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Krzysztof kuhy Rudnicki
cc06bc0cd8
749 findings across six labs and two projects. The mechanical half was scripted; everything below is what the scripts got wrong or could not do, and how it was caught. Three regressions I introduced and then found by RUNNING the code, not by reading it: - Converting print() to logger.info() made the programs SILENT. logging's last-resort handler only emits WARNING and above, so with no basicConfig call `lab2/main.py -t 1 1` printed nothing at all. Fifteen files across the repo now configure logging where they log. - The same conversion dropped `end=""`. lab2 draws its board one square at a time and lab1 draws the maze one cell at a time; both came out one glyph per line. Those thirteen call sites are sys.stdout.write again, and the rendered board now diffs byte-identically against the pre-change output. - The B007 fix renamed `col` to `_col` in the FIRST loop it matched, which was print_letters -- where the variable IS used. That would have been a NameError on the first call. And one the scripts caused silently: annotating 110 signatures dropped the DEFAULT VALUES from 22 of them, so `preprocessing(a, b, USER_THRESHOLD, ANIME_THRESHOLD)` would have raised TypeError. Every signature is now diffed against the pre-change version, name by name and default by default: zero mismatches. Real defects the lint surfaced: - `alpha_beta` ended with an unreachable `return None` that violated its own return type. Its maximising and minimising halves were mirror images, so they are one `_search_branch` now (C901 11 -> ok). - `get_data`'s `gpu` switch, `predict`'s `auto`, `create_model`'s `rows_number` and five of `preprocess_model_predict`'s fifteen parameters were never read. Gone, with their call sites. - np.random.* -> Generator throughout (NPY002), `.values` -> `.to_numpy()`, `datetime.now()` with a tzinfo, `inplace=True` removed, `except:` given a type, `random.*` -> `SystemRandom` (S311). - print_board's three nested helpers became methods (C901 17 -> ok); user_input's nine-branch if-ladder became a table (C901 12 -> ok). Verification, all by running: lab1 `-t` and lab2 `-t 1 1` produce output byte-identical to the pre-change code; lab2's rendered 8x8 board diffs clean; lab3's argument parsing returns the same dict for the same argv; lab4 fails identically before and after (no sklearn here). Also included: the logging configuration for NLP, MOM and ECRYPT_PROJECT, which had the same silence bug. Repo-wide: 2,775 findings -> 1,883. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
58 lines
1.9 KiB
Python
Executable File
58 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Ask GPT to align chunks, few-shot prompted with gold examples."""
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import logging
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from pathlib import Path
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import gpt_alignment
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import pandas as pd
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import processing
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logger = logging.getLogger(__name__)
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logging.basicConfig(format="%(message)s", level=logging.INFO)
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# Specify the output file path
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file_path = "alignments_with_training_headlines2.wa"
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# paths to students andsewrs database
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# paths to headlines
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chunked_path1 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
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chunked_path2 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
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alignment_path = "test_goldStandard/headlines/STSint.testinput.headlines.wa"
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# load data
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goldstandard_chunked = processing.load_chunked(chunked_path1, chunked_path2)
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goldstandard_alignment = processing.load_alignment(alignment_path)
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# get a nice anwser-student table
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data = pd.DataFrame.merge(
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goldstandard_chunked, goldstandard_alignment, left_index=True, right_index=True
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)
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train, test = processing.generate_train_test_split(data)
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data_for_chat, indexes = processing.get_chunks_as_text(test)
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_, indexes_of_training = processing.get_chunks_as_text(train)
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indexes_of_training = [i + 1 for i in indexes_of_training]
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indexes = [i + 1 for i in indexes]
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logger.info("%s", indexes_of_training)
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logger.info("%s", indexes)
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client = gpt_alignment.create_gpt()
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# Ten only: each call is a paid few-shot request, and ten was enough for the
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# report's comparison.
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SAMPLE_SIZE = 10
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responses = [
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[gpt_alignment.call_api_examples(client, train, data_for_chat[i]), indexes[i]]
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for i in range(SAMPLE_SIZE)
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]
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with Path(file_path).open("w") as file:
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for r in responses:
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file.write('<sentence id="' + str(r[1] + 1) + '" status="">\n')
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file.write("<alignment>\n")
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file.write(r[0])
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file.write("\n</alignment>\n")
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file.write("</sentence>\n\n")
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