StandardDeviationADChecker#

class skfp.applicability_domain.StandardDeviationADChecker(model: object | None = None, threshold: float = 1.0, n_jobs: int | None = None, verbose: int | dict = 0)#

Standard deviation method.

Defines applicability domain based on the spread (standard deviation) of predictions from individual estimators in an ensemble model [1]. The method assumes that greater consensus among estimators indicates higher prediction reliability, which typically occurs in densely sampled regions of the training data.

This approach supports both regression models (using .predict(X)) and binary classifiers (using .predict_proba(X) and the probability of the positive class). The ensemble model must expose the estimators_ attribute. If no model is provided, a default RandomForestRegressor is created and trained during fit().

At prediction time, each sample is passed to all estimators, and the standard deviation of their predictions (or predicted probabilities for classifiers) is calculated. The sample is considered in-domain if the standard deviation is less than or equal to the specified threshold.

Parameters:

  • model (object, default=None) – Fitted ensemble model with accessible estimators_ attribute and either .predict(X) or .predict_proba(X) method on each sub-estimator. If not provided, a default RandomForestRegressor will be created.

  • threshold (float, default=1.0) – Maximum allowed standard deviation of predictions. Samples with higher spread will be considered outside the applicability domain.

  • n_jobs (int, default=None) – The number of jobs to run in parallel. transform_x_y() and transform() are parallelized over the input molecules. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See scikit-learn documentation on n_jobs for more details.

  • verbose (int or dict, default=0) – Controls the verbosity when filtering molecules. If a dictionary is passed, it is treated as kwargs for tqdm(), and can be used to control the progress bar.

References

Examples

>>> import numpy as np
>>> from sklearn.ensemble import RandomForestRegressor
>>> from skfp.applicability_domain import StandardDeviationADChecker
>>> X_train = np.random.uniform(0, 1, size=(1000, 5))
>>> y_train = X_train.sum(axis=1)
>>> model = RandomForestRegressor(n_estimators=5, random_state=0)
>>> model.fit(X_train, y_train)
RandomForestRegressor(n_estimators=5, random_state=0)
>>> std_ad_checker = StandardDeviationADChecker(model=model, threshold=0.5)
>>> std_ad_checker.fit() 
StandardDeviationADChecker(model=RandomForestRegressor(n_estimators=5, random_state=0), threshold=0.5)

>>> X_test = np.random.uniform(0, 1, size=(100, 5))
>>> std_ad_checker.predict(X_test).shape
(100,)

Methods

fit([X, y])

Fit applicability domain estimator.

fit_predict(X[, y])

Perform fit on X and returns labels for X.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict labels (1 inside AD, 0 outside AD) of X according to the fitted model.

score_samples(X)

Calculate the applicability domain score of samples.

set_params(**params)

Set the parameters of this estimator.
fit(X: ndarray | None = None, y: ndarray | None = None)#

Fit applicability domain estimator.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – The input samples.

  • y (any) – Unused, kept for scikit-learn compatibility.

Returns:

self – Fitted estimator.

Return type:

object

fit_predict(X, y=None, **kwargs)#

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples.

  • y (Ignored) – Not used, present for API consistency by convention.

  • **kwargs (dict) –

    Arguments to be passed to fit.

    Added in version 1.4.

Returns:

y – 1 for inliers, -1 for outliers.

Return type:

ndarray of shape (n_samples,)

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

predict(X: ndarray) → ndarray#

Predict labels (1 inside AD, 0 outside AD) of X according to the fitted model.

Parameters:

X (array-like of shape (n_samples, n_features)) – The data matrix.

Returns:

is_inside_applicability_domain – Returns 1 for molecules inside the applicability domain, and 0 for those outside (outliers).

Return type:

ndarray of shape (n_samples,)

score_samples(X: ndarray) → ndarray#

Calculate the applicability domain score of samples. It is defined as the standard deviation of ensemble predictions.

Parameters:

X (array-like of shape (n_samples, n_features)) – The data matrix.

Returns:

scores – Standard deviations of predictions.

Return type:

ndarray of shape (n_samples,)

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance