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Replace module-level config loading in conftest.py with parameterized fixtures. dr_technique and forecast_technique are now pulled directly from the dimensionality_reduction_techniques and forecast_techniques dicts, creating a cross-product with existing indirect parametrization. Tests will now automatically cover any technique registered in those dicts. Note: This will cause a large increase in the number of test cases and cause some DR and forecasting techniques specific tests to fail or error.
Move PCA-specific tests from test_simulation.py and test_prediction.py into test_pca.py with local fixtures that hardcode DimensionalityReductionPCA, bypassing the conftest cross-product. Bundle (technique, comp) pairs in conftest's dr_technique fixture so each DR class gets an appropriate comp value — fixes KernelPCA InvalidParameterError from receiving a float. Remove unused dummy_sim_array fixture from test_simulation.py.
RecursiveForecaster hardcoded steps=30, ignoring the caller's forecast horizon. This caused shape mismatches when the caller requested a different number of steps. Derive steps from x_pred.shape[0] instead, matching how DirectForecaster works. Remove overly strict isinstance(y_hat, np.ndarray) checks in tests — shape and value assertions already cover checking for array-like data structures.
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Change Description
This PR decouples the tests from the dimensionality reduction and forecasting config files and begins to allow parameterisation of tests across custom methods.
Task List
conftest.pywith parameterized fixtures.test_simulation.pyandtest_prediction.pyintotest_pca.pywith local fixturesx_pred.shape[0]instead, matching how DirectForecaster works.isinstance(y_hat, np.ndarray)checks in tests