Remove Spanish-specific TSV tooling
This commit is contained in:
Pawel Sarkowicz
@@ -5,3 +5,5 @@ venv/
|
||||
*.egg-info/
|
||||
dist/
|
||||
build/
|
||||
.pytest_cache/
|
||||
.mypy_cache/
|
||||
|
||||
@@ -204,59 +204,36 @@ hablar 9
|
||||
Extract vocabulary from an Anki TSV export file instead of using AnkiConnect:
|
||||
|
||||
```shell
|
||||
saiki words --lang es --input Español.txt --field 2 --output words_es_content.txt --debug words_es_debug.tsv
|
||||
```
|
||||
|
||||
Additional file-based options:
|
||||
|
||||
```shell
|
||||
saiki words --lang es --input Español.txt --field 2 --include-proper-nouns
|
||||
saiki words --lang es --input Español.txt --field 2 --function-words
|
||||
saiki words --lang es --input Español.txt --field 2 --lemma-corrections my_fixes.tsv
|
||||
saiki words --lang es --input Español.txt --field 2 --bad-lemma-file bad_lemmas.txt
|
||||
saiki words --lang es --input Español.txt --field 2 --field-section first --output words_es_content.txt --debug words_es_debug.tsv
|
||||
saiki words --lang jp --input Japanese.txt --field 2 --output words_jp_content.txt
|
||||
```
|
||||
|
||||
When `--input` is provided, `--field` specifies the 1-based column index of the
|
||||
Spanish text (default 2). The audio column (index 1) and tags column are
|
||||
automatically skipped. File-based NLP extraction is currently Spanish-specific;
|
||||
the older AnkiConnect-based `saiki words jp` flow is unchanged.
|
||||
text field (default 2). Only that column is mined; other columns such as audio
|
||||
or Anki tags are ignored. By default, all blank-line-separated sections inside
|
||||
the selected field are kept. Use `--field-section first` when your card format
|
||||
stores target-language text before a translation or note in the same field.
|
||||
|
||||
The file-based pipeline:
|
||||
- Parses Anki `#` header lines (`#separator:tab`, `#html:true`, `#tags column:N`)
|
||||
- Uses Python's `csv` module for robust TSV parsing
|
||||
- Removes `[sound:...mp3]` markers, HTML tags, and English glosses after
|
||||
`<br><br>` from the field text
|
||||
- Applies safe Spanish lemmatisation (multi-word lemmas like `ayudar yo` or
|
||||
`lavar él` are rejected; bad lemmas like `comar` -> `comer` are corrected)
|
||||
- Removes `[sound:...mp3]` markers, HTML tags, URLs, and email addresses from
|
||||
the field text
|
||||
- Uses the configured language's spaCy model, token filter, and output format
|
||||
- Tracks POS counts, surface forms, example sentences, and source line numbers
|
||||
- Tracks original spaCy lemmas and correction/fallback reasons for debugging
|
||||
- Tracks original spaCy lemmas for debugging
|
||||
|
||||
Output files produced:
|
||||
- `words_es_content.txt` — cleaned content vocabulary (NOUN, VERB, ADJ, ADV)
|
||||
- `words_es_debug.tsv` — per-lemma debug info
|
||||
- `words_es_proper_nouns.txt` — proper nouns (only with `--include-proper-nouns`)
|
||||
- `words_es_function_words.txt` — function words (only with `--function-words`)
|
||||
- `words_es_suspicious_tokens.txt` — lemmas that required correction
|
||||
- `words_<lang>_content.txt` — cleaned vocabulary
|
||||
- `words_<lang>_debug.tsv` — per-entry debug info (only with `--debug`)
|
||||
|
||||
Debug TSV example:
|
||||
|
||||
```text
|
||||
lemma count pos_counts top_surface_forms example_sentences source_lines original_lemmas lemma_statuses status
|
||||
comer 8 VERB:8 como, come, comen Yo como manzanas.; Ustedes los comen con arroz. 70,979 comar, comer corrected:comar->comer:1, ok:7
|
||||
entry count pos_counts top_surface_forms example_sentences source_lines original_lemmas
|
||||
comer 8 VERB:8 como, come, comen Yo como manzanas.; Ustedes los comen con arroz. 70,979 comer
|
||||
```
|
||||
|
||||
### Lint Spanish Cards
|
||||
|
||||
Check an Anki TSV export for suspicious or awkward Spanish:
|
||||
|
||||
```shell
|
||||
saiki lint-anki-es --input Español.txt --field 2 --output suspicious_cards_es.tsv
|
||||
```
|
||||
|
||||
Detects known errors and rule-based suspicious patterns and produces a TSV
|
||||
report with: source line, original text, cleaned text, reason, and suggested
|
||||
fix.
|
||||
|
||||
### Compare
|
||||
|
||||
Compare deck vocabulary against a target list:
|
||||
@@ -270,7 +247,7 @@ Normalises case and optionally strips accents for matching, so `cómo` and
|
||||
`como` are treated as the same word. Use `--min-frequency` to ignore accidental
|
||||
low-frequency words in the deck.
|
||||
|
||||
|
||||
### YouTube
|
||||
|
||||
Mine vocabulary or sentence rows from YouTube subtitles.
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ def parse_anki_tsv(
|
||||
``text``, ``tags`` (if *include_tags* is True), ``line_number``,
|
||||
and ``raw_line``.
|
||||
|
||||
Header lines (``#``-prefixed) are skipped. The actual separator
|
||||
Leading header lines (``#``-prefixed) are skipped. The actual separator
|
||||
is detected from ``#separator:`` and defaults to tab.
|
||||
"""
|
||||
if field_index < 1:
|
||||
@@ -95,13 +95,15 @@ def parse_anki_tsv(
|
||||
with open(path, "r", encoding="utf-8") as fh:
|
||||
reader = csv.reader(fh, delimiter=separator)
|
||||
line_number = 0
|
||||
in_headers = True
|
||||
for row in reader:
|
||||
line_number += 1
|
||||
# skip header rows and completely empty rows
|
||||
if not row:
|
||||
continue
|
||||
if row and row[0].startswith("#"):
|
||||
if in_headers and row[0].startswith("#"):
|
||||
continue
|
||||
in_headers = False
|
||||
|
||||
if col_idx >= len(row):
|
||||
raise ValueError(
|
||||
|
||||
+9
-47
@@ -15,7 +15,7 @@ from .importer import (
|
||||
supported_tts_backends,
|
||||
synthesize_tts_sample,
|
||||
)
|
||||
from .words import compare_word_files, compare_word_lists, extract_words, extract_words_from_file, lint_anki_cards_main
|
||||
from .words import compare_word_files, compare_word_lists, extract_words, extract_words_from_file
|
||||
from .youtube import run_youtube
|
||||
|
||||
|
||||
@@ -98,12 +98,13 @@ def build_parser(config: Config | None = None) -> argparse.ArgumentParser:
|
||||
words.add_argument("--out", "--output", dest="out")
|
||||
words.add_argument("--full-field", action="store_true")
|
||||
words.add_argument("--spacy-model")
|
||||
words.add_argument("--include-proper-nouns", action="store_true", help="Include proper nouns (PROPN).")
|
||||
words.add_argument("--function-words", action="store_true", help="Extract function words separately.")
|
||||
words.add_argument("--debug", help="Path for debug TSV output.")
|
||||
words.add_argument("--lemma-corrections", help="Path to bad<TAB>good TSV of lemma corrections.")
|
||||
words.add_argument("--bad-lemma-file", help="Path to file listing lemmas to skip.")
|
||||
words.add_argument("--include-tags", action="store_true", help="Include tags column in parsing.")
|
||||
words.add_argument(
|
||||
"--field-section",
|
||||
choices=["all", "first"],
|
||||
default="all",
|
||||
help="Which blank-line-separated TSV field section to mine.",
|
||||
)
|
||||
words.add_argument("--debug-min-freq", type=int, default=0,
|
||||
help="Minimum frequency for debug output (default: all).")
|
||||
words.add_argument("--no-clean", action="store_true",
|
||||
@@ -121,12 +122,6 @@ def build_parser(config: Config | None = None) -> argparse.ArgumentParser:
|
||||
compare_new.add_argument("--min-frequency", type=int, default=0,
|
||||
help="Minimum frequency for deck words to count.")
|
||||
|
||||
lint_es = sub.add_parser("lint-anki-es", help="Check Spanish Anki cards for suspicious patterns.")
|
||||
lint_es.add_argument("--input", required=True, help="Anki TSV export file.")
|
||||
lint_es.add_argument("--field", type=int, default=2, help="1-based column index (default 2).")
|
||||
lint_es.add_argument("--output", default="suspicious_cards_es.tsv", help="Output TSV path.")
|
||||
lint_es.add_argument("--verbose", action="store_true", help="Output all cards, not just suspicious ones.")
|
||||
|
||||
youtube = sub.add_parser("youtube", help="Mine a YouTube transcript.")
