{
 "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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          "reduction_ratio": "The rolling reduction ratio with your given blocking rule(s) is 0.996. \nThis represents the reduction in the total number of comparisons due to your rule(s).",
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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_",
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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) \")"
   ]
  }
 ],
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   "display_name": "splink_demos",
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