Merge pull request #3 from jungtaekkim/0.1.6

0.1.6

Author: jungtaekkim

.github/workflows/pytest.yml

@@ -0,0 +1,29 @@
+name: pytest
+
+on: [push, pull_request]
+
+jobs:
+  test:
+    strategy:
+      matrix:
+        python-version:
+          - '3.6'
+          - '3.7'
+          - '3.8'
+          - '3.9'
+          - '3.10'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip setuptools
+          pip install pytest
+          pip install scipy
+      - name: Run pytest
+        run: |
+          pip install .
+          pytest

.travis.yml

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2019-2021 Jungtaek Kim
+Copyright (c) 2019-2022 Jungtaek Kim
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

LICENSE

@@ -1,9 +1,9 @@
 # BayesO Benchmarks
-[![Build Status](https://app.travis-ci.com/jungtaekkim/bayeso-benchmarks.svg?branch=main)](https://app.travis-ci.com/jungtaekkim/bayeso-benchmarks)
+[![Build Status](https://github.com/jungtaekkim/bayeso-benchmarks/actions/workflows/pytest.yml/badge.svg)](https://github.com/jungtaekkim/bayeso-benchmarks/actions/workflows/pytest.yml)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 Benchmarks for Bayesian optimization.
-The details of benchmark functions can be found in [these notes](http://jungtaek.github.io/notes/benchmarks_bo.pdf).
+The details of benchmark functions can be found in [these notes](https://jungtaek.github.io/notes/benchmarks_bo.pdf).
 
 ## Installation
 We recommend installing it with `virtualenv`.
@@ -65,10 +65,7 @@ Y_noise = obj_fun.output_gaussian_noise(X)
 ```
 
 ## Author
-* [Jungtaek Kim](http://jungtaek.github.io) (POSTECH)
-
-## Contact
-* Jungtaek Kim: [[email protected]](mailto:[email protected])
+* [Jungtaek Kim](https://jungtaek.github.io) (POSTECH)
 
 ## License
 [MIT License](LICENSE)

README.md

@@ -1,9 +1,10 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: June 24, 2021
+# last updated: October 23, 2021
 #
 
-__version__ = '0.1.5'
+
+__version__ = '0.1.6'
 
 
 from bayeso_benchmarks.inf_dim_ackley import Ackley
@@ -24,6 +25,9 @@
 from bayeso_benchmarks.two_dim_eggholder import Eggholder
 from bayeso_benchmarks.two_dim_goldsteinprice import GoldsteinPrice
 from bayeso_benchmarks.two_dim_holdertable import HolderTable
+from bayeso_benchmarks.two_dim_kim1 import Kim1
+from bayeso_benchmarks.two_dim_kim2 import Kim2
+from bayeso_benchmarks.two_dim_kim3 import Kim3
 from bayeso_benchmarks.two_dim_michalewicz import Michalewicz
 from bayeso_benchmarks.two_dim_sixhumpcamel import SixHumpCamel
 from bayeso_benchmarks.two_dim_threehumpcamel import ThreeHumpCamel

bayeso_benchmarks/__init__.py

@@ -74,6 +74,11 @@ def function(self, bx):
     def _output(self, X):
         assert isinstance(X, np.ndarray)
 
+        bounds = self.get_bounds()
+
+        assert np.all(X >= bounds[:, 0])
+        assert np.all(X <= bounds[:, 1])
+
         if len(X.shape) == 2:
             list_results = [self.function(bx) for bx in X]
         else:
@@ -220,6 +225,6 @@ def sample_uniform(self, num_points, seed=None):
         bounds = self.get_bounds()
 
         points = random_state_.uniform(size=(num_points, dim_problem))
-        points = (bounds[:, 0] + (bounds[:, 1] - bounds[:, 0])) * points
+        points = bounds[:, 0] + (bounds[:, 1] - bounds[:, 0]) * points
 
         return points

bayeso_benchmarks/benchmark_base.py

@@ -40,4 +40,7 @@ def __init__(self, dim_problem, seed=None):
 
         function = lambda bx: fun_target(bx, dim_problem)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Ackley, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)

bayeso_benchmarks/inf_dim_ackley.py

@@ -33,4 +33,7 @@ def __init__(self, dim_problem, seed=None):
 
         function = lambda bx: fun_target(bx, dim_problem)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Cosines, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)

bayeso_benchmarks/inf_dim_cosines.py

@@ -37,4 +37,7 @@ def __init__(self, dim_problem, seed=None):
 
         function = lambda bx: fun_target(bx, dim_problem)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Rosenbrock, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)

bayeso_benchmarks/inf_dim_rosenbrock.py

@@ -36,4 +36,7 @@ def __init__(self, dim_problem, seed=None):
 
         function = lambda bx: fun_target(bx, dim_problem)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Sphere, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)