the first value of changed average as a regression, or a progression.
:param data: Full data set with unavailable samples replaced by nan.
- :type data: pandas.Series
+ :type data: OrderedDict
:returns: Classification and trend values
:rtype: 2-tuple, list of strings and list of floats
"""
# Nan mean something went wrong.
# Use 0.0 to cause that being reported as a severe regression.
- bare_data = [0.0 if np.isnan(sample) else sample
+ bare_data = [0.0 if np.isnan(sample.avg) else sample
for _, sample in data.iteritems()]
# TODO: Put analogous iterator into jumpavg library.
groups = BitCountingClassifier().classify(bare_data)
values_left = 0
avg = 0.0
for _, sample in data.iteritems():
- if np.isnan(sample):
+ if np.isnan(sample.avg):
classification.append("outlier")
- avgs.append(sample)
+ avgs.append(sample.avg)
continue
if values_left < 1 or active_group is None:
values_left = 0