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\ No newline at end of file diff --git a/notebooks/xgboost_dla_xG.ipynb b/notebooks/xgboost_dla_xG.ipynb index 3206753..66071bd 100644 --- a/notebooks/xgboost_dla_xG.ipynb +++ b/notebooks/xgboost_dla_xG.ipynb @@ -341,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -360,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -427,56 +427,8 @@ "
\n", "38342 rows × 64 columns
\n", + "82821 rows × 64 columns
\n", "" ], "text/plain": [ " minute position_name shot_body_part_name shot_technique_name \\\n", - "0 0 18 3 4 \n", - "1 5 18 1 4 \n", - "2 5 4 3 2 \n", - "3 5 10 3 4 \n", - "4 5 17 1 4 \n", + "0 2 9 3 2 \n", + "1 5 7 1 6 \n", + "2 15 9 1 4 \n", + "3 16 3 0 4 \n", + "4 18 18 3 4 \n", "... ... ... ... ... \n", - "38337 61 3 3 4 \n", - "38338 66 0 3 4 \n", - "38339 73 3 1 6 \n", - "38340 75 13 1 4 \n", - "38341 90 3 3 4 \n", + "82816 79 0 3 2 \n", + "82817 80 20 3 4 \n", + "82818 82 0 3 4 \n", + "82819 84 21 3 4 \n", + "82820 88 8 1 2 \n", "\n", " shot_type_name shot_first_time shot_one_on_one shot_aerial_won \\\n", - "0 3 False False False \n", - "1 3 False False False \n", - "2 3 True False False \n", - "3 3 False False False \n", - "4 3 True False False \n", + "0 3 True False False \n", + "1 3 True False False \n", + "2 3 False False False \n", + "3 3 False False True \n", + "4 3 False False False \n", "... ... ... ... ... \n", - "38337 3 True False False \n", - "38338 3 True False False \n", - "38339 3 True False False \n", - "38340 3 False False False \n", - "38341 3 False False False \n", + "82816 3 True False False \n", + "82817 3 False False False \n", + "82818 3 True False False \n", + "82819 3 False False False \n", + "82820 3 False False False \n", "\n", " shot_open_goal shot_follows_dribble ... y_player_teammate_5 \\\n", "0 False False ... NaN \n", - "1 False False ... NaN \n", - "2 False False ... 48.9 \n", + "1 False False ... 20.6 \n", + "2 False False ... 29.0 \n", "3 False False ... NaN \n", - "4 False False ... NaN \n", + "4 False False ... 27.9 \n", "... ... ... ... ... \n", - "38337 False False ... 47.3 \n", - "38338 False False ... 47.6 \n", - "38339 False False ... 48.9 \n", - "38340 False False ... 29.1 \n", - "38341 False False ... 62.6 \n", + "82816 False False ... 30.9 \n", + "82817 False False ... 60.2 \n", + "82818 False False ... NaN \n", + "82819 False False ... NaN \n", + "82820 False False ... NaN \n", "\n", " y_player_teammate_6 y_player_teammate_7 y_player_teammate_8 \\\n", "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", + "1 32.8 NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", + "4 31.4 33.4 NaN \n", "... ... ... ... \n", - "38337 42.6 54.7 NaN \n", - "38338 39.4 43.1 NaN \n", - "38339 48.1 41.1 NaN \n", - "38340 33.6 40.9 NaN \n", - "38341 51.0 66.7 NaN \n", + "82816 NaN NaN NaN \n", + "82817 NaN NaN NaN \n", + "82818 NaN NaN NaN \n", + "82819 NaN NaN NaN \n", + "82820 NaN NaN NaN \n", "\n", " y_player_teammate_9 y_player_teammate_10 x_player_opponent_7 \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", + "0 NaN NaN 26.6 \n", + "1 NaN NaN 23.8 \n", + "2 NaN NaN 29.6 \n", + "3 NaN NaN 26.7 \n", + "4 NaN NaN 16.9 \n", "... ... ... ... \n", - "38337 NaN NaN 21.3 \n", - "38338 NaN NaN 19.0 \n", - "38339 NaN NaN 21.7 \n", - "38340 NaN NaN 21.2 \n", - "38341 NaN NaN 23.0 \n", + "82816 NaN NaN 30.8 \n", + "82817 NaN NaN 31.9 \n", + "82818 NaN NaN NaN \n", + "82819 NaN NaN NaN \n", + "82820 NaN NaN 20.0 \n", "\n", " y_player_opponent_7 x_player_teammate_Goalkeeper \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", + "0 53.1 NaN \n", + "1 31.2 NaN \n", + "2 55.3 NaN \n", + "3 60.4 NaN \n", + "4 40.1 NaN \n", "... ... ... \n", - "38337 50.9 NaN \n", - "38338 45.8 NaN \n", - "38339 29.6 NaN \n", - "38340 32.4 NaN \n", - "38341 45.5 NaN \n", + "82816 40.3 NaN \n", + "82817 47.7 NaN \n", + "82818 NaN NaN \n", + "82819 NaN NaN \n", + "82820 44.5 NaN \n", "\n", " y_player_teammate_Goalkeeper \n", "0 NaN \n", @@ -780,16 +780,16 @@ "3 NaN \n", "4 NaN \n", "... ... \n", - "38337 NaN \n", - "38338 NaN \n", - "38339 NaN \n", - "38340 NaN \n", - "38341 NaN \n", + "82816 NaN \n", + "82817 NaN \n", + "82818 NaN \n", + "82819 NaN \n", + "82820 NaN \n", "\n", - "[38342 rows x 64 columns]" + "[82821 rows x 64 columns]" ] }, - "execution_count": 46, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -809,7 +809,7 @@ "['labelEncoder.joblib']" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -820,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -892,19 +892,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Change the type of categorical features to 'category' \n", - "df[['minute',\n", - " 'position_name', \n", + "df[['position_name', \n", " 'shot_technique_name', \n", " 'shot_type_name', \n", " 'number_of_players_opponents', \n", " 'number_of_players_teammates', \n", - " 'shot_body_part_name']] = df[['minute',\n", - " 'position_name', \n", + " 'shot_body_part_name']] = df[['position_name', \n", " 'shot_technique_name', \n", " 'shot_type_name', \n", " 'number_of_players_opponents', \n", @@ -914,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -931,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -955,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -981,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -991,7 +989,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1004,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1029,15 +1027,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best parameters: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 250, 'scale_pos_weight': 1}\n", - "Model Training Time: 1550.563 seconds\n" + "Best parameters: {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 300, 'scale_pos_weight': 1}\n", + "Model Training Time: 1393.345 seconds\n" ] } ], @@ -1063,12 +1061,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "\n", - " | Training MAE | \n", - "Training RMSE | \n", - "Training R2 | \n", - "Testing MAE | \n", - "Testing RMSE | \n", - "Testing R2 | \n", - "Training Time (mins) | \n", + "Training MAE | \n", + "Training RMSE | \n", + "Training R2 | \n", + "Testing MAE | \n", + "Testing RMSE | \n", + "Testing R2 | \n", + "Training Time (mins) | \n", "|
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Model Name | \n", @@ -1258,23 +1256,23 @@ "|||||||||||||||
XG Boost | \n", - "0.09622 | \n", - "0.31019 | \n", - "0.01693 | \n", - "0.09949 | \n", - "0.31542 | \n", - "0.01620 | \n", - "25.84271 | \n", + "XG Boost | \n", + "0.09646 | \n", + "0.31058 | \n", + "0.01446 | \n", + "0.09919 | \n", + "0.31494 | \n", + "0.01918 | \n", + "23.22242 | \n", "