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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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43
tests/python/test_early_stopping.py
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@@ -2,24 +2,26 @@ import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1994)
class TestEarlyStopping(unittest.TestCase):
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping_nonparallel(self):
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
try:
from sklearn.model_selection import train_test_split
except:
except ImportError:
from sklearn.cross_validation import train_test_split
digits = load_digits(2)
X = digits['data']
y = digits['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y,
random_state=0)
clf1 = xgb.XGBClassifier()
clf1.fit(X_train, y_train, early_stopping_rounds=5, eval_metric="auc",
eval_set=[(X_test, y_test)])
@@ -35,36 +37,41 @@ class TestEarlyStopping(unittest.TestCase):
eval_set=[(X_test, y_test)])
assert clf3.best_score == 1
@pytest.mark.skipif(**tm.no_sklearn())
def evalerror(self, preds, dtrain):
tm._skip_if_no_sklearn()
from sklearn.metrics import mean_squared_error
labels = dtrain.get_label()
return 'rmse', mean_squared_error(labels, preds)
@pytest.mark.skipif(**tm.no_sklearn())
def test_cv_early_stopping(self):
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
digits = load_digits(2)
X = digits['data']
y = digits['target']
dm = xgb.DMatrix(X, label=y)
params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
params = {'max_depth': 2, 'eta': 1, 'silent': 1,
'objective': 'binary:logistic'}
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=10)
assert cv.shape[0] == 10
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=5)
assert cv.shape[0] == 3
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=1)
assert cv.shape[0] == 1
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, feval=self.evalerror,
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
early_stopping_rounds=10)
assert cv.shape[0] == 10
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, feval=self.evalerror,
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
early_stopping_rounds=5)
assert cv.shape[0] == 3
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
early_stopping_rounds=1)
assert cv.shape[0] == 1
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
feval=self.evalerror, early_stopping_rounds=10)
assert cv.shape[0] == 10
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
feval=self.evalerror, early_stopping_rounds=1)
assert cv.shape[0] == 5
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
feval=self.evalerror, maximize=True,
early_stopping_rounds=1)
assert cv.shape[0] == 5
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, feval=self.evalerror,
maximize=True, early_stopping_rounds=1)
assert cv.shape[0] == 1