{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real time linkage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we demonstrate splink's incremental and real time linkage capabilities - specifically:\n",
    "- the `linker.compare_two_records` function, that allows you to interactively explore the results of a linkage model; and\n",
    "- the `linker.find_matches_to_new_records` that allows you to incrementally find matches to a small number of new records"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Load a pre-trained linkage model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from splink.duckdb.duckdb_linker import DuckDBLinker\n",
    "import altair as alt\n",
    "alt.renderers.enable('mimetype')\n",
    "\n",
    "with open(\"demo_settings/real_time_settings.json\") as f:\n",
    "    trained_settings = json.load(f)\n",
    "\n",
    "df = pd.read_csv(\"./data/fake_1000.csv\")\n",
    "\n",
    "linker = DuckDBLinker(df, trained_settings)\n",
    "linker._initialise_df_concat_with_tf()\n",
    "linker.compute_tf_table(\"first_name\")\n",
    "linker.compute_tf_table(\"surname\")\n",
    "linker.compute_tf_table(\"dob\")\n",
    "linker.compute_tf_table(\"city\")\n",
    "t = linker.compute_tf_table(\"email\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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            "labelExpr": "format(1 / (1 + pow(2, -1*datum.value)), '.2r')",
            "orient": "right",
            "title": "Probability"
           },
           "field": "sum",
           "scale": {
            "zero": false
           },
           "type": "quantitative"
          }
         },
         "mark": {
          "color": "black",
          "strokeWidth": 2,
          "type": "rule",
          "x2Offset": 30,
          "xOffset": -30
         }
        }
       ],
       "params": [
        {
         "bind": {
          "input": "range",
          "max": 1,
          "min": 0,
          "step": 1
         },
         "description": "Filter by the interation number",
         "name": "record_number",
         "value": 0
        }
       ],
       "resolve": {
        "axis": {
         "y": "independent"
        }
       },
       "title": {
        "subtitle": "How each comparison contributes to the final match score",
        "text": "Match weights waterfall chart"
       },
       "transform": [
        {
         "filter": "(datum.record_number == record_number)"
        },
        {
         "filter": "(datum.bayes_factor !== 1.0)"
        },
        {
         "frame": [
          null,
          0
         ],
         "window": [
          {
           "as": "sum",
           "field": "log2_bayes_factor",
           "op": "sum"
          },
          {
           "as": "lead",
           "field": "column_name",
           "op": "lead"
          }
         ]
        },
        {
         "as": "sum",
         "calculate": "datum.column_name === \"Final score\" ? datum.sum - datum.log2_bayes_factor : datum.sum"
        },
        {
         "as": "lead",
         "calculate": "datum.lead === null ? datum.column_name : datum.lead"
        },
        {
         "as": "previous_sum",
         "calculate": "datum.column_name === \"Final score\" || datum.column_name === \"Prior match weight\" ? 0 : datum.sum - datum.log2_bayes_factor"
        },
        {
         "as": "top_label",
         "calculate": "datum.sum > datum.previous_sum ? datum.column_name : \"\""
        },
        {
         "as": "bottom_label",
         "calculate": "datum.sum < datum.previous_sum ? datum.column_name : \"\""
        },
        {
         "as": "sum_top",
         "calculate": "datum.sum > datum.previous_sum ? datum.sum : datum.previous_sum"
        },
        {
         "as": "sum_bottom",
         "calculate": "datum.sum < datum.previous_sum ? datum.sum : datum.previous_sum"
        },
        {
         "as": "center",
         "calculate": "(datum.sum + datum.previous_sum) / 2"
        },
        {
         "as": "text_log2_bayes_factor",
         "calculate": "(datum.log2_bayes_factor > 0 ? \"+\" : \"\") + datum.log2_bayes_factor"
        },
        {
         "as": "dy",
         "calculate": "datum.sum < datum.previous_sum ? 4 : -4"
        },
        {
         "as": "baseline",
         "calculate": "datum.sum < datum.previous_sum ? \"top\" : \"bottom\""
        },
        {
         "as": "prob",
         "calculate": "1. / (1 + pow(2, -1.*datum.sum))"
        },
        {
         "as": "zero",
         "calculate": "0*datum.sum"
        }
       ],
       "width": {
        "step": 75
       }
      },
      "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linker.waterfall_chart(linker.predict().as_record_dict(limit=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step  Comparing two records\n",
