# Options for Statistical Tests

## Available Options

* (deprecated)`feature_stattest_func` (default: `None`): define the Statistical Test for features in the DataDrift Dashboard or Profile:
  * `None` - use default Statistical Tests for all features (based on internal logic)
  * You can define a Statistical Test to be used for all the features in the dataset:
    * `str` - the name of StatTest to use across all features (see the available names below)
    * `Callable[[pd.Series, pd.Series, str, float], Tuple[float, bool]]` - custom StatTest function added (see the requirements for the custom StatTest function below)
    * `StatTest` - an instance of `StatTest`
  * You can define a Statistical Test to be used for individual features by passing a `dict` object where the key is a feature name and the value is one from the previous options (`str`, `Callable` or `StatTest`)
  * **Deprecated:** Use `all_features_stattest` or `per_feature_statttest` options.
* `all_features_stattest`(default: `None`): defines a custom statistical test for all features in DataDrift Dashboard or Profile.
* `cat_features_stattest` (default: `None`): defines a custom statistical test for categorical features in DataDrift Dashboard or Profile.
* `num_features_stattest` (default: `None`): defines a custom statistical test for numerical features in DataDrift Dashboard or Profile.
* `per_feature_stattest` (default: `None`): defines a custom statistical test per feature in DataDrift Dashboard or Profile as `dict` object where key is feature name and values is statistical test.
* `cat_target_stattest_func` (default: `None`): defines a custom statistical test to detect target drift in the Categorical Target Drift report. It follows the same logic as the `feature_stattest_func`, but without the `dict` option.
* `num_target_stattest_func` (default: `None`): defines a custom statistical test to detect target drift in the Numerical Target Drift report. It follows the same logic as the `feature_stattest_func`, but without the `dict` option.

### Example:

Change the StatTest for all the features in the Data Drift report:

```python
from evidently.options.data_drift import DataDriftOptions

options = DataDriftOptions(
  feature_stattest_func="ks",
) 
```

Change the StatTest for a single feature to a Custom (user-defined) function:

```python
from evidently.options.data_drift import DataDriftOptions


def my_stat_test(reference_data, current_data, feature_type, threshold):
  return 0.0, False


options = DataDriftOptions(
  feature_stattest_func={"feature_1": my_stat_test },
)
```

Change the StatTest for a single feature to Custom function (using a StatTest object):

```python
from evidently.calculations.stattests import StatTest
from evidently.options.data_drift import DataDriftOptions


def _my_stat_test(reference_data, current_data, feature_type, threshold):
  return 0.0, False


my_stat_test = StatTest(
  name="my_stat_test",
  display_name="My Stat Test",
  func=_my_stat_test,
  allowed_feature_types=["cat"],
)

options = DataDriftOptions(
  feature_stattest_func={"feature_1": my_stat_test},
)
```

## Custom StatTest function requirements:

The StatTest function should match `(reference_data: pd.Series, current_data: pd.Series, threshold: float) -> Tuple[float, bool]` signature:

* `reference_data: pd.Series` - reference data series
* `current_data: pd.Series` - current data series to compare
* `feature_type: str` - feature type
* `threshold: float` - Stat Test threshold for drift detection

Returns:

* `score: float` - Stat Test score (actual value)
* `drift_detected: bool` - indicates is drift detected with given threshold

### Example:

```python
from typing import Tuple

import numpy as np
import pandas as pd
from scipy.stats import anderson_ksamp


def anderson_stat_test(reference_data: pd.Series, current_data: pd.Series, _feature_type: str, threshold: float) -> Tuple[float, bool]:
  p_value = anderson_ksamp(np.array([reference_data, current_data]))[2]
  return p_value, p_value < threshold
```

## StatTest meta information (StatTest class):

To use the StatTest function, we recommended writing a specific instance of the StatTest class for that function:

To create the instance of the `StatTest` class, you need:

* `name: str` - a short name used to reference the Stat Test from the options (the StatTest should be registered globally)
* `display_name: str` - a long name displayed in the Dashboard and Profile
* `func: Callable` - a StatTest function
* `allowed_feature_types: List[str]` - the list of allowed feature types to which this function can be applied (available values: `cat`, `num`)

### Example:

```python
from evidently.calculations.stattests import StatTest


def example_stat_test(reference_data, current_data, feature_type, threshold):
  return 0.1, False


example_stat_test = StatTest(
  name="example_test",
  display_name="Example Stat Test (score)",
  func=example_stat_test,
  allowed_feature_types=["cat"],
)
```

## Available StatTest Functions:

* `ks` - Kolmogorov–Smirnov (K-S) test
  * default for numerical features
  * only for numerical features
  * returns `p_value`
  * drift detected when `p_value < threshold`
* `chisquare` - Chi-Square test
  * default for categorical features if the number of labels for feature > 2
  * only for categorical features
  * returns `p_value`
  * drift detected when `p_value < threshold`
* `z` - Z-test
  * default for categorical features if the number of labels for feature <= 2
  * only for categorical features
  * returns `p_value`
  * drift detected when `p_value < threshold`
* `wasserstein` - Wasserstein distance (normed)
  * only for numerical features
  * returns `distance`
  * drift detected when `distance >= threshold`
* `kl_div` - Kullback-Leibler divergence
  * for numerical and categorical features
  * returns `divergence`
  * drift detected when `divergence >= threshold`
* `psi` - Population Stability Index (PSI)
  * for numerical and categorical features
  * returns `psi_value`
  * drift detected when `psi_value >= threshold`
* `jensenshannon` - Jensen-Shannon distance
  * for numerical and categorical features
  * returns `distance`
  * drift detected when `distance >= threshold`


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