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[dask] Fix missing value for scikit-learn interface. (#5435)

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This commit is contained in:
Jiaming Yuan
2020-03-20 22:56:01 +08:00
committed by GitHub
parent 4b7e2b7bff
commit cd7d6f7d59
3 changed files with 77 additions and 12 deletions
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23
python-package/xgboost/dask.py
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@@ -572,7 +572,7 @@ def predict(client, model, data, *args, missing=numpy.nan):
return predictions
def _evaluation_matrices(client, validation_set, sample_weights):
def _evaluation_matrices(client, validation_set, sample_weights, missing):
'''
Parameters
----------
@@ -597,7 +597,8 @@ def _evaluation_matrices(client, validation_set, sample_weights):
for i, e in enumerate(validation_set):
w = (sample_weights[i]
if sample_weights is not None else None)
dmat = DaskDMatrix(client=client, data=e[0], label=e[1], weight=w)
dmat = DaskDMatrix(client=client, data=e[0], label=e[1], weight=w,
missing=missing)
evals.append((dmat, 'validation_{}'.format(i)))
else:
evals = None
@@ -672,10 +673,12 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
verbose=True):
_assert_dask_support()
dtrain = DaskDMatrix(client=self.client,
data=X, label=y, weight=sample_weights)
data=X, label=y, weight=sample_weights,
missing=self.missing)
params = self.get_xgb_params()
evals = _evaluation_matrices(self.client,
eval_set, sample_weight_eval_set)
eval_set, sample_weight_eval_set,
self.missing)
results = train(self.client, params, dtrain,
num_boost_round=self.get_num_boosting_rounds(),
@@ -688,7 +691,8 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
def predict(self, data): # pylint: disable=arguments-differ
_assert_dask_support()
test_dmatrix = DaskDMatrix(client=self.client, data=data)
test_dmatrix = DaskDMatrix(client=self.client, data=data,
missing=self.missing)
pred_probs = predict(client=self.client,
model=self.get_booster(), data=test_dmatrix)
return pred_probs
@@ -711,7 +715,8 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
verbose=True):
_assert_dask_support()
dtrain = DaskDMatrix(client=self.client,
data=X, label=y, weight=sample_weights)
data=X, label=y, weight=sample_weights,
missing=self.missing)
params = self.get_xgb_params()
# pylint: disable=attribute-defined-outside-init
@@ -728,7 +733,8 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
params["objective"] = "binary:logistic"
evals = _evaluation_matrices(self.client,
eval_set, sample_weight_eval_set)
eval_set, sample_weight_eval_set,
self.missing)
results = train(self.client, params, dtrain,
num_boost_round=self.get_num_boosting_rounds(),
evals=evals, verbose_eval=verbose)
@@ -739,7 +745,8 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
def predict(self, data): # pylint: disable=arguments-differ
_assert_dask_support()
test_dmatrix = DaskDMatrix(client=self.client, data=data)
test_dmatrix = DaskDMatrix(client=self.client, data=data,
missing=self.missing)
pred_probs = predict(client=self.client,
model=self.get_booster(), data=test_dmatrix)
return pred_probs