# `ABCD`

### *class* capymoa.drift.detectors.ABCD[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L18)

Bases: [`BaseDriftDetector`](capymoa.drift.base_detector.BaseDriftDetector.md#capymoa.drift.base_detector.BaseDriftDetector)

Adaptive Bernstein Change Detector (ABCD).

ABCD is a drift detector for **multivariate** data streams. It fits an
encoder-decoder model of the incoming feature vectors, monitors how well
that model reconstructs them, and signals a change when the reconstruction
error shifts by more than a Bernstein-bound-based test allows. Because it
watches the *input distribution* rather than a single value, it detects
drift that changes the features, not drift that only changes the labels.
The reconstruction model is selected with `model_id`: `"pca"` (default)
and `"kpca"` rely only on scikit-learn, while `"ae"` uses a PyTorch
autoencoder and therefore needs the `torch` extra.

## Example:

```pycon
>>> from capymoa.drift.detectors import ABCD
>>> from capymoa.stream.drift import AbruptDrift, DriftStream
>>> from capymoa.stream.generator import RandomRBFGenerator
>>>
>>> def rbf(seed):
...     return RandomRBFGenerator(
...         model_random_seed=seed,
...         instance_random_seed=seed,
...         number_of_attributes=6,
...         number_of_centroids=20,
...     )
...
>>> # A stream whose input distribution changes at instance 2000.
>>> stream = DriftStream(stream=[rbf(1), AbruptDrift(position=2000), rbf(99)])
>>> detector = ABCD(model_id="pca", maximum_absolute_value=0.2)
>>>
>>> i = 0
>>> while stream.has_more_instances() and i < 4000:
...     instance = stream.next_instance()
...     i += 1
...     detector.add_element(instance.x)
...     if detector.detected_change():
...         print(f"Change detected at index: {i}")
Change detected at index: 2324
```

## Reference:

Heyden, M., Fouché, E., Arzamasov, V., Fenn, T., Kalinke, F., & Böhm, K.
(2024). Adaptive Bernstein change detector for high-dimensional data
streams. Data Mining and Knowledge Discovery, 38(3), 1334-1363. Springer.

#### \_\_init_\_(delta_drift: [float](https://docs.python.org/3/builtins/functions.html#float) = 0.002, delta_warn: [float](https://docs.python.org/3/builtins/functions.html#float) = 0.01, model_id: [str](https://docs.python.org/3/builtins/stdtypes.html#str) = 'pca', split_type: [str](https://docs.python.org/3/builtins/stdtypes.html#str) = 'ed', encoding_factor: [float](https://docs.python.org/3/builtins/functions.html#float) = 0.5, update_epochs: [int](https://docs.python.org/3/builtins/functions.html#int) = 50, num_splits: [int](https://docs.python.org/3/builtins/functions.html#int) = 20, max_size: [int](https://docs.python.org/3/builtins/functions.html#int) = np.inf, subspace_threshold: [float](https://docs.python.org/3/builtins/functions.html#float) = 2.5, n_min: [int](https://docs.python.org/3/builtins/functions.html#int) = 100, maximum_absolute_value: [float](https://docs.python.org/3/builtins/functions.html#float) = 1.0, bonferroni: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L67)

* **Parameters:**
  * **delta_drift** – The desired confidence level at which a drift is detected
  * **delta_warn** – The desired confidence level at which a warning is detected
  * **model_id** – Which encoder-decoder to reconstruct instances with:
    `"pca"`, `"kpca"` or `"ae"`. `"pca"` is the default because
    it needs only scikit-learn, whereas `"ae"` is backed by PyTorch,
    which CapyMOA installs only as the `torch` extra.
  * **update_epochs** – The number of epochs to train the AE after a change occurred
  * **split_type** – Investigation of different split types
  * **subspace_threshold** – Called tau in the paper
  * **bonferroni** – Use bonferroni correction to account for multiple testing?
  * **encoding_factor** – The relative size of the bottleneck
  * **maximum_absolute_value** – The maximum absolute value that one can expect (e.g. 1.0 for normalized data). Smaller values can increase false alarms but speed up change detection
  * **num_splits** – The number of time point to evaluate
  * **max_size** – The maximum number of instances kept in the adaptive window; older instances beyond this size are discarded. Defaults to no limit.
  * **n_min** – The number of initial instances collected to pre-train the reconstruction model before change monitoring begins

#### 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) | [Instance](capymoa.core.Instance.md#capymoa.core.Instance)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L175)

Add the new element and also perform change detection
:param element: The new observation
:return:

#### 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?

#### *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_dims_p_values() → [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L271)

#### get_drift_dims() → [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L274)

#### 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/abcd.py#L153)

Get the hyper-parameters of the drift detector.

#### get_severity()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L288)

#### loss()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L268)

#### pre_train(data)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/drift/detectors/abcd.py#L170)

#### 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/abcd.py#L237)

Reset the detector state.

Clears the adaptive window, the reconstruction model, and all
bookkeeping so that replaying the same input reproduces the
as-constructed flag trace. Hyper-parameters (including
`model_id`) are preserved; the reconstruction model is re-trained
lazily from the next `n_min` elements, exactly as on construction.

* **Parameters:**
  **clean_history** – Whether to reset detection history, defaults to False

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

If `True`, this detector needs a reference distribution before
it can detect change. Concept drift detectors leave this as
`False` and only use [`add_element()`](#capymoa.drift.detectors.ABCD.add_element).