68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
import xgboost as xgb
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import testing as tm
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import numpy as np
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import unittest
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rng = np.random.RandomState(1994)
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class TestEarlyStopping(unittest.TestCase):
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def test_early_stopping_nonparallel(self):
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tm._skip_if_no_sklearn()
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from sklearn.datasets import load_digits
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from sklearn.cross_validation import train_test_split
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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clf1 = xgb.XGBClassifier()
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clf1.fit(X_train, y_train, early_stopping_rounds=5, eval_metric="auc",
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eval_set=[(X_test, y_test)])
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clf2 = xgb.XGBClassifier()
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clf2.fit(X_train, y_train, early_stopping_rounds=4, eval_metric="auc",
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eval_set=[(X_test, y_test)])
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# should be the same
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assert clf1.best_score == clf2.best_score
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assert clf1.best_score != 1
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# check overfit
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clf3 = xgb.XGBClassifier()
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clf3.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
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eval_set=[(X_test, y_test)])
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assert clf3.best_score == 1
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def evalerror(self, preds, dtrain):
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tm._skip_if_no_sklearn()
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from sklearn.metrics import mean_squared_error
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labels = dtrain.get_label()
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return 'rmse', mean_squared_error(labels, preds)
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def test_cv_early_stopping(self):
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tm._skip_if_no_sklearn()
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from sklearn.datasets import load_digits
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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dm = xgb.DMatrix(X, label=y)
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params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=10)
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assert cv.shape[0] == 10
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=5)
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assert cv.shape[0] == 3
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=1)
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assert cv.shape[0] == 1
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, feval=self.evalerror,
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early_stopping_rounds=10)
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assert cv.shape[0] == 10
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, feval=self.evalerror,
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early_stopping_rounds=1)
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assert cv.shape[0] == 5
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, feval=self.evalerror,
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maximize=True, early_stopping_rounds=1)
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assert cv.shape[0] == 1
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