from sklearn.decomposition.kernel_pca import KernelPCA as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class KernelPCAImpl():
def __init__(self, n_components=None, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=None):
self._hyperparams = {
'n_components': n_components,
'kernel': kernel,
'gamma': gamma,
'degree': degree,
'coef0': coef0,
'kernel_params': kernel_params,
'alpha': alpha,
'fit_inverse_transform': fit_inverse_transform,
'eigen_solver': eigen_solver,
'tol': tol,
'max_iter': max_iter,
'remove_zero_eig': remove_zero_eig,
'random_state': random_state,
'copy_X': copy_X,
'n_jobs': n_jobs}
[docs] def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for KernelPCA Kernel Principal component analysis (KPCA)',
'allOf': [{
'type': 'object',
'required': ['n_components', 'kernel', 'gamma', 'degree', 'coef0', 'kernel_params', 'alpha', 'fit_inverse_transform', 'eigen_solver', 'tol', 'max_iter', 'remove_zero_eig', 'random_state', 'copy_X', 'n_jobs'],
'relevantToOptimizer': ['n_components', 'kernel', 'degree', 'coef0', 'alpha', 'eigen_solver', 'tol', 'max_iter', 'remove_zero_eig', 'copy_X'],
'additionalProperties': False,
'properties': {
'n_components': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 256,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None,
'description': 'Number of components. If None, all non-zero components are kept.'},
'kernel': {
'enum': ['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed'],
'default': 'linear',
'description': 'Kernel. Default="linear".'},
'gamma': {
'XXX TODO XXX': 'float, default=1/n_features',
'description': 'Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other',
'enum': [None],
'default': None},
'degree': {
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 3,
'distribution': 'uniform',
'default': 3,
'description': 'Degree for poly kernels. Ignored by other kernels.'},
'coef0': {
'type': 'number',
'minimumForOptimizer': 0.0,
'maximumForOptimizer': 1.0,
'distribution': 'uniform',
'default': 1,
'description': 'Independent term in poly and sigmoid kernels.'},
'kernel_params': {
'XXX TODO XXX': 'mapping of string to any, default=None',
'description': 'Parameters (keyword arguments) and values for kernel passed as',
'enum': [None],
'default': None},
'alpha': {
'anyOf': [{
'type': 'integer',
'forOptimizer': False}, {
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform'}],
'default': 1.0,
'description': 'Hyperparameter of the ridge regression that learns the'},
'fit_inverse_transform': {
'type': 'boolean',
'default': False,
'description': 'Learn the inverse transform for non-precomputed kernels.'},
'eigen_solver': {
'enum': ['arpack', 'auto', 'dense'],
'default': 'auto',
'description': 'Select eigensolver to use. If n_components is much less than'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0,
'description': 'Convergence tolerance for arpack.'},
'max_iter': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None,
'description': 'Maximum number of iterations for arpack.'},
'remove_zero_eig': {
'type': 'boolean',
'default': False,
'description': 'If True, then all components with zero eigenvalues are removed, so'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'If int, random_state is the seed used by the random number generator;'},
'copy_X': {
'type': 'boolean',
'default': True,
'description': 'If True, input X is copied and stored by the model in the `X_fit_`'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'The number of parallel jobs to run.'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the model from data in X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training vector, where n_samples in the number of samples'},
},
}
_input_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transform X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
},
}
_output_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transform X.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'type': 'object',
'tags': {
'pre': [],
'op': ['transformer'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_transform': _input_transform_schema,
'output_transform': _output_transform_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
KernelPCA = lale.operators.make_operator(KernelPCAImpl, _combined_schemas)