    "\n",
    "It's now possible to compute a match weight for any two records using `linker.compare_two_records()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_weight</th>\n",
       "      <th>match_probability</th>\n",
       "      <th>unique_id_l</th>\n",
       "      <th>unique_id_r</th>\n",
       "      <th>first_name_l</th>\n",
       "      <th>first_name_r</th>\n",
       "      <th>gamma_first_name</th>\n",
       "      <th>tf_first_name_l</th>\n",
       "      <th>tf_first_name_r</th>\n",
       "      <th>bf_first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>tf_city_r</th>\n",
       "      <th>bf_city</th>\n",
       "      <th>bf_tf_adj_city</th>\n",
       "      <th>email_l</th>\n",
       "      <th>email_r</th>\n",
       "      <th>gamma_email</th>\n",
       "      <th>tf_email_l</th>\n",
       "      <th>tf_email_r</th>\n",
       "      <th>bf_email</th>\n",
       "      <th>bf_tf_adj_email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.161672</td>\n",
       "      <td>0.999891</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>Lucas</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.446404</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lucas.smith@hotmail.com</td>\n",
       "      <td>lucas.smith@hotmail.com</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.229168</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_weight  match_probability  unique_id_l  unique_id_r first_name_l  \\\n",
       "0     13.161672           0.999891            1            2        Lucas   \n",
       "\n",
       "  first_name_r  gamma_first_name  tf_first_name_l  tf_first_name_r  \\\n",
       "0        Lucas                 2         0.001203         0.001203   \n",
       "\n",
       "   bf_first_name  ...  tf_city_r   bf_city bf_tf_adj_city  \\\n",
       "0      87.571229  ...        NaN  0.446404            1.0   \n",
       "\n",
       "                   email_l                  email_r  gamma_email  tf_email_l  \\\n",
       "0  lucas.smith@hotmail.com  lucas.smith@hotmail.com            1         NaN   \n",
       "\n",
       "   tf_email_r    bf_email bf_tf_adj_email  \n",
       "0         NaN  263.229168             1.0  \n",
       "\n",
       "[1 rows x 39 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_1  = {\n",
    "     'unique_id':1,\n",
    "     'first_name': \"Lucas\",\n",
    "     'surname': \"Smith\",\n",
    "     'dob': \"1984-01-02\",\n",
    "     'city': \"London\",\n",
    "     'email': \"lucas.smith@hotmail.com\"\n",
    "}\n",
    "\n",
    "record_2  = {\n",
    "     'unique_id':2,\n",
    "     'first_name': \"Lucas\",\n",
    "     'surname': \"Smith\",\n",
    "     'dob': \"1983-02-12\",\n",
    "     'city': \"Machester\",\n",
    "     'email': \"lucas.smith@hotmail.com\"\n",
    "}\n",
    "linker._settings_obj_._retain_intermediate_calculation_columns = True\n",
    "linker._settings_obj_._retain_matching_columns = True\n",
    "\n",
    "df_two = linker.compare_two_records(record_1, record_2)\n",
    "df_two.as_pandas_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Interactive comparisons\n",
    "\n",
    "One interesting applicatin of `compare_two_records` is to create a simple interface that allows the user to input two records interactively, and get real time feedback.\n",
    "\n",
    "In the following cell we use `ipywidets` for this purpose.  ✨✨ Change the values in the text boxes to see the waterfall chart update in real time. ✨✨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0e1d6c19a0c4a4a9f1204f3448b3b91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Text(value='1', description='unique_id'), Text(value='Lucas', description='first…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4afb60cf43141b58803c58e9d193342",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "fields = [\"unique_id\", \"first_name\",\"surname\",\"dob\",\"email\",\"city\"]\n",
    "\n",
    "left_text_boxes = []\n",
    "right_text_boxes = []\n",
    "\n",
    "inputs_to_interactive_output = {}\n",
    "\n",
    "for f in fields:\n",
    "    wl = widgets.Text(description=f, value =str(record_1[f]))\n",
    "    left_text_boxes.append(wl)\n",
    "    inputs_to_interactive_output[f\"{f}_l\"] = wl\n",
    "    wr = widgets.Text( description=f, value =str(record_2[f]))\n",
    "    right_text_boxes.append(wr)\n",
    "    inputs_to_interactive_output[f\"{f}_r\"] = wr\n",
    "\n",
    "\n",
    "b1 = widgets.VBox(left_text_boxes)\n",
    "b2 = widgets.VBox(right_text_boxes)\n",
    "ui = widgets.HBox([b1,b2])\n",
    "\n",
    "def myfn(**kwargs):\n",
    "    my_args = dict(kwargs)\n",
    "    \n",
