ipr_data_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>FY</th>\n",
       "      <th>Trading Partner</th>\n",
       "      <th>Mode of Transportation</th>\n",
       "      <th>Centers of Excellence</th>\n",
       "      <th>Product</th>\n",
       "      <th>Unique Seizure ID</th>\n",
       "      <th>Line</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>CN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Other</td>\n",
       "      <td>202201000001</td>\n",
       "      <td>1</td>\n",
       "      <td>49314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>CN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>202201000002</td>\n",
       "      <td>1</td>\n",
       "      <td>45391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>CN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>202201000002</td>\n",
       "      <td>2</td>\n",
       "      <td>9754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Handbags/Wallets</td>\n",
       "      <td>202201000003</td>\n",
       "      <td>1</td>\n",
       "      <td>30800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Handbags/Wallets</td>\n",
       "      <td>202201000004</td>\n",
       "      <td>1</td>\n",
       "      <td>2840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FY Trading Partner Mode of Transportation  ... Unique Seizure ID Line  MSRP\n",
       "0  2022              CN                  Other  ...      202201000001    1 49314\n",
       "1  2022              CN                  Other  ...      202201000002    1 45391\n",
       "2  2022              CN                  Other  ...      202201000002    2  9754\n",
       "3  2022              HK    Express Consignment  ...      202201000003    1 30800\n",
       "4  2022              HK    Express Consignment  ...      202201000004    1  2840\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from data_util import load_ipr\n",
    "\n",
    "ipr_data_df = load_ipr()\n",
    "ipr_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 444113 entries, 0 to 444112\n",
      "Data columns (total 8 columns):\n",
      " #   Column                  Non-Null Count   Dtype  \n",
      "---  ------                  --------------   -----  \n",
      " 0   FY                      444113 non-null  int64  \n",
      " 1   Trading Partner         444113 non-null  object \n",
      " 2   Mode of Transportation  444113 non-null  object \n",
      " 3   Centers of Excellence   444113 non-null  object \n",
      " 4   Product                 444113 non-null  object \n",
      " 5   Unique Seizure ID       444113 non-null  int64  \n",
      " 6   Line                    444113 non-null  int64  \n",
      " 7   MSRP                    443918 non-null  float64\n",
      "dtypes: float64(1), int64(3), object(4)\n",
      "memory usage: 27.1+ MB\n"
     ]
    }
   ],
   "source": [
    "ipr_data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FY</th>\n",
       "      <th>Unique Seizure ID</th>\n",
       "      <th>Line</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>444113</td>\n",
       "      <td>444113</td>\n",
       "      <td>444113</td>\n",
       "      <td>443918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2021</td>\n",
       "      <td>202125006749</td>\n",
       "      <td>13</td>\n",
       "      <td>26885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1</td>\n",
       "      <td>135629500</td>\n",
       "      <td>34</td>\n",
       "      <td>581003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2019</td>\n",
       "      <td>201901000001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2020</td>\n",
       "      <td>202001017448</td>\n",
       "      <td>1</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2021</td>\n",
       "      <td>202101023542</td>\n",
       "      <td>4</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2022</td>\n",
       "      <td>202201019709</td>\n",
       "      <td>10</td>\n",
       "      <td>6600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2023</td>\n",
       "      <td>202301019875</td>\n",
       "      <td>752</td>\n",
       "      <td>226976300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FY  Unique Seizure ID   Line      MSRP\n",
       "count 444113             444113 444113    443918\n",
       "mean    2021       202125006749     13     26885\n",
       "std        1          135629500     34    581003\n",
       "min     2019       201901000001      1         0\n",
       "25%     2020       202001017448      1       575\n",
       "50%     2021       202101023542      4      1872\n",
       "75%     2022       202201019709     10      6600\n",
       "max     2023       202301019875    752 226976300"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_data_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Rows: 444113\n",
      "Index(['FY', 'Trading Partner', 'Mode of Transportation',\n",
