#pylint: skip-file import sys sys.path.append("../../tests/python") import xgboost as xgb import testing as tm import numpy as np import unittest rng = np.random.RandomState(1994) dpath = '../../demo/data/' ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train') ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test') class TestGPU(unittest.TestCase): def test_grow_gpu(self): tm._skip_if_no_sklearn() from sklearn.datasets import load_digits try: from sklearn.model_selection import train_test_split except: from sklearn.cross_validation import train_test_split ag_param = {'max_depth': 2, 'tree_method': 'exact', 'nthread': 1, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': 'auc'} ag_param2 = {'max_depth': 2, 'updater': 'grow_gpu', 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': 'auc'} ag_res = {} ag_res2 = {} num_rounds = 10 xgb.train(ag_param, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')], evals_result=ag_res) xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')], evals_result=ag_res2) assert ag_res['train']['auc'] == ag_res2['train']['auc'] assert ag_res['test']['auc'] == ag_res2['test']['auc'] digits = load_digits(2) X = digits['data'] y = digits['target'] X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) dtrain = xgb.DMatrix(X_train, y_train) dtest = xgb.DMatrix(X_test, y_test) param = {'objective': 'binary:logistic', 'updater': 'grow_gpu', 'max_depth': 3, 'eval_metric': 'auc'} res = {} xgb.train(param, dtrain, 10, [(dtrain, 'train'), (dtest, 'test')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert self.non_decreasing(res['test']['auc']) # fail-safe test for dense data from sklearn.datasets import load_svmlight_file X2, y2 = load_svmlight_file(dpath + 'agaricus.txt.train') X2 = X2.toarray() dtrain2 = xgb.DMatrix(X2, label=y2) param = {'objective': 'binary:logistic', 'updater': 'grow_gpu', 'max_depth': 2, 'eval_metric': 'auc'} res = {} xgb.train(param, dtrain2, 10, [(dtrain2, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 for j in range(X2.shape[1]): for i in rng.choice(X2.shape[0], size=10, replace=False): X2[i, j] = 2 dtrain3 = xgb.DMatrix(X2, label=y2) res = {} xgb.train(param, dtrain3, num_rounds, [(dtrain3, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 for j in range(X2.shape[1]): for i in np.random.choice(X2.shape[0], size=10, replace=False): X2[i, j] = 3 dtrain4 = xgb.DMatrix(X2, label=y2) res = {} xgb.train(param, dtrain4, 10, [(dtrain4, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 def test_grow_gpu_hist(self): tm._skip_if_no_sklearn() from sklearn.datasets import load_digits try: from sklearn.model_selection import train_test_split except: from sklearn.cross_validation import train_test_split # regression test --- hist must be same as exact on all-categorial data ag_param = {'max_depth': 2, 'tree_method': 'exact', 'nthread': 1, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': 'auc'} ag_param2 = {'max_depth': 2, 'updater': 'grow_gpu_hist', 'eta': 1, 'silent': 1, 'objective': 'binary:logistic', 'eval_metric': 'auc'} ag_res = {} ag_res2 = {} num_rounds = 10 xgb.train(ag_param, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')], evals_result=ag_res) xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')], evals_result=ag_res2) assert ag_res['train']['auc'] == ag_res2['train']['auc'] assert ag_res['test']['auc'] == ag_res2['test']['auc'] digits = load_digits(2) X = digits['data'] y = digits['target'] X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) dtrain = xgb.DMatrix(X_train, y_train) dtest = xgb.DMatrix(X_test, y_test) param = {'objective': 'binary:logistic', 'updater': 'grow_gpu_hist', 'max_depth': 3, 'eval_metric': 'auc'} res = {} xgb.train(param, dtrain, 10, [(dtrain, 'train'), (dtest, 'test')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert self.non_decreasing(res['test']['auc']) # fail-safe test for dense data from sklearn.datasets import load_svmlight_file X2, y2 = load_svmlight_file(dpath + 'agaricus.txt.train') X2 = X2.toarray() dtrain2 = xgb.DMatrix(X2, label=y2) param = {'objective': 'binary:logistic', 'updater': 'grow_gpu_hist', 'grow_policy': 'depthwise', 'max_depth': 2, 'eval_metric': 'auc'} res = {} xgb.train(param, dtrain2, 10, [(dtrain2, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 for j in range(X2.shape[1]): for i in rng.choice(X2.shape[0], size=10, replace=False): X2[i, j] = 2 dtrain3 = xgb.DMatrix(X2, label=y2) res = {} xgb.train(param, dtrain3, num_rounds, [(dtrain3, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 for j in range(X2.shape[1]): for i in np.random.choice(X2.shape[0], size=10, replace=False): X2[i, j] = 3 dtrain4 = xgb.DMatrix(X2, label=y2) res = {} xgb.train(param, dtrain4, 10, [(dtrain4, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 # fail-safe test for max_bin=2 param = {'objective': 'binary:logistic', 'updater': 'grow_gpu_hist', 'max_depth': 2, 'eval_metric': 'auc', 'max_bin': 2} res = {} xgb.train(param, dtrain2, 10, [(dtrain2, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 # subsampling param = {'objective': 'binary:logistic', 'updater': 'grow_gpu_hist', 'max_depth': 3, 'eval_metric': 'auc', 'colsample_bytree': 0.5, 'colsample_bylevel': 0.5, 'subsample': 0.5 } res = {} xgb.train(param, dtrain2, 10, [(dtrain2, 'train')], evals_result=res) assert self.non_decreasing(res['train']['auc']) assert res['train']['auc'][0] >= 0.85 def non_decreasing(self, L): return all((x - y) < 0.001 for x, y in zip(L, L[1:]))