✨ Readme and refactor

README.md

@@ -0,0 +1,59 @@
+# Introduction
+
+Voici MMIX, une application IA permettant de prédire à hauteur de 63% la probabilité de victore d'un combat entre deux combattants MMA. 
+
+Les statistiques sont issu d'un dataframe disponible sur [Kaggle](https://www.kaggle.com/datasets/rajeevw/ufcdata).
+
+Le model utilisé est une forêt aléatoire.
+
+# Les données
+
+Les données utilisées sont issu d'un dataframe reprenant les statistiques de la plus grande organisation MMA au monde, l'UFC (Ultimate Fighting Championship).
+
+## Qu'est ce que le MMA ?
+
+Le MMA, ou arts martiaux mixtes, est un sport de percussion-préhension (debout et au sol). 
+
+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. 
+
+Avant cette date, ce sport était pratiquer par nos français à l'extérieur de l'héxagone.
+
+Ce sport est très complexe, et seul les combattants maitrisant plusieurs style de combat peuvent gravir les échellons.
+
+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.
+
+Le MMA, comme en boxe, permet de combattre seulement contre sa catégorie de poids, sauf exception (devenir double champion, montée de catégories...).
+
+## Et nos français ?
+
+Actuellement à l'UFC, nous possédons 4 dans le top 15 mondial de l'UFC.
+
+[Ciryl Gane](https://www.ufc.com/athlete/ciryl-gane) - Top 2 dans la catégorie Poids Lourd Homme
+
+[Manon Fiorot](https://www.ufc.com/athlete/manon-fiorot) - Top 3 dans la catégorie Poids Mouche Femme
+
+[Nassourdine Imavov](https://www.ufc.com/athlete/nassourdine-imavov) - Top 8 dans la catégorie Poids Moyen Homme
+
+[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
+
+# Que retenir du dataset ?
+
+
+
+# LISTE DES VISUALISATIONS A PREVOIR
+
+**Taux de victoire par méthode de finition** : Analyser la fréquence à laquelle les combats se terminent par soumission, KO, décision unanime, décision partagée, etc
+
+**Durée moyenne des combats** : Calculer la durée moyenne des combats pour différentes catégories de poids ou pour l'ensemble de l'UFC
+
+**Taux de réussite des takedowns** : Examiner le pourcentage de tentatives de takedown réussies par les combattants
+
+**Taux de réussite des frappes** : Analyser le pourcentage de coups réussis par rapport au nombre total de coups tentés
+
+**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.)
+
+**Variation des performances avec l'âge** : Vérifier s'il existe une corrélation entre l'âge des combattants et leur succès dans l'UFC
+
+![LE DARON À ZAK](https://upload.wikimedia.org/wikipedia/commons/e/ec/Dana_White_-_London_2015_%28cropped%29.jpg)
+
+# DANA CACA WHITE

server.py

@@ -7,11 +7,20 @@ app = Flask(__name__)
 # Charger le DataFrame une seule fois pour économiser des ressources
 df = pd.read_csv('archive/data.csv')  # Assurez-vous de spécifier le bon chemin vers votre fichier de données
 
+# Before April 2001, there were almost no rules in UFC (no judges, no time limits, no rounds, etc.). 
+#It's up to this precise date that UFC started to implement a set of rules known as 
+#"Unified Rules of Mixed Martial Arts".
+#Therefore, we delete all fights before this major update in UFC's rules history.
+
+# Using this old data would not be representative of current fights, especially since this 
+#sport has become one of the most regulated due to its mixity and complexity.
 limit_date = '2001-04-01'
 df = df[(df['date'] > limit_date)]
 
+# Display NaN values
 displayNumberOfNaNValues(df)
 
+# Define the list of important features to impute
 imp_features = ['R_Weight_lbs', 'R_Height_cms', 'B_Height_cms', 'R_age', 'B_age', 'R_Reach_cms', 'B_Reach_cms']
 imp_median = SimpleImputer(missing_values=np.nan, strategy='median')
 
