def load_data_set(data_set_type, classification_method):
print("".join(["Loading ", data_set_type, " data set for ", classification_method, "..."]))
try:
data_set_file = classification_method + "_"
if data_set_type == DatasetType.TRAINING:
data_set_file += FilePath.TRAINING_DATA_SET_FILE_NAME
else:
data_set_file += FilePath.TEST_DATA_SET_FILE_NAME
return pickle.load(open(os.path.join(FilePath.DATA_FOLDER, data_set_file), "rb"))
except Exception as e:
raise UnableToLoadDatasetException(
"".join(["Unable to load: ", classification_method, " data set with cause: ", e.message, "\n"]))
def load_dataset(self, csv_file, one_hot, validation_size):
""" Load a data set.
Args:
csv_file: a CSV file containing ground truth and file names.
one_hot: a boolean. It True, will load the data set labels as a one-hot vector e.g. [0, 1, 0].
If False, will load the data set labels as integers.
validation_size: the specified user's validation data set size.
Returns:
A DataSet object containing training and validation set.
"""
try:
img_names, labels = self._read_labels(csv_file, one_hot)
return self._create_datasets(img_names, labels, validation_size)
except Exception as e:
raise UnableToLoadDatasetException(
"Unable to load galaxies data set with cause: " + str(e))
def load_dataset(self, csv_file, one_hot, validation_size):
""" Load a data set.
Args:
csv_file: a CSV file containing ground truth and file names.
feature_vector: a boolean. It True, will load the data set from a feature vector.
If False, will load the data set required to extract song features.
Returns:
A DataSet object containing training and validation set.
"""
try:
features, labels = self._read_labels(csv_file, one_hot)
return self._create_datasets(features, labels, validation_size)
except Exception as e:
raise UnableToLoadDatasetException(
"Unable to load music data set with cause: " + str(e))
def load_dataset(self, csv_file, one_hot, validation_size):
""" Load a data set.
Args:
csv_file: a CSV file containing ground truth and file names.
one_hot: a boolean. It True, will load the data set labels as a one-hot vector e.g. [0, 1, 0].
If False, will load the data set labels as integers.
validation_size: the specified user's validation data set size.
Returns:
A tuple containing the feature vectors and labels associated to these vectors.
"""
try:
features, labels = self._load_feature_vector(csv_file, one_hot)
return self.create_datasets(features, labels, validation_size)
except Exception as e:
raise UnableToLoadDatasetException(
"Unable to load galaxies data set with cause: " + str(e))
def load_dataset(self, csv_file, one_hot, validation_size):
""" Load a data set.
Args:
csv_file: a CSV file containing ground truth and file names.
feature_vector: a boolean. It True, will load the data set from a feature vector.
If False, will load the data set required to extract galaxy image features.
Returns:
A tuple containing the feature vectors and labels associated to these vectors.
"""
try:
feature_vectors, labels = self._load_feature_vector(
csv_file, one_hot)
return self._create_datasets(feature_vectors, labels,
validation_size)
except Exception as e:
raise UnableToLoadDatasetException(
"Unable to load spam data set with cause: " + str(e))