| # ------------------------------------------------------------- |
| # |
| # 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 math |
| import os |
| import random |
| import sys |
| import unittest |
| |
| import numpy as np |
| import scipy.stats as st |
| from systemds.context import SystemDSContext |
| from systemds.matrix.data_gen import rand |
| |
| np.random.seed(7) |
| # TODO Remove the randomness of the test, such that |
| # inputs for the random operation is predictable |
| shape = (random.randrange(1, 25), random.randrange(1, 25)) |
| dist_shape = (10, 15) |
| min_max = (0, 1) |
| sparsity = random.uniform(0.0, 1.0) |
| seed = 123 |
| distributions = ["norm", "uniform"] |
| |
| |
| class TestRand(unittest.TestCase): |
| |
| sds: SystemDSContext = None |
| |
| @classmethod |
| def setUpClass(cls): |
| cls.sds = SystemDSContext() |
| |
| @classmethod |
| def tearDownClass(cls): |
| cls.sds.close() |
| |
| def test_rand_shape(self): |
| m = rand(self.sds, rows=shape[0], cols=shape[1]).compute() |
| self.assertTrue(m.shape == shape) |
| |
| def test_rand_min_max(self): |
| m = ( |
| rand(self.sds, rows=shape[0], cols=shape[1], |
| min=min_max[0], max=min_max[1]) |
| .compute()) |
| self.assertTrue((m.min() >= min_max[0]) and (m.max() <= min_max[1])) |
| |
| def test_rand_sparsity(self): |
| m = rand(self.sds, rows=shape[0], cols=shape[1], |
| sparsity=sparsity, seed=0).compute() |
| non_zero_value_percent = np.count_nonzero(m) * 100 / np.prod(m.shape) |
| |
| self.assertTrue(math.isclose( |
| non_zero_value_percent, sparsity*100, rel_tol=5)) |
| |
| def test_rand_uniform_distribution(self): |
| m = ( |
| rand(self.sds, |
| rows=dist_shape[0], |
| cols=dist_shape[1], |
| pdf="uniform", |
| min=min_max[0], |
| max=min_max[1], |
| seed=0) |
| .compute()) |
| |
| dist = find_best_fit_distribution(m.flatten("F"), distributions) |
| self.assertTrue(dist == "uniform") |
| |
| def test_rand_normal_distribution(self): |
| m = ( |
| rand(self.sds, |
| rows=dist_shape[0], |
| cols=dist_shape[1], |
| pdf="normal", |
| min=min_max[0], |
| max=min_max[1], |
| seed=0) |
| .compute()) |
| |
| dist = find_best_fit_distribution(m.flatten("F"), distributions) |
| self.assertTrue(dist == "norm") |
| |
| def test_rand_zero_shape(self): |
| m = rand(self.sds, rows=0, cols=0).compute() |
| self.assertTrue(np.allclose(m, np.array([[]]))) |
| |
| def test_rand_invalid_shape(self): |
| with self.assertRaises(ValueError) as context: |
| rand(self.sds, rows=1, cols=-10).compute() |
| |
| def test_rand_invalid_pdf(self): |
| with self.assertRaises(ValueError) as context: |
| rand(self.sds, rows=1, cols=10, pdf="norm").compute() |
| |
| |
| def find_best_fit_distribution(data, distribution_lst): |
| """ |
| Finds and returns the distribution of the distributions list that fits the data the best. |
| :param data: flat numpy array |
| :param distribution_lst: distributions to check |
| :return: best distribution that fits the data |
| """ |
| result = dict() |
| |
| for dist in distribution_lst: |
| param = getattr(st, dist).fit(data) |
| |
| D, p_value = st.kstest(data, dist, args=param) |
| result[dist] = p_value |
| |
| best_dist = max(result, key=result.get) |
| return best_dist |
| |
| |
| if __name__ == "__main__": |
| unittest.main(exit=False) |