Dlasza praca nad logiką obliczeniową

app/src/flask-server/modele/modele.py

@@ -3,9 +3,9 @@ import pandas as pd
 from math import sqrt
 
 # Funkcja zwraca prawdopodobieństwo zdobycia gola
-def LogisticRegression_predict_proba(position_x, position_y, angle, match_minute, Number_Intervening_Opponents, Number_Intervening_Teammates, isFoot, isHead):
+def LogisticRegression_predict_proba(position_x, position_y, distance_to_goalM, angle, match_minute, Number_Intervening_Opponents, Number_Intervening_Teammates, isFoot, isHead):
     
-    distance_to_goalM = sqrt(( (position_x**2) + (position_y**2))) 
+    # distance_to_goalM = sqrt(( (position_x**2) + (position_y**2))) 
     
     model = load('regresja_logistyczna.joblib')
     X_new = pd.DataFrame(columns=['position_x', 'position_y', 'distance_to_goalM', 'angle','match_minute', 'Number_Intervening_Opponents','Number_Intervening_Teammates', 'isFoot', 'isHead'])
@@ -14,7 +14,79 @@ def LogisticRegression_predict_proba(position_x, position_y, angle, match_minute
 
 #kolejne modele
 
-def xgboost_predict_proba():
+def xgboost_predict_proba(minute, position_name, shot_body_part_name, shot_technique_name,
+       shot_type_name, shot_first_time, shot_one_on_one,
+       shot_aerial_won, shot_deflected, shot_open_goal,
+       shot_follows_dribble, shot_redirect, x1, y1,
+       number_of_players_opponents, number_of_players_teammates,
+       angle, distance, x_player_opponent_Goalkeeper,
+       x_player_opponent_8, x_player_opponent_1, x_player_opponent_2,
+       x_player_opponent_3, x_player_teammate_1, x_player_opponent_4,
+       x_player_opponent_5, x_player_opponent_6, x_player_teammate_2,
+       x_player_opponent_9, x_player_opponent_10, x_player_opponent_11,
+       x_player_teammate_3, x_player_teammate_4, x_player_teammate_5,
+       x_player_teammate_6, x_player_teammate_7, x_player_teammate_8,
+       x_player_teammate_9, x_player_teammate_10,
+       y_player_opponent_Goalkeeper, y_player_opponent_8,
+       y_player_opponent_1, y_player_opponent_2, y_player_opponent_3,
+       y_player_teammate_1, y_player_opponent_4, y_player_opponent_5,
+       y_player_opponent_6, y_player_teammate_2, y_player_opponent_9,
+       y_player_opponent_10, y_player_opponent_11, y_player_teammate_3,
+       y_player_teammate_4, y_player_teammate_5, y_player_teammate_6,
+       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,
+       shot_kick_off):
 
     model = load('xgboost.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',
+       'shot_aerial_won', 'shot_deflected', 'shot_open_goal',
+       'shot_follows_dribble', 'shot_redirect', 'x1', 'y1',
+       'number_of_players_opponents', 'number_of_players_teammates',
+       'angle', 'distance', 'x_player_opponent_Goalkeeper',
+       'x_player_opponent_8', 'x_player_opponent_1', 'x_player_opponent_2',
+       'x_player_opponent_3', 'x_player_teammate_1', 'x_player_opponent_4',
+       'x_player_opponent_5', 'x_player_opponent_6', 'x_player_teammate_2',
+       'x_player_opponent_9', 'x_player_opponent_10', 'x_player_opponent_11',
+       'x_player_teammate_3', 'x_player_teammate_4', 'x_player_teammate_5',
+       'x_player_teammate_6', 'x_player_teammate_7', 'x_player_teammate_8',
+       'x_player_teammate_9', 'x_player_teammate_10',
+       'y_player_opponent_Goalkeeper', 'y_player_opponent_8',
+       'y_player_opponent_1', 'y_player_opponent_2', 'y_player_opponent_3',
+       'y_player_teammate_1', 'y_player_opponent_4', 'y_player_opponent_5',
+       'y_player_opponent_6', 'y_player_teammate_2', 'y_player_opponent_9',
+       'y_player_opponent_10', 'y_player_opponent_11', 'y_player_teammate_3',
+       'y_player_teammate_4', 'y_player_teammate_5', 'y_player_teammate_6',
+       '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',
+       'shot_kick_off'])
+    
+    X_new.loc[len(X_new.index)] = [minute, position_name, shot_body_part_name, shot_technique_name,
+       shot_type_name, shot_first_time, shot_one_on_one,
+       shot_aerial_won, shot_deflected, shot_open_goal,
+       shot_follows_dribble, shot_redirect, x1, y1,
+       number_of_players_opponents, number_of_players_teammates,
+       angle, distance, x_player_opponent_Goalkeeper,
+       x_player_opponent_8, x_player_opponent_1, x_player_opponent_2,
+       x_player_opponent_3, x_player_teammate_1, x_player_opponent_4,
+       x_player_opponent_5, x_player_opponent_6, x_player_teammate_2,
+       x_player_opponent_9, x_player_opponent_10, x_player_opponent_11,
+       x_player_teammate_3, x_player_teammate_4, x_player_teammate_5,
+       x_player_teammate_6, x_player_teammate_7, x_player_teammate_8,
+       x_player_teammate_9, x_player_teammate_10,
+       y_player_opponent_Goalkeeper, y_player_opponent_8,
+       y_player_opponent_1, y_player_opponent_2, y_player_opponent_3,
+       y_player_teammate_1, y_player_opponent_4, y_player_opponent_5,
+       y_player_opponent_6, y_player_teammate_2, y_player_opponent_9,
+       y_player_opponent_10, y_player_opponent_11, y_player_teammate_3,
+       y_player_teammate_4, y_player_teammate_5, y_player_teammate_6,
+       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,
+       shot_kick_off]
+    return model.predict_proba(X_new)[0][1].round(2)
+
+
     

app/src/flask-server/server.py

@@ -1,12 +1,26 @@
 from flask import Flask, request, jsonify
 from flask_cors import CORS
-from modele.modele import LogisticRegression_predict_proba
+from modele.modele import LogisticRegression_predict_proba, xgboost_predict_proba
+import math
 
 app = Flask(__name__)
 