@@ -48,22 +57,34 @@ dfWithoutString = df.select_dtypes(include=['float64', 'int64'])
 plt.figure(figsize=(50, 40))
 corr_matrix = dfWithoutString.corr(method='pearson').abs()
 sns.heatmap(corr_matrix, annot=True)
+## Show the correlation matrix of the dataframe
+## Very laggy feature
+# plt.show()
+
 
+# Last year when data fight was not full and correct
 fighters = list_fighters(df,'2015-01-01')
 
+# Get all fight of every fighters
 df_train = build_df_all_but_last(df, fighters)
+
+# Get the last fight of every fighters for test the model
 df_test = build_df(df, fighters,0)
 
+#Creates a column transformer that encodes specified categorical columns ordinally 
+#while leaving other columns unchanged
 preprocessor = make_column_transformer((OrdinalEncoder(), ['weight_class', 'B_Stance', 'R_Stance']), remainder='passthrough')
 
-
+#These lines of code utilize LabelEncoder to encode the 'Winner' column into numerical labels for 
+#both training and testing datasets, followed by the separation of features and target variable for 
+#further processing.
 label_encoder = LabelEncoder()
 y_train = label_encoder.fit_transform(df_train['Winner'])
 y_test = label_encoder.transform(df_test['Winner'])
-
 X_train, X_test = df_train.drop(['Winner'], axis=1), df_test.drop(['Winner'], axis=1)
 
-# Random Forest composed of 100 decision trees. We optimized parameters using cross-validation and GridSearch tool paired together
+# Random Forest composed of 100 decision trees. We optimized parameters using cross-validation 
+#and GridSearch tool paired together
 random_forest = RandomForestClassifier(n_estimators=100, 
                                        criterion='entropy', 
                                        max_depth=10, 
@@ -71,6 +92,7 @@ random_forest = RandomForestClassifier(n_estimators=100,
                                        min_samples_leaf=1, 
                                        random_state=0)
 
+# Train data
 model = Pipeline([('encoding', preprocessor), ('random_forest', random_forest)])
 model.fit(X_train, y_train)
 
@@ -79,30 +101,25 @@ accuracies = cross_val_score(estimator=model, X=X_train, y=y_train, cv=5)
 print('Accuracy mean : ', accuracies.mean())
 print('Accuracy standard deviation : ', accuracies.std())
 
+# Test
 y_pred = model.predict(X_test)
 print('Testing accuracy : ', accuracy_score(y_test, y_pred), '\n')
 
+# Class definition
 target_names = ["Blue","Red"]
 print(classification_report(y_test, y_pred, labels=[0,1], target_names=target_names))
 
+# Declare feature
 feature_names = [col for col in X_train]
+# Set importances for every feature
 feature_importances = model['random_forest'].feature_importances_
+# Sort importances
 indices = np.argsort(feature_importances)[::-1]
 n = 30 # maximum feature importances displayed
 idx = indices[0:n] 
+# Standard deviation
 std = np.std([tree.feature_importances_ for tree in model['random_forest'].estimators_], axis=0)
-
+# Select tree from model
-#for f in range(n):
-#    print("%d. feature %s (%f)" % (f + 1, feature_names[idx[f]], feature_importances[idx[f]])) 
-
-# plt.figure(figsize=(30, 8))
-# plt.title("Feature importances")
-# plt.bar(range(n), feature_importances[idx], color="r", yerr=std[idx], align="center")
-# plt.xticks(range(n), [feature_names[id] for id in idx], rotation = 45) 
-# plt.xlim([-1, n]) 
-# plt.show()
-
-# Sélectionnez un arbre de votre modèle
 tree_estimator = model['random_forest'].estimators_[10]
 
 

test.py

@@ -37,72 +37,6 @@ def displayNumberOfNaNValues(df):
     print('Number of features with NaN values:', len([x[1] for x in na if x[1] > 0]))
     print("Total NaN in dataframe :" , df.isna().sum().sum())
 
