{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d729e302",
   "metadata": {},
   "source": [
    "# Specifying and estimating a linkage model\n",
    "\n",
    "We've just seen how to use Splink's exploratory analysis tools to understand our data.  \n",
    "\n",
    "Now it's time to build a linkage model.  This model will make pairwise comparisons of input records and output a match score, which is a prediction of whether the two records represent the same entity (e.g. are the same person).  You can read more about the theory behind probabilistic linkage models [here](https://www.robinlinacre.com/intro_to_probabilistic_linkage/).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa6a9e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Begin by reading in the tutorial data again\n",
    "from splink.duckdb.duckdb_linker import DuckDBLinker\n",
    "import pandas as pd \n",
    "import altair as alt\n",
    "alt.renderers.enable(\"mimetype\")\n",
    "df = pd.read_csv(\"./data/fake_1000.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f104340",
   "metadata": {},
   "source": [
    "##  Specifying a linkage model\n",
    "\n",
    "To produce a match score, `splink` needs to know how to compare the information in pairs records from the input dataset.\n",
    "\n",
    "To be concrete, here is an example pairwise record comparison from our input dataset:\n",
    "\n",
    "\n",
    "|   unique_id | first_name   | surname   | dob        | city   | email               |\n",
    "|------------:|:-------------|:----------|:-----------|:-------|:--------------------|\n",
    "|           1 | Robert       | Allen     | 1971-05-24 | nan    | roberta25@smith.net |\n",
    "|           2 | Rob          | Allen     | 1971-06-24 | London | roberta25@smith.net |\n",
    "\n",
    "What functions should we use to assess the similarity of `Rob` vs. `Robert` in the the `first_name` field?  Should similarity in the `dob` field be computed in the same way, or a different way?\n",
    "\n",
    "Your job as the developer of a linkage model is to decide what comparisons are most appropriate for the types of data you have.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a520392",
   "metadata": {},
   "source": [
    "### Comparisons\n",
    "\n",
    "The concept of a `Comparison` has a specific definition within Splink: it defines how data from one or more input columns is compared, using SQL expressions assess similarity.\n",
    "\n",
    "For example, one `Comparison` may represent how similarity is assessed for a person's date of birth. Another `Comparison` may represent the comparison of a person's name or location.\n",
    "\n",
    "A model will thereby be composed of many `Comparison`s, which between them assess the similarity of all of the columns being used for data linking.  \n",
    "\n",
    "Each `Comparison` contains two or more `ComparisonLevels` which define _n_ discrete gradations of similarity between the input columns within the Comparison.\n",
    "\n",
    "For example, for the date of birth `Comparison` there may be a `ComparisonLevel` for an exact match, another for a one-character difference, and another for all other comparisons.\n",
    "\n",
    "To summarise:\n",
    "\n",
    "```\n",
    "Data Linking Model\n",
    "├─-- Comparison: Date of birth\n",
    "│    ├─-- ComparisonLevel: Exact match\n",
    "│    ├─-- ComparisonLevel: One character difference\n",
    "│    ├─-- ComparisonLevel: All other\n",
    "├─-- Comparison: City\n",
    "│    ├─-- ComparisonLevel: Exact match on city\n",
    "│    ├─-- ComparisonLevel: All other\n",
    "│    etc.\n",
    "```\n",
    "\n",
    "More information about comparisons can be found [here](https://moj-analytical-services.github.io/splink/comparison.html).\n",
    "\n",
    "\n",
    "We will now use these concepts to build a data linking model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02000a24",
   "metadata": {},
   "source": [
    "### Specifying the model using comparisons\n",
    "\n",
    "Splink provides utility functions to help formulate some of the most common comparison types, which we'll make use of in this introductory example.\n",
    "\n",
    "Let's start by looking at a single comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd6143e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison 'Exact match vs. levenshtein at threshold 2 vs. anything else' of first_name.\n",
      "Similarity is assessed using the following ComparisonLevels:\n",
      "    - 'Null' with SQL rule: first_name_l IS NULL OR first_name_r IS NULL\n",
      "    - 'Exact match' with SQL rule: first_name_l = first_name_r\n",
      "    - 'levenshtein <= 2' with SQL rule: levenshtein(first_name_l, first_name_r) <= 2\n",
      "    - 'All other comparisons' with SQL rule: ELSE\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import splink.duckdb.duckdb_comparison_library as cl\n",
    "\n",
    "first_name_comparison = cl.levenshtein_at_thresholds(\"first_name\", 2)\n",
    "print(first_name_comparison.human_readable_description)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b7677a",
   "metadata": {},
   "source": [
    "## Specifying the full settings dictionary\n",
    "\n",
    "`Comparisons` are specified as part of the Splink `settings`, a Python dictionary which controls all of the configuration of a Splink model.\n",
    "\n",
    "Let's take a look at a full settings dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fa0611a",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings = {\n",
    "    \"probability_two_random_records_match\": 4/1000,\n",
    "    \"link_type\": \"dedupe_only\",\n",
    "    \"comparisons\": [\n",
    "        cl.levenshtein_at_thresholds(\"first_name\", 2),\n",
    "        cl.levenshtein_at_thresholds(\"surname\"),\n",
    "        cl.levenshtein_at_thresholds(\"dob\"),\n",
    "        cl.exact_match(\"city\", term_frequency_adjustments=True),\n",
    "        cl.levenshtein_at_thresholds(\"email\"),\n",
    "    ],\n",
    "    \"blocking_rules_to_generate_predictions\": [\n",
    "        \"l.first_name = r.first_name\",\n",
    "        \"l.surname = r.surname\",\n",
    "    ],\n",
    "    \"retain_matching_columns\": True,\n",
    "    \"retain_intermediate_calculation_columns\": True,\n",
