| # ------------------------------------------------------------- |
| # |
| # 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 os |
| import shutil |
| import sys |
| import unittest |
| |
| import numpy as np |
| import pandas as pd |
| from systemds.context import SystemDSContext |
| from systemds.operator.algorithm import hyperband |
| |
| |
| class TestHyperband(unittest.TestCase): |
| |
| sds: SystemDSContext = None |
| np.random.seed(42) |
| X_train = np.random.rand(50, 10) |
| y_train = np.sum(X_train, axis=1, keepdims=True) + np.random.rand(50, 1) |
| X_val = np.random.rand(50, 10) |
| y_val = np.sum(X_val, axis=1, keepdims=True) + np.random.rand(50, 1) |
| params = 'list("reg", "tol", "maxi")' |
| min_max_params = [[0, 20], [0.0001, 0.1], [5, 10]] |
| param_ranges = np.array(min_max_params) |
| |
| @classmethod |
| def setUpClass(cls): |
| cls.sds = SystemDSContext(capture_stdout=True, logging_level=50) |
| |
| @classmethod |
| def tearDownClass(cls): |
| cls.sds.close() |
| |
| def tearDown(self): |
| pass |
| |
| def test_hyperband(self): |
| x_train = self.sds.from_numpy(self.X_train) |
| y_train = self.sds.from_numpy(self.y_train) |
| x_val = self.sds.from_numpy(self.X_val) |
| y_val = self.sds.from_numpy(self.y_val) |
| paramRanges = self.sds.from_numpy(self.param_ranges) |
| params = self.params |
| [best_weights_mat, opt_hyper_params_df] = hyperband( |
| X_train=x_train, |
| y_train=y_train, |
| X_val=x_val, |
| y_val=y_val, |
| params=params, |
| paramRanges=paramRanges, |
| verbose=False, |
| ).compute() |
| self.assertTrue(isinstance(best_weights_mat, np.ndarray)) |
| self.assertTrue(best_weights_mat.shape[0] == self.X_train.shape[1]) |
| self.assertTrue(best_weights_mat.shape[1] == self.y_train.shape[1]) |
| |
| self.assertTrue(isinstance(opt_hyper_params_df, pd.DataFrame)) |
| self.assertTrue(opt_hyper_params_df.shape[1] == 1) |
| for i, hyper_param in enumerate(opt_hyper_params_df.values.flatten().tolist()): |
| self.assertTrue( |
| self.min_max_params[i][0] <= hyper_param <= self.min_max_params[i][1] |
| ) |
| |
| |
| if __name__ == "__main__": |
| unittest.main(exit=False) |