|
||||
youtube.add_argument("lang", choices=choices)
|
||||
youtube.add_argument("video")
|
||||
@@ -195,12 +190,6 @@ def main(argv: list[str] | None = None) -> int:
|
||||
if args.command == "words":
|
||||
if args.input:
|
||||
# File-based extraction
|
||||
if config.language_name(args.lang) != "spanish":
|
||||
print(
|
||||
"Error: TSV file-based word extraction is currently Spanish-only.",
|
||||
file=sys.stderr,
|
||||
)
|
||||
return 1
|
||||
if args.field and not args.field.isdigit():
|
||||
print(
|
||||
f"Error: --field must be a 1-based column index (integer) "
|
||||
@@ -221,25 +210,16 @@ def main(argv: list[str] | None = None) -> int:
|
||||
min_freq=args.min_freq,
|
||||
outdir=args.outdir,
|
||||
out=args.out,
|
||||
include_proper_nouns=args.include_proper_nouns,
|
||||
function_words=args.function_words,
|
||||
debug=args.debug,
|
||||
lemma_corrections=args.lemma_corrections,
|
||||
bad_lemma_file=args.bad_lemma_file,
|
||||
spacy_model=args.spacy_model,
|
||||
include_tags=args.include_tags,
|
||||
debug_min_freq=args.debug_min_freq,
|
||||
no_clean=args.no_clean,
|
||||
field_section=args.field_section,
|
||||
)
|
||||
print(f"Parsed {result['records']} records")
|
||||
print(f"Wrote {result['written']} content entries to: {result['out']}")
|
||||
print(f"Wrote {result['written']} entries to: {result['out']}")
|
||||
if result.get("debug"):
|
||||
print(f"Debug output: {result['debug']}")
|
||||
if result.get("proper_nouns"):
|
||||
print(f"Proper nouns: {result['proper_nouns']}")
|
||||
if result.get("function_words"):
|
||||
print(f"Function words: {result['function_words']}")
|
||||
print(f"Suspicious tokens: {result.get('suspicious', '')}")
|
||||
else:
|
||||
result = extract_words(
|
||||
config, args.lang, args.query, args.deck, args.field, args.min_freq,
|
||||
@@ -279,24 +259,6 @@ def main(argv: list[str] | None = None) -> int:
|
||||
print(f"Seen words written to: {args.seen_output}")
|
||||
return 0
|
||||
|
||||
if args.command == "lint-anki-es":
|
||||
if args.field < 1:
|
||||
print(
|
||||
f"Error: --field must be a 1-based column index >= 1, got: {args.field}",
|
||||
file=sys.stderr,
|
||||
)
|
||||
return 1
|
||||
result = lint_anki_cards_main(
|
||||
args.input,
|
||||
field_index=args.field,
|
||||
output_path=args.output,
|
||||
verbose=args.verbose,
|
||||
)
|
||||
print(f"Checked {result['records']} cards")
|
||||
print(f"Found {result['issues']} suspicious card(s)")
|
||||
print(f"Report written to: {result['path']}")
|
||||
return 0
|
||||
|
||||
if args.command == "youtube":
|
||||
result = run_youtube(
|
||||
config, args.lang, args.video, args.mode, args.top, args.no_stopwords,
|
||||
|
||||
+1
-1
@@ -13,7 +13,7 @@ from typing import Any
|
||||
|
||||
try:
|
||||
import yaml
|
||||
except Exception: # pragma: no cover - handled when config files are loaded
|
||||
except ImportError: # pragma: no cover - handled when config files are loaded
|
||||
yaml = None
|
||||
|
||||
|
||||
|
||||
@@ -1,719 +0,0 @@
|
||||
"""Spanish-specific NLP utilities for vocabulary extraction and card linting."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import html
|
||||
import re
|
||||
from collections import defaultdict
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Callable
|
||||
|
||||
# ── Regex patterns ─────────────────────────────────────────────────
|
||||
|
||||
SOUND_RE = re.compile(r"\[sound:[^\]]*\]")
|
||||
MEDIA_RE = re.compile(r"\[\s*(?:sound|image|media):[^\]]*\]", re.IGNORECASE)
|
||||
MEDIA_FILENAME_RE = re.compile(
|
||||
r"[^/\s]+\.(?:mp3|ogg|wav|m4a|flac|jpg|jpeg|png|gif|webp|mp4|webm)\b",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
BR_RE = re.compile(r"<\s*br\s*/?\s*>", re.IGNORECASE)
|
||||
DOUBLE_BR_RE = re.compile(
|
||||
r"<\s*br\s*/?\s*>\s*<\s*br\s*/?\s*>", re.IGNORECASE
|
||||
)
|
||||
HTML_TAG_RE = re.compile(r"<[^>]+>")
|
||||
URL_RE = re.compile(r"https?://\S+|www\.\S+", re.IGNORECASE)
|
||||
EMAIL_RE = re.compile(r"\S+@\S+\.\S+")
|
||||
MULTI_WS_RE = re.compile(r"[ \t]+")
|
||||
SPANISH_WORD_RE = re.compile(r"^[a-záéíóúüñ]+$", re.IGNORECASE)
|
||||
BAD_TOKEN_TEXT_RE = re.compile(r"[\d_/@#\\|+*=<>~`^]")
|
||||
LEMMA_PUNCT_RE = re.compile(r"[.,;:!?¿¡\"'()\[\]{}<>\-_/\\|`~@#$%^&*+=\d]")
|
||||
|
||||
_ENGLISH_PATTERNS: list[str] = [
|
||||
r"\bI\s+(?:am|have|had|will|would|could|should|was|were|did|do|don"
|
||||
r"|can|need|like|want|think|know|love|hate|see|hear|eat|drink"
|
||||
r"|go|come|take|give|make|say|tell|ask|get|put)\b",
|
||||
r"\byou\s+(?:are|have|had|will|would|could|should|were|did|do|don"
|
||||
r"|can|need|like|want|think|know|love|hate|see|hear)\b",
|
||||
r"\b(the|to|and|in|of|a|an|is|it|for|with|on|at|by|from|or|be|this|that)\b",
|
||||
r"\bsee you later\b",
|
||||
r"\bI love you\b",
|
||||
r"\bI want you\b",
|
||||
r"\bknowledge/understanding\b",
|
||||
r"\b(?:see|watch|read|write|speak|listen|learn|study|teach|explain)\s+"
|
||||
r"(?:you|him|her|it|us|them|me)\b",
|
||||
]
|
||||
ENGLISH_RE = re.compile("|".join(_ENGLISH_PATTERNS), re.IGNORECASE)
|
||||
|
||||
# ── Lemma corrections ──────────────────────────────────────────────
|
||||
|
||||
BUILTIN_LEMMA_CORRECTIONS: dict[str, str] = {
|
||||
"comar": "comer",
|
||||
"acabir": "acabar",
|
||||
"deliciós": "delicioso",
|
||||
"llover/llover": "llover",
|
||||
}
|
||||
|
||||
PRIR_SURFACE_FORMS = {"pide", "pido", "piden", "pidiendo", "pedir"}
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class LemmaInfo:
|
||||
"""A selected lemma plus audit metadata about how it was chosen."""
|
||||
|
||||
lemma: str
|
||||
original_lemma: str
|
||||
surface: str
|
||||
status: str = "ok"
|
||||
|
||||
# ── Known card issues (exact substring → fix → explanation) ────────
|
||||
|
||||
KNOWN_CARD_ISSUES: list[tuple[str, str, str]] = [
|
||||
(
|
||||
"Tú sabes nada",
|
||||
"Tú no sabes nada.",
|
||||
"Missing negation 'no' before 'sabes'",
|
||||
),
|
||||
(
|
||||
"Dulce sueños",
|
||||
"Dulces sueños.",
|
||||
"'Dulce' should agree in number with 'sueños'",
|
||||
),
|
||||
(
|
||||
"Ella soña con viajar.",
|
||||
"Ella sueña con viajar.",
|
||||
"'Soña' → 'sueña' (present indicative of soñar)",
|
||||
),
|
||||
(
|
||||
"Siento lastima",
|
||||
"Siento lástima",
|
||||
"'lastima' should be 'lástima' with accent",
|
||||
),
|
||||
(
|
||||
"Tengo uno libro",
|
||||
"Tengo un libro",
|
||||
"'uno' → 'un' before singular masculine noun",
|
||||
),
|
||||
(
|
||||
"Tengo veintiuno años",
|
||||
"Tengo veintiún años",
|
||||
"'veintiuno' → 'veintiún' before plural noun",
|
||||
),
|
||||
(
|
||||
"Dile a tu madre que feliz cumpleaños",
|
||||
'Dile a tu madre: "Feliz cumpleaños."',
|
||||
"Missing colon/quotes or restructured phrasing",
|
||||
),
|
||||
(
|
||||
"La pareja está en un bote observando a los peces nadan.",
|
||||
"La pareja está en un bote observando cómo nadan los peces.",
|
||||
"Missing 'cómo' or subordinating conjunction",
|
||||
),
|
||||
(
|
||||
"Los perros pequeños le vuelven loca.",
|
||||
"Los perros pequeños la vuelven loca.",
|
||||
"'le' → 'la' (direct object, not indirect)",
|
||||
),
|
||||
]
|
||||
|
||||
# ── Regex-based suspicious patterns ────────────────────────────────
|
||||
# (pattern, description, suggested_fix_or_None)
|
||||
|
||||
SUSPICIOUS_PATTERNS: list[tuple[str, str, str | None]] = [
|
||||
(
|
||||
r"AI-generated text-to-speech",
|
||||
"Metadata contamination: AI-generated TTS label in text field",
|
||||
None,
|
||||
),
|
||||
(
|
||||
r"\bsabes\s+nada\b(?!.*\bno\b)",
|
||||
"Missing negation: 'sabes nada' without 'no'",
|
||||
"Add 'no' before 'sabes': 'No sabes nada.'",
|
||||
),
|
||||
(
|
||||
r"\buno\s+(?:libro|coche|casa|perro|gato|hombre|mujer|niño"
|
||||
r"|amigo|día|año|mes|semana|minuto|segundo)\b",
|
||||
"'uno' instead of 'un' before masculine singular noun",
|
||||
"Replace 'uno' with 'un'",
|
||||
),
|
||||
(
|
||||
r"\bveintiuno\s+(?:años|días|meses|semanas|horas|minutos|segundos)\b",
|
||||
"'veintiuno' instead of 'veintiún' before plural noun",
|
||||
"Replace 'veintiuno' with 'veintiún'",
|
||||
),
|
||||
(
|
||||
r"observando\s+a\s+.*\bnadan\b",
|
||||
"Possible missing 'cómo' in 'observando cómo ...' construction",
|
||||
"Consider adding 'cómo': 'observando cómo ...'",
|
||||
),
|
||||
(
|
||||
r"\ble\s+vuelve\w*\s+loc[ao]s?\b",
|
||||
"'le vuelve/vuelven loco/a' may need a direct object pronoun",
|
||||
"Replace 'le' with lo/la/los/las as appropriate",
|
||||
),
|
||||
(
|
||||
r"\ble\s+vuelven\s+loc[ao]s?\b",
|
||||
"'le vuelven loco/a' may need a direct object pronoun",
|
||||
"Replace 'le' with lo/la/los/las as appropriate",
|
||||
),
|
||||
(
|
||||
r"\bNecesito\s+[a-záéíóúüñ]{2,}\b(?!\s+(?:un|una|el|la|los"
|
||||
r"|las|al|del|mi|tu|su|nuestro))\s*$",
|
||||
"Missing article before the noun after 'Necesito'",
|
||||
None,
|
||||
),
|
||||
(
|
||||
r"\bQu[ée]\s+es\s+la\s+respuesta\b",
|
||||
"'Qué es la respuesta' → 'Cuál es la respuesta'",
|
||||
"Replace 'Qué' with 'Cuál'",
|
||||
),
|
||||
(
|
||||
r"\bpeludos?\s+animales?\b",
|
||||
"'peludo(s) animal(es)' → 'animal(es) peludo(s)' (adjective placement)",
|
||||
"Place adjective after noun",
|
||||
),
|
||||
(
|
||||
r"\bhembras\s+y\s+varones\b",
|
||||
"'hembras y varones' — consider 'niños y niñas' or 'chicos y chicas'",
|
||||
None,
|
||||
),
|
||||
(
|
||||
r"\bt[ií]o\s+alto\s+con\s+uniforme\b",
|
||||
"'tío alto con uniforme' — check intent (Spain slang vs 'hombre')",
|
||||
None,
|
||||
),
|
||||
(
|
||||
r"\brepresentante\s+adulto\b",
|
||||
"'representante adulto' → 'adulto responsable'",
|
||||
"Consider 'adulto responsable'",
|
||||
),
|
||||
(
|
||||
r"\bpropiedad\s+a\s+este\s+representante\b",
|
||||
"'propiedad a este representante' — unusual phrasing",
|
||||
"Consider revising wording",
|
||||
),
|
||||
]
|
||||
|
||||
CONTENT_POS = frozenset({"NOUN", "VERB", "ADJ", "ADV"})
|
||||
FUNCTION_POS = frozenset(
|
||||
{"PRON", "ADP", "CCONJ", "SCONJ", "DET", "AUX", "PART", "INTJ"}
|
||||
)
|
||||
|
||||
# ── Text cleaning ──────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _clean_single_field_part(text: str) -> str:
|
||||
"""Clean one segment of an Anki field (no English-gloss filtering)."""
