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Add base margin to sklearn interface. (#5151)

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This commit is contained in:
Jiaming Yuan
2019-12-24 09:43:41 +08:00
committed by GitHub
parent 1d0ca49761
commit 0202e04a8e
5 changed files with 103 additions and 61 deletions
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39
python-package/xgboost/core.py
View File

@@ -434,9 +434,11 @@ class DMatrix(object):
_feature_names = None # for previous version's pickle
_feature_types = None
def __init__(self, data, label=None, missing=None,
weight=None, silent=False,
feature_names=None, feature_types=None,
def __init__(self, data, label=None, weight=None, base_margin=None,
missing=None,
silent=False,
feature_names=None,
feature_types=None,
nthread=None):
"""Parameters
----------
@@ -492,6 +494,7 @@ class DMatrix(object):
label = _maybe_pandas_label(label)
label = _maybe_dt_array(label)
weight = _maybe_dt_array(weight)
base_margin = _maybe_dt_array(base_margin)
if isinstance(data, (STRING_TYPES, os_PathLike)):
handle = ctypes.c_void_p()
@@ -518,19 +521,11 @@ class DMatrix(object):
' {}'.format(type(data).__name__))
if label is not None:
if isinstance(label, np.ndarray):
self.set_label_npy2d(label)
elif _use_columnar_initializer(label):
self.set_interface_info('label', label)
else:
self.set_label(label)
self.set_label(label)
if weight is not None:
if isinstance(weight, np.ndarray):
self.set_weight_npy2d(weight)
elif _use_columnar_initializer(label):
self.set_interface_info('weight', weight)
else:
self.set_weight(weight)
self.set_weight(weight)
if base_margin is not None:
self.set_base_margin(base_margin)
self.feature_names = feature_names
self.feature_types = feature_types
@@ -792,7 +787,12 @@ class DMatrix(object):
label: array like
The label information to be set into DMatrix
"""
self.set_float_info('label', label)
if isinstance(label, np.ndarray):
self.set_label_npy2d(label)
elif _use_columnar_initializer(label):
self.set_interface_info('label', label)
else:
self.set_float_info('label', label)
def set_label_npy2d(self, label):
"""Set label of dmatrix
@@ -820,7 +820,12 @@ class DMatrix(object):
data points within each group, so it doesn't make sense to assign
weights to individual data points.
"""
self.set_float_info('weight', weight)
if isinstance(weight, np.ndarray):
self.set_weight_npy2d(weight)
elif _use_columnar_initializer(weight):
self.set_interface_info('weight', weight)
else:
self.set_float_info('weight', weight)
def set_weight_npy2d(self, weight):
""" Set weight of each instance

