121 lines
4.6 KiB
Python
121 lines
4.6 KiB
Python
from __future__ import print_function
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import sys
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sys.path.append("../../tests/python")
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import xgboost as xgb
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import numpy as np
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import unittest
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from nose.plugins.attrib import attr
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from sklearn.datasets import load_digits, load_boston, load_breast_cancer, make_regression
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rng = np.random.RandomState(1994)
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def non_increasing(L, tolerance):
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return all((y - x) < tolerance for x, y in zip(L, L[1:]))
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#Check result is always decreasing and final accuracy is within tolerance
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def assert_accuracy(res, tree_method, comparison_tree_method, tolerance):
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assert non_increasing(res[tree_method], tolerance)
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assert np.allclose(res[tree_method][-1], res[comparison_tree_method][-1], 1e-3, 1e-2)
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def train_boston(param_in, comparison_tree_method):
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data = load_boston()
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dtrain = xgb.DMatrix(data.data, label=data.target)
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param = {}
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param.update(param_in)
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res_tmp = {}
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res = {}
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num_rounds = 10
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[param['tree_method']] = res_tmp['train']['rmse']
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param["tree_method"] = comparison_tree_method
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[comparison_tree_method] = res_tmp['train']['rmse']
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return res
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def train_digits(param_in, comparison_tree_method):
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data = load_digits()
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dtrain = xgb.DMatrix(data.data, label=data.target)
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param = {}
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param['objective'] = 'multi:softmax'
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param['num_class'] = 10
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param.update(param_in)
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res_tmp = {}
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res = {}
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num_rounds = 10
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[param['tree_method']] = res_tmp['train']['merror']
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param["tree_method"] = comparison_tree_method
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[comparison_tree_method] = res_tmp['train']['merror']
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return res
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def train_cancer(param_in, comparison_tree_method):
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data = load_breast_cancer()
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dtrain = xgb.DMatrix(data.data, label=data.target)
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param = {}
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param['objective'] = 'binary:logistic'
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param.update(param_in)
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res_tmp = {}
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res = {}
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num_rounds = 10
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[param['tree_method']] = res_tmp['train']['error']
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param["tree_method"] = comparison_tree_method
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[comparison_tree_method] = res_tmp['train']['error']
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return res
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def train_sparse(param_in, comparison_tree_method):
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n = 5000
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sparsity = 0.75
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X, y = make_regression(n, random_state=rng)
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X = np.array([[np.nan if rng.uniform(0, 1) < sparsity else x for x in x_row] for x_row in X])
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dtrain = xgb.DMatrix(X, label=y)
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param = {}
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param.update(param_in)
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res_tmp = {}
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res = {}
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num_rounds = 10
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bst = xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[param['tree_method']] = res_tmp['train']['rmse']
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param["tree_method"] = comparison_tree_method
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bst = xgb.train(param, dtrain, num_rounds, [(dtrain, 'train')], evals_result=res_tmp)
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res[comparison_tree_method] = res_tmp['train']['rmse']
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return res
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def assert_updater_accuracy(tree_method, comparison_tree_method, variable_param, tolerance):
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param = {'tree_method': tree_method}
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for k, set in variable_param.items():
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for val in set:
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param_tmp = param.copy()
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param_tmp[k] = val
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print(param_tmp, file=sys.stderr)
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assert_accuracy(train_boston(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance)
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assert_accuracy(train_digits(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance)
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assert_accuracy(train_cancer(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance)
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assert_accuracy(train_sparse(param_tmp, comparison_tree_method), tree_method, comparison_tree_method, tolerance)
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@attr('gpu')
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class TestGPU(unittest.TestCase):
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def test_gpu_hist(self):
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variable_param = {'max_depth': [2, 6, 11], 'max_bin': [2, 16, 1024], 'n_gpus': [1, -1]}
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assert_updater_accuracy('gpu_hist', 'hist', variable_param, 0.02)
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def test_gpu_exact(self):
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variable_param = {'max_depth': [2, 6, 15]}
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assert_updater_accuracy('gpu_exact', 'exact', variable_param, 0.02)
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def test_gpu_hist_experimental(self):
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variable_param = {'max_depth': [2, 6], 'max_leaves': [255, 4], 'max_bin': [2, 16, 1024]}
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assert_updater_accuracy('gpu_hist_experimental', 'hist', variable_param, 0.01)
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