def prepare_data_holdout_given_split(path_samples):
# ------ Fetch samples
samples_train = fetch_samples(os.path.join(path_samples, 'train'))
samples_test = fetch_samples(os.path.join(path_samples, 'test'))
#samples_test = fetch_samples(os.path.join(path_samples, 'validation'))
# ------ Create feature vector from already splitted dataset
X_train, X_test, Y_train, Y_test, class_names, fvector_labels = create_fvector_train_test(
samples_train, samples_test)
return X_train, X_test, Y_train, Y_test, class_names, fvector_labels, samples_train, samples_test
def prepare_data_gridCrossvalidation(path_samples):
# ------ Fetch samples
samples_train = fetch_samples(os.path.join(path_samples, 'train'))
samples_test = fetch_samples(os.path.join(path_samples, 'test'))
test_fold = []
for sample in samples_train:
test_fold.append(sample['fold'])
# ------ Create feature vector
X_train, X_test, Y_train, Y_test, class_names, fvector_labels = create_fvector_train_test(
samples_train, samples_test)
folds = PredefinedSplit(test_fold)
return X_train, X_test, Y_train, Y_test, class_names, fvector_labels, folds, samples_train, samples_test
def prepare_data_crossvalidation(path_samples):
# ------ Fetch samples
samples = fetch_samples(path_samples)
# ------ Create feature vector
X, Y, class_names, fvector_labels = create_fvector(samples)
return X, Y, class_names, fvector_labels, samples
def prepare_data_holdout_random_split(path_samples):
# ------ Fetch samples
samples = fetch_samples(path_samples)
# ------ Create feature vector
X, Y, class_names, fvector_labels = create_fvector(samples)
# ------ Split Dataset
X_train, X_test, Y_train, Y_test = split_dataset(X, Y)
return X_train, X_test, Y_train, Y_test, class_names, fvector_labels, samples
def prepare_data_crossvalidation_given_split(path_samples):
# ------ Fetch samples
samples = fetch_samples(path_samples)
test_fold = []
for sample in samples:
test_fold.append(sample['fold'])
# ------ Create feature vector
X, Y, class_names, fvector_labels = create_fvector(samples)
folds = PredefinedSplit(test_fold)
return X, Y, class_names, fvector_labels, folds, samples