76
python-package/xgboost/sklearn.py
View File

@@ -288,9 +288,9 @@ class XGBModel(XGBModelBase):
self._Booster = Booster({'n_jobs': self.n_jobs})
self._Booster.load_model(fname)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=None):
def fit(self, X, y, sample_weight=None, base_margin=None,
eval_set=None, eval_metric=None, early_stopping_rounds=None,
verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=None):
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""Fit gradient boosting model
@@ -302,6 +302,8 @@ class XGBModel(XGBModelBase):
Labels
sample_weight : array_like
instance weights
base_margin : array_like
global bias for each instance.
eval_set : list, optional
A list of (X, y) tuple pairs to use as validation sets, for which
metrics will be computed.
@@ -346,14 +348,10 @@ class XGBModel(XGBModelBase):
[xgb.callback.reset_learning_rate(custom_rates)]
"""
if sample_weight is not None:
trainDmatrix = DMatrix(X, label=y, weight=sample_weight,
missing=self.missing,
nthread=self.n_jobs)
else:
trainDmatrix = DMatrix(X, label=y, missing=self.missing,
nthread=self.n_jobs)
trainDmatrix = DMatrix(data=X, label=y, weight=sample_weight,
base_margin=base_margin,
missing=self.missing,
nthread=self.n_jobs)
evals_result = {}
if eval_set is not None:
@@ -404,7 +402,8 @@ class XGBModel(XGBModelBase):
self.best_ntree_limit = self._Booster.best_ntree_limit
return self
def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
def predict(self, data, output_margin=False, ntree_limit=None,
validate_features=True, base_margin=None):
"""
Predict with `data`.
@@ -442,7 +441,8 @@ class XGBModel(XGBModelBase):
prediction : numpy array
"""
# pylint: disable=missing-docstring,invalid-name
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
test_dmatrix = DMatrix(data, base_margin=base_margin,
missing=self.missing, nthread=self.n_jobs)
# get ntree_limit to use - if none specified, default to
# best_ntree_limit if defined, otherwise 0.
if ntree_limit is None:
@@ -621,7 +621,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
base_score=base_score, random_state=random_state, missing=missing,
**kwargs)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
def fit(self, X, y, sample_weight=None, base_margin=None,
eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=None):
# pylint: disable = attribute-defined-outside-init,arguments-differ
@@ -675,13 +676,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
raise ValueError(
'Please reshape the input data X into 2-dimensional matrix.')
self._features_count = X.shape[1]
if sample_weight is not None:
train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight,
missing=self.missing, nthread=self.n_jobs)
else:
train_dmatrix = DMatrix(X, label=training_labels,
missing=self.missing, nthread=self.n_jobs)
train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight,
base_margin=base_margin,
missing=self.missing, nthread=self.n_jobs)
self._Booster = train(xgb_options, train_dmatrix, self.get_num_boosting_rounds(),
evals=evals, early_stopping_rounds=early_stopping_rounds,
@@ -706,7 +703,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
fit.__doc__ = XGBModel.fit.__doc__.replace('Fit gradient boosting model',
'Fit gradient boosting classifier', 1)
def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
def predict(self, data, output_margin=False, ntree_limit=None,
validate_features=True, base_margin=None):
"""
Predict with `data`.
@@ -729,7 +727,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
Parameters
----------
data : DMatrix
data : array_like
The dmatrix storing the input.
output_margin : bool
Whether to output the raw untransformed margin value.
@@ -743,7 +741,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
-------
prediction : numpy array
"""
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
test_dmatrix = DMatrix(data, base_margin=base_margin,
missing=self.missing, nthread=self.n_jobs)
if ntree_limit is None:
ntree_limit = getattr(self, "best_ntree_limit", 0)
class_probs = self.get_booster().predict(test_dmatrix,
@@ -761,7 +760,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
column_indexes[class_probs > 0.5] = 1
return self._le.inverse_transform(column_indexes)
def predict_proba(self, data, ntree_limit=None, validate_features=True):
def predict_proba(self, data, ntree_limit=None, validate_features=True,
base_margin=None):
"""
Predict the probability of each `data` example being of a given class.
@@ -787,7 +787,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
prediction : numpy array
a numpy array with the probability of each data example being of a given class.
"""
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
test_dmatrix = DMatrix(data, base_margin=base_margin,
missing=self.missing, nthread=self.n_jobs)
if ntree_limit is None:
ntree_limit = getattr(self, "best_ntree_limit", 0)
class_probs = self.get_booster().predict(test_dmatrix,
@@ -1045,7 +1046,8 @@ class XGBRanker(XGBModel):
if "rank:" not in self.objective:
raise ValueError("please use XGBRanker for ranking task")
def fit(self, X, y, group, sample_weight=None, eval_set=None,
def fit(self, X, y, group, sample_weight=None, base_margin=None,
eval_set=None,
sample_weight_eval_set=None, eval_group=None, eval_metric=None,
early_stopping_rounds=None, verbose=False, xgb_model=None,
callbacks=None):
@@ -1072,6 +1074,8 @@ class XGBRanker(XGBModel):
data points within each group, so it doesn't make sense to assign
weights to individual data points.
base_margin : array_like
Global bias for each instance.
eval_set : list, optional
A list of (X, y) tuple pairs to use as validation sets, for which
metrics will be computed.
@@ -1138,14 +1142,10 @@ class XGBRanker(XGBModel):
ret.set_group(group)
return ret
if sample_weight is not None:
train_dmatrix = _dmat_init(
group, data=X, label=y, weight=sample_weight,
missing=self.missing, nthread=self.n_jobs)
else:
train_dmatrix = _dmat_init(
group, data=X, label=y,
missing=self.missing, nthread=self.n_jobs)
train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,
base_margin=base_margin,
missing=self.missing, nthread=self.n_jobs)
train_dmatrix.set_group(group)
evals_result = {}
@@ -1192,9 +1192,11 @@ class XGBRanker(XGBModel):
return self
def predict(self, data, output_margin=False, ntree_limit=0, validate_features=True):
def predict(self, data, output_margin=False,
ntree_limit=0, validate_features=True, base_margin=None):
test_dmatrix = DMatrix(data, missing=self.missing)
test_dmatrix = DMatrix(data, base_margin=base_margin,
missing=self.missing)
if ntree_limit is None:
ntree_limit = getattr(self, "best_ntree_limit", 0)