Remove benchmark scripts. (#9992)
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Jiaming Yuan
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@ -10,9 +10,7 @@ facilities.
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dependencies for tests, see conda files in `ci_build`.
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* python-gpu: Similar to python tests, but for GPU.
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* travis: CI facilities for Travis.
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* distributed: Test for distributed system.
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* benchmark: Legacy benchmark code. There are a number of benchmark projects for
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XGBoost with much better configurations.
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* test_distributed: Test for distributed systems including spark and dask.
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# Others
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* pytest.ini: Describes the `pytest` marker for python tests, some markers are generated
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@ -1,69 +0,0 @@
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#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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@ -1,86 +0,0 @@
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"""Run benchmark on the tree booster."""
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import argparse
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import ast
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import time
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import numpy as np
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import xgboost as xgb
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RNG = np.random.RandomState(1994)
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def run_benchmark(args):
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"""Runs the benchmark."""
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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 = RNG.rand(args.rows, args.columns)
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y = RNG.randint(0, 2, args.rows)
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if 0.0 < 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]
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for x_row in X])
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train_rows = int(args.rows * (1.0 - args.test_size))
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test_rows = int(args.rows * args.test_size)
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X_train = X[:train_rows, :]
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X_test = X[-test_rows:, :]
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y_train = y[:train_rows]
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y_test = y[-test_rows:]
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print("Generate Time: %s seconds" % (str(time.time() - tmp)))
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del X, y
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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, nthread=-1)
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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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del X_train, y_train, X_test, y_test
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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'}
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if args.params != '':
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param.update(ast.literal_eval(args.params))
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param['tree_method'] = args.tree_method
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print("Training with '%s'" % param['tree_method'])
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tmp = time.time()
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xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
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print("Train Time: %s seconds" % (str(time.time() - tmp)))
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def main():
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"""The main function.
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Defines and parses command line arguments and calls the benchmark.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('--tree_method', default='gpu_hist')
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parser.add_argument('--sparsity', type=float, default=0.0)
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parser.add_argument('--rows', type=int, default=1000000)
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parser.add_argument('--columns', type=int, default=50)
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parser.add_argument('--iterations', type=int, default=500)
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parser.add_argument('--test_size', type=float, default=0.25)
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parser.add_argument('--params', default='',
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help='Provide additional parameters as a Python dict string, e.g. --params '
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'\"{\'max_depth\':2}\"')
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args = parser.parse_args()
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run_benchmark(args)
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if __name__ == '__main__':
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main()
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@ -1,87 +0,0 @@
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"""Generate synthetic data in LIBSVM format."""
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import argparse
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import io
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import time
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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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RNG = np.random.RandomState(2019)
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def generate_data(args):
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"""Generates the data."""
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print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
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print("Sparsity {}".format(args.sparsity))
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print("{}/{} train/test split".format(1.0 - args.test_size, args.test_size))
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tmp = time.time()
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n_informative = args.columns * 7 // 10
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n_redundant = args.columns // 10
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n_repeated = args.columns // 10
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print("n_informative: {}, n_redundant: {}, n_repeated: {}".format(n_informative, n_redundant,
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n_repeated))
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x, y = make_classification(n_samples=args.rows, n_features=args.columns,
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n_informative=n_informative, n_redundant=n_redundant,
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n_repeated=n_repeated, shuffle=False, random_state=RNG)
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print("Generate Time: {} seconds".format(time.time() - tmp))
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tmp = time.time()
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=args.test_size,
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random_state=RNG, shuffle=False)
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print("Train/Test Split Time: {} seconds".format(time.time() - tmp))
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tmp = time.time()
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write_file('train.libsvm', x_train, y_train, args.sparsity)
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print("Write Train Time: {} seconds".format(time.time() - tmp))
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tmp = time.time()
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write_file('test.libsvm', x_test, y_test, args.sparsity)
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print("Write Test Time: {} seconds".format(time.time() - tmp))
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def write_file(filename, x_data, y_data, sparsity):
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with open(filename, 'w') as f:
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for x, y in zip(x_data, y_data):
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write_line(f, x, y, sparsity)
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def write_line(f, x, y, sparsity):
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with io.StringIO() as line:
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line.write(str(y))
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for i, col in enumerate(x):
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if 0.0 < sparsity < 1.0:
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if RNG.uniform(0, 1) > sparsity:
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write_feature(line, i, col)
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else:
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write_feature(line, i, col)
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line.write('\n')
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f.write(line.getvalue())
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def write_feature(line, index, feature):
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line.write(' ')
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line.write(str(index))
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line.write(':')
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line.write(str(feature))
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def main():
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"""The main function.
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Defines and parses command line arguments and calls the generator.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('--rows', type=int, default=1000000)
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parser.add_argument('--columns', type=int, default=50)
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parser.add_argument('--sparsity', type=float, default=0.0)
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parser.add_argument('--test_size', type=float, default=0.01)
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args = parser.parse_args()
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generate_data(args)
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if __name__ == '__main__':
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main()
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