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clone and minorly adapt python kll testing to classic quantiles
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| # 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. | ||
|
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| import unittest | ||
| from datasketches import quantiles_ints_sketch, quantiles_floats_sketch, quantiles_doubles_sketch | ||
| import numpy as np | ||
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| class QuantilesTest(unittest.TestCase): | ||
| def test_quantiles_example(self): | ||
| k = 128 | ||
| n = 2 ** 20 | ||
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| # create a sketch and inject ~1 million N(0,1) points as an array and as a single item | ||
| quantiles = quantiles_floats_sketch(k) | ||
| quantiles.update(np.random.normal(size=n-1)) | ||
| quantiles.update(0.0) | ||
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| # 0 should be near the median | ||
| self.assertAlmostEqual(0.5, quantiles.get_rank(0.0), delta=0.035) | ||
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| # the median should be near 0 | ||
| self.assertAlmostEqual(0.0, quantiles.get_quantile(0.5), delta=0.035) | ||
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| # we also track the min/max independently from the rest of the data | ||
| # which lets us know the full observed data range | ||
| self.assertLessEqual(quantiles.get_min_value(), quantiles.get_quantile(0.01)) | ||
| self.assertLessEqual(0.0, quantiles.get_rank(quantiles.get_min_value())) | ||
| self.assertGreaterEqual(quantiles.get_max_value(), quantiles.get_quantile(0.99)) | ||
| self.assertGreaterEqual(1.0, quantiles.get_rank(quantiles.get_max_value())) | ||
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| # we can also extract a list of values at a time, | ||
| # here the values should give us something close to [-2, -1, 0, 1, 2]. | ||
| # then get the CDF, which will return something close to | ||
| # the original values used in get_quantiles() | ||
| # finally, can check the normalized rank error bound | ||
| pts = quantiles.get_quantiles([0.0228, 0.1587, 0.5, 0.8413, 0.9772]) | ||
| cdf = quantiles.get_cdf(pts) # include 1.0 at end to account for all probability mass | ||
| self.assertEqual(len(cdf), len(pts)+1) | ||
| err = quantiles.normalized_rank_error(False) | ||
| self.assertEqual(err, quantiles_floats_sketch.get_normalized_rank_error(k, False)) | ||
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| # and a few basic queries about the sketch | ||
| self.assertFalse(quantiles.is_empty()) | ||
| self.assertTrue(quantiles.is_estimation_mode()) | ||
| self.assertEqual(quantiles.get_n(), n) | ||
| self.assertEqual(quantiles.get_k(), k) | ||
| self.assertLess(quantiles.get_num_retained(), n) | ||
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| # merging itself will double the number of items the sketch has seen | ||
| quantiles.merge(quantiles) | ||
| self.assertEqual(quantiles.get_n(), 2*n) | ||
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| # we can then serialize and reconstruct the sketch | ||
| quantiles_bytes = quantiles.serialize() | ||
| new_quantiles = quantiles.deserialize(quantiles_bytes) | ||
| self.assertEqual(quantiles.get_num_retained(), new_quantiles.get_num_retained()) | ||
| self.assertEqual(quantiles.get_min_value(), new_quantiles.get_min_value()) | ||
| self.assertEqual(quantiles.get_max_value(), new_quantiles.get_max_value()) | ||
| self.assertEqual(quantiles.get_quantile(0.7), new_quantiles.get_quantile(0.7)) | ||
| self.assertEqual(quantiles.get_rank(0.0), new_quantiles.get_rank(0.0)) | ||
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| def test_quantiles_ints_sketch(self): | ||
| k = 128 | ||
| n = 10 | ||
| quantiles = quantiles_ints_sketch(k) | ||
| for i in range(0, n): | ||
| quantiles.update(i) | ||
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| self.assertEqual(quantiles.get_min_value(), 0) | ||
| self.assertEqual(quantiles.get_max_value(), n-1) | ||
| self.assertEqual(quantiles.get_n(), n) | ||
| self.assertFalse(quantiles.is_empty()) | ||
| self.assertFalse(quantiles.is_estimation_mode()) # n < k | ||
| self.assertEqual(quantiles.get_k(), k) | ||
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| pmf = quantiles.get_pmf([round(n/2)]) | ||
| self.assertIsNotNone(pmf) | ||
| self.assertEqual(len(pmf), 2) | ||
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| cdf = quantiles.get_cdf([round(n/2)]) | ||
| self.assertIsNotNone(cdf) | ||
| self.assertEqual(len(cdf), 2) | ||
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| self.assertEqual(quantiles.get_quantile(0.5), round(n/2)) | ||
| quants = quantiles.get_quantiles([0.25, 0.5, 0.75]) | ||
| self.assertIsNotNone(quants) | ||
| self.assertEqual(len(quants), 3) | ||
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| self.assertEqual(quantiles.get_rank(round(n/2)), 0.5) | ||
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| # merge self | ||
| quantiles.merge(quantiles) | ||
| self.assertEqual(quantiles.get_n(), 2 * n) | ||
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| sk_bytes = quantiles.serialize() | ||
| self.assertTrue(isinstance(quantiles_ints_sketch.deserialize(sk_bytes), quantiles_ints_sketch)) | ||
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| def test_quantiles_floats_sketch(self): | ||
| # already tested ints and it's templatized, so just make sure it instantiates properly | ||
| k = 256 | ||
| quantiles = quantiles_floats_sketch(k) | ||
| self.assertTrue(quantiles.is_empty()) | ||
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| def test_quantiles_doubles_sketch(self): | ||
| # already tested ints and it's templatized, so just make sure it instantiates properly | ||
| k = 128 | ||
| quantiles = quantiles_doubles_sketch(k) | ||
| self.assertTrue(quantiles.is_empty()) | ||
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| if __name__ == '__main__': | ||
| unittest.main() |