# 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.
from sklearn.base import BaseEstimator
[docs]class XGBClassifierImpl(BaseEstimator):
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1,
objective='binary:logistic', booster='gbtree', n_jobs=1,
nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1,
colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0,
reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0,
seed=None, missing=None):
self.max_depth = max_depth
self.learning_rate = learning_rate
self.n_estimators = n_estimators
self.verbosity = verbosity
self.objective = objective
self.booster = booster
self.n_jobs = n_jobs
self.nthread = nthread
self.gamma = gamma
self.min_child_weight = min_child_weight
self.max_delta_step = max_delta_step
self.subsample = subsample
self.colsample_bytree = colsample_bytree
self.colsample_bylevel = colsample_bylevel
self.colsample_bynode = colsample_bynode
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.scale_pos_weight = scale_pos_weight
self.base_score = base_score
self.random_state = random_state
self.seed = seed
self.missing = missing
[docs] def fit(self, X, y, **fit_params):
result = XGBClassifierImpl(self.max_depth, self.learning_rate, self.n_estimators,
self.verbosity, self.objective, self.booster, self.n_jobs,
self.nthread, self.gamma, self.min_child_weight, self.max_delta_step, self.subsample,
self.colsample_bytree, self.colsample_bylevel, self.colsample_bynode, self.reg_alpha,
self.reg_lambda, self.scale_pos_weight, self.base_score, self.random_state,
self.seed, self.missing)
result._xgboost_model = XGBoostClassifier(
**self.get_params())
if fit_params is None:
result._xgboost_model.fit(X, y)
else:
result._xgboost_model.fit(X, y, **fit_params)
return result
[docs] def predict(self, X):
return self._xgboost_model.predict(X)
[docs] def predict_proba(self, X):
return self._xgboost_model.predict_proba(X)
from xgboost import XGBClassifier as XGBoostClassifier
import lale.helpers
import lale.operators
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Hyperparameter schema for a Lale wrapper for XGBoost.',
'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': ['max_depth', 'learning_rate','n_estimators',
'verbosity', 'objective', 'booster', 'n_jobs', 'gamma','min_child_weight',
'max_delta_step', 'subsample', 'colsample_bytree', 'colsample_bylevel',
'colsample_bynode', 'reg_alpha', 'reg_lambda', 'scale_pos_weight',
'base_score', 'random_state', 'missing'],
'relevantToOptimizer': ['max_depth','learning_rate','n_estimators','booster', 'min_child_weight',
'subsample', 'colsample_bytree', 'colsample_bylevel', 'reg_alpha', 'reg_lambda'],
#'objective', 'booster', ],
'properties': {
'max_depth': {
'description': 'Maximum tree depth for base learners.',
'type': 'integer',
'default': 3,
'minimum': 0,
'distribution':'uniform',
'minimumForOptimizer': 1,
'maximumForOptimizer': 20},
'learning_rate': {
'description': 'Boosting learning rate (xgb’s “eta”)',
'type': 'number',
'default': 0.1,
'distribution':'loguniform',
'minimumForOptimizer': 0.01,
'maximumForOptimizer': 1},
'n_estimators': {
'description': 'Number of trees to fit.',
'type': 'integer',
'default': 100,
'minimumForOptimizer': 10,
'maximumForOptimizer': 1500},
'verbosity': {
'description': 'The degree of verbosity.',
'type': 'integer',
'default': 1,
'minimum': 0,
'maximum': 3},
'objective': {
'description': 'Specify the learning task and the corresponding '
'learning objective or a custom objective function to be used.'
