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Refactor Python tests. (#3897)

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* Deprecate nose tests.
* Format python tests.
This commit is contained in:
Jiaming Yuan
2018-11-15 13:56:33 +13:00
committed by GitHub
parent c76d993681
commit 2ea0f887c1
23 changed files with 302 additions and 225 deletions
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63
tests/python/test_training_continuation.py
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@@ -2,6 +2,7 @@ import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1337)
@@ -27,8 +28,8 @@ class TestTrainingContinuation(unittest.TestCase):
'num_parallel_tree': num_parallel_tree
}
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation(self):
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
from sklearn.metrics import mean_squared_error
@@ -44,15 +45,19 @@ class TestTrainingContinuation(unittest.TestCase):
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)
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_2class, 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_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")
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
@@ -66,11 +71,14 @@ class TestTrainingContinuation(unittest.TestCase):
res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
assert res1 == res2
gbdt_03 = xgb.train(self.xgb_params_01, dtrain_2class, 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_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")
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
@@ -80,25 +88,42 @@ class TestTrainingContinuation(unittest.TestCase):
res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
assert res1 == res2
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
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
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
res2 = mean_squared_error(y_2class,
gbdt_04.predict(
dtrain_2class,
ntree_limit=gbdt_04.best_ntree_limit))
assert res1 == res2
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
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
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class, ntree_limit=gbdt_04.best_ntree_limit))
res2 = mean_squared_error(y_2class,
gbdt_04.predict(
dtrain_2class,
ntree_limit=gbdt_04.best_ntree_limit))
assert res1 == res2
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
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
res1 = gbdt_05.predict(dtrain_5class)
res2 = gbdt_05.predict(dtrain_5class, ntree_limit=gbdt_05.best_ntree_limit)
res2 = gbdt_05.predict(dtrain_5class,
ntree_limit=gbdt_05.best_ntree_limit)
np.testing.assert_almost_equal(res1, res2)