from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay, roc_curve, auc, RocCurveDisplay
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
import numpy as np
from sklearn import metrics

# Fonction pour calculer la matrice de confusion, le rapport de classification et l'exactitude
def calculateMatrix(y_true, y_pred):
    accuracy = accuracy_score(y_true, y_pred)
    confusion_matrix_result = confusion_matrix(y_true, y_pred)
    classification_report_result = classification_report(y_true, y_pred, zero_division=1)
    return accuracy, confusion_matrix_result, classification_report_result

# Fonction pour afficher une matrice de confusion
def seeMatrix(matrix, classes):
    cmap = plt.cm.Blues
    disp = ConfusionMatrixDisplay(confusion_matrix=matrix, display_labels=classes)
    disp.plot(cmap=cmap)
    plt.show()

# Fonction pour tracer la courbe ROC
def rocCurve(y_test, y_pred):
    fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)
    roc_auc = metrics.auc(fpr, tpr)
    display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
                                    estimator_name='example estimator')
    display.plot()

# Fonction pour visualiser la courbe d'apprentissage
def seeRocCurve(model, X_train, y_train, learning_reps):
    # Calcul des scores d'apprentissage et de test en fonction de la taille de l'ensemble d'apprentissage
    train_sizes, train_scores, test_scores = learning_curve(model, X_train, y_train, cv=learning_reps, scoring='accuracy', n_jobs=-1)
    # Calcul des moyennes des scores d'apprentissage et de test
    train_scores_mean = np.mean(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)

    plt.plot(train_sizes, train_scores_mean, label='Entrainement')
    plt.plot(train_sizes, test_scores_mean, label='Test')
    plt.xlabel("lol")
    plt.ylabel('Score')
    plt.title('Courbe d apprentissage')
    plt.legend()
    plt.show()