luevard
1 year ago
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# Introduction
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Voici MMIX, une application IA permettant de prédire à hauteur de 63% la probabilité de victore d'un combat entre deux combattants MMA.
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Les statistiques sont issu d'un dataframe disponible sur [Kaggle](https://www.kaggle.com/datasets/rajeevw/ufcdata).
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Le model utilisé est une forêt aléatoire.
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# Les données
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Les données utilisées sont issu d'un dataframe reprenant les statistiques de la plus grande organisation MMA au monde, l'UFC (Ultimate Fighting Championship).
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## Qu'est ce que le MMA ?
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Le MMA, ou arts martiaux mixtes, est un sport de percussion-préhension (debout et au sol).
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C'est un sport mis en avant par la médiatisation, comme le catch il y a quelques années. Il s'est démocratisé il y a peu de temps en France, car il n'est autorisé que depuis 2020.
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Avant cette date, ce sport était pratiquer par nos français à l'extérieur de l'héxagone.
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Ce sport est très complexe, et seul les combattants maitrisant plusieurs style de combat peuvent gravir les échellons.
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Le MMA permet de réunir plein de sorte de style de combat comme la boxe anglaise, le Jiu-jitsu brésilien, la lutte et le sambo.
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Le MMA, comme en boxe, permet de combattre seulement contre sa catégorie de poids, sauf exception (devenir double champion, montée de catégories...).
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## Et nos français ?
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Actuellement à l'UFC, nous possédons 4 dans le top 15 mondial de l'UFC.
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[Ciryl Gane](https://www.ufc.com/athlete/ciryl-gane) - Top 2 dans la catégorie Poids Lourd Homme
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[Manon Fiorot](https://www.ufc.com/athlete/manon-fiorot) - Top 3 dans la catégorie Poids Mouche Femme
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[Nassourdine Imavov](https://www.ufc.com/athlete/nassourdine-imavov) - Top 8 dans la catégorie Poids Moyen Homme
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[Benoit Saint Denis](https://www.ufc.com/athlete/mariya-agapova-0) - Top 12 dans la catégorie Poids Léger Homme, il est en série de 3 victoires très impréssionantes et va combattre début avril contre le Top 3 [Dustin Poirier](https://www.ufc.com/athlete/dustin-poirier) qui est une légende du MMA
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## Que retenir du dataset ?
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Le dataset contient 140 colonnes, il faut donc trier afin de récupérer seulement les informations importantes
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**B_avg_BODY_landed** : Nombre moyen de coups au corps réussis par le combattant du coin bleu
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**B_avg_HEAD_landed** : Nombre moyen de coups à la tête réussis par le combattant du coin bleu
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**B_avg_TD_att** : Nombre moyen de tentatives de takedown par le combattant du coin bleu
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**B_avg_TOTAL_STR_landed** : Nombre moyen total de coups réussis par le combattant du coin bleu
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**B_avg_opp_BODY_att** : Nombre moyen de tentatives de coups au corps par les adversaires contre le combattant du coin bleu
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**B_avg_opp_HEAD_landed** : Nombre moyen de coups à la tête réussis par les adversaires contre le combattant du coin bleu
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**B_avg_opp_LEG_landed** : Nombre moyen de coups à la jambe réussis par les adversaires contre le combattant du coin bleu
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**B_avg_opp_SIG_STR_att** : Nombre moyen de tentatives de coups significatifs par les adversaires contre le combattant du coin bleu
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**B_avg_opp_TOTAL_STR_att** : Nombre moyen total de tentatives de coups par les adversaires contre le combattant du coin bleu
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**R_avg_TD_att** : Nombre moyen de tentatives de takedown par le combattant du coin rouge
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**R_avg_opp_GROUND_att** : Nombre moyen de tentatives de coups au sol par les adversaires contre le combattant du coin rouge
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**R_avg_opp_SIG_STR_landed** : Nombre moyen de coups significatifs réussis par les adversaires contre le combattant du coin rouge
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**B_age** : Âge du combattant du coin bleu
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**R_age** : Âge du combattant du coin rouge
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# Comment lancer le projet ?
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Pour lancer le projet, il faut simple exécuter la commande : python3 server.py.
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Par contre le model met un peu de temps à charger, 2 minutes environs.
