from sklearn.linear_model.coordinate_descent import ElasticNetCV as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class ElasticNetCVImpl():
def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, precompute='auto', max_iter=1000, tol=0.0001, cv=3, copy_X=True, verbose=0, n_jobs=None, positive=False, random_state=None, selection='cyclic'):
self._hyperparams = {
'l1_ratio': l1_ratio,
'eps': eps,
'n_alphas': n_alphas,
'alphas': alphas,
'fit_intercept': fit_intercept,
'normalize': normalize,
'precompute': precompute,
'max_iter': max_iter,
'tol': tol,
'cv': cv,
'copy_X': copy_X,
'verbose': verbose,
'n_jobs': n_jobs,
'positive': positive,
'random_state': random_state,
'selection': selection}
[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 ElasticNetCV Elastic Net model with iterative fitting along a regularization path.',
'allOf': [{
'type': 'object',
'required': ['l1_ratio', 'eps', 'n_alphas', 'alphas', 'fit_intercept', 'normalize', 'precompute', 'max_iter', 'tol', 'cv', 'copy_X', 'verbose', 'n_jobs', 'positive', 'random_state', 'selection'],
'relevantToOptimizer': ['eps', 'n_alphas', 'fit_intercept', 'normalize', 'precompute', 'max_iter', 'tol', 'cv', 'copy_X', 'positive', 'selection'],
'additionalProperties': False,
'properties': {
'l1_ratio': {
'XXX TODO XXX': 'float or array of floats, optional',
'description': 'float between 0 and 1 passed to ElasticNet (scaling between',
'type': 'number',
'default': 0.5},
'eps': {
'type': 'number',
'minimumForOptimizer': 0.001,
'maximumForOptimizer': 0.1,
'distribution': 'uniform',
'default': 0.001,
'description': 'Length of the path. ``eps=1e-3`` means that'},
'n_alphas': {
'type': 'integer',
'minimumForOptimizer': 100,
'maximumForOptimizer': 101,
'distribution': 'uniform',
'default': 100,
'description': 'Number of alphas along the regularization path, used for each l1_ratio.'},
'alphas': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'numpy array, optional'}, {
'enum': [None]}],
'default': None,
'description': 'List of alphas where to compute the models.'},
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'whether to calculate the intercept for this model. If set'},
'normalize': {
'type': 'boolean',
'default': False,
'description': 'This parameter is ignored when ``fit_intercept`` is set to False.'},
'precompute': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': "True | False | 'auto' | array-like",
'forOptimizer': False}, {
'enum': ['auto']}],
'default': 'auto',
'description': 'Whether to use a precomputed Gram matrix to speed up'},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 1000,
'description': 'The maximum number of iterations'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'The tolerance for the optimization: if the updates are'},
'cv': {
'XXX TODO XXX': 'int, cross-validation generator or an iterable, optional',
'description': 'Determines the cross-validation splitting strategy.',
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 4,
'distribution': 'uniform',
'default': 3},
'copy_X': {
'type': 'boolean',
'default': True,
'description': 'If ``True``, X will be copied; else, it may be overwritten.'},
'verbose': {
'anyOf': [{
'type': 'boolean'}, {
'type': 'integer'}],
'default': 0,
'description': 'Amount of verbosity.'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'Number of CPUs to use during the cross validation.'},
'positive': {
'type': 'boolean',
'default': False,
'description': 'When set to ``True``, forces the coefficients to be positive.'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'The seed of the pseudo random number generator that selects a random'},
'selection': {
'enum': ['random', 'cyclic'],
'default': 'cyclic',
'description': "If set to 'random', a random coefficient is updated every iteration"},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit linear model with coordinate descent',
'type': 'object',
'properties': {
'X': {
'XXX TODO XXX': '{array-like}, shape (n_samples, n_features)',
'description': 'Training data. Pass directly as Fortran-contiguous data'},
'y': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'Target values'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict using the linear model',
'type': 'object',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'array_like or sparse matrix, shape (n_samples, n_features)'}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'Samples.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Returns predicted values.',
'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)
ElasticNetCV = lale.operators.make_operator(ElasticNetCVImpl, _combined_schemas)