evidently.metrics.data_drift
class ColumnDriftMetric(column_name: str, stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, stattest_threshold: Optional[float] = None)
Calculate drift metric for a column
column_name : str
stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
stattest_threshold : Optional[float]
get_parameters()
render_html(obj: ColumnDriftMetric)
render_json(obj: ColumnDriftMetric)
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]])
Bases:
object
column_name : str
column_type : str
current_scatter : Optional[Dict[str, list]]
drift_detected : bool
drift_score : Union[float, int]
plot_shape : Optional[Dict[str, float]]
stattest_name : str
stattest_threshold : float
x_name : Optional[str]
column_name : str
render_html(obj: ColumnValuePlot)
render_json(obj: ColumnValuePlot)
Bases:
object
column_name : str
current_scatter : DataFrame
datetime_column_name : Optional[str]
reference_scatter : DataFrame
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)
columns : Optional[List[str]]
get_parameters()
render_html(obj: DataDriftTable)
render_json(obj: DataDriftTable)
Bases:
object
dataset_drift : bool
number_of_columns : int
number_of_drifted_columns : int
share_of_drifted_columns : float
render_html(obj: DatasetDriftMetric)
render_json(obj: DatasetDriftMetric)
columns : Optional[List[str]]
drift_share : float
get_parameters()
Bases:
object
dataset_drift : bool
drift_share : float
number_of_columns : int
number_of_drifted_columns : int
share_of_drifted_columns : float
columns : Optional[List[str]]
render_html(obj: TargetByFeaturesTable)
render_json(obj: TargetByFeaturesTable)
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)
Bases:
object
columns : List[str]
current_plot_data : DataFrame
reference_plot_data : DataFrame
target_name : Optional[str]
task : str