training xgb model not fully accurate
This commit is contained in:
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.gitignore
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@@ -82,4 +82,5 @@ Desktop.ini
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*.bak
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*.swp
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data/*
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data/*
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models/*
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@@ -5694,7 +5694,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "99be3b8b",
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"metadata": {},
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"outputs": [
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@@ -11068,7 +11068,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 3,
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"id": "b58e7aed",
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"metadata": {},
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"outputs": [
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@@ -11281,7 +11281,7 @@
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"[5 rows x 26 columns]"
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]
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},
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"execution_count": 5,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -11454,6 +11454,601 @@
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"\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "be2469ce",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training rows: 466379\n",
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"Testing rows : 116374\n",
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"[0]\tvalidation_0-mae:32.71855\tvalidation_1-mae:32.46407\n",
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"[100]\tvalidation_0-mae:17.67243\tvalidation_1-mae:17.56379\n",
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"[200]\tvalidation_0-mae:15.99231\tvalidation_1-mae:16.02378\n",
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"[300]\tvalidation_0-mae:15.81573\tvalidation_1-mae:15.89000\n",
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"[400]\tvalidation_0-mae:15.76997\tvalidation_1-mae:15.86225\n",
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"[500]\tvalidation_0-mae:15.74462\tvalidation_1-mae:15.84885\n",
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"[600]\tvalidation_0-mae:15.72323\tvalidation_1-mae:15.83748\n",
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"[700]\tvalidation_0-mae:15.70448\tvalidation_1-mae:15.82844\n",
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"[800]\tvalidation_0-mae:15.68914\tvalidation_1-mae:15.82248\n",
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"[900]\tvalidation_0-mae:15.67203\tvalidation_1-mae:15.81510\n",
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"[1000]\tvalidation_0-mae:15.65865\tvalidation_1-mae:15.80998\n",
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"[1100]\tvalidation_0-mae:15.64647\tvalidation_1-mae:15.80701\n",
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"[1200]\tvalidation_0-mae:15.63444\tvalidation_1-mae:15.80456\n",
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"[1300]\tvalidation_0-mae:15.62326\tvalidation_1-mae:15.80293\n",
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"[1400]\tvalidation_0-mae:15.61287\tvalidation_1-mae:15.80151\n",
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"[1500]\tvalidation_0-mae:15.60263\tvalidation_1-mae:15.80194\n",
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"[1600]\tvalidation_0-mae:15.59322\tvalidation_1-mae:15.80148\n",
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"[1700]\tvalidation_0-mae:15.58498\tvalidation_1-mae:15.80152\n",
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"[1800]\tvalidation_0-mae:15.57652\tvalidation_1-mae:15.80163\n",
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"[1900]\tvalidation_0-mae:15.56848\tvalidation_1-mae:15.80177\n",
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"[1999]\tvalidation_0-mae:15.56159\tvalidation_1-mae:15.80210\n",
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"\n",
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"========================\n",
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"Results\n",
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"========================\n",
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"MAE : 15.802 runs\n",
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"RMSE : 22.194 runs\n",
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"R² : 0.7054\n",
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"\n",
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"Feature importance:\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>feature</th>\n",
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" <th>importance</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>run_rate_factor</td>\n",
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" <td>0.477130</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>run_rate</td>\n",
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" <td>0.272351</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>projected_score</td>\n",
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" <td>0.162744</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>delivery_number</td>\n",
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" <td>0.017518</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>18</th>\n",
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" <td>score_wicket_combo</td>\n",
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" <td>0.013470</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>19</th>\n",
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" <td>pressure_index</td>\n",
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" <td>0.010688</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>middle_overs</td>\n",
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" <td>0.010050</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20</th>\n",
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" <td>score_squared</td>\n",
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" <td>0.008396</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>legal_ball</td>\n",
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" <td>0.004335</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>score</td>\n",
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" <td>0.004045</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>wickets</td>\n",
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" <td>0.003218</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>wickets_remaining</td>\n",
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" <td>0.002742</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>batting_strength_remaining</td>\n",
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" <td>0.002630</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>overs_remaining</td>\n",
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" <td>0.002173</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>innings_progress</td>\n",
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" <td>0.002061</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>balls_remaining</td>\n",
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" <td>0.001827</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>wicket_fraction</td>\n",
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" <td>0.001551</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
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" <td>score_per_ball_remaining</td>\n",
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" <td>0.001476</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>22</th>\n",
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" <td>score_progress_interaction</td>\n",
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" <td>0.001058</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>overs_completed</td>\n",
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" <td>0.000537</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>powerplay</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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" <td>death_overs</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>21</th>\n",
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" <td>progress_squared</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" feature importance\n",
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"10 run_rate_factor 0.477130\n",
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"8 run_rate 0.272351\n",
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"9 projected_score 0.162744\n",
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"0 delivery_number 0.017518\n",
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"18 score_wicket_combo 0.013470\n",
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"19 pressure_index 0.010688\n",
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"15 middle_overs 0.010050\n",
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"20 score_squared 0.008396\n",
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"1 legal_ball 0.004335\n",
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"2 score 0.004045\n",
