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evidently.metric_preset

class ClassificationPreset(columns: Optional[List[str]] = None, probas_threshold: Optional[float] = None, k: Optional[int] = None)

Bases: MetricPreset

Metrics preset for classification performance.

Contains metrics:

  • ClassificationQualityMetric

  • ClassificationClassBalance

  • ClassificationConfusionMatrix

  • ClassificationQualityByClass

Attributes:

columns : Optional[List[str]]

k : Optional[int]

probas_threshold : Optional[float]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

class DataDriftPreset(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)

Bases: MetricPreset

Metric Preset for Data Drift analysis.

Contains metrics:

  • DatasetDriftMetric

  • DataDriftTable

Attributes:

cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

cat_stattest_threshold : Optional[float]

columns : Optional[List[str]]

drift_share : float

num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

num_stattest_threshold : Optional[float]

per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]

per_column_stattest_threshold : Optional[Dict[str, float]]

stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

stattest_threshold : Optional[float]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

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

Bases: MetricPreset

Metric preset for Data Quality analysis.

Contains metrics:

  • DatasetSummaryMetric

  • ColumnSummaryMetric for each column

  • DatasetMissingValuesMetric

  • DatasetCorrelationsMetric

  • Parameters

    columns – list of columns for analysis.

Attributes:

columns : Optional[List[str]]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

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

Bases: MetricPreset

Metric preset for Regression performance analysis.

Contains metrics:

  • RegressionQualityMetric

  • RegressionPredictedVsActualScatter

  • RegressionPredictedVsActualPlot

  • RegressionErrorPlot

  • RegressionAbsPercentageErrorPlot

  • RegressionErrorDistribution

  • RegressionErrorNormality

  • RegressionTopErrorMetric

  • RegressionErrorBiasTable

Attributes:

columns : Optional[List[str]]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

class TargetDriftPreset(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)

Bases: MetricPreset

Metric preset for Target Drift analysis.

Contains metrics:

  • ColumnDriftMetric - for target and prediction if present in datasets.

  • ColumnValuePlot - if task is regression.

  • ColumnCorrelationsMetric - for target and prediction if present in datasets.

  • TargetByFeaturesTable

Attributes:

cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

cat_stattest_threshold : Optional[float]

columns : Optional[List[str]]

num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

num_stattest_threshold : Optional[float]

per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]

per_column_stattest_threshold : Optional[Dict[str, float]]

stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

stattest_threshold : Optional[float]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

Submodules

classification_performance module

class ClassificationPreset(columns: Optional[List[str]] = None, probas_threshold: Optional[float] = None, k: Optional[int] = None)

Bases: MetricPreset

Metrics preset for classification performance.

Contains metrics:

  • ClassificationQualityMetric

  • ClassificationClassBalance

  • ClassificationConfusionMatrix

  • ClassificationQualityByClass

Attributes:

columns : Optional[List[str]]

k : Optional[int]

probas_threshold : Optional[float]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

data_drift module

class DataDriftPreset(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)

Bases: MetricPreset

Metric Preset for Data Drift analysis.

Contains metrics:

  • DatasetDriftMetric

  • DataDriftTable

Attributes:

cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

cat_stattest_threshold : Optional[float]

columns : Optional[List[str]]

drift_share : float

num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

num_stattest_threshold : Optional[float]

per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]

per_column_stattest_threshold : Optional[Dict[str, float]]

stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

stattest_threshold : Optional[float]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

data_quality module

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

Bases: MetricPreset

Metric preset for Data Quality analysis.

Contains metrics:

  • DatasetSummaryMetric

  • ColumnSummaryMetric for each column

  • DatasetMissingValuesMetric

  • DatasetCorrelationsMetric

  • Parameters

    columns – list of columns for analysis.

Attributes:

columns : Optional[List[str]]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

metric_preset module

class MetricPreset()

Bases: object

Base class for metric presets

Methods:

abstract generate_metrics(data: InputData, columns: DatasetColumns)

regression_performance module

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

Bases: MetricPreset

Metric preset for Regression performance analysis.

Contains metrics:

  • RegressionQualityMetric

  • RegressionPredictedVsActualScatter

  • RegressionPredictedVsActualPlot

  • RegressionErrorPlot

  • RegressionAbsPercentageErrorPlot

  • RegressionErrorDistribution

  • RegressionErrorNormality

  • RegressionTopErrorMetric

  • RegressionErrorBiasTable

Attributes:

columns : Optional[List[str]]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

target_drift module

class TargetDriftPreset(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)

Bases: MetricPreset

Metric preset for Target Drift analysis.

Contains metrics:

  • ColumnDriftMetric - for target and prediction if present in datasets.

  • ColumnValuePlot - if task is regression.

  • ColumnCorrelationsMetric - for target and prediction if present in datasets.

  • TargetByFeaturesTable

Attributes:

cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

cat_stattest_threshold : Optional[float]

columns : Optional[List[str]]

num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

num_stattest_threshold : Optional[float]

per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]

per_column_stattest_threshold : Optional[Dict[str, float]]

stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]

stattest_threshold : Optional[float]

Methods:

generate_metrics(data: InputData, columns: DatasetColumns)

Previousevidently.metrics.regression_performanceNextevidently.options

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