    "    record_left = {}\n",
    "    record_right = {}\n",
    "    \n",
    "    for key, value in my_args.items():\n",
    "        if value == '':\n",
    "            value = None\n",
    "        if key.endswith(\"_l\"):\n",
    "            record_left[key[:-2]] = value\n",
    "        if key.endswith(\"_r\"):\n",
    "            record_right[key[:-2]] = value\n",
    "            \n",
    "\n",
    "    linker._settings_obj_._retain_intermediate_calculation_columns = True\n",
    "    linker._settings_obj_._retain_matching_columns = True\n",
    "\n",
    "    df_two = linker.compare_two_records(record_left, record_right)\n",
    "\n",
    "    recs = df_two.as_pandas_dataframe().to_dict(orient=\"records\")\n",
    "    from splink.charts import waterfall_chart\n",
    "    display(linker.waterfall_chart(recs, filter_nulls=False))\n",
    "\n",
    "\n",
    "out = widgets.interactive_output(myfn, inputs_to_interactive_output)\n",
    "\n",
    "display(ui,out)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding matching records interactively\n",
    "\n",
    "It is also possible to search the records in the input dataset rapidly using the `linker.find_matches_to_new_records()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_weight</th>\n",
       "      <th>match_probability</th>\n",
       "      <th>unique_id_l</th>\n",
       "      <th>unique_id_r</th>\n",
       "      <th>first_name_l</th>\n",
       "      <th>first_name_r</th>\n",
       "      <th>gamma_first_name</th>\n",
       "      <th>tf_first_name_l</th>\n",
       "      <th>tf_first_name_r</th>\n",
       "      <th>bf_first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>tf_city_r</th>\n",
       "      <th>bf_city</th>\n",
       "      <th>bf_tf_adj_city</th>\n",
       "      <th>email_l</th>\n",
       "      <th>email_r</th>\n",
       "      <th>gamma_email</th>\n",
       "      <th>tf_email_l</th>\n",
       "      <th>tf_email_r</th>\n",
       "      <th>bf_email</th>\n",
       "      <th>bf_tf_adj_email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.531793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>123987</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Robert</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>263.229168</td>\n",
       "      <td>1.730964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.550320</td>\n",
       "      <td>0.999958</td>\n",
       "      <td>1</td>\n",
       "      <td>123987</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Robert</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>roberta25@smith.net</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.388623</td>\n",
       "      <td>0.999255</td>\n",
       "      <td>3</td>\n",
       "      <td>123987</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Robert</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>87.571229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>0.446404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.427256</td>\n",
       "      <td>0.843228</td>\n",
       "      <td>2</td>\n",
       "      <td>123987</td>\n",
       "      <td>Rob</td>\n",
       "      <td>Robert</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>0.218767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>10.484859</td>\n",
       "      <td>0.259162</td>\n",
       "      <td>roberta25@smith.net</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.123090</td>\n",
       "      <td>0.186697</td>\n",
       "      <td>8</td>\n",
       "      <td>123987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.205894</td>\n",
       "      <td>0.178139</td>\n",
       "      <td>754</td>\n",
       "      <td>123987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>j.c@whige.wort</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.802309</td>\n",
       "      <td>0.125383</td>\n",
       "      <td>750</td>\n",
       "      <td>123987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212792</td>\n",
       "      <td>10.484859</td>\n",
       "      <td>0.259162</td>\n",
       "      <td>j.c@white.org</td>\n",
       "      <td>robert255@smith.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.423438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_weight  match_probability  unique_id_l  unique_id_r first_name_l  \\\n",
       "2     23.531793           1.000000            0       123987       Robert   \n",
       "3     14.550320           0.999958            1       123987       Robert   \n",
       "4     10.388623           0.999255            3       123987       Robert   \n",
       "0      2.427256           0.843228            2       123987          Rob   \n",
       "6     -2.123090           0.186697            8       123987          NaN   \n",