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"3 wickets 0.003218\n",
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"11 wickets_remaining 0.002742\n",
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"13 batting_strength_remaining 0.002630\n",
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"7 overs_remaining 0.002173\n",
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"4 innings_progress 0.002061\n",
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"5 balls_remaining 0.001827\n",
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"12 wicket_fraction 0.001551\n",
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"17 score_per_ball_remaining 0.001476\n",
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"22 score_progress_interaction 0.001058\n",
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"6 overs_completed 0.000537\n",
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"14 powerplay 0.000000\n",
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"16 death_overs 0.000000\n",
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"21 progress_squared 0.000000"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Saved model\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"from sklearn.model_selection import GroupShuffleSplit\n",
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"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
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"\n",
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"from xgboost import XGBRegressor\n",
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"\n",
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"import joblib\n",
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"\n",
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"\n",
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"\n",
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"# =====================================\n",
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"# Prepare data\n",
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"# =====================================\n",
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"\n",
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"data = df.copy()\n",
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"\n",
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"\n",
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"# Remove identifiers / leakage\n",
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"\n",
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"drop_cols = [\n",
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" \"target_score\",\n",
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" \"match_id\",\n",
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" \"over_ball\"\n",
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"]\n",
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"\n",
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"\n",
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"X = data.drop(\n",
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" columns=drop_cols\n",
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")\n",
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"\n",
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"\n",
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"y = data[\"target_score\"]\n",
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"\n",
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"\n",
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"groups = data[\"match_id\"]\n",
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"\n",
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"\n",
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"\n",
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"# =====================================\n",
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"# Match-based split\n",
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"# =====================================\n",
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"\n",
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"splitter = GroupShuffleSplit(\n",
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" n_splits=1,\n",
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" test_size=0.2,\n",
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" random_state=42\n",
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")\n",
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"\n",
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"\n",
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"train_idx, test_idx = next(\n",
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" splitter.split(\n",
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" X,\n",
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" y,\n",
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" groups\n",
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" )\n",
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")\n",
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"\n",
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"\n",
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"X_train = X.iloc[train_idx]\n",
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"X_test = X.iloc[test_idx]\n",
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"\n",
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"\n",
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"y_train = y.iloc[train_idx]\n",
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"y_test = y.iloc[test_idx]\n",
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"\n",
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"\n",
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"\n",
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"print(\"Training rows:\", len(X_train))\n",
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"print(\"Testing rows :\", len(X_test))\n",
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"\n",
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"\n",
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"\n",
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"# =====================================\n",
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"# XGBoost model\n",
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"# =====================================\n",
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"\n",
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"model = XGBRegressor(\n",
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"\n",
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" # trees\n",
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" n_estimators=2000,\n",
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"\n",
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" # learning\n",
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" learning_rate=0.015,\n",
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"\n",
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" # tree complexity\n",
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" max_depth=6,\n",
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" min_child_weight=5,\n",
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"\n",
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" # randomness\n",
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" subsample=0.85,\n",
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" colsample_bytree=0.85,\n",
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"\n",
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" # regularization\n",
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" reg_alpha=0.1,\n",
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" reg_lambda=2,\n",
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"\n",
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" objective=\"reg:squarederror\",\n",
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"\n",
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" eval_metric=\"mae\",\n",
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"\n",
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" random_state=42,\n",
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"\n",
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" n_jobs=-1\n",
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")\n",
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"\n",
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"\n",
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"\n",
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"# =====================================\n",
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"# Train\n",
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"# =====================================\n",
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"\n",
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"model.fit(\n",
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" X_train,\n",
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" y_train,\n",
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"\n",
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" eval_set=[\n",
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" (X_train, y_train),\n",
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" (X_test, y_test)\n",
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" ],\n",
