improvements in en db generation

This commit is contained in:
2026-01-11 17:27:05 +01:00
parent 54de8672dc
commit 2a5069ed4b
5 changed files with 719 additions and 3 deletions

2
.gitignore vendored
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@ -160,3 +160,5 @@ cython_debug/
# option (not recommended) you can uncomment the following to ignore the entire idea folder. # option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/ #.idea/
.cache/

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@ -24,5 +24,7 @@ python -m multiplayer_crosswords.server.main
## start the webui ## start the webui
```bash ```bash
python -m multiplayer_crosswords.server.serve_frontend python -m multiplayer_crosswords.server.serve_frontend --port 8080
``` ```
Then open your browser and navigate to `http://localhost:8080`.

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@ -0,0 +1,671 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "e048da07",
"metadata": {},
"source": [
"# Create Dictionaies for crossword clues\n",
"\n",
"this notebook creates dictionaries for crossword clues.\n",
"\n",
"The final dictionaries will be saved as json file, containing a list of entries in the following format:\n",
"\n",
"```json\n",
"{\n",
" \"<unique_word_entry>\": {\n",
" \"word\": \"<word>\",\n",
" \"senses\": [\n",
" \"<definition_1>\",\n",
" \"<definition_2>\",\n",
" \"...\" \n",
" ],\n",
" \"synonyms\": [\n",
" \"<synonym_1>\",\n",
" \"<synonym_2>\",\n",
" \"...\" \n",
" ],\n",
" \"antonyms\": [\n",
" \"<antonym_1>\",\n",
" \"<antonym_2>\",\n",
" \"...\" \n",
" ],\n",
" \"word_frequency\": <frequency_value (from 0 to 100)>\n",
"\n",
" },\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"id": "28040681",
"metadata": {},
"source": [
"### Install some dependencies for that notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f0aecff7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"# install dependencies for this notebooks\n",
"\n",
"!pip install tqdm pandas requests ipywidgets pydantic nltk"
]
},
{
"cell_type": "markdown",
"id": "a0964dfc",
"metadata": {},
"source": [
"### Import Libraries and define Constants and source urls"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e7d3d24f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_157490/121043459.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
" from tqdm.autonotebook import tqdm\n"
]
}
],
"source": [
"# import necessary libraries\n",
"import pandas as pd\n",
"import requests\n",
"from tqdm.autonotebook import tqdm \n",
"from pathlib import Path\n",
"\n",
"# some constants\n",
"\n",
"CACHE_DIR = Path(\"./.cache\")\n",
"CACHE_DIR.mkdir(exist_ok=True)\n",
"\n",
"CRYPTICS_CROSSWORDS_DB_URL = \"https://cryptics.georgeho.org/data/clues.csv?_stream=on&_size=max\"\n",
"CRYPTICS_CROSSWORDS_DB_CSV = CACHE_DIR / \"cryptics_clues.csv\""
]
},
{
"cell_type": "markdown",
"id": "61263a61",
"metadata": {},
"source": [
"## Download External Data\n",
"\n",
"* Crypticts DB (\"https://cryptics.georgeho.org/\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "806a5c51",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n",
"[nltk_data] Downloading package omw-1.4 to /home/jonas/nltk_data...\n",
"[nltk_data] Package omw-1.4 is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# download the cryptics crosswords database if not already cached \n",
"if not CRYPTICS_CROSSWORDS_DB_CSV.exists():\n",
" response = requests.get(CRYPTICS_CROSSWORDS_DB_URL)\n",
" with open(CRYPTICS_CROSSWORDS_DB_CSV, \"wb\") as f:\n",
" f.write(response.content)\n",
"\n",
"# download wordnet from nltk\n",
"import nltk\n",
"nltk.download('wordnet')\n",
"nltk.download('omw-1.4') # optional, extra languages / lemmas\n",
"nltk.download('omw') # try the older omw package"
]
},
{
"cell_type": "markdown",
"id": "26060068",
"metadata": {},
"source": [
"## Define our Datastructures"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8c81708c",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import BaseModel\n",
"import re \n",
"\n",
"class WordEntry(BaseModel):\n",
" word: str\n",
" senses: list[str]\n",
" synonyms: list[str]\n",
" antonyms: list[str]\n",
" word_frequency: int # frequency rank of the word (0% - 100%)\n",
" source: str # source of the word entry (e.g., \"cryptics\", \"wordnet\", etc.)\n",
" categories: list[str] # categories or tags associated with the word entry\n",
"\n",
"class Dictionary(BaseModel):\n",
" entries: dict[str, WordEntry] # mapping from word to WordEntry\n",
" def add_entry(self, entry: WordEntry):\n",
" if entry.word not in self.entries: \n",
" self.entries[entry.word] = entry\n",
" else:\n",
" if entry.source == self.entries[entry.word].source:\n",
" # merge entries if word already exists\n",
" existing_entry = self.entries[entry.word]\n",
" existing_entry.senses = list(set(existing_entry.senses) | set(entry.senses))\n",
" existing_entry.synonyms = list(set(existing_entry.synonyms) | set(entry.synonyms))\n",
" existing_entry.antonyms = list(set(existing_entry.antonyms) | set(entry.antonyms))\n",
