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Introduce functionality for chunking experiments #10
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… different train functions
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #10 +/- ##
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+ Coverage 73.62% 74.21% +0.59%
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Files 8 8
Lines 906 927 +21
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+ Hits 667 688 +21
Misses 239 239 ☔ View full report in Codecov by Sentry. |
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This PR introduces functionality for chunking experiments. The following changes have been made:
generate_and_distribute_synthetic_dataset
without local or global imbalances equals the completely balanced dataset generated withmake_classification_dataset
when using the same random state. This ensures comparability of the strong scaling experiment series with and without chunking as the same datasets are created when passing the same random state.train_parallel_on_synthetic data
andtrain_parallel_on_balanced_synthetic_data
. This was completely missing in the former case. In addition, the argument parser was lacking some of the keyword arguments used insklearn
'smake_classification
andtrain_test_split
used under the hood.