95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
import xgboost as xgb
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import numpy as np
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from sklearn.cross_validation import train_test_split
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from sklearn.metrics import mean_squared_error
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from sklearn.datasets import load_digits
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import unittest
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rng = np.random.RandomState(1337)
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class TestEvalMetrics(unittest.TestCase):
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xgb_params_01 = {
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'silent': 1,
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'nthread': 1,
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'eval_metric': 'error'
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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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'eval_metric': ['error']
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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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'eval_metric': ['rmse', 'error']
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}
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xgb_params_04 = {
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'silent': 1,
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'nthread': 1,
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'eval_metric': ['error', 'rmse']
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}
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def evalerror_01(self, preds, dtrain):
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labels = dtrain.get_label()
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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def evalerror_02(self, preds, dtrain):
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labels = dtrain.get_label()
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return [('error', float(sum(labels != (preds > 0.0))) / len(labels))]
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def evalerror_03(self, preds, dtrain):
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labels = dtrain.get_label()
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return [('rmse', mean_squared_error(labels, preds)),
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('error', float(sum(labels != (preds > 0.0))) / len(labels))]
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def evalerror_04(self, preds, dtrain):
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labels = dtrain.get_label()
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return [('error', float(sum(labels != (preds > 0.0))) / len(labels)),
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('rmse', mean_squared_error(labels, preds))]
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def test_eval_metrics(self):
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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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Xt, Xv, yt, yv = train_test_split(X, y, test_size=0.2, random_state=0)
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dtrain = xgb.DMatrix(Xt, label=yt)
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dvalid = xgb.DMatrix(Xv, label=yv)
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watchlist = [(dtrain, 'train'), (dvalid, 'val')]
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gbdt_01 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10)
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gbdt_02 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=10)
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gbdt_03 = xgb.train(self.xgb_params_03, dtrain, num_boost_round=10)
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assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
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assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
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gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
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assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
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assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
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gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_01)
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gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_02)
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gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_03)
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gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_04)
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assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
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assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
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assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
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