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Ensuite, une interface web est disponible à l'adresse http://127.0.0.1:5000/ .
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# LISTE DES VISUALISATIONS A PREVOIR
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**Taux de victoire par méthode de finition** : Analyser la fréquence à laquelle les combats se terminent par soumission, KO, décision unanime, décision partagée, etc
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**Durée moyenne des combats** : Calculer la durée moyenne des combats pour différentes catégories de poids ou pour l'ensemble de l'UFC
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**Taux de réussite des takedowns** : Examiner le pourcentage de tentatives de takedown réussies par les combattants
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**Taux de réussite des frappes** : Analyser le pourcentage de coups réussis par rapport au nombre total de coups tentés
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**Distribution des finitions par round** : Déterminer dans quel round les combats sont le plus souvent terminés (par exemple, soumission au premier round, KO au deuxième round, etc.)
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**Variation des performances avec l'âge** : Vérifier s'il existe une corrélation entre l'âge des combattants et leur succès dans l'UFC
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from flask import Flask, render_template, request
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import pandas as pd
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from test import * # Assurez-vous d'avoir un fichier predict.py avec votre fonction predict
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app = Flask(__name__)
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colonnes = ['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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# Charger le DataFrame une seule fois pour économiser des ressources
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df = pd.read_csv('archive/data.csv') # Assurez-vous de spécifier le bon chemin vers votre fichier de données
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# Before April 2001, there were almost no rules in UFC (no judges, no time limits, no rounds, etc.).
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#It's up to this precise date that UFC started to implement a set of rules known as
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#"Unified Rules of Mixed Martial Arts".
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#Therefore, we delete all fights before this major update in UFC's rules history.
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# Using this old data would not be representative of current fights, especially since this
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#sport has become one of the most regulated due to its mixity and complexity.
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limit_date = '2001-04-01'
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df = df[(df['date'] > limit_date)]
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# Display NaN values
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displayNumberOfNaNValues(df)
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# Define the list of important features to impute
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imp_features = ['R_Weight_lbs', 'R_Height_cms', 'B_Height_cms', 'R_age', 'B_age', 'R_Reach_cms', 'B_Reach_cms']
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imp_median = SimpleImputer(missing_values=np.nan, strategy='median')
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# Iterate over each feature to impute missing values
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for feature in imp_features:
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# Fit and transform the feature using median imputation
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imp_feature = imp_median.fit_transform(df[feature].values.reshape(-1,1))
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# Assign the imputed values back to the DataFrame
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df[feature] = imp_feature
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# Impute missing values for 'R_Stance' using most frequent strategy
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imp_stance_R = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
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imp_R_stance = imp_stance_R.fit_transform(df['R_Stance'].values.reshape(-1,1))
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# Impute missing values for 'B_Stance' using most frequent strategy
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imp_stance_B = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
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imp_B_stance = imp_stance_B.fit_transform(df['B_Stance'].values.reshape(-1,1))
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# Create DataFrames for imputed stances
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df['R_Stance'] = pd.DataFrame(imp_R_stance, columns=['R_Stance'])
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df['B_Stance'] = pd.DataFrame(imp_B_stance, columns=['B_Stance'])
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df.drop(['Referee', 'location'], axis=1, inplace=True)
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# Drop column 'B_draw' and 'R_draw' and 'Draw' fight and 'Catch Weight' fight
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df.drop(['B_draw', 'R_draw'], axis=1, inplace=True)
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df = df[df['Winner'] != 'Draw']
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df = df[df['weight_class'] != 'Catch Weight']
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# Remove column when data type is not float or int
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dfWithoutString = df.select_dtypes(include=['float64', 'int64'])
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plt.figure(figsize=(50, 40))
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corr_matrix = dfWithoutString.corr(method='pearson').abs()
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sns.heatmap(corr_matrix, annot=True)
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## Show the correlation matrix of the dataframe
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## Very laggy feature
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# plt.show()
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# Last year when data fight was not full and correct
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fighters = list_fighters(df,'2015-01-01')
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df = df[colonnes]
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# Get all fight of every fighters
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df_train = build_df_all_but_last(df, fighters)
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# Get the last fight of every fighters for test the model
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df_test = build_df(df, fighters,0)
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#Creates a column transformer that encodes specified categorical columns ordinally
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#while leaving other columns unchanged
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preprocessor = make_column_transformer((OrdinalEncoder(), ['weight_class', 'B_Stance', 'R_Stance']), remainder='passthrough')
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#These lines of code utilize LabelEncoder to encode the 'Winner' column into numerical labels for
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#both training and testing datasets, followed by the separation of features and target variable for
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#further processing.