    "    \"additional_columns_to_retain\": [\"cluster\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "657a1fb8",
   "metadata": {},
   "source": [
    "In words, this setting dictionary says:\n",
    "\n",
    "* We have set a starting value for `probability_two_random_records_match` to 4/1000.  This is a starting value - we will later estimate this parameter\n",
    "* We are performing a `dedupe_only` (the other options are `link_only`, or `link_and_dedupe`, which may be used if there are multiple input datasets)\n",
    "* When comparing records, we will use information from the `first_name`, `surname`, `dob`, `city` and `email` columns to compute a match score.\n",
    "* The `blocking_rules_to_generate_predictions` states that we will only check for duplicates amongst records where either the `first_name` or `surname` is identical.\n",
    "* We have enabled term frequency adjustments for the 'city' column, because some values (e.g. `London`) appear much more frequently than others\n",
    "* We will retain the `cluster` column in the results even though this is not used as part of comparisons. Later we'll be able to use this to compare Splink scores to the ground truth.\n",
    "* We have set `retain_intermediate_calculation_columns` and `additional_columns_to_retain` to `True`  so that Splink outputs additional information that helps the user understand the calculations. If they were `False`, the computations would run faster."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afa31386",
   "metadata": {},
   "source": [
    "## Estimate the parameters of the model\n",
    "\n",
    "Now that we have specified our linkage model, we want to estimate its `m` and `u` parameters. \n",
    "\n",
    "- The `m` values are the proportion of records falling into each `ComparisonLevel` amongst truly *matching* records\n",
    "\n",
    "- The `u` values are the proportion of records falling into each `ComparisonLevel` amongst truly *non-matching* records\n",
    "\n",
    "You can read more about the theory of what these mean [here](https://www.robinlinacre.com/maths_of_fellegi_sunter/).\n",
    "\n",
    "We begin by using `estimate_u_using_random_sampling` method to compute the `u` values of the model.  This is a simple direct estimation algorithm.  The larger the random sample, the more accurate the predictions.  You control this using the `target_rows` parameter. For large datasets, we recommend using at least 10 million - but the higher the better and 1 billion is often appropriate for larger datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8d49e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "----- 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",
      "    - dob (no m values are trained).\n",
      "    - city (no m values are trained).\n",
      "    - email (no m values are trained).\n"
     ]
    }
   ],
   "source": [
    "linker = DuckDBLinker(df, settings)\n",
    "linker.estimate_u_using_random_sampling(target_rows=1e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73921b7",
   "metadata": {},
   "source": [
    "We then use the expectation maximisation algorithm to train the `m` values.\n",
    "\n",
    "This algorithm estimates the `m` values by generating pairwise record comparisons, and using them to maximise a likelihood function. \n",
    "\n",
    "Each estimation pass requires the user to configure an estimation blocking rule to reduce the number of record comparisons generated to a managable level.\n",
    "\n",
    "\n",
    "In our first estimation pass, we block on `first_name` and `surname`, meaning we will generate all record comparisons that have `first_name` and `surname` exactly equal.   \n",
    "\n",
    "Recall we are trying to estimate the `m` values of the model, i.e. proportion of records falling into each `ComparisonLevel` amongst truly matching records.\n",
    "\n",
    "This means that, in this training session, we cannot estimate parameter estimates for the `first_name` or `surname` columns, since we have forced them to be equal 100% of the time.\n",
    "\n",
    "We can, however, estimate parameter estimates for all of the other columns.  The output messages produced by Splink confirm this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "098f0a40",
   "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",
      "    - 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",
      "    - surname\n",
      "\n",
      "Iteration 1: Largest change in params was -0.531 in the m_probability of dob, level `Exact match`\n",
      "Iteration 2: Largest change in params was 0.0331 in probability_two_random_records_match\n",
      "Iteration 3: Largest change in params was 0.0128 in probability_two_random_records_match\n",
      "Iteration 4: Largest change in params was 0.00635 in probability_two_random_records_match\n",
      "Iteration 5: Largest change in params was 0.00363 in probability_two_random_records_match\n",
      "Iteration 6: Largest change in params was 0.00225 in probability_two_random_records_match\n",
      "Iteration 7: Largest change in params was 0.00146 in probability_two_random_records_match\n",
      "Iteration 8: Largest change in params was 0.000987 in probability_two_random_records_match\n",
      "Iteration 9: Largest change in params was 0.000681 in probability_two_random_records_match\n",
      "Iteration 10: Largest change in params was 0.000478 in probability_two_random_records_match\n",
      "Iteration 11: Largest change in params was 0.000339 in probability_two_random_records_match\n",
      "Iteration 12: Largest change in params was 0.000242 in probability_two_random_records_match\n",
      "Iteration 13: Largest change in params was 0.000174 in probability_two_random_records_match\n",
      "Iteration 14: Largest change in params was 0.000126 in probability_two_random_records_match\n",
      "Iteration 15: Largest change in params was 9.12e-05 in probability_two_random_records_match\n",
      "\n",
      "EM converged after 15 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"
     ]
    }
   ],
   "source": [
    "training_blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n",
    "training_session_fname_sname = linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92bd4a31",
   "metadata": {},
   "source": [
    "In a second estimation pass, we block on dob. This allows us to estimate parameters for the `first_name` and `surname` comparisons.\n",
