callback.py

# coding: utf-8
# pylint: disable = invalid-name, W0105, C0301
"""Callbacks library."""
from __future__ import absolute_import

import collections
import warnings
from operator import gt, lt

from .compat import range_


class EarlyStopException(Exception):
    """Exception of early stopping."""

    def __init__(self, best_iteration, best_score):
        """Create early stopping exception.

        Parameters
        ----------
        best_iteration : int
            The best iteration stopped.
        best_score : float
            The score of the best iteration.
        """
        super(EarlyStopException, self).__init__()
        self.best_iteration = best_iteration
        self.best_score = best_score


# Callback environment used by callbacks
CallbackEnv = collections.namedtuple(
    "LightGBMCallbackEnv",
    ["model",
     "params",
     "iteration",
     "begin_iteration",
     "end_iteration",
     "evaluation_result_list"])


def _format_eval_result(value, show_stdv=True):
    """Format metric string."""
    if len(value) == 4:
        return '%s\'s %s: %g' % (value[0], value[1], value[2])
    elif len(value) == 5:
        if show_stdv:
            return '%s\'s %s: %g + %g' % (value[0], value[1], value[2], value[4])
        else:
            return '%s\'s %s: %g' % (value[0], value[1], value[2])
    else:
        raise ValueError("Wrong metric value")


def print_evaluation(period=1, show_stdv=True):
    """Create a callback that prints the evaluation results.

    Parameters
    ----------
    period : int, optional (default=1)
        The period to print the evaluation results.
    show_stdv : bool, optional (default=True)
        Whether to show stdv (if provided).

    Returns
    -------
    callback : function
        The callback that prints the evaluation results every ``period`` iteration(s).
    """
    def _callback(env):
        if period > 0 and env.evaluation_result_list and (env.iteration + 1) % period == 0:
            result = '\t'.join([_format_eval_result(x, show_stdv) for x in env.evaluation_result_list])
            print('[%d]\t%s' % (env.iteration + 1, result))
    _callback.order = 10
    return _callback


def record_evaluation(eval_result):
    """Create a callback that records the evaluation history into ``eval_result``.

    Parameters
    ----------
    eval_result : dict
       A dictionary to store the evaluation results.

    Returns
    -------
    callback : function
        The callback that records the evaluation history into the passed dictionary.
    """
    if not isinstance(eval_result, dict):
        raise TypeError('eval_result should be a dictionary')
    eval_result.clear()

    def _init(env):
        for data_name, eval_name, _, _ in env.evaluation_result_list:
            eval_result.setdefault(data_name, collections.OrderedDict())
            eval_result[data_name].setdefault(eval_name, [])

    def _callback(env):
        if not eval_result:
            _init(env)
        for data_name, eval_name, result, _ in env.evaluation_result_list:
            eval_result[data_name][eval_name].append(result)
    _callback.order = 20
    return _callback


def reset_parameter(**kwargs):
    """Create a callback that resets the parameter after the first iteration.

    Note
    ----
    The initial parameter will still take in-effect on first iteration.

    Parameters
    ----------
    **kwargs : value should be list or function
        List of parameters for each boosting round
        or a customized function that calculates the parameter in terms of
        current number of round (e.g. yields learning rate decay).
        If list lst, parameter = lst[current_round].
        If function func, parameter = func(current_round).

    Returns
    -------
    callback : function
        The callback that resets the parameter after the first iteration.
    """
    def _callback(env):
        new_parameters = {}
        for key, value in kwargs.items():
            if key in ['num_class', 'num_classes',
                       'boosting', 'boost', 'boosting_type',
                       'metric', 'metrics', 'metric_types']:
                raise RuntimeError("Cannot reset {} during training".format(repr(key)))
            if isinstance(value, list):
                if len(value) != env.end_iteration - env.begin_iteration:
                    raise ValueError("Length of list {} has to equal to 'num_boost_round'."
                                     .format(repr(key)))
                new_param = value[env.iteration - env.begin_iteration]
            else:
                new_param = value(env.iteration - env.begin_iteration)
            if new_param != env.params.get(key, None):
                new_parameters[key] = new_param
        if new_parameters:
            env.model.reset_parameter(new_parameters)
            env.params.update(new_parameters)
    _callback.before_iteration = True
    _callback.order = 10
    return _callback


def early_stopping(stopping_rounds, first_metric_only=False, verbose=True):
    """Create a callback that activates early stopping.

