add tests for public API

tests/test_api/test_api_hyperactive.py

@@ -0,0 +1,34 @@
+import pytest
+
+from hyperactive import Hyperactive
+from surfaces.test_functions import test_functions
+
+
+test_functions_d = (
+    "test_function",
+    test_functions,
+)
+
+
+@pytest.mark.parametrize(*test_functions_d)
+def test_all_functions(test_function):
+    try:
+        test_function_ = test_function()
+    except TypeError:
+        test_function_ = test_function(n_dim=3)
+
+    try:
+        search_space = test_function_.search_space(value_types="list")
+    except TypeError:
+        search_space = test_function_.search_space()
+
+    objective_function = test_function_.objective_function
+    n_iter = 5
+
+    hyper = Hyperactive()
+    hyper.add_search(
+        objective_function,
+        search_space,
+        n_iter=n_iter,
+    )
+    hyper.run()

tests/test_api/test_input_type.py

@@ -0,0 +1,36 @@
+import pytest
+
+from surfaces.test_functions import mathematical_functions, machine_learning_functions
+
+
+mathematical_functions_d = (
+    "test_function",
+    mathematical_functions,
+)
+
+
+machine_learning_functions_d = (
+    "test_function",
+    machine_learning_functions,
+)
+
+
+@pytest.mark.parametrize(*mathematical_functions_d)
+def test_(test_function):
+    try:
+        test_function_ = test_function()
+    except TypeError:
+        test_function_ = test_function(n_dim=2)
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(value_types="array")
+    n_iter = 20
+
+
+@pytest.mark.parametrize(*machine_learning_functions_d)
+def test_all_(test_function):
+    test_function_ = test_function()
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space()
+    n_iter = 20

tests/test_api/test_metric.py

@@ -0,0 +1,36 @@
+import pytest
+
+from surfaces.test_functions import mathematical_functions, machine_learning_functions
+
+
+mathematical_functions_d = (
+    "test_function",
+    mathematical_functions,
+)
+
+
+machine_learning_functions_d = (
+    "test_function",
+    machine_learning_functions,
+)
+
+
+@pytest.mark.parametrize(*mathematical_functions_d)
+def test_(test_function):
+    try:
+        test_function_ = test_function()
+    except TypeError:
+        test_function_ = test_function(n_dim=2)
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(value_types="array")
+    n_iter = 20
+
+
+@pytest.mark.parametrize(*machine_learning_functions_d)
+def test_all_(test_function):
+    test_function_ = test_function()
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space()
+    n_iter = 20

tests/test_api/test_search_space.py

@@ -0,0 +1,116 @@
+import pytest
+from gradient_free_optimizers import RandomSearchOptimizer
+from hyperactive import Hyperactive
+
+
+from surfaces.test_functions import (
+    machine_learning_functions,
+)
+from surfaces.test_functions.mathematical import (
+    mathematical_functions_1d,
+    mathematical_functions_2d,
+    mathematical_functions_nd,
+)
+
+mathematical_functions_1d = (
+    "test_function",
+    mathematical_functions_1d,
+)
+
+mathematical_functions_2d = (
+    "test_function",
+    mathematical_functions_2d,
+)
+
+mathematical_functions_nd = (
+    "test_function",
+    mathematical_functions_nd,
+)
+
+
+machine_learning_functions_d = (
+    "test_function",
+    machine_learning_functions,
+)
+
+
+@pytest.mark.parametrize(*mathematical_functions_1d)
+def test_search_space_1d_0(test_function):
+    test_function_ = test_function()
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(min=-1, max=1, value_types="array")
+    n_iter = 20
+
+    opt = RandomSearchOptimizer(search_space)
+    opt.search(objective_function, n_iter=n_iter)
+
+
+@pytest.mark.parametrize(*mathematical_functions_2d)
+def test_search_space_2d_0(test_function):
+    print("\n test_function", test_function, "\n")
+    test_function_ = test_function()
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(min=-1, max=1, value_types="array")
+    n_iter = 20
+
+    opt = RandomSearchOptimizer(search_space)
+    opt.search(objective_function, n_iter=n_iter)
+
+
+@pytest.mark.parametrize(*mathematical_functions_2d)
+def test_search_space_2d_1(test_function):
+    test_function_ = test_function()
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(
+        min=[-1, -1], max=[1, 1], value_types="array"
+    )
+    n_iter = 20
+
+    opt = RandomSearchOptimizer(search_space)
+    opt.search(objective_function, n_iter=n_iter)
+
+
+@pytest.mark.parametrize(*mathematical_functions_nd)
+def test_search_space_nd_0(test_function):
+    test_function_ = test_function(n_dim=3)
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(min=-1, max=1, value_types="array")
+    n_iter = 20
+
+    opt = RandomSearchOptimizer(search_space)
+    opt.search(objective_function, n_iter=n_iter)
+
+
+@pytest.mark.parametrize(*mathematical_functions_nd)
+def test_search_space_nd_1(test_function):
+    test_function_ = test_function(n_dim=3)
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(
+        min=[-1, -1, -1], max=[1, 1, 1], value_types="array"
+    )
+    n_iter = 20
+
+    opt = RandomSearchOptimizer(search_space)
+    opt.search(objective_function, n_iter=n_iter)
+
+
+@pytest.mark.parametrize(*machine_learning_functions_d)
+def test_all_(test_function):
+    test_function_ = test_function()
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space()
+    n_iter = 3
+
+    hyper = Hyperactive()
+    hyper.add_search(
+        objective_function,
+        search_space,
+        n_iter=n_iter,
+    )
+    hyper.run()

tests/test_api/test_sleep.py

@@ -0,0 +1,36 @@
+import pytest
+
+from surfaces.test_functions import mathematical_functions, machine_learning_functions
+
+
+mathematical_functions_d = (
+    "test_function",
+    mathematical_functions,
+)
+
+
+machine_learning_functions_d = (
+    "test_function",
+    machine_learning_functions,
+)
+
+
+@pytest.mark.parametrize(*mathematical_functions_d)
+def test_(test_function):
+    try:
+        test_function_ = test_function()
+    except TypeError:
+        test_function_ = test_function(n_dim=2)
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space(value_types="array")
+    n_iter = 20
+
+
+@pytest.mark.parametrize(*machine_learning_functions_d)
+def test_all_(test_function):
+    test_function_ = test_function()
+
+    objective_function = test_function_.objective_function
+    search_space = test_function_.search_space()
+    n_iter = 20