    "\n",
    "Between the two estimation passes, we now have parameter estimates for all comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac8d3264",
   "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",
      "    - 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",
      "\n",
      "Iteration 1: Largest change in params was 0.48 in probability_two_random_records_match\n",
      "Iteration 2: Largest change in params was 0.151 in probability_two_random_records_match\n",
      "Iteration 3: Largest change in params was 0.0477 in probability_two_random_records_match\n",
      "Iteration 4: Largest change in params was 0.0177 in probability_two_random_records_match\n",
      "Iteration 5: Largest change in params was 0.00797 in probability_two_random_records_match\n",
      "Iteration 6: Largest change in params was 0.004 in probability_two_random_records_match\n",
      "Iteration 7: Largest change in params was 0.00213 in probability_two_random_records_match\n",
      "Iteration 8: Largest change in params was 0.00117 in probability_two_random_records_match\n",
      "Iteration 9: Largest change in params was 0.00065 in probability_two_random_records_match\n",
      "Iteration 10: Largest change in params was 0.000366 in probability_two_random_records_match\n",
      "Iteration 11: Largest change in params was 0.000207 in probability_two_random_records_match\n",
      "Iteration 12: Largest change in params was 0.000117 in probability_two_random_records_match\n",
      "Iteration 13: Largest change in params was 6.67e-05 in probability_two_random_records_match\n",
      "\n",
      "EM converged after 13 iterations\n",
      "\n",
      "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
     ]
    }
   ],
   "source": [
    "training_blocking_rule = \"l.dob = r.dob\"\n",
    "training_session_dob = linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efdb0c5f",
   "metadata": {},
   "source": [
    "Note that Splink includes other algorithms for estimating m and u values, which are documented [here](https://moj-analytical-services.github.io/splink/linkerest.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38355535",
   "metadata": {},
   "source": [
    "## Visualising model parameters\n",
    "\n",
    "The final estimated match weights can be viewed in the match weights chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a1e15cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "$schema": "https://vega.github.io/schema/vega-lite/v5.2.json",
       "config": {
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         "width": 400
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       "data": {
        "values": [
         {
          "bayes_factor": 0.009833850927123876,
          "bayes_factor_description": "The probability that two random records drawn at random match is 0.010 or one in  102.7 records.This is equivalent to a starting match weight of -6.668.",
          "comparison_name": "probability_two_random_records_match",
          "comparison_sort_order": -1,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "",
          "log2_bayes_factor": -6.66802779937637,
          "m_probability": null,
          "m_probability_description": null,
          "max_comparison_vector_value": 0,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": null,
          "tf_adjustment_column": null,
          "tf_adjustment_weight": null,
          "u_probability": null,
          "u_probability_description": null
         },
         {
          "bayes_factor": 85.5492422084233,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 85.55 times more likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 6.41868317032687,
          "m_probability": 0.49563564273680927,
          "m_probability_description": "Amongst matching record comparisons, 49.56% of records are in the exact match comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "first_name_l = first_name_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0057935713975033705,
          "u_probability_description": "Amongst non-matching record comparisons, 0.58% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 26.751309863975084,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 26.75 times more likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 4.741537628943031,
          "m_probability": 0.27072063394450885,
          "m_probability_description": "Amongst matching record comparisons, 27.07% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(first_name_l, first_name_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.010119901990634016,
          "u_probability_description": "Amongst non-matching record comparisons, 1.01% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.23742193089778274,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.21 times less likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.074474890589354,
          "m_probability": 0.23364372331868066,
          "m_probability_description": "Amongst matching record comparisons, 23.36% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9840865266118626,
          "u_probability_description": "Amongst non-matching record comparisons, 98.41% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 90.1703772843795,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 90.17 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 3,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 6.494581652935134,
          "m_probability": 0.4409309402659144,
          "m_probability_description": "Amongst matching record comparisons, 44.09% of records are in the exact match comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "surname_l = surname_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.004889975550122249,
          "u_probability_description": "Amongst non-matching record comparisons, 0.49% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 79.15014455822713,
          "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 79.15 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 1",
          "log2_bayes_factor": 6.306520080161341,
          "m_probability": 0.187141318174158,
          "m_probability_description": "Amongst matching record comparisons, 18.71% of records are in the levenshtein <= 1 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(surname_l, surname_r) <= 1",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.002364383782476692,
