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unittest for 'num_class > 2' added

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Faron 2015-11-12 08:21:19 +01:00
parent ce5930c365
commit 7f2628acd7
1 changed files with 44 additions and 20 deletions
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64
tests/python/test_training_continuation.py
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@ -1,5 +1,6 @@
import xgboost as xgb
import numpy as np
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.cross_validation import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.grid_search import GridSearchCV
@ -23,46 +24,69 @@ class TestTrainingContinuation(unittest.TestCase):
'num_parallel_tree': num_parallel_tree
}
xgb_params_03 = {
'silent': 1,
'nthread': 1,
'num_class': 5,
'num_parallel_tree': num_parallel_tree
}
def test_training_continuation(self):
digits = load_digits(2)
X = digits['data']
y = digits['target']
digits_2class = load_digits(2)
digits_5class = load_digits(5)
dtrain = xgb.DMatrix(X, label=y)
X_2class = digits_2class['data']
y_2class = digits_2class['target']
gbdt_01 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10)
X_5class = digits_5class['data']
y_5class = digits_5class['target']
dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
gbdt_01 = xgb.train(self.xgb_params_01, dtrain_2class, 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 = xgb.train(self.xgb_params_01, dtrain_2class, 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")
gbdt_02a = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model=gbdt_02)
gbdt_02b = xgb.train(self.xgb_params_01, dtrain_2class, 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))
assert mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class)) == \
mean_squared_error(y_2class, gbdt_02a.predict(dtrain_2class))
assert mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class)) == \
mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
gbdt_03 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=3)
gbdt_03 = xgb.train(self.xgb_params_01, dtrain_2class, 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")
gbdt_03a = xgb.train(self.xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model=gbdt_03)
gbdt_03b = xgb.train(self.xgb_params_01, dtrain_2class, 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))
assert mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class)) == \
mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
gbdt_04 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=3)
gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class, 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))
assert mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class)) == \
mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
gbdt_04 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=7, xgb_model=gbdt_04)
gbdt_04 = xgb.train(self.xgb_params_02, dtrain_2class, 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))
assert mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class)) == \
mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
gbdt_05 = xgb.train(self.xgb_params_03, dtrain_5class, num_boost_round=7)
assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration + 1) * self.num_parallel_tree
gbdt_05 = xgb.train(self.xgb_params_03, dtrain_5class, num_boost_round=3, xgb_model=gbdt_05)
assert gbdt_05.best_ntree_limit == (gbdt_05.best_iteration + 1) * self.num_parallel_tree
assert np.any(gbdt_05.predict(dtrain_5class) !=
gbdt_05.predict(dtrain_5class, ntree_limit=gbdt_05.best_ntree_limit)) == False