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69 lines
4.0 KiB
69 lines
4.0 KiB
9 months ago
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from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay, roc_curve, auc, RocCurveDisplay
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import matplotlib.pyplot as plt
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from sklearn.model_selection import learning_curve
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import numpy as np
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from sklearn import metrics
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from sklearn.preprocessing import LabelEncoder
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from cleanData import *
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import sys
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# Fonction permettant de récupérer la moyenne de statistiques des combats précédents
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def getFighterStats(df, label_encoder, fighter_name):
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# Définition des colonnes ou la moyenne sera appliquée
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columns = ['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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# Tentative de récupération du dataframe ou le fighter passée en paramètre combat
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df_temp = df[(df['R_fighter'] == fighter_name) | (df['B_fighter'] == fighter_name)]
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# Gestion d'erreur si le df est vide
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if df_temp.empty:
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print(f"{fighter_name} introuvable. Abandon de l'application")
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sys.exit(1)
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# sous-fonction permettant d'inverser le coté du combattant
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def swap_values_if_needed(row):
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if row['R_fighter'] == fighter_name:
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return swap_values_withoutran(row)
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return row
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# Permet de faire un foreach et d'appliquer la fonction swap_values_if_needed pour chaque ligne
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df_temp = df_temp.apply(swap_values_if_needed, axis=1)
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# Fait la moyenne des colonnes précédement renseignée dans la liste columns
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return df_temp[columns].mean()
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def predict(fighterStatsR,fighterStatsB,titlebout,model,weight):
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# Définition des colonnes attendus pour la prédiction
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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','weight_class']
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# Définition d'un dataframe issu des colonnes précedemment renseignée
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df = pd.DataFrame(columns=columns)
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# Association des valeurs liées au deux combattants pour la prédiction
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fight = {'B_fighter':0,'R_fighter':0,'title_bout':1,'B_avg_BODY_landed': fighterStatsB['B_avg_BODY_landed'],
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'B_avg_HEAD_landed': fighterStatsB['B_avg_HEAD_landed'], 'B_avg_TD_att': fighterStatsB['B_avg_TD_att'],
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'B_avg_TOTAL_STR_landed': fighterStatsB['B_avg_TOTAL_STR_landed'],
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'B_avg_opp_BODY_att': fighterStatsB['B_avg_opp_BODY_att'],
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'B_avg_opp_HEAD_landed': fighterStatsB['B_avg_opp_HEAD_landed'],
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'B_avg_opp_LEG_landed': fighterStatsB['B_avg_opp_LEG_landed'],
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'B_avg_opp_SIG_STR_att': fighterStatsB['B_avg_opp_SIG_STR_att'],
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'B_avg_opp_TOTAL_STR_att': fighterStatsB['B_avg_opp_TOTAL_STR_att'],
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'R_avg_BODY_landed': fighterStatsR['B_avg_BODY_landed'],
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'R_avg_HEAD_landed': fighterStatsR['B_avg_HEAD_landed'], 'R_avg_TD_att': fighterStatsR['B_avg_TD_att'],
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'R_avg_TOTAL_STR_landed': fighterStatsR['B_avg_TOTAL_STR_landed'],
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'R_avg_opp_BODY_att': fighterStatsR['B_avg_opp_BODY_att'],
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'R_avg_opp_HEAD_landed': fighterStatsR['B_avg_opp_HEAD_landed'],
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'R_avg_opp_LEG_landed': fighterStatsR['B_avg_opp_LEG_landed'],
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'R_avg_opp_SIG_STR_att': fighterStatsR['B_avg_opp_SIG_STR_att'],
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'R_avg_opp_TOTAL_STR_att': fighterStatsR['B_avg_opp_TOTAL_STR_att'],
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'weight_class': 1
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}
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# Ajout des valeurs dans le dataframe
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df = df._append(fight, ignore_index=True)
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# Retourne la valeur 'Winner' suite à la prédiction du model choisis
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return model.predict(df)
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