s452662/SystemyRozmyte
1
0
Fork 1
Code Issues Pull Requests Projects Releases Wiki Activity

improved model, fixed typos

Browse Source
This commit is contained in:
s452662 2024-01-31 11:14:00 +01:00
parent 882c09cdc3
commit a348fac129
2 changed files with 22 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
data_filters.py
View File

@ -2,12 +2,12 @@ import pandas as pd
from fuzzy import *
def zapisz_do_csv(nazwa_pliku, dataframe):
dataframe.to_csv(nazwa_pliku, mode='a', index=False, header=not pd.DataFrame().append(dataframe).empty)
def save_to_csv(filename, dataframe):
dataframe.to_csv(filename, mode='a', index=False, header=not pd.DataFrame().append(dataframe).empty)
def podziel_na_partie(dataframe, rozmiar_partii):
for i in range(0, len(dataframe), rozmiar_partii):
yield dataframe.iloc[i:i + rozmiar_partii]
def split_to_parts(dataframe, part_size):
for i in range(0, len(dataframe), part_size):
yield dataframe.iloc[i:i + part_size]
def przetwarzaj_co_50_rekordow(plik_wejsciowy, plik_wyjsciowy):
dataframe_wejsciowe = pd.read_csv(plik_wejsciowy)
@ -40,16 +40,16 @@ def generateFuzzyLogicData(dataframe):
def last5Matches(season, teamA, data, df):
# Wybierz rekordy dla danej pary drużyn i sezonu
subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]
# Filtruj dane, aby zawierały te przed daną datą
before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
# Posortuj wg daty w odwrotnej kolejności
before_given_date = before_given_date.sort_values(by='date', ascending=False)
# Wybierz 5 ostatnich przed daną datą
last_before_date = before_given_date.head(5)
return last_before_date, "_5m"

26
main.py
View File

@ -8,8 +8,7 @@ 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
from sklearn.ensemble import GradientBoostingClassifier
# Ostatnie 5 spotkań
@ -24,13 +23,12 @@ from sklearn.metrics import classification_report
if __name__ == "__main__":
df = pd.read_csv('df_full_premierleague.csv')
df = pd.read_csv('df_parts.csv')
'''
df = pd.read_csv('df_full_premierleague.csv')
result = last5Matches('10/11', 'Stoke City', '2010-10-02', df)[0]
#print(result.to_markdown())
@ -42,7 +40,6 @@ if __name__ == "__main__":
print(calculatePoints(result,'Blackburn Rovers'))
print(calculateGoalDifference(result, 'Blackburn Rovers'))
'''
# df = generateTrainingData(df)
# df = add_column(df, categorize_passes, "c_away_passes", "away_passes")
@ -158,8 +155,8 @@ if __name__ == "__main__":
df.to_csv('df.csv', index=False)
#TU sie zapisuje zbior
rozmiar_partii = 50
for part in podziel_na_partie(df, rozmiar_partii):
part_size = 50
for part in split_to_parts(df, part_size):
part = add_column(part,
@ -206,8 +203,8 @@ if __name__ == "__main__":
"c_away_passing_5btw")
zapisz_do_csv("df_parts", part)
save_to_csv("df_parts", part)
'''
df = generateFuzzyLogicData(df)
label_encoder = LabelEncoder()
@ -219,9 +216,12 @@ if __name__ == "__main__":
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']
#y = label_encoder.fit_transform(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 = RandomForestClassifier(n_estimators=500, random_state=42)
#model = GradientBoostingClassifier(learning_rate=0.1, n_estimators=100, random_state = 42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
@ -233,6 +233,8 @@ if __name__ == "__main__":
print(f'Dokładność modelu: {accuracy}')
print(classification_report(y_test, y_pred))
#print(model.feature_importances_)
#print(categorize_fuzzy_passes(450,50))