evidently.options

class OptionsProvider()
Bases: object
Methods:
     add(options)
     get(options_type: Type[TypeParam])
Submodules
color_scheme module
class ColorOptions(primary_color: str = '#ed0400', secondary_color: str = '#4d4d4d', current_data_color: Optional[str] = None, reference_data_color: Optional[str] = None, color_sequence: Sequence[str] = ('#ed0400', '#0a5f38', '#6c3461', '#71aa34', '#d8dcd6', '#6b8ba4'), fill_color: str = 'LightGreen', zero_line_color: str = 'green', non_visible_color: str = 'white', underestimation_color: str = '#6574f7', overestimation_color: str = '#ee5540', majority_color: str = '#1acc98', vertical_lines: str = 'green', heatmap: str = 'RdBu_r')
Bases: object
Collection of colors for data visualization
primary_color - basic color for data visualization.
Uses by default for all bars and lines for widgets with one dataset and as a default for current data.
secondary_color - basic color for second data visualization if we have two data sets, for example, reference data.
current_data_color - color for all current data, by default primary color is used
reference_data_color - color for reference data, by default secondary color is used
color_sequence - set of colors for drawing a number of lines in one graph, in for data quality, for example
fill_color - fill color for areas in line graphs
zero_line_color - color for base, zero line in line graphs
non_visible_color - color for technical, not visible dots or points for better scalability
underestimation_color - color for underestimation line in regression
overestimation_color - color for overestimation line in regression
majority_color - color for majority line in regression
lines - color for vertical lines
heatmap_colors - colors for heatmap
Attributes:
     color_sequence : Sequence[str] = ('#ed0400', '#0a5f38', '#6c3461', '#71aa34', '#d8dcd6', '#6b8ba4')
     current_data_color : Optional[str] = None
     fill_color : str = 'LightGreen'
     heatmap : str = 'RdBu_r'
     majority_color : str = '#1acc98'
     non_visible_color : str = 'white'
     overestimation_color : str = '#ee5540'
     primary_color : str = '#ed0400'
     reference_data_color : Optional[str] = None
     secondary_color : str = '#4d4d4d'
     underestimation_color : str = '#6574f7'
     vertical_lines : str = 'green'
     zero_line_color : str = 'green'
Methods:
     get_current_data_color()
     get_reference_data_color()
data_drift module
class DataDriftOptions(confidence: Optional[Union[float, Dict[str, float]]] = None, threshold: Optional[Union[float, Dict[str, float]]] = None, drift_share: float = 0.5, nbinsx: Union[int, Dict[str, int]] = 10, xbins: Optional[Dict[str, int]] = None, feature_stattest_func: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest, Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]] = None, all_features_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, cat_features_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, num_features_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, per_feature_stattest: Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]] = None, all_features_threshold: Optional[float] = None, cat_features_threshold: Optional[float] = None, num_features_threshold: Optional[float] = None, per_feature_threshold: Optional[Dict[str, float]] = None, cat_target_threshold: Optional[float] = None, num_target_threshold: Optional[float] = None, cat_target_stattest_func: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, num_target_stattest_func: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None)
Bases: object
Configuration for Data Drift calculations.
Parameters
confidence – Defines the confidence level for statistical tests. Applies to all features (if passed as float) or certain features (if passed as dictionary). (Deprecated) Use threshold to define confidence level for statistical tests as more universal solution.
threshold – Defines thresholds for statistical tests. Applies to all features (if passed as float) or certain features (if passed as dictionary).
drift_share – Sets the share of drifting features as a condition for Dataset Drift in the Data Drift report.
nbinsx – Defines the number of bins in a histogram. Applies to all features (if passed as int) or certain features (if passed as dictionary).
xbins – Defines the boundaries for the size of a specific bin in a histogram.
feature_stattest_func – Defines a custom statistical test for drift detection in the Data Drift report. Applies to all features (if passed as a function) or individual features (if a dict). (Deprecated) Use all_features_stattest or per_feature_stattest.
all_features_stattest – Defines a custom statistical test for drift detection in the Data Drift report for all features.
cat_features_stattest – Defines a custom statistical test for drift detection in the Data Drift report for categorical features only.
num_features_stattest – Defines a custom statistical test for drift detection in the Data Drift report for numerical features only.
per_feature_stattest – Defines a custom statistical test for drift detection in the Data Drift report per feature.
cat_target_stattest_func – Defines a custom statistical test to detect target drift in category target.
num_target_stattest_func – Defines a custom statistical test to detect target drift in numeric target.
Attributes:
     all_features_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None
     all_features_threshold : Optional[float] = None
     cat_features_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None
     cat_features_threshold : Optional[float] = None
     cat_target_stattest_func : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None
     cat_target_threshold : Optional[float] = None
     confidence : Optional[Union[float, Dict[str, float]]] = None
     drift_share : float = 0.5
     feature_stattest_func : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest, Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]] = None
     nbinsx : Union[int, Dict[str, int]] = 10
     num_features_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None
     num_features_threshold : Optional[float] = None
     num_target_stattest_func : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None
     num_target_threshold : Optional[float] = None
     per_feature_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]] = None
     per_feature_threshold : Optional[Dict[str, float]] = None
     threshold : Optional[Union[float, Dict[str, float]]] = None
     xbins : Optional[Dict[str, int]] = None
Methods:
     as_dict()
     get_feature_stattest_func(feature_name: str, feature_type: str)
     get_nbinsx(feature_name: str)
     get_threshold(feature_name: str, feature_type: str)
quality_metrics module
class QualityMetricsOptions(conf_interval_n_sigmas: int = 1, classification_threshold: float = 0.5, cut_quantile: Union[NoneType, Tuple[str, float], Dict[str, Tuple[str, float]]] = None)
Bases: object
Attributes:
     classification_threshold : float = 0.5
     conf_interval_n_sigmas : int = 1
     cut_quantile : Union[None, Tuple[str, float], Dict[str, Tuple[str, float]]] = None
Methods:
     as_dict()
     get_cut_quantile(feature_name: str)
evidently.metric_preset
evidently.pipeline
Last modified 4mo ago