-# Before April 2001, there were almost no rules in UFC (no judges, no time limits, no rounds, etc.). 
-#It's up to this precise date that UFC started to implement a set of rules known as 
-#"Unified Rules of Mixed Martial Arts".
-#Therefore, we delete all fights before this major update in UFC's rules history.
-
-# Using this old data would not be representative of current fights, especially since this 
-#sport has become one of the most regulated due to its mixity and complexity.
-
-    #limit_date = '2001-04-01'
-    #df = df[(df['date'] > limit_date)]
-
-# Display NaN values
-    #displayNumberOfNaNValues(df)
-
-# Define the list of important features to impute
-#imp_features = ['R_Weight_lbs', 'R_Height_cms', 'B_Height_cms', 'R_age', 'B_age', 'R_Reach_cms', 'B_Reach_cms']
-
-# Initialize a SimpleImputer to impute missing values with median
-#imp_median = SimpleImputer(missing_values=np.nan, strategy='median')
-
-# Iterate over each feature to impute missing values
-#for feature in imp_features:
-    # Fit and transform the feature using median imputation
-    #imp_feature = imp_median.fit_transform(df[feature].values.reshape(-1,1))
-    # Assign the imputed values back to the DataFrame
-    #df[feature] = imp_feature
-
-# Impute missing values for 'R_Stance' using most frequent strategy
-#imp_stance_R = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
-#imp_R_stance = imp_stance_R.fit_transform(df['R_Stance'].values.reshape(-1,1))
-#
-## Impute missing values for 'B_Stance' using most frequent strategy
-#imp_stance_B = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
-#imp_B_stance = imp_stance_B.fit_transform(df['B_Stance'].values.reshape(-1,1))
-#
-## Create DataFrames for imputed stances
-#df['R_Stance'] = pd.DataFrame(imp_R_stance, columns=['R_Stance'])
-#df['B_Stance'] = pd.DataFrame(imp_B_stance, columns=['B_Stance'])
-#
-## drop B_avg_BODY_att values in the dataframe
-#    # List of features with NaN values to drop
-#    #na_features = ['B_avg_BODY_att', 'R_avg_BODY_att']
-#
-#    # Drop rows with NaN values in specified features
-#    #df.dropna(subset=na_features, inplace=True)
-#
-## Drop columns 'Referee' and 'location' from the DataFrame
-## The value of references and location has a low impact in battles, which makes it irrelevant to keep
-#df.drop(['Referee', 'location'], axis=1, inplace=True)
-#
-## Drop column 'B_draw' and 'R_draw' and 'Draw' fight and 'Catch Weight' fight
-#df.drop(['B_draw', 'R_draw'], axis=1, inplace=True)
-#df = df[df['Winner'] != 'Draw']
-#df = df[df['weight_class'] != 'Catch Weight']
-#
-## Remove column when data type is not float or int
-#dfWithoutString = df.select_dtypes(include=['float64', 'int64'])
-#
-#plt.figure(figsize=(50, 40))
-#corr_matrix = dfWithoutString.corr(method='pearson').abs()
-#sns.heatmap(corr_matrix, annot=True)
-#
-## Show the correlation matrix of the dataframe
-## Very laggy feature
-#
-# plt.show()
 
 #  i = index of the fighter's fight, 0 means the last fight, -1 means first fight
 def select_fight_row(df, name, i): 
@@ -114,10 +48,6 @@ def select_fight_row(df, name, i):
     arr = df_temp.iloc[i,:].values
     return arr
 
-#  we get the last fight of Khabib :'(
-#print(select_fight_row(df, 'Khabib Nurmagomedov', 0))
-
-
 # get all active UFC fighters (according to the limit_date parameter)
 def list_fighters(df, limit_date):
     # Filter the DataFrame to include only fights occurring after the specified limit date
@@ -133,9 +63,6 @@ def list_fighters(df, limit_date):
     # Return the list of unique fighters
     return fighters
 
