import pandas as pd import os def get_ck(df, season, round_num, local, away, league=None): """Obtiene corners totales de un partido específico""" season_round = (df['season'] == season) & (df['round'] == round_num) if league is not None: season_round = season_round & (df['league'] == league) df = df[season_round] df_local = df[df['team'] == local] df_away = df[df['team'] == away] total_ck = df_local["Pass Types_CK"].sum() + df_away["Pass Types_CK"].sum() return total_ck def get_dataframes(df, season, round_num, local, away, league=None): """Retorna 8 DataFrames filtrados por equipo, venue y liga""" season_round = (df['season'] == season) & (df['round'] < round_num) if league is not None: season_round = season_round & (df['league'] == league) def filter_and_split(team_filter): filtered = df[season_round & team_filter].copy() home = filtered[filtered['venue'] == "Home"] away = filtered[filtered['venue'] == "Away"] return home, away local_home, local_away = filter_and_split(df['team'] == local) local_opp_home, local_opp_away = filter_and_split(df['opponent'] == local) away_home, away_away = filter_and_split(df['team'] == away) away_opp_home, away_opp_away = filter_and_split(df['opponent'] == away) return (local_home, local_away, local_opp_home, local_opp_away, away_home, away_away, away_opp_home, away_opp_away) def get_head_2_head(df, local, away, seasons=None, league=None): """Obtiene últimos 3 enfrentamientos directos""" if seasons is None: seasons = [] df_filtered = df[df['season'].isin(seasons)] if seasons else df if league is not None: df_filtered = df_filtered[df_filtered['league'] == league] local_h2h = df_filtered[(df_filtered['team'] == local) & (df_filtered['opponent'] == away)] away_h2h = df_filtered[(df_filtered['team'] == away) & (df_filtered['opponent'] == local)] if len(local_h2h) < 4: return local_h2h.tail(2), away_h2h.tail(2) return local_h2h.tail(3), away_h2h.tail(3) def get_points_from_result(result): """Convierte resultado (W/D/L) a puntos""" if result == 'W': return 3 elif result == 'D': return 1 else: return 0 # ✅ NUEVA FUNCIÓN: Calcular PPP (Puntos Por Partido) def get_team_ppp(df, team, season, round_num, league=None): """ Calcula puntos por partido (PPP) de un equipo Args: df: DataFrame completo team: Nombre del equipo season: Temporada round_num: Número de jornada (NO incluye esta jornada) league: Código de liga (opcional) Returns: float: Puntos por partido (0-3) """ team_matches = df[ (df['team'] == team) & (df['season'] == season) & (df['round'] < round_num) ] if league is not None: team_matches = team_matches[team_matches['league'] == league] if len(team_matches) == 0: return 0.0 total_points = team_matches['result'].apply(get_points_from_result).sum() ppp = total_points / len(team_matches) return ppp # ✅ NUEVA FUNCIÓN: Calcular diferencia de PPP def get_ppp_difference(df, local, away, season, round_num, league=None): """ Calcula la diferencia de puntos por partido entre local y visitante Args: df: DataFrame completo local: Equipo local away: Equipo visitante season: Temporada round_num: Jornada actual league: Código de liga (opcional) Returns: float: Diferencia de PPP (local - away) """ local_ppp = get_team_ppp(df, local, season, round_num, league) away_ppp = get_team_ppp(df, away, season, round_num, league) return local_ppp - away_ppp def get_average(df, is_team=False, lst_avg=None): """Calcula promedios de estadísticas""" if len(df) == 0: # Retornar valores por defecto si el DataFrame está vacío if is_team: return (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) return (0, 0, 0, 0, 0, 0, 0, 0) if is_team: # =========================== # ESTADÍSTICAS BÁSICAS (NORMALIZADAS) # =========================== avg_cross = (df['Performance_Crs'].sum() / len(df)) - lst_avg[3] avg_att_3rd = (df['Touches_Att 3rd'].sum() / len(df)) - lst_avg[4] avg_sca = (df['SCA Types_SCA'].sum() / len(df)) - lst_avg[2] avg_xg = (df['Expected_xG'].sum() / len(df)) - lst_avg[1] # ✅ CAMBIO: VARIANZA EN VEZ DE PROMEDIO DE CK var_ck = df['Pass Types_CK'].var() if len(df) > 1 else 0 avg_ck = (df['Pass Types_CK'].sum() / len(df)) - lst_avg[8] avg_poss = (df['Poss'].sum() / len(df)) - 50 avg_gf = (df['GF'].sum() / len(df)) - lst_avg[5] avg_ga = (df['GA'].sum() / len(df)) - lst_avg[6] # =========================== # MÉTRICAS OFENSIVAS AVANZADAS # =========================== # Precisión de tiros total_sh = df['Standard_Sh'].sum() sh_accuracy = (df['Standard_SoT'].sum() / total_sh) if total_sh > 0 else 0 # Eficiencia xG por tiro xg_shot = (df['Expected_xG'].sum() / total_sh) if total_sh > 0 else 0 # Presencia atacante (% toques en área rival) total_touches = df['Touches_Touches'].sum() attacking_presence = (df['Touches_Att 3rd'].sum() / total_touches) if total_touches > 0 else 0 # Tiros por posesión total_poss = df['Poss'].sum() possession_shot = (total_sh / total_poss) if total_poss > 0 else 0 # Distancia promedio de tiros standard_dist = df['Standard_Dist'].mean() if 'Standard_Dist' in df.columns else 0 # =========================== # MÉTRICAS DE CREACIÓN # =========================== # Ratio de pases progresivos total_passes = df['Total_Att'].sum() progressive_pass_ratio = (df['PrgP'].sum() / total_passes) if total_passes > 0 else 0 # Participación en último tercio final_third_passes = df['1/3'].sum() final_third_involvement = (final_third_passes / total_passes) if total_passes > 0 else 0 # Ratio de pases largos long_ball_ratio = (df['Long_Att'].sum() / total_passes) if total_passes > 0 else 0 # Asistencias por SCA total_sca = df['SCA Types_SCA'].sum() assist_sca = (df['Ast'].sum() / total_sca) if total_sca > 0 else 0 # Dependencia de centros cross_dependency = (df['Performance_Crs'].sum() / total_passes) if total_passes > 0 else 0 # Eficiencia creativa creative_efficiency = (total_sca / total_poss) if total_poss > 0 else 0 # =========================== # MÉTRICAS DEFENSIVAS # =========================== # Intensidad de presión alta total_tackles = df['Tackles_Tkl'].sum() high_press_intensity = (df['Tackles_Att 3rd'].sum() / total_tackles) if total_tackles > 0 else 0 # Ratio intercepciones/tackles interception_tackle = (df['Int'].sum() / total_tackles) if total_tackles > 0 else 0 # Ratio bloqueos/tackles blocks_tackle = (df['Blocks_Blocks'].sum() / total_tackles) if total_tackles > 0 else 0 # Ratio de despejes total_defensive_actions = total_tackles + df['Int'].sum() clearance_ratio = (df['Clr'].sum() / total_defensive_actions) if total_defensive_actions > 0 else 0 # =========================== # MÉTRICAS DE PORTERÍA # =========================== # Rendimiento del portero normalizado avg_save_pct = df['Performance_Save%'].mean() if 'Performance_Save%' in df.columns else 0 avg_xg_against = df['Expected_xG'].mean() if len(df) > 0 else 1 performance_save = (avg_save_pct / (1 / avg_xg_against)) if avg_xg_against > 0 else 0 # =========================== # MÉTRICAS DE POSESIÓN # =========================== # Ratio de conducciones progresivas total_carries = df['Carries_Carries'].sum() progressive_carry_ratio = (df['Carries_PrgC'].sum() / total_carries) if total_carries > 0 else 0 # Ratio de conducciones al área penalty_carry_ratio = (df['Carries_CPA'].sum() / total_carries) if total_carries > 0 else 0 # Balance conducción/pase progresivo total_prog_passes = df['PrgP'].sum() carry_pass_balance = (df['Carries_PrgC'].sum() / total_prog_passes) if total_prog_passes > 0 else 0 # =========================== # ÍNDICES COMPUESTOS # =========================== # Índice ofensivo avg_gf_raw = df['GF'].mean() avg_xg_raw = df['Expected_xG'].mean() avg_sot = df['Standard_SoT'].mean() avg_sh = df['Standard_Sh'].mean() offensive_index = (avg_gf_raw + avg_xg_raw) * (avg_sot / avg_sh) if avg_sh > 0 else 0 # Índice defensivo avg_int = df['Int'].mean() avg_tkl = df['Tackles_Tkl'].mean() avg_clr = df['Clr'].mean() defensive_index = avg_save_pct * (avg_int / (avg_tkl + avg_clr)) if (avg_tkl + avg_clr) > 0 else 0 # Índice de control de posesión avg_touches_att = df['Touches_Att 3rd'].mean() avg_carries_third = df['Carries_1/3'].mean() if 'Carries_1/3' in df.columns else 0 avg_touches_total = df['Touches_Touches'].mean() possession_control_index = ((avg_touches_att + avg_carries_third) / avg_touches_total) if avg_touches_total > 0 else 0 # Índice de transición avg_prgp = df['PrgP'].mean() avg_prgc = df['Carries_PrgC'].mean() avg_poss_raw = df['Poss'].mean() transition_index = ((avg_prgp + avg_prgc) / avg_poss_raw) if avg_poss_raw > 0 else 0 # ✅ RETORNAR TODAS LAS MÉTRICAS (23 valores) return ( avg_ck, var_ck, # 0 - ✅ CAMBIADO: varianza en vez de promedio avg_xg, # 1 avg_sca, # 2 avg_cross, # 3 avg_poss, # 4 avg_att_3rd, # 5 avg_gf, # 6 avg_ga, # 7 sh_accuracy, # 8 xg_shot, # 9 attacking_presence, # 10 possession_shot, # 11 progressive_pass_ratio, # 12 final_third_involvement, # 13 assist_sca, # 14 creative_efficiency, # 15 high_press_intensity, # 16 interception_tackle, # 17 clearance_ratio, # 18 progressive_carry_ratio, # 19 carry_pass_balance, # 20 offensive_index, # 21 transition_index # 22 ) # =========================== # PROMEDIOS DE LIGA (is_team=False) # =========================== avg_cross = df['Performance_Crs'].mean() avg_att_3rd = df['Touches_Att 3rd'].mean() avg_sca = df['SCA Types_SCA'].mean() avg_xg = df['Expected_xG'].mean() # ✅ CAMBIO: VARIANZA EN VEZ DE PROMEDIO DE CK var_ck = df['Pass Types_CK'].var() if len(df) > 1 else 0 avg_ck = df['Pass Types_CK'].mean() avg_gf = df['GF'].mean() avg_ga = df['GA'].mean() # ✅ AGREGAR MÉTRICAS BÁSICAS PARA NORMALIZACIÓN avg_sh = df['Standard_Sh'].mean() if 'Standard_Sh' in df.columns else 0 return ( var_ck, # 0 - ✅ CAMBIADO avg_xg, # 1 avg_sca, # 2 avg_cross, # 3 avg_att_3rd, # 4 avg_gf, # 5 avg_ga, # 6 avg_sh, # 7 - NUEVO avg_ck ) class PROCESS_DATA(): def __init__(self,use_one_hot_encoding): self.USE_ONE_HOT_ENCODING = use_one_hot_encoding self.init_variables() self.load_clean_dataset() self.process_all_matches() self.clean_and_ouput_dataset() # Excluir temporada 1718 si es necesario def init_variables(self): self.y = [] self.lst_data = [] self.lst_years = ["1819", "1920", "2021", "2122", "2223", "2324", "2425", "2526"] # ✅ CONSTRUIR VECTOR DE FEATURES CON NOMBRES DESCRIPTIVOS self.lst_base_advanced = [ "avg_ck","var_ck", # ✅ CAMBIADO "xg", "sca", "cross", "poss", "att_3rd", "gf", "ga", "sh_accuracy", "xg_shot", "attacking_presence", "possession_shot", "progressive_pass_ratio", "final_third_involvement", "assist_sca", "creative_efficiency", "high_press_intensity", "interception_tackle", "clearance_ratio", "progressive_carry_ratio", "carry_pass_balance", "offensive_index", "transition_index" ] self.lst_base_original = [ "var_ck","xg", "sca", "cross", "poss", "att_3rd", "gf", "ga","avg_ck" ] print("Variables inicializadas") def load_clean_dataset(self): #load clean dataset generated on