @@ def get_settings(disable_tf_exact_match_detection, tf_minimum_u_value=None):
where unique_id_l = 147
and unique_id_r = 975
"""
- res = linker.query_sql(sql).to_dict(orient="records")[0]
+ res = linker.misc.query_sql(sql).to_dict(orient="records")[0]
# Levenshtein match, tf exact match detection disabled, Taylor
# Splink makes the tf adjustment based on on the exact match level
# Lev match level has bf of 0.9/0.3
diff --git a/tests/test_estimate_prob_two_rr_match.py b/tests/test_estimate_prob_two_rr_match.py
index 46c7537a46..4faa104b88 100644
--- a/tests/test_estimate_prob_two_rr_match.py
+++ b/tests/test_estimate_prob_two_rr_match.py
@@ -34,7 +34,7 @@ def test_prob_rr_match_dedupe(test_helpers, dialect):
# Test dedupe only
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
deterministic_rules, recall=1.0
)
@@ -44,7 +44,7 @@ def test_prob_rr_match_dedupe(test_helpers, dialect):
# Test recall works
deterministic_rules = ["l.first_name = r.first_name and l.surname = r.surname"]
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
deterministic_rules, recall=0.9
)
@@ -87,7 +87,7 @@ def test_prob_rr_match_link_only(test_helpers, dialect):
# Test dedupe only
linker = helper.Linker([df_1, df_2], settings, **helper.extra_linker_args())
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
deterministic_rules, recall=1.0
)
@@ -127,7 +127,7 @@ def test_prob_rr_match_link_and_dedupe(test_helpers, dialect):
# Test dedupe only
linker = helper.Linker([df_1, df_2], settings, **helper.extra_linker_args())
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
deterministic_rules, recall=1.0
)
@@ -196,7 +196,7 @@ def test_prob_rr_match_link_only_multitable(test_helpers, dialect):
deterministic_rules = ["l.first_name = r.first_name", "l.surname = r.surname"]
linker = helper.Linker(dfs, settings, **helper.extra_linker_args())
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
deterministic_rules, recall=1.0
)
@@ -207,7 +207,7 @@ def test_prob_rr_match_link_only_multitable(test_helpers, dialect):
# if we define all record pairs to be a match, then the probability should be 1
linker = helper.Linker(dfs, settings, **helper.extra_linker_args())
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.city = r.city"], recall=1.0
)
prob = linker._settings_obj._probability_two_random_records_match
@@ -274,7 +274,7 @@ def test_prob_rr_match_link_and_dedupe_multitable(test_helpers, dialect):
deterministic_rules = ["l.first_name = r.first_name", "l.surname = r.surname"]
linker = helper.Linker(dfs, settings, **helper.extra_linker_args())
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
deterministic_rules, recall=1.0
)
@@ -285,7 +285,7 @@ def test_prob_rr_match_link_and_dedupe_multitable(test_helpers, dialect):
assert pytest.approx(prob) == 10 / 171
linker = helper.Linker(dfs, settings, **helper.extra_linker_args())
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.city = r.city"], recall=1.0
)
prob = linker._settings_obj._probability_two_random_records_match
@@ -352,7 +352,7 @@ def check_range(p):
with pytest.raises(ValueError):
# all comparisons matches using this rule, so we must have perfect recall
# using recall = 80% is inconsistent, so should get an error
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.first_name = r.first_name"], recall=0.8
)
check_range(linker._settings_obj._probability_two_random_records_match)
@@ -360,10 +360,10 @@ def check_range(p):
# matching on city gives 6 matches out of 15, so recall must be at least 6/15
recall_min_city = 6 / 15
with pytest.raises(ValueError):
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.city = r.city"], recall=(recall_min_city - 1e-6)
)
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.city = r.city"], recall=recall_min_city
)
check_range(linker._settings_obj._probability_two_random_records_match)
@@ -372,7 +372,7 @@ def check_range(p):
# this gives a linkage model that always predicts match_probability as 0,
# so should give a warning at this stage
with caplog.at_level(logging.WARNING):
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.surname = r.surname"], recall=0.7
)
assert "WARNING:" in caplog.text
@@ -381,7 +381,7 @@ def check_range(p):
# this gives prob as 1, so again should get a warning
# as we have a trivial linkage model
with caplog.at_level(logging.WARNING):
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.first_name = r.first_name"], recall=1.0
)
assert "WARNING:" in caplog.text
@@ -389,14 +389,14 @@ def check_range(p):
# check we get errors if we pass bogus values for recall
with pytest.raises(ValueError):
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.first_name = r.first_name"], recall=0.0
)
with pytest.raises(ValueError):
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.first_name = r.first_name"], recall=1.2
)
with pytest.raises(ValueError):
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.first_name = r.first_name"], recall=-0.4
)
diff --git a/tests/test_expectation_maximisation.py b/tests/test_expectation_maximisation.py
index c3c31c7c26..e2e4a393a7 100644
--- a/tests/test_expectation_maximisation.py
+++ b/tests/test_expectation_maximisation.py
@@ -30,12 +30,14 @@ def test_clear_error_when_empty_block():
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.debug_mode = True
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
- linker.estimate_parameters_using_expectation_maximisation("l.name = r.name")
+ linker._debug_mode = True
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.name = r.name"
+ )
# No record pairs for which surname matches, so we should get a nice handled error
with pytest.raises(EMTrainingException):
- linker.estimate_parameters_using_expectation_maximisation(
+ linker.training.estimate_parameters_using_expectation_maximisation(
"l.surname = r.surname"
)
@@ -63,7 +65,7 @@ def test_em_manual_deactivate():
db_api = DuckDBAPI()
linker = Linker(df, settings, database_api=db_api)
- linker.estimate_parameters_using_expectation_maximisation(
+ linker.training.estimate_parameters_using_expectation_maximisation(
"l.name = r.name", comparisons_to_deactivate=["name"]
)
@@ -88,11 +90,11 @@ def test_estimate_without_term_frequencies():
linker_1 = Linker(df, settings, database_api=db_api)
- session_fast = linker_0.estimate_parameters_using_expectation_maximisation(
+ session_fast = linker_0.training.estimate_parameters_using_expectation_maximisation(
blocking_rule="l.email = r.email",
estimate_without_term_frequencies=True,
)
- session_slow = linker_1.estimate_parameters_using_expectation_maximisation(
+ session_slow = linker_1.training.estimate_parameters_using_expectation_maximisation(
blocking_rule="l.email = r.email",
estimate_without_term_frequencies=False,
)
diff --git a/tests/test_find_new_matches.py b/tests/test_find_new_matches.py
index 97423e29f5..09646540d1 100644
--- a/tests/test_find_new_matches.py
+++ b/tests/test_find_new_matches.py
@@ -60,10 +60,10 @@ def test_tf_tables_init_works(test_helpers, dialect):
# 1. Does nothing if term frequencies are not used
# 2. Should use the cache and not break if tf adj is requested for fn
# 3. Use both the cache and also create surname in our final example
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("first_name")
# Running without _df_concat_with_tf
- linker.__deepcopy__(None).find_matches_to_new_records(
+ linker.__deepcopy__(None).inference.find_matches_to_new_records(
[record], blocking_rules=[], match_weight_threshold=-10000
)
@@ -71,7 +71,7 @@ def test_tf_tables_init_works(test_helpers, dialect):
pipeline = CTEPipeline()
compute_df_concat_with_tf(linker, pipeline)
- linker.find_matches_to_new_records(
+ linker.inference.find_matches_to_new_records(
[record], blocking_rules=[], match_weight_threshold=-10000
)
@@ -86,25 +86,29 @@ def test_matches_work(test_helpers, dialect):
linker = Linker(df, get_settings_dict(), **helper.extra_linker_args())