          "u_probability_description": "Amongst non-matching record comparisons, 0.24% of records are in the levenshtein <= 1 comparison level"
         },
         {
          "bayes_factor": 22.60384847607212,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 22.60 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 4.498496518176822,
          "m_probability": 0.11323146703102362,
          "m_probability_description": "Amongst matching record comparisons, 11.32% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(surname_l, surname_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.005009388872469557,
          "u_probability_description": "Amongst non-matching record comparisons, 0.50% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.26190825137661683,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  3.82 times less likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.9328665826115932,
          "m_probability": 0.2586962745289042,
          "m_probability_description": "Amongst matching record comparisons, 25.87% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9877362517949315,
          "u_probability_description": "Amongst non-matching record comparisons, 98.77% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 224.62100960803812,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 224.62 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 3,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 7.81134906426195,
          "m_probability": 0.39258086363927386,
          "m_probability_description": "Amongst matching record comparisons, 39.26% of records are in the exact match comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "dob_l = dob_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0017477477477477479,
          "u_probability_description": "Amongst non-matching record comparisons, 0.17% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 93.58478813959559,
          "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 93.58 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 1",
          "log2_bayes_factor": 6.5482021390219165,
          "m_probability": 0.14988554656992287,
          "m_probability_description": "Amongst matching record comparisons, 14.99% of records are in the levenshtein <= 1 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(dob_l, dob_r) <= 1",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0016016016016016017,
          "u_probability_description": "Amongst non-matching record comparisons, 0.16% of records are in the levenshtein <= 1 comparison level"
         },
         {
          "bayes_factor": 13.323287650611148,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 13.32 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 3.735878220238321,
          "m_probability": 0.2067443495092833,
          "m_probability_description": "Amongst matching record comparisons, 20.67% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(dob_l, dob_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.015517517517517518,
          "u_probability_description": "Amongst non-matching record comparisons, 1.55% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.25561183473710014,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  3.91 times less likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.9679734607884474,
          "m_probability": 0.25078924028151967,
          "m_probability_description": "Amongst matching record comparisons, 25.08% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9811331331331331,
          "u_probability_description": "Amongst non-matching record comparisons, 98.11% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 10.257652575272894,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 10.26 times more likely to be a match",
          "comparison_name": "city",
          "comparison_sort_order": 3,
          "comparison_vector_value": 1,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 3.3586287083381365,
          "m_probability": 0.5656846255360627,
          "m_probability_description": "Amongst matching record comparisons, 56.57% of records are in the exact match comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "city_l = city_r",
          "tf_adjustment_column": "city",
          "tf_adjustment_weight": 1,
          "u_probability": 0.0551475711801453,
          "u_probability_description": "Amongst non-matching record comparisons, 5.51% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 0.45966477009156764,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  2.18 times less likely to be a match",
          "comparison_name": "city",
          "comparison_sort_order": 3,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.1213459964112529,
          "m_probability": 0.43431537446393775,
          "m_probability_description": "Amongst matching record comparisons, 43.43% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9448524288198547,
          "u_probability_description": "Amongst non-matching record comparisons, 94.49% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 255.4199334194977,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 255.42 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 3,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 7.996727309659809,
          "m_probability": 0.5603584650366957,
          "m_probability_description": "Amongst matching record comparisons, 56.04% of records are in the exact match comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "email_l = email_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0021938713143283602,
          "u_probability_description": "Amongst non-matching record comparisons, 0.22% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 235.45821520840934,
          "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 235.46 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 1",
          "log2_bayes_factor": 7.879327249357033,
          "m_probability": 0.17269329250389986,
          "m_probability_description": "Amongst matching record comparisons, 17.27% of records are in the levenshtein <= 1 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(email_l, email_r) <= 1",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0007334349848487773,