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label_encoder = LabelEncoder()
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y_train = label_encoder.fit_transform(df_train['Winner'])
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y_test = label_encoder.transform(df_test['Winner'])
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X_train, X_test = df_train.drop(['Winner'], axis=1), df_test.drop(['Winner'], axis=1)
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# Random Forest composed of 100 decision trees. We optimized parameters using cross-validation
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#and GridSearch tool paired together
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random_forest = RandomForestClassifier(n_estimators=100,
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criterion='entropy',
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max_depth=10,
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min_samples_split=2,
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min_samples_leaf=1,
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random_state=0)
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# Train data
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model = Pipeline([('encoding', preprocessor), ('random_forest', random_forest)])
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model.fit(X_train, y_train)
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# We use cross-validation with 5-folds to have a more precise accuracy (reduce variation)
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accuracies = cross_val_score(estimator=model, X=X_train, y=y_train, cv=5)
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print('Accuracy mean : ', accuracies.mean())
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print('Accuracy standard deviation : ', accuracies.std())
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# Test
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y_pred = model.predict(X_test)
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print('Testing accuracy : ', accuracy_score(y_test, y_pred), '\n')
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# Class definition
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target_names = ["Blue","Red"]
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print(classification_report(y_test, y_pred, labels=[0,1], target_names=target_names))
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# Declare feature
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feature_names = [col for col in X_train]
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# Set importances for every feature
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feature_importances = model['random_forest'].feature_importances_
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# Sort importances
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indices = np.argsort(feature_importances)[::-1]
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n = 30 # maximum feature importances displayed
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idx = indices[0:n]
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# Standard deviation
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std = np.std([tree.feature_importances_ for tree in model['random_forest'].estimators_], axis=0)
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# Select tree from model
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tree_estimator = model['random_forest'].estimators_[10]
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/predict', methods=['POST'])
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def make_prediction():
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blue_fighter = request.form['blue_fighter']
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red_fighter = request.form['red_fighter']
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weightclass = request.form['weightclass']
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rounds = int(request.form['rounds'])
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title_bout = True if request.form['title_bout'] == 'True' else False
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prediction_proba = predict(df, model, blue_fighter, red_fighter, weightclass, rounds, title_bout)
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# Formatage du résultat pour l'afficher dans le navigateur
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result = ""
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if prediction_proba is not None:
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result = f"The predicted probability of {blue_fighter} winning is {round(prediction_proba[0][0] * 100, 2)}% and the predicted probability of {red_fighter} winning is {round(prediction_proba[0][1] * 100, 2)}%"
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return render_template('result.html', result=result)
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if __name__ == '__main__':
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app.run(debug=True)
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>UFC Fight Prediction</title>
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</head>
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<body>
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<h1>UFC Fight Prediction</h1>
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<form action="/predict" method="post">
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<label for="blue_fighter">Blue Fighter:</label>
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<input type="text" id="blue_fighter" name="blue_fighter"><br><br>
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<label for="red_fighter">Red Fighter:</label>
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<input type="text" id="red_fighter" name="red_fighter"><br><br>
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<label for="weightclass">Weight Class:</label>
