Perform the training with given parameters.

    Parameters
    ----------
    params : dict
        Parameters for training.
    train_set : Dataset
        Data to be trained on.
    num_boost_round : int, optional (default=100)
        Number of boosting iterations.
    valid_sets : list of Datasets or None, optional (default=None)
        List of data to be evaluated on during training.
    valid_names : list of strings or None, optional (default=None)
        Names of ``valid_sets``.
    fobj : callable or None, optional (default=None)
        Customized objective function.
    feval : callable or None, optional (default=None)
        Customized evaluation function.
        Should accept two parameters: preds, train_data,
        and return (eval_name, eval_result, is_higher_better) or list of such tuples.
        For multi-class task, the preds is group by class_id first, then group by row_id.
        If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
        To ignore the default metric corresponding to the used objective,
        set the ``metric`` parameter to the string ``"None"`` in ``params``.
    init_model : string, Booster or None, optional (default=None)
        Filename of LightGBM model or Booster instance used for continue training.
    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.
    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``.
        The index of iteration that has the best performance will be saved in the ``best_iteration`` field
        if early stopping logic is enabled by setting ``early_stopping_rounds``.
    evals_result: dict or None, optional (default=None)
        This dictionary used to store all evaluation results of all the items in ``valid_sets``.

        Example
        -------
        With a ``valid_sets`` = [valid_set, train_set],
        ``valid_names`` = ['eval', 'train']
        and a ``params`` = {'metric': 'logloss'}
        returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
        'eval': {'logloss': ['0.480385', '0.357756', ...]}}.

    verbose_eval : bool or int, optional (default=True)
        Requires at least one validation data.
        If True, the eval metric on the valid set is printed at each boosting stage.
        If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
        The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.

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

    learning_rates : list, callable or None, optional (default=None)
        List of learning rates for each boosting round
        or a customized function that calculates ``learning_rate``
        in terms of current number of round (e.g. yields learning rate decay).
    keep_training_booster : bool, optional (default=False)
        Whether the returned Booster will be used to keep training.
        If False, the returned value will be converted into _InnerPredictor before returning.
        You can still use _InnerPredictor as ``init_model`` for future continue training.
    callbacks : list of callables or None, optional (default=None)
        List of callback functions that are applied at each iteration.
        See Callbacks in Python API for more information.

    Returns
    -------
    booster : Booster
        The trained Booster model.

def train(params, train_set, num_boost_round=100,
          valid_sets=None, valid_names=None,
          fobj=None, feval=None, init_model=None,
          feature_name='auto', categorical_feature='auto',
          early_stopping_rounds=None, evals_result=None,
          verbose_eval=True, learning_rates=None,
          keep_training_booster=False, callbacks=None):
    """Perform the training with given parameters.

    Parameters
    ----------
    params : dict
        Parameters for training.
    train_set : Dataset
        Data to be trained on.
    num_boost_round : int, optional (default=100)
        Number of boosting iterations.
    valid_sets : list of Datasets or None, optional (default=None)
        List of data to be evaluated on during training.
    valid_names : list of strings or None, optional (default=None)
        Names of ``valid_sets``.
    fobj : callable or None, optional (default=None)
        Customized objective function.
    feval : callable or None, optional (default=None)
        Customized evaluation function.
        Should accept two parameters: preds, train_data,
        and return (eval_name, eval_result, is_higher_better) or list of such tuples.
        For multi-class task, the preds is group by class_id first, then group by row_id.
        If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
        To ignore the default metric corresponding to the used objective,
        set the ``metric`` parameter to the string ``"None"`` in ``params``.
    init_model : string, Booster or None, optional (default=None)
        Filename of LightGBM model or Booster instance used for continue training.
    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.
    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``.
        The index of iteration that has the best performance will be saved in the ``best_iteration`` field
        if early stopping logic is enabled by setting ``early_stopping_rounds``.
    evals_result: dict or None, optional (default=None)
        This dictionary used to store all evaluation results of all the items in ``valid_sets``.

        Example
        -------
        With a ``valid_sets`` = [valid_set, train_set],
        ``valid_names`` = ['eval', 'train']
        and a ``params`` = {'metric': 'logloss'}
        returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
        'eval': {'logloss': ['0.480385', '0.357756', ...]}}.

    verbose_eval : bool or int, optional (default=True)
        Requires at least one validation data.
        If True, the eval metric on the valid set is printed at each boosting stage.
        If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
        The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.

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

    learning_rates : list, callable or None, optional (default=None)
        List of learning rates for each boosting round
        or a customized function that calculates ``learning_rate``
        in terms of current number of round (e.g. yields learning rate decay).
    keep_training_booster : bool, optional (default=False)
        Whether the returned Booster will be used to keep training.
        If False, the returned value will be converted into _InnerPredictor before returning.
        You can still use _InnerPredictor as ``init_model`` for future continue training.
    callbacks : list of callables or None, optional (default=None)
        List of callback functions that are applied at each iteration.
        See Callbacks in Python API for more information.

