ipr_model_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "We'll now evaluate or model and see how it performs against a testing and cross-validation data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, load the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_util import load_model_pickle\n",
    "\n",
    "model = load_model_pickle('models/ipr_model_202408180449.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['line_count', 'msrp', 'trading_partner_cn', 'trading_partner_hk',\n",
       "       'trading_partner_other_countries', 'trading_partner_sg',\n",
       "       'trading_partner_tr'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_names_in_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the model loaded now we'll evaluate its performance against a triaining set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_util import load_split\n",
    "\n",
    "y_name = 'seized'\n",
    "\n",
    "X_train, y_train = load_split('data/processed/ipr_data_processed_train.csv', y_name)\n",
    "X_test, y_test = load_split('data/processed/ipr_data_processed_test.csv', y_name)\n",
    "X_cv, y_cv = load_split('data/processed/ipr_data_processed_cv.csv', y_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(       line_count     msrp  trading_partner_cn  trading_partner_hk  \\\n",
       " 0               8   4620.0                   0                   0   \n",
       " 1              89  30690.0                   1                   0   \n",
       " 2             166  22400.0                   1                   0   \n",
       " 3               6  11000.0                   0                   1   \n",
       " 4              14   3120.0                   0                   0   \n",
       " ...           ...      ...                 ...                 ...   \n",
       " 66612           1  19990.0                   0                   1   \n",
       " 66613           2  88800.0                   1                   0   \n",
       " 66614          23   1125.0                   0                   0   \n",
       " 66615          71     24.0                   1                   0   \n",
       " 66616          41    295.0                   0                   0   \n",
       " \n",
       "        trading_partner_other_countries  trading_partner_sg  trading_partner_tr  \n",
       " 0                                    0                   0                   1  \n",
       " 1                                    0                   0                   0  \n",
       " 2                                    0                   0                   0  \n",
       " 3                                    0                   0                   0  \n",
       " 4                                    1                   0                   0  \n",
       " ...                                ...                 ...                 ...  \n",
       " 66612                                0                   0                   0  \n",
       " 66613                                0                   0                   0  \n",
       " 66614                                1                   0                   0  \n",
       " 66615                                0                   0                   0  \n",
       " 66616                                0                   0                   1  \n",
       " \n",
       " [66617 rows x 7 columns],\n",
       " 0        1.0\n",
       " 1        1.0\n",
       " 2        1.0\n",
       " 3        1.0\n",
       " 4        1.0\n",
       "         ... \n",
       " 66612    1.0\n",
       " 66613    1.0\n",
       " 66614    1.0\n",
       " 66615    1.0\n",
       " 66616    1.0\n",
       " Name: seized, Length: 66617, dtype: float64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.78173742,  0.        ,  0.        , ..., 14.26912733,\n",
       "        4.70233679,  3.43884422])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = model.score_samples(X_test)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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.hist(scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.save('models/ipr_model_20240817.1', scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets also look at the classification report:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00         0\n",
      "         1.0       1.00      0.40      0.57     66617\n",
      "\n",
      "    accuracy                           0.40     66617\n",
      "   macro avg       0.50      0.20      0.28     66617\n",
      "weighted avg       1.00      0.40      0.57     66617\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'lll use the model on our company's product list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Records 4200\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>line_count</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121.571429</td>\n",
       "      <td>24.713262</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>253.094131</td>\n",
       "      <td>20.247834</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.767025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.935484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.909200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>30.101553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>740.000000</td>\n",
       "      <td>143.369176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        line_count         msrp  trading_partner_cn  trading_partner_hk  \\\n",
       "count  4200.000000  4200.000000         4200.000000         4200.000000   \n",
       "mean    121.571429    24.713262            0.166667            0.166667   \n",
       "std     253.094131    20.247834            0.372722            0.372722   \n",
       "min       1.000000     4.767025            0.000000            0.000000   \n",
       "25%       3.000000    11.935484            0.000000            0.000000   \n",
