Support dask dataframe as y for classifier. (#5077)

* Support dask dataframe as y for classifier.

* Lint.

python-package/xgboost/dask.py

@@ -632,7 +632,10 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
         params = self.get_xgb_params()
 
         # pylint: disable=attribute-defined-outside-init
-        self.classes_ = da.unique(y).compute()
+        if isinstance(y, (da.Array)):
+            self.classes_ = da.unique(y).compute()
+        else:
+            self.classes_ = y.drop_duplicates().compute()
         self.n_classes_ = len(self.classes_)
 
         if self.n_classes_ > 2:
@@ -640,7 +643,6 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
             params['num_class'] = self.n_classes_
         else:
             params["objective"] = "binary:logistic"
-        params.setdefault('num_class', self.n_classes_)
 
         evals = _evaluation_matrices(self.client,
                                      eval_set, sample_weight_eval_set)

tests/python/test_with_dask.py

@@ -109,3 +109,16 @@ def test_classifier(client):
     assert list(history['validation_0'].keys())[0] == 'merror'
     assert len(list(history['validation_0'])) == 1
     assert len(history['validation_0']['merror']) == 2
+
+    assert classifier.n_classes_ == 10
+
+    # Test with dataframe.
+    X_d = dd.from_dask_array(X)
+    y_d = dd.from_dask_array(y)
+    classifier.fit(X_d, y_d)
+
+    assert classifier.n_classes_ == 10
+    prediction = classifier.predict(X_d)
+
+    assert prediction.ndim == 1
+    assert prediction.shape[0] == kRows