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"\n",
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" verbose=100\n",
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")\n",
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"\n",
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"\n",
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"\n",
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"# =====================================\n",
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"# Evaluate\n",
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"# =====================================\n",
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"\n",
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"pred = model.predict(X_test)\n",
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"\n",
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"\n",
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"mae = mean_absolute_error(\n",
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" y_test,\n",
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" pred\n",
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")\n",
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"\n",
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"\n",
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"rmse = np.sqrt(\n",
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" mean_squared_error(\n",
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" y_test,\n",
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" pred\n",
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" )\n",
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")\n",
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"\n",
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"\n",
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"r2 = r2_score(\n",
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" y_test,\n",
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" pred\n",
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")\n",
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"\n",
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"\n",
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"print(\"\\n========================\")\n",
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"print(\"Results\")\n",
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"print(\"========================\")\n",
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"\n",
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"print(f\"MAE : {mae:.3f} runs\")\n",
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"print(f\"RMSE : {rmse:.3f} runs\")\n",
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"print(f\"R² : {r2:.4f}\")\n",
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"\n",
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"\n",
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"\n",
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"# =====================================\n",
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"# Feature importance\n",
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"# =====================================\n",
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"\n",
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"importance = pd.DataFrame(\n",
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" {\n",
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" \"feature\": X.columns,\n",
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" \"importance\": model.feature_importances_\n",
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" }\n",
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")\n",
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"\n",
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"\n",
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"importance = importance.sort_values(\n",
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" \"importance\",\n",
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" ascending=False\n",
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")\n",
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"\n",
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"\n",
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"print(\"\\nFeature importance:\")\n",
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"display(importance)\n",
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"\n",
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"\n",
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"\n",
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"# =====================================\n",
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"# Save model\n",
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"# =====================================\n",
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"\n",
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"joblib.dump(\n",
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" model,\n",
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" \"models/xgb_run_predictor.pkl\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"\\nSaved model\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "acfb9aa7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"======================\n",
|
||||
"DLS BASELINE RESULTS\n",
|
||||
"======================\n",
|
||||
"Rows evaluated : 116374\n",
|
||||
"MAE : 23.02 runs\n",
|
||||
"RMSE : 38.91 runs\n",
|
||||
"R² : 0.0947\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ==========================================\n",
|
||||
"# Create DLS-style resource table from train\n",
|
||||
"# ==========================================\n",
|
||||
"\n",
|
||||
"dls_train = X_train.copy()\n",
|
||||
"dls_train[\"target_score\"] = y_train.values\n",
|
||||
"\n",
|
||||
"dls_train[\"resource_used\"] = (\n",
|
||||
" dls_train[\"score\"] /\n",
|
||||
" dls_train[\"target_score\"]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Remove weird cases\n",
|
||||
"dls_train[\"resource_used\"] = (\n",
|
||||
" dls_train[\"resource_used\"]\n",
|
||||
" .replace([np.inf, -np.inf], np.nan)\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"dls_train = dls_train.dropna(\n",
|
||||
" subset=[\"resource_used\"]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"dls_train[\"resource_used\"] = (\n",
|
||||
" dls_train[\"resource_used\"]\n",
|
||||
" .clip(0.01, 1.0)\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Average resource remaining at each state\n",
|
||||
"resource_table = (\n",
|
||||
" dls_train\n",
|
||||
" .groupby(\n",
|
||||
" [\n",
|
||||
" \"legal_ball\",\n",
|
||||
" \"wickets\"\n",
|
||||
" ]\n",
|
||||
" )[\"resource_used\"]\n",
|
||||
" .mean()\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ==========================================\n",
|
||||
"# DLS prediction function\n",
|
||||
"# ==========================================\n",
|
||||
"\n",
|
||||
"def dls_predict(row):\n",
|
||||
"\n",
|
||||
" key = (\n",
|
||||
" row[\"legal_ball\"],\n",
|
||||
" row[\"wickets\"]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # exact state\n",
|
||||
" if key in resource_table.index:\n",
|
||||
" resource = resource_table[key]\n",
|
||||
"\n",
|
||||
" else:\n",
|
||||
" # nearest legal ball fallback\n",
|
||||
" subset = resource_table.reset_index()\n",
|
||||
"\n",
|
||||
" subset = subset[\n",
|
||||
" subset[\"wickets\"] == row[\"wickets\"]\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
" if len(subset) == 0:\n",
|
||||
" return np.nan\n",
|
||||
"\n",
|
||||
" idx = (\n",
|
||||
" abs(\n",
|
||||
" subset[\"legal_ball\"]\n",
|
||||
" - row[\"legal_ball\"]\n",
|
||||
" )\n",
|
||||
" .idxmin()\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" resource = subset.loc[\n",
|
||||
" idx,\n",
|
||||
" \"resource_used\"\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" return row[\"score\"] / resource\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ==========================================\n",
|
||||
"# Predict test set\n",
|
||||
"# ==========================================\n",
|
||||
"\n",
|
||||
"dls_predictions = X_test.apply(\n",
|
||||
" dls_predict,\n",
|
||||
" axis=1\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Remove failures\n",
|
||||
"mask = ~dls_predictions.isna()\n",
|
||||
"\n",
|
||||
"dls_predictions = dls_predictions[mask]\n",
|
||||
"dls_actual = y_test[mask]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ==========================================\n",
|
||||
"# Metrics\n",
|
||||
"# ==========================================\n",
|
||||
"\n",
|
||||
"mae = mean_absolute_error(\n",
|
||||
" dls_actual,\n",
|
||||
" dls_predictions\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"rmse = np.sqrt(\n",
|
||||
" mean_squared_error(\n",
|
||||
" dls_actual,\n",
|
||||
" dls_predictions\n",
|
||||
" )\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"r2 = r2_score(\n",
|
||||
" dls_actual,\n",
|
||||
" dls_predictions\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(\"======================\")\n",
|
||||
"print(\"DLS BASELINE RESULTS\")\n",
|
||||
"print(\"======================\")\n",
|
||||
"print(f\"Rows evaluated : {len(dls_actual)}\")\n",
|
||||
"print(f\"MAE : {mae:.2f} runs\")\n",
|
||||
"print(f\"RMSE : {rmse:.2f} runs\")\n",
|
||||
"print(f\"R² : {r2:.4f}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5853752b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
Reference in New Issue
Block a user