xgboost/tests/python/test_training_continuation.py

53 lines
2.0 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):
xgb_params = {
'colsample_bytree': 0.7,
'silent': 1,
'nthread': 1,
}
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, dtrain, num_boost_round=10)
ntrees_01 = len(gbdt_01.get_dump())
assert ntrees_01 == 10
gbdt_02 = xgb.train(self.xgb_params, dtrain, num_boost_round=0)
gbdt_02.save_model('xgb_tc.model')
gbdt_02a = xgb.train(self.xgb_params, dtrain, num_boost_round=10, xgb_model=gbdt_02)
gbdt_02b = xgb.train(self.xgb_params, 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, dtrain, num_boost_round=3)
gbdt_03.save_model('xgb_tc.model')
gbdt_03a = xgb.train(self.xgb_params, dtrain, num_boost_round=7, xgb_model=gbdt_03)
gbdt_03b = xgb.train(self.xgb_params, 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))