def cluster_label(self, cluster_label):
if not isinstance(cluster_label, int):
raise e.TypeError('`cluster_label` should be an integer')
if cluster_label < 0:
raise e.ValueError('`cluster_label` should be >= 0')
self._cluster_label = cluster_label
def predicted_label(self, predicted_label):
if not isinstance(predicted_label, int):
raise e.TypeError('`predicted_label` should be an integer')
if predicted_label < 0:
raise e.ValueError('`predicted_label` should be >= 0')
self._predicted_label = predicted_label
def label(self, label):
if not isinstance(label, int):
raise e.TypeError('`label` should be an integer')
if label < 1:
raise e.ValueError('`label` should be >= 1')
self._label = label
def min_k(self, min_k):
if not isinstance(min_k, int):
raise e.TypeError('`min_k` should be an integer')
if min_k < 1:
raise e.ValueError('`min_k` should be >= 1')
self._min_k = min_k
def pred(self, pred):
if not isinstance(pred, int):
raise e.TypeError('`pred` should be an integer')
if pred < c.NIL:
raise e.ValueError(
'`pred` should have a value larger than `NIL`, e.g., -1')
self._pred = pred
def _read_distances(self, file_path):
"""Reads the distance between nodes from a pre-defined file.
Args:
file_path (str): File to be loaded.
Returns:
A matrix with pre-computed distances.
"""
logger.debug('Running private method: read_distances().')
# Getting file extension
extension = file_path.split('.')[-1]
# Check if extension is .csv
if extension == 'csv':
# If yes, call the method that actually loads csv
distances = loader.load_csv(file_path)
# Check if extension is .txt
elif extension == 'txt':
# If yes, call the method that actually loads txt
distances = loader.load_txt(file_path)
# If extension is not recognized
else:
# Raises an ArgumentError exception
raise e.ArgumentError(
'File extension not recognized. It should be either `.csv` or .txt`')
# Check if distances have been properly loaded
if distances is None:
# If not, raises a ValueError
raise e.ValueError(
'Pre-computed distances could not been properly loaded')
return distances
def policy(self, policy):
if policy not in ['min', 'max']:
raise e.ValueError('`policy` should be `min` or `max`')
self._policy = policy