# Train our model to get more reasonable outputs...
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker.visualisations.match_weights_chart().save("mwc.html")
blocking_rule = block_on("first_name", "surname")
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
blocking_rule = "l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ # linker.visualisations.match_weights_chart().save("mwc.html")
brs = ["l.surname = r.surname"]
- matches = linker.find_matches_to_new_records(
+ matches = linker.inference.find_matches_to_new_records(
[record], blocking_rules=brs, match_weight_threshold=-10000
)
matches = matches.as_pandas_dataframe()
assert len(matches) == 10
- matches = linker.find_matches_to_new_records(
- [record], blocking_rules=brs, match_weight_threshold=0
+ # linker.visualisations.match_weights_chart().save("mwc.html")
+
+ matches = linker.inference.find_matches_to_new_records(
+ [record], blocking_rules=brs, match_weight_threshold=0.1
)
matches = matches.as_pandas_dataframe()
diff --git a/tests/test_full_example_athena.py b/tests/test_full_example_athena.py
index 37e798cff9..141aee7612 100644
--- a/tests/test_full_example_athena.py
+++ b/tests/test_full_example_athena.py
@@ -119,33 +119,36 @@
# ["surname", "city"],
# ]
# )
-# linker.compute_tf_table("city")
-# linker.compute_tf_table("first_name")
+# linker.table_management.compute_tf_table("city")
+# linker.table_management.compute_tf_table("first_name")
-# linker.estimate_u_using_random_sampling(max_pairs=1e6, seed=None)
+# linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=None)
# blocking_rule = "l.first_name = r.first_name and l.surname = r.surname"
-# linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+# linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
# blocking_rule = "l.dob = r.dob"
-# linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+# linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
-# df_predict = linker.predict()
+# df_predict = linker.inference.predict()
-# linker.comparison_viewer_dashboard(df_predict, "test_scv_athena.html", True, 2)
+# linker.visualisations.comparison_viewer_dashboard(
+# df_predict, "test_scv_athena.html", True, 2
+# )
# df_predict.as_pandas_dataframe()
-# df_clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.1)
+# df_clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(
+# df_predict, 0.1)
-# linker.cluster_studio_dashboard(
+# linker.visualisations.cluster_studio_dashboard(
# df_predict,
# df_clusters,
# sampling_method="by_cluster_size",
# out_path=os.path.join(tmp_path, "test_cluster_studio.html"),
# )
-# linker.unlinkables_chart(source_dataset="Testing")
+# linker.evaluation.unlinkables_chart(source_dataset="Testing")
# _test_table_registration(linker)
@@ -183,7 +186,7 @@
# ]
# )
-# predict = linker.predict()
+# predict = linker.inference.predict()
# return linker, path, predict
@@ -277,7 +280,7 @@
# input_table_aliases=table_aliases,
# )
-# df_predict = linker.predict()
+# df_predict = linker.inference.predict()
# df_predict.as_pandas_dataframe()
# linker.drop_all_tables_created_by_splink(delete_s3_folders=True)
diff --git a/tests/test_full_example_deterministic_link.py b/tests/test_full_example_deterministic_link.py
index 2d85a3e0ab..7ee4e056f2 100644
--- a/tests/test_full_example_deterministic_link.py
+++ b/tests/test_full_example_deterministic_link.py
@@ -40,11 +40,11 @@ def test_deterministic_link_full_example(dialect, tmp_path, test_helpers):
linker = Linker(df, settings, **helper.extra_linker_args())
- df_predict = linker.deterministic_link()
+ df_predict = linker.inference.deterministic_link()
- clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict)
+ clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(df_predict)
- linker.cluster_studio_dashboard(
+ linker.visualisations.cluster_studio_dashboard(
df_predict,
clusters,
out_path=os.path.join(tmp_path, "test_cluster_studio.html"),
diff --git a/tests/test_full_example_duckdb.py b/tests/test_full_example_duckdb.py
index fa186ed0ff..5388a90ae7 100644
--- a/tests/test_full_example_duckdb.py
+++ b/tests/test_full_example_duckdb.py
@@ -69,44 +69,46 @@ def test_full_example_duckdb(tmp_path):
)
completeness_chart(df, db_api)
- linker.compute_tf_table("city")
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("city")
+ linker.table_management.compute_tf_table("first_name")
- linker.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)
+ linker.training.estimate_probability_two_random_records_match(
["l.email = r.email"], recall=0.3
)
blocking_rule = 'l.first_name = r.first_name and l."SUR name" = r."SUR name"'
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
blocking_rule = "l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
- linker.comparison_viewer_dashboard(
+ linker.visualisations.comparison_viewer_dashboard(
df_predict, os.path.join(tmp_path, "test_scv_duckdb.html"), True, 2
)
df_e = df_predict.as_pandas_dataframe(limit=5)
records = df_e.to_dict(orient="records")
- linker.waterfall_chart(records)
+ linker.visualisations.waterfall_chart(records)
register_roc_data(linker)
- linker.accuracy_analysis_from_labels_table("labels")
+ linker.evaluation.accuracy_analysis_from_labels_table("labels")
- df_clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.1)
+ df_clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(
+ df_predict, 0.1
+ )
- linker.cluster_studio_dashboard(
+ linker.visualisations.cluster_studio_dashboard(
df_predict,
df_clusters,
sampling_method="by_cluster_size",
out_path=os.path.join(tmp_path, "test_cluster_studio.html"),
)
- linker.unlinkables_chart(name_of_data_in_title="Testing")
+ linker.evaluation.unlinkables_chart(name_of_data_in_title="Testing")
_test_table_registration(linker)
@@ -120,13 +122,13 @@ def test_full_example_duckdb(tmp_path):
"cluster": 10000,
}
- linker.find_matches_to_new_records(
+ linker.inference.find_matches_to_new_records(
[record], blocking_rules=[], match_weight_threshold=-10000
)
# Test saving and loading
path = os.path.join(tmp_path, "model.json")
