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62 lines
2.1 KiB
62 lines
2.1 KiB
import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import sklearn as sk
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from sklearn.preprocessing import LabelEncoder
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from sklearn.impute import SimpleImputer
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
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from models import *
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import random
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from analise import *
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columns = ['B_fighter','R_fighter','title_bout',
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'B_avg_BODY_landed', 'B_avg_HEAD_landed', 'B_avg_TD_att', 'B_avg_TOTAL_STR_landed',
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'B_avg_opp_BODY_att', 'B_avg_opp_HEAD_landed', 'B_avg_opp_LEG_landed',
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'B_avg_opp_SIG_STR_att', 'B_avg_opp_TOTAL_STR_att',
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'R_avg_BODY_landed', 'R_avg_HEAD_landed', 'R_avg_TD_att', 'R_avg_TOTAL_STR_landed',
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'R_avg_opp_BODY_att', 'R_avg_opp_HEAD_landed', 'R_avg_opp_LEG_landed',
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'R_avg_opp_SIG_STR_att', 'R_avg_opp_TOTAL_STR_att',
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'B_age', 'R_age','date','Winner','weight_class','B_Stance','R_Stance']
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def swap_values(row):
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if random.random() > 0.5:
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for column in columns:
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if column.startswith('B_'):
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opposite_column = 'R_' + column[2:]
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row[column], row[opposite_column] = row[opposite_column], row[column]
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if column.startswith('Winner'):
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print(row[column])
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if row[column] == 0:
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row[column] = 2
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elif row[column] == 2:
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row[column] = 0
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print(row[column])
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return row
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return row
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def getData():
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df = pd.read_csv('archive/data.csv')
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limit_date = '2001-04-01'
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df = df.loc[df['date'] > limit_date, columns]
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label_encoder = LabelEncoder()
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# Convertir les chaînes de caractères en nombres
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for column in df.select_dtypes(include=['object']).columns:
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df[column] = label_encoder.fit_transform(df[column])
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df = df.apply(swap_values, axis=1)
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median_values = df.median()
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df.fillna(median_values, inplace=True)
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imp_features = ['R_age', 'B_age']
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X=df.drop('Winner', axis=1)
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y=df['Winner']
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return X,y
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