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sinhrks
2016-04-24 16:34:46 +09:00
parent b3c9e6a0db
commit 8fc2456c87
19 changed files with 282 additions and 199 deletions
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43
python-package/xgboost/training.py
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@@ -6,12 +6,12 @@ from __future__ import absolute_import
import sys
import re
import os
import numpy as np
from .core import Booster, STRING_TYPES
from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold, XGBKFold)
from .core import Booster, STRING_TYPES, XGBoostError
from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold)
from . import rabit
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, learning_rates=None, xgb_model=None):
@@ -97,7 +97,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
verbose_eval = True if verbose_eval_every_line > 0 else False
if rabit.get_rank() != 0:
verbose_eval = False;
verbose_eval = False
if xgb_model is not None:
if not isinstance(xgb_model, STRING_TYPES):
@@ -135,8 +135,9 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
if isinstance(params, list):
if len(params) != len(dict(params).items()):
params = dict(params)
rabit.tracker_print("Multiple eval metrics have been passed: " \
"'{0}' will be used for early stopping.\n\n".format(params['eval_metric']))
msg = ("Multiple eval metrics have been passed: "
"'{0}' will be used for early stopping.\n\n")
rabit.tracker_print(msg.format(params['eval_metric']))
else:
params = dict(params)
@@ -173,7 +174,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
# Distributed code: need to resume to this point.
# Skip the first update if it is a recovery step.
if version % 2 == 0:
if version % 2 == 0:
bst.update(dtrain, i, obj)
bst.save_rabit_checkpoint()
version += 1
@@ -203,7 +204,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
evals_idx = evals_name.index(key)
res_per_eval = len(res) // len(evals_name)
for r in range(res_per_eval):
res_item = res[(evals_idx*res_per_eval) + r]
res_item = res[(evals_idx * res_per_eval) + r]
res_key = res_item[0]
res_val = res_item[1]
if res_key in evals_result[key]:
@@ -224,7 +225,8 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
elif i - best_iteration >= early_stopping_rounds:
best_msg = bst.attr('best_msg')
if verbose_eval:
rabit.tracker_print("Stopping. Best iteration:\n{}\n\n".format(best_msg))
msg = "Stopping. Best iteration:\n{}\n\n"
rabit.tracker_print(msg.format(best_msg))
break
# do checkpoint after evaluation, in case evaluation also updates booster.
bst.save_rabit_checkpoint()
@@ -290,6 +292,7 @@ def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
ret.append(CVPack(dtrain, dtest, plst))
return ret
def aggcv(rlist, show_stdv=True, verbose_eval=None, as_pandas=True, trial=0):
# pylint: disable=invalid-name
"""
@@ -405,8 +408,8 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
-------
evaluation history : list(string)
"""
if stratified == True and not SKLEARN_INSTALLED:
raise XGBoostError('sklearn needs to be installed in order to use stratified cv')
if stratified is True and not SKLEARN_INSTALLED:
raise XGBoostError('sklearn needs to be installed in order to use stratified cv')
if isinstance(metrics, str):
metrics = [metrics]
@@ -417,7 +420,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
if 'eval_metric' in params:
params['eval_metric'] = _metrics
else:
params= dict((k, v) for k, v in params.items())
params = dict((k, v) for k, v in params.items())
if len(metrics) == 0 and 'eval_metric' in params:
if isinstance(params['eval_metric'], list):
@@ -428,12 +431,14 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
params.pop("eval_metric", None)
if early_stopping_rounds is not None:
if len(metrics) > 1:
raise ValueError('Check your params. '\
'Early stopping works with single eval metric only.')
msg = ('Check your params. '
'Early stopping works with single eval metric only.')
raise ValueError(msg)
if verbose_eval:
sys.stderr.write("Will train until cv error hasn't decreased in {} rounds.\n".format(\
early_stopping_rounds))
msg = "Will train until cv error hasn't decreased in {} rounds.\n"
sys.stderr.write(msg.format(early_stopping_rounds))
maximize_score = False
if len(metrics) == 1:
@@ -466,10 +471,10 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
best_score = score
best_score_i = i
elif i - best_score_i >= early_stopping_rounds:
results = results[:best_score_i+1]
results = results[:best_score_i + 1]
if verbose_eval:
sys.stderr.write("Stopping. Best iteration:\n[{}] cv-mean:{}\tcv-std:{}\n".
format(best_score_i, results[-1][0], results[-1][1]))
msg = "Stopping. Best iteration:\n[{}] cv-mean:{}\tcv-std:{}\n"
sys.stderr.write(msg.format(best_score_i, results[-1][0], results[-1][1]))
break
if as_pandas:
try: