nannyml.usage_logging module
- class nannyml.usage_logging.SegmentUsageTracker(write_key: Optional[str] = None, machine_metadata: Optional[Dict[str, Any]] = None)[source]
Bases:
UsageLogger
- SEGMENT_WRITE_KEY = 'lIVZJNAdj2ZaMzAHHnFWP76g7CuwmzGz'
- write_key: str
- class nannyml.usage_logging.UsageEvent(value)[source]
Bases:
str
,Enum
Logged usage events
- CBPE_ESTIMATOR_FIT = 'CBPE estimator fit'
- CBPE_ESTIMATOR_RUN = 'CBPE estimator run'
- CBPE_PLOT = 'CBPE estimator plot'
- CLI_RUN = 'CLI run'
- DC_CALC_FIT = 'Domain Classifier calculator fit'
- DC_CALC_RUN = 'Domain Classifier calculator run'
- DC_RESULTS_PLOT = 'Domain Classifier results plot'
- DLE_ESTIMATOR_FIT = 'DLE estimator fit'
- DLE_ESTIMATOR_RUN = 'DLE estimator run'
- DLE_PLOT = 'DLE estimator plot'
- DQ_CALC_MISSING_VALUES_FIT = 'Data Quality Calculator Missing Values fit'
- DQ_CALC_MISSING_VALUES_PLOT = 'Data Quality Calculator Missing Values plot'
- DQ_CALC_MISSING_VALUES_RUN = 'Data Quality Calculator Missing Values run'
- DQ_CALC_UNSEEN_VALUES_FIT = 'Data Quality Calculator Unseen Values fit'
- DQ_CALC_UNSEEN_VALUES_PLOT = 'Data Quality Calculator Unseen Values plot'
- DQ_CALC_UNSEEN_VALUES_RUN = 'Data Quality Calculator Unseen Values run'
- MULTIVAR_DRIFT_CALC_FIT = 'Multivariate reconstruction error drift calculator fit'
- MULTIVAR_DRIFT_CALC_RUN = 'Multivariate reconstruction error drift calculator run'
- MULTIVAR_DRIFT_PLOT = 'Multivariate drift results plot'
- PERFORMANCE_CALC_FIT = 'Realized performance calculator fit'
- PERFORMANCE_CALC_RUN = 'Realized performance calculator run'
- PERFORMANCE_PLOT = 'Realized performance calculator plot'
- RANKER_ALERT_COUNT_RUN = 'Run ranker using alert count'
- RANKER_CORRELATION_FIT = 'Fit ranker using correlation with performance'
- RANKER_CORRELATION_RUN = 'Run ranker using correlation with performance'
- STATS_AVG_FIT = 'Simple Stats Avg fit'
- STATS_AVG_PLOT = 'Simple Stats Avg plot'
- STATS_AVG_RUN = 'Simple Stats Avg run'
- STATS_COUNT_FIT = 'Simple Stats Count fit'
- STATS_COUNT_PLOT = 'Simple Stats Count plot'
- STATS_COUNT_RUN = 'Simple Stats Count run'
- STATS_MEDIAN_FIT = 'Simple Stats Median fit'
- STATS_MEDIAN_PLOT = 'Simple Stats Median plot'
- STATS_MEDIAN_RUN = 'Simple Stats Median run'
- STATS_STD_FIT = 'Simple Stats Std fit'
- STATS_STD_PLOT = 'Simple Stats Std plot'
- STATS_STD_RUN = 'Simple Stats Std run'
- STATS_SUM_FIT = 'Simple Stats Sum fit'
- STATS_SUM_PLOT = 'Simple Stats Sum plot'
- STATS_SUM_RUN = 'Simple Stats Sum run'
- UNIVAR_DRIFT_CALC_FIT = 'Univariate drift calculator fit'
- UNIVAR_DRIFT_CALC_RUN = 'Univariate drift calculator run'
- UNIVAR_DRIFT_PLOT = 'Univariate drift results plot'
- WRITE_DB = 'Exported results with DatabaseWriter'
- WRITE_PICKLE = 'Exported results with PickleWriter'
- WRITE_RAW = 'Exported results with RawFilesWriter'
- class nannyml.usage_logging.UsageLogger[source]
Bases:
ABC
- log(usage_event: UsageEvent, metadata: Optional[Dict[str, Any]] = None)[source]
- nannyml.usage_logging.get_logger() UsageLogger [source]
- nannyml.usage_logging.log_usage(usage_event: ~nannyml.usage_logging.UsageEvent, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, metadata_from_self: ~typing.Optional[~typing.List[str]] = None, metadata_from_kwargs: ~typing.Optional[~typing.List[str]] = None, logger: ~nannyml.usage_logging.UsageLogger = <nannyml.usage_logging.SegmentUsageTracker object>) Callable[[Callable[[P], T]], Callable[[P], T]] [source]