Two moons test fit #175
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Two moons test fit #175
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commit 320f1ae Author: lars <[email protected]> Date: Tue Jun 18 16:23:01 2024 +0200 fix two moons simulated dtype commit 27f99cd Author: lars <[email protected]> Date: Tue Jun 18 16:09:45 2024 +0200 fix data modification for tensorflow compiled mode commit c8060fc Merge: 3150d11 e2355de Author: lars <[email protected]> Date: Tue Jun 18 15:35:59 2024 +0200 Merge remote-tracking branch 'origin/streamlined-backend' into streamlined-backend commit 3150d11 Author: lars <[email protected]> Date: Tue Jun 18 15:35:52 2024 +0200 add JAX Approximator finalize all Approximators commit e2355de Author: Chase Grajeda <[email protected]> Date: Tue Jun 18 22:15:37 2024 +0900 Configurator Unit Tests (stefanradev93#174) * First additions Added __init__.py for test module. Added test_configurators.py. Added basic fixtures and construction tests. * Remaining tests Added remaining unit tests * Added conftest Separated fixtures and placed them in conftest.py * Added requested changes Added batch_size, set_size, and num_features parameterizations in conftest.py. Combined repetitive fixtures in conftest.py. Combined repetitive tests in test_configurators.py. Parameterized Configurator initialization in conftest.py. Parameterized parameter selection in conftest.py. Removed initialization tests in test_configurators.py. Added summary_inputs and summary_conditions to parameters. Changed instances of '==None' to 'is None'. Removed 'config=Configurator' instances in test_configurators.py.
Added test for post-training loss < pre-training loss to test_fit.py::test_fit
Added test in test_fit.py::test_fit for vanishing weights
Added test to test_fit.py for verifying the simulator produces random and consistent data
Added MMD test to test_two_moons.py. Added MMD method to utils/ops.py. Added test_dataset to test_two_moons/conftest.py.
Added auto-formatting changes from ruff
LarsKue
approved these changes
Jun 24, 2024
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Great changes, thank you. I added some comments for future improvement, but I will merge this as-is. Also make sure to pass the linter check in the future (you can do this automatically by installing the pre-commit hook with conda env update -f environment.yaml && pre-commit install && pre-commit run --all-files.)
| @@ -7,3 +7,30 @@ def isclose(x1, x2, rtol=1e-5, atol=1e-8): | |||
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| def allclose(x1, x2, rtol=1e-5, atol=1e-8): | |||
| return keras.ops.all(isclose(x1, x2, rtol, atol)) | |||
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| def max_mean_discrepancy(x, y): | |||
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In the future, let's put these in bayesflow/metrics directly. This time, I will move this and make it a bit more customizable.
| # Test model weights have not vanished | ||
| for layer in approximator.layers: | ||
| for weight in layer.weights: | ||
| assert not keras.ops.any(keras.ops.isnan(weight)).numpy() |
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tensor.numpy() is not backend-agnostic and also not necessary here (when necessary, use keras.ops.convert_to_numpy instead)
| pre_loss = approximator.compute_metrics(train_dataset.data)["loss"] | ||
| pre_val_loss = approximator.compute_metrics(validation_dataset.data)["loss"] | ||
| x_before = approximator.inference_network(inf_vars, conditions=inf_conds) | ||
| mmd_before = max_mean_discrepancy(x_before, y) |
| approximator.compile(jit_compile=jit_compile, loss=keras.losses.KLDivergence()) | ||
| inf_vars = approximator.configurator.configure_inference_variables(test_dataset.data) | ||
| inf_conds = approximator.configurator.configure_inference_conditions(test_dataset.data) | ||
| y = test_dataset.data["x"] |
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relies on the internal structure of the dataset fixture, which is not good. Use test_batch = test_dataset[0]; observables = test_batch["x"] instead.
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stefanradev93:streamlined-backend
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Adds tests to verify convergence in
test_two_moons/test_two_moons.pytest_fit