[FEA] DaskXGBClassifier Early Stopping

[programmylife]

We (myself and @kylejn27) would like to update our model training to run with DaskXGBClassifier, but it requires early stopping. We would like DaskXGBClassifier to match the spec of XGBClassifier to include early stopping:

DaskXGBClassifier

class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
    # pylint: disable=missing-docstring
    _client = None
    def fit(self,
            X,
            y,
            sample_weights=None,
            eval_set=None,
            sample_weight_eval_set=None,
            verbose=True):

XGBClassifier

class XGBClassifier(XGBModel, XGBClassifierBase):
    # pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
    def __init__(self, objective="binary:logistic", **kwargs):
        super().__init__(objective=objective, **kwargs)
    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):

[trivialfis]

Yup. Will work on this, I just need to redactor the call back a little bit.