# -*- 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)