33 lines
994 B
Python
33 lines
994 B
Python
#pylint: skip-file
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import xgboost as xgb
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import numpy as np
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from sklearn.datasets import make_classification
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from sklearn.model_selection import train_test_split
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import time
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n = 1000000
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num_rounds = 100
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X,y = make_classification(n, n_features=50, random_state=7)
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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': 'exact',
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'updater': 'grow_gpu_hist',
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'max_depth': 8,
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'silent': 1,
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'eval_metric': 'auc'}
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res = {}
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tmp = time.time()
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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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print ("GPU: %s seconds" % (str(time.time() - tmp)))
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tmp = time.time()
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param['updater'] = 'grow_fast_histmaker'
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train'), (dtest, 'test')], evals_result=res)
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print ("CPU: %s seconds" % (str(time.time() - tmp)))
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