70 lines
2.8 KiB
Python
70 lines
2.8 KiB
Python
#pylint: skip-file
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import argparse
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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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import ast
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rng = np.random.RandomState(1994)
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def run_benchmark(args):
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try:
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dtest = xgb.DMatrix('dtest.dm')
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dtrain = xgb.DMatrix('dtrain.dm')
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if not (dtest.num_col() == args.columns \
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and dtrain.num_col() == args.columns):
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raise ValueError("Wrong cols")
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if not (dtest.num_row() == args.rows * args.test_size \
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and dtrain.num_row() == args.rows * (1-args.test_size)):
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raise ValueError("Wrong rows")
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except:
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print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
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print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
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tmp = time.time()
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X, y = make_classification(args.rows, n_features=args.columns, n_redundant=0, n_informative=args.columns, n_repeated=0, random_state=7)
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if args.sparsity < 1.0:
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X = np.array([[np.nan if rng.uniform(0, 1) < args.sparsity else x for x in x_row] for x_row in X])
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size, random_state=7)
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print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
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tmp = time.time()
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print ("DMatrix Start")
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dtrain = xgb.DMatrix(X_train, y_train)
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dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
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print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
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dtest.save_binary('dtest.dm')
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dtrain.save_binary('dtrain.dm')
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param = {'objective': 'binary:logistic','booster':'gblinear'}
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if args.params != '':
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param.update(ast.literal_eval(args.params))
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param['updater'] = args.updater
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print("Training with '%s'" % param['updater'])
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tmp = time.time()
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xgb.train(param, dtrain, args.iterations, evals=[(dtrain,"train")], early_stopping_rounds = args.columns)
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print ("Train Time: %s seconds" % (str(time.time() - tmp)))
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parser = argparse.ArgumentParser()
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parser.add_argument('--updater', default='coord_descent')
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parser.add_argument('--sparsity', type=float, default=0.0)
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parser.add_argument('--lambda', type=float, default=1.0)
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parser.add_argument('--tol', type=float, default=1e-5)
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parser.add_argument('--alpha', type=float, default=1.0)
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parser.add_argument('--rows', type=int, default=1000000)
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parser.add_argument('--iterations', type=int, default=10000)
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parser.add_argument('--columns', type=int, default=50)
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parser.add_argument('--test_size', type=float, default=0.25)
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parser.add_argument('--standardise', type=bool, default=False)
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parser.add_argument('--params', default='', help='Provide additional parameters as a Python dict string, e.g. --params \"{\'max_depth\':2}\"')
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args = parser.parse_args()
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run_benchmark(args)
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