from sklearn.linear_model.theil_sen import TheilSenRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class TheilSenRegressorImpl():
def __init__(self, fit_intercept=True, copy_X=True, max_subpopulation=10000, n_subsamples=None, max_iter=300, tol=0.001, random_state=None, n_jobs=None, verbose=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'copy_X': copy_X,
'max_subpopulation': max_subpopulation,
'n_subsamples': n_subsamples,
'max_iter': max_iter,
'tol': tol,
'random_state': random_state,
'n_jobs': n_jobs,
'verbose': verbose}
[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 TheilSenRegressor Theil-Sen Estimator: robust multivariate regression model.',
'allOf': [{
'type': 'object',
'required': ['fit_intercept', 'copy_X', 'max_subpopulation', 'n_subsamples', 'max_iter', 'tol', 'random_state', 'n_jobs', 'verbose'],
'relevantToOptimizer': ['fit_intercept', 'copy_X', 'max_subpopulation', 'max_iter', 'tol'],
'additionalProperties': False,
'properties': {
'fit_intercept': {
'type': 'boolean',
'default': True,
'description': 'Whether to calculate the intercept for this model. If set'},
'copy_X': {
'type': 'boolean',
'default': True,
'description': 'If True, X will be copied; else, it may be overwritten.'},
'max_subpopulation': {
'type': 'integer',
'minimumForOptimizer': 10000,
'maximumForOptimizer': 10001,
'distribution': 'uniform',
'default': 10000,
'description': "Instead of computing with a set of cardinality 'n choose k', where n is"},
'n_subsamples': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'Number of samples to calculate the parameters. This is at least the'},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 300,
'description': 'Maximum number of iterations for the calculation of spatial median.'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.001,
'description': 'Tolerance when calculating spatial median.'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'A random number generator instance to define the state of the random'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'Number of CPUs to use during the cross validation.'},
'verbose': {
'type': 'boolean',
'default': False,
'description': 'Verbose mode when fitting the model.'},
}}, {
'XXX TODO XXX': "Parameter: max_subpopulation > only a stochastic subpopulation of a given maximal size if 'n choose k' is larger than max_subpopulation"}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit linear model.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training data'},
'y': {
'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)
TheilSenRegressor = lale.operators.make_operator(TheilSenRegressorImpl, _combined_schemas)