Build a gradient boosting model from the training set (X, y).

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]
            Input feature matrix.
        y : array-like of shape = [n_samples]
            The target values (class labels in classification, real numbers in regression).
        sample_weight : array-like of shape = [n_samples] or None, optional (default=None)
            Weights of training data.
        init_score : array-like of shape = [n_samples] or None, optional (default=None)
            Init score of training data.
        group : array-like or None, optional (default=None)
            Group data of training data.
        eval_set : list or None, optional (default=None)
            A list of (X, y) tuple pairs to use as validation sets.
        eval_names : list of strings or None, optional (default=None)
            Names of eval_set.
        eval_sample_weight : list of arrays or None, optional (default=None)
            Weights of eval data.
        eval_class_weight : list or None, optional (default=None)
            Class weights of eval data.
        eval_init_score : list of arrays or None, optional (default=None)
            Init score of eval data.
        eval_group : list of arrays or None, optional (default=None)
            Group data of eval data.
        eval_metric : string, list of strings, callable or None, optional (default=None)
            If string, it should be a built-in evaluation metric to use.
            If callable, it should be a custom evaluation metric, see note below for more details.
            In either case, the ``metric`` from the model parameters will be evaluated and used as well.
            Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker.
        early_stopping_rounds : int or None, optional (default=None)
            Activates early stopping. The model will train until the validation score stops improving.
            Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
            to continue training.
            Requires at least one validation data and one metric.
            If there's more than one, will check all of them. But the training data is ignored anyway.
            To check only the first metric you can pass in ``callbacks``
            ``early_stopping`` callback with ``first_metric_only=True``.
        verbose : bool or int, optional (default=True)
            Requires at least one evaluation data.
            If True, the eval metric on the eval set is printed at each boosting stage.
            If int, the eval metric on the eval set is printed at every ``verbose`` boosting stage.
            The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.

            Example
            -------
            With ``verbose`` = 4 and at least one item in ``eval_set``,
            an evaluation metric is printed every 4 (instead of 1) boosting stages.

        feature_name : list of strings or 'auto', optional (default='auto')
            Feature names.
            If 'auto' and data is pandas DataFrame, data columns names are used.
        categorical_feature : list of strings or int, or 'auto', optional (default='auto')
            Categorical features.
            If list of int, interpreted as indices.
            If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
            If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
            All values in categorical features should be less than int32 max value (2147483647).
            Large values could be memory consuming. Consider using consecutive integers starting from zero.
            All negative values in categorical features will be treated as missing values.
        callbacks : list of callback functions or None, optional (default=None)
            List of callback functions that are applied at each iteration.
            See Callbacks in Python API for more information.

        Returns
        -------
        self : object
            Returns self.

        Note
        ----
        Custom eval function expects a callable with following signatures:
        ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or
        ``func(y_true, y_pred, weight, group)``
        and returns (eval_name, eval_result, is_bigger_better) or
        list of (eval_name, eval_result, is_bigger_better):

            y_true : array-like of shape = [n_samples]
                The target values.
            y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
                The predicted values.
            weight : array-like of shape = [n_samples]
                The weight of samples.
            group : array-like
                Group/query data, used for ranking task.
            eval_name : string
                The name of evaluation.
            eval_result : float
                The eval result.
            is_bigger_better : bool
                Is eval result bigger better, e.g. AUC is bigger_better.

        For multi-class task, the y_pred is group by class_id first, then group by row_id.
        If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].

def fit(self, X, y,
            sample_weight=None, init_score=None, group=None,
            eval_set=None, eval_names=None, eval_sample_weight=None,
            eval_class_weight=None, eval_init_score=None, eval_group=None,
            eval_metric=None, early_stopping_rounds=None, verbose=True,
            feature_name='auto', categorical_feature='auto', callbacks=None):
        """Build a gradient boosting model from the training set (X, y).

