Remove dependecy on lazy fixture

.github/workflows/codecov.yml

@@ -15,10 +15,10 @@ jobs:
 
       - uses: actions/checkout@v4
 
-      - name: Set up Python 3.7
+      - name: Set up Python 3.10
         uses: actions/setup-python@v5
         with:
-          python-version: 3.7
+          python-version: "3.10"
 
       - name: Install dependencies
         run: |

.github/workflows/format.yml

@@ -11,10 +11,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
-    - name: Set up Python 3.7
+    - name: Set up Python 3.10
       uses: actions/setup-python@v5
       with:
-        python-version: 3.7
+        python-version: "3.10"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/tests.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v4
@@ -40,15 +40,15 @@ jobs:
 
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
+        os: [ubuntu-latest, macos-latest]
     runs-on: ${{ matrix.os }}
 
     steps:
     - uses: actions/checkout@v4
-    - name: Set up Python 3.7
+    - name: Set up Python 3.10
       uses: actions/setup-python@v5
       with:
-        python-version: 3.7
+        python-version: "3.10"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

emukit/sensitivity/monte_carlo/monte_carlo_sensitivity.py

@@ -51,9 +51,7 @@ def saltelli_estimators(
         Saltelli estimators of the total mean and variance
         """
 
-        variable_main_variance = (
-            sum(f_main_sample * f_new_fixing_sample) / (num_monte_carlo_points - 1) - total_mean**2
-        )
+        variable_main_variance = sum(f_main_sample * f_new_fixing_sample) / (num_monte_carlo_points - 1) - total_mean**2
         variable_total_variance = (
             total_variance - sum(f_fixing_sample * f_new_fixing_sample) / (num_monte_carlo_points - 1) + total_mean**2
         )

integration_tests/emukit/quadrature/test_wsabil_loop.py

@@ -6,7 +6,6 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_array_equal
-from pytest_lazyfixture import lazy_fixture
 
 from emukit.core.loop.user_function import UserFunctionWrapper
 from emukit.model_wrappers.gpy_quadrature_wrappers import BaseGaussianProcessGPy, QuadratureRBFGaussianMeasure, RBFGPy
@@ -58,12 +57,12 @@ def loop_fixed(wsabil_fixed):
     return emukit_loop, Y.shape[0], X, Y
 
 
-wsabi_test_list = [lazy_fixture("loop_adapt"), lazy_fixture("loop_fixed")]
+wsabi_test_list = ["loop_adapt", "loop_fixed"]
 
 
 @pytest.mark.parametrize("loop", wsabi_test_list)
-def test_wsabil_loop(loop):
-    emukit_loop, init_size, _, _ = loop
+def test_wsabil_loop(loop, request):
+    emukit_loop, init_size, _, _ = request.getfixturevalue(loop)
     num_iter = 5
 
     emukit_loop.run_loop(user_function=UserFunctionWrapper(func), stopping_condition=num_iter)
@@ -73,8 +72,8 @@ def test_wsabil_loop(loop):
 
 
 @pytest.mark.parametrize("loop", wsabi_test_list)
-def test_wsabil_loop_initial_state(loop):
-    emukit_loop, _, x_init, y_init = loop
+def test_wsabil_loop_initial_state(loop, request):
+    emukit_loop, _, x_init, y_init = request.getfixturevalue(loop)
 
     assert_array_equal(emukit_loop.loop_state.X, x_init)
     assert_array_equal(emukit_loop.loop_state.Y, y_init)

requirements/requirements.txt

@@ -1,10 +1,10 @@
 setuptools>=40.8.0
 # numpy deprecated np.bool in 1.24, and it is still used in GPy
 # until GPy is updated we need to avoid 1.24 or higher
-numpy>=1.14.5,<1.24
+numpy>=1.23
 # This is unfortunate - we don't need matplotlib
 # but until GPy and GPyOpt get their dependencies straight
 # we need GPy's plotting extra to ensure smooth installation
-GPy[plotting]>=1.10.0
+GPy[plotting]>=1.13.0
 emcee>=2.2.1
 scipy>=1.1.0

requirements/test_requirements.txt

@@ -6,7 +6,6 @@ black
 pytest>=3.5.1
 pytest-cov>=2.5.1
 mock>=2.0.0
-pytest-lazy-fixture>=0.4.2
 
