Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
mat = sp.io.loadmat(os.path.join('', 'datasets', mat_file))
X = mat['X']
y = mat['y']
# split dataset into train and test
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.4, random_state=42)
# standardize data to be digestible for most algorithms
X_train, X_test = standardizer(X_train, X_test)
contamination = y.sum() / len(y)
# get estimators for training and prediction
base_estimators = get_estimators(contamination=contamination)
##########################################################################
model = SUOD(base_estimators=base_estimators, rp_flag_global=True,
approx_clf=approx_clf,
n_jobs=n_jobs, bps_flag=True, contamination=contamination,
approx_flag_global=True)
start = time.time()
model.fit(X_train) # fit all models with X
print('Fit time:', time.time() - start)
print()
start = time.time()
model.approximate(X_train) # conduct model approximation if it is enabled
print('Approximation time:', time.time() - start)
print()