        Parameters
        ----------
        X : array-like or sparse matrix of shape = [n_samples, n_features]
            Input feature matrix.
        y : array-like of shape = [n_samples]
            The target values (class labels in classification, real numbers in regression).
        sample_weight : array-like of shape = [n_samples] or None, optional (default=None)
            Weights of training data.
        init_score : array-like of shape = [n_samples] or None, optional (default=None)
            Init score of training data.
        group : array-like or None, optional (default=None)
            Group data of training data.
        eval_set : list or None, optional (default=None)
            A list of (X, y) tuple pairs to use as validation sets.
        eval_names : list of strings or None, optional (default=None)
            Names of eval_set.
        eval_sample_weight : list of arrays or None, optional (default=None)
            Weights of eval data.
        eval_class_weight : list or None, optional (default=None)
            Class weights of eval data.
        eval_init_score : list of arrays or None, optional (default=None)
            Init score of eval data.
        eval_group : list of arrays or None, optional (default=None)
            Group data of eval data.
        eval_metric : string, list of strings, callable or None, optional (default=None)
            If string, it should be a built-in evaluation metric to use.
            If callable, it should be a custom evaluation metric, see note below for more details.
            In either case, the ``metric`` from the model parameters will be evaluated and used as well.
            Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker.
        early_stopping_rounds : int or None, optional (default=None)
            Activates early stopping. The model will train until the validation score stops improving.
            Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
            to continue training.
            Requires at least one validation data and one metric.
            If there's more than one, will check all of them. But the training data is ignored anyway.
            To check only the first metric you can pass in ``callbacks``
            ``early_stopping`` callback with ``first_metric_only=True``.
        verbose : bool or int, optional (default=True)
            Requires at least one evaluation data.
            If True, the eval metric on the eval set is printed at each boosting stage.
            If int, the eval metric on the eval set is printed at every ``verbose`` boosting stage.
            The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.

            Example
            -------
            With ``verbose`` = 4 and at least one item in ``eval_set``,
            an evaluation metric is printed every 4 (instead of 1) boosting stages.

        feature_name : list of strings or 'auto', optional (default='auto')
            Feature names.
            If 'auto' and data is pandas DataFrame, data columns names are used.
        categorical_feature : list of strings or int, or 'auto', optional (default='auto')
            Categorical features.
            If list of int, interpreted as indices.
            If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
            If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
            All values in categorical features should be less than int32 max value (2147483647).
            Large values could be memory consuming. Consider using consecutive integers starting from zero.
            All negative values in categorical features will be treated as missing values.
        callbacks : list of callback functions or None, optional (default=None)
            List of callback functions that are applied at each iteration.
            See Callbacks in Python API for more information.

        Returns
        -------
        self : object
            Returns self.

        Note
        ----
        Custom eval function expects a callable with following signatures:
        ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or
        ``func(y_true, y_pred, weight, group)``
        and returns (eval_name, eval_result, is_bigger_better) or
        list of (eval_name, eval_result, is_bigger_better):

            y_true : array-like of shape = [n_samples]
                The target values.
            y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
                The predicted values.
            weight : array-like of shape = [n_samples]
                The weight of samples.
            group : array-like
                Group/query data, used for ranking task.
            eval_name : string
                The name of evaluation.
            eval_result : float
                The eval result.
            is_bigger_better : bool
                Is eval result bigger better, e.g. AUC is bigger_better.

        For multi-class task, the y_pred is group by class_id first, then group by row_id.
        If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].
        """
        if self._objective is None:
            if isinstance(self, LGBMRegressor):
                self._objective = "regression"
            elif isinstance(self, LGBMClassifier):
                self._objective = "binary"
            elif isinstance(self, LGBMRanker):
                self._objective = "lambdarank"
            else:
                raise ValueError("Unknown LGBMModel type.")
        if callable(self._objective):
            self._fobj = _objective_function_wrapper(self._objective)
        else:
            self._fobj = None
        evals_result = {}
        params = self.get_params()
        # user can set verbose with kwargs, it has higher priority
        if not any(verbose_alias in params for verbose_alias in ('verbose', 'verbosity')) and self.silent:
            params['verbose'] = -1
        params.pop('silent', None)
        params.pop('importance_type', None)
        params.pop('n_estimators', None)
        params.pop('class_weight', None)
        if self._n_classes is not None and self._n_classes > 2:
            params['num_class'] = self._n_classes
        if hasattr(self, '_eval_at'):
            params['eval_at'] = self._eval_at
        params['objective'] = self._objective
        if self._fobj:
            params['objective'] = 'None'  # objective = nullptr for unknown objective

