Initial version of scikit-learn DBSCAN benchmark

Author: bibikar

sklearn/bench.py

@@ -121,7 +121,7 @@ def parse_args(parser, size=None, dtypes=None, loop_types=(),
 
     n_jobs = None
     if n_jobs_supported and not daal_version:
-        n_jobs = num_threads = params.num_threads
+        n_jobs = num_threads = params.threads
 
     # Set threading and DAAL related params here
     setattr(params, 'threads', num_threads)

sklearn/dbscan.py

@@ -0,0 +1,57 @@
+# 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
+import numpy as np
+from sklearn.cluster import DBSCAN
+
+parser = argparse.ArgumentParser(description='scikit-learn DBSCAN 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=0.5,
+                    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, loop_types=('fit', 'predict'), n_jobs_supported=True)
+
+# Load generated data
+X = np.load(params.filex)
+X_mult = np.vstack((X,) * params.data_multiplier)
+
+# Create our clustering object
+dbscan = DBSCAN(eps=params.eps, n_jobs=params.n_jobs,
+                min_samples=params.min_samples, metric='euclidean',
+                algorithm='auto')
+
+# N.B. algorithm='auto' will select DAAL's brute force method when running
+# daal4py-patched scikit-learn.
+
+columns = ('batch', 'arch', 'prefix', 'function', 'threads', 'dtype', 'size',
+           'n_clusters', 'time')
+params.size = size_str(X.shape)
+params.dtype = X.dtype
+print_header(columns, params)
+
+# Time fit
+fit_time, _ = time_mean_min(dbscan.fit, X,
+                            outer_loops=params.fit_outer_loops,
+                            inner_loops=params.fit_inner_loops,
+                            goal_outer_loops=params.fit_goal,
+                            time_limit=params.fit_time_limit,
+                            verbose=params.verbose)
+params.n_clusters = len(dbscan.core_sample_indices_)
+print_row(columns, params, function='DBSCAN.fit', time=fit_time)
+
+# Time predict
+predict_time, _ = time_mean_min(dbscan.fit_predict, X,
+                                outer_loops=params.predict_outer_loops,
+                                inner_loops=params.predict_inner_loops,
+                                goal_outer_loops=params.predict_goal,
+                                time_limit=params.predict_time_limit,
+                                verbose=params.verbose)
+print_row(columns, params, function='DBSCAN.fit_predict', time=predict_time)