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Krzysztof kuhy Rudnicki
ffa5dad02a
The 250-line cap applies to prose too, and the reports were the worst
offenders -- ENUME's projectA.tex alone was 1,876 lines. Each is now a
preamble plus one \input{} per section, which is how a long LaTeX document is
normally organised; not one byte of prose was rewritten.
"The PDF is unchanged" is checked by a script rather than by eye, because
hashing the PDF cannot work: pdflatex stamps a /CreationDate and an /ID into
every build, so two runs of the SAME source already differ. tex_fingerprint.sh
compares what a reader sees instead -- page count plus a hash of the extracted
text -- and it is stable across rebuilds of untouched sources, which is the
property that makes it usable as a gate.
It earned its keep immediately. Both ECOTE reports quote an example LaTeX
document inside an lstlisting block, so \end{document} appears in the middle
of the file; splitting on the FIRST one stranded everything after it. Same
page count, different text, caught automatically. EOPSY lab3 exposed a second
bug: a \section{} title that runs onto the next line.
Result: 19 .tex over the cap -> 1. The survivor,
ELAC/.../03-building-nfa-...tex, is 418 lines of consecutive tikz figures
with no subsection to split on and needs a hand-placed boundary.
Verified: 29 splits across two passes (section, then subsection for the
pieces still over), every one reporting `unchanged` from
scripts/tex_fingerprint.sh --check.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
19 lines
1.6 KiB
TeX
19 lines
1.6 KiB
TeX
\section{Definition of quality measures that will be used}
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In order to evaluate the quality of our recommendation system, we will use a combination of precision, recall, F1 score, and MAP. Precision measures the proportion of relevant items among the recommended items, while recall measures the proportion of relevant items that were actually recommended. F1 score is the harmonic mean of precision and recall, providing a balanced measure of the system's performance.
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MAP is calculated by taking the average of the average precision (AP) for each user. AP is calculated as the mean of the precision values obtained at each relevant item position in the ranked list of recommended items.
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To calculate MAP, we need to define a threshold for relevance, which can be either binary (relevant or not relevant) or graded (assigning a relevance score to each anime). In our case, we will use a binary threshold, where an anime is considered relevant if has been rated 8 or higher. We will also calculate MAP for different values of K (number of recommended items), as the quality of the recommendations may vary depending on the number of items displayed to the user.
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%Quality Measures:
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%We will use the following quality measures to evaluate the performance of the model:
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% Precision: the ratio of true positive predictions to the total number of predicted positives.
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% Recall: the ratio of true positive predictions to the total number of actual positives.
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% F1-score: the harmonic mean of precision and recall.
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%We will also use the mean average precision (MAP) as an additional quality measure to evaluate the model's performance.
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