        if callable(eval_metric):
            feval = _eval_function_wrapper(eval_metric)
        else:
            feval = None
            # register default metric for consistency with callable eval_metric case
            original_metric = self._objective if isinstance(self._objective, string_type) else None
            if original_metric is None:
                # try to deduce from class instance
                if isinstance(self, LGBMRegressor):
                    original_metric = "l2"
                elif isinstance(self, LGBMClassifier):
                    original_metric = "multi_logloss" if self._n_classes > 2 else "binary_logloss"
                elif isinstance(self, LGBMRanker):
                    original_metric = "ndcg"
            # overwrite default metric by explicitly set metric
            for metric_alias in ['metric', 'metrics', 'metric_types']:
                if metric_alias in params:
                    original_metric = params.pop(metric_alias)
            # concatenate metric from params (or default if not provided in params) and eval_metric
            original_metric = [original_metric] if isinstance(original_metric, (string_type, type(None))) else original_metric
            eval_metric = [eval_metric] if isinstance(eval_metric, (string_type, type(None))) else eval_metric
            params['metric'] = set(original_metric + eval_metric)

        if not isinstance(X, (DataFrame, DataTable)):
            _X, _y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2)
            _LGBMCheckConsistentLength(_X, _y, sample_weight)
        else:
            _X, _y = X, y

        if self.class_weight is not None:
            class_sample_weight = _LGBMComputeSampleWeight(self.class_weight, y)
            if sample_weight is None or len(sample_weight) == 0:
                sample_weight = class_sample_weight
            else:
                sample_weight = np.multiply(sample_weight, class_sample_weight)

        self._n_features = _X.shape[1]

        def _construct_dataset(X, y, sample_weight, init_score, group, params):
            ret = Dataset(X, label=y, weight=sample_weight, group=group, params=params)
            return ret.set_init_score(init_score)

        train_set = _construct_dataset(_X, _y, sample_weight, init_score, group, params)

        valid_sets = []
        if eval_set is not None:

            def _get_meta_data(collection, i):
                if collection is None:
                    return None
                elif isinstance(collection, list):
                    return collection[i] if len(collection) > i else None
                elif isinstance(collection, dict):
                    return collection.get(i, None)
                else:
                    raise TypeError('eval_sample_weight, eval_class_weight, eval_init_score, and eval_group '
                                    'should be dict or list')

            if isinstance(eval_set, tuple):
                eval_set = [eval_set]
            for i, valid_data in enumerate(eval_set):
                # reduce cost for prediction training data
                if valid_data[0] is X and valid_data[1] is y:
                    valid_set = train_set
                else:
                    valid_weight = _get_meta_data(eval_sample_weight, i)
                    if _get_meta_data(eval_class_weight, i) is not None:
                        valid_class_sample_weight = _LGBMComputeSampleWeight(_get_meta_data(eval_class_weight, i),
                                                                             valid_data[1])
                        if valid_weight is None or len(valid_weight) == 0:
                            valid_weight = valid_class_sample_weight
                        else:
                            valid_weight = np.multiply(valid_weight, valid_class_sample_weight)
                    valid_init_score = _get_meta_data(eval_init_score, i)
                    valid_group = _get_meta_data(eval_group, i)
                    valid_set = _construct_dataset(valid_data[0], valid_data[1],
                                                   valid_weight, valid_init_score, valid_group, params)
                valid_sets.append(valid_set)

        self._Booster = train(params, train_set,
                              self.n_estimators, valid_sets=valid_sets, valid_names=eval_names,
                              early_stopping_rounds=early_stopping_rounds,
                              evals_result=evals_result, fobj=self._fobj, feval=feval,
                              verbose_eval=verbose, feature_name=feature_name,
                              categorical_feature=categorical_feature,
                              callbacks=callbacks)

        if evals_result:
            self._evals_result = evals_result

        if early_stopping_rounds is not None:
            self._best_iteration = self._Booster.best_iteration

        self._best_score = self._Booster.best_score

        # free dataset
        self.booster_.free_dataset()
        del train_set, valid_sets
        return self