def _analyze_root_causes(self):
"""
Conduct root cause analysis.
The first metric of the list is taken as the root cause right now.
"""
causes = {}
for a in self.anomalies:
try:
causes[a] = self.correlations[a][0]
except IndexError:
raise exceptions.InvalidDataFormat('luminol.luminol: dict correlations contains empty list.')
self.causes = causes
def read_csv(csv_name):
"""
Read data from a csv file into a dictionary.
:param str csv_name: path to a csv file.
:return dict: a dictionary represents the data in file.
"""
data = {}
if not isinstance(csv_name, (str, unicode)):
raise exceptions.InvalidDataFormat('luminol.utils: csv_name has to be a string!')
with open(csv_name, 'r') as csv_data:
reader = csv.reader(csv_data, delimiter=',', quotechar='|')
for row in reader:
try:
key = to_epoch(row[0])
value = float(row[1])
data[key] = value
except ValueError:
pass
return data
def _get_algorithm_and_params(self, algorithm_name, algorithm_params):
"""
Get the specific algorithm and merge the algorithm params.
:param str algorithm: name of the algorithm to use.
:param dict algorithm_params: additional params for the specific algorithm.
"""
algorithm_name = algorithm_name or CORRELATOR_ALGORITHM
try:
self.algorithm = correlator_algorithms[algorithm_name]
except KeyError:
raise exceptions.AlgorithmNotFound('luminol.Correlator: ' +
str(algorithm_name) +
' not found.')
# Merge parameters.
if algorithm_params:
if not isinstance(algorithm_params, dict):
raise exceptions.InvalidDataFormat(
'luminol.Correlator: algorithm_params passed is not a dictionary.'
)
else:
self.algorithm_params = dict(algorithm_params.items() +
self.algorithm_params.items())
