Merge branch 'develop' into uri/split_initial_point_generation

.github/workflows/develop-checks.yaml

@@ -18,7 +18,6 @@ on:
   workflow_dispatch:
   push:
     branches: develop
-    tags: v[0-9]+.[0-9]+.[0-9]+*
 
 jobs:
   slowtests:

.github/workflows/release-checks.yaml

@@ -0,0 +1,114 @@
+# Copyright 2024 The Trieste Contributors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: Release checks
+
+on:
+  workflow_dispatch:
+  push:
+    tags: v[0-9]+.[0-9]+.[0-9]+*
+
+jobs:
+  types:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.10"
+    - run: pip install tox
+    - run: tox -e types
+
+  types_old:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.7"
+    - run: pip install tox
+    - run: tox -e types_old
+
+  format:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.10"
+    - run: pip install tox
+    - run: tox -e format
+
+  tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        part: [ "1", "2", "3", "4" ]
+    name: tests (part${{ matrix.part }})
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.10"
+    - run: pip install tox
+    - run: tox -e tests_${{ matrix.part }}
+
+  tests_old:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        part: [ "1", "2", "3", "4" ]
+    name: tests_old (part${{ matrix.part }})
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.7"
+    - run: pip install tox
+    - run: tox -e tests_old_${{ matrix.part }}
+
+  slowtests:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.10"
+    - run: pip install tox
+    - run: tox -e tests -- --runslow only
+
+  slowtests_old:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.7"
+    - run: pip install tox
+    - run: tox -e tests_old -- --runslow only
+
+  fulldocs:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: actions/setup-python@v2
+      with:
+        python-version: "3.10"
+    - run: pip install tox
+    - run: |
+        TEMP_DEB="$(mktemp)" &&
+        wget -O "$TEMP_DEB" 'https://github.com/jgm/pandoc/releases/download/2.10.1/pandoc-2.10.1-1-amd64.deb' &&
+        sudo dpkg -i "$TEMP_DEB"
+        rm -f "$TEMP_DEB"
+    - run: tox -e docs

README.md

@@ -2,8 +2,8 @@
 
 [![PyPI](https://img.shields.io/pypi/v/trieste.svg)](https://pypi.org/project/trieste)
 [![License](https://img.shields.io/badge/license-Apache-green.svg)](LICENSE)
-[![Quality checks](https://github.com/secondmind-labs/trieste/actions/workflows/develop-checks.yaml/badge.svg)](https://github.com/secondmind-labs/trieste/actions?query=workflows%3Adevelop-checks)
-[![Docs](https://github.com/secondmind-labs/trieste/actions/workflows/deploy.yaml/badge.svg)](https://github.com/secondmind-labs/trieste/actions/workflows/deploy.yaml)
+[![Release](https://img.shields.io/github/actions/workflow/status/secondmind-labs/trieste/release-checks.yaml?logo=github&label=release%20checks)](https://github.com/secondmind-labs/trieste/actions/workflows/release-checks.yaml)
+[![Develop](https://img.shields.io/github/actions/workflow/status/secondmind-labs/trieste/develop-checks.yaml?logo=github&label=develop%20checks)](https://github.com/secondmind-labs/trieste/actions/workflows/develop-checks.yaml)
 [![Codecov](https://img.shields.io/codecov/c/github/secondmind-labs/trieste/coverage.svg?branch=develop)](https://app.codecov.io/github/secondmind-labs/trieste/tree/develop)
 [![Slack Status](https://img.shields.io/badge/slack-trieste-green.svg?logo=Slack)](https://join.slack.com/t/secondmind-labs/shared_invite/zt-ph07nuie-gMlkle__tjvXBay4FNSLkw)
 

docs/notebooks/active_learning_for_binary_classification.pct.py

@@ -10,7 +10,6 @@
 import trieste
 from trieste.acquisition.function import BayesianActiveLearningByDisagreement
 from trieste.acquisition.rule import OBJECTIVE
-from trieste.models.gpflow.models import VariationalGaussianProcess
 from trieste.objectives.utils import mk_observer
 
 np.random.seed(1793)

docs/notebooks/asynchronous_greedy_multiprocessing.pct.py

@@ -211,7 +211,7 @@ def terminate_processes(processes):
                 f"Process {pid}: Main     : received data {new_data}",
                 flush=True,
             )
-        except:
+        except Exception:
             continue
 
         # new_data is a tuple of (point, observation value)

docs/notebooks/deep_gaussian_processes.pct.py

@@ -18,7 +18,6 @@
 # The Michalewicz functions are highly non-stationary and have a global minimum that's hard to find, so DGPs might be more suitable than standard GPs, which may struggle because they typically have stationary kernels that cannot easily model non-stationarities.
 
