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[Breaking] Update sklearn interface. (#4929)

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* Remove nthread, seed, silent. Add tree_method, gpu_id, num_parallel_tree. Fix #4909.
* Check data shape. Fix #4896.
* Check element of eval_set is tuple. Fix #4875
*  Add doc for random_state with hogwild. Fixes #4919
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Jiaming Yuan 2019-10-12 02:50:09 -04:00 committed by GitHub
parent c2cce4fac3
commit 4bbf062ed3
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5 changed files with 177 additions and 128 deletions
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1
include/xgboost/generic_parameters.h
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@ -35,6 +35,7 @@ struct GenericParameter : public dmlc::Parameter<GenericParameter> {
DMLC_DECLARE_PARAMETER(GenericParameter) { DMLC_DECLARE_PARAMETER(GenericParameter) {
DMLC_DECLARE_FIELD(seed).set_default(0).describe( DMLC_DECLARE_FIELD(seed).set_default(0).describe(
"Random number seed during training."); "Random number seed during training.");
DMLC_DECLARE_ALIAS(seed, random_state);
DMLC_DECLARE_FIELD(seed_per_iteration) DMLC_DECLARE_FIELD(seed_per_iteration)
.set_default(false) .set_default(false)
.describe( .describe(

232
python-package/xgboost/sklearn.py
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@ -62,17 +62,18 @@ class XGBModel(XGBModelBase):
Number of trees to fit. Number of trees to fit.
verbosity : int verbosity : int
The degree of verbosity. Valid values are 0 (silent) - 3 (debug). The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
silent : boolean
Whether to print messages while running boosting. Deprecated. Use verbosity instead.
objective : string or callable objective : string or callable
Specify the learning task and the corresponding learning objective or Specify the learning task and the corresponding learning objective or
a custom objective function to be used (see note below). a custom objective function to be used (see note below).
booster: string booster: string
Specify which booster to use: gbtree, gblinear or dart. Specify which booster to use: gbtree, gblinear or dart.
nthread : int tree_method: string
Number of parallel threads used to run xgboost. (Deprecated, please use ``n_jobs``) Specify which tree method to use. Default to auto. If this parameter
is set to default, XGBoost will choose the most conservative option
available. It's recommended to study this option from parameters
document.
n_jobs : int n_jobs : int
Number of parallel threads used to run xgboost. (replaces ``nthread``) Number of parallel threads used to run xgboost.
gamma : float gamma : float
Minimum loss reduction required to make a further partition on a leaf node of the tree. Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int min_child_weight : int
@ -95,13 +96,17 @@ class XGBModel(XGBModelBase):
Balancing of positive and negative weights. Balancing of positive and negative weights.
base_score: base_score:
The initial prediction score of all instances, global bias. The initial prediction score of all instances, global bias.
seed : int
Random number seed. (Deprecated, please use random_state)
random_state : int random_state : int
Random number seed. (replaces seed) Random number seed.
.. note:: Using gblinear booster with shotgun updater is
nondeterministic as it uses Hogwild algorithm.
missing : float, optional missing : float, optional
Value in the data which needs to be present as a missing value. If Value in the data which needs to be present as a missing value. If
None, defaults to np.nan. None, defaults to np.nan.
num_parallel_tree: int
Used for boosting random forest.
importance_type: string, default "gain" importance_type: string, default "gain"
The feature importance type for the feature_importances\\_ property: The feature importance type for the feature_importances\\_ property:
either "gain", "weight", "cover", "total_gain" or "total_cover". either "gain", "weight", "cover", "total_gain" or "total_cover".
@ -131,25 +136,27 @@ class XGBModel(XGBModelBase):
The value of the gradient for each sample point. The value of the gradient for each sample point.