- linker.save_model_to_json(path)
+ linker.misc.save_model_to_json(path)
db_api = DuckDBAPI()
linker_2 = Linker(df, settings=simple_settings, database_api=db_api)
@@ -181,7 +183,7 @@ def test_link_only(input, source_l, source_r):
db_api = DuckDBAPI()
linker = Linker(input, settings, database_api=db_api)
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
assert len(df_predict) == 7257
assert set(df_predict.source_dataset_l.values) == source_l
@@ -227,7 +229,7 @@ def test_duckdb_load_from_file(df):
database_api=db_api,
)
- assert len(linker.predict().as_pandas_dataframe()) == 3167
+ assert len(linker.inference.predict().as_pandas_dataframe()) == 3167
settings["link_type"] = "link_only"
@@ -239,7 +241,7 @@ def test_duckdb_load_from_file(df):
input_table_aliases=["testing1", "testing2"],
)
- assert len(linker.predict().as_pandas_dataframe()) == 7257
+ assert len(linker.inference.predict().as_pandas_dataframe()) == 7257
@mark_with_dialects_including("duckdb")
@@ -269,7 +271,7 @@ def test_duckdb_arrow_array():
},
database_api=db_api,
)
- df = linker.deterministic_link().as_pandas_dataframe()
+ df = linker.inference.deterministic_link().as_pandas_dataframe()
assert len(df) == 2
@@ -314,11 +316,11 @@ def test_small_example_duckdb(tmp_path):
db_api = DuckDBAPI()
linker = Linker(df, settings_dict, database_api=db_api)
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
blocking_rule = "l.full_name = r.full_name"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
blocking_rule = "l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
- linker.predict()
+ linker.inference.predict()
diff --git a/tests/test_full_example_postgres.py b/tests/test_full_example_postgres.py
index 1a9f1a3599..0a61d13073 100644
--- a/tests/test_full_example_postgres.py
+++ b/tests/test_full_example_postgres.py
@@ -60,44 +60,46 @@ def test_full_example_postgres(tmp_path, pg_engine):
completeness_chart(df, db_api=db_api)
- linker.compute_tf_table("city")
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("city")
+ linker.table_management.compute_tf_table("first_name")
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker.training.estimate_probability_two_random_records_match(
["l.email = r.email"], recall=0.3
)
blocking_rule = 'l.first_name = r.first_name and l."surname" = r."surname"'
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
blocking_rule = "l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
- linker.comparison_viewer_dashboard(
+ linker.visualisations.comparison_viewer_dashboard(
df_predict, os.path.join(tmp_path, "test_scv_postgres.html"), True, 2
)
df_e = df_predict.as_pandas_dataframe(limit=5)
records = df_e.to_dict(orient="records")
- linker.waterfall_chart(records)
+ linker.visualisations.waterfall_chart(records)
register_roc_data(linker)
- linker.accuracy_analysis_from_labels_table("labels")
+ linker.evaluation.accuracy_analysis_from_labels_table("labels")
- df_clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.1)
+ df_clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(
+ df_predict, 0.1
+ )
- linker.cluster_studio_dashboard(
+ linker.visualisations.cluster_studio_dashboard(
df_predict,
df_clusters,
sampling_method="by_cluster_size",
out_path=os.path.join(tmp_path, "test_cluster_studio.html"),
)
- linker.unlinkables_chart(name_of_data_in_title="Testing")
+ linker.evaluation.unlinkables_chart(name_of_data_in_title="Testing")
_test_table_registration(linker)
@@ -111,13 +113,13 @@ def test_full_example_postgres(tmp_path, pg_engine):
"cluster": 10000,
}
- linker.find_matches_to_new_records(
+ linker.inference.find_matches_to_new_records(
[record], blocking_rules=[], match_weight_threshold=-10000
)
# Test saving and loading
path = os.path.join(tmp_path, "model.json")
- linker.save_model_to_json(path)
+ linker.misc.save_model_to_json(path)
Linker(df, path, database_api=db_api)
@@ -137,4 +139,4 @@ def test_postgres_use_existing_table(tmp_path, pg_engine):
database_api=db_api,
settings=settings_dict,
)
- linker.predict()
+ linker.inference.predict()
diff --git a/tests/test_full_example_spark.py b/tests/test_full_example_spark.py
index a8aa5c3b98..571c707bf6 100644
--- a/tests/test_full_example_spark.py
+++ b/tests/test_full_example_spark.py
@@ -25,7 +25,7 @@ def test_full_example_spark(spark, df_spark, tmp_path, spark_api):
# Test that writing to files works as expected
def spark_csv_read(x):
- return linker.db_api.spark.read.csv(x, header=True).toPandas()
+ return linker._db_api.spark.read.csv(x, header=True).toPandas()
_test_write_functionality(linker, spark_csv_read)
@@ -81,29 +81,31 @@ def spark_csv_read(x):
),
)
- linker.compute_tf_table("city")
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("city")
+ linker.table_management.compute_tf_table("first_name")
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.email = r.email"], recall=0.3
)
- linker.estimate_u_using_random_sampling(max_pairs=1e5, seed=1)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e5, seed=1)
blocking_rule = "l.first_name = r.first_name and l.surname = r.surname"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
blocking_rule = "l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
- linker.comparison_viewer_dashboard(
+ linker.visualisations.comparison_viewer_dashboard(
df_predict, os.path.join(tmp_path, "test_scv_spark.html"), True, 2
)
- df_clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.2)
+ df_clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(
+ df_predict, 0.2
+ )
- linker.cluster_studio_dashboard(
+ linker.visualisations.cluster_studio_dashboard(
df_predict,
df_clusters,
cluster_ids=[0, 4],
@@ -111,7 +113,7 @@ def spark_csv_read(x):
out_path=os.path.join(tmp_path, "test_cluster_studio.html"),
)
- linker.unlinkables_chart(name_of_data_in_title="Testing")
+ linker.evaluation.unlinkables_chart(name_of_data_in_title="Testing")
# Test that writing to files works as expected
# spark_csv_read = lambda x: linker.spark.read.csv(x, header=True).toPandas()
# _test_write_functionality(linker, spark_csv_read)
@@ -125,7 +127,7 @@ def spark_csv_read(x):