      "       'Centers of Excellence', 'Product', 'Unique Seizure ID', 'Line',\n",
      "       'MSRP'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "FY                          0 (%0.00)\n",
       "Trading Partner             0 (%0.00)\n",
       "Mode of Transportation      0 (%0.00)\n",
       "Centers of Excellence       0 (%0.00)\n",
       "Product                     0 (%0.00)\n",
       "Unique Seizure ID           0 (%0.00)\n",
       "Line                        0 (%0.00)\n",
       "MSRP                      195 (%0.04)\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from format_util import to_percent\n",
    "\n",
    "total_rows = len(ipr_data_df)\n",
    "print(\"Total Rows:\", total_rows)\n",
    "\n",
    "rows_with_missing_data = ipr_data_df.isnull().sum()\n",
    "rows_with_missing_data = rows_with_missing_data.apply(\n",
    "    lambda v: '{:d} (%{:.2f})'.format(v, to_percent(v, total_rows))\n",
    ")\n",
    "print(rows_with_missing_data.index)\n",
    "rows_with_missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Values Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure general float display options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:.0f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Unique Seizure ID'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Records: 444113\n"
     ]
    }
   ],
   "source": [
    "print('Total Records:', len(ipr_data_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Unique Seizures: 121751\n"
     ]
    }
   ],
   "source": [
    "print('Total Unique Seizures:', len(ipr_data_df['Unique Seizure ID'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if those non-unique seizure ids are actually equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{201901000001: [68222], 201901000002: [68223], 201901000003: [68224], 201901000004: [68225], 201901000005: [68226], 201901000006: [68227], 201901000007: [68228], 201901000008: [68229, 68230, 68231], 201901000009: [68232], 201901000010: [68233, 68234], 201901000011: [68235, 68236], 201901000012: [68237], 201901000013: [68238, 68239], 201901000014: [68240, 68241], 201901000015: [68242, 68243], 201901000016: [68244], 201901000017: [68245], 201901000018: [68246, 68247, 68248], 201901000019: [68249], 201901000020: [68250, 68251, 68252, 68253, 68254], 201901000021: [68255], 201901000022: [68256, 68257, 68258, 68259, 68260, 68261, 68262, 68263, 68264, 68265, 68266, 68267, 68268, 68269], 201901000023: [68270, 68271, 68272, 68273, 68274, 68275, 68276, 68277], 201901000024: [68278], 201901000025: [68279, 68280], 201901000026: [68281, 68282, 68283, 68284, 68285], 201901000027: [68286], 201901000028: [68287, 68288, 68289, 68290], 201901000029: [68291, 68292], 201901000030: [68293, 68294], 201901000031: [68295], 201901000032: [68296, 68297, 68298], 201901000033: [68299], 201901000034: [68300, 68301], 201901000035: [68302, 68303, 68304, 68305], 201901000036: [68306, 68307], 201901000037: [68308], 201901000038: [68309, 68310, 68311, 68312, 68313, 68314], 201901000039: [68315], 201901000040: [68316, 68317, 68318, 68319, 68320, 68321, 68322, 68323, 68324, 68325, 68326], 201901000041: [68327, 68328], 201901000042: [68329, 68330, 68331, 68332], 201901000043: [68333, 68334, 68335, 68336, 68337, 68338, 68339, 68340, 68341, 68342, 68343, 68344, 68345, 68346, 68347, 68348, 68349, 68350, 68351, 68352, 68353], 201901000044: [68354], 201901000045: [68355], 201901000046: [68356], 201901000047: [68357, 68358, 68359], 201901000048: [68360], 201901000049: [68361], 201901000050: [68362], 201901000051: [68363], 201901000052: [68364, 68365], 201901000053: [68366, 68367], 201901000054: [68368], 201901000055: [68369], 201901000056: [68370, 68371, 68372, 68373, 68374], 201901000057: [68375, 68376, 68377, 68378, 68379, 68380, 68381, 68382, 68383, 68384, 68385, 68386, 68387], 201901000058: [68388], 201901000059: [68389], 201901000060: [68390], 201901000061: [68391], 201901000062: [68392], 201901000063: [68393], 201901000064: [68394, 68395, 68396, 68397, 68398], 201901000065: [68399], 201901000066: [68400], 201901000067: [68401, 68402, 68403, 68404], 201901000068: [68405], 201901000069: [68406, 68407, 68408, 68409, 68410, 68411, 68412], 201901000070: [68413], 201901000071: [68414], 201901000072: [68415, 68416], 201901000073: [68417, 68418, 68419, 68420, 68421, 68422, 68423, 68424, 68425, 68426], 201901000074: [68427], 201901000075: [68428, 68429], 201901000076: [68430], 201901000077: [68431], 201901000078: [68432], 201901000079: [68433], 201901000080: [68434], 201901000081: [68435, 68436], 201901000082: [68437], 201901000083: [68438, 68439, 68440], 201901000084: [68441, 68442], 201901000085: [68443], 201901000086: [68444, 68445, 68446, 68447], 201901000087: [68448], 201901000088: [68449], 201901000089: [68450, 68451], 201901000090: [68452, 68453], 201901000091: [68454, 68455], 201901000092: [68456, 68457, 68458, 68459, 68460, 68461], 201901000093: [68462], 201901000094: [68463, 68464, 68465], 201901000095: [68466], 201901000096: [68467, 68468, 68469], 201901000097: [68470], 201901000098: [68471, 68472], 201901000099: [68473], 201901000100: [68474], ...}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_data_df.groupby('Unique Seizure ID').groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we grab one of the groups to check the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FY</th>\n",
