mirror of
https://github.com/kuhyx/WUT_Computer_Science.git
synced 2026-11-10 21:47:08 +01:00
Krzysztof kuhy Rudnicki
bd24dd57ea
kuhy overruled the "do not fix coursework to be better than it was submitted" rule: git history is the archive, so the working tree can be clean. This lands the config and the mechanical half of the cleanup -- 9,281 ruff findings under the fleet config down to 3,686, with no per-file-ignores anywhere. What is in here: - pyproject.toml, the first one in this repo. select = ["ALL"], mypy strict, and the vendored list mirrored into ruff exclude and coverage omit. Five rules are off and each says why; CPY001 is the only judgement call, because stamping a copyright header onto 137 university assignments would assert a licence that was never claimed. No hook is wired up yet -- a gate lands only once its subject is clean. - ruff format + the safe fixes, then a second pass over the docstring rules ruff marks unsafe (they only edit docstring text). 108 files. - TRAK/sightpy is vendored, not coursework: it is rafael-fuente/Python-Raytracer (BSD 3-Clause). Verified against a fresh upstream clone with line endings normalised -- 30 of 32 files byte-identical, ray.py and scene.py carrying a 12- and 13-line local patch that counts rays for the report. 35 files that should never have been linted, now excluded and restored. - WDWR, SPD, MOM and ECRYPT are fully clean, by hand and verified by running them: WDWR's three plot scripts print the same statistics as before, MOM's PuLP model solves to the same cost. WDWR's graph.py had a 342-character CSV header embedded as a string literal, which cannot be wrapped -- the data moved to data/pareto_front.csv. Two 0-byte .py stubs are deleted; an empty file is not a deliverable and git still has it. Three harness bugs found by actually running things: - `run` now forces MPLBACKEND=Agg. TRAK finished its ray trace in 37 seconds and then sat for 45 minutes: plt.show() had opened an image viewer and was waiting for someone to close the window. - TRAK's main.py only pops that viewer with --show now, and its runner writes to a gitignored build/ via the new --entry-args. The default output path is outputs/output.png, which is TRACKED -- a plain run overwrote a submitted deliverable. - course_lib.sh was 357 lines, over this repo's own cap. Split into course_probe.sh and course_run.sh. Verified: ECOTE 26 tests pass, ECRYPT_PROJECT 99 pass, TRAK renders cornell_box in 37s and exits 0, every module compiles, and every import ruff removed was checked to be genuinely unused. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import gpt_alignment
|
|
import pandas as pd
|
|
import processing
|
|
|
|
# Specify the output file path
|
|
file_path = "alignments_with_training_headlines2.wa"
|
|
|
|
# paths to students andsewrs database
|
|
# chunked_path1 = "test_goldStandard/student/STSint.testinput.answers-students.sent1.chunk.txt"
|
|
# chunked_path2 = "test_goldStandard/student/STSint.testinput.answers-students.sent2.chunk.txt"
|
|
# alignment_path = "test_goldStandard/student/STSint.testinput.answers-students.wa"
|
|
|
|
# paths to headlines
|
|
chunked_path1 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
|
|
chunked_path2 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
|
|
alignment_path = "test_goldStandard/headlines/STSint.testinput.headlines.wa"
|
|
|
|
# load data
|
|
goldstandard_chunked = processing.load_chunked(chunked_path1, chunked_path2)
|
|
goldstandard_alignment = processing.load_alignment(alignment_path)
|
|
|
|
# get a nice anwser-student table
|
|
data = pd.merge(
|
|
goldstandard_chunked, goldstandard_alignment, left_index=True, right_index=True
|
|
)
|
|
# print(data)
|
|
|
|
train, test = processing.generate_train_test_split(data)
|
|
|
|
data_for_chat, indexes = processing.get_chunks_as_text(test)
|
|
_, indexes_of_training = processing.get_chunks_as_text(train)
|
|
indexes_of_training = [i + 1 for i in indexes_of_training]
|
|
indexes = [i + 1 for i in indexes]
|
|
print(indexes_of_training)
|
|
print(indexes)
|
|
|
|
client = gpt_alignment.createGPT()
|
|
responses = []
|
|
for i in range(10): # len(data_for_chat)):
|
|
responses.append(
|
|
[gpt_alignment.callApi_examples(client, train, data_for_chat[i]), indexes[i]]
|
|
)
|
|
|
|
with open(file_path, "w") as file:
|
|
for i, r in enumerate(responses):
|
|
file.write('<sentence id="' + str(r[1] + 1) + '" status="">\n')
|
|
file.write("<alignment>\n")
|
|
file.write(r[0])
|
|
file.write("\n</alignment>\n")
|
|
file.write("</sentence>\n\n")
|