Add daal4py dbscan benchmark

Author: bibikar

daal4py/dbscan.py

@@ -0,0 +1,55 @@
+# Copyright (C) 2017-2019 Intel Corporation
+#
+# SPDX-License-Identifier: MIT
+
+import argparse
+from bench import parse_args, time_mean_min, print_header, print_row, size_str
+from daal4py import dbscan
+from daal4py.sklearn.utils import getFPType
+import numpy as np
+
+parser = argparse.ArgumentParser(description='daal4py DBSCAN clustering '
+                                             'benchmark')
+parser.add_argument('-x', '--filex', '--fileX', '--input', required=True,
+                    type=str, help='Points to cluster')
+parser.add_argument('-e', '--eps', '--epsilon', type=float, default=10,
+                    help='Radius of neighborhood of a point')
+parser.add_argument('-m', '--data-multiplier', default=100,
+                    type=int, help='Data multiplier')
+parser.add_argument('-M', '--min-samples', default=5, type=int,
+                    help='The minimum number of samples required in a '
+                    'neighborhood to consider a point a core point')
+params = parse_args(parser, prefix='daal4py')
+
+# Load generated data
+X = np.load(params.filex)
+X_mult = np.vstack((X,) * params.data_multiplier)
+
+params.size = size_str(X.shape)
+params.dtype = X.dtype
+
+
+# Define functions to time
+def test_dbscan(X):
+    algorithm = dbscan(
+        fptype=getFPType(X),
+        epsilon=params.eps,
+        minObservations=params.min_samples,
+        resultsToCompute='computeCoreIndices'
+    )
+    return algorithm.compute(X)
+
+
+columns = ('batch', 'arch', 'prefix', 'function', 'threads', 'dtype', 'size',
+           'n_clusters', 'time')
+print_header(columns, params)
+
+# Time clustering
+time, result = time_mean_min(test_dbscan, X,
+                             outer_loops=params.outer_loops,
+                             inner_loops=params.inner_loops,
+                             goal_outer_loops=params.goal,
+                             time_limit=params.time_limit,
+                             verbose=params.verbose)
+params.n_clusters = result.nClusters[0, 0]
+print_row(columns, params, function='DBSCAN', time=time)