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Merge remote-tracking branch 'upstream/master'

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This commit is contained in:
Paulo Alves
2016-02-29 08:53:48 -03:00
parent 592004b38f 5f70b4df7a
commit b7985466a4
14 changed files with 114 additions and 82 deletions
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111
python-package/xgboost/training.py
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@@ -24,7 +24,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
Data to be trained.
num_boost_round: int
Number of boosting iterations.
watchlist (evals): list of pairs (DMatrix, string)
evals: list of pairs (DMatrix, string)
List of items to be evaluated during training, this allows user to watch
performance on the validation set.
obj : function
@@ -117,48 +117,13 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
evals_result.clear()
evals_result.update(dict([(key, {}) for key in evals_name]))
if not early_stopping_rounds:
for i in range(nboost, nboost + num_boost_round):
bst.update(dtrain, i, obj)
nboost += 1
if len(evals) != 0:
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, STRING_TYPES):
msg = bst_eval_set
else:
msg = bst_eval_set.decode()
if verbose_eval:
if verbose_eval_every_line:
if i % verbose_eval_every_line == 0 or i == num_boost_round - 1:
sys.stderr.write(msg + '\n')
else:
sys.stderr.write(msg + '\n')
if evals_result is not None:
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]
bst.best_iteration = (nboost - 1)
bst.best_ntree_limit = nboost * num_parallel_tree
return bst
else:
# early stopping
# early stopping
if early_stopping_rounds is not None:
if len(evals) < 1:
raise ValueError('For early stopping you need at least one set in evals.')
if verbose_eval:
sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(\
sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(
evals[-1][1], early_stopping_rounds))
# is params a list of tuples? are we using multiple eval metrics?
@@ -166,7 +131,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
if len(params) != len(dict(params).items()):
params = dict(params)
sys.stderr.write("Multiple eval metrics have been passed: " \
"'{0}' will be used for early stopping.\n\n".format(params['eval_metric']))
"'{0}' will be used for early stopping.\n\n".format(params['eval_metric']))
else:
params = dict(params)
@@ -184,20 +149,23 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
else:
best_score = float('inf')
best_msg = ''
best_score_i = (nboost - 1)
best_msg = ''
best_score_i = (nboost - 1)
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'.")
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(nboost, nboost + 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)
nboost += 1
for i in range(nboost, nboost + 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)
nboost += 1
# check evaluation result.
if len(evals) != 0:
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, STRING_TYPES):
@@ -226,22 +194,28 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
else:
evals_result[key][res_key] = [res_val]
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
best_score = score
best_score_i = (nboost - 1)
best_msg = msg
elif i - best_score_i >= early_stopping_rounds:
if verbose_eval:
sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg))
bst.best_score = best_score
bst.best_iteration = best_score_i
break
bst.best_score = best_score
if early_stopping_rounds:
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
best_score = score
best_score_i = (nboost - 1)
best_msg = msg
elif i - best_score_i >= early_stopping_rounds:
if verbose_eval:
sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg))
# best iteration will be assigned in the end.
bst.best_score = best_score
bst.best_iteration = best_score_i
break
if early_stopping_rounds:
best_score = best_score
bst.best_iteration = best_score_i
bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
return bst
else:
bst.best_iteration = nboost - 1
bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
return bst
class CVPack(object):
@@ -299,7 +273,7 @@ def aggcv(rlist, show_stdv=True, verbose_eval=None, as_pandas=True, trial=0):
# pylint: disable=invalid-name
"""
Aggregate cross-validation results.
If verbose_eval is true, progress is displayed in every call. If
verbose_eval is an integer, progress will only be displayed every
`verbose_eval` trees, tracked via trial.
@@ -486,4 +460,3 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
results = np.array(results)
return results