-# Last year when data fight was not full and correct
-#fighters = list_fighters(df,'2015-01-01')
-
 def build_df(df, fighters, i):      
     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]
     cols = [col for col in df] 
@@ -168,77 +95,6 @@ def build_df_all_but_last(df, fighters):
 
     return df_fights
 
-#
-#df_train = build_df_all_but_last(df, fighters)
-#df_test = build_df(df, fighters,0)
-#
-#preprocessor = make_column_transformer((OrdinalEncoder(), ['weight_class', 'B_Stance', 'R_Stance']), remainder='passthrough')
-#
-## If the winner is from the Red corner, Winner label will be encoded as 1, otherwise it will be 0 (Blue corner)
-#label_encoder = LabelEncoder()
-#y_train = label_encoder.fit_transform(df_train['Winner'])
-#y_test = label_encoder.transform(df_test['Winner'])
-#
-#X_train, X_test = df_train.drop(['Winner'], axis=1), df_test.drop(['Winner'], axis=1)
-#
-## Random Forest composed of 100 decision trees. We optimized parameters using cross-validation and GridSearch tool paired together
-#random_forest = RandomForestClassifier(n_estimators=100, 
-#                                       criterion='entropy', 
-#                                       max_depth=10, 
-#                                       min_samples_split=2,
-#                                       min_samples_leaf=1, 
-#                                       random_state=0)
-#
-#model = Pipeline([('encoding', preprocessor), ('random_forest', random_forest)])
-#model.fit(X_train, y_train)
-#
-## We use cross-validation with 5-folds to have a more precise accuracy (reduce variation)
-#accuracies = cross_val_score(estimator=model, X=X_train, y=y_train, cv=5)
-#print('Accuracy mean : ', accuracies.mean())
-#print('Accuracy standard deviation : ', accuracies.std())
-#
-#y_pred = model.predict(X_test)
-#print('Testing accuracy : ', accuracy_score(y_test, y_pred), '\n')
-#
-#target_names = ["Blue","Red"]
-#print(classification_report(y_test, y_pred, labels=[0,1], target_names=target_names))
-#
-## cm = confusion_matrix(y_test, y_pred) 
-## ax = plt.subplot()
-# sns.heatmap(cm, annot = True, ax = ax, fmt = "d")
-# ax.set_xlabel('Actual')
-# ax.set_ylabel('Predicted')
-# ax.set_title("Confusion Matrix")
-# ax.xaxis.set_ticklabels(['Blue', 'Red'])
-# ax.yaxis.set_ticklabels(['Blue', 'Red'])
-# plt.show()
-
-#feature_names = [col for col in X_train]
-#feature_importances = model['random_forest'].feature_importances_
-#indices = np.argsort(feature_importances)[::-1]
-#n = 30 # maximum feature importances displayed
-#idx = indices[0:n] 
-#std = np.std([tree.feature_importances_ for tree in model['random_forest'].estimators_], axis=0)
-
-#for f in range(n):
-#    print("%d. feature %s (%f)" % (f + 1, feature_names[idx[f]], feature_importances[idx[f]])) 
-
-# plt.figure(figsize=(30, 8))
-# plt.title("Feature importances")
-# plt.bar(range(n), feature_importances[idx], color="r", yerr=std[idx], align="center")
-# plt.xticks(range(n), [feature_names[id] for id in idx], rotation = 45) 
-# plt.xlim([-1, n]) 
-# plt.show()
-
-# Sélectionnez un arbre de votre modèle
-#tree_estimator = model['random_forest'].estimators_[10]
-
-# Tracez l'arbre
-# plt.figure(figsize=(1, 1))
-# plot_tree(tree_estimator, feature_names=df_train.columns, filled=True, rounded=True, fontsize=10)
-# plt.savefig('tree.png', dpi=600)  # Enregistrez l'image au format PNG
-# plt.show()
-
 def predict(df, pipeline, blue_fighter, red_fighter, weightclass, rounds, title_bout=False): 
     try:
         #We build two dataframes, one for each figther 
@@ -285,7 +141,6 @@ def predict(df, pipeline, blue_fighter, red_fighter, weightclass, rounds, title_
 #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)