       "      <th>Trading Partner</th>\n",
       "      <th>Mode of Transportation</th>\n",
       "      <th>Centers of Excellence</th>\n",
       "      <th>Product</th>\n",
       "      <th>Unique Seizure ID</th>\n",
       "      <th>Line</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68309</th>\n",
       "      <td>2019</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>201901000038</td>\n",
       "      <td>1</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68310</th>\n",
       "      <td>2019</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>201901000038</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68311</th>\n",
       "      <td>2019</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>201901000038</td>\n",
       "      <td>3</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68312</th>\n",
       "      <td>2019</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>201901000038</td>\n",
       "      <td>4</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68313</th>\n",
       "      <td>2019</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>201901000038</td>\n",
       "      <td>5</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68314</th>\n",
       "      <td>2019</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>201901000038</td>\n",
       "      <td>6</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FY Trading Partner  ... Line MSRP\n",
       "68309  2019              HK  ...    1 3600\n",
       "68310  2019              HK  ...    2  600\n",
       "68311  2019              HK  ...    3 1800\n",
       "68312  2019              HK  ...    4 1800\n",
       "68313  2019              HK  ...    5 1800\n",
       "68314  2019              HK  ...    6  550\n",
       "\n",
       "[6 rows x 8 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_data_df[ipr_data_df['Unique Seizure ID'] == 201901000038]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the seizure ID is not unique but is rather like an \"Invoice\" and the `Line` property helps discern items within the seizure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 'Fiscal Year'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipr_data_df['FY'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'Line'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipr_data_df['Line'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'MSRP'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      443918\n",
       "mean        26885\n",
       "std        581003\n",
       "min             0\n",
       "25%           575\n",
       "50%          1872\n",
       "75%          6600\n",
       "max     226976300\n",
       "Name: MSRP, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_data_df['MSRP'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at MSRP descriptions we observe that most 75% of the produces have a price of about 7.5k, we'll use 10,000 as the cut-off to inspect distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipr_data_df[ipr_data_df['MSRP'] <= 10_000]['MSRP'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipr_data_df['Trading Partner'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution by Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "ipr_data_df['Product'].hist();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that CA and SG many less observations compared to the rest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Set Cleanup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be performing the following actions before exploring feature selection\n",
    "\n",
    "  1. Normalize column naming, that is remove special characters and replace with underscores, all columns names in lowercase.\n",
    "  2. Removing features with 20% or more missing values\n",
    "  3. Imputing the average for features with less than 20% missing values\n",
    "  4. Normalize string values\n",
    "  5. Remove columns that might not be of interest\n",
    "     1. `centers_of_excellence` - Typically the \n",
    "     2. `mode_of_transportation` - We don't have an equivalent on the product list for this column\n",
    "  6. We'll create a new column called `line_count` which contains the total number of lines\n",
    "  7. We'll merge the `line` and `unique_seizure_id` into a true single identifier\n",
    "  8. Finally we'll drop duplicate `unique_seizure_id`-`line` values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>FY</th>\n",
       "      <th>Trading Partner</th>\n",