|
||||
text = BR_RE.sub(" ", text)
|
||||
text = HTML_TAG_RE.sub("", text)
|
||||
text = html.unescape(text)
|
||||
text = URL_RE.sub("", text)
|
||||
text = EMAIL_RE.sub("", text)
|
||||
return text.strip()
|
||||
|
||||
|
||||
def _looks_like_english_gloss(text: str) -> bool:
|
||||
"""Heuristic: does *text* read like an English gloss/translation?"""
|
||||
stripped = text.strip().strip("()[]（）【】")
|
||||
if not stripped:
|
||||
return True
|
||||
return bool(ENGLISH_RE.search(stripped))
|
||||
|
||||
|
||||
def clean_anki_field_text(text: str) -> str:
|
||||
"""Clean Anki field text for Spanish NLP.
|
||||
|
||||
* Removes ``[sound:...]`` and ``[media:...]`` markers.
|
||||
* Splits on ``<br><br>`` to separate Spanish from English glosses.
|
||||
* Strips HTML tags and unescapes entities.
|
||||
* Removes URLs, emails.
|
||||
* Drops segments that look like English translations.
|
||||
* Normalises whitespace.
|
||||
* Preserves Spanish accents and meaningful content.
|
||||
"""
|
||||
if not text:
|
||||
return ""
|
||||
|
||||
text = SOUND_RE.sub("", text)
|
||||
text = MEDIA_RE.sub("", text)
|
||||
|
||||
# Split on double <br> to isolate Spanish from glosses
|
||||
parts = DOUBLE_BR_RE.split(text)
|
||||
cleaned_parts: list[str] = []
|
||||
for i, part in enumerate(parts):
|
||||
cleaned = _clean_single_field_part(part)
|
||||
if not cleaned:
|
||||
continue
|
||||
if i > 0 and _looks_like_english_gloss(cleaned):
|
||||
continue
|
||||
cleaned_parts.append(cleaned)
|
||||
|
||||
result = " ".join(cleaned_parts)
|
||||
result = MULTI_WS_RE.sub(" ", result).strip()
|
||||
return result
|
||||
|
||||
|
||||
# ── Safe lemmatisation ─────────────────────────────────────────────
|
||||
|
||||
|
||||
def load_lemma_corrections(path: str) -> dict[str, str]:
|
||||
"""Load a ``bad<TAB>good`` TSV of manual lemma fixes."""
|
||||
corrections: dict[str, str] = {}
|
||||
with open(path, "r", encoding="utf-8") as fh:
|
||||
for line in fh:
|
||||
line = line.strip()
|
||||
if not line or line.startswith("#"):
|
||||
continue
|
||||
parts = line.split("\t")
|
||||
if len(parts) >= 2:
|
||||
bad = parts[0].strip().lower()
|
||||
good = parts[1].strip().lower()
|
||||
if bad and good:
|
||||
corrections[bad] = good
|
||||
return corrections
|
||||
|
||||
|
||||
def load_bad_lemmas(path: str) -> set[str]:
|
||||
"""Load a list of lemmas (one per line) to skip and report."""
|
||||
bad: set[str] = set()
|
||||
with open(path, "r", encoding="utf-8") as fh:
|
||||
for line in fh:
|
||||
lemma = line.strip()
|
||||
if lemma and not lemma.startswith("#"):
|
||||
bad.add(lemma.lower())
|
||||
return bad
|
||||
|
||||
|
||||
def safe_spanish_lemma(
|
||||
token,
|
||||
extra_corrections: dict[str, str] | None = None,
|
||||
) -> str:
|
||||
"""Return only the selected lemma for callers that do not need metadata."""
|
||||
return safe_spanish_lemma_info(token, extra_corrections=extra_corrections).lemma
|
||||
|
||||
|
||||
def safe_spanish_lemma_info(
|
||||
token,
|
||||
extra_corrections: dict[str, str] | None = None,
|
||||
) -> LemmaInfo:
|
||||
"""Return a safe Spanish lemma for a spaCy token.
|
||||
|
||||
Falls back to ``token.text.lower()`` when the spaCy lemma is empty,
|
||||
multi-word, contains punctuation, or looks non-Spanish. Applies a
|
||||
built-in and optionally an external correction map, and records why
|
||||
fallback/correction happened.
|
||||
"""
|
||||
manual = dict(BUILTIN_LEMMA_CORRECTIONS)
|
||||
if extra_corrections:
|
||||
manual.update({k.lower(): v.lower() for k, v in extra_corrections.items()})
|
||||
|
||||
lemma = (token.lemma_ or "").strip().lower()
|
||||
surface = (token.text or "").strip().lower()
|
||||
|
||||
# Manual correction map wins
|
||||
if lemma in manual:
|
||||
return LemmaInfo(
|
||||
manual[lemma],
|
||||
original_lemma=lemma,
|
||||
surface=surface,
|
||||
status=f"corrected:{lemma}->{manual[lemma]}",
|
||||
)
|
||||
|
||||
# Empty or one-char fallback
|
||||
if not lemma or len(lemma) <= 1:
|
||||
return LemmaInfo(
|
||||
surface or lemma,
|
||||
original_lemma=lemma,
|
||||
surface=surface,
|
||||
status="fallback_empty_or_short_lemma",
|
||||
)
|
||||
|
||||
# Multi-word lemma → suspicious spaCy output
|
||||
if " " in lemma:
|
||||
return LemmaInfo(
|
||||
surface,
|
||||
original_lemma=lemma,
|
||||
surface=surface,
|
||||
status="fallback_multi_word_lemma",
|
||||
)
|
||||
|
||||
# Punctuation or digits in lemma → fall back
|
||||
if LEMMA_PUNCT_RE.search(lemma):
|
||||
return LemmaInfo(
|
||||
surface,
|
||||
original_lemma=lemma,
|
||||
surface=surface,
|
||||
status="fallback_bad_lemma_characters",
|
||||
)
|
||||
|
||||
# Special case: "prir" might be spaCy failing on pedir forms
|
||||
if lemma == "prir":
|
||||
if surface in PRIR_SURFACE_FORMS:
|
||||
return LemmaInfo(
|
||||
"pedir",
|
||||
original_lemma=lemma,
|
||||
surface=surface,
|
||||
status="corrected:prir->pedir",
|
||||
)
|
||||
# otherwise return surface so it shows up in debug as suspicious
|
||||
return LemmaInfo(
|
||||
surface,
|
||||
original_lemma=lemma,
|
||||
surface=surface,
|
||||
status="suspicious_prir",
|
||||
)
|
||||
|
||||
if not SPANISH_WORD_RE.match(lemma):
|
||||
return LemmaInfo(
|
||||
surface,
|
||||
original_lemma=lemma,
|
||||
surface=surface,
|
||||
status="fallback_non_spanish_lemma",
|
||||
)
|
||||
|
||||
return LemmaInfo(lemma, original_lemma=lemma, surface=surface)
|
||||
|
||||
|
||||
# ── Token filters ──────────────────────────────────────────────────
|
||||
|
||||
|
||||
def spanish_content_filter(
|
||||
token,
|
||||
include_proper_nouns: bool = False,
|
||||
) -> bool:
|
||||
"""Return True for Spanish content words (NOUN, VERB, ADJ, ADV).
|
||||
|
||||
Excludes punctuation, digits, symbols, URLs, emails, media
|
||||
filenames, and one-character junk.
|
||||
"""
|
||||
text = (token.text or "").strip()
|
||||
if not text:
|
||||
return False
|
||||
if token.is_punct or token.is_space or token.is_digit:
|
||||
return False
|
||||
if token.is_currency:
|
||||
return False
|
||||
if token.like_url or token.like_email:
|
||||
return False
|
||||
if MEDIA_FILENAME_RE.search(text):
|
||||
return False
|
||||
if BAD_TOKEN_TEXT_RE.search(text):
|
||||
return False
|
||||
if len(text) <= 1 and text not in ("a", "y", "e", "o", "u", "él"):
|
||||
return False
|
||||
pos = getattr(token, "pos_", "") or ""
|
||||
if pos in CONTENT_POS:
|
||||
return True
|
||||
if include_proper_nouns and pos == "PROPN":
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def spanish_function_word_filter(token) -> bool:
|
||||
"""Return True for Spanish function words (PRON, ADP, CCONJ, …)."""
|
||||
text = (token.text or "").strip()
|
||||
if not text:
|
||||
return False
|
||||
if token.is_punct or token.is_space or token.like_url or token.like_email:
|
||||
return False
|
||||
if MEDIA_FILENAME_RE.search(text) or BAD_TOKEN_TEXT_RE.search(text):
|
||||
return False
|
||||
pos = getattr(token, "pos_", "") or ""
|
||||
return pos in FUNCTION_POS
|
||||
|
||||
|
||||
# ── Suspicious-card detection ──────────────────────────────────────
|
||||
|
||||
|
||||
def check_card_for_issues(text: str, cleaned: str) -> list[dict[str, Any]]:
|
||||
"""Check a single card's text for suspicious patterns.
|
||||
|
||||
Returns a list of issue dicts (normally 0 or 1 per card, but one
|
||||
card can match multiple patterns).