 # For Latin design
 PyDOE>=0.3.0

setup.py

@@ -31,7 +31,7 @@
     include_package_data=True,
     install_requires=requires,
     extras_require={"benchmarking": ["matplotlib"]},
-    python_requires=">=3",
+    python_requires=">=3.9",
     license="Apache License 2.0",
     classifiers=(
         # https://pypi.org/pypi?%3Aaction=list_classifiers
@@ -40,9 +40,6 @@
         "Intended Audience :: Education",
         "Intended Audience :: Science/Research",
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.4",
-        "Programming Language :: Python :: 3.5",
-        "Programming Language :: Python :: 3.6",
         "License :: OSI Approved :: Apache Software License",
         "Operating System :: OS Independent",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",

tests/emukit/model_wrappers/test_gpy_wrappers_quadrature.py

@@ -7,7 +7,6 @@
 import GPy
 import numpy as np
 import pytest
-from pytest_lazyfixture import lazy_fixture
 
 from emukit.model_wrappers.gpy_quadrature_wrappers import (
     BaseGaussianProcessGPy,
@@ -204,21 +203,22 @@ def wrapper_matern52_2(dim1, gpy_matern52):
 
 
 gpy_test_list = [
-    lazy_fixture("wrapper_rbf_1"),
-    lazy_fixture("wrapper_rbf_2"),
-    lazy_fixture("wrapper_brownian_1"),
-    lazy_fixture("wrapper_brownian_2"),
-    lazy_fixture("wrapper_matern12_1"),
-    lazy_fixture("wrapper_matern12_2"),
-    lazy_fixture("wrapper_matern32_1"),
-    lazy_fixture("wrapper_matern32_2"),
-    lazy_fixture("wrapper_matern52_1"),
-    lazy_fixture("wrapper_matern52_2"),
+    "wrapper_rbf_1",
+    "wrapper_rbf_2",
+    "wrapper_brownian_1",
+    "wrapper_brownian_2",
+    "wrapper_matern12_1",
+    "wrapper_matern12_2",
+    "wrapper_matern32_1",
+    "wrapper_matern32_2",
+    "wrapper_matern52_1",
+    "wrapper_matern52_2",
 ]
 
 
-@pytest.mark.parametrize("wrapper", gpy_test_list)
-def test_create_emukit_model_from_gpy_model_types(wrapper):
+@pytest.mark.parametrize("wrapper_name", gpy_test_list)
+def test_create_emukit_model_from_gpy_model_types(wrapper_name, request):
+    wrapper = request.getfixturevalue(wrapper_name)
     gpy_model = GPy.models.GPRegression(kernel=wrapper["gpy_kernel"], X=wrapper["data"][0], Y=wrapper["data"][1])
     emukit_gp = create_emukit_model_from_gpy_model(gpy_model=gpy_model, measure=wrapper["measure"])
 
@@ -252,7 +252,7 @@ def test_create_emukit_model_from_gpy_model_raises_warns():
         create_emukit_model_from_gpy_model(gpy_model=gpy_model, integral_bounds=bounds, measure=measure)
 
 
-def test_base_gp_gpy_raises(gpy_prodbrownian):
+def test_base_gp_gpy_raises():
     incompatible_offset = -3
 
     n_dim = 2

tests/emukit/multi_fidelity/test_kernels.py

@@ -10,7 +10,7 @@
 """
 import GPy
 import numpy as np
-from GPy.testing.kernel_tests import check_kernel_gradient_functions
+from GPy.testing.test_kernel import check_kernel_gradient_functions
 
 import emukit.multi_fidelity
 

tests/emukit/quadrature/test_measures.py

@@ -9,7 +9,6 @@
 
 import numpy as np
 import pytest
-from pytest_lazyfixture import lazy_fixture
 from utils import check_grad
 
 from emukit.quadrature.measures import BoxDomain, GaussianMeasure, LebesgueMeasure
@@ -77,10 +76,10 @@ def gauss_measure():
 
 
 measure_test_list = [
-    lazy_fixture("lebesgue_measure"),
-    lazy_fixture("lebesgue_measure_normalized"),
-    lazy_fixture("gauss_iso_measure"),
-    lazy_fixture("gauss_measure"),
+    DataLebesgueMeasure(),
+    DataLebesgueNormalizedMeasure(),
+    DataGaussIsoMeasure(),
+    DataGaussMeasure(),
 ]
 
