evidently.metrics.data_quality

Submodules

column_correlations_metric module

class ColumnCorrelationsMetric(column_name: str)

Bases: Metric[ColumnCorrelationsMetricResult]

Calculates correlations between the selected column and all the other columns. In the current and reference (if presented) datasets

Attributes:

column_name : str

Methods:

calculate(data: InputData)

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

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ColumnCorrelationsMetric)

render_json(obj: ColumnCorrelationsMetric)

class ColumnCorrelationsMetricResult(column_name: str, current: Dict[str, ColumnCorrelations], reference: Optional[Dict[str, ColumnCorrelations]] = None)

Bases: object

Attributes:

column_name : str

current : Dict[str, ColumnCorrelations]

reference : Optional[Dict[str, ColumnCorrelations]] = None

column_distribution_metric module

class ColumnDistributionMetric(column_name: str)

Bases: Metric[ColumnDistributionMetricResult]

Calculates distribution for the column

Attributes:

column_name : str

Methods:

calculate(data: InputData)

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

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ColumnDistributionMetric)

render_json(obj: ColumnDistributionMetric)

class ColumnDistributionMetricResult(column_name: str, current: Distribution, reference: Optional[Distribution] = None)

Bases: object

Attributes:

column_name : str

current : Distribution

reference : Optional[Distribution] = None

column_quantile_metric module

class ColumnQuantileMetric(column_name: str, quantile: float)

Bases: Metric[ColumnQuantileMetricResult]

Calculates quantile with specified range

Attributes:

column_name : str

quantile : float

Methods:

calculate(data: InputData)

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

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ColumnQuantileMetric)

render_json(obj: ColumnQuantileMetric)

class ColumnQuantileMetricResult(column_name: str, quantile: float, current: float, current_distribution: Distribution, reference: Optional[float] = None, reference_distribution: Optional[Distribution] = None)

Bases: object

Attributes:

column_name : str

current : float

current_distribution : Distribution

quantile : float

reference : Optional[float] = None

reference_distribution : Optional[Distribution] = None

column_value_list_metric module

class ColumnValueListMetric(column_name: str, values: Optional[list] = None)

Bases: Metric[ColumnValueListMetricResult]

Calculates count and shares of values in the predefined values list

Attributes:

column_name : str

values : Optional[list]

Methods:

calculate(data: InputData)

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

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ColumnValueListMetric)

render_json(obj: ColumnValueListMetric)

class ColumnValueListMetricResult(column_name: str, values: List[Any], current: ValueListStat, reference: Optional[ValueListStat] = None)

Bases: object

Attributes:

column_name : str

current : ValueListStat

reference : Optional[ValueListStat] = None

values : List[Any]

class ValueListStat(number_in_list: int, number_not_in_list: int, share_in_list: float, share_not_in_list: float, values_in_list: Dict[Any, int], values_not_in_list: Dict[Any, int], rows_count: int)

Bases: object

Attributes:

number_in_list : int

number_not_in_list : int

rows_count : int

share_in_list : float

share_not_in_list : float

values_in_list : Dict[Any, int]

values_not_in_list : Dict[Any, int]

column_value_range_metric module

class ColumnValueRangeMetric(column_name: str, left: Optional[Union[float, int]] = None, right: Optional[Union[float, int]] = None)

Bases: Metric[ColumnValueRangeMetricResult]

Calculates count and shares of values in the predefined values range

Attributes:

column_name : str

left : Optional[Union[float, int]]

right : Optional[Union[float, int]]

Methods:

calculate(data: InputData)

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

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ColumnValueRangeMetric)

render_json(obj: ColumnValueRangeMetric)

class ColumnValueRangeMetricResult(column_name: str, left: Union[float, int], right: Union[float, int], current: ValuesInRangeStat, current_distribution: Distribution, reference: Optional[ValuesInRangeStat] = None, reference_distribution: Optional[Distribution] = None)

Bases: object

Attributes:

column_name : str

current : ValuesInRangeStat

current_distribution : Distribution

left : Union[float, int]

reference : Optional[ValuesInRangeStat] = None

reference_distribution : Optional[Distribution] = None

right : Union[float, int]

class ValuesInRangeStat(number_in_range: int, number_not_in_range: int, share_in_range: float, share_not_in_range: float, number_of_values: int)

Bases: object

Attributes:

number_in_range : int

number_not_in_range : int

number_of_values : int

share_in_range : float

share_not_in_range : float

dataset_correlations_metric module

class CorrelationStats(target_prediction_correlation: Optional[float] = None, abs_max_target_features_correlation: Optional[float] = None, abs_max_prediction_features_correlation: Optional[float] = None, abs_max_correlation: Optional[float] = None, abs_max_features_correlation: Optional[float] = None)

Bases: object

Attributes:

abs_max_correlation : Optional[float] = None

abs_max_features_correlation : Optional[float] = None

abs_max_prediction_features_correlation : Optional[float] = None

abs_max_target_features_correlation : Optional[float] = None

target_prediction_correlation : Optional[float] = None

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

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: DatasetCorrelationsMetric)

render_json(obj: DatasetCorrelationsMetric)

class DatasetCorrelation(correlation: Dict[str, pandas.core.frame.DataFrame], stats: Dict[str, CorrelationStats])

Bases: object

Attributes:

correlation : Dict[str, DataFrame]

stats : Dict[str, CorrelationStats]

class DatasetCorrelationsMetric()

Bases: Metric[DatasetCorrelationsMetricResult]

Calculate different correlations with target, predictions and features

Methods:

calculate(data: InputData)

class DatasetCorrelationsMetricResult(current: DatasetCorrelation, reference: Optional[DatasetCorrelation])

Bases: object

Attributes:

current : DatasetCorrelation

reference : Optional[DatasetCorrelation]

stability_metric module

class DataQualityStabilityMetric()

Bases: Metric[DataQualityStabilityMetricResult]

Calculates stability by target and prediction

Methods:

calculate(data: InputData)

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

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: DataQualityStabilityMetric)

render_json(obj: DataQualityStabilityMetric)

class DataQualityStabilityMetricResult(number_not_stable_target: Optional[int] = None, number_not_stable_prediction: Optional[int] = None)

Bases: object

Attributes:

number_not_stable_prediction : Optional[int] = None

number_not_stable_target : Optional[int] = None