Support multiple batches in gpu_hist (#5014)

* Initial external memory training support for GPU Hist tree method.

tests/benchmark/generate_libsvm.py

@@ -0,0 +1,87 @@
+"""Generate synthetic data in LibSVM format."""
+
+import argparse
+import io
+import time
+
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+
+RNG = np.random.RandomState(2019)
+
+
+def generate_data(args):
+    """Generates the data."""
+    print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
+    print("Sparsity {}".format(args.sparsity))
+    print("{}/{} train/test split".format(1.0 - args.test_size, args.test_size))
+
+    tmp = time.time()
+    n_informative = args.columns * 7 // 10
+    n_redundant = args.columns // 10
+    n_repeated = args.columns // 10
+    print("n_informative: {}, n_redundant: {}, n_repeated: {}".format(n_informative, n_redundant,
+                                                                      n_repeated))
+    x, y = make_classification(n_samples=args.rows, n_features=args.columns,
+                               n_informative=n_informative, n_redundant=n_redundant,
+                               n_repeated=n_repeated, shuffle=False, random_state=RNG)
+    print("Generate Time: {} seconds".format(time.time() - tmp))
+
+    tmp = time.time()
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=args.test_size,
+                                                        random_state=RNG, shuffle=False)
+    print("Train/Test Split Time: {} seconds".format(time.time() - tmp))
+
+    tmp = time.time()
+    write_file('train.libsvm', x_train, y_train, args.sparsity)
+    print("Write Train Time: {} seconds".format(time.time() - tmp))
+
+    tmp = time.time()
+    write_file('test.libsvm', x_test, y_test, args.sparsity)
+    print("Write Test Time: {} seconds".format(time.time() - tmp))
+
+
+def write_file(filename, x_data, y_data, sparsity):
+    with open(filename, 'w') as f:
+        for x, y in zip(x_data, y_data):
+            write_line(f, x, y, sparsity)
+
+
+def write_line(f, x, y, sparsity):
+    with io.StringIO() as line:
+        line.write(str(y))
+        for i, col in enumerate(x):
+            if 0.0 < sparsity < 1.0:
+                if RNG.uniform(0, 1) > sparsity:
+                    write_feature(line, i, col)
+            else:
+                write_feature(line, i, col)
+        line.write('\n')
+        f.write(line.getvalue())
+
+
+def write_feature(line, index, feature):
+    line.write(' ')
+    line.write(str(index))
+    line.write(':')
+    line.write(str(feature))
+
+
+def main():
+    """The main function.
+
+    Defines and parses command line arguments and calls the generator.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--rows', type=int, default=1000000)
+    parser.add_argument('--columns', type=int, default=50)
+    parser.add_argument('--sparsity', type=float, default=0.0)
+    parser.add_argument('--test_size', type=float, default=0.01)
+    args = parser.parse_args()
+
+    generate_data(args)
+
+
+if __name__ == '__main__':
+    main()