|
||||
"""
|
||||
issues: list[dict[str, Any]] = []
|
||||
seen_reasons: set[str] = set()
|
||||
|
||||
for bad_text, fix, explanation in KNOWN_CARD_ISSUES:
|
||||
if bad_text in text:
|
||||
if explanation not in seen_reasons:
|
||||
seen_reasons.add(explanation)
|
||||
issues.append({
|
||||
"reason": explanation,
|
||||
"suggested_fix": fix,
|
||||
})
|
||||
|
||||
for pattern, description, fix in SUSPICIOUS_PATTERNS:
|
||||
if re.search(pattern, text, re.IGNORECASE):
|
||||
if description not in seen_reasons:
|
||||
seen_reasons.add(description)
|
||||
issues.append({
|
||||
"reason": description,
|
||||
"suggested_fix": fix or "",
|
||||
})
|
||||
|
||||
# Check for English after <br><br>
|
||||
parts = DOUBLE_BR_RE.split(text)
|
||||
for part in parts[1:]:
|
||||
cleaned_part = _clean_single_field_part(part)
|
||||
if _looks_like_english_gloss(cleaned_part):
|
||||
desc = "English gloss after <br><br>"
|
||||
if desc not in seen_reasons:
|
||||
seen_reasons.add(desc)
|
||||
issues.append({"reason": desc, "suggested_fix": ""})
|
||||
|
||||
# Check for missing final punctuation in full-sentence cards
|
||||
stripped_text = text.rstrip()
|
||||
if (
|
||||
stripped_text
|
||||
and not stripped_text.endswith((".", "!", "?", "…", '"', "'"))
|
||||
and stripped_text[0].isupper()
|
||||
and len(stripped_text.split()) >= 3
|
||||
):
|
||||
desc = "Missing final punctuation for full-sentence card"
|
||||
if desc not in seen_reasons:
|
||||
seen_reasons.add(desc)
|
||||
issues.append({"reason": desc, "suggested_fix": ""})
|
||||
|
||||
return issues
|
||||
|
||||
|
||||
def lint_anki_cards(
|
||||
records: list[dict[str, Any]],
|
||||
verbose: bool = False,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Run all suspicious-card checks across parsed Anki records.
|
||||
|
||||
When *verbose* is True every record is returned, not just those
|
||||
with issues.
|
||||
|
||||
Returns a list of issue dicts with keys:
|
||||
``source_line``, ``original_text``, ``cleaned_text``,
|
||||
``reason``, ``suggested_fix``.
|
||||
"""
|
||||
results: list[dict[str, Any]] = []
|
||||
for record in records:
|
||||
text = record.get("text", "")
|
||||
if not text:
|
||||
if verbose:
|
||||
results.append({
|
||||
"source_line": record.get("line_number", ""),
|
||||
"original_text": record.get("raw_line", ""),
|
||||
"cleaned_text": "",
|
||||
"reason": "empty field",
|
||||
"suggested_fix": "",
|
||||
})
|
||||
continue
|
||||
cleaned = clean_anki_field_text(text)
|
||||
issues = check_card_for_issues(text, cleaned)
|
||||
if verbose or issues:
|
||||
if issues:
|
||||
combined_reason = "; ".join(
|
||||
sorted({i["reason"] for i in issues})
|
||||
)
|
||||
suggested = ""
|
||||
for i in issues:
|
||||
if i.get("suggested_fix"):
|
||||
suggested = i["suggested_fix"]
|
||||
break
|
||||
else:
|
||||
combined_reason = ""
|
||||
suggested = ""
|
||||
results.append({
|
||||
"source_line": record.get("line_number", ""),
|
||||
"original_text": record.get("raw_line", text),
|
||||
"cleaned_text": cleaned,
|
||||
"reason": combined_reason,
|
||||
"suggested_fix": suggested,
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
|
||||
# ── Detailed extraction helpers ─────────────────────────────────────
|
||||
|
||||
|
||||
def extract_detailed_counts(
|
||||
texts: list[dict[str, Any]],
|
||||
nlp,
|
||||
token_filter: Callable,
|
||||
lemma_fn: Callable,
|
||||
max_examples: int = 3,
|
||||
clean: bool = True,
|
||||
) -> dict[str, dict[str, Any]]:
|
||||
"""Build detailed per-lemma statistics from a list of ``{text, line_number}`` records.
|
||||
|
||||
When *clean* is False the raw text is passed to the NLP pipeline
|
||||
without going through ``clean_anki_field_text``.
|
||||
|
||||
Returns ::
|
||||
|
||||
{lemma: {"count": int,
|
||||
"pos_counts": {POS: int, …},
|
||||
"surface_forms": [str, …],
|
||||
"example_sentences": [str, …],
|
||||
"source_lines": [str, …]}}
|
||||
"""
|
||||
stats: dict[str, dict[str, Any]] = {}
|
||||
|
||||
for record in texts:
|
||||
raw = record.get("text", "")
|
||||
line_number = record.get("line_number", 0)
|
||||
if not raw:
|
||||
continue
|
||||
txt = clean_anki_field_text(raw) if clean else raw.strip()
|
||||
if not txt:
|
||||
continue
|
||||
|
||||
doc = nlp(txt)
|
||||
for token in doc:
|
||||
if not token_filter(token):
|
||||
continue
|
||||
lemma_value = lemma_fn(token)
|
||||
if lemma_value is None:
|
||||
continue
|
||||
if isinstance(lemma_value, LemmaInfo):
|
||||
lemma = lemma_value.lemma
|
||||
original_lemma = lemma_value.original_lemma
|
||||
lemma_status = lemma_value.status
|
||||
else:
|
||||
lemma = str(lemma_value)
|
||||
original_lemma = (getattr(token, "lemma_", "") or "").strip().lower()
|
||||
lemma_status = "ok"
|
||||
if not lemma:
|
||||
continue
|
||||
|
||||
if lemma not in stats:
|
||||
stats[lemma] = {
|
||||
"count": 0,
|
||||
"pos_counts": defaultdict(int),
|
||||
"surface_forms": set(),
|
||||
"original_lemmas": set(),
|
||||
"lemma_statuses": defaultdict(int),
|
||||
"example_sentences": [],
|
||||
"source_lines": [],
|
||||
}
|
||||
|
||||
s = stats[lemma]
|
||||
s["count"] += 1
|
||||
s["pos_counts"][token.pos_] += 1
|
||||
s["surface_forms"].add(token.text.lower())
|
||||
if original_lemma:
|
||||
s["original_lemmas"].add(original_lemma)
|
||||
s["lemma_statuses"][lemma_status] += 1
|
||||
if len(s["example_sentences"]) < max_examples:
|
||||
sent_text = txt
|
||||
if sent_text not in s["example_sentences"]:
|
||||
s["example_sentences"].append(sent_text)
|
||||
if line_number:
|
||||
s["source_lines"].append(str(line_number))
|
||||
|
||||
# Convert defaultdicts/sets to plain types for serialisation
|
||||
for lemma, s in stats.items():
|
||||
s["pos_counts"] = dict(s["pos_counts"])
|
||||
s["surface_forms"] = sorted(s["surface_forms"])
|
||||
s["original_lemmas"] = sorted(s.get("original_lemmas", []))
|
||||
s["lemma_statuses"] = dict(s.get("lemma_statuses", {}))
|
||||
s["source_lines"] = list(dict.fromkeys(s["source_lines"])) # dedup, preserve order
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
# ── Output helpers ─────────────────────────────────────────────────
|
||||
|
||||
|
||||
def write_debug_tsv(
|
||||
stats: dict[str, dict[str, Any]],
|
||||
path: str,
|
||||
bad_lemmas: set[str] | None = None,
|
||||
min_freq: int = 0,
|
||||
) -> None:
|
||||
"""Write a debug TSV with columns::
|
||||
|
||||
lemma count pos_counts top_surface_forms example_sentences source_lines
|
||||
|
||||
Appends a special line per lemma in *bad_lemmas* with an
|
||||
``(EXCLUDED)`` marker. When *min_freq* > 0 only lemmas with
|
||||
count >= min_freq are included.
|
||||
"""
|
||||
import os
|
||||
|
||||
os.makedirs(os.path.dirname(os.path.abspath(path)) or ".", exist_ok=True)
|
||||
|
||||
bad = bad_lemmas or set()
|
||||
|
||||
with open(path, "w", encoding="utf-8") as fh:
|
||||
fh.write(
|
||||
"lemma\tcount\tpos_counts\ttop_surface_forms"
|
||||
"\texample_sentences\tsource_lines\toriginal_lemmas"
|
||||
"\tlemma_statuses\tstatus\n"
|
||||
)
|
||||
sorted_lemmas = sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0]))
|
||||
for lemma, s in sorted_lemmas:
|
||||
if min_freq > 0 and s["count"] < min_freq:
|
||||
continue
|
||||
pos_counts_str = ", ".join(
|
||||
f"{pos}:{cnt}"
|
||||
for pos, cnt in sorted(
|
||||
s["pos_counts"].items(), key=lambda x: -x[1]
|
||||
)
|
||||
)
|
||||
surfaces_str = ", ".join(s["surface_forms"])
|
||||
examples_str = "; ".join(s["example_sentences"])
|
||||
lines_str = ", ".join(s["source_lines"])
|
||||
original_lemmas_str = ", ".join(s.get("original_lemmas", []))
|
||||
lemma_statuses_str = ", ".join(
|
||||
f"{status}:{cnt}"
|
||||
for status, cnt in sorted(
|
||||
s.get("lemma_statuses", {}).items(),
|
||||
key=lambda x: (-x[1], x[0]),
|
||||
)
|
||||
)
|
||||
|
||||
status = "EXCLUDED" if lemma in bad else ""
|
||||
fh.write(
|
||||
f"{lemma}\t{s['count']}\t{pos_counts_str}\t{surfaces_str}"
|
||||
f"\t{examples_str}\t{lines_str}\t{original_lemmas_str}"
|
||||
f"\t{lemma_statuses_str}\t{status}\n"
|
||||
)
|
||||
|
||||
|
||||
def write_suspicious_tokens(
|
||||
stats: dict[str, dict[str, Any]],
|
||||
path: str,
|
||||
bad_lemmas: set[str] | None = None,
|
||||
) -> None:
|
||||
"""Write a file of suspicious lemmas (multi-word, punctuation, etc.)
|
||||
|
||||
Any lemma that was corrected, fell back from a suspicious spaCy lemma,
|
||||
or appears in the user blocklist is listed here.