generate_dataset.py self.df_dataset_historic = pd.read_csv("dataset/cleaned/dataset_cleaned.csv") if os.path.exists(r"dataset/cleaned/dataset_cleaned_current_year.csv"): self.df_dataset_current_year = pd.read_csv("dataset/cleaned/dataset_cleaned_current_year.csv") self.df_dataset = pd.concat([self.df_dataset_historic,self.df_dataset_current_year]) else: self.df_dataset = self.df_dataset_historic self.df_dataset["season"] = self.df_dataset["season"].astype(str) self.df_dataset["Performance_Save%"].fillna(0) self.df_dataset_export = self.df_dataset.copy() #filter data to get key elements on mathces self.df_dataset_export = self.df_dataset_export.drop_duplicates(subset=["game", "league"]) self.df_dataset_export = self.df_dataset_export[["local", "away", "round", "season", "date", "league"]] #load all unique matches on a list to process self.lst_matches = self.df_dataset_export.values.tolist() self.lst_matches = [row for row in self.lst_matches if row[3] != "1718"] print("dataset loaded") def process_all_matches(self): for i in self.lst_matches: if i[2] < 5: continue local = i[0] away = i[1] round_num = i[2] season = i[3] date = i[4] league_code = i[5] dic_df = {} # Promedios de liga lst_avg = get_average( self.df_dataset[ (self.df_dataset['season'] == season) & (self.df_dataset['round'] < round_num) & (self.df_dataset['league'] == league_code) ], is_team=False ) # ✅ FUNCIÓN MEJORADA: Maneja métricas originales y avanzadas def create_line(df, is_form=True, is_team=False, use_advanced=True): """ Args: df: DataFrame con datos del equipo is_form: Si True, toma solo últimos 8 partidos is_team: Si True, normaliza contra promedios de liga use_advanced: Si True, incluye métricas avanzadas (23 valores) Si False, solo métricas originales (8 valores) """ if is_form: df = df[-6:] if use_advanced: # Retorna 23 valores (todas las métricas) return get_average(df, is_team, lst_avg) else: # Retorna solo 8 valores originales result = get_average(df, is_team, lst_avg) return result[:9] # Primeros 8 valores # Extraer DataFrames (team1_home, team1_away, team1_opp_home, team1_opp_away, team2_home, team2_away, team2_opp_home, team2_opp_away) = get_dataframes( self.df_dataset, season, round_num, local, away, league=league_code ) # Corners reales ck = get_ck(self.df_dataset, season, round_num, local, away, league=league_code) self.y.append(ck) # Head to Head index = self.lst_years.index(season) result = self.lst_years[:index+1] team1_h2h, team2_h2h = get_head_2_head( self.df_dataset, local, away, seasons=result, league=league_code ) # ✅ PPP local_ppp = get_team_ppp(self.df_dataset, local, season, round_num, league=league_code) away_ppp = get_team_ppp(self.df_dataset, away, season, round_num, league=league_code) ppp_diff = local_ppp - away_ppp dic_df['ppp_local'] = (local_ppp,) dic_df['ppp_away'] = (away_ppp,) dic_df['ppp_difference'] = (ppp_diff,) # ✅ FEATURES CON MÉTRICAS AVANZADAS (23 valores cada una) dic_df['lst_team1_home_form'] = create_line(team1_home, True, True, use_advanced=True) dic_df['lst_team1_home_general'] = create_line(team1_home, False, True, use_advanced=True) dic_df['lst_team1_away_form'] = create_line(team1_away, True, True, use_advanced=True) dic_df['lst_team1_away_general'] = create_line(team1_away, False, True, use_advanced=True) dic_df['lst_team2_home_form'] = create_line(team2_home, True, True, use_advanced=True) dic_df['lst_team2_home_general'] = create_line(team2_home, False, True, use_advanced=True) dic_df['lst_team2_away_form'] = create_line(team2_away, True, True, use_advanced=True) dic_df['lst_team2_away_general'] = create_line(team2_away, False, True, use_advanced=True) dic_df['lst_team1_h2h'] = create_line(team1_h2h, False, True, use_advanced=True) dic_df['lst_team2_h2h'] = create_line(team2_h2h, False, True, use_advanced=True) # ✅ FEATURES CON MÉTRICAS ORIGINALES (8 valores) - SOLO PARA OPONENTES dic_df['lst_team1_opp_away'] = create_line(team1_opp_away, False, True, use_advanced=False) dic_df['lst_team2_opp_home'] = create_line(team2_opp_home, False, True, use_advanced=False) # One-Hot Encoding if self.USE_ONE_HOT_ENCODING: league_dummies = { 'league_ESP': 1 if league_code == 'ESP' else 0, 'league_GER': 1 if league_code == 'GER' else 0, 'league_FRA': 1 if league_code == 'FRA' else 0, 'league_ITA': 1 if league_code == 'ITA' else 0, 'league_NED': 1 if league_code == 'NED' else 0, 'league_ENG': 1 if league_code == 'ENG' else 0, 'league_POR': 1 if league_code == 'POR' else 0, 'league_BEL': 1 if league_code == 'BEL' else 0 } for key, value in league_dummies.items(): dic_df[key] = (value,) lst_features_values = [] self.lst_features_values = [] for key in dic_df: lst_features_values.extend(list(dic_df[key])) # Casos especiales if key in ['ppp_local', 'ppp_away', 'ppp_difference']: self.lst_features_values.append(key) elif key.startswith('league_'): self.lst_features_values.append(key) elif key in ['lst_team1_opp_away', 'lst_team2_opp_home']: # ✅ Métricas ORIGINALES (8 valores) self.lst_features_values.extend([f"{key}_{col}" for col in self.lst_base_original]) else: # ✅ Métricas AVANZADAS (23 valores) self.lst_features_values.extend([f"{key}_{col}" for col in self.lst_base_advanced]) self.lst_data.append(lst_features_values) print("Dataset processed") def clean_and_ouput_dataset(self): self.df_data = pd.DataFrame(data=self.lst_data, columns=self.lst_features_values) print(f"\n✅ PROCESAMIENTO COMPLETADO:") print(f" Shape inicial: {self.df_data.shape}") print(f" Total partidos: {len(self.df_data)}") print(f" Features totales: {self.df_data.shape[1]}") # =========================== # LIMPIEZA DE DATOS NULOS # =========================== print(f"\n🧹 LIMPIANDO DATOS NULOS...") import numpy as np nulos_antes_X = self.df_data.isnull().sum().sum() nulos_antes_y = np.isnan(self.y).sum() if isinstance(self.y, np.ndarray) else sum(pd.isna(self.y)) print(f" Nulos en X (antes): {nulos_antes_X}") print(f" Nulos en Y (antes): {nulos_antes_y}") y_array = np.array(self.y).flatten() mask_valid_X = ~self.df_data.isnull().any(axis=1) mask_valid_y = ~np.isnan(y_array) mask_combined = mask_valid_X & mask_valid_y self.df_data = self.df_data[mask_combined].reset_index(drop=True) y_array = y_array[mask_combined] print(f"\n✅ LIMPIEZA COMPLETADA:") print(f" Nulos en X (después): {self.df_data.isnull().sum().sum()}") print(f" Nulos en Y (después): {np.isnan(y_array).sum()}") print(f" Filas eliminadas: {len(mask_combined) - mask_combined.sum()}") print(f" Shape final: {self.df_data.shape}") # =========================== # VERIFICACIÓN FINAL # =========================== print(f"\n🔍 VERIFICACIÓN DE NUEVAS FEATURES:") print(f" ✅ Features con 'var_ck': {len([c for c in self.df_data.columns if 'var_ck' in c])}") print(f" ✅ Features con métricas avanzadas: {len([c for c in self.df_data.columns if any(m in c for m in ['sh_accuracy', 'offensive_index'])])}") print(f" ✅ Features de oponentes (8 valores): {len([c for c in self.df_data.columns if 'opp' in c])}") print("\n" + "=" * 80) print("✅ PROCESO COMPLETADO - DATOS LISTOS PARA ENTRENAMIENTO") print("=" * 80) self.y = y_array.tolist() self.df_data["y"] = self.y self.df_data.to_csv("dataset\processed\dataset_processed.csv",index=False) print("Dataset") #a = PROCESS_DATA(True)