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<input type="text" id="weightclass" name="weightclass"><br><br>
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<label for="rounds">Number of Rounds:</label>
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<input type="number" id="rounds" name="rounds" min="1" max="5" value="3"><br><br>
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<label for="title_bout">Title Bout:</label>
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<select id="title_bout" name="title_bout">
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<option value="True">Yes</option>
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<option value="False" selected>No</option>
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</select><br><br>
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<input type="submit" value="Predict">
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</form>
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</body>
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</html>
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Prediction Result</title>
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</head>
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<body>
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<h2>Prediction Result</h2>
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<p>{{ result }}</p>
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<p><a href="/">Make Another Prediction</a></p>
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</body>
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</html>
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import re
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import pandas as pd
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import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.tree import export_graphviz
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from sklearn.tree import plot_tree
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from sklearn.pipeline import Pipeline
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import classification_report
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from sklearn.metrics import confusion_matrix
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from sklearn.model_selection import cross_val_score
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from sklearn.preprocessing import OrdinalEncoder, LabelEncoder
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from sklearn.compose import make_column_transformer
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from sklearn.impute import SimpleImputer
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pd.options.display.max_columns = None
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pd.options.display.max_rows = None
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import sklearn
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def displayNumberOfNaNValues(df):
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# Create an empty list to store tuples of column index and number of NaN values
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na = []
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# Loop through each column in the DataFrame
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for index, col in enumerate(df):
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# Count the number of NaN values in each column and append the index and count to 'na'
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na.append((index, df[col].isna().sum()))
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# Make a copy of 'na' and sort it based on the count of NaN values in descending order
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na.sort(key=lambda x: x[1], reverse=True)
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# Iterate through the sorted list of columns
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for i in range(len(df.columns)):
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# Check if the count of NaN values for the current column is not zero
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if na[i][1] != 0:
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# Print the column name, count of NaN values, and "NaN"
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print(df.columns[na[i][0]], ":", na[i][1], "NaN")
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# Calculate and print the total number of features with NaN values
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print('Number of features with NaN values:', len([x[1] for x in na if x[1] > 0]))
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print("Total NaN in dataframe :" , df.isna().sum().sum())
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# i = index of the fighter's fight, 0 means the last fight, -1 means first fight
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def select_fight_row(df, name, i):
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df_temp = df[(df['R_fighter'] == name) | (df['B_fighter'] == name)] # filter df on fighter's name
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df_temp.reset_index(drop=True, inplace=True) # as we created a new temporary dataframe, we have to reset indexes
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idx = max(df_temp.index) # get the index of the oldest fight
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if i > idx: # if we are looking for a fight that didn't exist, we return nothing
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return
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arr = df_temp.iloc[i,:].values
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return arr
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# get all active UFC fighters (according to the limit_date parameter)
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def list_fighters(df, limit_date):
