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