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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)