Merge pull request #1 from jungtaekkim/0.1.4

0.1.4

.gitignore

@@ -129,6 +129,6 @@ dmypy.json
 .pyre/
 
 # Jungtaek
-*.swp
 .DS_Store
 __MACOSX/
+*.swp

.travis.yml

@@ -6,6 +6,7 @@ python:
   - "3.6"
   - "3.7"
   - "3.8"
+  - "3.9"
 install:
   - pip install .
 script:

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2019 Jungtaek Kim
+Copyright (c) 2019-2021 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

MANIFEST.in

@@ -1,2 +1,2 @@
-include LICENSE
 include requirements.txt
+include LICENSE

README.md

@@ -6,7 +6,7 @@ Benchmarks for Bayesian optimization.
 The details of benchmark functions can be found in [these notes](http://jungtaek.github.io/notes/benchmarks_bo.pdf).
 
 ## Installation
-We recommend it should be installed in `virtualenv`.
+We recommend installing it with `virtualenv`.
 You can choose one of three installation options.
 
 * Using PyPI repository (for user installation)
@@ -51,7 +51,7 @@ The following `requirements` files include the package list, the purpose of whic
 * `requirements-dev.txt`: It is for developing the `bayeso-benchmarks` package.
 
 ## Author
-* [Jungtaek Kim](http://mlg.postech.ac.kr/~jtkim/) (POSTECH)
+* [Jungtaek Kim](http://jungtaek.github.io) (POSTECH)
 
 ## Contact
 * Jungtaek Kim: [[email protected]](mailto:[email protected])

benchmarks/__init__.py → bayeso_benchmarks/__init__.py

@@ -3,4 +3,4 @@
 # last updated: November 5, 2020
 #
 
-__version__ = '0.1.3'
+__version__ = '0.1.4'

benchmarks/benchmark_base.py → bayeso_benchmarks/benchmark_base.py

@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
@@ -9,7 +9,7 @@
 
 
 class Function(object):
-    def __init__(self, dimensionality, bounds, global_minimizers, global_minimum, function, dim_problem=None):
+    def __init__(self, dimensionality, bounds, global_minimizers, global_minimum, function, dim_problem=None, seed=None):
         assert isinstance(dimensionality, int) or dimensionality is np.inf
         assert isinstance(bounds, np.ndarray)
         assert isinstance(global_minimizers, np.ndarray)
@@ -27,6 +27,7 @@ def __init__(self, dimensionality, bounds, global_minimizers, global_minimum, fu
         self._function = function
 
         self.dim_problem = dim_problem
+        self.random_state = np.random.RandomState(seed)
 
         self.validate_properties()
 
@@ -70,7 +71,7 @@ def function(self, bx):
 
         return self._function(bx)
 
-    def output(self, X):
+    def _output(self, X):
         assert isinstance(X, np.ndarray)
 
         if len(X.shape) == 2:
@@ -79,22 +80,20 @@ def output(self, X):
             list_results = [self.function(X)]
 
         by = np.array(list_results)
+        return by
+
+    def output(self, X):
+        by = self._output(X)
         Y = np.expand_dims(by, axis=1)
 
         assert len(Y.shape) == 2
         assert Y.shape[1] == 1
         return Y
 
     def output_constant_noise(self, X, scale_noise=0.01):
-        assert isinstance(X, np.ndarray)
         assert isinstance(scale_noise, float)
 
-        if len(X.shape) == 2:
-            list_results = [self.function(bx) for bx in X]
-        else:
-            list_results = [self.function(X)]
-
-        by = np.array(list_results)
+        by = self._output(X)
         by += scale_noise
 
         Y = np.expand_dims(by, axis=1)
@@ -104,16 +103,10 @@ def output_constant_noise(self, X, scale_noise=0.01):
         return Y
 
     def output_gaussian_noise(self, X, scale_noise=0.01):
-        assert isinstance(X, np.ndarray)
         assert isinstance(scale_noise, float)
 
-        if len(X.shape) == 2:
-            list_results = [self.function(bx) for bx in X]
-        else:
-            list_results = [self.function(X)]
-
-        by = np.array(list_results)
-        by += scale_noise * np.random.randn(by.shape[0])
+        by = self._output(X)
+        by += scale_noise * self.random_state.randn(by.shape[0])
 
         Y = np.expand_dims(by, axis=1)
 
@@ -122,23 +115,20 @@ def output_gaussian_noise(self, X, scale_noise=0.01):
         return Y
 
     def output_sparse_gaussian_noise(self, X, scale_noise=0.1, sparsity=0.01):
-        assert isinstance(X, np.ndarray)
         assert isinstance(scale_noise, float)
         assert isinstance(sparsity, float)
         assert sparsity >= 0.0 and sparsity <= 1.0
         assert sparsity < 0.5
 
+        by = self._output(X)
+
         if len(X.shape) == 2:
             num_X = X.shape[0]
-            list_results = [self.function(bx) for bx in X]
         else:
             num_X = 1
-            list_results = [self.function(X)]
 
-        by = np.array(list_results)
-
-        noise = np.random.randn(num_X)
-        mask = np.random.uniform(low=0.0, high=1.0, size=num_X) < sparsity
+        noise = self.random_state.randn(num_X)
+        mask = self.random_state.uniform(low=0.0, high=1.0, size=num_X) < sparsity
         noise *= mask.astype(np.float)
         by += scale_noise * noise
 

benchmarks/inf_dim_ackley.py → bayeso_benchmarks/inf_dim_ackley.py

@@ -1,11 +1,11 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx,

benchmarks/inf_dim_cosines.py → bayeso_benchmarks/inf_dim_cosines.py

@@ -1,11 +1,11 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx):

benchmarks/inf_dim_rosenbrock.py → bayeso_benchmarks/inf_dim_rosenbrock.py

@@ -1,6 +1,11 @@
+#
+# author: Jungtaek Kim ([email protected])
+# last updated: February 8, 2021
+#
+
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx):
@@ -17,6 +22,7 @@ def fun_target(bx, dim_bx):
 class Rosenbrock(Function):
     def __init__(self, dim_problem):
         assert isinstance(dim_problem, int)
+        assert dim_problem > 1
 
         dim_bx = np.inf
         bounds = np.array([

benchmarks/inf_dim_sphere.py → bayeso_benchmarks/inf_dim_sphere.py

@@ -1,11 +1,11 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx):

benchmarks/one_dim_constant.py → bayeso_benchmarks/one_dim_constant.py

@@ -1,11 +1,11 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx, constant):

benchmarks/one_dim_gramacyandlee2012.py → bayeso_benchmarks/one_dim_gramacyandlee2012.py

@@ -1,11 +1,11 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx):

benchmarks/one_dim_linear.py → bayeso_benchmarks/one_dim_linear.py

@@ -1,11 +1,11 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx, slope):

benchmarks/one_dim_step.py → bayeso_benchmarks/one_dim_step.py

@@ -1,11 +1,11 @@
 #
 # author: Jungtaek Kim ([email protected])
-# last updated: November 5, 2020
+# last updated: February 8, 2021
 #
 
 import numpy as np
 
-from benchmarks.benchmark_base import Function
+from bayeso_benchmarks.benchmark_base import Function
 
 
 def fun_target(bx, dim_bx, steps, step_values):