          "u_probability_description": "Amongst non-matching record comparisons, 0.07% of records are in the levenshtein <= 1 comparison level"
         },
         {
          "bayes_factor": 206.63645011925308,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 206.64 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 7.690950953987196,
          "m_probability": 0.12828947158266213,
          "m_probability_description": "Amongst matching record comparisons, 12.83% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(email_l, email_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.000620846281034272,
          "u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.13915250519710004,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  7.19 times less likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.845261212787023,
          "m_probability": 0.13865877087674205,
          "m_probability_description": "Amongst matching record comparisons, 13.87% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9964518474197885,
          "u_probability_description": "Amongst non-matching record comparisons, 99.65% of records are in the all other comparisons comparison level"
         }
        ]
       },
       "resolve": {
        "axis": {
         "y": "independent"
        },
        "scale": {
         "y": "independent"
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       },
       "selection": {
        "zoom_selector": {
         "bind": "scales",
         "encodings": [
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         ],
         "type": "interval"
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       },
       "title": {
        "subtitle": "Use mousewheel to zoom",
        "text": "Model parameters (components of final match weight)"
       },
       "vconcat": [
        {
         "encoding": {
          "color": {
           "field": "log2_bayes_factor",
           "scale": {
            "domain": [
             -10,
             0,
             10
            ],
            "range": [
             "red",
             "orange",
             "green"
            ]
           },
           "title": "Match weight",
           "type": "quantitative"
          },
          "tooltip": [
           {
            "field": "comparison_name",
            "title": "Comparison name",
            "type": "nominal"
           },
           {
            "field": "probability_two_random_records_match",
            "format": ".4f",
            "title": "Probability two random records match",
            "type": "nominal"
           },
           {
            "field": "log2_bayes_factor",
            "format": ",.4f",
            "title": "Equivalent match weight",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor_description",
            "title": "Match weight description",
            "type": "nominal"
           }
          ],
          "x": {
           "axis": {
            "domain": false,
            "labels": false,
            "ticks": false,
            "title": ""
           },
           "field": "log2_bayes_factor",
           "scale": {
            "domain": [
             -10,
             10
            ]
           },
           "type": "quantitative"
          },
          "y": {
           "axis": {
            "title": "Prior (starting) match weight",
            "titleAlign": "right",
            "titleAngle": 0,
            "titleFontWeight": "normal"
           },
           "field": "label_for_charts",
           "sort": {
            "field": "comparison_vector_value",
            "order": "descending"
           },
           "type": "nominal"
          }
         },
         "height": 30,
         "mark": {
          "clip": true,
          "height": 20,
          "type": "bar"
         },
         "selection": {
          "zoom_selector": {
           "bind": "scales",
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           "type": "interval"
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         },
         "transform": [
          {
           "filter": "(datum.comparison_name == 'probability_two_random_records_match')"
          }
         ]
        },
        {
         "encoding": {
          "color": {
           "field": "log2_bayes_factor",
           "scale": {
            "domain": [
             -10,
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             10
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            "range": [
             "red",
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             "green"
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           },
           "title": "Match weight",
           "type": "quantitative"
          },
          "row": {
           "field": "comparison_name",
           "header": {
            "labelAlign": "left",
            "labelAnchor": "middle",
            "labelAngle": 0
           },
           "sort": {
            "field": "comparison_sort_order"
           },
           "type": "nominal"
          },
          "tooltip": [
           {
            "field": "comparison_name",
            "title": "Comparison name",
            "type": "nominal"
           },
           {
            "field": "label_for_charts",
            "title": "Label",
            "type": "ordinal"
           },
           {
            "field": "sql_condition",
            "title": "SQL condition",
            "type": "nominal"
           },
           {
            "field": "m_probability",
            "format": ".4f",
            "title": "M probability",
            "type": "quantitative"
           },
           {
            "field": "u_probability",
            "format": ".4f",
            "title": "U probability",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor",
            "format": ",.4f",
            "title": "Bayes factor = m/u",
            "type": "quantitative"
           },
           {
            "field": "log2_bayes_factor",
            "format": ",.4f",
            "title": "Match weight = log2(m/u)",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor_description",
            "title": "Match weight description",
            "type": "nominal"
           }
          ],
          "x": {
           "axis": {
            "title": "Comparison level match weight = log2(m/u)"