       "      <th>Mode of Transportation</th>\n",
       "      <th>Centers of Excellence</th>\n",
       "      <th>Product</th>\n",
       "      <th>Unique Seizure ID</th>\n",
       "      <th>Line</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>CN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Other</td>\n",
       "      <td>202201000001</td>\n",
       "      <td>1</td>\n",
       "      <td>49314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>CN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>202201000002</td>\n",
       "      <td>1</td>\n",
       "      <td>45391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>CN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Apparel, Footwear &amp; Textiles</td>\n",
       "      <td>Wearing Apparel/Accessories</td>\n",
       "      <td>202201000002</td>\n",
       "      <td>2</td>\n",
       "      <td>9754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Handbags/Wallets</td>\n",
       "      <td>202201000003</td>\n",
       "      <td>1</td>\n",
       "      <td>30800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>HK</td>\n",
       "      <td>Express Consignment</td>\n",
       "      <td>Consumer Products &amp; Mass Merchandising</td>\n",
       "      <td>Handbags/Wallets</td>\n",
       "      <td>202201000004</td>\n",
       "      <td>1</td>\n",
       "      <td>2840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FY Trading Partner Mode of Transportation  ... Unique Seizure ID Line  MSRP\n",
       "0  2022              CN                  Other  ...      202201000001    1 49314\n",
       "1  2022              CN                  Other  ...      202201000002    1 45391\n",
       "2  2022              CN                  Other  ...      202201000002    2  9754\n",
       "3  2022              HK    Express Consignment  ...      202201000003    1 30800\n",
       "4  2022              HK    Express Consignment  ...      202201000004    1  2840\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "remove_columns_missing: set()\n",
      "join_columns: unique_seizure_id_line\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:\n",
      "msrp\n",
      "trading_partner_cn\n",
      "trading_partner_hk\n",
      "trading_partner_other_countries\n",
      "trading_partner_sg\n",
      "trading_partner_tr\n",
      "product_ball_bearings\n",
      "product_cigarettes\n",
      "product_computers_accessories\n",
      "product_consumer_electronics\n",
      "product_consumer_products\n",
      "product_food\n",
      "product_footwear\n",
      "product_handbags_wallets\n",
      "product_labels_tags\n",
      "product_optical_media\n",
      "product_other\n",
      "product_pharmaceuticals_personal_care\n",
      "product_sporting_goods\n",
      "product_toys\n",
      "product_watches_jewelry\n",
      "product_wearing_apparel_accessories\n",
      "line_count\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "      <th></th>\n",
       "      <th>msrp</th>\n",
       "      <th>trading_partner_cn</th>\n",
       "      <th>trading_partner_hk</th>\n",
       "      <th>trading_partner_other_countries</th>\n",
       "      <th>trading_partner_sg</th>\n",
       "      <th>trading_partner_tr</th>\n",
       "      <th>product_ball_bearings</th>\n",
       "      <th>product_cigarettes</th>\n",
       "      <th>product_computers_accessories</th>\n",
       "      <th>product_consumer_electronics</th>\n",
       "      <th>product_consumer_products</th>\n",
       "      <th>product_food</th>\n",
       "      <th>product_footwear</th>\n",
       "      <th>product_handbags_wallets</th>\n",
       "      <th>product_labels_tags</th>\n",
       "      <th>product_optical_media</th>\n",
       "      <th>product_other</th>\n",
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       "      <th>product_toys</th>\n",
       "      <th>product_watches_jewelry</th>\n",
       "      <th>product_wearing_apparel_accessories</th>\n",
       "      <th>line_count</th>\n",
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       "      <th>2</th>\n",
       "      <td>9754</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2840</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   msrp  trading_partner_cn  ...  product_wearing_apparel_accessories  line_count\n",
       "0 49314                   1  ...                                    0           1\n",
       "1 45391                   1  ...                                    1           2\n",
       "2  9754                   1  ...                                    1           2\n",
       "3 30800                   0  ...                                    0           1\n",
       "4  2840                   0  ...                                    0           4\n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from etl_ipr_data import transform\n",
    "\n",
    "ipr_data_processed_df = transform(ipr_data_df)\n",
    "\n",