|
||||
"""
|
||||
import os
|
||||
|
||||
os.makedirs(os.path.dirname(os.path.abspath(path)) or ".", exist_ok=True)
|
||||
bad = bad_lemmas or set()
|
||||
|
||||
with open(path, "w", encoding="utf-8") as fh:
|
||||
fh.write("lemma\tcount\tsurface_forms\toriginal_lemmas\tnote\n")
|
||||
fh.write(
|
||||
"# Lemmas that required correction or fallback are listed here\n"
|
||||
)
|
||||
for lemma, s in sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0])):
|
||||
statuses = {
|
||||
status: count
|
||||
for status, count in s.get("lemma_statuses", {}).items()
|
||||
if status != "ok"
|
||||
}
|
||||
if lemma in bad:
|
||||
statuses["blocked_bad_lemma"] = s["count"]
|
||||
if not statuses:
|
||||
continue
|
||||
surfaces_str = ", ".join(s["surface_forms"])
|
||||
originals_str = ", ".join(s.get("original_lemmas", []))
|
||||
note = ", ".join(
|
||||
f"{status}:{count}"
|
||||
for status, count in sorted(statuses.items())
|
||||
)
|
||||
fh.write(
|
||||
f"{lemma}\t{s['count']}\t{surfaces_str}"
|
||||
f"\t{originals_str}\t{note}\n"
|
||||
)
|
||||
+174
-143
@@ -2,9 +2,9 @@
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import html
|
||||
import os
|
||||
from collections import Counter
|
||||
from collections import Counter, defaultdict
|
||||
|
||||
import regex as re
|
||||
from typing import Any, Callable
|
||||
@@ -12,18 +12,6 @@ from typing import Any, Callable
|
||||
from .ankiconnect import anki_request
|
||||
from .anki_tsv import parse_anki_tsv
|
||||
from .config import Config
|
||||
from .spanish import (
|
||||
clean_anki_field_text,
|
||||
extract_detailed_counts,
|
||||
lint_anki_cards,
|
||||
load_bad_lemmas,
|
||||
load_lemma_corrections,
|
||||
safe_spanish_lemma_info,
|
||||
spanish_content_filter,
|
||||
spanish_function_word_filter,
|
||||
write_debug_tsv,
|
||||
write_suspicious_tokens,
|
||||
)
|
||||
from .text import extract_first_visible_line, extract_visible_text, normalize_word_key
|
||||
|
||||
ACCENT_MAP = str.maketrans("áéíóúüñÁÉÍÓÚÜÑ", "aeiouunAEIOUUN")
|
||||
@@ -38,19 +26,67 @@ JAPANESE_GRAMMAR_EXCLUDE = {
|
||||
"て", "た", "ます", "れる", "てる", "ぬ", "ん", "しまう", "いる", "ない", "なる", "ある", "だ", "です",
|
||||
}
|
||||
JAPANESE_ALLOWED_POS = {"NOUN", "PROPN", "VERB", "ADJ"}
|
||||
SPANISH_ALLOWED_POS = {"NOUN", "VERB", "ADJ", "ADV"}
|
||||
FIELD_SECTION_CHOICES = {"all", "first"}
|
||||
|
||||
|
||||
def setup_logging(logfile: str) -> None:
|
||||
"""Configure file logging for word extraction scripts."""
|
||||
os.makedirs(os.path.dirname(os.path.abspath(logfile)), exist_ok=True)
|
||||
logging.basicConfig(filename=logfile, level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
|
||||
|
||||
SOUND_RE = re.compile(r"\[sound:[^\]]*\]")
|
||||
MEDIA_RE = re.compile(r"\[\s*(?:sound|image|media):[^\]]*\]", re.IGNORECASE)
|
||||
BR_RE = re.compile(r"<\s*br\s*/?\s*>", re.IGNORECASE)
|
||||
DOUBLE_BR_RE = re.compile(
|
||||
r"<\s*br\s*/?\s*>\s*<\s*br\s*/?\s*>", re.IGNORECASE
|
||||
)
|
||||
HTML_TAG_RE = re.compile(r"<[^>]+>")
|
||||
URL_RE = re.compile(r"https?://\S+|www\.\S+", re.IGNORECASE)
|
||||
EMAIL_RE = re.compile(r"\S+@\S+\.\S+")
|
||||
MULTI_WS_RE = re.compile(r"[ \t]+")
|
||||
|
||||
def build_query_from_decks(decks: list[str]) -> str:
|
||||
"""Build an Anki search query that matches any configured deck."""
|
||||
return " OR ".join(f'deck:"{d}"' for d in decks)
|
||||
|
||||
|
||||
def _clean_single_field_part(text: str) -> str:
|
||||
"""Clean one segment of an Anki field before NLP."""
|
||||
text = BR_RE.sub(" ", text)
|
||||
text = HTML_TAG_RE.sub("", text)
|
||||
text = html.unescape(text)
|
||||
text = URL_RE.sub("", text)
|
||||
text = EMAIL_RE.sub("", text)
|
||||
return text.strip()
|
||||
|
||||
|
||||
def clean_anki_field_text(text: str | None, field_section: str = "all") -> str:
|
||||
"""Clean Anki field text before NLP.
|
||||
|
||||
Removes media markers, strips HTML/URLs/emails, normalizes spaces, and can
|
||||
optionally keep only the first blank-line-separated field section.
|
||||
"""
|
||||
if not text:
|
||||
return ""
|
||||
if field_section not in FIELD_SECTION_CHOICES:
|
||||
raise ValueError(
|
||||
f"field_section must be one of {sorted(FIELD_SECTION_CHOICES)}, "
|
||||
f"got {field_section!r}"
|
||||
)
|
||||
|
||||
text = SOUND_RE.sub("", text)
|
||||
text = MEDIA_RE.sub("", text)
|
||||
|
||||
parts = DOUBLE_BR_RE.split(text)
|
||||
if field_section == "first":
|
||||
parts = parts[:1]
|
||||
|
||||
cleaned_parts: list[str] = []
|
||||
for part in parts:
|
||||
cleaned = _clean_single_field_part(part)
|
||||
if not cleaned:
|
||||
continue
|
||||
cleaned_parts.append(cleaned)
|
||||
|
||||
result = " ".join(cleaned_parts)
|
||||
return MULTI_WS_RE.sub(" ", result).strip()
|
||||
|
||||
|
||||
def japanese_filter(token) -> bool:
|
||||
"""Return whether a spaCy token is useful Japanese vocabulary.
|
||||
|
||||
@@ -74,7 +110,13 @@ def japanese_filter(token) -> bool:
|
||||
|
||||
def spanish_filter(token) -> bool:
|
||||
"""Return whether a spaCy token is useful Spanish vocabulary."""
|
||||
return bool(getattr(token, "is_alpha", False)) and not bool(getattr(token, "is_stop", False))
|
||||
if not bool(getattr(token, "is_alpha", False)):
|
||||
return False
|
||||
if bool(getattr(token, "is_stop", False)):
|
||||
return False
|
||||
if bool(getattr(token, "like_url", False)) or bool(getattr(token, "like_email", False)):
|
||||
return False
|
||||
return getattr(token, "pos_", None) in SPANISH_ALLOWED_POS
|
||||
|
||||
|
||||
def spanish_format(token) -> str:
|
||||
@@ -219,18 +261,109 @@ def extract_words(
|
||||
return {"query": search_query, "notes": len(notes), "unique": len(counter), "written": written, "out": out_path}
|
||||
|
||||
|
||||
def extract_detailed_counts(
|
||||
texts: list[dict[str, Any]],
|
||||
nlp,
|
||||
token_filter: Callable,
|
||||
output_format: Callable,
|
||||
max_examples: int = 3,
|
||||
clean: bool = True,
|
||||
field_section: str = "all",
|
||||
) -> dict[str, dict[str, Any]]:
|
||||
"""Build detailed per-entry statistics from parsed TSV records."""
|
||||
stats: dict[str, dict[str, Any]] = {}
|
||||
|
||||
for record in texts:
|
||||
raw = record.get("text", "")
|
||||
line_number = record.get("line_number", 0)
|
||||
if not raw:
|
||||
continue
|
||||
text = clean_anki_field_text(raw, field_section=field_section) if clean else raw.strip()
|
||||
if not text:
|
||||
continue
|
||||
|
||||
for token in nlp(text):
|
||||
if not token_filter(token):
|
||||
continue
|
||||
key = str(output_format(token)).strip()
|
||||
if not key:
|
||||
continue
|
||||
|
||||
if key not in stats:
|
||||
stats[key] = {
|
||||
"count": 0,
|
||||
"pos_counts": defaultdict(int),
|
||||
"surface_forms": set(),
|
||||
"original_lemmas": set(),
|
||||
"example_sentences": [],
|
||||
"source_lines": [],
|
||||
}
|
||||
|
||||
item = stats[key]
|
||||
item["count"] += 1
|
||||
item["pos_counts"][getattr(token, "pos_", "") or ""] += 1
|
||||
item["surface_forms"].add((getattr(token, "text", "") or "").lower())
|
||||
original_lemma = (getattr(token, "lemma_", "") or "").strip().lower()
|
||||
if original_lemma:
|
||||
item["original_lemmas"].add(original_lemma)
|
||||
if len(item["example_sentences"]) < max_examples and text not in item["example_sentences"]:
|
||||
item["example_sentences"].append(text)
|
||||
if line_number:
|
||||
item["source_lines"].append(str(line_number))
|
||||
|
||||
for item in stats.values():
|
||||
item["pos_counts"] = dict(item["pos_counts"])
|
||||
item["surface_forms"] = sorted(item["surface_forms"])
|
||||
item["original_lemmas"] = sorted(item["original_lemmas"])
|
||||
item["source_lines"] = list(dict.fromkeys(item["source_lines"]))
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
def write_debug_tsv(
|
||||
stats: dict[str, dict[str, Any]],
|
||||
path: str,
|
||||
min_freq: int = 0,
|
||||
) -> None:
|
||||
"""Write a debug TSV for detailed extraction statistics."""
|
||||
os.makedirs(os.path.dirname(os.path.abspath(path)) or ".", exist_ok=True)
|
||||
|
||||
with open(path, "w", encoding="utf-8") as fh:
|
||||
fh.write(
|
||||
"entry\tcount\tpos_counts\ttop_surface_forms"
|
||||
"\texample_sentences\tsource_lines\toriginal_lemmas\n"
|
||||
)
|
||||
sorted_entries = sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0]))
|
||||
for entry, item in sorted_entries:
|
||||
if min_freq > 0 and item["count"] < min_freq:
|
||||
continue
|
||||
pos_counts = ", ".join(
|
||||
f"{pos}:{count}"
|
||||
for pos, count in sorted(
|
||||
item["pos_counts"].items(), key=lambda x: -x[1]
|
||||
)
|
||||
if pos
|
||||
)
|
||||
surfaces = ", ".join(item["surface_forms"])
|
||||
examples = "; ".join(item["example_sentences"])
|
||||
lines = ", ".join(item["source_lines"])
|
||||
original_lemmas = ", ".join(item.get("original_lemmas", []))
|
||||
fh.write(
|
||||
f"{entry}\t{item['count']}\t{pos_counts}\t{surfaces}"
|
||||
f"\t{examples}\t{lines}\t{original_lemmas}\n"
|
||||
)
|
||||
|
||||
|
||||
def _write_content_words(
|
||||
stats: dict[str, dict[str, Any]],
|
||||
path: str,
|
||||
min_freq: int,
|
||||
bad_lemmas: set[str] | None = None,
|
||||
) -> int:
|
||||
"""Write a sorted ``lemma count`` file from detailed stats."""
|
||||
bad = bad_lemmas or set()
|
||||
"""Write a sorted ``entry count`` file from detailed stats."""
