if __name__ == "__main__":
# We can use this to run this file as a script and test the Preprocessor
if len(
argv
) == 1: # Use the default input and output directories if no arguments are provided
input_dir = "../public_data"
output_dir = "../results"
else:
input_dir = argv[1]
output_dir = argv[2]
basename = 'Iris'
D = DataManager(basename, input_dir) # Load data
print("*** Original data ***")
print(D)
Prepro = Preprocessor()
# Preprocess on the data and load it back into D
D.data['X_train'] = Prepro.fit_transform(D.data['X_train'],
D.data['Y_train'])
D.data['X_valid'] = Prepro.transform(D.data['X_valid'])
D.data['X_test'] = Prepro.transform(D.data['X_test'])
D.feat_name = np.array(['PC1', 'PC2'])
D.feat_type = np.array(['Numeric', 'Numeric'])
# Here show something that proves that the preprocessing worked fine
print("*** Transformed data ***")
print(D)
if __name__ == "__main__":
# We can use this to run this file as a script and test the Preprocessor
if len(
argv
) == 1: # Use the default input and output directories if no arguments are provided
input_dir = "../iris"
output_dir = "../results"
else:
input_dir = argv[1]
output_dir = argv[2]
basename = 'iris'
D = DataManager(basename, input_dir) # Load data
print("*** Original data ***")
print(D)
Prepro = Preprocessor()
# Preprocess on the data and load it back into D
D.data['X_train'] = Prepro.fit_transform(D.data['X_train'],
D.data['Y_train'])
D.data['X_valid'] = Prepro.transform(D.data['X_valid'])
D.data['X_test'] = Prepro.transform(D.data['X_test'])
D.feat_name = np.array(['Feat1', 'Feat2'])
D.feat_type = np.array(['Numeric', 'Numeric'])
# Here show something that proves that the preprocessing worked fine
print("*** Transformed data ***")
print(D)