Fit gradient boosting classifier

        Parameters
        ----------
        X : array_like
            Feature matrix
        y : array_like
            Labels
        sample_weight : array_like
            Weight for each instance
        eval_set : list, optional
            A list of (X, y) pairs to use as a validation set for
            early-stopping
        sample_weight_eval_set : list, optional
            A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of
            instance weights on the i-th validation set.
        eval_metric : str, callable, optional
            If a str, should be a built-in evaluation metric to use. See
            doc/parameter.rst. If callable, a custom evaluation metric. The call
            signature is func(y_predicted, y_true) where y_true will be a
            DMatrix object such that you may need to call the get_label
            method. It must return a str, value pair where the str is a name
            for the evaluation and value is the value of the evaluation
            function. This objective is always minimized.
        early_stopping_rounds : int, optional
            Activates early stopping. Validation error needs to decrease at
            least every <early_stopping_rounds> round(s) to continue training.
            Requires at least one item in evals. If there's more than one,
            will use the last. If early stopping occurs, the model will have
            three additional fields: bst.best_score, bst.best_iteration and
            bst.best_ntree_limit (bst.best_ntree_limit is the ntree_limit parameter
            default value in predict method if not any other value is specified).
            (Use bst.best_ntree_limit to get the correct value if num_parallel_tree
            and/or num_class appears in the parameters)
        verbose : bool
            If `verbose` and an evaluation set is used, writes the evaluation
            metric measured on the validation set to stderr.
        xgb_model : str
            file name of stored xgb model or 'Booster' instance Xgb model to be
            loaded before training (allows training continuation).
        callbacks : list of callback functions
            List of callback functions that are applied at end of each iteration.
            It is possible to use predefined callbacks by using :ref:`callback_api`.
            Example:

            .. code-block:: python

                [xgb.callback.reset_learning_rate(custom_rates)]

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):
        # pylint: disable = attribute-defined-outside-init,arguments-differ
        """
        Fit gradient boosting classifier

        Parameters
        ----------
        X : array_like
            Feature matrix
        y : array_like
            Labels
        sample_weight : array_like
            Weight for each instance
        eval_set : list, optional
            A list of (X, y) pairs to use as a validation set for
            early-stopping
        sample_weight_eval_set : list, optional
            A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of
            instance weights on the i-th validation set.
        eval_metric : str, callable, optional
            If a str, should be a built-in evaluation metric to use. See
            doc/parameter.rst. If callable, a custom evaluation metric. The call
            signature is func(y_predicted, y_true) where y_true will be a
            DMatrix object such that you may need to call the get_label
            method. It must return a str, value pair where the str is a name
            for the evaluation and value is the value of the evaluation
            function. This objective is always minimized.
        early_stopping_rounds : int, optional
            Activates early stopping. Validation error needs to decrease at
            least every <early_stopping_rounds> round(s) to continue training.
            Requires at least one item in evals. If there's more than one,
            will use the last. If early stopping occurs, the model will have
            three additional fields: bst.best_score, bst.best_iteration and
            bst.best_ntree_limit (bst.best_ntree_limit is the ntree_limit parameter
            default value in predict method if not any other value is specified).
            (Use bst.best_ntree_limit to get the correct value if num_parallel_tree
            and/or num_class appears in the parameters)
        verbose : bool
            If `verbose` and an evaluation set is used, writes the evaluation
            metric measured on the validation set to stderr.
        xgb_model : str
            file name of stored xgb model or 'Booster' instance Xgb model to be
            loaded before training (allows training continuation).
        callbacks : list of callback functions
            List of callback functions that are applied at end of each iteration.
            It is possible to use predefined callbacks by using :ref:`callback_api`.
            Example:

            .. code-block:: python

                [xgb.callback.reset_learning_rate(custom_rates)]
        """
        evals_result = {}
        self.classes_ = np.unique(y)
        self.n_classes_ = len(self.classes_)

        xgb_options = self.get_xgb_params()

        if callable(self.objective):
            obj = _objective_decorator(self.objective)
            # Use default value. Is it really not used ?
            xgb_options["objective"] = "binary:logistic"
        else:
            obj = None

        if self.n_classes_ > 2:
            # Switch to using a multiclass objective in the underlying XGB instance
            xgb_options["objective"] = "multi:softprob"
            xgb_options['num_class'] = self.n_classes_

        feval = eval_metric if callable(eval_metric) else None
        if eval_metric is not None:
            if callable(eval_metric):
                eval_metric = None
            else:
                xgb_options.update({"eval_metric": eval_metric})

        self._le = XGBLabelEncoder().fit(y)
        training_labels = self._le.transform(y)

        if eval_set is not None:
            if sample_weight_eval_set is None:
                sample_weight_eval_set = [None] * len(eval_set)
            evals = list(
                DMatrix(eval_set[i][0], label=self._le.transform(eval_set[i][1]),
                        missing=self.missing, weight=sample_weight_eval_set[i],
                        nthread=self.n_jobs)
                for i in range(len(eval_set))
            )
            nevals = len(evals)
            eval_names = ["validation_{}".format(i) for i in range(nevals)]
            evals = list(zip(evals, eval_names))
        else:
            evals = ()

        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)

        self._Booster = train(xgb_options, train_dmatrix, self.get_num_boosting_rounds(),
                              evals=evals, early_stopping_rounds=early_stopping_rounds,
                              evals_result=evals_result, obj=obj, feval=feval,
                              verbose_eval=verbose, xgb_model=xgb_model,
                              callbacks=callbacks)

        self.objective = xgb_options["objective"]
        if evals_result:
            for val in evals_result.items():
                evals_result_key = list(val[1].keys())[0]
                evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
            self.evals_result_ = evals_result

        if early_stopping_rounds is not None:
            self.best_score = self._Booster.best_score
            self.best_iteration = self._Booster.best_iteration
            self.best_ntree_limit = self._Booster.best_ntree_limit

        return self