classification_report.py

class Report:
    def __init__(self, X_test, y_test):
        self.X = X_test
        self.y = y_test
    
    def metrics(self, model):
        y_pred = model.predict(self.X)
        
        print('Accuracy score:\n')
        print(accuracy_score(self.y, y_pred))
        
        print('\nConfusion Matrix:\n')
        print(confusion_matrix(self.y, y_pred))
        
        print('\nClassification Report:\n')
        print(classification_report(self.y, y_pred))
    
    def plot_roc_curve(self, model, part='h1', save=False):
        probs = model.predict_proba(self.X)
        preds = probs[:, 1]

        fpr, tpr, threshold = roc_curve(self.y, preds)
        roc_auc = auc(fpr, tpr)

        plt.title('Receiver Operating Characteristic')
        plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % roc_auc)
        plt.legend(loc='lower right')
        plt.plot([0, 1], [0, 1], 'r--')
        plt.xlim([0, 1])
        plt.ylim([0, 1])
        plt.ylabel('True Positive Rate')
        plt.xlabel('False Positive Rate')
        
        if save:
            name = model.__class__.__name__
            plt.savefig(f'./images/{name}_{part}.png')
        plt.show()