 # %%
-import gpflow
 from trieste.objectives import Michalewicz2, Michalewicz5
 from trieste.objectives.utils import mk_observer
 from trieste.experimental.plotting import plot_function_plotly
@@ -167,9 +166,6 @@ def build_dgp_model(data, search_space):
 # We now compare to a GP model with priors over the hyperparameters. We do not expect this to do as well because GP models cannot deal with non-stationary functions well.
 
 # %%
-import gpflow
-import tensorflow_probability as tfp
-
 from trieste.models.gpflow import GaussianProcessRegression, build_gpr
 
 gpflow_model = build_gpr(initial_data, search_space, likelihood_variance=1e-7)

docs/notebooks/explicit_constraints.pct.py

@@ -204,7 +204,6 @@ def constraint(input_data):
 def plot_bo_results():
     dataset = result.try_get_final_dataset()
     query_points = dataset.query_points.numpy()
-    observations = dataset.observations.numpy()
 
     _, ax = plot_function_2d(
         ScaledBranin.objective,

docs/notebooks/multifidelity_modelling.pct.py

@@ -260,13 +260,13 @@ def __call__(self, x, add_noise=True):
 
 # Plot gpr results
 mean, var = gpr_predictions
-ax.plot(X, mean, label=f"GPR", color="tab:blue")
+ax.plot(X, mean, label="GPR", color="tab:blue")
 ax.plot(X, mean + 1.96 * tf.math.sqrt(var), alpha=0.2, color="tab:blue")
 ax.plot(X, mean - 1.96 * tf.math.sqrt(var), alpha=0.2, color="tab:blue")
 
 # Plot gpr results
 mean, var = multifidelity_predictions
-ax.plot(X, mean, label=f"MultifidelityAutoregressive", color="tab:orange")
+ax.plot(X, mean, label="MultifidelityAutoregressive", color="tab:orange")
 ax.plot(X, mean + 1.96 * tf.math.sqrt(var), alpha=0.2, color="tab:orange")
 ax.plot(X, mean - 1.96 * tf.math.sqrt(var), alpha=0.2, color="tab:orange")
 
@@ -275,13 +275,13 @@ def __call__(self, x, add_noise=True):
 ax.plot(
     X,
     observer(X_for_multifid, add_noise=False).observations,
-    label=f"True function",
+    label="True function",
     color="tab:green",
 )
 
 # Scatter the data
 ax.scatter(
-    hf_data.query_points, hf_data.observations, label=f"Data", color="tab:green"
+    hf_data.query_points, hf_data.observations, label="Data", color="tab:green"
 )
 plt.legend()
 plt.show()

pyproject.toml

@@ -85,10 +85,12 @@ disable = [
     "invalid-unary-operand-type",
     "line-too-long",
     "locally-disabled",
+    "missing-class-docstring",
     "missing-module-docstring",
     "no-else-return",
     "no-self-use",
     "no-value-for-parameter",
+    "protected-access",
     "redefined-builtin",
     "redundant-keyword-arg",
     "suppressed-message",
@@ -98,10 +100,12 @@ disable = [
     "too-many-instance-attributes",
     "too-many-public-methods",
     "too-many-arguments",
+    "too-many-branches",
     "too-many-locals",
     "too-many-statements",
     "too-many-boolean-expressions",
     "too-many-nested-blocks",
+    "typevar-name-incorrect-variance",
     "unexpected-keyword-arg",
     "unused-argument",
     "unsubscriptable-object",

tests/integration/models/multifidelity/test_multifidelity_models.py

@@ -129,7 +129,7 @@ def test_multifidelity_nonlinear_autoregressive_results_better_than_linear() ->
         build_multifidelity_autoregressive_models(initial_data, n_fidelities, input_search_space)
     )
 
