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[Breaking] Remove learning_rates in Python. (#5155)

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* Remove `learning_rates`.

It's been deprecated since we have callback.

* Set `before_iteration` of `reset_learning_rate` to False to preserve 
  the initial learning rate, and comply to the term "reset".

Closes #4709.

* Tests for various `tree_method`.
This commit is contained in:
Jiaming Yuan
2019-12-24 14:25:48 +08:00
committed by GitHub
parent 73b1bd2789
commit 298ebe68ac
5 changed files with 75 additions and 34 deletions
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8
python-package/xgboost/callback.py
View File

@@ -134,14 +134,16 @@ def reset_learning_rate(learning_rates):
if context == 'train':
bst, i, n = env.model, env.iteration, env.end_iteration
bst.set_param('learning_rate', get_learning_rate(i, n, learning_rates))
bst.set_param(
'learning_rate', get_learning_rate(i, n, learning_rates))
elif context == 'cv':
i, n = env.iteration, env.end_iteration
for cvpack in env.cvfolds:
bst = cvpack.bst
bst.set_param('learning_rate', get_learning_rate(i, n, learning_rates))
bst.set_param(
'learning_rate', get_learning_rate(i, n, learning_rates))
callback.before_iteration = True
callback.before_iteration = False
return callback

25
python-package/xgboost/training.py
View File

@@ -4,7 +4,6 @@
"""Training Library containing training routines."""
from __future__ import absolute_import
import warnings
import numpy as np
from .core import Booster, STRING_TYPES, XGBoostError, CallbackEnv, EarlyStopException
from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold)
@@ -54,9 +53,11 @@ def _train_internal(params, dtrain,
nboost += start_iteration
callbacks_before_iter = [
cb for cb in callbacks if cb.__dict__.get('before_iteration', False)]
cb for cb in callbacks
if cb.__dict__.get('before_iteration', False)]
callbacks_after_iter = [
cb for cb in callbacks if not cb.__dict__.get('before_iteration', False)]
cb for cb in callbacks
if not cb.__dict__.get('before_iteration', False)]
for i in range(start_iteration, num_boost_round):
for cb in callbacks_before_iter:
@@ -113,7 +114,7 @@ def _train_internal(params, dtrain,
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
maximize=False, early_stopping_rounds=None, evals_result=None,
verbose_eval=True, xgb_model=None, callbacks=None, learning_rates=None):
verbose_eval=True, xgb_model=None, callbacks=None):
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
"""Train a booster with given parameters.
@@ -169,11 +170,6 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
/ the boosting stage found by using **early_stopping_rounds** is also printed.
Example: with ``verbose_eval=4`` and at least one item in **evals**, an evaluation metric
is printed every 4 boosting stages, instead of every boosting stage.
learning_rates: list or function (deprecated - use callback API instead)
List of learning rate for each boosting round
or a customized function that calculates eta in terms of
current number of round and the total number of boosting round (e.g. yields
learning rate decay)
xgb_model : file name of stored xgb model or 'Booster' instance
Xgb model to be loaded before training (allows training continuation).
callbacks : list of callback functions
@@ -206,11 +202,6 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
if evals_result is not None:
callbacks.append(callback.record_evaluation(evals_result))
if learning_rates is not None:
warnings.warn("learning_rates parameter is deprecated - use callback API instead",
DeprecationWarning)
callbacks.append(callback.reset_learning_rate(learning_rates))
return _train_internal(params, dtrain,
num_boost_round=num_boost_round,
evals=evals,
@@ -485,9 +476,11 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
callbacks.append(callback.print_evaluation(verbose_eval, show_stdv=show_stdv))
callbacks_before_iter = [
cb for cb in callbacks if cb.__dict__.get('before_iteration', False)]
cb for cb in callbacks if
cb.__dict__.get('before_iteration', False)]
callbacks_after_iter = [
cb for cb in callbacks if not cb.__dict__.get('before_iteration', False)]
cb for cb in callbacks if
not cb.__dict__.get('before_iteration', False)]
for i in range(num_boost_round):
for cb in callbacks_before_iter: