evidently.calculations.stattests
Available statistical tests. For detailed information about statistical tests see module documentation.
Anderson-Darling test of two samples.
Name: “anderson”
Import:
>>> from evidently.calculations.stattests import anderson_darling_test
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import anderson_darling_test
>>> options = DataDriftOptions(all_features_stattest=anderson_darling_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="anderson")
Chisquare test of two samples.
Name: “chisquare”
Import:
>>> from evidently.calculations.stattests import chi_stat_test
Properties:
- only for categorical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import chi_stat_test
>>> options = DataDriftOptions(all_features_stattest=chi_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="chisquare")
Cramer-Von-mises test of two samples.
Name: “cramer_von_mises”
Import:
>>> from evidently.calculations.stattests import cramer_von_mises
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import cramer_von_mises
>>> options = DataDriftOptions(all_features_stattest=cramer_von_mises)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="cramer_von_mises")
Bases:
object
Energy-distance test of two samples.
Name: “ed”
Import:
>>> from evidently.calculations.stattests import energy_dist_test
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import energy_dist_test
>>> options = DataDriftOptions(all_features_stattest=energy_dist_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="ed")
Epps-Singleton test of two samples.
Name: “es”
Import:
>>> from evidently.calculations.stattests import epps_singleton_test
Properties:
- only for numerical features
- returns p-value
- default threshold 0.05
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import epps_singleton_test
>>> options = DataDriftOptions(all_features_stattest=epps_singleton_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="es")
Fisher’s exact test of two samples.
Name: “fisher_exact”
Import:
>>> from evidently.calculations.stattests import fisher_exact_test
Properties:
- only for categorical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import fisher_exact_test
>>> options = DataDriftOptions(all_features_stattest=fisher_exact_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="fisher_exact")
G-test of two samples.
Name: “g_test”
Import:
>>> from evidently.calculations.stattests import g_test
Properties:
- only for categorical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import g_test
>>> options = DataDriftOptions(all_features_stattest=g_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="g_test")
Hellinger distance of two samples.
Name: “hellinger”
Import:
>>> from evidently.calculations.stattests import hellinger_stat_test
Properties:
- only for categorical and numerical features
- returns distance
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import hellinger_stat_test
>>> options = DataDriftOptions(all_features_stattest=hellinger_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="hellinger")
Jensen-Shannon distance of two samples.
Name: “jensenshannon”
Import:
>>> from evidently.calculations.stattests import jensenshannon_stat_test
Properties:
- only for categorical and numerical features
- returns distance
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import jensenshannon_stat_test
>>> options = DataDriftOptions(all_features_stattest=jensenshannon_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="jensenshannon")
Kullback-Leibler divergence of two samples.
Name: “kl_div”
Import:
>>> from evidently.calculations.stattests import kl_div_stat_test
Properties:
- only for categorical and numerical features
- returns divergence
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import kl_div_stat_test
>>> options = DataDriftOptions(all_features_stattest=kl_div_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="kl_div")
Kolmogorov-Smirnov test of two samples.
Name: “ks”
Import:
>>> from evidently.calculations.stattests import ks_stat_test
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import ks_stat_test
>>> options = DataDriftOptions(all_features_stattest=ks_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="ks")
Mann-Whitney U-rank test of two samples.
Name: “mannw”
Import:
>>> from evidently.calculations.stattests import mann_whitney_u_stat_test
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import mann_whitney_u_stat_test
>>> options = DataDriftOptions(all_features_stattest=mann_whitney_u_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="mannw")
PSI of two samples.
Name: “psi”
Import:
>>> from evidently.calculations.stattests import psi_stat_test
Properties:
- only for categorical and numerical features
- returns PSI value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import psi_stat_test
>>> options = DataDriftOptions(all_features_stattest=psi_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="psi")
Bases:
object
allowed_feature_types : List[str]
default_threshold : float = 0.05
display_name : str
func : Callable[[Series, Series, str, float], Tuple[float, bool]]
name : str
Bases:
ValueError
Bases:
ValueError
Bases:
object
actual_threshold : float
drift_score : float
drifted : bool
T test of two samples.
Name: “t_test”
Import:
>>> from evidently.calculations.stattests import t_test
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import t_test
>>> options = DataDriftOptions(all_features_stattest=t_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="t_test")
Total variation distance of two samples.
Name: “TVD”
Import:
>>> from evidently.calculations.stattests import tvd_test
Properties:
- only for numerical features
- returns distance
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import tvd_test
>>> options = DataDriftOptions(all_features_stattest=tvd_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="TVD")
Generate 2x2 contingency matrix for fisher exact test :param reference_data: reference data :param current_data: current data
- Raises
ValueError
– if reference_data and current_data are not of equal length - Returnscontingency_matrix for binary data
- Return typecontingency_matrix
Split variable into n buckets based on reference quantiles :param reference_data: reference data :param current_data: current data :param feature_type: feature type :param n: number of quantiles
- Returns% of records in each bucket for reference current_percents: % of records in each bucket for current
- Return typereference_percents
Get unique values from current and reference series, drop NaNs
Perform a two-sided permutation test :param reference_data: reference data :param current_data: current data :param observed: observed value :param test_statistic_func: the test statistic function :param iterations: number of times to permute
- Returnstwo-sided p_value
- Return typep_value
Wasserstein distance of two samples.
Name: “wasserstein”
Import:
>>> from evidently.calculations.stattests import wasserstein_stat_test
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import wasserstein_stat_test
>>> options = DataDriftOptions(all_features_stattest=wasserstein_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="wasserstein")
Mann-Whitney U-rank test of two samples.
Name: “mannw”
Import:
>>> from evidently.calculations.stattests import mann_whitney_u_stat_test
Properties:
- only for numerical features
- returns p-value
Using by object:
>>> from evidently.options import DataDriftOptions
>>> from evidently.calculations.stattests import mann_whitney_u_stat_test
>>> options = DataDriftOptions(all_features_stattest=mann_whitney_u_stat_test)
Using by name:
>>> from evidently.options import DataDriftOptions
>>> options = DataDriftOptions(all_features_stattest="mannw")
Last modified 6mo ago