whylogs.datasets.ecommerce#

Module Contents#

Classes#

Ecommerce

Ecommerce Dataset

EcommerceDatasetIterator

Iterator to retrieve inference batches, when multiple batches are required.

Attributes#

logger

base_config

whylogs.datasets.ecommerce.logger#
whylogs.datasets.ecommerce.base_config#
class whylogs.datasets.ecommerce.Ecommerce(version: str = 'base')#

Bases: whylogs.datasets.base.Dataset

Ecommerce Dataset

Parameters

version (str) –

baseline_df: pandas.DataFrame#
inference_df: pandas.DataFrame#
inference_interval: str = '1d'#
number_days: int = 1#
unit: str = 'D'#
url: str#
baseline_timestamp: Union[datetime.date, datetime.datetime]#
inference_start_timestamp: Union[datetime.date, datetime.datetime]#
original: bool = False#
dataset_config: Optional[whylogs.datasets.configs.DatasetConfig]#
classmethod config() → whylogs.datasets.configs.DatasetConfig#
Return type

whylogs.datasets.configs.DatasetConfig

classmethod describe_versions() → Tuple[str]#
Return type

Tuple[str]

classmethod describe() → Optional[str]#
Return type

Optional[str]

get_baseline() → whylogs.datasets.base.Batch#
Return type

whylogs.datasets.base.Batch

get_inference_data(target_date: Optional[Union[datetime.date, datetime.datetime]] = None, number_batches: Optional[int] = None) → Union[whylogs.datasets.base.Batch, Iterable[whylogs.datasets.base.Batch]]#

Get batch(es) from inference dataset.

Parameters

  • target_date (Optional[Union[date, datetime]], optional) – Target date for single batch. If datetime is passed, only date will be considered, by default None

  • number_batches (Optional[int], optional) – Number of batches to be retrieved. Each batch will have a time interval as defined by inference_interval from set_parameters. By default None

Returns

Can return a single batch or an interator of batches, depending on input parameters

Return type

Union[Batch, Iterable[Batch]]

set_parameters(inference_interval: Optional[str] = None, baseline_timestamp: Optional[Union[datetime.date, datetime.datetime]] = None, inference_start_timestamp: Optional[Union[datetime.date, datetime.datetime]] = None, original: Optional[bool] = None) → None#

Set timestamp and interval parameters for the dataset object.

Parameters

  • inference_interval (Optional[str], optional) – Interval for the inference batches. If none is passed, daily inference batches will be returned, by default None

  • baseline_timestamp (Optional[Union[date, datetime]], optional) – Timestamp for the baseline dataset. If none is passed, timestamp will be equal to the current day, by default None

  • inference_start_timestamp (Optional[Union[date, datetime]], optional) – Timestamp for the start of the inference dataset. If none is passed, timestamp will be equal to tomorrow’s date, by default None

  • original (Optional[bool], optional) – _If true, sets both baseline and inference timestamps to the dataset’s original timestamp, by default None

Return type

None

class whylogs.datasets.ecommerce.EcommerceDatasetIterator(df: pandas.DataFrame, number_days: int, number_batches: int, version: str, config=DatasetConfig)#

Iterator to retrieve inference batches, when multiple batches are required.

Parameters