       "5     -2.205894           0.178139          754       123987          NaN   \n",
       "1     -2.802309           0.125383          750       123987          NaN   \n",
       "\n",
       "  first_name_r  gamma_first_name  tf_first_name_l  tf_first_name_r  \\\n",
       "2       Robert                 2         0.003610          0.00361   \n",
       "3       Robert                 2         0.003610          0.00361   \n",
       "4       Robert                 2         0.003610          0.00361   \n",
       "0       Robert                 0         0.001203          0.00361   \n",
       "6       Robert                -1              NaN          0.00361   \n",
       "5       Robert                -1              NaN          0.00361   \n",
       "1       Robert                -1              NaN          0.00361   \n",
       "\n",
       "   bf_first_name  ...  tf_city_r    bf_city bf_tf_adj_city  \\\n",
       "2      87.571229  ...   0.212792   1.000000       1.000000   \n",
       "3      87.571229  ...   0.212792   1.000000       1.000000   \n",
       "4      87.571229  ...   0.212792   0.446404       1.000000   \n",
       "0       0.218767  ...   0.212792  10.484859       0.259162   \n",
       "6       1.000000  ...   0.212792   1.000000       1.000000   \n",
       "5       1.000000  ...   0.212792   1.000000       1.000000   \n",
       "1       1.000000  ...   0.212792  10.484859       0.259162   \n",
       "\n",
       "               email_l              email_r  gamma_email  tf_email_l  \\\n",
       "2  robert255@smith.net  robert255@smith.net            1    0.001267   \n",
       "3  roberta25@smith.net  robert255@smith.net            0    0.002535   \n",
       "4                  NaN  robert255@smith.net           -1         NaN   \n",
       "0  roberta25@smith.net  robert255@smith.net            0    0.002535   \n",
       "6                  NaN  robert255@smith.net           -1         NaN   \n",
       "5       j.c@whige.wort  robert255@smith.net            0    0.001267   \n",
       "1        j.c@white.org  robert255@smith.net            0    0.002535   \n",
       "\n",
       "   tf_email_r    bf_email bf_tf_adj_email  \n",
       "2    0.001267  263.229168        1.730964  \n",
       "3    0.001267    0.423438        1.000000  \n",
       "4    0.001267    1.000000        1.000000  \n",
       "0    0.001267    0.423438        1.000000  \n",
       "6    0.001267    1.000000        1.000000  \n",
       "5    0.001267    0.423438        1.000000  \n",
       "1    0.001267    0.423438        1.000000  \n",
       "\n",
       "[7 rows x 39 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record = {'unique_id': 123987,\n",
    " 'first_name': \"Robert\",\n",
    " 'surname': \"Alan\",\n",
    " 'dob': \"1971-05-24\",\n",
    " 'city': \"London\",\n",
    " 'email': \"robert255@smith.net\"\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "df_inc = linker.find_matches_to_new_records([record], blocking_rules=[]).as_pandas_dataframe()\n",
    "df_inc.sort_values(\"match_weight\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive interface for finding records\n",
    "\n",
    "Again, we can use `ipywidgets` to build an interactive interface for the `linker.find_matches_to_new_records` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6887322a0d2d459ca04a2cddeee41da2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='Robert', description='first_name'), Text(value='Alan', description='surname'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from splink.charts import waterfall_chart\n",
    "\n",
    "@widgets.interact(first_name='Robert', surname=\"Alan\", dob=\"1971-05-24\", city=\"London\", email=\"robert255@smith.net\")\n",
    "def interactive_link(first_name, surname, dob, city, email):    \n",
    "\n",
    "    record = {'unique_id': 123987,\n",
    "     'first_name': first_name,\n",
    "     'surname': surname,\n",
    "     'dob': dob,\n",
    "     'city': city,\n",
    "     'email': email,\n",
    "     'group': 0}\n",
    "\n",
    "    for key in record.keys():\n",
    "        if type(record[key]) == str:\n",
    "            if record[key].strip() == \"\":\n",
    "                record[key] = None\n",
    "\n",
    "    \n",
    "    df_inc = linker.find_matches_to_new_records([record], blocking_rules=[f\"(true)\"]).as_pandas_dataframe()\n",
    "    df_inc = df_inc.sort_values(\"match_weight\", ascending=False)\n",
    "    recs = df_inc.to_dict(orient=\"records\")\n",
    "    \n",
    "\n",
    "\n",
    "    display(linker.waterfall_chart(recs, filter_nulls=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v4+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v5.2.json",