    Returns
    -------
    booster : Booster
        The trained Booster model.
    """
    # create predictor first
    params = copy.deepcopy(params)
    if fobj is not None:
        params['objective'] = 'none'
    for alias in ["num_iterations", "num_iteration", "n_iter", "num_tree", "num_trees",
                  "num_round", "num_rounds", "num_boost_round", "n_estimators"]:
        if alias in params:
            num_boost_round = int(params.pop(alias))
            warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
            break
    for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping"]:
        if alias in params and params[alias] is not None:
            early_stopping_rounds = int(params.pop(alias))
            warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
            break

    if num_boost_round <= 0:
        raise ValueError("num_boost_round should be greater than zero.")
    if isinstance(init_model, string_type):
        predictor = _InnerPredictor(model_file=init_model, pred_parameter=params)
    elif isinstance(init_model, Booster):
        predictor = init_model._to_predictor(dict(init_model.params, **params))
    else:
        predictor = None
    init_iteration = predictor.num_total_iteration if predictor is not None else 0
    # check dataset
    if not isinstance(train_set, Dataset):
        raise TypeError("Training only accepts Dataset object")

    train_set._update_params(params) \
             ._set_predictor(predictor) \
             .set_feature_name(feature_name) \
             .set_categorical_feature(categorical_feature)

    is_valid_contain_train = False
    train_data_name = "training"
    reduced_valid_sets = []
    name_valid_sets = []
    if valid_sets is not None:
        if isinstance(valid_sets, Dataset):
            valid_sets = [valid_sets]
        if isinstance(valid_names, string_type):
            valid_names = [valid_names]
        for i, valid_data in enumerate(valid_sets):
            # reduce cost for prediction training data
            if valid_data is train_set:
                is_valid_contain_train = True
                if valid_names is not None:
                    train_data_name = valid_names[i]
                continue
            if not isinstance(valid_data, Dataset):
                raise TypeError("Traninig only accepts Dataset object")
            reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set))
            if valid_names is not None and len(valid_names) > i:
                name_valid_sets.append(valid_names[i])
            else:
                name_valid_sets.append('valid_' + str(i))
    # process callbacks
    if callbacks is None:
        callbacks = set()
    else:
        for i, cb in enumerate(callbacks):
            cb.__dict__.setdefault('order', i - len(callbacks))
        callbacks = set(callbacks)

    # Most of legacy advanced options becomes callbacks
    if verbose_eval is True:
        callbacks.add(callback.print_evaluation())
    elif isinstance(verbose_eval, integer_types):
        callbacks.add(callback.print_evaluation(verbose_eval))

    if early_stopping_rounds is not None:
        callbacks.add(callback.early_stopping(early_stopping_rounds, verbose=bool(verbose_eval)))

    if learning_rates is not None:
        callbacks.add(callback.reset_parameter(learning_rate=learning_rates))

    if evals_result is not None:
        callbacks.add(callback.record_evaluation(evals_result))

    callbacks_before_iter = {cb for cb in callbacks if getattr(cb, 'before_iteration', False)}
    callbacks_after_iter = callbacks - callbacks_before_iter
    callbacks_before_iter = sorted(callbacks_before_iter, key=attrgetter('order'))
    callbacks_after_iter = sorted(callbacks_after_iter, key=attrgetter('order'))

    # construct booster
    try:
        booster = Booster(params=params, train_set=train_set)
        if is_valid_contain_train:
            booster.set_train_data_name(train_data_name)
        for valid_set, name_valid_set in zip_(reduced_valid_sets, name_valid_sets):
            booster.add_valid(valid_set, name_valid_set)
    finally:
        train_set._reverse_update_params()
        for valid_set in reduced_valid_sets:
            valid_set._reverse_update_params()
    booster.best_iteration = 0

    # start training
    for i in range_(init_iteration, init_iteration + num_boost_round):
        for cb in callbacks_before_iter:
            cb(callback.CallbackEnv(model=booster,
                                    params=params,
                                    iteration=i,
                                    begin_iteration=init_iteration,
                                    end_iteration=init_iteration + num_boost_round,
                                    evaluation_result_list=None))

        booster.update(fobj=fobj)

        evaluation_result_list = []
        # check evaluation result.
        if valid_sets is not None:
            if is_valid_contain_train:
                evaluation_result_list.extend(booster.eval_train(feval))
            evaluation_result_list.extend(booster.eval_valid(feval))
        try:
            for cb in callbacks_after_iter:
                cb(callback.CallbackEnv(model=booster,
                                        params=params,
                                        iteration=i,
                                        begin_iteration=init_iteration,
                                        end_iteration=init_iteration + num_boost_round,
                                        evaluation_result_list=evaluation_result_list))
        except callback.EarlyStopException as earlyStopException:
            booster.best_iteration = earlyStopException.best_iteration + 1
            evaluation_result_list = earlyStopException.best_score
            break
    booster.best_score = collections.defaultdict(dict)
    for dataset_name, eval_name, score, _ in evaluation_result_list:
        booster.best_score[dataset_name][eval_name] = score
    if not keep_training_booster:
        booster.model_from_string(booster.model_to_string(), False).free_dataset()
    return booster