phenome_core.core.base.base_dataset module

class BaseDataset(record_frequency, period_type, model_classtype, data_model)

Bases: object

append(rows_list)
get_aggregated_data(group_by_columns, data_columns, period, estimate_if_incomplete)
get_column_stats(data_columns, prediction_outputs)
get_data(data_columns, period_start_ms, period_end_ms, object_id_filter, object_model_id_filter=None, period_id=None, predicted=None)
get_estimated_sample_count(period)
get_memory_usage()
get_predictions(prediction_id=None, period_id=None, include_current=False)
get_predictions_by_object(obj_id, period_id=None, include_current=False)
get_predictions_by_object_id_and_prediction_id(obj_id, prediction_id, period_id=None, include_current=False)
insert(rows_list)
is_fully_populated(period)
update_predictions(prediction_model_id, metrics, predictions, time_period=<_TIME_PERIOD_CODES_.ONE_MINUTE: 1>, timestamp=None)

Updates dataframe with a number of predicted values for a single KPI and multiple objects Currently uses the current time

Parameters

  • prediction_model_id – The ID of the predictive model

  • metrics – The KPI(s) being predicted

  • predictions – dict of predicted values keyed by Object ID

  • time_period – The _TIME_PERIOD_CODES_ time period ID, defaults to ONE_MINUTE (1)

  • timestamp – datetime object (specify None for now())

Returns

None

update_values(model_id, kpi, values, time_period=<_TIME_PERIOD_CODES_.ONE_MINUTE: 1>, timestamp=None)

Updates dataframe with a number of current values for a single KPI and multiple objects

Parameters

  • model_id – The object model_id

  • kpi – The KPI being collected

  • values – dict of values keyed by Object ID

  • time_period – The _TIME_PERIOD_CODES_ time period ID, defaults to ONE_MINUTE (1)

  • timestamp – datetime object (specify None for now())

Returns

None