HotellingT2TestADChecker#

class skfp.applicability_domain.HotellingT2TestADChecker(alpha: float = 0.1, n_jobs: int | None = None, verbose: int | dict = 0)#

Hotelling’s T^2 test method.

Applicability domain is defined by the Hotelling’s T^2 statistical test, which is a multidimensional generalization of Student’s t-test [1]. It measures the Mahalanobis distance of a new sample from the mean of the training data, scaled by the covariance structure of the training data.

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. In case of Hotelling’s T^2 test, using PCA beforehand to obtain orthogonal features is particularly beneficial.

This method scales relatively well with number of samples, but not the number of features, as it requires computing the pseudoinverse of the covariance matrix, which scales as \(O(d^3)\).

Parameters:

alpha (float, default=0.1) – Statistical test significance level, in range [0, 1]. Lower values are more conservative and correspond to a smaller 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 skfp.applicability_domain import HotellingT2TestADChecker
>>> 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]])
>>> hotelling_t2_test_ad_checker = HotellingT2TestADChecker()
>>> hotelling_t2_test_ad_checker
HotellingT2TestADChecker()

>>> hotelling_t2_test_ad_checker.fit(X_train)
HotellingT2TestADChecker()

>>> hotelling_t2_test_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, i.e. the T^2 statistic value for 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, i.e. the T^2 statistic value for samples. 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

HotellingT2TestADChecker#

This Page