update

app/package.json

@@ -1,7 +1,7 @@
 {
   "name": "react_bs_web",
   "private": true,
-  "proxy": "http://localhost:5173",
+  "proxy": "http://127.0.0.1:5000/",
   "version": "0.0.0",
   "type": "module",
   "scripts": {

app/src/components/Hero.jsx

@@ -10,7 +10,7 @@ const Hero = () => {
   const toggleDropdown = () => setIsOpen(!isOpen);
   const handleMinuteChange = (e) => setGameMinute(e.target.value);
   
-  const [data,setData] = useState([{}])
+  const [data, setData] = useState("");
   //zmienne globalne
   //zmienna mówiąca który przycisk jest aktywny, jest ona wykorzystywana 
   //przez listener nanoszący zawodników na boisko. 1 - strzelec , 2 - bramkarz , 3 - obronca, 4 - napasnik. zmienna przez aktywacje przycskówk bb1, bb2, bb3 i bb4
@@ -108,10 +108,10 @@ const Hero = () => {
       var pName = "Bramkarz"
       var pColor = "#03e7fc"
     }else if(possition == 2){
-      var pName = "Napastnik"
+      var pName = "Obrońca"
       var pColor = "#0324fc"
     }else if (possition ==3){
-      var pName = "Obrońca"
+      var pName = "Napastnik"
       var pColor = "#fc6703"
     }
     var player = document.createElement('div');
@@ -189,6 +189,8 @@ const Hero = () => {
           ball.style.left = x + "%"
           ball.style.top = y + "%"
         }
+        //var shooterX = konwerturX(ball.style.left) 
+        //var shooterY = konwetujY(ball.style.top)
         posytion.innerHTML =  "x:" + konwerturX(ball.style.left) + "m " + "y:" + konwetujY(ball.style.top) + "m";
       bojo.addEventListener("mouseup", function(){
           ball.style.background = pColor
@@ -200,22 +202,38 @@ const Hero = () => {
     })
   }
 
-
+  // // Wyłanie zapytania do serwera
-
-  // Wyłanie zapytania do serwera
   function sentQuestion() {
-      if (numer_od_shooters > 0) {
+    var ball = document.querySelector('.football');
-          fetch("http://localhost:5173/members").then
+
-          (res => res.json()).then(res => {
+    if (ball) {
-            let token = res.token;
+      // Użyj backticksów zamiast zwykłych cudzysłowów
-            console.log("token: ", token);
+      //console.log('Wysyłanie wartości: ', ball);
-        })
+      shooterX = konwerturX(ball.style.left)
-        eX = data.response
+      shooterY = konwetujY(ball.style.top)
+      console.log('Wysyłanie wartości: ', shooterX, shooterY);
+      fetch(`http://127.0.0.1:5000/get_model?x=${shooterX}&y=${shooterY}`).then(
+        res => res.json()
+      ).then(
+        data => {
+          setData(data);
+          console.log(data);
+          // Przenieś tę linię do środka bloku .then(), aby uniknąć błędów
+          let eX = data.response;
+          document.getElementById("ex").innerHTML = "Współczynnik xG: " + eX;
+        }
+      ).catch(error => {
+        console.error('Błąd:', error);
+      });
     } else {
       alert('Piłka nie jest obecnie na boisku.');
     }  
   }
 
+
+
+  
+
   /* Funkcja dodająca listener do boiska*/
   function boiskoListener(ev){
     ev.preventDefault()

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'])
@@ -13,3 +13,80 @@ def LogisticRegression_predict_proba(position_x, position_y, angle, match_minute
     return model.predict_proba(X_new)[0][1].round(2)
 
 #kolejne modele
+
+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

@@ -1,36 +1,116 @@
-from flask import Flask
+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
+<<<<<<< HEAD
 @app.route("/members")
 def members():
     return "dnjss"
+=======
+# @app.route("/members")
+# def members():
+#     return{"members":["Member1","Member2","Member3"]}
+>>>>>>> 0b5e2681184a215a830217fb15fc4be882e06e8c
 
 # zapytanie o regresje logistyczną na podstawie pozycji piłki
 
-@app.route("/LRegresion<x>&<y>")
+# @app.route("/LRegresion<x>&<y>")
-def LRegresion(x,y):
+@app.route("/get_model", methods = ['GET'])
+# def get_model():
 
-    x = int(x[0:2])
+#     # x = int(x[0:2])
-    y = int(y[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])
+    # 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 = 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,
                                                 isFoot=1,
                                                 isHead=0)
-    print(x)
+    #print(x)
-    print(y)
+    #print(y)
-    print(response)
+    #print(response)
-    return {"response":[str(response)]}
+    res = str(response)
+    #return {"response":res}
+    return jsonify({"response":res})
 
 # uruchomienie serwera
 if __name__ == "__main__":

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 ##############################