ce5930c365
- best_ntree_limit as new booster atrribute added - usage of bst.best_ntree_limit in python doc added - fixed wrong 'best_iteration' after training continuation
69 lines
2.8 KiB
Python
69 lines
2.8 KiB
Python
import xgboost as xgb
|
|
import numpy as np
|
|
from sklearn.cross_validation import KFold, train_test_split
|
|
from sklearn.metrics import mean_squared_error
|
|
from sklearn.grid_search import GridSearchCV
|
|
from sklearn.datasets import load_iris, load_digits, load_boston
|
|
import unittest
|
|
|
|
rng = np.random.RandomState(1337)
|
|
|
|
|
|
class TestTrainingContinuation(unittest.TestCase):
|
|
num_parallel_tree = 3
|
|
|
|
xgb_params_01 = {
|
|
'silent': 1,
|
|
'nthread': 1,
|
|
}
|
|
|
|
xgb_params_02 = {
|
|
'silent': 1,
|
|
'nthread': 1,
|
|
'num_parallel_tree': num_parallel_tree
|
|
}
|
|
|
|
def test_training_continuation(self):
|
|
digits = load_digits(2)
|
|
X = digits['data']
|
|
y = digits['target']
|
|
|
|
dtrain = xgb.DMatrix(X, label=y)
|
|
|
|
gbdt_01 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10)
|
|
ntrees_01 = len(gbdt_01.get_dump())
|
|
assert ntrees_01 == 10
|
|
|
|
gbdt_02 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=0)
|
|
gbdt_02.save_model('xgb_tc.model')
|
|
|
|
gbdt_02a = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10, xgb_model=gbdt_02)
|
|
gbdt_02b = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10, xgb_model="xgb_tc.model")
|
|
ntrees_02a = len(gbdt_02a.get_dump())
|
|
ntrees_02b = len(gbdt_02b.get_dump())
|
|
assert ntrees_02a == 10
|
|
assert ntrees_02b == 10
|
|
assert mean_squared_error(y, gbdt_01.predict(dtrain)) == mean_squared_error(y, gbdt_02a.predict(dtrain))
|
|
assert mean_squared_error(y, gbdt_01.predict(dtrain)) == mean_squared_error(y, gbdt_02b.predict(dtrain))
|
|
|
|
gbdt_03 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=3)
|
|
gbdt_03.save_model('xgb_tc.model')
|
|
|
|
gbdt_03a = xgb.train(self.xgb_params_01, dtrain, num_boost_round=7, xgb_model=gbdt_03)
|
|
gbdt_03b = xgb.train(self.xgb_params_01, dtrain, num_boost_round=7, xgb_model="xgb_tc.model")
|
|
ntrees_03a = len(gbdt_03a.get_dump())
|
|
ntrees_03b = len(gbdt_03b.get_dump())
|
|
assert ntrees_03a == 10
|
|
assert ntrees_03b == 10
|
|
assert mean_squared_error(y, gbdt_03a.predict(dtrain)) == mean_squared_error(y, gbdt_03b.predict(dtrain))
|
|
|
|
gbdt_04 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=3)
|
|
assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration + 1) * self.num_parallel_tree
|
|
assert mean_squared_error(y, gbdt_04.predict(dtrain)) == \
|
|
mean_squared_error(y, gbdt_04.predict(dtrain, ntree_limit=gbdt_04.best_ntree_limit))
|
|
|
|
gbdt_04 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=7, xgb_model=gbdt_04)
|
|
assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration + 1) * self.num_parallel_tree
|
|
assert mean_squared_error(y, gbdt_04.predict(dtrain)) == \
|
|
mean_squared_error(y, gbdt_04.predict(dtrain, ntree_limit=gbdt_04.best_ntree_limit))
|