hess: array_like of shape [n_samples] hess: array_like of shape [n_samples]
The value of the second derivative for each sample point The value of the second derivative for each sample point
""" """
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
verbosity=1, silent=None, objective="reg:squarederror", verbosity=1, objective="reg:squarederror",
booster='gbtree', n_jobs=1, nthread=None, gamma=0, booster='gbtree', tree_method='auto', n_jobs=1, gamma=0,
min_child_weight=1, max_delta_step=0, subsample=1, min_child_weight=1, max_delta_step=0, subsample=1,
colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5,
random_state=0, seed=None, missing=None, random_state=0, missing=None, num_parallel_tree=1,
importance_type="gain", **kwargs): importance_type="gain", **kwargs):
if not SKLEARN_INSTALLED: if not SKLEARN_INSTALLED:
raise XGBoostError('sklearn needs to be installed in order to use this module') raise XGBoostError(
'sklearn needs to be installed in order to use this module')
self.max_depth = max_depth self.max_depth = max_depth
self.learning_rate = learning_rate self.learning_rate = learning_rate
self.n_estimators = n_estimators self.n_estimators = n_estimators
self.verbosity = verbosity self.verbosity = verbosity
self.silent = silent
self.objective = objective self.objective = objective
self.booster = booster self.booster = booster
self.tree_method = tree_method
self.gamma = gamma self.gamma = gamma
self.min_child_weight = min_child_weight self.min_child_weight = min_child_weight
self.max_delta_step = max_delta_step self.max_delta_step = max_delta_step
@ -162,11 +169,10 @@ class XGBModel(XGBModelBase):
self.scale_pos_weight = scale_pos_weight self.scale_pos_weight = scale_pos_weight
self.base_score = base_score self.base_score = base_score
self.missing = missing if missing is not None else np.nan self.missing = missing if missing is not None else np.nan
self.num_parallel_tree = num_parallel_tree
self.kwargs = kwargs self.kwargs = kwargs
self._Booster = None self._Booster = None
self.seed = seed
self.random_state = random_state self.random_state = random_state
self.nthread = nthread
self.n_jobs = n_jobs self.n_jobs = n_jobs
self.importance_type = importance_type self.importance_type = importance_type
@ -227,33 +233,6 @@ class XGBModel(XGBModelBase):
def get_xgb_params(self): def get_xgb_params(self):
"""Get xgboost type parameters.""" """Get xgboost type parameters."""
xgb_params = self.get_params() xgb_params = self.get_params()
random_state = xgb_params.pop('random_state')
if 'seed' in xgb_params and xgb_params['seed'] is not None:
warnings.warn('The seed parameter is deprecated as of version .6.'
'Please use random_state instead.'
'seed is deprecated.', DeprecationWarning)
else:
xgb_params['seed'] = random_state
n_jobs = xgb_params.pop('n_jobs')
if 'nthread' in xgb_params and xgb_params['nthread'] is not None:
warnings.warn('The nthread parameter is deprecated as of version .6.'
'Please use n_jobs instead.'
'nthread is deprecated.', DeprecationWarning)
else:
xgb_params['nthread'] = n_jobs
if 'silent' in xgb_params and xgb_params['silent'] is not None:
warnings.warn('The silent parameter is deprecated.'
'Please use verbosity instead.'
'silent is depreated', DeprecationWarning)
# TODO(canonizer): set verbosity explicitly if silent is removed from xgboost,
# but remains in this API
else:
# silent=None shouldn't be passed to xgboost
xgb_params.pop('silent', None)
if xgb_params['nthread'] <= 0:
xgb_params.pop('nthread', None)
return xgb_params return xgb_params
def get_num_boosting_rounds(self): def get_num_boosting_rounds(self):
@ -301,7 +280,7 @@ class XGBModel(XGBModelBase):
Input file name or memory buffer(see also save_raw) Input file name or memory buffer(see also save_raw)
""" """
if self._Booster is None: if self._Booster is None:
self._Booster = Booster({'nthread': self.n_jobs}) self._Booster = Booster({'n_jobs': self.n_jobs})
self._Booster.load_model(fname) self._Booster.load_model(fname)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
@ -364,13 +343,17 @@ class XGBModel(XGBModelBase):
""" """
if sample_weight is not None: if sample_weight is not None:
trainDmatrix = DMatrix(X, label=y, weight=sample_weight, trainDmatrix = DMatrix(X, label=y, weight=sample_weight,
