def test_feature_importances(self): #Load model model = joblib.load(os.path.join(models_path,'feature_importances_model.pkl')) #Generate plot fi = plots.feature_importance(model) #Save it fi.savefig(os.path.join(result_path, 'feature_importances.png')) #Compare result = equal_images(expected='baseline_images/feature_importances.png', actual='result_images/feature_importances.png', tol=tol, basepath=module_path) self.assertTrue(result)
def test_multi_roc(self): #Load y_score, y_test y_score = joblib.load(os.path.join(models_path,'multi_roc_y_score.pkl')) y_test = joblib.load(os.path.join(models_path,'multi_roc_y_test.pkl')) #Generate plot pr = plots.roc(y_test, y_score) #Save plot pr.savefig(os.path.join(result_path, 'multi_roc.png')) #Compare result = equal_images(expected='baseline_images/multi_roc.png', actual='result_images/multi_roc.png', tol=tol, basepath=module_path) self.assertTrue(result)
def test_normalized_confusion_matrix(self): #Load y_pred, y_test y_pred = joblib.load(os.path.join(models_path,'confusion_matrix_y_pred.pkl')) y_test = joblib.load(os.path.join(models_path,'confusion_matrix_y_test.pkl')) #Generate plot ncf = plots.confusion_matrix_(y_test, y_pred, target_names=['setosa', 'versicolor', 'virginica'], normalize=True, title="Normalized confusion matrix") #Save it ncf.savefig(os.path.join(result_path, 'normalized_confusion_matrix.png')) #Compare result = equal_images(expected='baseline_images/normalized_confusion_matrix.png', actual='result_images/normalized_confusion_matrix.png', tol=tol, basepath=module_path) self.assertTrue(result)