Merge pull request #3 from jungtaekkim/0.1.6

0.1.6

.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

LICENSE

@@ -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

README.md

@@ -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)

bayeso_benchmarks/__init__.py

@@ -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/benchmark_base.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/inf_dim_ackley.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_cosines.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_rosenbrock.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_sphere.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)

bayeso_benchmarks/one_dim_constant.py

@@ -43,4 +43,7 @@ def __init__(self,
         global_minimum = constant
         function = lambda bx: fun_target(bx, dim_bx, constant)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Constant, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/one_dim_gramacyandlee2012.py

@@ -30,4 +30,7 @@ def __init__(self, seed=None):
         global_minimum = -0.86901113
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(GramacyAndLee2012, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/one_dim_linear.py

@@ -47,4 +47,7 @@ def __init__(self,
             global_minimum = slope * bounds[0, 1]
         function = lambda bx: fun_target(bx, dim_bx, slope)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Linear, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/one_dim_step.py

@@ -55,4 +55,7 @@ def __init__(self,
         global_minimum = np.min(step_values)
         function = lambda bx: fun_target(bx, dim_bx, steps, step_values)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Step, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/six_dim_hartmann6d.py

@@ -63,4 +63,7 @@ def __init__(self,
         global_minimum = -3.322368
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Hartmann6D, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/three_dim_hartmann3d.py

@@ -60,4 +60,7 @@ def __init__(self,
         global_minimum = -3.86278
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Hartmann3D, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_beale.py

@@ -31,4 +31,7 @@ def __init__(self, seed=None):
         global_minimum = 0.0
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Beale, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_bohachevsky.py

@@ -31,4 +31,7 @@ def __init__(self, seed=None):
         global_minimum = 0.0
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Bohachevsky, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_branin.py

@@ -53,4 +53,7 @@ def __init__(self,
         global_minimum = 0.3978874
         function = lambda bx: fun_target(bx, dim_bx, a, b, c, r, s, t)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Branin, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_dejong5.py

@@ -48,4 +48,7 @@ def __init__(self, seed=None):
         global_minimum = 0.9980038
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(DeJong5, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_dropwave.py

@@ -31,4 +31,7 @@ def __init__(self, seed=None):
         global_minimum = -1.0
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(DropWave, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_eggholder.py

@@ -38,4 +38,7 @@ def __init__(self,
         global_minimum = -959.6406627
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Eggholder, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_goldsteinprice.py

@@ -39,4 +39,7 @@ def __init__(self, seed=None):
         global_minimum = 3.0
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(GoldsteinPrice, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_holdertable.py

@@ -34,4 +34,7 @@ def __init__(self, seed=None):
         global_minimum = -19.2085026
         function = lambda bx: fun_target(bx, dim_bx)
 
-        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(HolderTable, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_kim1.py

@@ -0,0 +1,37 @@
+#
+# author: Jungtaek Kim ([email protected])
+# last updated: October 27, 2021
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    y = np.sin(bx[0]) + np.cos(bx[1]) + 0.016 * (bx[0] - 5.0)**2 + 0.008 * (bx[1] - 5.0)**2
+    return y
+
+
+class Kim1(Function):
+    def __init__(self, seed=None):
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = 2
+        bounds = np.array([
+            [-16.0, 16.0],
+            [-16.0, 16.0],
+        ])
+        global_minimizers = np.array([
+            [4.72130726, 3.17086303],
+        ])
+        global_minimum = -1.9715232347905773
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Kim1, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)

bayeso_benchmarks/two_dim_kim2.py

@@ -0,0 +1,42 @@
+#
+# author: Jungtaek Kim ([email protected])
+# last updated: October 27, 2021
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    y = np.sin(bx[0] / 1.0) + np.cos(bx[1] / 1.0) \
+        + np.sin(bx[0] / 2.0) + np.cos(bx[1] / 2.0) \
+        + np.sin(bx[0] / 4.0) + np.cos(bx[1] / 4.0) \
+        + np.sin(bx[0] / 8.0) + np.cos(bx[1] / 8.0) \
+        + np.sin(bx[0] / 16.0) + np.cos(bx[1] / 16.0) \
+        + 0.0032 * (bx[0] - 20.0)**2 + 0.0016 * (bx[1] - 20.0)**2
+    return y
+
+
+class Kim2(Function):
+    def __init__(self, seed=None):
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = 2
+        bounds = np.array([
+            [-128.0, 128.0],
+            [-128.0, 128.0],
+        ])
+        global_minimizers = np.array([
+            [-2.1013466, 34.14526252],
+        ])
+        global_minimum = -3.454387473489018
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Kim2, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)