' string or callable.',
'enum': ['binary:logistic', 'binary:logitraw', 'binary:hinge', 'multi:softprob','multi:softmax'],
'default': 'binary:logistic'},
'booster': {
'description':
'Specify which booster to use.',
'enum': ['gbtree', 'gblinear', 'dart'],
'default': 'gbtree'
},
'n_jobs': {
'type': 'integer',
'description': 'Number of parallel threads used to run xgboost. (replaces ``nthread``)',
'default': 1
},
'nthread': {
'anyOf': [
{'type':'integer'},
{'enum':[None]}],
'default': None,
'description': 'Number of parallel threads used to run xgboost. Deprecated, please use n_jobs'},
'gamma': {
'type': 'number',
'description': 'Minimum loss reduction required to make a further partition on a leaf node of the tree.',
'default': 0,
'minimum': 0
},
'min_child_weight': {
'type': 'integer',
'description': 'Minimum sum of instance weight(hessian) needed in a child.',
'default': 1,
'distribution': 'uniform',
'minimumForOptimizer' : 1,
'maximumForOptimizer': 20
},
'max_delta_step': {
'type': 'integer',
'description': "Maximum delta step we allow each tree's weight estimation to be.",
'default': 0,
},
'subsample': {
'type': 'number',
'description': 'Subsample ratio of the training instance.',
'default': 1,
'minimum': 0,
'exclusiveMinimum': True,
'distribution': 'uniform',
'minimumForOptimizer' : 0.01,
'maximumForOptimizer': 1.0
},
'colsample_bytree': {
'type': 'number',
'description': 'Subsample ratio of columns when constructing each tree.',
'default': 1,
'minimum': 0,
'exclusiveMinimum': True,
'maximum': 1,
'distribution': 'uniform',
'minimumForOptimizer' : 0.1,
'maximumForOptimizer': 1.0
},
'colsample_bylevel': {
'type': 'number',
'description': 'Subsample ratio of columns for each split, in each level.',
'default': 1,
'minimum': 0,
'exclusiveMinimum': True,
'maximum': 1,
'distribution': 'uniform',
'minimumForOptimizer' : 0.1,
'maximumForOptimizer': 1.0
},
'colsample_bynode': {
'type': 'number',
'description': 'Subsample ratio of columns for each split.',
'default': 1,
'minimum': 0,
'exclusiveMinimum': True,
'maximum': 1
},
'reg_alpha': {
'type': 'number',
'description': 'L1 regularization term on weights',
'default': 0,
'distribution': 'loguniform',
'minimumForOptimizer': 0.0,
'maximumForOptimizer': 1.0
},
'reg_lambda': {
'type': 'number',
'description': 'L2 regularization term on weights',
'default': 1,
'distribution': 'loguniform',
'minimumForOptimizer': 0.1,
'maximumForOptimizer': 1.0
},
'scale_pos_weight': {
'type': 'number',
'description': 'Balancing of positive and negative weights.',
'default': 1
},
'base_score': {
'type': 'number',
'description': 'The initial prediction score of all instances, global bias.',
'default': 0.5
},
'random_state': {
'type': 'integer',
'description': 'Random number seed. (replaces seed)',
'default': 0
},
'missing': {
'anyOf': [{
'type': 'number',
}, {
'enum': [None],
}],
'default': None,
'description': 'Value in the data which needs to be present as a missing value. If'
' If None, defaults to np.nan.'
},
},
}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit gradient boosting classifier',
'type': 'object',
'required': ['X', 'y'],
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Feature matrix',
},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Labels',
},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'description': 'Weight for each instance',
'default': None
},
'eval_set': {
'anyOf': [{
'type': 'array',
}, {
'enum': [None],
}],
'default': None,
'description': 'A list of (X, y) pairs to use as a validation set for',
},
'sample_weight_eval_set': {
'anyOf': [{
'type': 'array',
}, {
'enum': [None],
}],
'default': None,
'description': 'A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of',
},
'eval_metric': {
'anyOf': [{
'type': 'array',
'items': {'type':'string'}}, {
'type': 'string'},{
'enum': [None]},{
'type': 'object'}],
'default': None,
'description': 'If a str, should be a built-in evaluation metric to use. See',
},
'early_stopping_rounds': {
'anyOf': [{
'type': 'integer',
}, {
'enum': [None],
}],
'default': None,
'description': 'Activates early stopping. Validation error needs to decrease at',
},
'verbose': {
'type': 'boolean',
'description': 'If `verbose` and an evaluation set is used, writes the evaluation',
'default': True
},
'xgb_model': {
'anyOf':[{
'type': 'string'},{
'enum': [None]}],
'description': "file name of stored xgb model or 'Booster' instance Xgb model to be",
'default': None
},
'callbacks': {
'anyOf': [{
'type': 'array',
'items': {'type':'object'}}, {
'enum': [None]}],
'default': None,
'description': 'List of callback functions that are applied at each iteration. '}
}
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict with `data`.',
'allOf': [{
'type': 'object',
'required': ['X'],
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The dmatrix storing the input.',
},
'output_margin': {
'type': 'boolean',
'default': False,
'description': 'Whether to output the raw untransformed margin value.',
},
'ntree_limit': {
'anyOf':[
{'type': 'integer'},
{'enum': [None]}],
'description': 'Limit number of trees in the prediction; defaults to best_ntree_limit if defined',
},
'validate_features': {
'type': 'boolean',
'default': True,
'description': "When this is True, validate that the Booster's and data's feature_names are identical.",
}},
}],
}
_output_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Output data schema for predictions (target class labels).',
'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://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn',
'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)
XGBClassifier = lale.operators.make_operator(XGBClassifierImpl, _combined_schemas)