/tests/python/test_basic.py
Python | 209 lines | 145 code | 36 blank | 28 comment | 6 complexity | 1b0dde26f8cc4ae39f2decde2d95d5b8 MD5 | raw file
- # -*- coding: utf-8 -*-
- import numpy as np
- import xgboost as xgb
- import unittest
- dpath = 'demo/data/'
- rng = np.random.RandomState(1994)
- class TestBasic(unittest.TestCase):
- def test_basic(self):
- dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
- dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
- param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
- # specify validations set to watch performance
- watchlist = [(dtest, 'eval'), (dtrain, 'train')]
- num_round = 2
- bst = xgb.train(param, dtrain, num_round, watchlist)
- # this is prediction
- preds = bst.predict(dtest)
- labels = dtest.get_label()
- err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
- # error must be smaller than 10%
- assert err < 0.1
- # save dmatrix into binary buffer
- dtest.save_binary('dtest.buffer')
- # save model
- bst.save_model('xgb.model')
- # load model and data in
- bst2 = xgb.Booster(model_file='xgb.model')
- dtest2 = xgb.DMatrix('dtest.buffer')
- preds2 = bst2.predict(dtest2)
- # assert they are the same
- assert np.sum(np.abs(preds2 - preds)) == 0
- def test_record_results(self):
- dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
- dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
- param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
- # specify validations set to watch performance
- watchlist = [(dtest, 'eval'), (dtrain, 'train')]
- num_round = 2
- result = {}
- res2 = {}
- xgb.train(param, dtrain, num_round, watchlist,
- callbacks=[xgb.callback.record_evaluation(result)])
- xgb.train(param, dtrain, num_round, watchlist,
- evals_result=res2)
- assert result['train']['error'][0] < 0.1
- assert res2 == result
- def test_multiclass(self):
- dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
- dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
- param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'num_class': 2}
- # specify validations set to watch performance
- watchlist = [(dtest, 'eval'), (dtrain, 'train')]
- num_round = 2
- bst = xgb.train(param, dtrain, num_round, watchlist)
- # this is prediction
- preds = bst.predict(dtest)
- labels = dtest.get_label()
- err = sum(1 for i in range(len(preds)) if preds[i] != labels[i]) / float(len(preds))
- # error must be smaller than 10%
- assert err < 0.1
- # save dmatrix into binary buffer
- dtest.save_binary('dtest.buffer')
- # save model
- bst.save_model('xgb.model')
- # load model and data in
- bst2 = xgb.Booster(model_file='xgb.model')
- dtest2 = xgb.DMatrix('dtest.buffer')
- preds2 = bst2.predict(dtest2)
- # assert they are the same
- assert np.sum(np.abs(preds2 - preds)) == 0
- def test_dmatrix_init(self):
- data = np.random.randn(5, 5)
- # different length
- self.assertRaises(ValueError, xgb.DMatrix, data,
- feature_names=list('abcdef'))
- # contains duplicates
- self.assertRaises(ValueError, xgb.DMatrix, data,
- feature_names=['a', 'b', 'c', 'd', 'd'])
- # contains symbol
- self.assertRaises(ValueError, xgb.DMatrix, data,
- feature_names=['a', 'b', 'c', 'd', 'e<1'])
- dm = xgb.DMatrix(data)
- dm.feature_names = list('abcde')
- assert dm.feature_names == list('abcde')
- dm.feature_types = 'q'
- assert dm.feature_types == list('qqqqq')
- dm.feature_types = list('qiqiq')
- assert dm.feature_types == list('qiqiq')
- def incorrect_type_set():
- dm.feature_types = list('abcde')
- self.assertRaises(ValueError, incorrect_type_set)
- # reset
- dm.feature_names = None
- self.assertEqual(dm.feature_names, ['f0', 'f1', 'f2', 'f3', 'f4'])
- assert dm.feature_types is None
- def test_feature_names(self):
- data = np.random.randn(100, 5)
- target = np.array([0, 1] * 50)
- cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'],
- [u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']]
- for features in cases:
- dm = xgb.DMatrix(data, label=target,
- feature_names=features)
- assert dm.feature_names == features
- assert dm.num_row() == 100
- assert dm.num_col() == 5
- params = {'objective': 'multi:softprob',
- 'eval_metric': 'mlogloss',
- 'eta': 0.3,
- 'num_class': 3}
- bst = xgb.train(params, dm, num_boost_round=10)
- scores = bst.get_fscore()
- assert list(sorted(k for k in scores)) == features
- dummy = np.random.randn(5, 5)
- dm = xgb.DMatrix(dummy, feature_names=features)
- bst.predict(dm)
- # different feature name must raises error
- dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
- self.assertRaises(ValueError, bst.predict, dm)
- def test_feature_importances(self):
- data = np.random.randn(100, 5)
- target = np.array([0, 1] * 50)
- features = ['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5']
- dm = xgb.DMatrix(data, label=target,
- feature_names=features)
- params = {'objective': 'multi:softprob',
- 'eval_metric': 'mlogloss',
- 'eta': 0.3,
- 'num_class': 3}
- bst = xgb.train(params, dm, num_boost_round=10)
- # number of feature importances should == number of features
- scores1 = bst.get_score()
- scores2 = bst.get_score(importance_type='weight')
- scores3 = bst.get_score(importance_type='cover')
- scores4 = bst.get_score(importance_type='gain')
- assert len(scores1) == len(features)
- assert len(scores2) == len(features)
- assert len(scores3) == len(features)
- assert len(scores4) == len(features)
- # check backwards compatibility of get_fscore
- fscores = bst.get_fscore()
- assert scores1 == fscores
- def test_load_file_invalid(self):
- self.assertRaises(xgb.core.XGBoostError, xgb.Booster,
- model_file='incorrect_path')
- self.assertRaises(xgb.core.XGBoostError, xgb.Booster,
- model_file=u'不正なパス')
- def test_dmatrix_numpy_init(self):
- data = np.random.randn(5, 5)
- dm = xgb.DMatrix(data)
- assert dm.num_row() == 5
- assert dm.num_col() == 5
- data = np.matrix([[1, 2], [3, 4]])
- dm = xgb.DMatrix(data)
- assert dm.num_row() == 2
- assert dm.num_col() == 2
- # 0d array
- self.assertRaises(ValueError, xgb.DMatrix, np.array(1))
- # 1d array
- self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3]))
- # 3d array
- data = np.random.randn(5, 5, 5)
- self.assertRaises(ValueError, xgb.DMatrix, data)
- # object dtype
- data = np.array([['a', 'b'], ['c', 'd']])
- self.assertRaises(ValueError, xgb.DMatrix, data)
- def test_cv(self):
- dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
- params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
- # return np.ndarray
- cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
- assert isinstance(cv, dict)
- assert len(cv) == (4)