)
register_roc_data(linker)
- linker.accuracy_analysis_from_labels_table("labels")
+ linker.evaluation.accuracy_analysis_from_labels_table("labels")
record = {
"unique_id": 1,
@@ -137,7 +139,7 @@ def spark_csv_read(x):
"cluster": 10000,
}
- linker.find_matches_to_new_records(
+ linker.inference.find_matches_to_new_records(
[record], blocking_rules=[], match_weight_threshold=-10000
)
@@ -156,7 +158,7 @@ def spark_csv_read(x):
# Test saving and loading
path = os.path.join(tmp_path, "model.json")
- linker.save_model_to_json(path)
+ linker.misc.save_model_to_json(path)
Linker(df_spark, settings=path, database_api=spark_api)
@@ -178,7 +180,7 @@ def test_link_only(spark, df_spark, spark_api):
num_partitions_on_repartition=2,
),
)
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
assert len(df_predict) == 7257
assert set(df_predict.source_dataset_l.values) == {"my_left_ds"}
@@ -204,4 +206,4 @@ def test_spark_load_from_file(df, spark, spark_api):
spark_api,
)
- assert len(linker.predict().as_pandas_dataframe()) == 3167
+ assert len(linker.inference.predict().as_pandas_dataframe()) == 3167
diff --git a/tests/test_full_example_sqlite.py b/tests/test_full_example_sqlite.py
index 20a21d59b8..50968cf92a 100644
--- a/tests/test_full_example_sqlite.py
+++ b/tests/test_full_example_sqlite.py
@@ -33,36 +33,36 @@ def test_full_example_sqlite(tmp_path):
profile_columns(df, db_api, ["first_name", "surname", "first_name || surname"])
- linker.compute_tf_table("city")
- linker.compute_tf_table("first_name")
+ linker.table_management.compute_tf_table("city")
+ linker.table_management.compute_tf_table("first_name")
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.email = r.email"], recall=0.3
)
- linker.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6, seed=1)
blocking_rule = "l.first_name = r.first_name and l.surname = r.surname"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
blocking_rule = "l.dob = r.dob"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
- linker.comparison_viewer_dashboard(
+ linker.visualisations.comparison_viewer_dashboard(
df_predict, os.path.join(tmp_path, "test_scv_sqlite.html"), True, 2
)
- linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.5)
+ linker.clustering.cluster_pairwise_predictions_at_threshold(df_predict, 0.5)
- linker.unlinkables_chart(name_of_data_in_title="Testing")
+ linker.evaluation.unlinkables_chart(name_of_data_in_title="Testing")
_test_table_registration(linker)
register_roc_data(linker)
- linker.accuracy_analysis_from_labels_table("labels")
+ linker.evaluation.accuracy_analysis_from_labels_table("labels")
@mark_with_dialects_including("sqlite")
@@ -84,7 +84,7 @@ def test_small_link_example_sqlite():
input_table_aliases=["fake_data_1", "fake_data_2"],
)
- linker.predict()
+ linker.inference.predict()
@mark_with_dialects_including("sqlite")
@@ -96,4 +96,4 @@ def test_default_conn_sqlite(tmp_path):
db_api = SQLiteAPI()
linker = Linker(df, settings_dict, db_api)
- linker.predict()
+ linker.inference.predict()
diff --git a/tests/test_graph_metrics.py b/tests/test_graph_metrics.py
index 0f92a48bfe..ba9c94e2de 100644
--- a/tests/test_graph_metrics.py
+++ b/tests/test_graph_metrics.py
@@ -39,10 +39,12 @@ def test_size_density_dedupe():
linker = Linker(df_1, settings, database_api=db_api)
- df_predict = linker.predict()
- df_clustered = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.9)
+ df_predict = linker.inference.predict()
+ df_clustered = linker.clustering.cluster_pairwise_predictions_at_threshold(
+ df_predict, 0.9
+ )
- df_result = linker.compute_graph_metrics(
+ df_result = linker.clustering.compute_graph_metrics(
df_predict, df_clustered
).clusters.as_pandas_dataframe()
# not testing this here - it's not relevant for small clusters anyhow
@@ -76,11 +78,13 @@ def test_size_density_link():
database_api=db_api,
)
- df_predict = linker.predict()
- df_clustered = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.9)
+ df_predict = linker.inference.predict()
+ df_clustered = linker.clustering.cluster_pairwise_predictions_at_threshold(
+ df_predict, 0.9
+ )
df_result = (
- linker.compute_graph_metrics(
+ linker.clustering.compute_graph_metrics(
df_predict, df_clustered, threshold_match_probability=0.99
)
.clusters.as_pandas_dataframe()
@@ -230,10 +234,14 @@ def test_metrics(dialect, test_helpers):
{"link_type": "dedupe_only"},
**helper.extra_linker_args(),
)
- df_predict = linker.register_table(helper.convert_frame(df_e), "predict")
- df_clustered = linker.register_table(helper.convert_frame(df_c), "clusters")
+ df_predict = linker.table_management.register_table(
+ helper.convert_frame(df_e), "predict"
+ )
+ df_clustered = linker.table_management.register_table(
+ helper.convert_frame(df_c), "clusters"
+ )
- cm = linker.compute_graph_metrics(
+ cm = linker.clustering.compute_graph_metrics(
df_predict, df_clustered, threshold_match_probability=0.95
)
df_cm = cm.clusters.as_pandas_dataframe()
@@ -342,11 +350,15 @@ def test_is_bridge(dialect, test_helpers):
{"link_type": "dedupe_only"},
**helper.extra_linker_args(),
)
- df_predict = linker.register_table(helper.convert_frame(df_e), "br_predict")
- df_clustered = linker.register_table(helper.convert_frame(df_c), "br_clusters")
+ df_predict = linker.table_management.register_table(
+ helper.convert_frame(df_e), "br_predict"
+ )
+ df_clustered = linker.table_management.register_table(
+ helper.convert_frame(df_c), "br_clusters"
+ )
# linker.debug_mode = True
- cm = linker.compute_graph_metrics(
+ cm = linker.clustering.compute_graph_metrics(
df_predict, df_clustered, threshold_match_probability=0.95
)
df_em = cm.edges.as_pandas_dataframe()
@@ -392,12 +404,14 @@ def test_edges_without_igraph():
}
linker = Linker(df_1, settings, DuckDBAPI())
- df_predict = linker.predict()
- df_clustered = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.9)
+ df_predict = linker.inference.predict()
