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)