    "print(\"Features:\")\n",
    "print(\"\\n\".join(ipr_data_processed_df.columns))\n",
    "\n",
    "ipr_data_processed_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll add a constant column which is the target class of the column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipr_data_processed_df['seized'] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>msrp</th>\n",
       "      <th>trading_partner_cn</th>\n",
       "      <th>trading_partner_hk</th>\n",
       "      <th>trading_partner_other_countries</th>\n",
       "      <th>trading_partner_sg</th>\n",
       "      <th>trading_partner_tr</th>\n",
       "      <th>product_ball_bearings</th>\n",
       "      <th>product_cigarettes</th>\n",
       "      <th>product_computers_accessories</th>\n",
       "      <th>product_consumer_electronics</th>\n",
       "      <th>product_consumer_products</th>\n",
       "      <th>product_food</th>\n",
       "      <th>product_footwear</th>\n",
       "      <th>product_handbags_wallets</th>\n",
       "      <th>product_labels_tags</th>\n",
       "      <th>product_optical_media</th>\n",
       "      <th>product_other</th>\n",
       "      <th>product_pharmaceuticals_personal_care</th>\n",
       "      <th>product_sporting_goods</th>\n",
       "      <th>product_toys</th>\n",
       "      <th>product_watches_jewelry</th>\n",
       "      <th>product_wearing_apparel_accessories</th>\n",
       "      <th>line_count</th>\n",
       "      <th>seized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45391</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9754</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2840</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   msrp  trading_partner_cn  ...  line_count  seized\n",
       "0 49314                   1  ...           1       1\n",
       "1 45391                   1  ...           2       1\n",
       "2  9754                   1  ...           2       1\n",
       "3 30800                   0  ...           1       1\n",
       "4  2840                   0  ...           4       1\n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_data_processed_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First the total number of features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "msrp\n",
      "trading_partner_cn\n",
      "trading_partner_hk\n",
      "trading_partner_other_countries\n",
      "trading_partner_sg\n",
      "trading_partner_tr\n",
      "product_ball_bearings\n",
      "product_cigarettes\n",
      "product_computers_accessories\n",
      "product_consumer_electronics\n",
      "product_consumer_products\n",
      "product_food\n",
      "product_footwear\n",
      "product_handbags_wallets\n",
      "product_labels_tags\n",
      "product_optical_media\n",
      "product_other\n",
      "product_pharmaceuticals_personal_care\n",
      "product_sporting_goods\n",
      "product_toys\n",
      "product_watches_jewelry\n",
      "product_wearing_apparel_accessories\n",
      "line_count\n",
      "seized\n"
     ]
    }
   ],
   "source": [
    "print(len(ipr_data_processed_df.columns))\n",
    "print(\"\\n\".join(ipr_data_processed_df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RFE(estimator=DecisionTreeClassifier(), n_features_to_select=24)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RFE</label><div class=\"sk-toggleable__content\"><pre>RFE(estimator=DecisionTreeClassifier(), n_features_to_select=24)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RFE(estimator=DecisionTreeClassifier(), n_features_to_select=24)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "N_FEATURES = len(ipr_data_processed_df.columns)\n",
    "X = ipr_data_processed_df[ipr_data_processed_df.columns.difference(['seized'])]\n",
    "y = ipr_data_processed_df['seized']\n",
    "\n",
    "rfe = RFE(estimator=DecisionTreeClassifier(), n_features_to_select=N_FEATURES)\n",
    "rfe.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>index</th>\n",
       "      <th>feature_name</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>line_count</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>msrp</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>product_ball_bearings</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>product_cigarettes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>product_computers_accessories</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>product_consumer_electronics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>product_consumer_products</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>product_food</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>product_footwear</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>product_handbags_wallets</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>product_labels_tags</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>product_optical_media</