Code Documentation: Domain

mastml.domain Module

This module contains a collection of routines to perform domain of applicability evaluations

Classes

AgglomerativeClustering([n_clusters, ...])	Agglomerative Clustering.
BootstrappedLeaveClusterOut(clust, ...)	Custom splitting class which pre-clusters data and then splits to folds.
Composition(*args[, strict])	Represents a Composition, a mapping of {element/species: amount} with enhanced functionality tailored for handling chemical compositions.
ConstantKernel([constant_value, ...])	Constant kernel.
Domain(check_type[, preprocessor, model, ...])
GaussianProcessRegressor([kernel, alpha, ...])	Gaussian process regression (GPR).
GridSearchCV(estimator, param_grid, *[, ...])	Exhaustive search over specified parameter values for an estimator.
Matern([length_scale, length_scale_bounds, nu])	Matern kernel.
Pipeline(steps, *[, transform_input, ...])	A sequence of data transformers with an optional final predictor.
RepeatedKFold(*[, n_splits, n_repeats, ...])	Repeated K-Fold cross validator.
ShuffleSplit([n_splits, test_size, ...])	Random permutation cross-validator.
StandardScaler(*[, copy, with_mean, with_std])	Standardize features by removing the mean and scaling to unit variance.
WhiteKernel([noise_level, noise_level_bounds])	White kernel.
calibration([uq_func, params, prior])	A UQ model for calibration of uncertainties.
combine(gs_model, ds_model, uq_model[, ...])	Combine distance, UQ, and ensemble regression models.
dissimilarity([dis, kernel, bandwidth, scale])
nested_cv(model, X, y[, g, splitters, n_jobs])	Class to do nested CV.

Class Inheritance Diagram

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