WUT_Computer_Science/Programming/NLP/format_alignments.py
Krzysztof kuhy Rudnicki cc06bc0cd8 EARIN: zero findings, verified against byte-identical program output
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
2026-08-28 20:39:53 +02:00

180 lines
6.1 KiB
Python
Executable File

#!/usr/bin/env python3
"""Turn the raw GPT alignment replies into the SemEval .wa format."""
import ast
import copy
import logging
import re
from pathlib import Path
import pandas as pd
import processing
logger = logging.getLogger(__name__)
logging.basicConfig(format="%(message)s", level=logging.INFO)
# input file name
file_path = "alignments_unformatted_headlines.txt"
# output file path
output_file_path = "headlines_fixed_format.wa"
# paths to headlines database
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"
# paths to students andsewrs database
# load data
goldstandard_chunked = processing.load_chunked(chunked_path1, chunked_path2)
goldstandard_alignment = processing.load_alignment(alignment_path)
# ASCII letters mark a chunk the numbering pass failed to replace, and a
# formatted alignment always has at least the four //-separated fields.
ASCII_MAX = 128
FULL_FIELDS = 4
SHORT_FIELDS = 3
# get a nice table
data = pd.DataFrame.merge(
goldstandard_chunked, goldstandard_alignment, left_index=True, right_index=True
)
# open generated alignments
# The file holds one repr()'d string per line (see create_alignments.py),
# so literal_eval is enough -- eval() here would execute whatever GPT
# happened to return.
with Path(file_path).open() as file:
responses = [ast.literal_eval(line.strip()) for line in file]
for i, r in enumerate(responses):
logger.info("%s", "\nresponse number " + str(i))
logger.info("%s", r)
unformatted = copy.deepcopy(responses)
for i, response in enumerate(responses):
temp = response.lstrip("\n")
temp = temp.rstrip("\n")
temp = re.sub(r"(?<==)>", "> ", temp) # add space after >
temp = re.sub(r"(?<!\n) +(?!\n)", " ", temp) # remove double space
temp = temp.replace("]", "")
temp = temp.replace("[", "")
temp = temp.replace("'", "")
temp = temp.replace(" ==> ", " <==> ")
temp = temp.replace(" => ", " <==> ")
temp = temp.replace(" <=> ", " <==> ")
temp = temp.replace(" <== ", " <==> ")
temp = temp.replace("<==> //", " <==> 0 //")
temp = temp.replace("NOALI <==>", "0 <==>")
temp = temp.replace("<==> NOALI", "<==> 0")
temp = temp.replace("\n// NOALI", "\n0 <==> 0 // NOALI")
temp = temp.replace("\n // ", "\n0 <==> ")
temp = temp.replace("\n// ", "\n0 <==> ")
temp = re.sub(r"(^|[^<])(==>+)", r" <==>", temp)
temp = temp.replace("// - //", "// 0 //")
temp = temp.replace("// score", "// ")
temp = temp.replace("// alignment type", "// NOALI")
temp = temp.replace("> -", "> 0")
temp = temp.replace("- <", "0 <")
temp = temp.replace("// //", "// NOALI //")
temp = temp.replace("equi", "EQUI")
temp = re.sub(r"\d\. ", "", temp) # remove 1., 2. ...
temp = temp.upper()
temp = re.sub(r"^(<==>)", r"0 \1", temp)
temp = re.sub(r"(?<!\n) +(?!\n)", " ", temp) # remove double space
temp = temp.split("\n")
for k, t in enumerate(temp):
if "<==>" not in t:
temp[k] = "0 <==> " + temp[k]
temp[k] = temp[k].split("<==>")
temp[k] = [temp[k][0], *temp[k][1].split("//")]
chunk1arr = data.iloc[i]["chunked_sentance1"]
q = 1
number_list = []
for raw_chunk in chunk1arr:
chunk = re.sub(r"(?<!\n) +(?!\n)", " ", raw_chunk)
n_of_words = len(chunk.strip().split(" "))
index_str = ""
for qq in range(q, q + n_of_words):
index_str = index_str + str(qq) + " "
number_list.append(index_str)
q = q + n_of_words
for j, chunk in enumerate(data.iloc[i]["chunked_sentance1"]):
pattern = re.compile(chunk, re.IGNORECASE)
temp[k][0] = pattern.sub(number_list[j], temp[k][0])
chunk1arr = data.iloc[i]["chunked_sentance2"]
q = 1
number_list = []
for raw_chunk in chunk1arr:
chunk = re.sub(r"(?<!\n) +(?!\n)", " ", raw_chunk)
n_of_words = len(chunk.strip().split(" "))
index_str = ""
for qq in range(q, q + n_of_words):
index_str = index_str + str(qq) + " "
number_list.append(index_str)
q = q + n_of_words
for j, chunk in enumerate(data.iloc[i]["chunked_sentance2"]):
pattern = re.compile(chunk, re.IGNORECASE)
temp[k][1] = pattern.sub(number_list[j], temp[k][1])
if any(char.isalpha() and ord(char) < ASCII_MAX for char in temp[k][0]) or any(
char.isalpha() and ord(char) < ASCII_MAX for char in temp[k][1]
):
temp[k] = ""
if len(temp[k]) >= FULL_FIELDS:
temp[k][3] = temp[k][3].replace("NOALI", "0")
if temp[k][3] == "":
temp[k][3] = " 0 "
if temp[k][3] == " ":
temp[k][3] = " 0 "
temp[k] = (
temp[k][0]
+ " <==> "
+ temp[k][1]
+ " // "
+ temp[k][2]
+ " // "
+ temp[k][3]
)
elif len(temp[k]) == SHORT_FIELDS:
temp[k] = temp[k][0] + " <==> " + temp[k][1] + " // " + temp[k][2] + " // 0"
temp[k] = re.sub(r"\s{2,}", " ", temp[k]) # remove double space
temp = [x for x in temp if x != ""]
responses[i] = "\n".join(temp).strip()
indexes = []
responses_final = []
rejected_indexes = []
for n, r in enumerate(responses):
if r == "":
rejected_indexes.append(n + 1)
continue
if r == "\n":
rejected_indexes.append(n + 1)
continue
indexes.append(n + 1)
responses_final.append(r)
logger.info("rejected indexes:")
logger.info("%s", rejected_indexes)
with Path(output_file_path).open("w") as file:
for i, r in zip(indexes, responses_final, strict=False):
file.write('<sentence id="' + str(i + 1) + '" status="">\n')
file.write("<alignment>\n")
file.write(r)
file.write("\n</alignment>\n")
file.write("</sentence>\n\n")