       "config": {
        "header": {
         "title": null
        },
        "mark": {
         "tooltip": null
        },
        "title": {
         "anchor": "middle"
        },
        "view": {
         "height": 60,
         "width": 400
        }
       },
       "data": {
        "values": [
         {
          "bayes_factor": 0.023816582302252427,
          "bayes_factor_description": "The probability that two random records drawn at random match is 0.023 or one in  43.0 records.This is equivalent to a starting match weight of -5.392.",
          "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": -5.391889789559854,
          "m_probability": null,
          "m_probability_description": null,
          "max_comparison_vector_value": 0,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": null,
          "tf_adjustment_column": null,
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          "u_probability": null,
          "u_probability_description": null
         },
         {
          "bayes_factor": 87.57122888658395,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 87.57 times more likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 2,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 6.452385051922501,
          "m_probability": 0.5073501669215337,
          "m_probability_description": "Amongst matching record comparisons, 50.74% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "first_name_l = first_name_r",
          "tf_adjustment_column": "first_name",
          "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": 27.407809072486973,
          "bayes_factor_description": "If comparison level is `levenstein <= 2` then comparison is 27.41 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": "Levenstein <= 2",
          "log2_bayes_factor": 4.776515101395072,
          "m_probability": 0.27736434159157797,
          "m_probability_description": "Amongst matching record comparisons, 27.74% of records are in the levenstein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "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 levenstein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.21876683164040506,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.57 times less likely to be a match",
          "comparison_name": "first_name",
          "comparison_sort_order": 0,
          "comparison_vector_value": 0,
          "has_tf_adjustments": false,
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          "label_for_charts": "All other comparisons",
          "log2_bayes_factor": -2.1925340745596764,
          "m_probability": 0.21528549148688833,
          "m_probability_description": "Amongst matching record comparisons, 21.53% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "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": 92.38584465067325,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 92.39 times more likely to be a match",
          "comparison_name": "surname",
          "comparison_sort_order": 1,
          "comparison_vector_value": 2,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 6.529599913880287,
          "m_probability": 0.4517645215191846,
          "m_probability_description": "Amongst matching record comparisons, 45.18% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 2,
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          "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": 41.74477904659683,
          "bayes_factor_description": "If comparison level is `levenstein <= 2` then comparison is 41.74 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": "Levenstein <= 2",
          "log2_bayes_factor": 5.383523868172574,
          "m_probability": 0.3078165102205689,
          "m_probability_description": "Amongst matching record comparisons, 30.78% of records are in the levenstein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "levenshtein(surname_l, surname_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.007373772654946249,
          "u_probability_description": "Amongst non-matching record comparisons, 0.74% of records are in the levenstein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.24340401379756268,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.11 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": -2.03857513621085,
          "m_probability": 0.24041896826024636,
          "m_probability_description": "Amongst matching record comparisons, 24.04% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "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": 232.03049287476935,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 232.03 times more likely to be a match",
          "comparison_name": "dob",
