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record training progress

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This commit is contained in:
Yizheng Liao 2015-04-23 21:24:24 -07:00
parent 4aa1ea2d44
commit 44d1043031
1 changed files with 36 additions and 4 deletions
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40
wrapper/xgboost.py
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@ -11,6 +11,7 @@ from __future__ import absolute_import
import os
import sys
import re
import ctypes
import collections
@ -530,7 +531,7 @@ class Booster(object):
return fmap
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, early_stopping_rounds=None):
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, early_stopping_rounds=None,evals_result=None):
"""
Train a booster with given parameters.
@ -542,7 +543,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
Data to be trained.
num_boost_round: int
Number of boosting iterations.
watchlist : list of pairs (DMatrix, string)
watchlist (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
@ -557,6 +558,8 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
Returns the model from the last iteration (not the best one).
If early stopping occurs, the model will have two additional fields:
bst.best_score and bst.best_iteration.
evals_result: dict
This dictionary stores the evaluation results of all the items in watchlist
Returns
-------
@ -566,15 +569,39 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
evals = list(evals)
bst = Booster(params, [dtrain] + [d[0] for d in evals])
if evals_result is not None:
if type(evals_result) is not dict:
raise TypeError('evals_result has to be a dictionary')
else:
evals_name = [d[1] for d in evals]
evals_result.clear()
evals_result.update({key:[] for key in evals_name})
if not early_stopping_rounds:
for i in range(num_boost_round):
bst.update(dtrain, i, obj)
if len(evals) != 0:
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, string_types):
sys.stderr.write(bst_eval_set + '\n')
msg = bst_eval_set
#sys.stderr.write(bst_eval_set + '\n')
# if evals_result is not None:
# res = re.findall(":([0-9.]+).",bst_eval_set)
# for key,val in zip(evals_name,res):
# evals_result[key].append(val)
else:
sys.stderr.write(bst_eval_set.decode() + '\n')
msg = bst_eval_set.decode()
# sys.stderr.write(bst_eval_set.decode() + '\n')
# if evals_result is not None:
# res = re.findall(":([0-9.]+).",bst_eval_set.decode())
# for key,val in zip(evals_name,res):
# evals_result[key].append(val)
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)
return bst
else:
@ -617,6 +644,11 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
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)
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):