Naprawa kategorycznych, Zmiana zapisywania/wczytywania xgboost

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()

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 

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
+    print('CHECKLISTA')
+    print('shot_first_time', shot_first_time)
+    print('shot_one_on_one', shot_one_on_one)
+    print('shot_aerial_won', shot_aerial_won)
+    print('shot_open_goal', shot_open_goal)
+    print('shot_follows_dribble', shot_follows_dribble)
+    print('shot_redirect', shot_redirect)
+
     # MODEL XGBOOST
-    response = xgboost_predict_proba(minute=gameMinute, 
+    response = xgboost_predict_proba(minute=int(gameMinute), #minute=0,
                                      position_name=position_name, 
                                      shot_body_part_name=body_part, 
                                      shot_technique_name=technique,