missing=self.missing, nthread=self.n_jobs) missing=self.missing,
nthread=self.n_jobs)
else: else:
trainDmatrix = DMatrix(X, label=y, missing=self.missing, nthread=self.n_jobs) trainDmatrix = DMatrix(X, label=y, missing=self.missing,
nthread=self.n_jobs)
evals_result = {} evals_result = {}
if eval_set is not None: if eval_set is not None:
if not isinstance(eval_set[0], (list, tuple)):
raise TypeError('Unexpected input type for `eval_set`')
if sample_weight_eval_set is None: if sample_weight_eval_set is None:
sample_weight_eval_set = [None] * len(eval_set) sample_weight_eval_set = [None] * len(eval_set)
evals = list( evals = list(
@ -610,22 +593,27 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
__doc__ = "Implementation of the scikit-learn API for XGBoost classification.\n\n" \ __doc__ = "Implementation of the scikit-learn API for XGBoost classification.\n\n" \
+ '\n'.join(XGBModel.__doc__.split('\n')[2:]) + '\n'.join(XGBModel.__doc__.split('\n')[2:])
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, def __init__(self, max_depth=3, learning_rate=0.1,
verbosity=1, silent=None, n_estimators=100, verbosity=1,
objective="binary:logistic", booster='gbtree', objective="binary:logistic", booster='gbtree',
n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, tree_method='auto', n_jobs=1, gpu_id=-1, gamma=0,
subsample=1, colsample_bytree=1, colsample_bylevel=1, min_child_weight=1, max_delta_step=0, subsample=1,
colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1,
base_score=0.5, random_state=0, seed=None, missing=None, **kwargs): reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5,
random_state=0, missing=None, **kwargs):
super(XGBClassifier, self).__init__( super(XGBClassifier, self).__init__(
max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate,
verbosity=verbosity, silent=silent, objective=objective, booster=booster, n_estimators=n_estimators, verbosity=verbosity,
n_jobs=n_jobs, nthread=nthread, gamma=gamma, objective=objective, booster=booster, tree_method=tree_method,
min_child_weight=min_child_weight, max_delta_step=max_delta_step, n_jobs=n_jobs, gpu_id=gpu_id, gamma=gamma,
subsample=subsample, colsample_bytree=colsample_bytree, min_child_weight=min_child_weight,
colsample_bylevel=colsample_bylevel, colsample_bynode=colsample_bynode, max_delta_step=max_delta_step, subsample=subsample,
reg_alpha=reg_alpha, reg_lambda=reg_lambda, scale_pos_weight=scale_pos_weight, colsample_bytree=colsample_bytree,
base_score=base_score, random_state=random_state, seed=seed, missing=missing, colsample_bylevel=colsample_bylevel,
colsample_bynode=colsample_bynode,
reg_alpha=reg_alpha, reg_lambda=reg_lambda,
scale_pos_weight=scale_pos_weight,
base_score=base_score, random_state=random_state, missing=missing,
**kwargs) **kwargs)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
@ -676,6 +664,11 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
else: else:
evals = () evals = ()
if len(X.shape) != 2:
# Simply raise an error here since there might be many
# different ways of reshaping
raise ValueError(
'Please reshape the input data X into 2-dimensional matrix.')
self._features_count = X.shape[1] self._features_count = X.shape[1]
if sample_weight is not None: if sample_weight is not None:
@ -846,26 +839,27 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
class XGBRFClassifier(XGBClassifier): class XGBRFClassifier(XGBClassifier):
# pylint: disable=missing-docstring # pylint: disable=missing-docstring
__doc__ = "Experimental implementation of the scikit-learn API "\ __doc__ = "scikit-learn API for XGBoost random forest classification.\n\n"\
+ "for XGBoost random forest classification.\n\n"\
+ '\n'.join(XGBModel.__doc__.split('\n')[2:]) + '\n'.join(XGBModel.__doc__.split('\n')[2:])
def __init__(self, max_depth=3, learning_rate=1, n_estimators=100, def __init__(self, max_depth=3, learning_rate=1, n_estimators=100,
verbosity=1, silent=None, verbosity=1, objective="binary:logistic", n_jobs=1,
objective="binary:logistic", n_jobs=1, nthread=None, gamma=0, gpu_id=-1, gamma=0, min_child_weight=1, max_delta_step=0,
min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1, subsample=0.8, colsample_bytree=1, colsample_bylevel=1,
colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1e-5, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1e-5,
scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, scale_pos_weight=1, base_score=0.5, random_state=0,
missing=None, **kwargs): missing=None, **kwargs):
super(XGBRFClassifier, self).__init__( super(XGBRFClassifier, self).__init__(
max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate,
verbosity=verbosity, silent=silent, objective=objective, booster='gbtree', n_estimators=n_estimators, verbosity=verbosity,
n_jobs=n_jobs, nthread=nthread, gamma=gamma, objective=objective, booster='gbtree', n_jobs=n_jobs,
min_child_weight=min_child_weight, max_delta_step=max_delta_step, gpu_id=gpu_id, gamma=gamma, min_child_weight=min_child_weight,
max_delta_step=max_delta_step,
subsample=subsample, colsample_bytree=colsample_bytree, subsample=subsample, colsample_bytree=colsample_bytree,
colsample_bylevel=colsample_bylevel, colsample_bynode=colsample_bynode, colsample_bylevel=colsample_bylevel,
reg_alpha=reg_alpha, reg_lambda=reg_lambda, scale_pos_weight=scale_pos_weight, colsample_bynode=colsample_bynode, reg_alpha=reg_alpha,
base_score=base_score, random_state=random_state, seed=seed, missing=missing, reg_lambda=reg_lambda, scale_pos_weight=scale_pos_weight,
base_score=base_score, random_state=random_state, missing=missing,
**kwargs) **kwargs)
def get_xgb_params(self): def get_xgb_params(self):
@ -885,26 +879,28 @@ class XGBRegressor(XGBModel, XGBRegressorBase):
class XGBRFRegressor(XGBRegressor): class XGBRFRegressor(XGBRegressor):
# pylint: disable=missing-docstring # pylint: disable=missing-docstring
__doc__ = "Experimental implementation of the scikit-learn API "\ __doc__ = "scikit-learn API for XGBoost random forest regression.\n\n"\
+ "for XGBoost random forest regression.\n\n"\
+ '\n'.join(XGBModel.__doc__.split('\n')[2:]) + '\n'.join(XGBModel.__doc__.split('\n')[2:])
def __init__(self, max_depth=3, learning_rate=1, n_estimators=100, def __init__(self, max_depth=3, learning_rate=1, n_estimators=100,
verbosity=1, silent=None, verbosity=1, objective="reg:squarederror", n_jobs=1,
objective="reg:squarederror", n_jobs=1, nthread=None, gamma=0, gpu_id=-1, gamma=0, min_child_weight=1,
min_child_weight=1, max_delta_step=0, subsample=0.8, colsample_bytree=1, max_delta_step=0, subsample=0.8, colsample_bytree=1,
colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0, reg_lambda=1e-5, colsample_bylevel=1, colsample_bynode=0.8, reg_alpha=0,
scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, reg_lambda=1e-5, scale_pos_weight=1, base_score=0.5,
missing=None, **kwargs): random_state=0, missing=None, **kwargs):
super(XGBRFRegressor, self).__init__( super(XGBRFRegressor, self).__init__(
max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate,
verbosity=verbosity, silent=silent, objective=objective, booster='gbtree', n_estimators=n_estimators, verbosity=verbosity,
n_jobs=n_jobs, nthread=nthread, gamma=gamma, objective=objective, booster='gbtree', n_jobs=n_jobs,
min_child_weight=min_child_weight, max_delta_step=max_delta_step, gpu_id=gpu_id, gamma=gamma, min_child_weight=min_child_weight,
subsample=subsample, colsample_bytree=colsample_bytree, max_delta_step=max_delta_step, subsample=subsample,
colsample_bylevel=colsample_bylevel, colsample_bynode=colsample_bynode, colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha, reg_lambda=reg_lambda, scale_pos_weight=scale_pos_weight, colsample_bylevel=colsample_bylevel,
base_score=base_score, random_state=random_state, seed=seed, missing=missing, colsample_bynode=colsample_bynode,
reg_alpha=reg_alpha, reg_lambda=reg_lambda,
scale_pos_weight=scale_pos_weight,
base_score=base_score, random_state=random_state, missing=missing,
**kwargs) **kwargs)
def get_xgb_params(self): def get_xgb_params(self):
@ -930,17 +926,13 @@ class XGBRanker(XGBModel):
Number of boosted trees to fit. Number of boosted trees to fit.
verbosity : int verbosity : int
The degree of verbosity. Valid values are 0 (silent) - 3 (debug). The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
silent : boolean
Whether to print messages while running boosting. Deprecated. Use verbosity instead.
objective : string objective : string
Specify the learning task and the corresponding learning objective. Specify the learning task and the corresponding learning objective.