+ df_clustered = linker.clustering.cluster_pairwise_predictions_at_threshold(
+ df_predict, 0.9
+ )
# pretend we don't have igraph installed
with patch("builtins.__import__", side_effect=mock_no_igraph_installed):
- graph_metrics = linker.compute_graph_metrics(
+ graph_metrics = linker.clustering.compute_graph_metrics(
df_predict, df_clustered, threshold_match_probability=0.9
)
df_edge_metrics = graph_metrics.edges.as_pandas_dataframe()
@@ -428,12 +442,12 @@ def test_no_threshold_provided():
settings = {"link_type": "dedupe_only"}
linker = Linker(df_1, settings, DuckDBAPI())
- df_predict = linker.register_table(df_e, "predict")
- df_clustered = linker.register_table(df_c, "clusters")
+ df_predict = linker.table_management.register_table(df_e, "predict")
+ df_clustered = linker.table_management.register_table(df_c, "clusters")
with raises(TypeError):
# no threshold_match_probability, no metadata
- _ = linker.compute_graph_metrics(df_predict, df_clustered)
+ _ = linker.clustering.compute_graph_metrics(df_predict, df_clustered)
def test_override_metadata_threshold():
@@ -450,12 +464,12 @@ def test_override_metadata_threshold():
settings = {"link_type": "dedupe_only"}
linker = Linker(df_1, settings, DuckDBAPI())
# linker.debug_mode = True
- df_predict = linker.register_table(df_e, "predict")
- df_clustered = linker.register_table(df_c, "clusters")
+ df_predict = linker.table_management.register_table(df_e, "predict")
+ df_clustered = linker.table_management.register_table(df_c, "clusters")
df_clustered.metadata["threshold_match_probability"] = 0.95
- gm_results_95 = linker.compute_graph_metrics(df_predict, df_clustered)
- gm_results_9 = linker.compute_graph_metrics(
+ gm_results_95 = linker.clustering.compute_graph_metrics(df_predict, df_clustered)
+ gm_results_9 = linker.clustering.compute_graph_metrics(
df_predict, df_clustered, threshold_match_probability=0.9
)
df_expected_95 = pd.DataFrame(
diff --git a/tests/test_join_type_for_estimate_u_and_predict_are_efficient.py b/tests/test_join_type_for_estimate_u_and_predict_are_efficient.py
index d9d92b7cfa..c90ad586a1 100644
--- a/tests/test_join_type_for_estimate_u_and_predict_are_efficient.py
+++ b/tests/test_join_type_for_estimate_u_and_predict_are_efficient.py
@@ -122,7 +122,7 @@ def test_dedupe_only():
)
logging.getLogger("splink").setLevel(1)
- linker.estimate_u_using_random_sampling(max_pairs=1000)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1000)
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
@@ -133,7 +133,7 @@ def test_dedupe_only():
handler.log_list.clear()
- linker.predict()
+ linker.inference.predict()
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
@@ -177,7 +177,7 @@ def test_link_and_dedupe():
handler.log_list.clear()
logging.getLogger("splink").setLevel(1)
- linker.estimate_u_using_random_sampling(max_pairs=1000)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1000)
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
@@ -188,7 +188,7 @@ def test_link_and_dedupe():
log_list.clear()
- linker.predict()
+ linker.inference.predict()
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
@@ -233,7 +233,7 @@ def test_link_only_two():
log_list.clear()
logging.getLogger("splink").setLevel(1)
- linker.estimate_u_using_random_sampling(max_pairs=1000)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1000)
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
@@ -244,7 +244,7 @@ def test_link_only_two():
log_list.clear()
- linker.predict()
+ linker.inference.predict()
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
@@ -290,7 +290,7 @@ def test_link_only_three():
log_list.clear()
logging.getLogger("splink").setLevel(1)
- linker.estimate_u_using_random_sampling(max_pairs=1000)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1000)
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
@@ -301,7 +301,7 @@ def test_link_only_three():
log_list.clear()
- linker.predict()
+ linker.inference.predict()
all_log_messages = "\n".join(log_list)
all_log_messages = re.sub(r"\s+", " ", all_log_messages)
diff --git a/tests/test_km_distance_level.py b/tests/test_km_distance_level.py
index fbfae2cc90..2ad2734007 100644
--- a/tests/test_km_distance_level.py
+++ b/tests/test_km_distance_level.py
@@ -125,9 +125,9 @@ def test_km_distance_levels(dialect, test_helpers):
df = helper.convert_frame(df)
linker = helper.Linker(df, settings_cl, **helper.extra_linker_args())
- cl_df_e = linker.predict().as_pandas_dataframe()
+ cl_df_e = linker.inference.predict().as_pandas_dataframe()
linker = helper.Linker(df, settings_cll, **helper.extra_linker_args())
- cll_df_e = linker.predict().as_pandas_dataframe()
+ cll_df_e = linker.inference.predict().as_pandas_dataframe()
linker_outputs = {
"cl": cl_df_e,
@@ -227,7 +227,7 @@ def test_haversine_level():
db_api = DuckDBAPI()
linker = Linker(df, settings, input_table_aliases="test", database_api=db_api)
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
row = dict(df_e.query("id_l == 1 and id_r == 2").iloc[0])
assert row["gamma_lat_long"] == 3
diff --git a/tests/test_linker_variants.py b/tests/test_linker_variants.py
index c5ec84b900..dfb7b85797 100644
--- a/tests/test_linker_variants.py
+++ b/tests/test_linker_variants.py
@@ -70,7 +70,7 @@ def test_dedupe_only_join_condition():
linker = Linker(df.copy(), s, database_api=db_api)
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
assert len(df_predict) == (6 * 5) / 2
@@ -95,7 +95,7 @@ def test_link_only_two_join_condition():
linker = Linker([sds_d_only, sds_b_only], s, database_api=db_api)
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
assert len(df_predict) == 4
@@ -124,7 +124,7 @@ def test_link_only_three_join_condition():
linker = Linker([sds_d_only, sds_b_only, sds_c_only], s, database_api=db_api)
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
assert len(df_predict) == 12
@@ -153,7 +153,7 @@ def test_link_and_dedupe_two_join_condition():
linker = Linker([sds_d_only, sds_b_only], s, database_api=db_api)