|
||||
items = [
|
||||
(lemma, data["count"])
|
||||
for lemma, data in stats.items()
|
||||
if data["count"] >= min_freq and lemma not in bad
|
||||
(entry, data["count"])
|
||||
for entry, data in stats.items()
|
||||
if data["count"] >= min_freq
|
||||
]
|
||||
items.sort(key=lambda x: (-x[1], x[0]))
|
||||
os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
|
||||
@@ -240,19 +373,6 @@ def _write_content_words(
|
||||
return len(items)
|
||||
|
||||
|
||||
def _write_function_words(
|
||||
stats: dict[str, dict[str, Any]],
|
||||
path: str,
|
||||
) -> int:
|
||||
"""Write a function-word frequency file from detailed stats."""
|
||||
os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
|
||||
items = sorted(stats.items(), key=lambda x: (-x[1]["count"], x[0]))
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
for word, data in items:
|
||||
f.write(f"{word} {data['count']}\n")
|
||||
return len(items)
|
||||
|
||||
|
||||
def extract_words_from_file(
|
||||
config: Config,
|
||||
lang: str,
|
||||
@@ -261,84 +381,64 @@ def extract_words_from_file(
|
||||
min_freq: int = 2,
|
||||
outdir: str | None = None,
|
||||
out: str | None = None,
|
||||
include_proper_nouns: bool = False,
|
||||
function_words: bool = False,
|
||||
debug: str | None = None,
|
||||
lemma_corrections: str | None = None,
|
||||
bad_lemma_file: str | None = None,
|
||||
spacy_model: str | None = None,
|
||||
include_tags: bool = False,
|
||||
debug_min_freq: int = 0,
|
||||
no_clean: bool = False,
|
||||
field_section: str = "all",
|
||||
) -> dict[str, Any]:
|
||||
"""Extract frequent Spanish vocabulary from an Anki TSV export file.
|
||||
"""Extract frequent vocabulary from an Anki TSV export file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
config:
|
||||
Saiki configuration.
|
||||
lang:
|
||||
Language code (e.g. ``"es"``).
|
||||
Language code (e.g. ``"es"`` or ``"jp"``).
|
||||
input_path:
|
||||
Path to the Anki TSV export file.
|
||||
field_index:
|
||||
1-based column index of the Spanish text (default 2).
|
||||
1-based column index of the text field (default 2).
|
||||
min_freq:
|
||||
Minimum frequency to include in output.
|
||||
outdir:
|
||||
Output directory (defaults to config word output root).
|
||||
out:
|
||||
Output filename or path for content words.
|
||||
include_proper_nouns:
|
||||
Include ``PROPN`` tokens.
|
||||
function_words:
|
||||
Also extract a separate function-word list.
|
||||
debug:
|
||||
Path for the debug TSV.
|
||||
lemma_corrections:
|
||||
Path to a ``bad\\tgood`` TSV of lemma corrections.
|
||||
bad_lemma_file:
|
||||
Path to a list of lemmas to skip.
|
||||
spacy_model:
|
||||
SpaCy model name override.
|
||||
field_section:
|
||||
Which blank-line-separated field section to mine when cleaning:
|
||||
``"all"`` or ``"first"``.
|
||||
|
||||
Returns
|
||||
-------
|
||||
A dict with keys ``records``, ``stats``, ``written``, ``out``,
|
||||
``debug``, ``proper_nouns``, ``function_words``, ``suspicious``.
|
||||
A dict with keys ``records``, ``stats``, ``written``, ``out``, and
|
||||
optional debug/supporting output paths.
|
||||
"""
|
||||
language_bucket = config.language_name(lang)
|
||||
profile = LANGUAGE_PROFILES[language_bucket]
|
||||
out_dir = os.path.expanduser(outdir) if outdir else os.path.join(config.word_output_root, language_bucket)
|
||||
out_path = os.path.expanduser(out) if out else os.path.join(out_dir, f"words_{lang}_content.txt")
|
||||
model_name = spacy_model or str(config.language(lang).get("word_model"))
|
||||
nlp = load_spacy_model(model_name)
|
||||
|
||||
corrections = None
|
||||
if lemma_corrections:
|
||||
corrections = load_lemma_corrections(lemma_corrections)
|
||||
bad_lemmas = None
|
||||
if bad_lemma_file:
|
||||
bad_lemmas = load_bad_lemmas(bad_lemma_file)
|
||||
|
||||
records = parse_anki_tsv(
|
||||
input_path,
|
||||
field_index=field_index,
|
||||
include_tags=include_tags,
|
||||
)
|
||||
if not records:
|
||||
return {"records": 0, "stats": {}, "written": 0, "out": out_path}
|
||||
|
||||
# Build token filter
|
||||
def _content_filter(token) -> bool:
|
||||
return spanish_content_filter(token, include_proper_nouns=include_proper_nouns)
|
||||
|
||||
stats = extract_detailed_counts(
|
||||
records, nlp, _content_filter,
|
||||
lambda t: safe_spanish_lemma_info(t, extra_corrections=corrections),
|
||||
records, nlp, profile["token_filter"], profile["output_format"],
|
||||
clean=not no_clean,
|
||||
field_section=field_section,
|
||||
)
|
||||
|
||||
written = _write_content_words(stats, out_path, min_freq, bad_lemmas=bad_lemmas)
|
||||
written = _write_content_words(stats, out_path, min_freq)
|
||||
|
||||
result: dict[str, Any] = {
|
||||
"records": len(records),
|
||||
@@ -350,81 +450,12 @@ def extract_words_from_file(
|
||||
# Debug output
|
||||
if debug:
|
||||
debug_path = os.path.expanduser(debug)
|
||||
write_debug_tsv(stats, debug_path, bad_lemmas=bad_lemmas, min_freq=debug_min_freq)
|
||||
write_debug_tsv(stats, debug_path, min_freq=debug_min_freq)
|
||||
result["debug"] = debug_path
|
||||
|
||||
# Proper nouns
|
||||
if include_proper_nouns:
|
||||
proper_path = os.path.join(out_dir, f"words_{lang}_proper_nouns.txt")
|
||||
proper_items = [
|
||||
(lemma, data["count"])
|
||||
for lemma, data in stats.items()
|
||||
if data["pos_counts"].get("PROPN", 0) > 0
|
||||
]
|
||||
proper_items.sort(key=lambda x: (-x[1], x[0]))
|
||||
os.makedirs(os.path.dirname(os.path.abspath(proper_path)), exist_ok=True)
|
||||
with open(proper_path, "w", encoding="utf-8") as f:
|
||||
for w, c in proper_items:
|
||||
f.write(f"{w} {c}\n")
|
||||
result["proper_nouns"] = proper_path
|
||||
|
||||
# Function words (separate extraction pass)
|
||||
if function_words:
|
||||
fw_path = os.path.join(out_dir, f"words_{lang}_function_words.txt")
|
||||
fw_stats = extract_detailed_counts(
|
||||
records, nlp, spanish_function_word_filter,
|
||||
lambda t: safe_spanish_lemma_info(t, extra_corrections=corrections),
|
||||
)
|
||||
fw_written = _write_function_words(fw_stats, fw_path)
|
||||
result["function_words"] = fw_path
|
||||
result["function_words_written"] = fw_written
|
||||
|
||||
# Suspicious tokens
|
||||
susp_path = os.path.join(out_dir, f"words_{lang}_suspicious_tokens.txt")
|
||||
write_suspicious_tokens(stats, susp_path, bad_lemmas=bad_lemmas)
|
||||
result["suspicious"] = susp_path
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def lint_anki_cards_main(
|
||||
input_path: str,
|
||||
field_index: int = 2,
|
||||
output_path: str = "suspicious_cards_es.tsv",
|
||||
verbose: bool = False,
|
||||
) -> dict[str, Any]:
|
||||
"""Read an Anki TSV export and produce a suspicious-card report.
|
||||
|
||||
When *verbose* is True every record is written to the report, not
|
||||
only those with issues.
|
||||
|
||||
Returns ``{"records": …, "issues": …, "path": output_path}``.
|
||||
"""
|
||||
records = parse_anki_tsv(input_path, field_index=field_index)
|
||||
issues = lint_anki_cards(records, verbose=verbose)
|
||||
|
||||
os.makedirs(os.path.dirname(os.path.abspath(output_path)) or ".", exist_ok=True)
|
||||
with open(output_path, "w", encoding="utf-8") as fh:
|
||||
fh.write(
|
||||
"source_line\toriginal_text\tcleaned_text\treason\tsuggested_fix\n"
|
||||
)
|
||||
for issue in issues:
|
||||
fh.write(
|
||||
f"{issue['source_line']}\t"
|
||||
f"{_tsv_escape(issue['original_text'])}\t"
|
||||
f"{_tsv_escape(issue['cleaned_text'])}\t"
|
||||
f"{_tsv_escape(issue['reason'])}\t"
|
||||
f"{_tsv_escape(issue['suggested_fix'])}\n"
|
||||
)
|
||||
|
||||
return {"records": len(records), "issues": len(issues), "path": output_path}
|
||||
|
||||
|
||||
def _tsv_escape(value: str) -> str:
|
||||
"""Escape a value for TSV output (backslash-escape tabs and newlines)."""
|
||||
return value.replace("\\", "\\\\").replace("\t", "\\t").replace("\n", "\\n")
|
||||
|
||||
|
||||
def normalize_word_for_comparison(word: str) -> str:
|
||||
"""Normalise a word for file-comparison: lowercase, strip accents."""
