[GPU-Plugin] Fix gpu_hist to allow matrices with more than just 2^{32} elements. Also fixed CPU hist algorithm. (#2518)
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PSEUDOTENSOR / Jonathan McKinney
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Rory Mitchell
Rory Mitchell
parent
c85bf9859e
commit
ca7fc9fda3
134
plugin/updater_gpu/test/python/test_large.py
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134
plugin/updater_gpu/test/python/test_large.py
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from __future__ import print_function
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#pylint: skip-file
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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 testing as tm
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import numpy as np
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import unittest
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from sklearn.datasets import make_classification
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def eprint(*args, **kwargs):
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print(*args, file=sys.stderr, **kwargs) ; sys.stderr.flush()
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print(*args, file=sys.stdout, **kwargs) ; sys.stdout.flush()
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eprint("Testing Big Data (this may take a while)")
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rng = np.random.RandomState(1994)
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# "realistic" size based upon http://stat-computing.org/dataexpo/2009/ , which has been processed to one-hot encode categoricalsxsy
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cols = 31
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# reduced to fit onto 1 gpu but still be large
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rows2 = 5000 # medium
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#rows2 = 4032 # fake large for testing
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rows1 = 42360032 # large
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#rows2 = 152360032 # can do this for multi-gpu test (very large)
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rowslist = [rows1, rows2]
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class TestGPU(unittest.TestCase):
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def test_large(self):
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eprint("Starting test for large data")
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tm._skip_if_no_sklearn()
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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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for rows in rowslist:
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eprint("Creating train data rows=%d cols=%d" % (rows,cols))
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X, y = make_classification(rows, n_features=cols, random_state=7)
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rowstest = int(rows*0.2)
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eprint("Creating test data rows=%d cols=%d" % (rowstest,cols))
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# note the new random state. if chose same as train random state, exact methods can memorize and do very well on test even for random data, while hist cannot
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Xtest, ytest = make_classification(rowstest, n_features=cols, random_state=8)
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eprint("Starting DMatrix(X,y)")
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ag_dtrain = xgb.DMatrix(X,y)
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eprint("Starting DMatrix(Xtest,ytest)")
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ag_dtest = xgb.DMatrix(Xtest,ytest)
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max_depth=6
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max_bin=1024
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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': 1,
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'eta': 1,
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'silent': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_paramb = {'max_depth': max_depth,
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'tree_method': 'hist',
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#'nthread': 1,
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'eta': 1,
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'silent': 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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'tree_method': 'gpu_hist',
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'eta': 1,
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'silent': 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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'tree_method': 'gpu_hist',
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'eta': 1,
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'silent': 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_param4 = {'max_depth': max_depth,
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# 'tree_method': 'gpu_exact',
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# 'eta': 1,
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# 'silent': 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_res = {}
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ag_resb = {}
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ag_res2 = {}
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ag_res3 = {}
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#ag_res4 = {}
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num_rounds = 1
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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("hist updater")
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xgb.train(ag_paramb, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_resb)
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eprint("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("gpu_hist updater all 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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#eprint("gpu_exact updater")
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#xgb.train(ag_param4, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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# evals_result=ag_res4)
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assert np.fabs(ag_res['train']['auc'][0] - ag_resb['train']['auc'][0])<0.001
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assert np.fabs(ag_res['train']['auc'][0] - ag_res2['train']['auc'][0])<0.001
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assert np.fabs(ag_res['train']['auc'][0] - ag_res3['train']['auc'][0])<0.001
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#assert np.fabs(ag_res['train']['auc'][0] - ag_res4['train']['auc'][0])<0.001
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assert np.fabs(ag_res['test']['auc'][0] - ag_resb['test']['auc'][0])<0.01
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assert np.fabs(ag_res['test']['auc'][0] - ag_res2['test']['auc'][0])<0.01
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assert np.fabs(ag_res['test']['auc'][0] - ag_res3['test']['auc'][0])<0.01
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#assert np.fabs(ag_res['test']['auc'][0] - ag_res4['test']['auc'][0])<0.01
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