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
assert len(df_predict) == (4 * 3) / 2
@@ -182,7 +182,7 @@ def test_link_and_dedupe_three_join_condition():
linker = Linker([sds_d_only, sds_b_only, sds_c_only], s, database_api=db_api)
- df_predict = linker.predict().as_pandas_dataframe()
+ df_predict = linker.inference.predict().as_pandas_dataframe()
assert len(df_predict) == (6 * 5) / 2
diff --git a/tests/test_m_train.py b/tests/test_m_train.py
index 7e4b407dd2..ac331373ba 100644
--- a/tests/test_m_train.py
+++ b/tests/test_m_train.py
@@ -26,7 +26,7 @@ def test_m_train():
linker = Linker(df, settings, database_api=db_api)
- linker.estimate_m_from_label_column("cluster")
+ linker.training.estimate_m_from_label_column("cluster")
cc_name = linker._settings_obj.comparisons[0]
cl_exact = cc_name._get_comparison_level_by_comparison_vector_value(2)
@@ -57,8 +57,8 @@ def test_m_train():
linker_pairwise = Linker(df, settings, database_api=db_api)
- linker_pairwise.register_table(df_labels, "labels")
- linker_pairwise.estimate_m_from_pairwise_labels("labels")
+ linker_pairwise.table_management.register_table(df_labels, "labels")
+ linker_pairwise.training.estimate_m_from_pairwise_labels("labels")
cc_name = linker_pairwise._settings_obj.comparisons[0]
cl_exact = cc_name._get_comparison_level_by_comparison_vector_value(2)
diff --git a/tests/test_new_comparison_levels.py b/tests/test_new_comparison_levels.py
index a6dc045fd4..6564d4410d 100644
--- a/tests/test_new_comparison_levels.py
+++ b/tests/test_new_comparison_levels.py
@@ -76,7 +76,7 @@ def test_cll_creators_run_predict(dialect, test_helpers):
df = helper.load_frame_from_csv("./tests/datasets/fake_1000_from_splink_demos.csv")
linker = helper.Linker(df, cll_settings, **helper.extra_linker_args())
- linker.predict()
+ linker.inference.predict()
@mark_with_dialects_excluding()
@@ -172,7 +172,7 @@ def test_cl_creators_run_predict(dialect, test_helpers):
linker = helper.Linker(df, cl_settings, **helper.extra_linker_args())
- linker.predict()
+ linker.inference.predict()
@mark_with_dialects_excluding("sqlite")
@@ -201,7 +201,7 @@ def test_regex_fall_through(dialect, test_helpers):
}
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
# only entry should be in Else level
assert df_e["gamma_name"][0] == 0
@@ -231,7 +231,7 @@ def test_null_pattern_match(dialect, test_helpers):
}
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- df_e = linker.predict().as_pandas_dataframe()
+ df_e = linker.inference.predict().as_pandas_dataframe()
# only entry should be in Null level
assert df_e["gamma_name"][0] == -1
@@ -285,7 +285,7 @@ def test_ctl_creators_run_predict(dialect, test_helpers):
df = helper.load_frame_from_csv("./tests/datasets/fake_1000_from_splink_demos.csv")
linker = helper.Linker(df, ctl_settings, **helper.extra_linker_args())
- linker.predict()
+ linker.inference.predict()
def test_custom_dialect_no_string_lookup():
diff --git a/tests/test_new_db_api.py b/tests/test_new_db_api.py
index 356c3e3a2a..c88a2bc2ef 100644
--- a/tests/test_new_db_api.py
+++ b/tests/test_new_db_api.py
@@ -69,7 +69,7 @@ def test_run_predict(dialect, test_helpers):
cl_settings,
db_api,
)
- linker.predict()
+ linker.inference.predict()
@mark_with_dialects_excluding()
@@ -83,25 +83,27 @@ def test_full_run(dialect, test_helpers, tmp_path):
cl_settings,
db_api,
)
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.first_name = r.first_name AND l.surname = r.surname"],
0.6,
)
- linker.estimate_u_using_random_sampling(500)
- linker.estimate_parameters_using_expectation_maximisation(
+ linker.training.estimate_u_using_random_sampling(500)
+ linker.training.estimate_parameters_using_expectation_maximisation(
"l.first_name = r.first_name"
)
- linker.estimate_parameters_using_expectation_maximisation("l.surname = r.surname")
- df_e = linker.predict()
- df_c = linker.cluster_pairwise_predictions_at_threshold(df_e, 0.99)
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.surname = r.surname"
+ )
+ df_e = linker.inference.predict()
+ df_c = linker.clustering.cluster_pairwise_predictions_at_threshold(df_e, 0.99)
- linker.comparison_viewer_dashboard(
+ linker.visualisations.comparison_viewer_dashboard(
df_e,
os.path.join(tmp_path, "test_cvd_duckdb.html"),
overwrite=True,
num_example_rows=2,
)
- linker.cluster_studio_dashboard(
+ linker.visualisations.cluster_studio_dashboard(
df_e,
df_c,
os.path.join(tmp_path, "test_csd_duckdb.html"),
@@ -126,18 +128,20 @@ def test_charts(dialect, test_helpers, tmp_path):
linker = Linker(df, cl_settings, db_api)
- linker.estimate_probability_two_random_records_match(
+ linker.training.estimate_probability_two_random_records_match(
["l.first_name = r.first_name AND l.surname = r.surname"],
0.6,
)
- linker.estimate_u_using_random_sampling(500)
- linker.estimate_parameters_using_expectation_maximisation(
+ linker.training.estimate_u_using_random_sampling(500)
+ linker.training.estimate_parameters_using_expectation_maximisation(
"l.first_name = r.first_name"
)
- linker.estimate_parameters_using_expectation_maximisation("l.surname = r.surname")
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.surname = r.surname"
+ )
- linker.match_weights_chart()
- linker.m_u_parameters_chart()
+ linker.visualisations.match_weights_chart()
+ linker.visualisations.m_u_parameters_chart()
@mark_with_dialects_excluding()
diff --git a/tests/test_regex_param.py b/tests/test_regex_param.py
index a9d02642b4..774fe5a5fd 100644
--- a/tests/test_regex_param.py
+++ b/tests/test_regex_param.py
@@ -131,7 +131,7 @@ def test_regex(dialect, test_helpers, level_set, record_pairs_gamma):
df = helper.convert_frame(df_pandas)
linker = helper.Linker(df, settings, **helper.extra_linker_args())
- linker_output = linker.predict().as_pandas_dataframe()
+ linker_output = linker.inference.predict().as_pandas_dataframe()
for gamma, id_pairs in record_pairs_gamma.items():
for left, right in id_pairs:
diff --git a/tests/test_salting_len.py b/tests/test_salting_len.py
