evidently.metrics.data_drift

Submodules

column_drift_metric module

class ColumnDriftMetric(column_name: str, stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, stattest_threshold: Optional[float] = None)

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class ColumnDriftMetricRenderer(color_options: Optional[ColorOptions] = None)

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class ColumnDriftMetricResults(column_name: str, column_type: str, stattest_name: str, stattest_threshold: float, drift_score: Union[float, int], drift_detected: bool, current_distribution: Distribution, reference_distribution: Distribution, current_scatter: Optional[Dict[str, list]], x_name: Optional[str], plot_shape: Optional[Dict[str, float]])

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column_value_plot module

class ColumnValuePlot(column_name: str)

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class ColumnValuePlotRenderer(color_options: Optional[ColorOptions] = None)

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class ColumnValuePlotResults(column_name: str, datetime_column_name: Optional[str], current_scatter: pandas.core.frame.DataFrame, reference_scatter: pandas.core.frame.DataFrame)

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data_drift_table module

class DataDriftTable(columns: Optional[List[str]] = None, stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, cat_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, num_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, per_column_stattest: Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]] = None, stattest_threshold: Optional[float] = None, cat_stattest_threshold: Optional[float] = None, num_stattest_threshold: Optional[float] = None, per_column_stattest_threshold: Optional[Dict[str, float]] = None)

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class DataDriftTableRenderer(color_options: Optional[ColorOptions] = None)

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class DataDriftTableResults(number_of_columns: int, number_of_drifted_columns: int, share_of_drifted_columns: float, dataset_drift: bool, drift_by_columns: Dict[str, ColumnDataDriftMetrics], dataset_columns: DatasetColumns)

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dataset_drift_metric module

class DataDriftMetricsRenderer(color_options: Optional[ColorOptions] = None)

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class DatasetDriftMetric(columns: Optional[List[str]] = None, drift_share: float = 0.5, stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, cat_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, num_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, per_column_stattest: Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]] = None, stattest_threshold: Optional[float] = None, cat_stattest_threshold: Optional[float] = None, num_stattest_threshold: Optional[float] = None, per_column_stattest_threshold: Optional[Dict[str, float]] = None)

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class DatasetDriftMetricResults(drift_share: float, number_of_columns: int, number_of_drifted_columns: int, share_of_drifted_columns: float, dataset_drift: bool)

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target_by_features_table module

class TargetByFeaturesTable(columns: Optional[List[str]] = None)

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class TargetByFeaturesTableRenderer(color_options: Optional[ColorOptions] = None)

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class TargetByFeaturesTableResults(current_plot_data: pandas.core.frame.DataFrame, reference_plot_data: pandas.core.frame.DataFrame, target_name: Optional[str], curr_predictions: Optional[PredictionData], ref_predictions: Optional[PredictionData], columns: List[str], task: str)

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