nannyml.analytics module

class nannyml.analytics.SegmentUsageTracker(write_key: Optional[str] = None, user_metadata: Optional[Dict[str, Any]] = None)

Bases: nannyml.analytics.UsageTracker

SEGMENT_WRITE_KEY = 'lIVZJNAdj2ZaMzAHHnFWP76g7CuwmzGz'

TEST_UUID = '58f6bfdf-fc5d-4166-aaa3-4949ceda8bdc'

write_key: str

class nannyml.analytics.UsageEvent(value)

Bases: str, enum.Enum

Tracked usage events

CBPE_ESTIMATOR_FIT = 'CBPE estimator fit'

CBPE_ESTIMATOR_RUN = 'CBPE estimator run'

MULTIVAR_RECONST_DRIFT_CALC_FIT = 'Multivariate reconstruction error drift calculator fit'

MULTIVAR_RECONST_DRIFT_CALC_RUN = 'Multivariate reconstruction error drift calculator run'

OUTPUT_DRIFT_CALC_FIT = 'Output drift calculator fit'

OUTPUT_DRIFT_CALC_RUN = 'Output drift calculator run'

PERFORMANCE_CALC_FIT = 'Realized performance calculator fit'

PERFORMANCE_CALC_RUN = 'Realized performance calculator run'

TARGET_DISTRIBUTION_DRIFT_CALC_FIT = 'Target distribution drift calculator fit'

TARGET_DISTRIBUTION_DRIFT_CALC_RUN = 'Target distribution drift calculator run'

UNIVAR_STAT_DRIFT_CALC_FIT = 'Univariate statistical drift calculator fit'

UNIVAR_STAT_DRIFT_CALC_RUN = 'Univariate statistical drift calculator run'

class nannyml.analytics.UsageTracker

Bases: abc.ABC

track(usage_event: nannyml.analytics.UsageEvent, metadata: Optional[Dict[str, Any]] = None)

user_metadata: Dict[str, Any] = {}

nannyml.analytics.track(usage_event: nannyml.analytics.UsageEvent, metadata: typing.Optional[typing.Dict[str, typing.Any]] = None, tracker: nannyml.analytics.UsageTracker = <nannyml.analytics.SegmentUsageTracker object>)