# `AndersonDarling`

### *class* capymoa.drift.detectors.AndersonDarling[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/anderson_darling.py#L17)

Bases: [`BaseDataDriftDetector`](capymoa.drift.detectors.BaseDataDriftDetector.md#capymoa.drift.detectors.BaseDataDriftDetector)

Anderson-Darling K-Sample Test

Univariate statistical test that compares two samples by measuring
the integrated squared difference between their empirical CDFs,
weighted to give more sensitivity in the tails than the
Kolmogorov-Smirnov test.

Applied per feature, with Bonferroni correction by default.

#### NOTE
p-values are bounded between 0.001 and 0.25 by
[`scipy.stats.anderson_ksamp()`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anderson_ksamp.html#scipy.stats.anderson_ksamp). With `correction="bonferroni"`,
[`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) raises [`ValueError`](https://docs.python.org/3/builtins/exceptions.html#ValueError) if `alpha / n_features`
falls at or below the 0.001 floor, since drift could then never be
declared regardless of the data.

## Example:

```pycon
>>> import numpy as np
>>> from capymoa.drift.detectors import AndersonDarling
>>> rng = np.random.default_rng(42)
>>> detector = AndersonDarling(window_size=50)
>>> detector.fit(rng.normal(0, 1, size=(200, 2)))
>>> for x in rng.normal(0.8, 1, size=(50, 2)):
...     detector.add_element(x)
>>> detector.detected_change()
True
```

## Reference:

Scholz, Fritz W., and Michael A. Stephens. “K-sample Anderson-Darling tests.”
Journal of the American Statistical Association 82.399 (1987): 918-924.

#### \_\_init_\_(window_size: [int](https://docs.python.org/3/builtins/functions.html#int), alpha: [float](https://docs.python.org/3/builtins/functions.html#float) = 0.05, correction: [Literal](https://docs.python.org/3/library/typing.html#typing.Literal)['bonferroni', 'none'] = 'bonferroni', auto_fit_samples: [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None) = None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/anderson_darling.py#L57)

Create an Anderson-Darling data drift detector.

* **Parameters:**
  * **window_size** – Number of observations in the sliding window.
  * **alpha** – Significance level for drift decisions.
  * **correction** – Multiple-testing correction across features.
  * **auto_fit_samples** – Number of initial samples for auto-fit.

#### \_fit(X: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/anderson_darling.py#L73)

Store or preprocess the reference data.

Called by [`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) after validation. *X* is guaranteed to be a
2-D `np.ndarray` with shape `(n_samples, n_features)`.

At minimum, implementations should set `self._X_ref = X`.

#### \_test(x_ref: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray), x_test: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) → [DataDriftResult](capymoa.drift.detectors.DataDriftResult.md#capymoa.drift.detectors.DataDriftResult)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/anderson_darling.py#L87)

Run the underlying statistical test or distance measure.

When `IS_UNIVARIATE = True` this receives **one feature** at a
time: both arrays are 1-D with shapes `(n_ref,)` and
`(n_test,)`. The subclass should set `statistic` and
`p_value` (if available) on the returned result.

If `p_value` is set, the base class applies the configured
`correction` and ignores the per-feature `is_drift` flag.
Example:

```default
def _test(self, x_ref, x_test):
    stat, p = scipy.stats.ks_2samp(x_ref, x_test)
    return DataDriftResult(
        is_drift=False, statistic=stat, p_value=p
    )
```

If `p_value` is left `None` (distance- or score-based tests),
the base class uses the per-feature `is_drift` flag directly and
`correction` has no effect – the subclass is responsible for its
own per-feature decision (e.g. comparing a distance to a
threshold).

When `IS_UNIVARIATE = False` this receives **all features**:
both arrays are 2-D with shapes `(n_ref, n_features)` and
`(n_test, n_features)`. The subclass is responsible for
the full `DataDriftResult` including `is_drift`.

#### add_element(element: [float](https://docs.python.org/3/builtins/functions.html#float) | [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/base.py#L300)

Add one observation and check for drift.

The observation is appended to a sliding window of size
[`window_size`](#capymoa.drift.detectors.AndersonDarling.window_size). Once full, the detector compares the window
against the reference on every call.

If the detector is not yet fitted and [`auto_fit_samples`](#capymoa.drift.detectors.AndersonDarling.auto_fit_samples)
is set, those initial observations build the reference.
No comparison happens until then. If it is not fitted and
auto-fit is not enabled, this raises [`RuntimeError`](https://docs.python.org/3/builtins/exceptions.html#RuntimeError).

* **Parameters:**
  **element** – A single observation – a scalar for univariate
  data, or a 1-D array for multivariate data.
* **Raises:**
  * [**RuntimeError**](https://docs.python.org/3/builtins/exceptions.html#RuntimeError) – If the detector is not fitted and
    auto-fit mode is not enabled. Call [`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) first, or
    construct with `auto_fit_samples`.
  * [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError) – If the observation has a different number of
    features than the reference.

#### compare(X_test: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)) → [DataDriftResult](capymoa.drift.detectors.DataDriftResult.md#capymoa.drift.detectors.DataDriftResult)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/base.py#L354)

One-shot batch comparison against the reference.

Unlike [`add_element()`](#capymoa.drift.detectors.AndersonDarling.add_element), this does not update the internal
window or detection history. It is useful for offline evaluation.
`auto_fit_samples` does not apply here; call [`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) first.