-    mses = list()
+    mses = []
     for model in [nonlinear_model, linear_model]:
         model.update(initial_data)
         model.optimize(initial_data)

tests/integration/test_multifidelity_bayesian_optimization.py

@@ -61,7 +61,7 @@ def _build_nested_multifidelity_dataset(
 ) -> Dataset:
     num_fidelities = problem.num_fidelities
     initial_sample_sizes = [10 + 2 * (num_fidelities - i) for i in range(num_fidelities)]
-    fidelity_samples = list()
+    fidelity_samples = []
     lowest_fidelity_sample = problem.search_space.sample(initial_sample_sizes[0])
     lowest_fidelity_sample = add_fidelity_column(lowest_fidelity_sample, 0)
     fidelity_samples.append(lowest_fidelity_sample)

tests/unit/acquisition/function/test_active_learning.py

@@ -405,7 +405,7 @@ def test_integrated_variance_reduction_builder_updates_without_retracing() -> No
     ],
 )
 def test_bayesian_active_learning_by_disagreement_is_correct(at: tf.Tensor) -> None:
-    """ "
+    """
     We perform an MC check as in Section 5 of Houlsby 2011 paper. We check only the
     2nd, more complicated term.
     """

trieste/acquisition/combination.py

@@ -46,7 +46,8 @@ def __init__(self, *builders: AcquisitionFunctionBuilder[ProbabilisticModelType]
 
     def __repr__(self) -> str:
         """"""
-        return "{}({})".format(self.__class__.__name__, ", ".join(map(repr, self._acquisitions)))
+        builders = ", ".join(map(repr, self._acquisitions))
+        return f"{self.__class__.__name__}({builders})"
 
     def prepare_acquisition_function(
         self,

trieste/acquisition/function/active_learning.py

@@ -64,7 +64,7 @@ def prepare_acquisition_function(
         if not isinstance(model, SupportsPredictJoint):
             raise NotImplementedError(
                 f"PredictiveVariance only works with models that support "
-                f"predict_joint; received {model.__repr__()}"
+                f"predict_joint; received {model!r}"
             )
 
         return predictive_variance(model, self._jitter)
@@ -282,8 +282,7 @@ def prepare_acquisition_function(
         """
         if not isinstance(model, FastUpdateModel):
             raise NotImplementedError(
-                f"PredictiveVariance only works with FastUpdateModel models; "
-                f"received {model.__repr__()}"
+                f"PredictiveVariance only works with FastUpdateModel models; received {model!r}"
             )
 
         return integrated_variance_reduction(model, self._integration_points, self._threshold)

trieste/acquisition/function/continuous_thompson_sampling.py

@@ -70,7 +70,7 @@ def prepare_acquisition_function(
         if not isinstance(model, HasTrajectorySampler):
             raise ValueError(
                 f"Thompson sampling from trajectory only supports models with a trajectory_sampler "
-                f"method; received {model.__repr__()}"
+                f"method; received {model!r}"
             )
 
         self._trajectory_sampler = model.trajectory_sampler()
@@ -147,7 +147,7 @@ def prepare_acquisition_function(
         if not isinstance(model, HasTrajectorySampler):
             raise ValueError(
                 f"Thompson sampling from trajectory only supports models with a trajectory_sampler "
-                f"method; received {model.__repr__()}"
+                f"method; received {model!r}"
             )
 
         self._trajectory_sampler = model.trajectory_sampler()

trieste/acquisition/function/entropy.py

@@ -221,8 +221,6 @@ class SupportsCovarianceObservationNoise(
 ):
     """A model that supports both covariance_between_points and get_observation_noise."""
 
-    pass
-
 
 class SupportsCovarianceObservationNoiseTrajectory(
     HasTrajectorySampler, SupportsCovarianceObservationNoise, Protocol
@@ -337,7 +335,7 @@ def prepare_acquisition_function(
         if not isinstance(model, SupportsCovarianceObservationNoise):
             raise NotImplementedError(
                 f"GIBBON only works with models that support "
-                f"covariance_between_points and get_observation_noise; received {model.__repr__()}"
+                f"covariance_between_points and get_observation_noise; received {model!r}"
             )
 
         tf.debugging.Assert(dataset is not None, [tf.constant([])])
@@ -630,8 +628,6 @@ class SupportsCovarianceWithTopFidelityPredictY(
 ):
     """A model that is both multifidelity and supports predict_y."""
 
-    pass
-
 
 MUMBOModelType = TypeVar(
     "MUMBOModelType", bound=SupportsCovarianceWithTopFidelityPredictY, contravariant=True