The objective name must start with "rank:". The objective name must start with "rank:".
booster: string booster: string
Specify which booster to use: gbtree, gblinear or dart. Specify which booster to use: gbtree, gblinear or dart.
nthread : int
Number of parallel threads used to run xgboost. (Deprecated, please use ``n_jobs``)
n_jobs : int n_jobs : int
Number of parallel threads used to run xgboost. (replaces ``nthread``) Number of parallel threads used to run xgboost.
gamma : float gamma : float
Minimum loss reduction required to make a further partition on a leaf node of the tree. Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int min_child_weight : int
@ -963,10 +955,12 @@ class XGBRanker(XGBModel):
Balancing of positive and negative weights. Balancing of positive and negative weights.
base_score: base_score:
The initial prediction score of all instances, global bias. The initial prediction score of all instances, global bias.
seed : int
Random number seed. (Deprecated, please use random_state)
random_state : int random_state : int
Random number seed. (replaces seed) Random number seed.
.. note:: Using gblinear booster with shotgun updater is
nondeterministic as it uses Hogwild algorithm.
missing : float, optional missing : float, optional
Value in the data which needs to be present as a missing value. If Value in the data which needs to be present as a missing value. If
None, defaults to np.nan. None, defaults to np.nan.
@ -1015,33 +1009,39 @@ class XGBRanker(XGBModel):
+-------+-----------+---------------+ +-------+-----------+---------------+
then your group array should be ``[3, 4]``. then your group array should be ``[3, 4]``.
""" """
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
verbosity=1, silent=None, objective="rank:pairwise", booster='gbtree', verbosity=1, objective="rank:pairwise", booster='gbtree',
n_jobs=-1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, tree_method='auto', n_jobs=-1, gpu_id=-1, gamma=0,
subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, min_child_weight=1, max_delta_step=0, subsample=1,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1,
base_score=0.5, random_state=0, seed=None, missing=None, **kwargs): reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5,
random_state=0, missing=None, **kwargs):
super(XGBRanker, self).__init__( super(XGBRanker, self).__init__(
max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate,
verbosity=verbosity, silent=silent, objective=objective, booster=booster, n_estimators=n_estimators, verbosity=verbosity,
n_jobs=n_jobs, nthread=nthread, gamma=gamma, objective=objective, booster=booster, tree_method=tree_method,
n_jobs=n_jobs, gpu_id=gpu_id, gamma=gamma,
min_child_weight=min_child_weight, max_delta_step=max_delta_step, min_child_weight=min_child_weight, max_delta_step=max_delta_step,
subsample=subsample, colsample_bytree=colsample_bytree, subsample=subsample, colsample_bytree=colsample_bytree,
colsample_bylevel=colsample_bylevel, colsample_bynode=colsample_bynode, colsample_bylevel=colsample_bylevel,
reg_alpha=reg_alpha, reg_lambda=reg_lambda, colsample_bynode=colsample_bynode, reg_alpha=reg_alpha,
scale_pos_weight=scale_pos_weight, base_score=base_score, reg_lambda=reg_lambda, scale_pos_weight=scale_pos_weight,
random_state=random_state, seed=seed, missing=missing, **kwargs) base_score=base_score, random_state=random_state, missing=missing,
**kwargs)
if callable(self.objective): if callable(self.objective):
raise ValueError("custom objective function not supported by XGBRanker") raise ValueError(
"custom objective function not supported by XGBRanker")
if "rank:" not in self.objective: if "rank:" not in self.objective:
raise ValueError("please use XGBRanker for ranking task") raise ValueError("please use XGBRanker for ranking task")
def fit(self, X, y, group, sample_weight=None, eval_set=None, sample_weight_eval_set=None, def fit(self, X, y, group, sample_weight=None, eval_set=None,
eval_group=None, eval_metric=None, early_stopping_rounds=None, sample_weight_eval_set=None, eval_group=None, eval_metric=None,
