from sklearn.gaussian_process.gpr import GaussianProcessRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class GaussianProcessRegressorImpl():
def __init__(self, kernel=None, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None):
self._hyperparams = {
'kernel': kernel,
'alpha': alpha,
'optimizer': optimizer,
'n_restarts_optimizer': n_restarts_optimizer,
'normalize_y': normalize_y,
'copy_X_train': copy_X_train,
'random_state': random_state}
[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
[docs] def predict(self, X):
return self._sklearn_model.predict(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for GaussianProcessRegressor Gaussian process regression (GPR).',
'allOf': [{
'type': 'object',
'required': ['kernel', 'alpha', 'optimizer', 'n_restarts_optimizer', 'normalize_y', 'copy_X_train', 'random_state'],
'relevantToOptimizer': ['alpha', 'optimizer', 'n_restarts_optimizer', 'normalize_y'],
'additionalProperties': False,
'properties': {
'kernel': {
'XXX TODO XXX': 'kernel object',
'description': 'The kernel specifying the covariance function of the GP. If None is',
'enum': [None],
'default': None},
'alpha': {
'anyOf': [{
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform'}, {
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'float or array-like, optional (default: 1e-10)',
'forOptimizer': False}],
'default': 1e-10,
'description': 'Value added to the diagonal of the kernel matrix during fitting.'},
'optimizer': {
'anyOf': [{
'type': 'object',
'forOptimizer': False}, {
'enum': ['fmin_l_bfgs_b']}],
'default': 'fmin_l_bfgs_b',
'description': 'Can either be one of the internally supported optimizers for optimizing'},
'n_restarts_optimizer': {
'type': 'integer',
'minimumForOptimizer': 0,
'maximumForOptimizer': 1,
'distribution': 'uniform',
'default': 0,
'description': "The number of restarts of the optimizer for finding the kernel's"},
'normalize_y': {
'type': 'boolean',
'default': False,
'description': 'Whether the target values y are normalized, i.e., the mean of the'},
'copy_X_train': {
'type': 'boolean',
'default': True,
'description': 'If True, a persistent copy of the training data is stored in the'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'The generator used to initialize the centers. If int, random_state is'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit Gaussian process regression model.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training data'},
'y': {
'XXX TODO XXX': 'array-like, shape = (n_samples, [n_output_dims])',
'description': 'Target values'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the Gaussian process regression model',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Query points where the GP is evaluated'},
'return_std': {
'type': 'boolean',
'default': False,
'description': 'If True, the standard-deviation of the predictive distribution at'},
'return_cov': {
'type': 'boolean',
'default': False,
'description': 'If True, the covariance of the joint predictive distribution at'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the Gaussian process regression model',
}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'type': 'object',
'tags': {
'pre': [],
'op': ['estimator'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_predict': _input_predict_schema,
'output_predict': _output_predict_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
GaussianProcessRegressor = lale.operators.make_operator(GaussianProcessRegressorImpl, _combined_schemas)