diff --git a/app/src/components/Hero.jsx b/app/src/components/Hero.jsx index 20ad951..7c34c5a 100644 --- a/app/src/components/Hero.jsx +++ b/app/src/components/Hero.jsx @@ -25,12 +25,12 @@ const Hero = () => { const handleOneOnOneChange = (event) => { SetoneOnOne(event.target.checked); - sentQuestion() + sentQuestion(); }; const handleAfterAirDuelChange = (event) => { SetafterAirDuele(event.target.checked); - sentQuestion() + sentQuestion(); }; const handleOpenGoalChange = (event) => { @@ -301,7 +301,7 @@ const handleRedirectChange = (event) => { ///Dziwny Blad loadPlayers() if (number_of_shooters_rev.current == 1) { - console.log('Wysyłanie wartości: ', bodyPart, technique, actionType, shooterPossition, gameMinute, firstShot); + //console.log('Wysyłanie wartości: ', bodyPart, technique, actionType, shooterPossition, gameMinute, firstShot); // Użyj backticksów zamiast zwykłych cudzysłowów fetch(`http://127.0.0.1:5000/get_model?shooter=${shooter}&goalkeeper=${goalkeeper}&defenders=${defenders}&strickers=${stricers}&bodyPart=${bodyPart}&technique=${technique}&actionType=${actionType}&shooterPossition=${shooterPossition}&gameMinute=${gameMinute}&shot_first_time=${firstShot}&shot_one_on_one=${oneOnOne}&shot_aerial_won=${afterAirDuel}&shot_open_goal=${openGoal}&shot_follows_dribble=${afterDribbling}&shot_redirect=${redirect}`).then( res => res.json() diff --git a/app/src/flask-server/labelEncoder.joblib b/app/src/flask-server/labelEncoder.joblib new file mode 100644 index 0000000..9fb67fa Binary files /dev/null and b/app/src/flask-server/labelEncoder.joblib differ diff --git a/app/src/flask-server/modele/__pycache__/modele.cpython-312.pyc b/app/src/flask-server/modele/__pycache__/modele.cpython-312.pyc new file mode 100644 index 0000000..29caed1 Binary files /dev/null and b/app/src/flask-server/modele/__pycache__/modele.cpython-312.pyc differ diff --git a/app/src/flask-server/modele/modele.py b/app/src/flask-server/modele/modele.py index 3b890bf..ca15111 100644 --- a/app/src/flask-server/modele/modele.py +++ b/app/src/flask-server/modele/modele.py @@ -3,6 +3,8 @@ import pandas as pd from math import sqrt import math import numpy as np +import xgboost +from sklearn.preprocessing import OrdinalEncoder # Funkcja zwraca prawdopodobieństwo zdobycia gola def LogisticRegression_predict_proba(position_x, position_y, distance_to_goalM, angle, match_minute, Number_Intervening_Opponents, Number_Intervening_Teammates, isFoot, isHead): @@ -35,8 +37,12 @@ def xgboost_predict_proba(minute=0, position_name='Center Forward', shot_body_pa y_player_teammate_7=np.nan, y_player_teammate_8=np.nan, y_player_teammate_9=np.nan, y_player_teammate_10=np.nan, x_player_opponent_7=np.nan, y_player_opponent_7=np.nan, x_player_teammate_Goalkeeper=np.nan, y_player_teammate_Goalkeeper=np.nan): - - model = load('xgboost.joblib') + + model = xgboost.XGBClassifier() + model.load_model('xgboost.json') + + enc = OrdinalEncoder() + enc = load('labelEncoder.joblib') X_new = pd.DataFrame(columns=['minute', 'position_name', 'shot_body_part_name', 'shot_technique_name', 'shot_type_name', 'shot_first_time', 'shot_one_on_one', @@ -83,21 +89,40 @@ def xgboost_predict_proba(minute=0, position_name='Center Forward', shot_body_pa y_player_teammate_7, y_player_teammate_8, y_player_teammate_9, y_player_teammate_10, x_player_opponent_7, y_player_opponent_7, x_player_teammate_Goalkeeper, y_player_teammate_Goalkeeper] - + X_new[['position_name', + 'shot_technique_name', + 'shot_type_name', + 'shot_body_part_name']] = enc.transform(X_new[['position_name', 'shot_technique_name', 'shot_type_name', 'shot_body_part_name']], ) + + X_new[['minute', + 'position_name', 'shot_technique_name', 'shot_type_name', 'number_of_players_opponents', 'number_of_players_teammates', - 'shot_body_part_name']] = X_new[['position_name', + 'shot_body_part_name']] = X_new[['minute', + 'position_name', 'shot_technique_name', 'shot_type_name', 'number_of_players_opponents', 'number_of_players_teammates', - 'shot_body_part_name']].astype('category') - - X_new['minute'] = X_new['minute'].astype(int) + 'shot_body_part_name']].astype(int) +# X_new[['minute', +# 'position_name', +# 'shot_technique_name', +# 'shot_type_name', +# 'number_of_players_opponents', +# 'number_of_players_teammates', +# 'shot_body_part_name']] = X_new[['minute', +# 'position_name', +# 'shot_technique_name', +# 'shot_type_name', +# 'number_of_players_opponents', +# 'number_of_players_teammates', +# 'shot_body_part_name']].astype('category') + X_new[['shot_first_time', 'shot_one_on_one', 'shot_aerial_won', @@ -108,8 +133,11 @@ def xgboost_predict_proba(minute=0, position_name='Center Forward', shot_body_pa 'shot_aerial_won', 'shot_open_goal', 'shot_follows_dribble', - 