c-dickens commented on code in PR #354: URL: https://github.com/apache/datasketches-cpp/pull/354#discussion_r1159518470
########## python/tests/count_min_test.py: ########## @@ -0,0 +1,86 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +import unittest +from datasketches import count_min_sketch + +class CountMinTest(unittest.TestCase): + def test_count_min_example(self): + # we'll define target confidence and relative error and use the built-in + # methods to determine how many hashes and buckets to use + confidence = 0.95 + num_hashes = count_min_sketch.suggest_num_hashes(confidence) + relative_error = 0.01 + num_buckets = count_min_sketch.suggest_num_buckets(relative_error) + + # now we can create a few empty sketches + cm = count_min_sketch(num_hashes, num_buckets) + cm2 = count_min_sketch(num_hashes, num_buckets) + self.assertTrue(cm.is_empty()) + + # we'll use a moderate number of distinct items with + # increasing weights, with each item's weight being + # equal to its value + n = 1000 + total_wt = 0 + for i in range(1, n+1): + cm.update(i, i) + total_wt += i + self.assertFalse(cm.is_empty()) + self.assertEqual(cm.get_total_weight(), total_wt) + + # querying the items, each of them should + # have a non-zero count. the estimate should + # be at least i with appropriately behaved bounds. + for i in range(1, n+1): + val = cm.get_estimate(i) + self.assertGreaterEqual(val, i) + self.assertGreaterEqual(val, cm.get_lower_bound(i)) + self.assertGreater(cm.get_upper_bound(i), val) + + # values not in the sketch should have lower estimates, but + # are not guaranteed to be zero and will succeed + self.assertIsNotNone(cm.get_estimate("not in set")) + + # we can create another sketch with partial overlap + # and merge them + for i in range(int(n / 2), int(3 * n / 2)): + cm2.update(i, i) + cm.merge(cm2) + + # and the estimated weight for the meerged values should now be + # at least 2x the value + self.assertGreaterEqual(cm.get_estimate(n), 2 * n) Review Comment: Is it worth specifying that this only applies to the items in the overlap interval [n/2, n] ? Items n+1, ... 3n / 2 - 1 have all been included only once with their weight. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