index 5a13966a68..fde8a4f850 100644
--- a/tests/test_salting_len.py
+++ b/tests/test_salting_len.py
@@ -39,7 +39,7 @@ def generate_linker_output(
linker = Linker(df, settings, spark_api)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
df_predict = df_predict.as_pandas_dataframe()
return df_predict.sort_values(by=["unique_id_l", "unique_id_r"], ignore_index=True)
diff --git a/tests/test_settings_options.py b/tests/test_settings_options.py
index d6f35f5ac9..4953cc21e5 100644
--- a/tests/test_settings_options.py
+++ b/tests/test_settings_options.py
@@ -4,6 +4,7 @@
import splink.internals.comparison_library as cl
from splink import block_on
+from splink.internals.linker import Linker
from .decorator import mark_with_dialects_excluding
@@ -56,14 +57,20 @@ def test_model_heavily_customised_settings(test_helpers, dialect, tmp_path):
"term_frequency_adjustment_column_prefix": "term_freq__",
"comparison_vector_value_column_prefix": "cvv__",
}
- linker = helper.Linker([df_l, df_r], settings, **helper.extra_linker_args())
+ linker = Linker([df_l, df_r], settings, **helper.extra_linker_args())
# run through a few common operations to check functioning
- linker.estimate_probability_two_random_records_match(["l.dob = r.dob"], 0.5)
- linker.estimate_u_using_random_sampling(2e4)
- linker.estimate_parameters_using_expectation_maximisation("l.dob = r.dob")
- df_predict = linker.predict(0.1)
- df_clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict, 0.1)
- linker.comparison_viewer_dashboard(df_predict, os.path.join(tmp_path, "csv.html"))
- linker.cluster_studio_dashboard(
+ linker.training.estimate_probability_two_random_records_match(
+ ["l.dob = r.dob"], 0.5
+ )
+ linker.training.estimate_u_using_random_sampling(2e4)
+ linker.training.estimate_parameters_using_expectation_maximisation("l.dob = r.dob")
+ df_predict = linker.inference.predict(0.1)
+ df_clusters = linker.clustering.cluster_pairwise_predictions_at_threshold(
+ df_predict, 0.1
+ )
+ linker.visualisations.comparison_viewer_dashboard(
+ df_predict, os.path.join(tmp_path, "csv.html")
+ )
+ linker.visualisations.cluster_studio_dashboard(
df_predict, df_clusters, os.path.join(tmp_path, "csd.html")
)
diff --git a/tests/test_spark_udfs.py b/tests/test_spark_udfs.py
index 955952106b..a65a82457a 100644
--- a/tests/test_spark_udfs.py
+++ b/tests/test_spark_udfs.py
@@ -65,13 +65,13 @@ def test_udf_registration(spark_api):
settings,
spark_api,
)
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
blocking_rule = "l.first_name = r.first_name"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
blocking_rule = "l.surname = r.surname"
- linker.estimate_parameters_using_expectation_maximisation(blocking_rule)
+ linker.training.estimate_parameters_using_expectation_maximisation(blocking_rule)
- linker.predict()
+ linker.inference.predict()
@mark_with_dialects_including("spark")
@@ -105,7 +105,7 @@ def test_damerau_levenshtein(spark_api):
where l.id < r.id
"""
- udf_out = linker.query_sql(sql)
+ udf_out = linker.misc.query_sql(sql)
# Set accuracy level
decimals = 4
@@ -192,7 +192,7 @@ def test_jaro(spark_api):
where l.id < r.id
"""
- udf_out = linker.query_sql(sql)
+ udf_out = linker.misc.query_sql(sql)
# Set accuracy level
decimals = 4
@@ -274,7 +274,7 @@ def test_jaro_winkler(spark_api):
where l.id < r.id
"""
- udf_out = linker.query_sql(sql)
+ udf_out = linker.misc.query_sql(sql)
# Set accuracy level
decimals = 4
diff --git a/tests/test_splink_datasets.py b/tests/test_splink_datasets.py
index 60fd5b108e..5bc920d33e 100644
--- a/tests/test_splink_datasets.py
+++ b/tests/test_splink_datasets.py
@@ -16,4 +16,4 @@ def test_datasets_basic_link(test_helpers):
},
**helper.extra_linker_args(),
)
- linker.predict()
+ linker.inference.predict()
diff --git a/tests/test_term_frequencies.py b/tests/test_term_frequencies.py
index e458e6afa8..29cd2f574f 100644
--- a/tests/test_term_frequencies.py
+++ b/tests/test_term_frequencies.py
@@ -82,7 +82,7 @@ def test_tf_basic():
db_api = DuckDBAPI(connection=":memory:")
linker = Linker(data, settings, database_api=db_api)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
results = filter_results(df_predict)
bf_no_adj = results["London"]["bf_city"]
@@ -119,7 +119,7 @@ def test_tf_clamp():
db_api = DuckDBAPI(connection=":memory:")
linker = Linker(data, settings, database_api=db_api)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
results = filter_results(df_predict)
bf_no_adj = results["London"]["bf_city"]
@@ -157,7 +157,7 @@ def test_weight():
db_api = DuckDBAPI(connection=":memory:")
linker = Linker(data, settings, database_api=db_api)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
results = filter_results(df_predict)
bf_no_adj = results["London"]["bf_city"]
@@ -208,7 +208,7 @@ def test_weightand_clamp():
db_api = DuckDBAPI(connection=":memory:")
linker = Linker(data, settings, database_api=db_api)
- df_predict = linker.predict()
+ df_predict = linker.inference.predict()
results = filter_results(df_predict)
bf_no_adj = results["London"]["bf_city"]
diff --git a/tests/test_train_vs_predict.py b/tests/test_train_vs_predict.py
index 08f3cbb0aa..779a20750a 100644
--- a/tests/test_train_vs_predict.py
+++ b/tests/test_train_vs_predict.py
@@ -22,14 +22,16 @@ def test_train_vs_predict(test_helpers, dialect):
settings_dict["blocking_rules_to_generate_predictions"] = ["l.surname = r.surname"]
linker = helper.Linker(df, settings_dict, **helper.extra_linker_args())
- training_session = linker.estimate_parameters_using_expectation_maximisation(
- "l.surname = r.surname", fix_u_probabilities=False
+ training_session = (
+ linker.training.estimate_parameters_using_expectation_maximisation(
+ "l.surname = r.surname", fix_u_probabilities=False
+ )
)
expected = training_session.core_model_settings.probability_two_random_records_match
# We expect the probability_two_random_records_match to be the same as for a predict
- df = linker.predict().as_pandas_dataframe()
+ df = linker.inference.predict().as_pandas_dataframe()
