# evidently.calculations ## Subpackages * [evidently.calculations.stattests package](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations/evidently.calculations.stattests) * [Submodules](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#submodules) * [anderson\_darling\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.anderson_darling_stattest) * [chisquare\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.chisquare_stattest) * [cramer\_von\_mises\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.cramer_von_mises_stattest) * [`CramerVonMisesResult`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.cramer_von_mises_stattest.CramerVonMisesResult) * [energy\_distance module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.energy_distance) * [epps\_singleton\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.epps_singleton_stattest) * [fisher\_exact\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.fisher_exact_stattest) * [g\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.g_stattest) * [hellinger\_distance module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.hellinger_distance) * [jensenshannon module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.jensenshannon) * [kl\_div module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.kl_div) * [ks\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.ks_stattest) * [mann\_whitney\_urank\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.mann_whitney_urank_stattest) * [psi module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.psi) * [registry module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.registry) * [`StatTest`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTest) * [`StatTest.allowed_feature_types`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTest.allowed_feature_types) * [`StatTest.default_threshold`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTest.default_threshold) * [`StatTest.display_name`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTest.display_name) * [`StatTest.func`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTest.func) * [`StatTest.name`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTest.name) * [`StatTestInvalidFeatureTypeError`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTestInvalidFeatureTypeError) * [`StatTestNotFoundError`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTestNotFoundError) * [`StatTestResult`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTestResult) * [`StatTestResult.actual_threshold`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTestResult.actual_threshold) * [`StatTestResult.drift_score`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTestResult.drift_score) * [`StatTestResult.drifted`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.StatTestResult.drifted) * [`get_stattest()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.get_stattest) * [`register_stattest()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.registry.register_stattest) * [t\_test module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.t_test) * [tvd\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.tvd_stattest) * [utils module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.utils) * [`generate_fisher2x2_contingency_table()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.utils.generate_fisher2x2_contingency_table) * [`get_binned_data()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.utils.get_binned_data) * [`get_unique_not_nan_values_list_from_series()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.utils.get_unique_not_nan_values_list_from_series) * [`permutation_test()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.utils.permutation_test) * [wasserstein\_distance\_norm module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.wasserstein_distance_norm) * [z\_stattest module](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#module-evidently.calculations.stattests.z_stattest) * [`proportions_diff_z_stat_ind()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.z_stattest.proportions_diff_z_stat_ind) * [`proportions_diff_z_test()`](https://francesco.gitbook.io/docs.evidentlyai.com/reference/api-reference/evidently.calculations.stattests#evidently.calculations.stattests.z_stattest.proportions_diff_z_test) ## Submodules ## classification\_performance module ### class ConfusionMatrix(labels: Sequence\[Union\[str, int]], values: list) Bases: `object` #### Attributes: **labels : Sequence\[Union\[str, int]]** **values : list** ### class DatasetClassificationQuality(accuracy: float, precision: float, recall: float, f1: float, roc\_auc: Optional\[float] = None, log\_loss: Optional\[float] = None, tpr: Optional\[float] = None, tnr: Optional\[float] = None, fpr: Optional\[float] = None, fnr: Optional\[float] = None, rate\_plots\_data: Optional\[Dict] = None, plot\_data: Optional\[Dict] = None) Bases: `object` #### Attributes: **accuracy : float** **f1 : float** **fnr : Optional\[float] = None** **fpr : Optional\[float] = None** **log\_loss : Optional\[float] = None** **plot\_data : Optional\[Dict] = None** **precision : float** **rate\_plots\_data : Optional\[Dict] = None** **recall : float** **roc\_auc : Optional\[float] = None** **tnr : Optional\[float] = None** **tpr : Optional\[float] = None** ### class