{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choosing blocking rules to optimise runtime\n",
    "\n",
    "\n",
    "To link records, we need to compare pairs of records, and decide which pairs are matches and non matches.\n",
    "\n",
    "For most large datasets, it is computationally intractable to compare every row with every other row, since the number of comparisons rises quadratically with the number of records.  \n",
    "\n",
    "Instead we rely on blocking rules, which specify which pairwise comparisons to generate.  For example, we could generate the subset of pairwise comparisons where either first name or surname matches.\n",
    "\n",
    "This is part of a two step process to link data:\n",
    "\n",
    "1.  Use blocking rules to generate candidate pairwise record comparisons\n",
    "\n",
    "2.  Use a probabilistic linkage model to score these candidate pairs, to determine which ones should be linked\n",
    "\n",
    "**Blocking rules are the most important determinant of the performance of your linkage job**.  \n",
    "\n",
    "When deciding on your blocking rules, you're trading off accuracy for performance:\n",
    "\n",
    "- If your rules are too loose, your linkage job may fail.  \n",
    "- If they're too tight, you may miss some valid links. \n",
    "\n",
    "This tutorial clarifies what blocking rules are, and how to choose good rules.\n",
    "\n",
    "\n",
    "## Blocking rules in Splink\n",
    "\n",
    "In Splink, blocking rules are specified as SQL expressions. \n",
    "\n",
    "For example, to generate the subset of record comparisons where the first name matches, we can specify the following blocking rule:\n",
    "\n",
    "`l.first_name = r.first_name`\n",
    "\n",
    "Since blocking rules are SQL expressions, they can be arbitrarily complex.  For example, you could create record comparisons where the initial of the first name and the surname match with the following rule:\n",
    "\n",
    "`substr(l.first_name, 1,1) = substr(r.first_name, 1,1) and l.surname = r.surname`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Devising effective blocking rules \n",
    "\n",
    "\n",
    "The aims of your blocking rules are twofold:\n",
    "1. Eliminate enough non-matching comparison pairs so your record linkage job is small enough to compute\n",
    "2. Eliminate as few truly matching pairs as possible (ideally none)\n",
    "\n",
    "It is usually impossible to find a single blocking rule which achieves both aims, so we recommend using multiple blocking rules.  \n",
    "\n",
    "When we specify multiple blocking rules, Splink will generate all comparison pairs that meet any one of the rules.\n",
    "\n",
    "For example, consider the following blocking rule:\n",
    "\n",
    "`l.first_name = r.first_name and l.dob = r.dob`\n",
    "\n",
    "This rule is likely to be effective in reducing the number of comparison pairs.  It will retain all truly matching pairs, except those with errors or nulls in either the `first_name` or `dob` fields.\n",
    "\n",
    "Now consider a second blocking rule:\n",
    "\n",
    "`l.email and r.email`.\n",
    "\n",
    "This will retain all truly matching pairs, except those with errors or nulls in the `email` column.\n",
    "\n",
    "\n",
    "Individually, these blocking rules are problematic because they exclude true matches where the records contain typos of certain types.  But between them, they might do quite a good job.  \n",
    "\n",
    "For a true match to be eliminated by the use of these two blocking rules, it would have to have an error in _both_  email AND (first name or date of birth).  \n",
    "\n",
    "This is not completely implausible, but it is significantly less likely than if we'd just used a single rule.\n",
    "\n",
    "More generally, we can often specify multiple blocking rules such that it becomes highly implausible that a true match would not meet at least one of these blocking critera.  This is the recommended approach in Splink.  Generally we would recommend between about 3 and 10, though even more is possible.\n",
    "\n",
    "The question then becomes how to choose what to put in this list.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splink tools to help choose your blocking rules\n",
    "\n",
    "Splink contains a number of tools to help you choose effective blocking rules.  Let's try them out, using our small test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import altair as alt\n",
    "alt.renderers.enable('mimetype')\n",
    "\n",
    "df = pd.read_csv(\"./data/fake_1000.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting the number of comparisons created by a single blocking rule\n",
    "\n",
    "On large datasets, some blocking rules imply the creation of trillions of record comparisons, which would cause a linkage job to fail.\n",
    "\n",
    "Before using a blocking rule in a linkage job, it's therefore a good idea to count the number of records it generates to ensure it is not too loose:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of comparisons generated by 'substr(l.first_name,1,1) = substr(r.first_name,1,1) and l.surname = r.surname': 473\n",
      "Number of comparisons generated by 'l.surname = r.surname': 1,638\n",
      "Number of comparisons generated by 'l.email = r.email': 682\n",
