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Update training.py

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Made changes to training.py to make sure all eval_metric information get passed to evals_result. Previous version lost and mislabeled data in evals_result when using more than one eval_metric.

Structure of eval_metric is now:
eval_metric[evals][eval_metric] = list of metrics

Example:

>>> dtrain = xgb.DMatrix('agaricus.txt.train', silent=True)
>>> dtest = xgb.DMatrix('agaricus.txt.test', silent=True)

>>> param = [('max_depth', 2), ('objective', 'binary:logistic'), ('bst:eta', 0.01), ('eval_metric', 'logloss'), ('eval_metric', 'error')]

>>> watchlist  = [(dtest,'eval'), (dtrain,'train')]
>>> num_round = 3
>>> evals_result = {}
>>> bst = xgb.train(param, dtrain, num_round, watchlist, evals_result=evals_result)

>>> print(evals_result['eval']['logloss'])
>>> print(evals_result)

Prints:

['0.684877', '0.676767', '0.668817']

{'train': {'logloss': ['0.684954', '0.676917', '0.669036'], 'error': ['0.04652', '0.04652', '0.04652']}, 'eval': {'logloss': ['0.684877', '0.676767', '0.668817'], 'error': ['0.042831', '0.042831', '0.042831']}}
This commit is contained in:
quansie 2015-10-11 01:09:05 +02:00
parent e9edb03eff
commit 1ca737ed55
1 changed files with 25 additions and 7 deletions
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32
python-package/xgboost/training.py
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@ -56,7 +56,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
else:
evals_name = [d[1] for d in evals]
evals_result.clear()
evals_result.update({key: [] for key in evals_name})
evals_result.update({key: {} for key in evals_name})
if not early_stopping_rounds:
for i in range(num_boost_round):
@ -71,9 +71,18 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
if verbose_eval:
sys.stderr.write(msg + '\n')
if evals_result is not None:
res = re.findall(":-?([0-9.]+).", msg)
for key, val in zip(evals_name, res):
evals_result[key].append(val)
res = re.findall("([0-9a-zA-Z@]+[-]*):-?([0-9.]+).", msg)
for key in evals_name:
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_key = res_item[0]
res_val = res_item[1]
if res_key in evals_result[key]:
evals_result[key][res_key].append(res_val)
else:
evals_result[key][res_key] = [res_val]
return bst
else:
@ -119,9 +128,18 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
sys.stderr.write(msg + '\n')
if evals_result is not None:
res = re.findall(":-?([0-9.]+).", msg)
for key, val in zip(evals_name, res):
evals_result[key].append(val)
res = re.findall("([0-9a-zA-Z@]+[-]*):-?([0-9.]+).", msg)
for key in evals_name:
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_key = res_item[0]
res_val = res_item[1]
if res_key in evals_result[key]:
evals_result[key][res_key].append(res_val)
else:
evals_result[key][res_key] = [res_val]
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \