Improve reproducibility of results in Dfmc and Dfmf #11
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mzganec
commented
May 27, 2021
| @@ -95,7 +94,7 @@ def fuse(self, fusion_graph): | |||
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| parallelizer = Parallel(n_jobs=self.n_jobs, max_nbytes=1e3, verbose=self.verbose) | |||
| task_iter = (delayed(parallel_dfmc_wrapper)( | |||
| R=R, M=M, Theta=T, obj_types=object_types, | |||
| R=R, M=M, Theta=T, obj_types=self.fusion_graph.object_types, | |||
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Iteration order of obj_types in matrix initialization is not conserved if obj_types is a set. Consequently, matrix initialization is not always reproducible even if the same random_state is used, leading to irreproducibility of results.
Uniqueness of keys in self.fusion_graph.object_types is guaranteed already by its type, so conversion into a set is redundant.
| @@ -84,6 +84,19 @@ def postprocessor(data): | |||
| np.testing.assert_almost_equal(fuser.complete(relation), trnf) | |||
| np.testing.assert_equal(fusion_graph.get_relation('R').data, R12) | |||
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| def test_reproducibility(self): | |||
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Without this fix test_reproducibility occasionally fails depending on the iteration order of obj_types in matrix initialization. With the proposed fix this test always finishes successfully.
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