       "50%      19.000000    17.909200            0.000000            0.000000   \n",
       "75%      56.000000    30.101553            0.000000            0.000000   \n",
       "max     740.000000   143.369176            1.000000            1.000000   \n",
       "\n",
       "       trading_partner_other_countries  trading_partner_sg  trading_partner_tr  \n",
       "count                      4200.000000         4200.000000         4200.000000  \n",
       "mean                          0.166667            0.166667            0.166667  \n",
       "std                           0.372722            0.372722            0.372722  \n",
       "min                           0.000000            0.000000            0.000000  \n",
       "25%                           0.000000            0.000000            0.000000  \n",
       "50%                           0.000000            0.000000            0.000000  \n",
       "75%                           0.000000            0.000000            0.000000  \n",
       "max                           1.000000            1.000000            1.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from etl_hvi_data import load_processed\n",
    "\n",
    "hvi_processed_df = load_processed()\n",
    "hvi_processed_df = hvi_processed_df[model.feature_names_in_]\n",
    "\n",
    "print(\"Total Records\", len(hvi_processed_df))\n",
    "hvi_processed_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1, -1, ..., -1, -1, -1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seized_pred = model.predict(hvi_processed_df)\n",
    "seized_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "hvi_risk_df = hvi_processed_df.copy()\n",
    "hvi_risk_df['seized_pred'] = seized_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>line_count</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>seized_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121.571429</td>\n",
       "      <td>24.713262</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.489048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>253.094131</td>\n",
       "      <td>20.247834</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.872361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.767025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.935484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.909200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>30.101553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>740.000000</td>\n",
       "      <td>143.369176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        line_count         msrp  trading_partner_cn  trading_partner_hk  \\\n",
       "count  4200.000000  4200.000000         4200.000000         4200.000000   \n",
       "mean    121.571429    24.713262            0.166667            0.166667   \n",
       "std     253.094131    20.247834            0.372722            0.372722   \n",
       "min       1.000000     4.767025            0.000000            0.000000   \n",
       "25%       3.000000    11.935484            0.000000            0.000000   \n",
       "50%      19.000000    17.909200            0.000000            0.000000   \n",
       "75%      56.000000    30.101553            0.000000            0.000000   \n",
       "max     740.000000   143.369176            1.000000            1.000000   \n",
       "\n",
       "       trading_partner_other_countries  trading_partner_sg  \\\n",
       "count                      4200.000000         4200.000000   \n",
       "mean                          0.166667            0.166667   \n",
       "std                           0.372722            0.372722   \n",
       "min                           0.000000            0.000000   \n",
       "25%                           0.000000            0.000000   \n",
       "50%                           0.000000            0.000000   \n",
       "75%                           0.000000            0.000000   \n",
       "max                           1.000000            1.000000   \n",
       "\n",
       "       trading_partner_tr  seized_pred  \n",
       "count         4200.000000  4200.000000  \n",
       "mean             0.166667    -0.489048  \n",
       "std              0.372722     0.872361  \n",
       "min              0.000000    -1.000000  \n",
       "25%              0.000000    -1.000000  \n",
       "50%              0.000000    -1.000000  \n",
       "75%              0.000000     1.000000  \n",
       "max              1.000000     1.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4200.000000\n",
       "mean       -0.489048\n",
       "std         0.872361\n",
       "min        -1.000000\n",
       "25%        -1.000000\n",
       "50%        -1.000000\n",
       "75%         1.000000\n",
       "max         1.000000\n",
       "Name: seized_pred, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.seized_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seized_pred\n",
       "-1    3127\n",
       " 1    1073\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.seized_pred.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we save to disk, we'll convert back the dummy or one-hot encoded trading partner columns to a categorical one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['trading_partner_cn', 'trading_partner_hk', 'trading_partner_other_countries', 'trading_partner_sg', 'trading_partner_tr']\n",
      "['cn', 'hk', 'other countries', 'sg', 'tr']\n"
     ]
    }
   ],
   "source": [
    "trading_partner_columns = [c for c in hvi_risk_df.columns if c.startswith(\"trading_partner_\")]\n",
    "trading_partner_values = [' '.join(c.split(\"_\")[2:]) for c in trending_partner_columns]\n",
    "\n",
    "print(trading_partner_columns)\n",
    "print(trading_partner_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trading_partner_categorical(x):\n",
    "    \"\"\"\n",
    "    Reverse encodes categorical values from dummy / OHE features to categorical labels\n",
    "    \"\"\"\n",
