improved model, fixed typos
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@ -2,12 +2,12 @@ import pandas as pd
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from fuzzy import *
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def zapisz_do_csv(nazwa_pliku, dataframe):
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dataframe.to_csv(nazwa_pliku, mode='a', index=False, header=not pd.DataFrame().append(dataframe).empty)
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def save_to_csv(filename, dataframe):
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dataframe.to_csv(filename, mode='a', index=False, header=not pd.DataFrame().append(dataframe).empty)
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def podziel_na_partie(dataframe, rozmiar_partii):
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for i in range(0, len(dataframe), rozmiar_partii):
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yield dataframe.iloc[i:i + rozmiar_partii]
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def split_to_parts(dataframe, part_size):
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for i in range(0, len(dataframe), part_size):
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yield dataframe.iloc[i:i + part_size]
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def przetwarzaj_co_50_rekordow(plik_wejsciowy, plik_wyjsciowy):
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dataframe_wejsciowe = pd.read_csv(plik_wejsciowy)
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@ -40,16 +40,16 @@ def generateFuzzyLogicData(dataframe):
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def last5Matches(season, teamA, data, df):
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# Wybierz rekordy dla danej pary drużyn i sezonu
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subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]
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# Filtruj dane, aby zawierały te przed daną datą
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before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
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# Posortuj wg daty w odwrotnej kolejności
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before_given_date = before_given_date.sort_values(by='date', ascending=False)
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# Wybierz 5 ostatnich przed daną datą
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last_before_date = before_given_date.head(5)
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return last_before_date, "_5m"
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26
main.py
26
main.py
@ -8,8 +8,7 @@ from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.preprocessing import LabelEncoder
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from sklearn.metrics import classification_report
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from sklearn.ensemble import GradientBoostingClassifier
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# Ostatnie 5 spotkań
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@ -24,13 +23,12 @@ from sklearn.metrics import classification_report
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if __name__ == "__main__":
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df = pd.read_csv('df_full_premierleague.csv')
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df = pd.read_csv('df_parts.csv')
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'''
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df = pd.read_csv('df_full_premierleague.csv')
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result = last5Matches('10/11', 'Stoke City', '2010-10-02', df)[0]
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#print(result.to_markdown())
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@ -42,7 +40,6 @@ if __name__ == "__main__":
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print(calculatePoints(result,'Blackburn Rovers'))
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print(calculateGoalDifference(result, 'Blackburn Rovers'))
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'''
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# df = generateTrainingData(df)
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# df = add_column(df, categorize_passes, "c_away_passes", "away_passes")
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@ -158,8 +155,8 @@ if __name__ == "__main__":
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df.to_csv('df.csv', index=False)
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#TU sie zapisuje zbior
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rozmiar_partii = 50
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for part in podziel_na_partie(df, rozmiar_partii):
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part_size = 50
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for part in split_to_parts(df, part_size):
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part = add_column(part,
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@ -206,8 +203,8 @@ if __name__ == "__main__":
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"c_away_passing_5btw")
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zapisz_do_csv("df_parts", part)
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save_to_csv("df_parts", part)
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'''
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df = generateFuzzyLogicData(df)
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label_encoder = LabelEncoder()
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@ -219,9 +216,12 @@ if __name__ == "__main__":
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df[['home_team', 'away_team']] = temp.unstack()
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X = df.drop(['result_full', 'date', 'c_home_result', 'c_away_result'], axis=1)
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y = df['c_home_result']
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#y = label_encoder.fit_transform(df['c_home_result'])
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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model = RandomForestClassifier(n_estimators=500, random_state=42)
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#model = GradientBoostingClassifier(learning_rate=0.1, n_estimators=100, random_state = 42)
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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@ -233,6 +233,8 @@ if __name__ == "__main__":
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print(f'Dokładność modelu: {accuracy}')
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print(classification_report(y_test, y_pred))
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#print(model.feature_importances_)
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#print(categorize_fuzzy_passes(450,50))
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