          "comparison_sort_order": 2,
          "comparison_vector_value": 2,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 7.858170603008739,
          "m_probability": 0.405530771330678,
          "m_probability_description": "Amongst matching record comparisons, 40.55% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "dob_l = dob_r",
          "tf_adjustment_column": "dob",
          "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": 21.492671620753597,
          "bayes_factor_description": "If comparison level is `levenstein <= 2` then comparison is 21.49 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": "Levenstein <= 2",
          "log2_bayes_factor": 4.425772921275592,
          "m_probability": 0.3679356056637918,
          "m_probability_description": "Amongst matching record comparisons, 36.79% of records are in the levenstein <= 2 comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "levenshtein(dob_l, dob_r) <= 2",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.01711911911911912,
          "u_probability_description": "Amongst non-matching record comparisons, 1.71% of records are in the levenstein <= 2 comparison level"
         },
         {
          "bayes_factor": 0.23088978993311773,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  4.33 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": -2.114723717091652,
          "m_probability": 0.22653362300553073,
          "m_probability_description": "Amongst matching record comparisons, 22.65% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 2,
          "probability_two_random_records_match": 0.02326254791526835,
          "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": 10.484858675056154,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 10.48 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": 3.3902355104306197,
          "m_probability": 0.5782144900964232,
          "m_probability_description": "Amongst matching record comparisons, 57.82% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "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": 0.4464035832880252,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  2.24 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": -1.1635794871398053,
          "m_probability": 0.4217855099035769,
          "m_probability_description": "Amongst matching record comparisons, 42.18% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "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": 263.2291676754963,
          "bayes_factor_description": "If comparison level is `exact_match` then comparison is 263.23 times more likely to be a match",
          "comparison_name": "email",
          "comparison_sort_order": 4,
          "comparison_vector_value": 1,
          "has_tf_adjustments": true,
          "is_null_level": false,
          "label_for_charts": "exact_match",
          "log2_bayes_factor": 8.04017554864013,
          "m_probability": 0.5774909200578013,
          "m_probability_description": "Amongst matching record comparisons, 57.75% of records are in the exact_match comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "email_l = email_r",
          "tf_adjustment_column": "email",
          "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": 0.4234380485302649,
          "bayes_factor_description": "If comparison level is `all other comparisons` then comparison is  2.36 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": -1.239777184635766,
          "m_probability": 0.42250907994219916,
          "m_probability_description": "Amongst matching record comparisons, 42.25% of records are in the all other comparisons comparison level",
          "max_comparison_vector_value": 1,
          "probability_two_random_records_match": 0.02326254791526835,
          "sql_condition": "ELSE",
          "tf_adjustment_column": null,
          "tf_adjustment_weight": 1,
          "u_probability": 0.9978061286856716,
          "u_probability_description": "Amongst non-matching record comparisons, 99.78% 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": [
          "x"
         ],
         "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",
           "encodings": [
            "x"
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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,
             0,
             10
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            "range": [
             "red",
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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"
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         },
         "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()"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}