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>product_other</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>product_pharmaceuticals_personal_care</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>product_sporting_goods</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>product_toys</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>product_watches_jewelry</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>product_wearing_apparel_accessories</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>trading_partner_cn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>trading_partner_hk</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>trading_partner_other_countries</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>trading_partner_sg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>trading_partner_tr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index                           feature_name  rank\n",
       "0       0                             line_count     1\n",
       "1       1                                   msrp     1\n",
       "2       2                  product_ball_bearings     1\n",
       "3       3                     product_cigarettes     1\n",
       "4       4          product_computers_accessories     1\n",
       "5       5           product_consumer_electronics     1\n",
       "6       6              product_consumer_products     1\n",
       "7       7                           product_food     1\n",
       "8       8                       product_footwear     1\n",
       "9       9               product_handbags_wallets     1\n",
       "10     10                    product_labels_tags     1\n",
       "11     11                  product_optical_media     1\n",
       "12     12                          product_other     1\n",
       "13     13  product_pharmaceuticals_personal_care     1\n",
       "14     14                 product_sporting_goods     1\n",
       "15     15                           product_toys     1\n",
       "16     16                product_watches_jewelry     1\n",
       "17     17    product_wearing_apparel_accessories     1\n",
       "18     18                     trading_partner_cn     1\n",
       "19     19                     trading_partner_hk     1\n",
       "20     20        trading_partner_other_countries     1\n",
       "21     21                     trading_partner_sg     1\n",
       "22     22                     trading_partner_tr     1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = []\n",
    "for i in range(X.shape[1]):\n",
    "    if not rfe.support_[i]:\n",
    "        continue\n",
    "    features.append({'index': i, 'feature_name': X.columns[i], 'rank': rfe.ranking_[i]})\n",
    "\n",
    "features_df = pd.DataFrame(features, columns=['index', 'feature_name', 'rank'])\n",
    "features_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From looking at the results it seems that all features are ranked equally; this may be because the categorical product features indicate a direct seizure on our data set. \n",
    "\n",
    "Since we're mostly interested in country and our full product list is all of the same category, we'll remove all `product_` features and try another feature extractor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>msrp</th>\n",
       "      <th>trading_partner_cn</th>\n",
       "      <th>trading_partner_hk</th>\n",
       "      <th>trading_partner_other_countries</th>\n",
       "      <th>trading_partner_sg</th>\n",
       "      <th>trading_partner_tr</th>\n",
       "      <th>line_count</th>\n",
       "      <th>seized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49314</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45391</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9754</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2840</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444108</th>\n",
       "      <td>6980</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444109</th>\n",
       "      <td>780</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444110</th>\n",
       "      <td>11130</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444111</th>\n",
       "      <td>1927</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444112</th>\n",
       "      <td>5490</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>444113 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        msrp  trading_partner_cn  ...  line_count  seized\n",
       "0      49314                   1  ...           1       1\n",
       "1      45391                   1  ...           2       1\n",
       "2       9754                   1  ...           2       1\n",
       "3      30800                   0  ...           1       1\n",
       "4       2840                   0  ...           4       1\n",
       "...      ...                 ...  ...         ...     ...\n",
       "444108  6980                   0  ...           3       1\n",