           },
           "field": "log2_bayes_factor",
           "scale": {
            "domain": [
             -10,
             10
            ]
           },
           "type": "quantitative"
          },
          "y": {
           "axis": {
            "title": null
           },
           "field": "label_for_charts",
           "sort": {
            "field": "comparison_vector_value",
            "order": "descending"
           },
           "type": "nominal"
          }
         },
         "mark": {
          "clip": true,
          "type": "bar"
         },
         "resolve": {
          "axis": {
           "y": "independent"
          },
          "scale": {
           "y": "independent"
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         },
         "selection": {
          "zoom_selector": {
           "bind": "scales",
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           "type": "interval"
          }
         },
         "transform": [
          {
           "filter": "(datum.comparison_name != 'probability_two_random_records_match')"
          }
         ]
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 4 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linker.match_weights_chart()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8576c042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v4+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
       "config": {
        "header": {
         "title": null
        },
        "title": {
         "anchor": "middle",
         "offset": 10
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "values": [
         {
          "bayes_factor": 85.5492422084233,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 85.55 times more likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 6.41868317032687,
          "m_probability": 0.49563564273680927,
          "m_probability_description": "Amongst matching record comparisons, 49.56% of records are in the exact match comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "first_name_l = first_name_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0057935713975033705,
          "u_probability_description": "Amongst non-matching record comparisons, 0.58% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 26.751309863975084,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 26.75 times more likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 4.741537628943031,
          "m_probability": 0.27072063394450885,
          "m_probability_description": "Amongst matching record comparisons, 27.07% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(first_name_l, first_name_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.010119901990634016,
          "u_probability_description": "Amongst non-matching record comparisons, 1.01% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.23742193089778274,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.21 times less likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.074474890589354,
          "m_probability": 0.23364372331868066,
          "m_probability_description": "Amongst matching record comparisons, 23.36% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9840865266118626,
          "u_probability_description": "Amongst non-matching record comparisons, 98.41% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 90.1703772843795,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 90.17 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 3,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 6.494581652935134,
          "m_probability": 0.4409309402659144,
          "m_probability_description": "Amongst matching record comparisons, 44.09% of records are in the exact match comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "surname_l = surname_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.004889975550122249,
          "u_probability_description": "Amongst non-matching record comparisons, 0.49% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 79.15014455822713,
          "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 79.15 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 1",
          "log2_bayes_factor": 6.306520080161341,
          "m_probability": 0.187141318174158,
          "m_probability_description": "Amongst matching record comparisons, 18.71% of records are in the levenshtein <= 1 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(surname_l, surname_r) <= 1",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.002364383782476692,
          "u_probability_description": "Amongst non-matching record comparisons, 0.24% of records are in the levenshtein <= 1 comparison level"
         },
         {
          "bayes_factor": 22.60384847607212,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 22.60 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 4.498496518176822,
          "m_probability": 0.11323146703102362,
          "m_probability_description": "Amongst matching record comparisons, 11.32% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(surname_l, surname_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.005009388872469557,
          "u_probability_description": "Amongst non-matching record comparisons, 0.50% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.26190825137661683,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  3.82 times less likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.9328665826115932,
          "m_probability": 0.2586962745289042,
          "m_probability_description": "Amongst matching record comparisons, 25.87% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9877362517949315,
          "u_probability_description": "Amongst non-matching record comparisons, 98.77% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 224.62100960803812,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 224.62 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 3,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 7.81134906426195,
          "m_probability": 0.39258086363927386,
          "m_probability_description": "Amongst matching record comparisons, 39.26% of records are in the exact match comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "dob_l = dob_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0017477477477477479,
