# 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 sklearn.naive_bayes
import lale.helpers
import lale.operators
[docs]class GaussianNBImpl():
def __init__(self, priors=None, var_smoothing=1e-09):
self._hyperparams = {
'priors': priors,
'var_smoothing': var_smoothing}
[docs] def fit(self, X, y=None):
self._sklearn_model = sklearn.naive_bayes.GaussianNB(**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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Gaussian Naive Bayes (GaussianNB)',
'allOf': [{
'type': 'object',
'required': ['priors'],
'relevantToOptimizer': [],
'additionalProperties': False,
'properties': {
'priors': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'Prior probabilities of the classes. If specified the priors are not'},
'var_smoothing': {
'type': 'number',
'default': 1e-09,
'description': 'Portion of the largest variance of all features that is added to'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit Gaussian Naive Bayes according to X, y',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training vectors, where n_samples is the number of samples'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Target values.'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'Weights applied to individual samples (1. for unweighted).'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Perform classification on an array of test vectors X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predicted target values for X',
'type': 'array',
'items': {
'type': 'number'},
}
_input_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Return probability estimates for the test vector X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
},
}
_output_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Returns the probability of the samples for each class in',
'type': 'array',
'items': {
'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.naive_bayes.GaussianNB.html',
'type': 'object',
'tags': {
'pre': [],
'op': ['estimator'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_predict': _input_predict_schema,
'output': _output_predict_schema,
'input_predict_proba': _input_predict_proba_schema,
'output_predict_proba': _output_predict_proba_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
GaussianNB = lale.operators.make_operator(GaussianNBImpl, _combined_schemas)