BoundingBoxADChecker#

class skfp.applicability_domain.BoundingBoxADChecker(percentile_lower: float | str = 0, percentile_upper: float | str = 100, num_allowed_violations: int = 0, n_jobs: int | None = None, verbose: int | dict = 0)#

Bounding box method.

Defines applicability domain based on feature ranges in the training data. This creates a “bounding box” using their extreme values, and new molecules should lie in this distribution, i.e. have properties in the same ranges [1].

Typically, physicochemical properties (continuous features) are used as inputs. Consider scaling, normalizing, or transforming them before computing AD to lessen effects of outliers, e.g. with PowerTransformer or RobustScaler. This is particularly important if "three_sigma" is used as the percentile bound, as it assumes normal distribution.

By default, the full range of training descriptors are allowed as AD. For stricter check, use percentile_lower and percentile_upper arguments to disallow extremely low or large values, respectively. For looser check, use num_allowed_violations to allow a number of desrciptors to lie outside the given ranges.

This method scales very well with both the number of samples and features.

Parameters:

  • percentile_lower (float or "three_sigma", default=0) – Lower bound of accepted feature value ranges. Float or integer value is interpreted as a percentile of descriptors in the training data for each feature. "three_sigma" uses 3 standard deviations from the mean, a common rule-of-thumb for outliers assuming the normal distribution.

  • percentile_upper (float or "three_sigma", default=100) – Upper bound of accepted feature value ranges. Float or integer value is interpreted as a percentile of descriptors in the training data for each feature. "three_sigma" uses 3 standard deviations from the mean, a common rule-of-thumb for outliers assuming the normal distribution.

  • num_allowed_violations (int, default=0) – Number of allowed violations of feature ranges. By default, all descriptors must lie inside the bounding box.

  • 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 skfp.applicability_domain import BoundingBoxADChecker
>>> X_train = np.array([[0.1, 0.2, 0.3], [1.0, 0.9, 0.8], [0.5, 0.5, 0.5]])
>>> X_test = np.array([[0.3, 0.3, 0.3], [0.6, 0.6, 0.6], [0.0, 0.9, 1.0]])
>>> bb_ad_checker = BoundingBoxADChecker()
>>> bb_ad_checker
BoundingBoxADChecker()

>>> bb_ad_checker.fit(X_train)
BoundingBoxADChecker()

>>> bb_ad_checker.predict(X_test)
array([ True,  True, False])

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, 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 the number of feature ranges fulfilled by samples. It ranges between 0 and num_features, where 0 means all descriptors inside training data ranges.

Parameters:

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

Returns:

scores – Applicability domain scores of samples.

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