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28/statistical hypothesis testing - #31
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…h args to SASRecTrainingPipeline.train() method to let the caller explicitly specify where to save best-performing model checkpoint and where corresponding ImpactConfig YAML snapshot
…DCG@k metrics to allow collecting raw, unaggregated metrics (for statistical testing), make the functions computing aggregated metrics (means) simple wrappers over per-sample computations
…eval pipeline to calculate both batch-level metric sums (unnormalized) and raw per-sample metric values for statistical testing
…onPipeline to allow the caller specify whether only aggregated metrics should be computed or, optionally, aggregated metrics + raw per-sample metrics (for statistical testing)
…ain entrypoint to let the caller optionally save raw metrics (for statistical testing) specifying where
…pothesis testing implementations (e.g. built-in Wilcoxon test)
…r IMPACT evaluation using Wilcoxon signed-rank test; add scipy-stubs library to resolve typing issues
…point, tables_data.py for pure table data computation and tables_render_latex.py for rendering the computed data into LaTeX tables
…tput LaTeX code for the data funnel stats table (positioning and number format)
…final form of it used in the thesis
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