'shot_redirect']].astype(bool) - + 'shot_redirect']].astype(int) + print(X_new) + print(X_new.dtypes) + + return model.predict_proba(X_new)[0][1].round(3) #XgBoost_2 diff --git a/app/src/flask-server/server.py b/app/src/flask-server/server.py index 9893f92..1478414 100644 --- a/app/src/flask-server/server.py +++ b/app/src/flask-server/server.py @@ -91,7 +91,7 @@ def get_model(): atakujacy_macierz = zmienWMaciez(atakujacy_lista) if gameMinute == "": - gameMinute = 1 # to change + gameMinute = 1.0 # to change print("wspolrzedne obroncow: ", obroncy) print("wspolrzedne atakujacych: ", atakujacy) @@ -124,11 +124,15 @@ def get_model(): posortowani_atakujacy = sort_coordinates_by_distance(atakujacy_macierz, [shooter_x, shooter_y]) print("Posortowani obroncy: ", posortowani_obroncy) - print("Posortowani atakujacy: ", posortowani_atakujacy) - - number_of_players_opponents = np.sum(~np.isnan(posortowani_obroncy).all(axis=1)) - number_of_players_teammates = np.sum(~np.isnan(posortowani_atakujacy).all(axis=1)) + print("Posortowani atakujacy: ", posortowani_atakujacy) + + + number_of_players_opponents = float(np.sum(~np.isnan(posortowani_obroncy).all(axis=1))) + number_of_players_teammates = float(np.sum(~np.isnan(posortowani_atakujacy).all(axis=1))) + + print("Liczba obroncow:", number_of_players_opponents) + print("Liczba atakujacych:", number_of_players_teammates) ## add angle, match minutes and number of players def loc2angle(x, y): @@ -155,8 +159,42 @@ def get_model(): print("bramkarz:", goalkepper_x, goalkepper_y) + if shot_first_time == 'true': + shot_first_time = True + else: + shot_first_time = False + if shot_one_on_one == 'true': + shot_one_on_one = True + else: + shot_one_on_one = False + if shot_aerial_won == 'true': + shot_aerial_won = True + else: + shot_aerial_won = False + if shot_open_goal == 'true': + shot_open_goal = True + else: + shot_open_goal = False + if shot_follows_dribble == 'true': + shot_follows_dribble = True + else: + shot_follows_dribble = False + if shot_redirect == 'true': + shot_redirect = True + else: + shot_redirect = False + + #### CHECKLIST + 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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 4d68323..32a9685 100644 --- a/notebooks/xgboost_dla_xG.ipynb +++ b/notebooks/xgboost_dla_xG.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ " dtype='object')" ] }, - "execution_count": 15, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -310,7 +310,7 @@ "[5 rows x 64 columns]" ] }, - "execution_count": 16, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -328,17 +328,583 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# df[['minute', \n", + "# 'number_of_players_opponents', \n", + "# 'number_of_players_teammates']] = df[['minute', \n", + "# 'number_of_players_opponents', \n", + "# 'number_of_players_teammates']].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OrdinalEncoder\n", + "\n", + "enc = OrdinalEncoder()\n", + "\n", + "df[['position_name', \n", + " 'shot_technique_name', \n", + " 'shot_type_name', \n", + " 'shot_body_part_name']] = enc.fit_transform(df[['position_name', \n", + " 'shot_technique_name', \n", + " 'shot_type_name',\n", + " 'shot_body_part_name']])" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "df[['minute', \n", + " 'position_name', \n", + " 'shot_technique_name', \n", + " 'shot_type_name', \n", + " 'shot_body_part_name']] = df[['minute', \n", + " 'position_name', \n", + " 'shot_technique_name', \n", + " 'shot_type_name', \n", + " 'shot_body_part_name']].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
38337 | \n", + "61 | \n", + "3 | \n", + "3 | \n", + "4 | \n", + "3 | \n", + "True | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "... | \n", + "47.3 | \n", + "42.6 | \n", + "54.7 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "21.3 | \n", + "50.9 | \n", + "NaN | \n", + "NaN | \n", + "
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38341 | \n", + "90 | \n", + "3 | \n", + "3 | \n", + "4 | \n", + "3 | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "False | \n", + "... | \n", + "62.6 | \n", + "51.0 | \n", + "66.7 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "23.0 | \n", + "45.5 | \n", + "NaN | \n", + "NaN | \n", + "
38342 rows × 64 columns
\n", + "\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", "|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Model Name | \n", @@ -692,23 +1258,23 @@ "|||||||||||||||
XG Boost | \n", - "0.10305 | \n", - "0.32102 | \n", - "0.00230 | \n", - "0.10497 | \n", - "0.32399 | \n", - "0.00009 | \n", - "15.20037 | \n", + "XG Boost | \n", + "0.10309 | \n", + "0.32107 | \n", + "0.00199 | \n", + "0.10549 | \n", + "0.32479 | \n", + "-0.00488 | \n", + "0.27337 | \n", "