* **Parameters:**
  **X_test** – Test data with the same number of features as the
  reference.
* **Raises:**
  * [**RuntimeError**](https://docs.python.org/3/builtins/exceptions.html#RuntimeError) – If [`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) has not been called.
  * [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError) – If *X_test* has a different number of features
    than the reference.
* **Returns:**
  Comparison result.

#### detected_change() → [bool](https://docs.python.org/3/builtins/functions.html#bool)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/base_detector.py#L52)

Is the detector currently detecting a concept drift?

#### detected_warning() → [bool](https://docs.python.org/3/builtins/functions.html#bool)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/base_detector.py#L56)

Is the detector currently warning of an upcoming concept drift?

#### fit(X: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) | [Any](https://docs.python.org/3/library/typing.html#typing.Any), feature_names: [Sequence](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)[[str](https://docs.python.org/3/builtins/stdtypes.html#str)] | [None](https://docs.python.org/3/builtins/constants.html#None) = None) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/base.py#L254)

Set the reference distribution.

This detector has [`REQUIRES_FIT`](#capymoa.drift.detectors.AndersonDarling.REQUIRES_FIT) set to `True`. Call
[`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) before [`add_element()`](#capymoa.drift.detectors.AndersonDarling.add_element) or [`compare()`](#capymoa.drift.detectors.AndersonDarling.compare), unless
`auto_fit_samples` was set so [`add_element()`](#capymoa.drift.detectors.AndersonDarling.add_element) can collect
the reference. Calling [`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) again replaces the reference
(a sliding reference window).

* **Parameters:**
  * **X** – Reference data, shape `(n_samples,)` or
    `(n_samples, n_features)`. May also be a pandas DataFrame,
    in which case column names are extracted automatically.
  * **feature_names** – Optional names for each feature. If
    provided, must have length equal to the number of features.
    Overrides column names extracted from a DataFrame.
    When set, per-feature dicts in [`DataDriftResult`](capymoa.drift.detectors.DataDriftResult.md#capymoa.drift.detectors.DataDriftResult) use
    these names as keys instead of integer indices.
* **Raises:**
  [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError) – If *X* is empty or *feature_names* length
  does not match the number of features.

#### *classmethod* from_params(schema: [Any](https://docs.python.org/3/library/typing.html#typing.Any) = None, params: [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)] | [None](https://docs.python.org/3/builtins/constants.html#None) = None, random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1) → [Any](https://docs.python.org/3/library/typing.html#typing.Any)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L170)

Construct an instance from parameters produced by `get_params`.

#### get_params() → [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)][[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/anderson_darling.py#L105)

Return the hyper-parameters of this detector.

#### reset(clean_history: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/data_drift/base.py#L382)

Reset the detector state.

* **Parameters:**
  **clean_history** – If `True`, also clear the reference data
  and detection history. If `False` (default), only the
  sliding window and current result are cleared; the reference
  and detection indices are preserved.

#### IS_UNIVARIATE *: [bool](https://docs.python.org/3/builtins/functions.html#bool)* *= True*

If `True` the test is applied to each feature separately and
results are combined. If `False` the test runs on the joint
distribution.

#### REQUIRES_FIT *: [bool](https://docs.python.org/3/builtins/functions.html#bool)* *= True*

Data drift detectors need a reference distribution. Call
[`fit()`](#capymoa.drift.detectors.AndersonDarling.fit) or set `auto_fit_samples` so [`add_element()`](#capymoa.drift.detectors.AndersonDarling.add_element)
can collect it.

#### *property* X_ref *: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) | [None](https://docs.python.org/3/builtins/constants.html#None)*

The reference data set with `fit`.

#### *property* alpha *: [float](https://docs.python.org/3/builtins/functions.html#float)*

Significance level for drift decisions.

#### *property* auto_fit_samples *: [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None)*

Number of samples for auto-fit, or `None` if explicit fit.

#### *property* correction *: [str](https://docs.python.org/3/builtins/stdtypes.html#str)*

Multiple-testing correction across features.

#### *property* feature_names *: [list](https://docs.python.org/3/builtins/stdtypes.html#list)[[str](https://docs.python.org/3/builtins/stdtypes.html#str)] | [None](https://docs.python.org/3/builtins/constants.html#None)*

Feature names passed to [`fit()`](#capymoa.drift.detectors.AndersonDarling.fit), or `None`.

#### *property* is_fitted *: [bool](https://docs.python.org/3/builtins/functions.html#bool)*

Whether the reference distribution has been set.

`True` after [`fit()`](#capymoa.drift.detectors.AndersonDarling.fit), or after [`add_element()`](#capymoa.drift.detectors.AndersonDarling.add_element) has
collected [`auto_fit_samples`](#capymoa.drift.detectors.AndersonDarling.auto_fit_samples) observations.

#### *property* n_features *: [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None)*

Number of features in the reference data, or `None` before fit.

#### *property* result *: [DataDriftResult](capymoa.drift.detectors.DataDriftResult.md#capymoa.drift.detectors.DataDriftResult) | [None](https://docs.python.org/3/builtins/constants.html#None)*

Most recent comparison result, or `None` during warm-up.

#### *property* window_size *: [int](https://docs.python.org/3/builtins/functions.html#int)*

Size of the sliding test window.