 CORS(app)
 app.config['CORS_HEADERS'] = 'Content-Type'
 
+def loc2angle(x, y):
+    rads = math.atan(7.32 * x / (x**2 + (y - 34)**2 - (7.32/2)**2))
+    rads = math.pi + rads if rads < 0 else rads
+    deg = math.degrees(rads)
+    return deg
+
+def loc2distance(x, y):
+    return math.sqrt(x**2 + (y - 34)**2)
+
+def loc2locdistance(x1, y1, x2, y2):
+    return math.sqrt((x1 - x2)**2 + (y1 - y2)**2)
+
+
 # model Api
 # @app.route("/members")
 # def members():
@@ -16,6 +30,51 @@ app.config['CORS_HEADERS'] = 'Content-Type'
 
 # @app.route("/LRegresion<x>&<y>")
 @app.route("/get_model", methods = ['GET'])
+# def get_model():
+
+#     # x = int(x[0:2])
+#     # y = int(y[0:2])
+#     x = request.args.get('x', type=float)
+#     y = request.args.get('y', type=float)
+#     ## change model on xgboost
+#     ## add angle, match minutes and number of players
+
+#     angle = loc2angle(x = x, y = y)
+#     dist = loc2distance(x = x, y = y)
+
+#     if y is None and x is None:
+#         return jsonify({"error": "Brak wymaganych parametrów"}), 400
+
+#     response = xgboost_predict_proba(minute = 20, position_name, shot_body_part_name, shot_technique_name,
+#                                      shot_type_name, shot_first_time, shot_one_on_one,
+#                                      shot_aerial_won, shot_deflected, shot_open_goal,
+#                                      shot_follows_dribble, shot_redirect, x1 = x, y1 = y,
+#                                      number_of_players_opponents, number_of_players_teammates,
+#                                      angle = angle, distance = dist, x_player_opponent_Goalkeeper,
+#                                      x_player_opponent_8, x_player_opponent_1, x_player_opponent_2,
+#                                      x_player_opponent_3, x_player_teammate_1, x_player_opponent_4,
+#                                      x_player_opponent_5, x_player_opponent_6, x_player_teammate_2,
+#                                      x_player_opponent_9, x_player_opponent_10, x_player_opponent_11,
+#                                      x_player_teammate_3, x_player_teammate_4, x_player_teammate_5,
+#                                      x_player_teammate_6, x_player_teammate_7, x_player_teammate_8,
+#                                      x_player_teammate_9, x_player_teammate_10,
+#                                      y_player_opponent_Goalkeeper, y_player_opponent_8,
+#                                      y_player_opponent_1, y_player_opponent_2, y_player_opponent_3,
+#                                      y_player_teammate_1, y_player_opponent_4, y_player_opponent_5,
+#                                      y_player_opponent_6, y_player_teammate_2, y_player_opponent_9,
+#                                      y_player_opponent_10, y_player_opponent_11, y_player_teammate_3,
+#                                      y_player_teammate_4, y_player_teammate_5, y_player_teammate_6,
+#                                      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,
+#                                      shot_kick_off)
+#     #print(x)
+#     #print(y)
+#     #print(response)
+#     res = str(response)
+#     #return {"response":res}
+#     return jsonify({"response":res})
+
 def get_model():
 
     # x = int(x[0:2])
@@ -25,12 +84,16 @@ def get_model():
     ## change model on xgboost
     ## add angle, match minutes and number of players
 
+    angle = loc2angle(x = x, y = y)
+    dist = loc2distance(x = x, y = y)
+
     if y is None and x is None:
         return jsonify({"error": "Brak wymaganych parametrów"}), 400
 
     response = LogisticRegression_predict_proba(position_x=x,
                                                 position_y=y,
-                                                angle = 13.67,
+                                                distance_to_goalM = dist,
+                                                angle = angle,
                                                 match_minute=13,
                                                 Number_Intervening_Opponents=3,
                                                 Number_Intervening_Teammates=0,

notebooks/dataCleaning.R

@@ -114,7 +114,7 @@ loc2angle <- function(x, y) {
 
 # distance to goal
 loc2distance <- function(x, y) {
-  sqrt(x^2 + y^2)
+  sqrt(x^2 + (y - 34)^2)
 }
 
 # distance between two points on the pitch
@@ -345,5 +345,5 @@ cols <- names(data3_final)[grepl(pattern, names(data3_final))]
 data_final <- data3_final %>% unnest(all_of(cols))
 skimr::skim(data_final)
 write_csv(data_final, file = "data/final_data.csv")
-# df_test <- read.csv("data/final_data.csv", nrows = 100)
+df_test <- read.csv("data/final_data.csv", nrows = 1000)
 ##################### The fourth dataset ##############################