    Note
    ----
    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 set ``first_metric_only`` to True.

    Parameters
    ----------
    stopping_rounds : int
       The possible number of rounds without the trend occurrence.
    first_metric_only : bool, optional (default=False)
       Whether to use only the first metric for early stopping.
    verbose : bool, optional (default=True)
        Whether to print message with early stopping information.

    Returns
    -------
    callback : function
        The callback that activates early stopping.
    """
    best_score = []
    best_iter = []
    best_score_list = []
    cmp_op = []
    enabled = [True]
    first_metric = ['']

    def _init(env):
        enabled[0] = not any((boost_alias in env.params
                              and env.params[boost_alias] == 'dart') for boost_alias in ('boosting',
                                                                                         'boosting_type',
                                                                                         'boost'))
        if not enabled[0]:
            warnings.warn('Early stopping is not available in dart mode')
            return
        if not env.evaluation_result_list:
            raise ValueError('For early stopping, '
                             'at least one dataset and eval metric is required for evaluation')

        if verbose:
            msg = "Training until validation scores don't improve for {} rounds"
            print(msg.format(stopping_rounds))

        # split is needed for "<dataset type> <metric>" case (e.g. "train l1")
        first_metric[0] = env.evaluation_result_list[0][1].split(" ")[-1]
        for eval_ret in env.evaluation_result_list:
            best_iter.append(0)
            best_score_list.append(None)
            if eval_ret[3]:
                best_score.append(float('-inf'))
                cmp_op.append(gt)
            else:
                best_score.append(float('inf'))
                cmp_op.append(lt)

    def _final_iteration_check(env, eval_name_splitted, i):
        if env.iteration == env.end_iteration - 1:
            if verbose:
                print('Did not meet early stopping. Best iteration is:\n[%d]\t%s' % (
                    best_iter[i] + 1, '\t'.join([_format_eval_result(x) for x in best_score_list[i]])))
                if first_metric_only:
                    print("Evaluated only: {}".format(eval_name_splitted[-1]))
            raise EarlyStopException(best_iter[i], best_score_list[i])

    def _callback(env):
        if not cmp_op:
            _init(env)
        if not enabled[0]:
            return
        for i in range_(len(env.evaluation_result_list)):
            score = env.evaluation_result_list[i][2]
            if best_score_list[i] is None or cmp_op[i](score, best_score[i]):
                best_score[i] = score
                best_iter[i] = env.iteration
                best_score_list[i] = env.evaluation_result_list
            # split is needed for "<dataset type> <metric>" case (e.g. "train l1")
            eval_name_splitted = env.evaluation_result_list[i][1].split(" ")
            if first_metric_only and first_metric[0] != eval_name_splitted[-1]:
                continue  # use only the first metric for early stopping
            if ((env.evaluation_result_list[i][0] == "cv_agg" and eval_name_splitted[0] == "train"
                 or env.evaluation_result_list[i][0] == env.model._train_data_name)):
                _final_iteration_check(env, eval_name_splitted, i)
                continue  # train data for lgb.cv or sklearn wrapper (underlying lgb.train)
            elif env.iteration - best_iter[i] >= stopping_rounds:
                if verbose:
                    print('Early stopping, best iteration is:\n[%d]\t%s' % (
                        best_iter[i] + 1, '\t'.join([_format_eval_result(x) for x in best_score_list[i]])))
                    if first_metric_only:
                        print("Evaluated only: {}".format(eval_name_splitted[-1]))
                raise EarlyStopException(best_iter[i], best_score_list[i])
            _final_iteration_check(env, eval_name_splitted, i)
    _callback.order = 30
    return _callback