from sklearn.neighbors.regression import KNeighborsRegressor as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class KNeighborsRegressorImpl():
def __init__(self, n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None):
self._hyperparams = {
'n_neighbors': n_neighbors,
'weights': weights,
'algorithm': algorithm,
'leaf_size': leaf_size,
'p': p,
'metric': metric,
'metric_params': metric_params,
'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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for KNeighborsRegressor Regression based on k-nearest neighbors.',
'allOf': [{
'type': 'object',
'required': ['n_neighbors', 'weights', 'algorithm', 'leaf_size', 'p', 'metric', 'metric_params', 'n_jobs'],
'relevantToOptimizer': ['n_neighbors', 'weights', 'algorithm', 'leaf_size', 'p', 'metric'],
'additionalProperties': False,
'properties': {
'n_neighbors': {
'type': 'integer',
'minimumForOptimizer': 5,
'maximumForOptimizer': 20,
'distribution': 'uniform',
'default': 5,
'description': 'Number of neighbors to use by default for :meth:`kneighbors` queries.'},
'weights': {
'anyOf': [{
'type': 'object',
'forOptimizer': False}, {
'enum': ['distance', 'uniform']}],
'default': 'uniform',
'description': 'weight function used in prediction. Possible values:'},
'algorithm': {
'enum': ['auto', 'ball_tree', 'kd_tree', 'brute'],
'default': 'auto',
'description': 'Algorithm used to compute the nearest neighbors:'},
'leaf_size': {
'type': 'integer',
'minimumForOptimizer': 30,
'maximumForOptimizer': 31,
'distribution': 'uniform',
'default': 30,
'description': 'Leaf size passed to BallTree or KDTree. This can affect the'},
'p': {
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 3,
'distribution': 'uniform',
'default': 2,
'description': 'Power parameter for the Minkowski metric. When p = 1, this is'},
'metric': {
'anyOf': [{
'type': 'object',
'forOptimizer': False}, {
'enum': ['euclidean', 'manhattan', 'minkowski', 'precomputed']}],
'default': 'minkowski',
'description': 'the distance metric to use for the tree. The default metric is'},
'metric_params': {
'anyOf': [{
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'Additional keyword arguments for the metric function.'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'The number of parallel jobs to run for neighbors search.'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the model using X as training data and y as target values',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': '{array-like, sparse matrix, BallTree, KDTree}',
'description': 'Training data. If array or matrix, shape [n_samples, n_features],'},
'y': {
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': '{array-like, sparse matrix}',
'description': 'Target values, array of float values, shape = [n_samples]'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict the target for the provided data',
'type': 'object',
'properties': {
'X': {
'XXX TODO XXX': "array-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == 'precomputed'",
'description': 'Test samples.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Target values',
'XXX TODO XXX': 'array of int, shape = [n_samples] or [n_samples, n_outputs]',
}
_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)
KNeighborsRegressor = lale.operators.make_operator(KNeighborsRegressorImpl, _combined_schemas)