|
||||
return word.lower().translate(ACCENT_MAP)
|
||||
|
||||
+104
-296
@@ -26,27 +26,18 @@ from saiki.importer import (
|
||||
synthesize_tts_sample,
|
||||
supported_tts_backends,
|
||||
)
|
||||
from saiki.spanish import (
|
||||
BUILTIN_LEMMA_CORRECTIONS,
|
||||
check_card_for_issues,
|
||||
clean_anki_field_text,
|
||||
extract_detailed_counts,
|
||||
lint_anki_cards,
|
||||
load_lemma_corrections,
|
||||
safe_spanish_lemma,
|
||||
safe_spanish_lemma_info,
|
||||
spanish_content_filter,
|
||||
spanish_function_word_filter,
|
||||
write_debug_tsv,
|
||||
write_suspicious_tokens,
|
||||
)
|
||||
from saiki.text import extract_first_visible_line, extract_visible_text
|
||||
from saiki.words import (
|
||||
build_query_from_decks,
|
||||
clean_anki_field_text,
|
||||
compare_word_files,
|
||||
compare_word_lists,
|
||||
extract_detailed_counts,
|
||||
extract_words_from_file,
|
||||
normalize_word_for_comparison,
|
||||
read_word_file,
|
||||
spanish_filter,
|
||||
write_debug_tsv,
|
||||
)
|
||||
from saiki.youtube import TranscriptLine, extract_video_id, sentence_vocab, write_sentence_export
|
||||
|
||||
@@ -177,6 +168,15 @@ class AnkiTsvTests(unittest.TestCase):
|
||||
self.assertEqual(records[0]["line_number"], 2)
|
||||
self.assertEqual(records[1]["line_number"], 3)
|
||||
|
||||
def test_parse_tsv_preserves_hash_data_rows_after_headers(self):
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
path = os.path.join(tmp, "test.txt")
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
f.write("#separator:tab\nalpha\tbeta\n#hashtag\tvalue\n")
|
||||
records = parse_anki_tsv(path, field_index=1)
|
||||
self.assertEqual([r["text"] for r in records], ["alpha", "#hashtag"])
|
||||
self.assertEqual(records[1]["line_number"], 3)
|
||||
|
||||
def test_parse_tsv_include_tags(self):
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
path = os.path.join(tmp, "test.txt")
|
||||
@@ -215,17 +215,19 @@ class CliValidationTests(unittest.TestCase):
|
||||
self.assertEqual(code, 1)
|
||||
self.assertIn("1-based", stderr.getvalue())
|
||||
|
||||
def test_words_input_is_spanish_only(self):
|
||||
def test_words_input_accepts_japanese_tsv(self):
|
||||
from io import StringIO
|
||||
|
||||
with patch("saiki.cli.importlib.metadata.version", return_value="0"):
|
||||
with patch("sys.stderr", new_callable=StringIO) as stderr:
|
||||
code = main(["words", "jp", "--input", "deck.tsv", "--field", "2"])
|
||||
self.assertEqual(code, 1)
|
||||
self.assertIn("Spanish-only", stderr.getvalue())
|
||||
with patch("saiki.cli.extract_words_from_file") as extract:
|
||||
extract.return_value = {"records": 0, "written": 0, "out": "out.txt"}
|
||||
with patch("sys.stdout", new_callable=StringIO):
|
||||
code = main(["words", "jp", "--input", "deck.tsv", "--field", "2"])
|
||||
self.assertEqual(code, 0)
|
||||
extract.assert_called_once()
|
||||
|
||||
|
||||
class SpanishTextCleaningTests(unittest.TestCase):
|
||||
class AnkiFieldCleaningTests(unittest.TestCase):
|
||||
def test_remove_sound_marker(self):
|
||||
self.assertEqual(
|
||||
clean_anki_field_text("[sound:es_001.mp3] Hola mundo"),
|
||||
@@ -236,17 +238,23 @@ class SpanishTextCleaningTests(unittest.TestCase):
|
||||
result = clean_anki_field_text("<span>Para el lunes...</span>")
|
||||
self.assertEqual(result, "Para el lunes...")
|
||||
|
||||
def test_english_after_brbr_removed(self):
|
||||
result = clean_anki_field_text("hasta luego<br><br>(see you later)")
|
||||
self.assertEqual(result, "hasta luego")
|
||||
def test_double_br_sections_are_preserved_by_default(self):
|
||||
result = clean_anki_field_text("Target sentence<br><br>(Translation sentence)")
|
||||
self.assertEqual(result, "Target sentence (Translation sentence)")
|
||||
|
||||
def test_english_gloss_after_brbr_removed(self):
|
||||
result = clean_anki_field_text("Te quiero.<br><br>I love you / I want you")
|
||||
self.assertEqual(result, "Te quiero.")
|
||||
def test_first_field_section_drops_later_sections_without_phrase_rules(self):
|
||||
result = clean_anki_field_text("Target sentence<br><br>Translation sentence")
|
||||
self.assertEqual(result, "Target sentence Translation sentence")
|
||||
|
||||
def test_spanish_after_brbr_preserved(self):
|
||||
result = clean_anki_field_text("Hola<br><br>Otra línea")
|
||||
self.assertEqual(result, "Hola Otra línea")
|
||||
result = clean_anki_field_text(
|
||||
"Target sentence<br><br>Translation sentence",
|
||||
field_section="first",
|
||||
)
|
||||
self.assertEqual(result, "Target sentence")
|
||||
|
||||
def test_later_field_sections_are_preserved_by_default(self):
|
||||
result = clean_anki_field_text("First line<br><br>Second line")
|
||||
self.assertEqual(result, "First line Second line")
|
||||
|
||||
def test_normalize_whitespace(self):
|
||||
result = clean_anki_field_text(" Hola mundo ")
|
||||
@@ -283,230 +291,13 @@ class MockToken:
|
||||
self.sent = None
|
||||
|
||||
|
||||
class SpanishLemmaTests(unittest.TestCase):
|
||||
def test_builtin_correction_comar(self):
|
||||
token = MockToken(text="como", lemma="comar")
|
||||
self.assertEqual(safe_spanish_lemma(token), "comer")
|
||||
class DetailedCountsTests(unittest.TestCase):
|
||||
def test_spanish_filter_uses_content_pos(self):
|
||||
self.assertTrue(spanish_filter(MockToken(text="casa", lemma="casa", pos="NOUN")))
|
||||
self.assertTrue(spanish_filter(MockToken(text="rápido", lemma="rápido", pos="ADV")))
|
||||
self.assertFalse(spanish_filter(MockToken(text="Madrid", lemma="Madrid", pos="PROPN")))
|
||||
self.assertFalse(spanish_filter(MockToken(text="para", lemma="para", pos="ADP")))
|
||||
|
||||
def test_builtin_correction_acabir(self):
|
||||
token = MockToken(text="acabas", lemma="acabir")
|
||||
self.assertEqual(safe_spanish_lemma(token), "acabar")
|
||||
|
||||
def test_builtin_correction_delicius(self):
|
||||
token = MockToken(text="delicioso", lemma="deliciós")
|
||||
self.assertEqual(safe_spanish_lemma(token), "delicioso")
|
||||
|
||||
def test_builtin_correction_llover_slash(self):
|
||||
token = MockToken(text="llueve", lemma="llover/llover")
|
||||
self.assertEqual(safe_spanish_lemma(token), "llover")
|
||||
|
||||
def test_multi_word_lemma_falls_back(self):
|
||||
token = MockToken(text="ayudar", lemma="ayudar yo")
|
||||
self.assertEqual(safe_spanish_lemma(token), "ayudar")
|
||||
|
||||
def test_multi_word_lemma_lavar_el(self):
|
||||
token = MockToken(text="lava", lemma="lavar él")
|
||||
self.assertEqual(safe_spanish_lemma(token), "lava")
|
||||
|
||||
def test_prir_corrected_when_source_is_pedir(self):
|
||||
for form in ["pide", "pido", "piden", "pidiendo", "pedir"]:
|
||||
token = MockToken(text=form, lemma="prir")
|
||||
self.assertEqual(safe_spanish_lemma(token), "pedir")
|
||||
|
||||
def test_prir_not_corrected_for_unknown_form(self):
|
||||
token = MockToken(text="prir", lemma="prir")
|
||||
self.assertEqual(safe_spanish_lemma(token), "prir")
|
||||
|
||||
def test_empty_lemma_falls_back(self):
|
||||
token = MockToken(text="hola", lemma="")
|
||||
self.assertEqual(safe_spanish_lemma(token), "hola")
|
||||
|
||||
def test_lemma_with_punctuation_falls_back(self):
|
||||
token = MockToken(text="comiendo", lemma="comiendo,")
|
||||
self.assertEqual(safe_spanish_lemma(token), "comiendo")
|
||||
|
||||
def test_normal_lemma_passes_through(self):
|
||||
token = MockToken(text="comiendo", lemma="comer")
|
||||
self.assertEqual(safe_spanish_lemma(token), "comer")
|
||||
|
||||
def test_lemma_info_tracks_correction_reason(self):
|
||||
token = MockToken(text="como", lemma="comar")
|
||||
info = safe_spanish_lemma_info(token)
|
||||
self.assertEqual(info.lemma, "comer")
|
||||
self.assertEqual(info.original_lemma, "comar")
|
||||
self.assertIn("corrected", info.status)
|
||||
|
||||
def test_non_spanish_lemma_falls_back(self):
|
||||
token = MockToken(text="hola", lemma="hello_world")
|
||||
info = safe_spanish_lemma_info(token)
|
||||
self.assertEqual(info.lemma, "hola")
|
||||
self.assertEqual(info.status, "fallback_bad_lemma_characters")
|
||||
|
||||
def test_extra_corrections_override_builtin(self):
|
||||
extra = {"comar": "comprar"}
|
||||
token = MockToken(text="como", lemma="comar")
|
||||
self.assertEqual(
|
||||
safe_spanish_lemma(token, extra_corrections=extra),
|
||||
"comprar",
|
||||
)
|
||||
|
||||
def test_load_lemma_corrections(self):
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
path = os.path.join(tmp, "corrections.tsv")
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
f.write("malo\tbueno\nfeo\tbonito\n")
|
||||
corrections = load_lemma_corrections(path)
|
||||
self.assertEqual(corrections, {"malo": "bueno", "feo": "bonito"})
|
||||
|
||||
def test_load_lemma_corrections_skips_comments(self):
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
path = os.path.join(tmp, "corrections.tsv")
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
f.write("# comment\nmalo\tbueno\n")
|
||||
corrections = load_lemma_corrections(path)
|
||||
self.assertEqual(corrections, {"malo": "bueno"})
|
||||
|
||||
|
||||
class SpanishFilterTests(unittest.TestCase):
|
||||
def test_content_filter_keeps_noun(self):
|
||||
token = MockToken(text="casa", lemma="casa", pos="NOUN")
|
||||
self.assertTrue(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_keeps_verb(self):
|
||||
token = MockToken(text="come", lemma="comer", pos="VERB")
|
||||
self.assertTrue(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_keeps_adj(self):
|
||||
token = MockToken(text="grande", lemma="grande", pos="ADJ")
|
||||
self.assertTrue(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_keeps_adv(self):
|
||||
token = MockToken(text="bien", lemma="bien", pos="ADV")
|
||||
self.assertTrue(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_excludes_propn_by_default(self):
|
||||
token = MockToken(text="Madrid", lemma="Madrid", pos="PROPN")
|
||||
self.assertFalse(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_includes_propn_with_flag(self):
|
||||
token = MockToken(text="Madrid", lemma="Madrid", pos="PROPN")
|
||||
self.assertTrue(
|
||||
spanish_content_filter(token, include_proper_nouns=True)
|
||||
)
|
||||
|
||||
def test_content_filter_excludes_punct(self):
|
||||
token = MockToken(text=".", lemma=".", pos="PUNCT", is_punct=True)
|
||||
self.assertFalse(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_excludes_digit(self):
|
||||
token = MockToken(text="123", lemma="123", pos="NUM", is_digit=True)
|
||||
self.assertFalse(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_excludes_url(self):
|
||||
token = MockToken(
|
||||
text="http://x.com", lemma="http://x.com", pos="X", like_url=True
|
||||
)
|
||||
self.assertFalse(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_excludes_media_filename(self):
|
||||
token = MockToken(text="es_001.mp3", lemma="es_001.mp3", pos="NOUN")
|
||||
self.assertFalse(spanish_content_filter(token))
|
||||
|
||||
def test_content_filter_excludes_one_char_non_vowel(self):
|
||||
token = MockToken(text="x", lemma="x", pos="NOUN")
|
||||
self.assertFalse(spanish_content_filter(token))
|
||||
|
||||
def test_function_word_filter_keeps_preposition(self):
|
||||
token = MockToken(text="para", lemma="para", pos="ADP")
|
||||
self.assertTrue(spanish_function_word_filter(token))
|
||||
|
||||
def test_function_word_filter_keeps_conjunction(self):
|
||||
token = MockToken(text="y", lemma="y", pos="CCONJ")
|
||||
self.assertTrue(spanish_function_word_filter(token))
|
||||
|
||||
def test_function_word_filter_rejects_noun(self):
|
||||
token = MockToken(text="casa", lemma="casa", pos="NOUN")
|
||||
self.assertFalse(spanish_function_word_filter(token))
|
||||
|
||||
|
||||
class SpanishLintTests(unittest.TestCase):
|
||||
def _check(self, text: str) -> list:
|
||||
return check_card_for_issues(text, clean_anki_field_text(text))
|
||||
|
||||
def test_detect_tu_sabes_nada(self):
|
||||
issues = self._check("Tú sabes nada")
|
||||
self.assertTrue(any("Missing negation" in i["reason"] for i in issues))
|
||||
|
||||
def test_detect_dulce_suenos(self):
|
||||
issues = self._check("Dulce sueños")
|
||||
self.assertTrue(
|
||||
any("agree in number" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_detect_ella_sona(self):
|
||||
issues = self._check("Ella soña con viajar.")
