Hook post-hoc binary metrics and plots into standard evaluation framework#492
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Hook post-hoc binary metrics and plots into standard evaluation framework#492
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Added an index filtering step to FeatureConcatenator. Previously, if different featurizers dropped different molecules, the raw arrays were still concatenated, resulting in shape mismatches or mismatched rows. The features are now strictly masked to the common indices prior to concatenation.
This overhaul replaces slow, high-dependency integration tests with true unit tests utilizing pytest-mock and synthetic data fixtures. Key changes include swapping tautological file-writing mocks for internal state assertions, enforcing strict disjoint set validation for chemical splitters, and implementing rigorous mathematical validation for uncertainty quantification and evaluation metrics. These updates significantly improve execution speed and cross-platform stability by replacing fragile floating-point equality with robust approximate comparisons and isolating testing boundaries for featurizers, inference orchestration, and CLI logic.
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Updated PosthocBinaryMetrics and created PosthocBinaryPlots to conform to the standard evaluate API, returning nested metric dictionaries and matplotlib objects. Registered both classes and added strict unit tests to verify mathematical accuracy and figure generation.
for more information, see https://pre-commit.ci
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FYI we just did this to demo copilot functionality. Probably still worth merging for posterity, but generally we're less interested in binary classification workflows. |
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Will resolve #143 |
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Description
This PR integrates post-hoc binary classification into the standard
openadmetevaluation and orchestration framework. By mirroring the established regression pattern,PosthocBinaryMetricsand the newly createdPosthocBinaryPlotscan now be dynamically instantiated and called by the pipeline.Key Changes
PosthocBinaryMetrics.evaluateto accepty_true,y_pred, andcutoff. It now returns the standard nested dictionary format expected by the workflow ({"task_0": {"precision": {"value": ...}}}).PosthocBinaryPlotsto generate post-hoc classification scatter plots and confusion matrices. It returns a dictionary ofmatplotlib.figure.Figureobjects.@evaluators.registerdecorator.test_posthoc_binary_metrics_evaluateandtest_posthoc_binary_plots_evaluatetotest_eval.py. These tests strictly verify mathematical outputs and object instantiation, adhering to the project's rule against tautological or lazy (assert True) testing.Status
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