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# Filter the DataFrame to include only fights occurring after the specified limit date
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df_temp = df[df['date'] > limit_date]
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# Create a set of all fighters from the red corner ('R_fighter') in the filtered DataFrame
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set_R = set(df_temp['R_fighter'])
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# Create a set of all fighters from the blue corner ('B_fighter') in the filtered DataFrame
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set_B = set(df_temp['B_fighter'])
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# Combine the sets of fighters from the red and blue corners to get all unique fighters
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fighters = list(set_R.union(set_B))
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# Print the number of unique fighters included in the list
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# print("Number of fighters: " + str(len(fighters)))
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# Return the list of unique fighters
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return fighters
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def build_df(df, fighters, i):
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arr = [select_fight_row(df, fighters[f], i) for f in range(len(fighters)) if select_fight_row(df, fighters[f], i) is not None]
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cols = [col for col in df]
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df_fights = pd.DataFrame(data=arr, columns=cols)
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df_fights.drop_duplicates(inplace=True)
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df_fights['title_bout'] = df_fights['title_bout'].map({True: 1, False: 0})
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df_fights.drop(['R_fighter', 'B_fighter', 'date'], axis=1, inplace=True)
|
||||
return df_fights
|
||||
|
||||
def build_df_all_but_last(df, fighters):
|
||||
cols = [col for col in df]
|
||||
print(len(cols))
|
||||
df_fights=pd.DataFrame(columns=cols)
|
||||
for f in range(len(fighters)):
|
||||
i=0
|
||||
while True:
|
||||
fight_row = select_fight_row(df, fighters[f], i)
|
||||
if fight_row is None:
|
||||
if not df_fights.empty:
|
||||
df_fights = df_fights.iloc[:-1]
|
||||
break
|
||||
fight_row = list(fight_row)
|
||||
dfTemp = pd.DataFrame(data=[fight_row], columns=cols)
|
||||
df_fights = df_fights.dropna(axis=1, how='all')
|
||||
df_fights = pd.concat([df_fights, dfTemp], ignore_index=True)
|
||||
i=i+1
|
||||
df_fights.drop_duplicates(inplace=True)
|
||||
df_fights = df_fights[~df_fights.apply(lambda row: 'Open Stance' in row.values, axis=1)].reset_index(drop=True)
|
||||
df_fights['title_bout'] = df_fights['title_bout'].map({True: 1, False: 0})
|
||||
df_fights.drop(['R_fighter', 'B_fighter', 'date'], axis=1, inplace=True)
|
||||
|
||||
return df_fights
|
||||
|
||||
def predict(df, pipeline, blue_fighter, red_fighter, weightclass, rounds, title_bout=False):
|
||||
#We build two dataframes, one for each figther
|
||||
f1 = df[(df['R_fighter'] == blue_fighter) | (df['B_fighter'] == blue_fighter)].copy()
|
||||
f1.reset_index(drop=True, inplace=True)
|
||||
f1 = f1[:1]
|
||||
f2 = df[(df['R_fighter'] == red_fighter) | (df['B_fighter'] == red_fighter)].copy()
|
||||
f2.reset_index(drop=True, inplace=True)
|
||||
f2 = f2[:1]
|
||||
|
||||
print("OK 1")
|
||||
|
||||
# if the fighter was red/blue corner on his last fight, we filter columns to only keep his statistics (and not the other fighter)
|
||||
# then we rename columns according to the color of the corner in the parameters using re.sub()
|
||||
if (f1.loc[0, ['R_fighter']].values[0]) == blue_fighter:
|
||||
result1 = f1.filter(regex='^R', axis=1).copy() #here we keep the red corner stats
|
||||
result1.rename(columns = lambda x: re.sub('^R','B', x), inplace=True) #we rename it with "B_" prefix because he's in the blue_corner
|
||||
else:
|
||||
result1 = f1.filter(regex='^B', axis=1).copy()
|
||||
if (f2.loc[0, ['R_fighter']].values[0]) == red_fighter:
|
||||
result2 = f2.filter(regex='^R', axis=1).copy()
|
||||
else:
|
||||
result2 = f2.filter(regex='^B', axis=1).copy()
|
||||
result2.rename(columns = lambda x: re.sub('^B','R', x), inplace=True)
|
||||
|
||||
print("OK 2")
|
||||
|
||||
fight = pd.concat([result1, result2], axis = 1) # we concatenate the red and blue fighter dataframes (in columns)
|
||||
fight.drop(['R_fighter','B_fighter'], axis = 1, inplace = True) # we remove fighter names
|
||||
fight.insert(0, 'title_bout', title_bout) # we add tittle_bout, weight class and number of rounds data to the dataframe
|
||||
fight.insert(1, 'weight_class', weightclass)
|
||||
fight.insert(2, 'no_of_rounds', rounds)
|
||||
fight['title_bout'] = fight['title_bout'].map({True: 1, False: 0})
|
||||
|
||||
print("OK 3")
|
||||
|
||||
pred = pipeline.predict(fight)
|
||||
proba = pipeline.predict_proba(fight)
|
||||
|
||||
print("OK 4")
|
||||
|
||||
if (pred == 1.0):
|
||||
print("The predicted winner is", red_fighter, 'with a probability of', round(proba[0][1] * 100, 2), "%")
|
||||
else:
|
||||
print("The predicted winner is", blue_fighter, 'with a probability of ', round(proba[0][0] * 100, 2), "%")
|
||||
return proba
|
||||
|
||||
|
||||
#predict(df, model, 'Kamaru Usman', 'Colby Covington', 'Welterweight', 3, True)
|
||||
#predict(df, model, 'Leon Edwards', 'Belal Muhammad', 'Welterweight', 3, True)
|
||||
#predict(df, model, 'Conor McGregor', 'Khabib Nurmagomedov', 'Lightweight', 5, True)
|
||||
#predict(df, model, 'Conor McGregor', 'Tai Tuivasa', 'Heavyweight', 5, True)
|
||||
#predict(df,model,'Charles Oliveira','Conor McGregor','Lightweight',5,True)
|
||||
#predict(df,model,'Charles Oliveira','Khabib Nurmagomedov','Lightweight',5,True)
|
||||
#predict(df, model, 'Leon Edwards', 'Kamaru Usman', 'Welterweight', 5, True)
|
||||
Loading…
Reference in new issue