          "u_probability_description": "Amongst non-matching record comparisons, 0.17% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 93.58478813959559,
          "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 93.58 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 1",
          "log2_bayes_factor": 6.5482021390219165,
          "m_probability": 0.14988554656992287,
          "m_probability_description": "Amongst matching record comparisons, 14.99% of records are in the levenshtein <= 1 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(dob_l, dob_r) <= 1",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0016016016016016017,
          "u_probability_description": "Amongst non-matching record comparisons, 0.16% of records are in the levenshtein <= 1 comparison level"
         },
         {
          "bayes_factor": 13.323287650611148,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 13.32 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 3.735878220238321,
          "m_probability": 0.2067443495092833,
          "m_probability_description": "Amongst matching record comparisons, 20.67% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(dob_l, dob_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.015517517517517518,
          "u_probability_description": "Amongst non-matching record comparisons, 1.55% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.25561183473710014,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  3.91 times less likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.9679734607884474,
          "m_probability": 0.25078924028151967,
          "m_probability_description": "Amongst matching record comparisons, 25.08% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9811331331331331,
          "u_probability_description": "Amongst non-matching record comparisons, 98.11% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 10.257652575272894,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 10.26 times more likely to be a match",
          "comparison_name": "city",
          "comparison_sort_order": 3,
          "comparison_vector_value": 1,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 3.3586287083381365,
          "m_probability": 0.5656846255360627,
          "m_probability_description": "Amongst matching record comparisons, 56.57% of records are in the exact match comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "city_l = city_r",
          "tf_adjustment_column": "city",
          "tf_adjustment_weight": 1,
          "u_probability": 0.0551475711801453,
          "u_probability_description": "Amongst non-matching record comparisons, 5.51% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 0.45966477009156764,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  2.18 times less likely to be a match",
          "comparison_name": "city",
          "comparison_sort_order": 3,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -1.1213459964112529,
          "m_probability": 0.43431537446393775,
          "m_probability_description": "Amongst matching record comparisons, 43.43% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9448524288198547,
          "u_probability_description": "Amongst non-matching record comparisons, 94.49% of records are in the all other comparisons comparison level"
         },
         {
          "bayes_factor": 255.4199334194977,
          "bayes_factor_description": "If comparison level is `exact match` then comparison is 255.42 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 3,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "Exact match",
          "log2_bayes_factor": 7.996727309659809,
          "m_probability": 0.5603584650366957,
          "m_probability_description": "Amongst matching record comparisons, 56.04% of records are in the exact match comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "email_l = email_r",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0021938713143283602,
          "u_probability_description": "Amongst non-matching record comparisons, 0.22% of records are in the exact match comparison level"
         },
         {
          "bayes_factor": 235.45821520840934,
          "bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 235.46 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 2,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 1",
          "log2_bayes_factor": 7.879327249357033,
          "m_probability": 0.17269329250389986,
          "m_probability_description": "Amongst matching record comparisons, 17.27% of records are in the levenshtein <= 1 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(email_l, email_r) <= 1",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.0007334349848487773,
          "u_probability_description": "Amongst non-matching record comparisons, 0.07% of records are in the levenshtein <= 1 comparison level"
         },
         {
          "bayes_factor": 206.63645011925308,
          "bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 206.64 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 1,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "levenshtein <= 2",
          "log2_bayes_factor": 7.690950953987196,
          "m_probability": 0.12828947158266213,
          "m_probability_description": "Amongst matching record comparisons, 12.83% of records are in the levenshtein <= 2 comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "levenshtein(email_l, email_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.000620846281034272,
          "u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.13915250519710004,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  7.19 times less likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
          "is_null_level": false,
          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.845261212787023,
          "m_probability": 0.13865877087674205,
          "m_probability_description": "Amongst matching record comparisons, 13.87% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 3,
          "probability_two_random_records_match": 0.009738088021208104,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9964518474197885,
          "u_probability_description": "Amongst non-matching record comparisons, 99.65% of records are in the all other comparisons comparison level"