PredictionData(predictions: pandas.core.series.Series, prediction\_probas: Optional\[pandas.core.frame.DataFrame], labels: List\[Union\[str, int]]) Bases: `object` #### Attributes: **labels : List\[Union\[str, int]]** **prediction\_probas : Optional\[DataFrame]** **predictions : Series** ### calculate\_confusion\_by\_classes(confusion\_matrix: ndarray, class\_names: Sequence\[Union\[str, int]]) Calculate metrics: * TP (true positive) * TN (true negative) * FP (false positive) * FN (false negative) for each class from confusion matrix. * **Returns** a dict like: ```default { "class_1_name": { "tp": 1, "tn": 5, "fp": 0, "fn": 3, }, "class_1_name": { "tp": 1, "tn": 5, "fp": 0, "fn": 3, }, } ``` ### calculate\_matrix(target: Series, prediction: Series, labels: List\[Union\[str, int]]) ### calculate\_metrics(column\_mapping: [ColumnMapping](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.pipeline#evidently.pipeline.column_mapping.ColumnMapping), confusion\_matrix: ConfusionMatrix, target: Series, prediction: PredictionData) ### calculate\_pr\_table(binded) ### collect\_plot\_data(prediction\_probas: DataFrame) ### get\_prediction\_data(data: DataFrame, data\_columns: [DatasetColumns](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns), pos\_label: Optional\[Union\[str, int]], threshold: float = 0.5) Get predicted values and optional prediction probabilities from source data. Also take into account a threshold value - if a probability is less than the value, do not take it into account. Return and object with predicted values and an optional prediction probabilities. ### k\_probability\_threshold(prediction\_probas: DataFrame, k: Union\[int, float]) ### threshold\_probability\_labels(prediction\_probas: DataFrame, pos\_label: Union\[str, int], neg\_label: Union\[str, int], threshold: float) Get prediction values by probabilities with the threshold apply ## data\_drift module Methods and types for data drift calculations. ### class ColumnDataDriftMetrics(column\_name: str, column\_type: str, stattest\_name: str, drift\_score: float, drift\_detected: bool, threshold: float, current\_distribution: [Distribution](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.visualizations.Distribution), reference\_distribution: [Distribution](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.visualizations.Distribution), current\_small\_distribution: Optional\[list] = None, reference\_small\_distribution: Optional\[list] = None, current\_scatter: Optional\[Dict\[str, list]] = None, x\_name: Optional\[str] = None, plot\_shape: Optional\[Dict\[str, float]] = None, current\_correlations: Optional\[Dict\[str, float]] = None, reference\_correlations: Optional\[Dict\[str, float]] = None) Bases: `object` One column drift metrics. #### Attributes: **column\_name : str** **column\_type : str** **current\_correlations : Optional\[Dict\[str, float]] = None** **current\_distribution :** [**Distribution**](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.visualizations.Distribution) **current\_scatter : Optional\[Dict\[str, list]] = None** **current\_small\_distribution : Optional\[list] = None** **drift\_detected : bool** **drift\_score : float** **plot\_shape : Optional\[Dict\[str, float]] = None** **reference\_correlations : Optional\[Dict\[str, float]] = None** **reference\_distribution :** [**Distribution**](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.visualizations.Distribution) **reference\_small\_distribution : Optional\[list] = None** **stattest\_name : str** **threshold : float** **x\_name : Optional\[str] = None** ### class DatasetDrift(number\_of\_drifted\_columns: int, dataset\_drift\_score: float, dataset\_drift: bool) Bases: `object` Dataset drift calculation results #### Attributes: **dataset\_drift : bool** **dataset\_drift\_score : float** **number\_of\_drifted\_columns : int** ### class DatasetDriftMetrics(number\_of\_columns: int, number\_of\_drifted\_columns: int, share\_of\_drifted\_columns: float, dataset\_drift: bool, drift\_by\_columns: Dict\[str, ColumnDataDriftMetrics], options: [DataDriftOptions](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.options#evidently.options.data_drift.DataDriftOptions), dataset\_columns: [DatasetColumns](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns)) Bases: `object` #### Attributes: **dataset\_columns :** [**DatasetColumns**](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns) **dataset\_drift : bool** **drift\_by\_columns : Dict\[str, ColumnDataDriftMetrics]** **number\_of\_columns : int** **number\_of\_drifted\_columns : int** **options :** [**DataDriftOptions**](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.options#evidently.options.data_drift.DataDriftOptions) **share\_of\_drifted\_columns : float** ### ensure\_prediction\_column\_is\_string(\*, prediction\_column: Optional\[Union\[str, Sequence]], current\_data: DataFrame, reference\_data: DataFrame, threshold: float = 0.5) Update dataset by predictions type: * if prediction column is None or a string, no dataset changes * (binary classification) if predictions is a list and its length equals 2 set predicted\_labels column by threshold * (multy label classification) if predictions is a list and its length is greater than 2 set predicted\_labels from probability values in columns by prediction column * **Returns** prediction column name. ### get\_dataset\_drift(drift\_metrics, drift\_share=0.5) ### get\_drift\_for\_columns(\*, current\_data: DataFrame, reference\_data: DataFrame, dataset\_columns: [DatasetColumns](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns), data\_drift\_options: [DataDriftOptions](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.options#evidently.options.data_drift.DataDriftOptions), drift\_share\_threshold: Optional\[float] = None, columns: Optional\[List\[str]] = None) ### get\_one\_column\_drift(\*, current\_data: DataFrame, reference\_data: DataFrame, column\_name: str, options: [DataDriftOptions](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.options#evidently.options.data_drift.DataDriftOptions), dataset\_columns: [DatasetColumns](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns), column\_type: Optional\[str] = None) ## data\_integration module ### get\_number\_of\_all\_pandas\_missed\_values(dataset: DataFrame) Calculate the number of missed - nulls by pandas - values in a dataset ### get\_number\_of\_almost\_constant\_columns(dataset: DataFrame, threshold: float) Calculate the number of almost constant columns in a dataset ### get\_number\_of\_almost\_duplicated\_columns(dataset: DataFrame, threshold: float) Calculate the number of almost duplicated columns in a dataset ### get\_number\_of\_constant\_columns(dataset: DataFrame) Calculate the number of constant columns in a dataset ### get\_number\_of\_duplicated\_columns(dataset: DataFrame) Calculate the number of duplicated columns in a dataset ### get\_number\_of\_empty\_columns(dataset: DataFrame) Calculate the number of empty columns in a dataset ## data\_quality module Methods for overall dataset quality calculations - rows count, a specific values count, etc. ### class ColumnCorrelations(column\_name: str, kind: str, values: [Distribution](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.visualizations.Distribution)) Bases: `object` #### Attributes: **column\_name : str** **kind : str** **values :** [**Distribution**](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.visualizations.Distribution) ### class DataQualityGetPlotData() Bases: `object` #### Methods: **calculate\_data\_by\_target(curr: DataFrame, ref: Optional\[DataFrame], feature\_name: str, feature\_type: str, target\_name: str, target\_type: str, merge\_small\_cat: Optional\[int] = 5)** **calculate\_data\_in\_time(curr: DataFrame, ref: Optional\[DataFrame], feature\_name: str, feature\_type: str, datetime\_name: str, merge\_small\_cat: Optional\[int] = 5)** **calculate\_main\_plot(curr: DataFrame, ref: Optional\[DataFrame], feature\_name: str, feature\_type: str, merge\_small\_cat: Optional\[int] = 5)** ### class DataQualityPlot(bins\_for\_hist: Dict\[str, pandas.core.frame.DataFrame]) Bases: `object` #### Attributes: **bins\_for\_hist : Dict\[str, DataFrame]** ### class DataQualityStats(rows\_count: int, num\_features\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, cat\_features\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, datetime\_features\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, target\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None, prediction\_stats: Optional\[Dict\[str, FeatureQualityStats]] = None) Bases: `object` #### Attributes: **cat\_features\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None** **datetime\_features\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None** **num\_features\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None** **prediction\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None** **rows\_count : int** **target\_stats : Optional\[Dict\[str, FeatureQualityStats]] = None** #### Methods: **get\_all\_features()** ### class FeatureQualityStats(feature\_type: str, number\_of\_rows: int = 0, count: int = 0, infinite\_count: Optional\[int] = None, infinite\_percentage: Optional\[float] = None, missing\_count: Optional\[int] = None, missing\_percentage: Optional\[float] = None, unique\_count: Optional\[int] = None, unique\_percentage: Optional\[float] = None, percentile\_25: Optional\[float] = None, percentile\_50: Optional\[float] = None, percentile\_75: Optional\[float] = None, max: Optional\[Union\[int, float, bool, str]] = None, min: Optional\[Union\[int, float, bool, str]] = None, mean: Optional\[float] = None, most\_common\_value: Optional\[Union\[int, float, bool, str]] = None, most\_common\_value\_percentage: Optional\[float] = None, std: Optional\[float] = None, most\_common\_not\_null\_value: Optional\[Union\[int, float, bool, str]] = None, most\_common\_not\_null\_value\_percentage: Optional\[float] = None, new\_in\_current\_values\_count: Optional\[int] = None, unused\_in\_current\_values\_count: Optional\[int] = None) Bases: `object` Class for all features data quality metrics store. A type of the feature is stored in feature\_type field. Concrete stat kit depends on the feature type. Is a metric is not applicable - leave None value for it. Metrics for all feature types: ``` - feature type - cat for category, num for numeric, datetime for datetime features - count - quantity of a meaningful values (do not take into account NaN values) - missing_count - quantity of meaningless (NaN) values - missing_percentage - the percentage of the missed values - unique_count - quantity of unique values - unique_percentage - the percentage of the unique values - max - maximum value (not applicable for category features) - min - minimum value (not applicable for category features) - most_common_value - the most common value in the feature values - most_common_value_percentage - the percentage of the most common value - most_common_not_null_value - if most_common_value equals NaN - the next most common value. Otherwise - None - most_common_not_null_value_percentage - the percentage of most_common_not_null_value if it is defined. If most_common_not_null_value is not defined, equals None too. ``` Metrics for numeric features only: ``` - infinite_count - quantity infinite values (for numeric features only) - infinite_percentage - the percentage of infinite values (for numeric features only) - percentile_25 - 25% percentile for meaningful values - percentile_50 - 50% percentile for meaningful values - percentile_75 - 75% percentile for meaningful values - mean - the sum of the meaningful values divided by the number of the meaningful values - std - standard deviation of the values ``` Metrics for category features only: * new\_in\_current\_values\_count - quantity of new values in the current dataset after the reference ``` Defined for reference dataset only. ``` * new\_in\_current\_values\_count - quantity of values in the reference dataset that not presented in the current Defined for reference dataset only. #### Attributes: **count : int = 0** **feature\_type : str** **infinite\_count : Optional\[int] = None** **infinite\_percentage : Optional\[float] = None** **max : Optional\[Union\[int, float, bool, str]] = None** **mean : Optional\[float] = None** **min : Optional\[Union\[int, float, bool, str]] = None** **missing\_count : Optional\[int] = None** **missing\_percentage : Optional\[float] = None** **most\_common\_not\_null\_value : Optional\[Union\[int, float, bool, str]] = None** **most\_common\_not\_null\_value\_percentage : Optional\[float] = None** **most\_common\_value : Optional\[Union\[int, float, bool, str]] = None** **most\_common\_value\_percentage : Optional\[float] = None** **new\_in\_current\_values\_count : Optional\[int] = None** **number\_of\_rows : int = 0** **percentile\_25 : Optional\[float] = None** **percentile\_50 : Optional\[float] = None** **percentile\_75 : Optional\[float] = None** **std : Optional\[float] = None** **unique\_count : Optional\[int] = None** **unique\_percentage : Optional\[float] = None** **unused\_in\_current\_values\_count : Optional\[int] = None** #### Methods: **as\_dict()** **is\_category()** Checks that the object store stats for a category feature **is\_datetime()** Checks that the object store stats for a datetime feature **is\_numeric()** Checks that the object store stats for a numeric feature ### calculate\_category\_column\_correlations(column\_name: str, dataset: DataFrame, columns: List\[str]) For category columns calculate cramer\_v correlation ### calculate\_column\_distribution(column: Series, column\_type: str) ### calculate\_correlations(dataset: DataFrame, columns: [DatasetColumns](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns)) ### calculate\_cramer\_v\_correlation(column\_name: str, dataset: DataFrame, columns: List\[str]) ### calculate\_data\_quality\_stats(dataset: DataFrame, columns: [DatasetColumns](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns), task: Optional\[str]) ### calculate\_numerical\_column\_correlations(column\_name: str, dataset: DataFrame, columns: List\[str]) ### get\_features\_stats(feature: Series, feature\_type: str) ### get\_pairwise\_correlation(df, func: Callable\[\[Series, Series], float]) Compute pairwise correlation of columns :param df: initial data frame. :param func: function for computing pairwise correlation. * **Returns** Correlation matrix. ### get\_rows\_count(data: Union\[DataFrame, Series]) Count quantity of rows in a dataset ## regression\_performance module ### class ErrorWithQuantiles(error, quantile\_top, quantile\_other) Bases: `object` ### class FeatureBias(feature\_type: str, majority: float, under: float, over: float, range: float) Bases: `object` #### Attributes: **feature\_type : str** **majority : float** **over : float** **range : float** **under : float** #### Methods: **as\_dict(prefix)** ### class RegressionPerformanceMetrics(mean\_error: float, mean\_abs\_error: float, mean\_abs\_perc\_error: float, error\_std: float, abs\_error\_max: float, abs\_error\_std: float, abs\_perc\_error\_std: float, error\_normality: dict, underperformance: dict, error\_bias: dict) Bases: `object` #### Attributes: **abs\_error\_max : float** **abs\_error\_std : float** **abs\_perc\_error\_std : float** **error\_bias : dict** **error\_normality : dict** **error\_std : float** **mean\_abs\_error : float** **mean\_abs\_perc\_error : float** **mean\_error : float** **underperformance : dict** ### calculate\_regression\_performance(dataset: DataFrame, columns: [DatasetColumns](https://francesco.gitbook.io/docs.evidentlyai.com/reference/evidently.utils#evidently.utils.data_operations.DatasetColumns), error\_bias\_prefix: str) ### error\_bias\_table(dataset, err\_quantiles, num\_feature\_names, cat\_feature\_names) ### error\_with\_quantiles(dataset, prediction\_column, target\_column, quantile: float)