TrainTestPredictionDrift#

class TrainTestPredictionDrift[source]#

Calculate prediction drift between train dataset and test dataset, using statistical measures.

Check calculates a drift score for the prediction in the test dataset, by comparing its distribution to the train dataset.

For numerical columns, we use the Earth Movers Distance. See https://en.wikipedia.org/wiki/Wasserstein_metric

For categorical distributions, we use the Cramer’s V. See https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V We also support Population Stability Index (PSI). See https://www.lexjansen.com/wuss/2017/47_Final_Paper_PDF.pdf.

Parameters
margin_quantile_filter: float, default: 0.025

float in range [0,0.5), representing which margins (high and low quantiles) of the distribution will be filtered out of the EMD calculation. This is done in order for extreme values not to affect the calculation disproportionally. This filter is applied to both distributions, in both margins.

max_num_categories_for_drift: int, default: 10

Only for categorical columns. Max number of allowed categories. If there are more, they are binned into an “Other” category. If None, there is no limit.

max_num_categories_for_display: int, default: 10

Max number of categories to show in plot.

show_categories_by: str, default: ‘largest_difference’

Specify which categories to show for categorical features’ graphs, as the number of shown categories is limited by max_num_categories_for_display. Possible values: - ‘train_largest’: Show the largest train categories. - ‘test_largest’: Show the largest test categories. - ‘largest_difference’: Show the largest difference between categories.

categorical_drift_method: str, default: “cramer_v”

decides which method to use on categorical variables. Possible values are: “cramers_v” for Cramer’s V, “PSI” for Population Stability Index (PSI).

max_num_categories: int, default: None

Deprecated. Please use max_num_categories_for_drift and max_num_categories_for_display instead

__init__(margin_quantile_filter: float = 0.025, max_num_categories_for_drift: int = 10, max_num_categories_for_display: int = 10, show_categories_by: str = 'largest_difference', categorical_drift_method='cramer_v', max_num_categories: Optional[int] = None, **kwargs)[source]#
__new__(*args, **kwargs)#

Methods

TrainTestPredictionDrift.add_condition(name, ...)

Add new condition function to the check.

TrainTestPredictionDrift.add_condition_drift_score_not_greater_than([...])

Add condition - require drift score to not be more than a certain threshold.

TrainTestPredictionDrift.clean_conditions()

Remove all conditions from this check instance.

TrainTestPredictionDrift.conditions_decision(result)

Run conditions on given result.

TrainTestPredictionDrift.finalize_check_result(...)

Finalize the check result by adding the check instance and processing the conditions.

TrainTestPredictionDrift.metadata([...])

Return check metadata.

TrainTestPredictionDrift.name()

Name of class in split camel case.

TrainTestPredictionDrift.params([show_defaults])

Return parameters to show when printing the check.

TrainTestPredictionDrift.remove_condition(index)

Remove given condition by index.

TrainTestPredictionDrift.run(train_dataset, ...)

Run check.

TrainTestPredictionDrift.run_logic(context)

Calculate drift for all columns.

Examples#