LeverageADChecker#

class skfp.applicability_domain.LeverageADChecker(threshold: float | str = 'auto', n_jobs: int | None = None, verbose: int | dict = 0)#

Leverage method.

Defines applicability domain based on the leverage statistic [1] [2] [3], which is a general distance of new point from the space of training data. Leverage is defined using the hat (projection / influence) matrix, and its formula is:

\[leverage(x_i) = x^T_i (X^T X)^{-1} x_i\]

This way, the new molecule is projected orthogonally into the space spanned by the training data, taking into consideration feature correlations via Gram matrix \(X^T X\). Distance from the average leverage of the training data is used as an outlier score. Typical threshold is \(3(d+1)/n\), where d is the number of features and n is the number of training molecules.

Typically, physicochemical properties (continous 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. Features should not be too strongly correlated, as this can result in near-singular matrix that is not invertible.

This method scales relatively well with number of samples, but not the number of features, as it builds \(d \times d\) Gram matrix.

Parameters:

threshold (float or "auto", default="auto") – Maximal leverage allowed for applicability domain. New points with larger leverage, i.e. distance to training set, are assumed to lie outside AD.
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 LeverageADChecker
>>> X_train = np.array([[0.0, 1.0], [0.0, 3.0], [3.0, 1.0]])
>>> X_test = np.array([[1.0, 1.0], [1.0, 2.0], [20.0, 3.0]])
>>> leverage_ad_checker = LeverageADChecker()
>>> leverage_ad_checker
LeverageADChecker()

>>> leverage_ad_checker.fit(X_train)
LeverageADChecker()

>>> leverage_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 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 | csr_array) → ndarray#

Predict labels (1 inside AD, 0 outside AD) of X according to 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 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 equal to the leverage of each sample. Note that here lower score indicates sample more firmly inside AD.

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

LeverageADChecker#

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