SystemyRozmyte/main.py
2024-01-22 15:59:58 +01:00

89 lines
3.5 KiB
Python

import pandas as pd
from simpful import *
from rules import *
from data_filters import *
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import classification_report
# Ostatnie 5 spotkań
#Forma: 0-6 punktow = słaba, średnia 6-10, dobra 10-15 punktow
#Bilans bramek ujemny dodatni
#Strzały 6- mało pomiędzy średnio 12 - dużo
#Posiadanie piłki słabe 30-40, średnie = 40-55, dobre = 56-64
#Podania ponizej 300-400 słabo powyżej 500 dużo
if __name__ == "__main__":
df = pd.read_csv('df_full_premierleague.csv')
result = last5Matches('10/11', 'Stoke City', '2010-10-02', df)
#print(result.to_markdown())
#print(result)
result = last5Matches('10/11', 'Blackburn Rovers', '2010-10-02', df)
#print(result.to_markdown())
#print(result)
print(calculatePoints(result,'Blackburn Rovers'))
print(calculateGoalDifference(result, 'Blackburn Rovers'))
# df = generateTrainingData(df)
# df = add_column(df, categorize_passes, "c_away_passes", "away_passes")
# df = add_column(df, categorize_passes, "c_home_passes", "home_passes")
# df = add_column(df, categorize_possesion, "c_away_possession", "away_possession")
# df = add_column(df, categorize_possesion, "c_home_possession", "home_possession")
# df = add_column(df, categorize_shots, "c_away_shots", "away_shots")
# df = add_column(df, categorize_shots, "c_home_shots", "home_shots")
# print(df.columns)
df = add_column(df, get_method(df, True, categorize_points, last5Matches), "c_home_form_5m")
df = add_column(df, get_method(df, False, categorize_points, last5Matches), "c_away_form_5m")
df = add_column(df, get_method(df, True, categorize_diff, last5Matches), "c_home_diff_5m")#categorize_diff
df = add_column(df, get_method(df, False, categorize_diff,last5Matches), "c_away_diff_5m")
df = add_column(df, get_method(df, True, categorize_points, seasonMatches), "c_home_form_season")
df = add_column(df, get_method(df, False, categorize_points, seasonMatches), "c_away_form_season")
df = add_column(df, get_method(df, True, categorize_diff, seasonMatches), "c_home_diff_season")#categorize_diff
df = add_column(df, get_method(df, False, categorize_diff,seasonMatches), "c_away_diff_season")
df = generateFuzzyLogicData(df)
label_encoder = LabelEncoder()
df['season'] = label_encoder.fit_transform(df['season'])
df['c_home_result'] = get_result_list(df,True)
df['c_away_result'] = get_result_list(df,True)
temp = df[['home_team', 'away_team']].stack()
temp[:] = temp.factorize()[0]
df[['home_team', 'away_team']] = temp.unstack()
X = df.drop(['result_full', 'date', 'c_home_result', 'c_away_result'], axis=1)
y = df['c_home_result']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Dokładność modelu: {accuracy}')
accuracy = accuracy_score(y_test, y_pred)
print(f'Dokładność modelu: {accuracy}')
print(classification_report(y_test, y_pred))
result = last5Matches('10/11', 'Manchester United', '2010-12-16', df)
print(calculatePoints(result,'Manchester United'))
print(calculateGoalDifference(result, 'Manchester United'))
print(categorize_fuzzy_passes(450,50))