6b375f6ad8
* Multi-threaded XGDMatrixCreateFromMat for faster DMatrix creation from numpy arrays for python interface.
104 lines
3.9 KiB
Python
104 lines
3.9 KiB
Python
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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rows3 = 5000 # small
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rows2 = 4360032 # medium
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rows1 = 42360032 # large
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#rows1 = 152360032 # can do this for multi-gpu test (very large)
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rowslist = [rows1, rows2, rows3]
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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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for rows in rowslist:
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eprint("Creating train data rows=%d cols=%d" % (rows,cols))
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np.random.seed(7)
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X = np.random.rand(rows, cols)
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y = np.random.rand(rows)
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eprint("Starting DMatrix(X,y)")
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ag_dtrain = xgb.DMatrix(X,y,nthread=0)
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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': 0,
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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': 0,
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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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'nthread': 0,
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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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'nthread': 0,
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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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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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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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