      "Number of comparisons generated by 'l.city = r.city and l.first_name = r.first_name': 315\n"
     ]
    }
   ],
   "source": [
    "from splink.duckdb.duckdb_linker import DuckDBLinker\n",
    "settings = {\"link_type\": \"dedupe_only\"}\n",
    "linker = DuckDBLinker(df, settings)\n",
    "\n",
    "blocking_rule_1 = \"substr(l.first_name,1,1) = substr(r.first_name,1,1) and l.surname = r.surname\"\n",
    "count = linker.count_num_comparisons_from_blocking_rule(blocking_rule_1)\n",
    "print(f\"Number of comparisons generated by '{blocking_rule_1}': {count:,.0f}\")\n",
    "\n",
    "blocking_rule_2 = \"l.surname = r.surname\"\n",
    "count = linker.count_num_comparisons_from_blocking_rule(blocking_rule_2)\n",
    "print(f\"Number of comparisons generated by '{blocking_rule_2}': {count:,.0f}\")\n",
    "\n",
    "blocking_rule_3 = \"l.email = r.email\"\n",
    "count = linker.count_num_comparisons_from_blocking_rule(blocking_rule_3)\n",
    "print(f\"Number of comparisons generated by '{blocking_rule_3}': {count:,.0f}\")\n",
    "\n",
    "blocking_rule_3 = \"l.city = r.city and l.first_name = r.first_name\"\n",
    "count = linker.count_num_comparisons_from_blocking_rule(blocking_rule_3)\n",
    "print(f\"Number of comparisons generated by '{blocking_rule_3}': {count:,.0f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum number of comparisons that you can compute will be affected by your choice of SQL backend, and how powerful your computer is.\n",
    "\n",
    "For linkages in DuckDB on a standard laptop, we suggest using blocking rules that create no more than about 20 million comparisons.  For Spark and Athena, try starting with fewer than a a billion comparisons, before scaling up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting the number of comparisons created by a list of blocking rules\n",
    "\n",
    "As noted above, it's usually a good idea to use multiple blocking rules.  It's therefore useful to know how many record comparisons will be generated when these rules are applied.\n",
    "\n",
    "Since the same record comparison may be created by several blocking rules, and Splink automatically deduplicates these comparisons, we cannot simply total the number of comparisons generated by each rule individually.  \n",
    "\n",
    "Splink provides a chart that shows the marginal (additional) comparisons generated by each blocking rule, after deduplication:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocking_rules = [blocking_rule_1, blocking_rule_2, blocking_rule_3]\n",
    "linker.cumulative_num_comparisons_from_blocking_rules_chart(blocking_rules)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding why certain blocking rules create large numbers of comparisons\n",
    "\n",
    "Finally, we can use the `profile_columns` function we saw in the previous tutorial to understand a specific blocking rule in more depth.\n",
    "\n",
    "Suppose we're interested in blocking on city and first initial.  \n",
    "\n",
    "Within each distinct value of `(city, first initial)`, all possible pairwise comparisons will be generated.\n",
    "\n",
    "So for instance, if there are 15 distinct records with `London,J` then these records will result in `n(n-1)/2 = 105` pairwise comparisons being generated.\n",
    "\n",
    "In a larger dataset, we might observe 10,000 `London,J` records, which would then be responsible for `49,995,000` comparisons.  \n",
    "\n",
    "These high-frequency values therefore have a disproportionate influence on the overall number of pairwise comparisons, and so it can be useful to analyse skew, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
        {
         "hconcat": [
          {
           "data": {
            "values": [
             {
              "distinct_value_count": 352,
              "group_name": "city_left_first_name_1_",
              "percentile_ex_nulls": 0.9777448177337646,
              "percentile_inc_nulls": 0.9850000143051147,
              "sum_tokens_in_value_count_group": 15,
              "total_non_null_rows": 674,
              "total_rows_inc_nulls": 1000,
              "value_count": 15
             },
             {
              "distinct_value_count": 352,
              "group_name": "city_left_first_name_1_",
              "percentile_ex_nulls": 0.924332320690155,
              "percentile_inc_nulls": 0.9490000009536743,
              "sum_tokens_in_value_count_group": 36,
              "total_non_null_rows": 674,
              "total_rows_inc_nulls": 1000,
              "value_count": 12
             },
             {
              "distinct_value_count": 352,
              "group_name": "city_left_first_name_1_",
              "percentile_ex_nulls": 0.9094955325126648,
              "percentile_inc_nulls": 0.9390000104904175,
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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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linker.profile_columns(\"city || left(first_name,1) \")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "splink_demos",
   "language": "python",
   "name": "splink_demos"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "3b53fa520a31e303a9636a08ff10a3bbc14893ee50cb37445791fa59628fc75b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}