from sklearn.linear_model.least_angle import LassoLarsIC as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class LassoLarsICImpl():
def __init__(self, criterion='aic', fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, positive=False):
self._hyperparams = {
'criterion': criterion,
'fit_intercept': fit_intercept,
'verbose': verbose,
'normalize': normalize,
'precompute': precompute,
'max_iter': max_iter,
'eps': eps,
'copy_X': copy_X,
'positive': positive}
[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 LassoLarsIC Lasso model fit with Lars using BIC or AIC for model selection',
'allOf': [{
'type': 'object',
'required': ['criterion', 'fit_intercept', 'verbose', 'normalize', 'precompute', 'max_iter', 'eps', 'copy_X', 'positive'],
'relevantToOptimizer': ['criterion', 'fit_intercept', 'normalize', 'precompute', 'max_iter', 'eps', 'copy_X', 'positive'],
'additionalProperties': False,
'properties': {
'criterion': {
'enum': ['bic', 'aic'],
'default': 'aic',
'description': 'The type of criterion to use.'},
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'whether to calculate the intercept for this model. If set'},
'verbose': {
'anyOf': [{
'type': 'boolean'}, {
'type': 'integer'}],
'default': False,
'description': 'Sets the verbosity amount'},
'normalize': {
'type': 'boolean',
'default': True,
'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': 500,
'description': 'Maximum number of iterations to perform. Can be used for'},
'eps': {
'type': 'number',
'minimumForOptimizer': 0.001,
'maximumForOptimizer': 0.1,
'distribution': 'uniform',
'default': 2.220446049250313e-16,
'description': 'The machine-precision regularization in the computation of the'},
'copy_X': {
'type': 'boolean',
'default': True,
'description': 'If True, X will be copied; else, it may be overwritten.'},
'positive': {
'type': 'boolean',
'default': False,
'description': 'Restrict coefficients to be >= 0. Be aware that you might want to'},
}}, {
'XXX TODO XXX': 'Parameter: positive > only coefficients up to the smallest alpha value (alphas_[alphas_ > 0'}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the model using X, y as training data.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'training data.'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': "target values. Will be cast to X's dtype if necessary"},
'copy_X': {
'type': 'boolean',
'default': True,
'description': 'If ``True``, X will be copied; else, it may be overwritten.'},
},
}
_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)
LassoLarsIC = lale.operators.make_operator(LassoLarsICImpl, _combined_schemas)