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        ]
       },
       "hconcat": [
        {
         "encoding": {
          "color": {
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          "row": {
           "field": "comparison_name",
           "header": {
            "labelAlign": "left",
            "labelAnchor": "middle",
            "labelAngle": 0
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           "sort": {
            "field": "comparison_sort_order"
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           "type": "nominal"
          },
          "tooltip": [
           {
            "field": "m_probability_description",
            "title": "m probability description",
            "type": "nominal"
           },
           {
            "field": "comparison_name",
            "title": "Comparison column name",
            "type": "nominal"
           },
           {
            "field": "label_for_charts",
            "title": "Label",
            "type": "ordinal"
           },
           {
            "field": "sql_condition",
            "title": "SQL condition",
            "type": "nominal"
           },
           {
            "field": "m_probability",
            "format": ".4p",
            "title": "m probability",
            "type": "quantitative"
           },
           {
            "field": "u_probability",
            "format": ".4p",
            "title": "u probability",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor",
            "format": ",.4f",
            "title": "Bayes factor = m/u",
            "type": "quantitative"
           },
           {
            "field": "log2_bayes_factor",
            "format": ",.4f",
            "title": "Match weight = log2(m/u)",
            "type": "quantitative"
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          ],
          "x": {
           "axis": {
            "title": "Proportion of record comparisons"
           },
           "field": "m_probability",
           "type": "quantitative"
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          "y": {
           "axis": {
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           },
           "field": "label_for_charts",
           "sort": {
            "field": "comparison_vector_value",
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           "type": "nominal"
          }
         },
         "height": 50,
         "mark": "bar",
         "resolve": {
          "scale": {
           "y": "independent"
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         },
         "title": {
          "fontSize": 12,
          "fontWeight": "bold",
          "text": "Amongst matching record comparisons:"
         },
         "transform": [
          {
           "filter": "(datum.bayes_factor != 'no-op filter due to vega lite issue 4680')"
          }
         ],
         "width": 150
        },
        {
         "encoding": {
          "color": {
           "value": "red"
          },
          "row": {
           "field": "comparison_name",
           "header": {
            "labels": false
           },
           "sort": {
            "field": "comparison_sort_order"
           },
           "type": "nominal"
          },
          "tooltip": [
           {
            "field": "u_probability_description",
            "title": "u probability description",
            "type": "nominal"
           },
           {
            "field": "comparison_name",
            "title": "Comparison column name",
            "type": "nominal"
           },
           {
            "field": "label_for_charts",
            "title": "Label",
            "type": "ordinal"
           },
           {
            "field": "sql_condition",
            "title": "SQL condition",
            "type": "nominal"
           },
           {
            "field": "m_probability",
            "format": ".4p",
            "title": "m probability",
            "type": "quantitative"
           },
           {
            "field": "u_probability",
            "format": ".4p",
            "title": "u probability",
            "type": "quantitative"
           },
           {
            "field": "bayes_factor",
            "format": ",.4f",
            "title": "Bayes factor = m/u",
            "type": "quantitative"
           },
           {
            "field": "log2_bayes_factor",
            "format": ",.4f",
            "title": "Match weight = log2(m/u)",
            "type": "quantitative"
           }
          ],
          "x": {
           "axis": {
            "title": "Proportion of record comparisons"
           },
           "field": "u_probability",
           "type": "quantitative"
          },
          "y": {
           "axis": {
            "title": null
           },
           "field": "label_for_charts",
           "sort": {
            "field": "comparison_vector_value",
            "order": "descending"
           },
           "type": "nominal"
          }
         },
         "height": 50,
         "mark": "bar",
         "resolve": {
          "scale": {
           "y": "independent"
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         },
         "title": {
          "fontSize": 12,
          "fontWeight": "bold",
          "text": "Amongst non-matching record comparisons:"
         },
         "transform": [
          {
           "filter": "(datum.bayes_factor != 'no-op filter2 due to vega lite issue 4680')"
          }
         ],
         "width": 150
        }
       ],
       "title": {
        "subtitle": "(m and u probabilities)",
        "text": "Proportion of record comparisons in each comparison level by match status"
       }
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 4 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linker.m_u_parameters_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c44fcc26",
   "metadata": {},
   "source": [
    "### Saving the model\n",
    "\n",
    "Finally we can save the model, including our estimated parameters, to a `.json` file, so we can use it in the next tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "992703a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "linker.save_settings_to_json(\"./demo_settings/saved_model_from_demo.json\", overwrite=True)"
   ]
  }
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