4.Data_Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Playing It With The Sample Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_true = pd.DataFrame(\n",
    "        {\n",
    "            \"USER\": [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
    "            \"ITEM\": [1, 2, 3, 1, 4, 5, 6, 7, 2, 5, 6, 8, 9, 10, 11, 12, 13, 14],\n",
    "            \"RATING\": [5, 4, 3, 5, 5, 3, 3, 1, 5, 5, 5, 4, 4, 3, 3, 3, 2, 1],\n",
    "        }\n",
    "    )\n",
    "\n",
    "df_pred = pd.DataFrame(\n",
    "    {\n",
    "        \"USER\": [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
    "        \"ITEM\": [3, 10, 12, 10, 3, 11, 5, 13, 4, 10, 7, 13, 1, 3, 5, 2, 11, 14],\n",
    "        \"RATING\": [14, 13, 12, 14, 13, 12, 11, 10, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER</th>\n",
       "      <th>ITEM</th>\n",
       "      <th>RATING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    USER  ITEM  RATING\n",
       "0      1     1       5\n",
       "1      1     2       4\n",
       "2      1     3       3\n",
       "3      2     1       5\n",
       "4      2     4       5\n",
       "5      2     5       3\n",
       "6      2     6       3\n",
       "7      2     7       1\n",
       "8      3     2       5\n",
       "9      3     5       5\n",
       "10     3     6       5\n",
       "11     3     8       4\n",
       "12     3     9       4\n",
       "13     3    10       3\n",
       "14     3    11       3\n",
       "15     3    12       3\n",
       "16     3    13       2\n",
       "17     3    14       1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from recoflow.metrics import MeanAbsoluteError, MeanSquaredError, RootMeanSquaredError\n",
    "from recoflow.metrics import PrecisionK, RecallK, NDCGK, MeanReciprocalRankK, MeanAveragePrecisionK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.106335201775948"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RootMeanSquaredError(df_true, df_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanSquaredError(df_true, df_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.25"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanAbsoluteError(df_true, df_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2222222222222222"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PrecisionK(df_true, df_pred, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14444444444444446"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RecallK(df_true, df_pred, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2551202123295406"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NDCGK(df_true, df_pred, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12777777777777777"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanAveragePrecisionK(df_true, df_pred, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16666666666666666"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MeanReciprocalRankK(df_true, df_pred, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_true = df_true\n",
    "rating_pred = df_pred\n",
    "k = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_users = set(rating_true[\"USER\"]).intersection(set(rating_pred[\"USER\"]))\n",
    "rating_true_common = rating_true[rating_true[\"USER\"].isin(common_users)]\n",
    "rating_pred_common = rating_pred[rating_pred[\"USER\"].isin(common_users)]\n",
    "n_users = len(common_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER</th>\n",
       "      <th>ITEM</th>\n",
       "      <th>RATING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      "text/plain": [
       "   USER  ITEM  RATING\n",
       "0     1     3      14\n",
       "1     1    10      13\n",
       "2     1    12      12\n",
       "3     2    10      14\n",
       "4     2     3      13\n",
       "5     2    11      12\n",
       "6     2     5      11\n",
       "7     2    13      10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_pred_common.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from recoflow.metrics import _GetTopKItems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER</th>\n",
       "      <th>ITEM</th>\n",
       "      <th>RATING</th>\n",
       "      <th>rank</th>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    USER  ITEM  RATING  rank\n",
       "0      1     3      14     1\n",
       "1      1    10      13     2\n",
       "2      1    12      12     3\n",
       "3      2    10      14     1\n",
       "4      2     3      13     2\n",
       "5      2    11      12     3\n",
       "6      2     5      11     4\n",
       "7      2    13      10     5\n",
       "8      3     4      14     1\n",
       "9      3    10      13     2\n",
       "10     3     7      12     3\n",
       "11     3    13      11     4\n",
       "12     3     1      10     5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hit = _GetTopKItems(rating_pred_common, \"USER\", \"RATING\", k)\n",
    "df_hit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER</th>\n",
       "      <th>ITEM</th>\n",
       "      <th>RATING</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "   USER  ITEM  RATING\n",
       "0     1     1       5\n",
       "1     1     2       4\n",
       "2     1     3       3\n",
       "3     2     1       5\n",
       "4     2     4       5\n",
       "5     2     5       3"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_true_common.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "   USER  ITEM  RATING  rank\n",
       "0     1     3      14     1\n",
       "1     1    10      13     2\n",
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       "3     2    10      14     1\n",
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   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hit = pd.merge(df_hit, rating_true_common, on=[\"USER\", \"ITEM\"])[\n",
    "        [\"USER\", \"ITEM\", \"rank\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   USER  ITEM  rank\n",
       "0     1     3     1\n",
       "1     2     5     4\n",
       "2     3    10     2\n",
       "3     3    13     4"
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     "execution_count": 27,
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    "df_hit"
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   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count the number of hits vs actual relevant items per user\n",
    "df_hit_count = pd.merge(\n",
    "    df_hit.groupby(\"USER\", as_index=False)[\"USER\"].agg({\"hit\": \"count\"}),\n",
    "    rating_true_common.groupby(\"USER\", as_index=False)[\"USER\"].agg(\n",
    "        {\"actual\": \"count\"}\n",
    "    ),\n",
    "    on=\"USER\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   USER  hit  actual\n",
       "0     1    1       3\n",
       "1     2    1       5\n",
       "2     3    2      10"
      ]
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   "source": [
    "df_hit_count"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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