# Copyright 2019 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import lale.helpers
import lale.operators
import sklearn.neighbors
[docs]class KNeighborsClassifierImpl():
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
[docs] def fit(self, X, y=None):
self._sklearn_model = sklearn.neighbors.KNeighborsClassifier(**self._hyperparams)
self._sklearn_model.fit(X, y)
return self
[docs] def predict(self, X):
return self._sklearn_model.predict(X)
[docs] def predict_proba(self, X):
return self._sklearn_model.predict_proba(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Hyperparameter schema for the KNeighborsClassifier model from scikit-learn.',
'allOf': [{
'description':
'This first sub-object lists all constructor arguments with their '
'types, one at a time, omitting cross-argument constraints.',
'type': 'object',
'additionalProperties': False,
'required': [
'n_neighbors', 'weights', 'algorithm', 'leaf_size', 'p', 'metric',
'metric_params', 'n_jobs'],
'relevantToOptimizer': [
'n_neighbors', 'weights', 'algorithm', 'p', 'metric'],
'properties': {
'n_neighbors': {
'description': 'Number of neighbors to use by default for kneighbors queries.',
'type': 'integer',
'distribution': 'loguniform',
'minimum': 1,
'default': 5,
'maximumForOptimizer': 100},
'weights': {
'description': 'Weight function used in prediction.',
'enum': ['uniform', 'distance'],
'default': 'uniform'},
'algorithm': {
'description': 'Algorithm used to compute the nearest neighbors.',
'enum': ['ball_tree', 'kd_tree', 'brute', 'auto'],
'default': 'auto'},
'leaf_size': {
'description': 'Leaf size passed to BallTree or KDTree.',
'type': 'integer',
'distribution': 'loguniform',
'minimum': 1,
'default': 30},
'p': {
'description': 'Power parameter for the Minkowski metric.',
'type': 'integer',
'distribution': 'uniform',
'minimum': 1,
'default': 2,
'maximumForOptimizer': 3},
'metric': {
'description': 'The distance metric to use for the tree.',
'enum': ['euclidean', 'manhattan', 'minkowski'],
'default': 'minkowski'},
'metric_params': {
'description': 'Additional keyword arguments for the metric function.',
'anyOf': [{
'enum': [None]}, {
'type': 'object',
'propertyNames': {
'pattern': '[_a-zA-Z][_a-zA-Z0-9]*'},
}],
'default': None},
'n_jobs': {
'description': 'Number of parallel jobs to run for the neighbor search.',
'anyOf': [{
'description': '1 unless in joblib.parallel_backend context.',
'enum': [None]}, {
'description': 'Use all processors.',
'enum': [(- 1)]}, {
'description': 'Number of CPU cores.',
'type': 'integer',
'minimum': 1}],
'default': None}}},
{ 'description': 'The leaf size only matters for tree algorithms.',
'anyOf': [
{ 'type': 'object',
'properties': {
'algorithm': {
'enum': ['ball_tree', 'kd_tree']},
}},
{ 'type': 'object',
'properties': {
'leaf_size': {
'enum': [30]},
}}]},
{ 'description': 'The power parameter is specific to the minkowski metric.',
'anyOf': [
{ 'type': 'object',
'properties': {
'metric': {
'enum': ['minkowski']},
}},
{ 'type': 'object',
'properties': {
'p': {
'enum': [2]},
}}]}]}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Input data schema for training the KNeighborsClassifier model from scikit-learn.',
'type': 'object',
'required': ['X', 'y'],
'additionalProperties': False,
'properties': {
'X': {
'description': 'Features; the outer array is over samples.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
'y': {
'description': 'Target class labels; the array is over samples.',
'anyOf': [
{ 'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
{ 'type': 'array',
'items': {
'type': 'number'},
}]}}}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Input data schema for predictions using the KNeighborsClassifier model from scikit-learn.',
'type': 'object',
'required': ['X'],
'additionalProperties': False,
'properties': {
'X': {
'description': 'Features; the outer array is over samples.',
'type': 'array',
'items': {
'type': 'array',
'items': { 'type': 'number'}}}}}
_output_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Output data schema for predictions (target class labels) using the KNeighborsClassifier model from scikit-learn.',
'anyOf': [
{ 'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
{ 'type': 'array',
'items': {
'type': 'number'}}]}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'documentation_url': 'https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html',
'type': 'object',
'tags': {
'pre': ['~categoricals'],
'op': ['estimator', 'classifier', 'interpretable'],
'post': ['probabilities']},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_predict': _input_predict_schema,
'output': _output_schema,
},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
KNeighborsClassifier = lale.operators.make_operator(KNeighborsClassifierImpl, _combined_schemas)