       "444109   780                   0  ...           3       1\n",
       "444110 11130                   0  ...           1       1\n",
       "444111  1927                   0  ...           2       1\n",
       "444112  5490                   0  ...           2       1\n",
       "\n",
       "[444113 rows x 8 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_data_processed_df.drop(columns=[c for c in ipr_data_processed_df.columns if c.startswith(\"product_\")], inplace=True)\n",
    "ipr_data_processed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>index</th>\n",
       "      <th>feature_name</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>line_count</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>msrp</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>trading_partner_cn</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>trading_partner_hk</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>trading_partner_other_countries</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>trading_partner_sg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>trading_partner_tr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                     feature_name  rank\n",
       "0      0                       line_count     1\n",
       "1      1                             msrp     1\n",
       "2      2               trading_partner_cn     1\n",
       "3      3               trading_partner_hk     1\n",
       "4      4  trading_partner_other_countries     1\n",
       "5      5               trading_partner_sg     1\n",
       "6      6               trading_partner_tr     1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from feature_util import create_rfe_df\n",
    "features_no_product_df = create_rfe_df(ipr_data_processed_df, 9, 'seized')\n",
    "features_no_product_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the results it seems like removing `product_` yields no difference in feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First since all features seem to have the same ranking we'll proceed to train our model; first we'll split our data set in training, we'll use the following splits for our data:\n",
    "\n",
    "  * Training: 70%\n",
    "  * Cross-Validation: 15%\n",
    "  * Testing: 15%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y_name = \"seized\"\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    ipr_data_processed_df[ipr_data_processed_df.columns.difference([y_name])],\n",
    "    ipr_data_processed_df[y_name],\n",
    "    test_size=0.3,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "X_test, X_cv, y_test, y_cv = train_test_split(\n",
    "    X_test, y_test, test_size=0.5, random_state=21\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: 444113\n",
      "-----\n",
      "Train: 310879 310879\n",
      "Test: 66617 66617\n",
      "CV: 66617 66617\n"
     ]
    }
   ],
   "source": [
    "print(\"Total:\", len(ipr_data_processed_df))\n",
    "print(\"-----\")\n",
    "print(\"Train:\", len(X_train), len(y_train))\n",
    "print(\"Test:\", len(X_test), len(y_test))\n",
    "print(\"CV:\", len(X_cv), len(y_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        line_count  msrp  ...  trading_partner_sg  trading_partner_tr\n",
      "165092           3  1550  ...                   0                   0\n",
      "5400             9  2300  ...                   0                   0\n",
      "361631           8  2190  ...                   0                   0\n",
      "161397          94  2000  ...                   0                   0\n",
      "30555            2  1550  ...                   0                   1\n",
      "...            ...   ...  ...                 ...                 ...\n",
      "259178          11 11840  ...                   0                   0\n",
      "365838          11  1700  ...                   0                   0\n",
      "131932           4  8000  ...                   0                   0\n",
      "146867           7   900  ...                   0                   0\n",
      "121958           8  1350  ...                   0                   0\n",
      "\n",
      "[310879 rows x 7 columns] 165092   1 \n",
      "5400     1 \n",
      "361631   1 \n",
      "161397   1 \n",
      "30555    1 \n",
      "         ..\n",
      "259178   1 \n",
      "365838   1 \n",
      "131932   1 \n",
      "146867   1 \n",
      "121958   1 \n",
      "Name: seized, Length: 310879, dtype: float64\n",
      "        line_count  msrp  ...  trading_partner_sg  trading_partner_tr\n",
      "208176           8  4620  ...                   0                   1\n",
      "273547          89 30690  ...                   0                   0\n",
      "35073          166 22400  ...                   0                   0\n",
      "223983           6 11000  ...                   0                   0\n",
      "437476          14  3120  ...                   0                   0\n",
      "...            ...   ...  ...                 ...                 ...\n",
      "267392           1 19990  ...                   0                   0\n",
      "403200           2 88800  ...                   0                   0\n",
      "68117           23  1125  ...                   0                   0\n",
      "49995           71    24  ...                   0                   0\n",
      "262337          41   295  ...                   0                   1\n",
      "\n",
      "[66617 rows x 7 columns] 208176   1 \n",
      "273547   1 \n",
      "35073    1 \n",
      "223983   1 \n",
      "437476   1 \n",
      "         ..\n",
      "267392   1 \n",
      "403200   1 \n",
      "68117    1 \n",
      "49995    1 \n",
      "262337   1 \n",
      "Name: seized, Length: 66617, dtype: float64\n",
      "        line_count   msrp  ...  trading_partner_sg  trading_partner_tr\n",
      "407805         277   3898  ...                   0                   0\n",
      "413011          10     15  ...                   0                   0\n",
      "207877          25   8400  ...                   0                   1\n",
      "243055           1 299000  ...                   0                   0\n",
      "130540           7    815  ...                   0                   0\n",
      "...            ...    ...  ...                 ...                 ...\n",
      "15570           12  39000  ...                   0                   0\n",
      "233164           4   1000  ...                   0                   0\n",
      "287962          38   2800  ...                   0                   0\n",
      "331901           8   4830  ...                   0                   0\n",
      "354212          39      2  ...                   0                   0\n",
      "\n",
      "[66617 rows x 7 columns] 407805   1 \n",
      "413011   1 \n",
      "207877   1 \n",
      "243055   1 \n",
      "130540   1 \n",
      "         ..\n",
      "15570    1 \n",
      "233164   1 \n",
      "287962   1 \n",
      "331901   1 \n",
      "354212   1 \n",
      "Name: seized, Length: 66617, dtype: float64\n"
     ]
    },
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     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msrp</th>\n",
       "      <th>trading_partner_cn</th>\n",
       "      <th>trading_partner_hk</th>\n",
       "      <th>trading_partner_other_countries</th>\n",
       "      <th>trading_partner_sg</th>\n",
       "      <th>trading_partner_tr</th>\n",
       "      <th>line_count</th>\n",
       "      <th>seized</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287962</th>\n",
       "      <td>2800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>331901</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66617 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         msrp  trading_partner_cn  ...  line_count  seized\n",
       "407805   3898                   1  ...         277       1\n",
       "413011     15                   0  ...          10       1\n",
       "207877   8400                   0  ...          25       1\n",
       "243055 299000                   0  ...           1       1\n",
       "130540    815                   0  ...           7       1\n",
       "...       ...                 ...  ...         ...     ...\n",
       "15570   39000                   1  ...          12       1\n",
       "233164   1000                   0  ...           4       1\n",
       "287962   2800                   1  ...          38       1\n",
       "331901   4830                   1  ...           8       1\n",
       "354212      2                   0  ...          39       1\n",
       "\n",
       "[66617 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from feature_util import write_split\n",
    "\n",
    "write_split(X_train, y_train, ipr_data_processed_df.columns, 'ipr_data_processed_train.csv')\n",
    "write_split(X_test, y_test, ipr_data_processed_df.columns, 'ipr_data_processed_test.csv')\n",
    "write_split(X_cv, y_cv, ipr_data_processed_df.columns, 'ipr_data_processed_cv.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data sets split for training and validation data sets we can now proceed to train our model. Since our dataset is biased toward a single class we'll be training a `OneClassClassifier` model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneClassSVM</label><div class=\"sk-toggleable__content\"><pre>OneClassSVM(gamma=&#x27;auto&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneClassSVM(gamma='auto')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from feature_util import fit_and_save\n",
    "from sklearn.svm import OneClassSVM\n",
    "\n",
    "model = OneClassSVM(gamma='auto')\n",
    "\n",
    "fit_and_save(model, X_train, 'ipr_model')"
   ]
  }
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