actual = df["match_probability"].mean()
# Will not be exactly equal because expected represents the
diff --git a/tests/test_u_train.py b/tests/test_u_train.py
index b1d87fa79d..e9fc85d6d1 100644
--- a/tests/test_u_train.py
+++ b/tests/test_u_train.py
@@ -30,8 +30,8 @@ def test_u_train(test_helpers, dialect):
df_linker = helper.convert_frame(df)
linker = helper.Linker(df_linker, settings, **helper.extra_linker_args())
- linker.debug_mode = True
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker._debug_mode = True
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
cc_name = linker._settings_obj.comparisons[0]
denom = (6 * 5) / 2 # n(n-1) / 2
@@ -79,8 +79,8 @@ def test_u_train_link_only(test_helpers, dialect):
df_r = helper.convert_frame(df_r)
linker = helper.Linker([df_l, df_r], settings, **helper.extra_linker_args())
- linker.debug_mode = True
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker._debug_mode = True
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
cc_name = linker._settings_obj.comparisons[0]
check_blocking_sql = """
@@ -90,7 +90,7 @@ def test_u_train_link_only(test_helpers, dialect):
pipeline = CTEPipeline()
pipeline.enqueue_sql(check_blocking_sql, "__splink__df_blocked_same_table_count")
- self_table_count = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ self_table_count = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
result = self_table_count.as_record_dict()
self_table_count.drop_table_from_database_and_remove_from_cache()
@@ -141,9 +141,9 @@ def test_u_train_link_only_sample(test_helpers, dialect):
input_table_aliases=["_a", "_b"],
**helper.extra_linker_args(),
)
- linker.debug_mode = True
+ linker._debug_mode = True
- linker.estimate_u_using_random_sampling(max_pairs=max_pairs)
+ linker.training.estimate_u_using_random_sampling(max_pairs=max_pairs)
# count how many pairs we _actually_ generated in random sampling
check_blocking_sql = """
@@ -152,7 +152,7 @@ def test_u_train_link_only_sample(test_helpers, dialect):
pipeline = CTEPipeline()
pipeline.enqueue_sql(check_blocking_sql, "__splink__df_blocked_same_table_count")
- self_table_count = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ self_table_count = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
result = self_table_count.as_record_dict()
self_table_count.drop_table_from_database_and_remove_from_cache()
@@ -266,8 +266,8 @@ def test_u_train_multilink(test_helpers, dialect):
}
linker = helper.Linker(dfs, settings, **helper.extra_linker_args())
- linker.debug_mode = True
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker._debug_mode = True
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
cc_name = linker._settings_obj.comparisons[0]
check_blocking_sql = """
@@ -277,7 +277,7 @@ def test_u_train_multilink(test_helpers, dialect):
pipeline = CTEPipeline()
pipeline.enqueue_sql(check_blocking_sql, "__splink__df_blocked_same_table_count")
- self_table_count = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ self_table_count = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
result = self_table_count.as_record_dict()
self_table_count.drop_table_from_database_and_remove_from_cache()
@@ -298,8 +298,8 @@ def test_u_train_multilink(test_helpers, dialect):
# also check the numbers on a link + dedupe with same inputs
settings["link_type"] = "link_and_dedupe"
linker = helper.Linker(dfs, settings, **helper.extra_linker_args())
- linker.debug_mode = True
- linker.estimate_u_using_random_sampling(max_pairs=1e6)
+ linker._debug_mode = True
+ linker.training.estimate_u_using_random_sampling(max_pairs=1e6)
cc_name = linker._settings_obj.comparisons[0]
check_blocking_sql = """
@@ -309,7 +309,7 @@ def test_u_train_multilink(test_helpers, dialect):
pipeline = CTEPipeline()
pipeline.enqueue_sql(check_blocking_sql, "__splink__df_blocked_same_table_count")
- self_table_count = linker.db_api.sql_pipeline_to_splink_dataframe(pipeline)
+ self_table_count = linker._db_api.sql_pipeline_to_splink_dataframe(pipeline)
result = self_table_count.as_record_dict()
self_table_count.drop_table_from_database_and_remove_from_cache()
@@ -343,9 +343,9 @@ def test_seed_u_outputs(test_helpers, dialect):
linker_2 = helper.Linker(df, settings, **helper.extra_linker_args())
linker_3 = helper.Linker(df, settings, **helper.extra_linker_args())
- linker_1.estimate_u_using_random_sampling(max_pairs=1e3, seed=1)
- linker_2.estimate_u_using_random_sampling(max_pairs=1e3, seed=1)
- linker_3.estimate_u_using_random_sampling(max_pairs=1e3, seed=2)
+ linker_1.training.estimate_u_using_random_sampling(max_pairs=1e3, seed=1)
+ linker_2.training.estimate_u_using_random_sampling(max_pairs=1e3, seed=1)
+ linker_3.training.estimate_u_using_random_sampling(max_pairs=1e3, seed=2)
assert (
linker_1._settings_obj._parameter_estimates_as_records
| \n", + " | cluster_id | \n", + "n_nodes | \n", + "n_edges | \n", + "density | \n", + "cluster_centralisation | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "Q98761652-1 | \n", + "5 | \n", + "8.0 | \n", + "0.800000 | \n", + "0.333333 | \n", + "
| 1 | \n", + "Q10307857-1 | \n", + "11 | \n", + "35.0 | \n", + "0.636364 | \n", + "0.200000 | \n", + "
| 2 | \n", + "Q18910925-1 | \n", + "20 | \n", + "172.0 | \n", + "0.905263 | \n", + "0.105263 | \n", + "
| 3 | \n", + "Q13530025-1 | \n", + "11 | \n", + "32.0 | \n", + "0.581818 | \n", + "0.266667 | \n", + "
| 4 | \n", + "Q15966633-11 | \n", + "3 | \n", + "3.0 | \n", + "1.000000 | \n", + "0.000000 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 21530 | \n", + "Q5006750-7 | \n", + "1 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 21531 | \n", + "Q5166888-13 | \n", + "1 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 21532 | \n", + "Q5546247-8 | \n", + "1 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 21533 | \n", + "Q6698372-5 | \n", + "1 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 21534 | \n", + "Q7794499-6 | \n", + "1 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
21535 rows × 5 columns
\n", + "