from sklearn.kernel_ridge import KernelRidge as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class KernelRidgeImpl():
def __init__(self, alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None):
self._hyperparams = {
'alpha': alpha,
'kernel': kernel,
'gamma': gamma,
'degree': degree,
'coef0': coef0,
'kernel_params': kernel_params}
[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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for KernelRidge Kernel ridge regression.',
'allOf': [{
'type': 'object',
'required': ['alpha', 'kernel', 'gamma', 'degree', 'coef0', 'kernel_params'],
'relevantToOptimizer': ['alpha', 'kernel', 'degree', 'coef0'],
'additionalProperties': False,
'properties': {
'alpha': {
'XXX TODO XXX': '{float, array-like}, shape = [n_targets]',
'description': 'Small positive values of alpha improve the conditioning of the problem',
'type': 'integer',
'minimumForOptimizer': 1,
'maximumForOptimizer': 2,
'distribution': 'uniform',
'default': 1},
'kernel': {
'anyOf': [{
'type': 'object',
'forOptimizer': False}, {
'enum': ['linear', 'poly', 'rbf', 'sigmoid']}],
'default': 'linear',
'description': 'Kernel mapping used internally. A callable should accept two arguments'},
'gamma': {
'anyOf': [{
'type': 'number'}, {
'enum': [None]}],
'default': None,
'description': 'Gamma parameter for the RBF, laplacian, polynomial, exponential chi2'},
'degree': {
'type': 'number',
'minimumForOptimizer': 0.0,
'maximumForOptimizer': 1.0,
'distribution': 'uniform',
'default': 3,
'description': 'Degree of the polynomial kernel. Ignored by other kernels.'},
'coef0': {
'type': 'number',
'minimumForOptimizer': 0.0,
'maximumForOptimizer': 1.0,
'distribution': 'uniform',
'default': 1,
'description': 'Zero coefficient for polynomial and sigmoid kernels.'},
'kernel_params': {
'XXX TODO XXX': 'mapping of string to any, optional',
'description': 'Additional parameters (keyword arguments) for kernel function passed',
'enum': [None],
'default': None},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit Kernel Ridge regression model',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training data. If kernel == "precomputed" this is instead'},
'y': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'Target values'},
'sample_weight': {
'anyOf': [{
'type': 'number'}, {
'type': 'array',
'items': {
'type': 'number'},
}],
'description': 'Individual weights for each sample, ignored if None is passed.'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the kernel ridge model',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Samples. If kernel == "precomputed" this is instead a'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Returns predicted values.',
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'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': ['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)
KernelRidge = lale.operators.make_operator(KernelRidgeImpl, _combined_schemas)