import numpy as np import xgboost as xgb import unittest dpath = 'demo/data/' dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train') dtest = xgb.DMatrix(dpath + 'agaricus.txt.test') rng = np.random.RandomState(1994) class TestModels(unittest.TestCase): def test_glm(self): param = {'silent': 1, 'objective': 'binary:logistic', 'booster': 'gblinear', 'alpha': 0.0001, 'lambda': 1, 'nthread': 1} watchlist = [(dtest, 'eval'), (dtrain, 'train')] num_round = 4 bst = xgb.train(param, dtrain, num_round, watchlist) assert isinstance(bst, xgb.core.Booster) 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)) assert err < 0.2 def test_dart(self): dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train') dtest = xgb.DMatrix(dpath + 'agaricus.txt.test') param = {'max_depth': 5, 'objective': 'binary:logistic', 'booster': 'dart', 'silent': False} # 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, ntree_limit=num_round) 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.dart') # load model and data in bst2 = xgb.Booster(params=param, model_file='xgb.model.dart') dtest2 = xgb.DMatrix('dtest.buffer') preds2 = bst2.predict(dtest2, ntree_limit=num_round) # assert they are the same assert np.sum(np.abs(preds2 - preds)) == 0 # check whether sample_type and normalize_type work num_round = 50 param['silent'] = True param['learning_rate'] = 0.1 param['rate_drop'] = 0.1 preds_list = [] for p in [[p0, p1] for p0 in ['uniform', 'weighted'] for p1 in ['tree', 'forest']]: param['sample_type'] = p[0] param['normalize_type'] = p[1] bst = xgb.train(param, dtrain, num_round, watchlist) preds = bst.predict(dtest, ntree_limit=num_round) err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds)) assert err < 0.1 preds_list.append(preds) for ii in range(len(preds_list)): for jj in range(ii + 1, len(preds_list)): assert np.sum(np.abs(preds_list[ii] - preds_list[jj])) > 0 def test_eta_decay(self): watchlist = [(dtest, 'eval'), (dtrain, 'train')] num_round = 4 # learning_rates as a list # init eta with 0 to check whether learning_rates work param = {'max_depth': 2, 'eta': 0, 'silent': 1, 'objective': 'binary:logistic'} evals_result = {} bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5], evals_result=evals_result) eval_errors = list(map(float, evals_result['eval']['error'])) assert isinstance(bst, xgb.core.Booster) # validation error should decrease, if eta > 0 assert eval_errors[0] > eval_errors[-1] # init learning_rate with 0 to check whether learning_rates work param = {'max_depth': 2, 'learning_rate': 0, 'silent': 1, 'objective': 'binary:logistic'} evals_result = {} bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5], evals_result=evals_result) eval_errors = list(map(float, evals_result['eval']['error'])) assert isinstance(bst, xgb.core.Booster) # validation error should decrease, if learning_rate > 0 assert eval_errors[0] > eval_errors[-1] # check if learning_rates override default value of eta/learning_rate param = {'max_depth': 2, 'silent': 1, 'objective': 'binary:logistic'} evals_result = {} bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0, 0, 0, 0], evals_result=evals_result) eval_errors = list(map(float, evals_result['eval']['error'])) assert isinstance(bst, xgb.core.Booster) # validation error should not decrease, if eta/learning_rate = 0 assert eval_errors[0] == eval_errors[-1] # learning_rates as a customized decay function def eta_decay(ithround, num_boost_round): return num_boost_round / (ithround + 1) bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay) assert isinstance(bst, xgb.core.Booster) def test_custom_objective(self): param = {'max_depth': 2, 'eta': 1, 'silent': 1} watchlist = [(dtest, 'eval'), (dtrain, 'train')] num_round = 2 def logregobj(preds, dtrain): labels = dtrain.get_label() preds = 1.0 / (1.0 + np.exp(-preds)) grad = preds - labels hess = preds * (1.0 - preds) return grad, hess def evalerror(preds, dtrain): labels = dtrain.get_label() return 'error', float(sum(labels != (preds > 0.0))) / len(labels) # test custom_objective in training bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror) assert isinstance(bst, xgb.core.Booster) 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)) assert err < 0.1 # test custom_objective in cross-validation xgb.cv(param, dtrain, num_round, nfold=5, seed=0, obj=logregobj, feval=evalerror) # test maximize parameter def neg_evalerror(preds, dtrain): labels = dtrain.get_label() return 'error', float(sum(labels == (preds > 0.0))) / len(labels) bst2 = xgb.train(param, dtrain, num_round, watchlist, logregobj, neg_evalerror, maximize=True) preds2 = bst2.predict(dtest) err2 = sum(1 for i in range(len(preds2)) if int(preds2[i] > 0.5) != labels[i]) / float(len(preds2)) assert err == err2 def test_multi_eval_metric(self): watchlist = [(dtest, 'eval'), (dtrain, 'train')] param = {'max_depth': 2, 'eta': 0.2, 'silent': 1, 'objective': 'binary:logistic'} param['eval_metric'] = ["auc", "logloss", 'error'] evals_result = {} bst = xgb.train(param, dtrain, 4, watchlist, evals_result=evals_result) assert isinstance(bst, xgb.core.Booster) assert len(evals_result['eval']) == 3 assert set(evals_result['eval'].keys()) == {'auc', 'error', 'logloss'} def test_fpreproc(self): param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'} num_round = 2 def fpreproc(dtrain, dtest, param): label = dtrain.get_label() ratio = float(np.sum(label == 0)) / np.sum(label == 1) param['scale_pos_weight'] = ratio return (dtrain, dtest, param) xgb.cv(param, dtrain, num_round, nfold=5, metrics={'auc'}, seed=0, fpreproc=fpreproc) def test_show_stdv(self): param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'} num_round = 2 xgb.cv(param, dtrain, num_round, nfold=5, metrics={'error'}, seed=0, show_stdv=False) def test_feature_names_validation(self): X = np.random.random((10, 3)) y = np.random.randint(2, size=(10,)) dm1 = xgb.DMatrix(X, y) dm2 = xgb.DMatrix(X, y, feature_names=("a", "b", "c")) bst = xgb.train([], dm1) bst.predict(dm1) # success self.assertRaises(ValueError, bst.predict, dm2) bst.predict(dm1) # success bst = xgb.train([], dm2) bst.predict(dm2) # success self.assertRaises(ValueError, bst.predict, dm1) bst.predict(dm2) # success