 

tests/emukit/quadrature/test_quadrature_acquisitions.py

@@ -8,7 +8,6 @@
 import GPy
 import numpy as np
 import pytest
-from pytest_lazyfixture import lazy_fixture
 from utils import check_grad
 
 from emukit.model_wrappers.gpy_quadrature_wrappers import BaseGaussianProcessGPy, RBFGPy
@@ -58,50 +57,49 @@ def model_gaussian(gpy_model):
 
 
 model_test_list = [
-    lazy_fixture("model_gaussian"),
-    lazy_fixture("model_lebesgue"),
-    lazy_fixture("model_lebesgue_normalized"),
+    "model_gaussian",
+    "model_lebesgue",
+    "model_lebesgue_normalized",
 ]
 
-
-@pytest.fixture(params=model_test_list)
-def model_test_list_fixture(request):
-    return request.param
-
-
 # === acquisition fixtures start here
 
 
 @pytest.fixture
-def mutual_information(model_test_list_fixture):
-    return MutualInformation(model_test_list_fixture)
+def mutual_information():
+    return lambda model: MutualInformation(model)
 
 
 @pytest.fixture
-def squared_correlation(model_test_list_fixture):
-    return SquaredCorrelation(model_test_list_fixture)
+def squared_correlation():
+    return lambda model: SquaredCorrelation(model)
 
 
 @pytest.fixture
-def integral_variance_reduction(model_test_list_fixture):
-    return IntegralVarianceReduction(model_test_list_fixture)
+def integral_variance_reduction():
+    return lambda model: IntegralVarianceReduction(model)
 
 
 @pytest.fixture
-def uncertainty_sampling(model_test_list_fixture):
-    return UncertaintySampling(model_test_list_fixture)
+def uncertainty_sampling():
+    return lambda model: UncertaintySampling(model)
 
 
-acquisitions_test_list = [
-    lazy_fixture("mutual_information"),
-    lazy_fixture("squared_correlation"),
-    lazy_fixture("integral_variance_reduction"),
-    lazy_fixture("uncertainty_sampling"),
+acquisition_test_list = [
+    "mutual_information",
+    "squared_correlation",
+    "integral_variance_reduction",
+    "uncertainty_sampling",
 ]
 
 
-@pytest.mark.parametrize("aq", acquisitions_test_list)
-def test_quadrature_acquisition_shapes(aq):
+@pytest.mark.parametrize("model_name", model_test_list)
+@pytest.mark.parametrize("aq_name", acquisition_test_list)
+def test_quadrature_acquisition_shapes(model_name, aq_name, request):
+    model = request.getfixturevalue(model_name)
+    aq_factory = request.getfixturevalue(aq_name)
+    aq = aq_factory(model)
+
     x = np.array([[-1, 1], [0, 0], [-2, 0.1]])
 
     # value
@@ -114,8 +112,13 @@ def test_quadrature_acquisition_shapes(aq):
     assert res[1].shape == (3, 2)
 
 
-@pytest.mark.parametrize("aq", acquisitions_test_list)
-def test_quadrature_acquisition_gradient_values(aq):
+@pytest.mark.parametrize("model_name", model_test_list)
+@pytest.mark.parametrize("aq_name", acquisition_test_list)
+def test_quadrature_acquisition_gradient_values(model_name, aq_name, request):
+    model = request.getfixturevalue(model_name)
+    aq_factory = request.getfixturevalue(aq_name)
+    aq = aq_factory(model)
+
     func = lambda x: aq.evaluate(x)[:, 0]
     dfunc = lambda x: aq.evaluate_with_gradients(x)[1].T
     check_grad(func, dfunc, in_shape=(3, 2), bounds=aq.model.X.shape[1] * [(-3, 3)])