|
||||
self.assertTrue(
|
||||
any("present indicative" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_detect_tengo_uno_libro(self):
|
||||
issues = self._check("Tengo uno libro en la mesa.")
|
||||
self.assertTrue(
|
||||
any("instead of 'un'" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_detect_observando_nadan(self):
|
||||
issues = self._check(
|
||||
"La pareja está en un bote observando a los peces nadan."
|
||||
)
|
||||
self.assertTrue(
|
||||
any("'cómo'" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_detect_le_vuelven_loca(self):
|
||||
issues = self._check("Los perros pequeños le vuelven loca.")
|
||||
self.assertTrue(
|
||||
any("direct object" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_detect_ai_generated_tts(self):
|
||||
issues = self._check("AI-generated text-to-speech")
|
||||
self.assertTrue(
|
||||
any("Metadata contamination" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_detect_necesito_missing_article(self):
|
||||
issues = self._check("Necesito jardinero")
|
||||
self.assertTrue(
|
||||
any("Missing article" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_detect_le_vuelven_loco(self):
|
||||
issues = self._check("Estos problemas le vuelven loco.")
|
||||
self.assertTrue(
|
||||
any("direct object" in i["reason"] or "direct object pronoun" in i["reason"] for i in issues)
|
||||
)
|
||||
|
||||
def test_veintiuno_anos(self):
|
||||
issues = self._check("Tengo veintiuno años de edad.")
|
||||
self.assertTrue(
|
||||
any("veintiún" in i["suggested_fix"] for i in issues)
|
||||
)
|
||||
|
||||
def test_lint_anki_cards_multiple_records(self):
|
||||
records = [
|
||||
{"text": "Hola mundo", "line_number": 1, "raw_line": "Hola mundo"},
|
||||
{"text": "Tú sabes nada", "line_number": 2, "raw_line": "Tú sabes nada"},
|
||||
{"text": "Ella soña con viajar.", "line_number": 3, "raw_line": "Ella soña con viajar."},
|
||||
]
|
||||
results = lint_anki_cards(records)
|
||||
self.assertEqual(len(results), 2)
|
||||
reasons = results[0]["reason"] + results[1]["reason"]
|
||||
self.assertIn("Missing negation", reasons)
|
||||
self.assertIn("present indicative", reasons)
|
||||
|
||||
|
||||
class SpanishDetailedCountsTests(unittest.TestCase):
|
||||
def test_extract_detailed_counts_basic(self):
|
||||
records = [
|
||||
{"text": "Yo como manzanas.", "line_number": 1},
|
||||
@@ -525,7 +316,7 @@ class SpanishDetailedCountsTests(unittest.TestCase):
|
||||
if "como" in text:
|
||||
return MockDoc([
|
||||
MockToken(text="Yo", lemma="yo", pos="PRON"),
|
||||
MockToken(text="como", lemma="comar", pos="VERB"),
|
||||
MockToken(text="como", lemma="comer", pos="VERB"),
|
||||
MockToken(text="manzanas", lemma="manzana", pos="NOUN"),
|
||||
MockToken(text=".", lemma=".", pos="PUNCT", is_punct=True),
|
||||
])
|
||||
@@ -540,12 +331,10 @@ class SpanishDetailedCountsTests(unittest.TestCase):
|
||||
records,
|
||||
MockNLP(),
|
||||
lambda t: t.pos_ in {"NOUN", "VERB", "ADJ", "ADV"},
|
||||
safe_spanish_lemma,
|
||||
lambda t: (t.lemma_ or t.text).lower(),
|
||||
)
|
||||
|
||||
# "comar" should be corrected to "comer"
|
||||
self.assertIn("comer", stats)
|
||||
# "comer" should have count 2 (comes + como -> comer)
|
||||
self.assertEqual(stats["comer"]["count"], 2)
|
||||
self.assertIn("manzana", stats)
|
||||
self.assertIn("pan", stats)
|
||||
@@ -571,27 +360,6 @@ class SpanishDetailedCountsTests(unittest.TestCase):
|
||||
self.assertIn("Yo como.", content)
|
||||
self.assertIn("1, 2, 3", content)
|
||||
|
||||
def test_suspicious_tokens_output(self):
|
||||
stats = {
|
||||
"comer": {
|
||||
"count": 3,
|
||||
"pos_counts": {"VERB": 3},
|
||||
"surface_forms": ["como", "comes"],
|
||||
"original_lemmas": ["comar", "comer"],
|
||||
"lemma_statuses": {"corrected:comar->comer": 1, "ok": 2},
|
||||
"example_sentences": ["Yo como."],
|
||||
"source_lines": ["1"],
|
||||
}
|
||||
}
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
path = os.path.join(tmp, "suspicious.tsv")
|
||||
write_suspicious_tokens(stats, path)
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
self.assertIn("comer", content)
|
||||
self.assertIn("comar", content)
|
||||
self.assertIn("corrected:comar->comer", content)
|
||||
|
||||
def test_debug_tsv_min_freq_filters(self):
|
||||
stats = {
|
||||
"comer": {"count": 5, "pos_counts": {"VERB": 5},
|
||||
@@ -638,29 +406,69 @@ class SpanishDetailedCountsTests(unittest.TestCase):
|
||||
lambda t: t.text.lower(),
|
||||
clean=True,
|
||||
)
|
||||
# With clean=True, the sound marker is stripped → empty text → no stats
|
||||
# With clean=True, the sound marker is stripped, so there is no text to count.
|
||||
self.assertEqual(len(stats_clean), 0)
|
||||
|
||||
def test_lint_anki_cards_verbose_includes_all(self):
|
||||
records = [
|
||||
{"text": "Hola mundo", "line_number": 1, "raw_line": "Hola mundo"},
|
||||
{"text": "Tú sabes nada", "line_number": 2, "raw_line": "Tú sabes nada"},
|
||||
]
|
||||
# Without verbose – only suspicious
|
||||
results = lint_anki_cards(records, verbose=False)
|
||||
self.assertEqual(len(results), 1)
|
||||
def test_extract_detailed_counts_can_use_first_field_section(self):
|
||||
class MockDoc:
|
||||
def __init__(self, tokens):
|
||||
self.tokens = tokens
|
||||
def __iter__(self):
|
||||
return iter(self.tokens)
|
||||
|
||||
# With verbose – both records
|
||||
results = lint_anki_cards(records, verbose=True)
|
||||
self.assertEqual(len(results), 2)
|
||||
class MockNLP:
|
||||
def __call__(self, text):
|
||||
return MockDoc([
|
||||
MockToken(text=part, lemma=part.lower(), pos="NOUN")
|
||||
for part in text.split()
|
||||
])
|
||||
|
||||
def test_lint_anki_cards_verbose_empty_field(self):
|
||||
records = [
|
||||
{"text": "", "line_number": 1, "raw_line": ""},
|
||||
]
|
||||
results = lint_anki_cards(records, verbose=True)
|
||||
self.assertEqual(len(results), 1)
|
||||
self.assertEqual(results[0]["reason"], "empty field")
|
||||
records = [{"text": "Target<br><br>Translation", "line_number": 1}]
|
||||
stats = extract_detailed_counts(
|
||||
records,
|
||||
MockNLP(),
|
||||
lambda t: True,
|
||||
lambda t: t.lemma_,
|
||||
field_section="first",
|
||||
)
|
||||
self.assertIn("target", stats)
|
||||
self.assertNotIn("translation", stats)
|
||||
|
||||
def test_extract_words_from_file_uses_language_profile(self):
|
||||
class MockDoc:
|
||||
def __init__(self, tokens):
|
||||
self.tokens = tokens
|
||||
def __iter__(self):
|
||||
return iter(self.tokens)
|
||||
|
||||
class MockNLP:
|
||||
def __call__(self, text):
|
||||
return MockDoc([
|
||||
MockToken(text="猫", lemma="猫", pos="NOUN"),
|
||||
MockToken(text="です", lemma="です", pos="AUX"),
|
||||
])
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp:
|
||||
source = os.path.join(tmp, "deck.tsv")
|
||||
out = os.path.join(tmp, "words_jp.txt")
|
||||
with open(source, "w", encoding="utf-8") as f:
|
||||
f.write("#separator:tab\n[sound:jp_001.mp3]\t猫です。\n")
|
||||
|
||||
with patch("saiki.words.load_spacy_model", return_value=MockNLP()):
|
||||
result = extract_words_from_file(
|
||||
Config(deepcopy(DEFAULT_CONFIG)),
|
||||
"jp",
|
||||
source,
|
||||
out=out,
|
||||
min_freq=1,
|
||||
)
|
||||
|
||||
with open(out, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
|
||||
self.assertEqual(result["records"], 1)
|
||||
self.assertIn("猫 1", content)
|
||||
self.assertNotIn("です", content)
|
||||
|
||||
|
||||
class CompareWordListsTests(unittest.TestCase):
|
||||
|
||||
Reference in New Issue
Block a user