Merge branch 'stefanradev93:Development' into Development

Author: rusty-electron

.github/workflows/docs.yml

@@ -1,7 +1,7 @@
 # From https://github.com/eeholmes/readthedoc-test/blob/main/.github/workflows/docs_pages.yml
 name: docs
 
-# execute this workflow automatically when a we push to master
+# execute this workflow automatically when we push to master
 on:
   push:
     branches:
@@ -28,7 +28,7 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
+          cache: "pip"
 
       - name: Install dependencies
         run: |

.github/workflows/publish.yml

@@ -7,10 +7,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - run: python3 -m pip install --upgrade build && python3 -m build
+      - run: python3 -m pip install -U build && python3 -m build
       - name: Publish package
         uses: pypa/gh-action-pypi-publish@release/v1
         with:

.github/workflows/tests.yml

@@ -3,7 +3,7 @@ name: Tests
 on:
   pull_request:
   push:
-    branches: 
+    branches:
       - master
       - Development
 
@@ -14,17 +14,18 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ['3.9']
+        python-version: ["3.10", "3.11"]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip
-        pip install tox tox-gh-actions
+        python -m ensurepip
+        python -m pip install -U pip setuptools wheel
+        python -m pip install tox tox-gh-actions
     - name: Test with tox
       run: tox

INSTALL.rst

@@ -4,7 +4,7 @@ Full Installation Instructions
 Requirements
 ------------
 
-This package requires Python 3.9 or later.
+This package requires Python 3.10 or later.
 A simple installation is possible via `Miniconda <https://docs.conda.io/en/latest/miniconda.html>`_,
 e.g. (on Linux)
 

environment.yaml

@@ -26,7 +26,7 @@ no_implicit_reexport = true
 
 [tool.black]
 line-length = 120
-target-version = ["py39", "py310"]
+target-version = ["py310", "py311"]
 
 [tool.isort]
 profile = "black"

pyproject.toml

@@ -1,5 +1,6 @@
 flake8>=6.1.0
 tox>=4.10.0
 pytest>=7.2.0
+pytest-xdist>=3.5.0
 pytest-cov>=4.1.0
 myp>=1.5.1

requirements_dev.txt

@@ -30,7 +30,7 @@ keywords =
     model comparison
 
 [options]
-python_requires = >=3.9
+python_requires = >=3.10
 packages = find:
 install_requires =
     h5py >= 3.7
@@ -51,6 +51,7 @@ testing =
     flake8 >= 3.9
     tox >= 3.24
     pytest >= 6.0
+    pytest-xdist >= 2.4.0
     pytest-cov >= 2.10.0
     mypy >= 0.910
 docs =

setup.cfg

@@ -1,12 +1,11 @@
+from copy import deepcopy
 from functools import partial
 from unittest.mock import MagicMock, Mock
 
 import numpy as np
 import pytest
 import tensorflow as tf
 
-from copy import deepcopy
-
 from bayesflow import computational_utilities, sensitivity, simulation
 from tests.test_trainers import _create_training_setup, _prior, _simulator
 
@@ -26,7 +25,7 @@ def _trainer_amortizer_sample_mock(input_dict, n_samples):
 
 
 class TestMisspecificationExperiment:
-    def setup(self):
+    def setup_method(self):
         self.trainer = _create_training_setup(mode="posterior")
 
         # Mock the approximate posterior sampling of the amortizer, return np.ones of shape (n_sim, n_samples)

tests/test_sensitivity.py

@@ -68,8 +68,9 @@ def _create_training_setup(mode):
     trainer = Trainer(generative_model=model, amortizer=amortizer)
     return trainer
 
+
 class TestTrainer:
-    def setup(self):
+    def setup_method(self):
         trainer_posterior = _create_training_setup("posterior")
         trainer_likelihood = _create_training_setup("likelihood")
         trainer_joint = _create_training_setup("joint")
@@ -111,7 +112,6 @@ def test_train_online(self, mode, reuse_optimizer, validation_sims):
             assert type(h["train_losses"]) is DataFrame
             assert type(h["val_losses"]) is DataFrame
 
-
     @pytest.mark.parametrize("mode", ["posterior", "joint"])
     @pytest.mark.parametrize("reuse_optimizer", [True, False])
     @pytest.mark.parametrize("validation_sims", [20, None])
@@ -202,34 +202,3 @@ def test_train_rounds(self, mode, reuse_optimizer, validation_sims):
             assert type(h) is dict
             assert type(h["train_losses"]) is DataFrame
             assert type(h["val_losses"]) is DataFrame
-
-    @pytest.mark.parametrize("reference_data", [None, "dict", "numpy"])
-    @pytest.mark.parametrize("observed_data_type", ["dict", "numpy"])
-    @pytest.mark.parametrize("bootstrap", [True, False])
-    def mmd_hypothesis_test_no_reference(self, reference_data, observed_data_type, bootstrap):
-        trainer = self.trainers["posterior"]
-        _ = trainer.train_online(epochs=1, iterations_per_epoch=1, batch_size=4)
-
-        num_reference_simulations = 10
-        num_observed_simulations = 2
-        num_null_samples = 5
-
-        if reference_data is None:
-            if reference_data == "dict":
-                reference_data = trainer.configurator(trainer.generative_model(num_reference_simulations))
-            elif reference_data == "numpy":
-                reference_data = trainer.configurator(trainer.generative_model(num_reference_simulations))['summary_conditions']
-
-        if observed_data_type == "dict":
-            observed_data = trainer.configurator(trainer.generative_model(num_observed_simulations))
-        elif observed_data_type == "numpy":
-            observed_data = trainer.configurator(trainer.generative_model(num_observed_simulations))['summary_conditions']
-
-        MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data=observed_data,
-                                                                              reference_data=reference_data,
-                                                                              num_reference_simulations=num_reference_simulations,
-                                                                              num_null_samples=num_null_samples,
-                                                                              bootstrap=bootstrap)
-
-        assert MMD_sampling_distribution.shape[0] == num_reference_simulations
-        assert np.all(MMD_sampling_distribution > 0)