93 lines
4.0 KiB
Python
93 lines
4.0 KiB
Python
import xgboost as xgb
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import numpy as np
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from sklearn.preprocessing import MultiLabelBinarizer
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from sklearn.cross_validation import KFold, train_test_split
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from sklearn.metrics import mean_squared_error
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from sklearn.grid_search import GridSearchCV
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from sklearn.datasets import load_iris, load_digits, load_boston
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import unittest
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rng = np.random.RandomState(1337)
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class TestTrainingContinuation(unittest.TestCase):
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num_parallel_tree = 3
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xgb_params_01 = {
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'silent': 1,
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'nthread': 1,
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}
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xgb_params_02 = {
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'silent': 1,
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'nthread': 1,
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'num_parallel_tree': num_parallel_tree
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}
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xgb_params_03 = {
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'silent': 1,
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'nthread': 1,
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'num_class': 5,
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'num_parallel_tree': num_parallel_tree
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}
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def test_training_continuation(self):
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digits_2class = load_digits(2)
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digits_5class = load_digits(5)
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X_2class = digits_2class['data']
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y_2class = digits_2class['target']
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X_5class = digits_5class['data']
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y_5class = digits_5class['target']
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dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
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dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
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gbdt_01 = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=10)
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ntrees_01 = len(gbdt_01.get_dump())
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assert ntrees_01 == 10
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gbdt_02 = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=0)
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gbdt_02.save_model('xgb_tc.model')
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gbdt_02a = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model=gbdt_02)
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gbdt_02b = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model="xgb_tc.model")
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ntrees_02a = len(gbdt_02a.get_dump())
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ntrees_02b = len(gbdt_02b.get_dump())
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assert ntrees_02a == 10
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assert ntrees_02b == 10
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assert mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class)) == \
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mean_squared_error(y_2class, gbdt_02a.predict(dtrain_2class))
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assert mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class)) == \
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mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
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gbdt_03 = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=3)
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gbdt_03.save_model('xgb_tc.model')
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gbdt_03a = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model=gbdt_03)
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gbdt_03b = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model="xgb_tc.model")
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ntrees_03a = len(gbdt_03a.get_dump())
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ntrees_03b = len(gbdt_03b.get_dump())
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assert ntrees_03a == 10
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assert ntrees_03b == 10
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assert mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class)) == \
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mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
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gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class, num_boost_round=3)
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assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration + 1) * self.num_parallel_tree
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assert mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class)) == \
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mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
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gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class, num_boost_round=7, xgb_model=gbdt_04)
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assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration + 1) * self.num_parallel_tree
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assert mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class)) == \
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mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
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gbdt_05 = xgb.train(self.xgb_params_03, dtrain_5class, num_boost_round=7)
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assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration + 1) * self.num_parallel_tree
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gbdt_05 = xgb.train(self.xgb_params_03, dtrain_5class, num_boost_round=3, xgb_model=gbdt_05)
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assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration + 1) * self.num_parallel_tree
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assert np.any(gbdt_05.predict(dtrain_5class) !=
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gbdt_05.predict(dtrain_5class, ntree_limit=gbdt_05.best_ntree_limit)) == False
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