Merge branch 'master' of https://codefirst.iut.uca.fr/git/PyPloteam/MMIX
luevard
1 year ago
commit
cad4dc579f
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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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# 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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limit_date = '2001-04-01'
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df = df[(df['date'] > limit_date)]
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displayNumberOfNaNValues(df)
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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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fighters = list_fighters(df,'2015-01-01')
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df_train = build_df_all_but_last(df, fighters)
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df_test = build_df(df, fighters,0)
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preprocessor = make_column_transformer((OrdinalEncoder(), ['weight_class', 'B_Stance', 'R_Stance']), remainder='passthrough')
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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 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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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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y_pred = model.predict(X_test)
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print('Testing accuracy : ', accuracy_score(y_test, y_pred), '\n')
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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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feature_names = [col for col in X_train]
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feature_importances = model['random_forest'].feature_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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std = np.std([tree.feature_importances_ for tree in model['random_forest'].estimators_], axis=0)
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#for f in range(n):
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# print("%d. feature %s (%f)" % (f + 1, feature_names[idx[f]], feature_importances[idx[f]]))
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# plt.figure(figsize=(30, 8))
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# plt.title("Feature importances")
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# plt.bar(range(n), feature_importances[idx], color="r", yerr=std[idx], align="center")
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# plt.xticks(range(n), [feature_names[id] for id in idx], rotation = 45)
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# plt.xlim([-1, n])
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# plt.show()
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# Sélectionnez un arbre de votre modèle
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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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@ -0,0 +1,32 @@
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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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@ -0,0 +1,13 @@
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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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