323 lines
13 KiB
Python
323 lines
13 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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rng = np.random.RandomState(1994)
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dpath = 'demo/data/'
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def eprint(*args, **kwargs):
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print(*args, file=sys.stderr, **kwargs)
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print(*args, file=sys.stdout, **kwargs)
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@attr('gpu')
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class TestGPU(unittest.TestCase):
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def test_grow_gpu(self):
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from sklearn.datasets import load_digits
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try:
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from sklearn.model_selection import train_test_split
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except:
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from sklearn.cross_validation import train_test_split
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ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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ag_param = {'max_depth': 2,
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'tree_method': 'exact',
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'nthread': 0,
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_param2 = {'max_depth': 2,
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'tree_method': 'gpu_exact',
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'nthread': 0,
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_res = {}
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ag_res2 = {}
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num_rounds = 10
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xgb.train(ag_param, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res)
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xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res2)
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assert ag_res['train']['auc'] == ag_res2['train']['auc']
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assert ag_res['test']['auc'] == ag_res2['test']['auc']
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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dtrain = xgb.DMatrix(X_train, y_train)
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dtest = xgb.DMatrix(X_test, y_test)
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_exact',
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'max_depth': 3,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert self.non_decreasing(res['test']['auc'])
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# fail-safe test for dense data
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from sklearn.datasets import load_svmlight_file
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X2, y2 = load_svmlight_file(dpath + 'agaricus.txt.train')
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X2 = X2.toarray()
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dtrain2 = xgb.DMatrix(X2, label=y2)
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_exact',
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'max_depth': 2,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in rng.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 2
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dtrain3 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain3, num_rounds, [(dtrain3, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in np.random.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 3
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dtrain4 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain4, num_rounds, [(dtrain4, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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def test_grow_gpu_hist(self):
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n_gpus = -1
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from sklearn.datasets import load_digits
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try:
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from sklearn.model_selection import train_test_split
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except:
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from sklearn.cross_validation import train_test_split
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ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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for max_depth in range(3, 10): # TODO: Doesn't work with 2 for some tests
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# eprint("max_depth=%d" % (max_depth))
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for max_bin_i in range(3, 11):
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max_bin = np.power(2, max_bin_i)
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# eprint("max_bin=%d" % (max_bin))
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# regression test --- hist must be same as exact on all-categorial data
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ag_param = {'max_depth': max_depth,
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'tree_method': 'exact',
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'nthread': 0,
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_param2 = {'max_depth': max_depth,
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'n_gpus': 1,
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'objective': 'binary:logistic',
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'max_bin': max_bin,
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'eval_metric': 'auc'}
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ag_param3 = {'max_depth': max_depth,
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'n_gpus': n_gpus,
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'objective': 'binary:logistic',
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'max_bin': max_bin,
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'eval_metric': 'auc'}
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ag_res = {}
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ag_res2 = {}
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ag_res3 = {}
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num_rounds = 10
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# eprint("normal updater");
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xgb.train(ag_param, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res)
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# eprint("grow_gpu_hist updater 1 gpu");
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xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res2)
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# eprint("grow_gpu_hist updater %d gpus" % (n_gpus));
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xgb.train(ag_param3, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res3)
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# assert 1==0
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assert ag_res['train']['auc'] == ag_res2['train']['auc']
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assert ag_res['test']['auc'] == ag_res2['test']['auc']
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assert ag_res['test']['auc'] == ag_res3['test']['auc']
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######################################################################
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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dtrain = xgb.DMatrix(X_train, y_train)
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dtest = xgb.DMatrix(X_test, y_test)
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param = {'objective': 'binary:logistic',
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'tree_method': 'gpu_hist',
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'nthread': 0,
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'max_depth': max_depth,
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'n_gpus': 1,
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'max_bin': max_bin,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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# eprint("digits: grow_gpu_hist updater 1 gpu");
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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# assert self.non_decreasing(res['test']['auc'])
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param2 = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'max_bin': max_bin,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res2 = {}
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# eprint("digits: grow_gpu_hist updater %d gpus" % (n_gpus));
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xgb.train(param2, dtrain, num_rounds, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res2)
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assert self.non_decreasing(res2['train']['auc'])
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# assert self.non_decreasing(res2['test']['auc'])
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assert res['train']['auc'] == res2['train']['auc']
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# assert res['test']['auc'] == res2['test']['auc']
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######################################################################
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# fail-safe test for dense data
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from sklearn.datasets import load_svmlight_file
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X2, y2 = load_svmlight_file(dpath + 'agaricus.txt.train')
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X2 = X2.toarray()
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dtrain2 = xgb.DMatrix(X2, label=y2)
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'max_bin': max_bin,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin > 32:
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in rng.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 2
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dtrain3 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain3, num_rounds, [(dtrain3, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin > 32:
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in np.random.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 3
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dtrain4 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain4, num_rounds, [(dtrain4, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin > 32:
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assert res['train']['auc'][0] >= 0.85
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######################################################################
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# fail-safe test for max_bin
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'debug_verbose': 0,
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'eval_metric': 'auc',
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'max_bin': max_bin}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin > 32:
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assert res['train']['auc'][0] >= 0.85
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######################################################################
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# subsampling
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'eval_metric': 'auc',
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'colsample_bytree': 0.5,
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'colsample_bylevel': 0.5,
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'subsample': 0.5,
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'debug_verbose': 0,
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'max_bin': max_bin}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin > 32:
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assert res['train']['auc'][0] >= 0.85
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######################################################################
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# fail-safe test for max_bin=2
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': 2,
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'n_gpus': n_gpus,
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'debug_verbose': 0,
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'eval_metric': 'auc',
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'max_bin': 2}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin > 32:
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assert res['train']['auc'][0] >= 0.85
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def non_decreasing(self, L):
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return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
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