from sklearn.linear_model.base import LinearRegression as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class LinearRegressionImpl():
def __init__(self, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None):
self._hyperparams = {
'fit_intercept': fit_intercept,
'normalize': normalize,
'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
[docs] def predict(self, X):
return self._sklearn_model.predict(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for LinearRegression Ordinary least squares Linear Regression.',
'allOf': [{
'type': 'object',
'required': ['fit_intercept', 'normalize', 'copy_X', 'n_jobs'],
'relevantToOptimizer': ['fit_intercept', 'normalize', 'copy_X'],
'additionalProperties': False,
'properties': {
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'whether to calculate the intercept for this model. If set'},
'normalize': {
'type': 'boolean',
'default': False,
'description': 'This parameter is ignored when ``fit_intercept`` is set to False.'},
'copy_X': {
'type': 'boolean',
'default': True,
'description': 'If True, X will be copied; else, it may be overwritten.'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'The number of jobs to use for the computation. This will only provide'},
}}, {
'XXX TODO XXX': 'Parameter: n_jobs > only provide speedup for n_targets > 1 and sufficient large problems'}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit 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': 'Training data'},
'y': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': "Target values. Will be cast to X's dtype if necessary"},
'sample_weight': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Individual weights for each sample'},
},
}
_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)
LinearRegression = lale.operators.make_operator(LinearRegressionImpl, _combined_schemas)