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Merge pull request #563 from Far0n/eta_decay

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learning_rates per boosting round
This commit is contained in:
Yuan (Terry) Tang 2015-10-30 10:21:33 -05:00
commit c16a6222f3
1 changed files with 13 additions and 1 deletions
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14
python-package/xgboost/training.py
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@ -10,7 +10,7 @@ import numpy as np
from .core import Booster, STRING_TYPES from .core import Booster, STRING_TYPES
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
early_stopping_rounds=None, evals_result=None, verbose_eval=True): early_stopping_rounds=None, evals_result=None, verbose_eval=True, learning_rates=None):
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init # pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
"""Train a booster with given parameters. """Train a booster with given parameters.
@ -46,6 +46,10 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
verbose_eval : bool verbose_eval : bool
If `verbose_eval` then the evaluation metric on the validation set, if If `verbose_eval` then the evaluation metric on the validation set, if
given, is printed at each boosting stage. given, is printed at each boosting stage.
learning_rates: list or function
Learning rate for each boosting round (yields learning rate decay).
- list l: eta = l[boosting round]
- function f: eta = f(boosting round, num_boost_round)
Returns Returns
------- -------
@ -119,7 +123,15 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
best_msg = '' best_msg = ''
best_score_i = 0 best_score_i = 0
if isinstance(learning_rates, list) and len(learning_rates) < num_boost_round:
raise ValueError("Length of list 'learning_rates' has to equal 'num_boost_round'.")
for i in range(num_boost_round): for i in range(num_boost_round):
if learning_rates is not None:
if isinstance(learning_rates, list):
bst.set_param({'eta': learning_rates[i]})
else:
bst.set_param({'eta': learning_rates(i, num_boost_round)})
bst.update(dtrain, i, obj) bst.update(dtrain, i, obj)
bst_eval_set = bst.eval_set(evals, i, feval) bst_eval_set = bst.eval_set(evals, i, feval)