    "    try:\n",
    "        index = list(x[trending_partner_columns]).index(1)\n",
    "    except:\n",
    "        index = -1\n",
    "    trading_partner = trading_partner_values[index] if index != -1 else 'ca'\n",
    "    return trading_partner\n",
    "\n",
    "hvi_risk_df['trading_partner'] = hvi_risk_df.apply(trading_partner_categorical, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['line_count', 'msrp', 'trading_partner_cn', 'trading_partner_hk',\n",
       "       'trading_partner_other_countries', 'trading_partner_sg',\n",
       "       'trading_partner_tr', 'seized_pred', 'trading_partner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     4200\n",
       "unique       6\n",
       "top         cn\n",
       "freq       700\n",
       "Name: trading_partner, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.trading_partner.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "hvi_risk_df.drop(columns=trading_partner_columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['line_count', 'msrp', 'seized_pred', 'trading_partner'], dtype='object')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, now that we've run our model we can get an estimate of risk, the model will have predicted whether an particular product would've been seized or not and we'll use the sum of those seized vs not as the risk indicator\n",
    "\n",
    "  * `-1` -> Inliner or seized\n",
    "  * `1` -> Outlier or not seized\n",
    "\n",
    "To make it easier to identify we'll replace the values with values that simplify categorical and numerical opertions,\n",
    "\n",
    "  * `1` -> Seized\n",
    "  * `0` -> Not Seized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "hvi_risk_df['seized_pred'] = hvi_risk_df.seized_pred.apply(lambda x: 0 if x == 1 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "hvi_risk_df.drop(columns=[1, -1], inplace=True, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>line_count</th>\n",
       "      <th>msrp</th>\n",
       "      <th>seized_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>4200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121.571429</td>\n",
       "      <td>24.713262</td>\n",
       "      <td>0.744524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>253.094131</td>\n",
       "      <td>20.247834</td>\n",
       "      <td>0.436180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.767025</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.935484</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.909200</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>30.101553</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>740.000000</td>\n",
       "      <td>143.369176</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        line_count         msrp  seized_pred\n",
       "count  4200.000000  4200.000000  4200.000000\n",
       "mean    121.571429    24.713262     0.744524\n",
       "std     253.094131    20.247834     0.436180\n",
       "min       1.000000     4.767025     0.000000\n",
       "25%       3.000000    11.935484     0.000000\n",
       "50%      19.000000    17.909200     1.000000\n",
       "75%      56.000000    30.101553     1.000000\n",
       "max     740.000000   143.369176     1.000000"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seized_pred\n",
       "1    3127\n",
       "0    1073\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hvi_risk_df.seized_pred.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll save merge our risk dataset with the original product items to be able to draw conclusions from the risk profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>product_id</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>trading_partner</th>\n",
       "      <th>line_count</th>\n",
       "      <th>msrp</th>\n",
       "      <th>seized_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2296012</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>cn</td>\n",
       "      <td>24</td>\n",
       "      <td>17.909200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13780156</td>\n",
       "      <td>LOCOMOTIVE</td>\n",
       "      <td>cn</td>\n",
       "      <td>24</td>\n",
       "      <td>13.727599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11895958</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>cn</td>\n",
       "      <td>24</td>\n",
       "      <td>16.714456</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4335679</td>\n",
       "      <td>Zivame</td>\n",
       "      <td>cn</td>\n",
       "      <td>24</td>\n",
       "      <td>15.471924</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11690882</td>\n",
       "      <td>Roadster</td>\n",
       "      <td>cn</td>\n",
       "      <td>24</td>\n",
       "      <td>7.156511</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  brand_name trading_partner  line_count       msrp  seized_pred\n",
       "0     2296012    Roadster              cn          24  17.909200            1\n",
       "1    13780156  LOCOMOTIVE              cn          24  13.727599            0\n",
       "2    11895958    Roadster              cn          24  16.714456            1\n",
       "3     4335679      Zivame              cn          24  15.471924            1\n",
       "4    11690882    Roadster              cn          24   7.156511            1"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from etl_hvi_data import load_processed\n",
    "\n",
    "hvi_products = load_processed()\n",
    "hvi_products_temp_df = pd.concat([hvi_risk_df, hvi_products[['product_id', 'brand_name']]], axis='columns')\n",
    "hvi_processed_df = hvi_products_temp_df[['product_id', 'brand_name', 'trading_partner', 'line_count', 'msrp', 'seized_pred']]\n",
    "hvi_processed_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "hvi_processed_df.to_csv(\"data/processed/hvi_products_risk.csv\", index=False)"
   ]
  }
 ],
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