verbose=False, xgb_model=None, callbacks=None): early_stopping_rounds=None, verbose=False, xgb_model=None,
callbacks=None):
# pylint: disable = attribute-defined-outside-init,arguments-differ # pylint: disable = attribute-defined-outside-init,arguments-differ
""" """
Fit gradient boosting ranker Fit gradient boosting ranker
@ -1132,10 +1132,12 @@ class XGBRanker(XGBModel):
return ret return ret
if sample_weight is not None: if sample_weight is not None:
train_dmatrix = _dmat_init(group, data=X, label=y, weight=sample_weight, train_dmatrix = _dmat_init(
group, data=X, label=y, weight=sample_weight,
missing=self.missing, nthread=self.n_jobs) missing=self.missing, nthread=self.n_jobs)
else: else:
train_dmatrix = _dmat_init(group, data=X, label=y, train_dmatrix = _dmat_init(
group, data=X, label=y,
missing=self.missing, nthread=self.n_jobs) missing=self.missing, nthread=self.n_jobs)
evals_result = {} evals_result = {}

2
src/learner.cc
View File

@ -601,7 +601,7 @@ class LearnerImpl : public Learner {
gbm_->Configure(args); gbm_->Configure(args);
if (this->gbm_->UseGPU()) { if (this->gbm_->UseGPU()) {
if (cfg_.find("gpu_id") == cfg_.cend()) { if (generic_param_.gpu_id == -1) {
generic_param_.gpu_id = 0; generic_param_.gpu_id = 0;
} }
} }

31
tests/python-gpu/test_gpu_with_sklearn.py Normal file
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@ -0,0 +1,31 @@
import xgboost as xgb
import pytest
import sys
import numpy as np
sys.path.append("tests/python")
import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
rng = np.random.RandomState(1994)
def test_gpu_binary_classification():
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
digits = load_digits(2)
y = digits['target']
X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
for train_index, test_index in kf.split(X, y):
xgb_model = cls(
random_state=42, tree_method='gpu_hist',
n_estimators=4, gpu_id='0').fit(X[train_index], y[train_index])
preds = xgb_model.predict(X[test_index])
labels = y[test_index]
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1

29
tests/python/test_with_sklearn.py
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@ -175,6 +175,21 @@ def test_feature_importances_gain():
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp) np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
def test_num_parallel_tree():
from sklearn.datasets import load_boston
reg = xgb.XGBRegressor(n_estimators=4, num_parallel_tree=4,
tree_method='hist')
boston = load_boston()
bst = reg.fit(X=boston['data'], y=boston['target'])
dump = bst.get_booster().get_dump(dump_format='json')
assert len(dump) == 16
reg = xgb.XGBRFRegressor(n_estimators=4)
bst = reg.fit(X=boston['data'], y=boston['target'])
dump = bst.get_booster().get_dump(dump_format='json')
assert len(dump) == 4
def test_boston_housing_regression(): def test_boston_housing_regression():
from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston from sklearn.datasets import load_boston
@ -430,18 +445,18 @@ def test_split_value_histograms():
def test_sklearn_random_state(): def test_sklearn_random_state():
clf = xgb.XGBClassifier(random_state=402) clf = xgb.XGBClassifier(random_state=402)
assert clf.get_xgb_params()['seed'] == 402 assert clf.get_xgb_params()['random_state'] == 402
clf = xgb.XGBClassifier(seed=401) clf = xgb.XGBClassifier(random_state=401)
assert clf.get_xgb_params()['seed'] == 401 assert clf.get_xgb_params()['random_state'] == 401
def test_sklearn_n_jobs(): def test_sklearn_n_jobs():
clf = xgb.XGBClassifier(n_jobs=1) clf = xgb.XGBClassifier(n_jobs=1)
assert clf.get_xgb_params()['nthread'] == 1 assert clf.get_xgb_params()['n_jobs'] == 1
clf = xgb.XGBClassifier(nthread=2) clf = xgb.XGBClassifier(n_jobs=2)
assert clf.get_xgb_params()['nthread'] == 2 assert clf.get_xgb_params()['n_jobs'] == 2
def test_kwargs(): def test_kwargs():
@ -482,7 +497,7 @@ def test_kwargs_error():
def test_sklearn_clone(): def test_sklearn_clone():
from sklearn.base import clone from sklearn.base import clone
clf = xgb.XGBClassifier(n_jobs=2, nthread=3) clf = xgb.XGBClassifier(n_jobs=2)
clf.n_jobs = -1 clf.n_jobs = -1
clone(clf) clone(clf)