" existing_entry.categories = list(set(existing_entry.categories) | set(entry.categories))\n",
" existing_entry.word_frequency = max(existing_entry.word_frequency, entry.word_frequency)\n",
" else:\n",
" # create a new entry\n",
" word = entry.word\n",
" i = 1\n",
" while f\"{word}_{i}\" in self.entries:\n",
" i += 1 \n",
" self.entries[f\"{word}_{i}\"] = entry\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"id": "b1397355",
"metadata": {},
"source": [
"## Parse Data (EN)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "56988415",
"metadata": {},
"outputs": [],
"source": [
"en_db = Dictionary (entries={})"
]
},
{
"cell_type": "markdown",
"id": "d5b35c1b",
"metadata": {},
"source": [
"### Parse cryptics DB"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e9e1ee43",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c5f6f1c7c594df6946e51686feeee34",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/42 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# csv structure: we will use the column clue as senses and the column answer (lowercase) as word.\n",
"# words that have spaces will be skipped\n",
"\n",
"# read the file in batches to avoid memory issues \n",
"batch_size = 1000\n",
"\n",
"# Calculate total lines safely\n",
"try:\n",
" total_lines = sum(1 for line in open(CRYPTICS_CROSSWORDS_DB_CSV))\n",
"except Exception as e:\n",
" print(f\"Error counting lines: {e}\")\n",
" total_lines = 0\n",
"\n",
"cryptics_cols = \"rowid\tclue\tanswer\tdefinition\tclue_number\tpuzzle_date\tpuzzle_name\tsource_url\tsource\".split()\n",
"\n",
"for start_row in tqdm(range(1, total_lines, batch_size)):\n",
" try:\n",
" # Use on_bad_lines='skip' to handle rows with too many fields\n",
" df = pd.read_csv(CRYPTICS_CROSSWORDS_DB_CSV, skiprows=start_row, nrows=batch_size, names=cryptics_cols, on_bad_lines='skip')\n",
" except Exception as e:\n",
" print(f\"Error reading batch starting at {start_row}: {e}\")\n",
" continue\n",
"\n",
" for index, row in df.iterrows():\n",
" # Check if answer is a string (handles NaN)\n",
" if not isinstance(row['answer'], str):\n",
" continue\n",
"\n",
" word = row['answer'].lower()\n",
" if ' ' in word:\n",
" continue\n",
" \n",
" # Check if clue is a string\n",
" if not isinstance(row['clue'], str):\n",
" continue\n",
"\n",
" clue = row['clue']\n",
" # replace - and _ with empty string\n",
" word = word.replace(\"-\", \"\").replace(\"_\", \"\")\n",
" word = word.lower()\n",
"\n",
" # remove numbers in parentheses or brackets (e.g. (5), [4], (3,4), [1-9])\n",
" clue = re.sub(r'\\s*[(\\[][\\d,\\-\\s]+[)\\]]$', '', clue).strip()\n",
"\n",
" # if the word is not alphabetic, skip it\n",
" if not word.isalpha():\n",
" continue \n",
" en_db.add_entry(WordEntry(\n",
" word=word,\n",
" senses=[clue],\n",
" synonyms=[],\n",
" antonyms=[],\n",
" word_frequency=-1, # placeholder frequency\n",
" source=\"cryptics\",\n",
" categories=[\"cryptic_clue\"]\n",
" ))"
]
},
{
"cell_type": "markdown",
"id": "62734af4",
"metadata": {},
"source": [
"### Parse Wordnet Data\n",
"\n",
"* import necessary stuff:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "32280691",
"metadata": {},
"outputs": [],
"source": [
"from nltk.corpus import wordnet as wn\n"
]
},
{
"cell_type": "markdown",
"id": "e13bf2f3",
"metadata": {},
"source": [
"* Parse synsets"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b21721e2",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c411da19479944298117158962a08ea3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/117659 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_synsets = wn.all_synsets()\n",
"\n",
"def frequency_metric(lemma, word):\n",
" # derive a frequency metric, based on polysemy and lemma count:\n",
" # 1. SemCor frequency count for this specific sense\n",
" semcor_count = lemma.count()\n",
" # 2. Polysemy: number of synsets for this word\n",
" polysemy_count = len(wn.synsets(word))\n",
" \n",
" return semcor_count + polysemy_count\n",
" \n",
"\n",
"for synset in tqdm(list(all_synsets)):\n",
" #print(synset.name(), synset.definition() )\n",
"\n",
" # find the first \"good\" lemma name (only alphabetic characters) \n",
" # WE NEED LEMMA OBJECTS NOW, NOT JUST NAMES\n",
" good_lemmas = [lemma for lemma in synset.lemmas() if lemma.name().isalpha()] \n",
" if not good_lemmas:\n",
" continue \n",
"\n",
" target_lemma = good_lemmas[0]\n",
" word = target_lemma.name().lower()\n",
" clue = synset.definition()\n",
"\n",
" # Calculate frequency\n",
" raw_metric = frequency_metric(target_lemma, word)\n",
" \n",
" # Normalize to 0-100 range\n",
" # Values can range from 1 to >200 for very common words.\n",
" # We apply a factor and clamp.\n",
" # Using factor 1.0 means 100 count -> 100 frequency.\n",
" word_frequency = min(100, int(raw_metric))\n",
"\n",
" en_db.add_entry(WordEntry(\n",
" word=word,\n",
" senses=[clue],\n",
" synonyms=[],\n",
" antonyms=[],\n",
" word_frequency=word_frequency,\n",
" source=\"wordnet\",\n",
" categories=[\"wordnet\"]\n",
" ))\n",
" \n",
" #break"
]
},
{
"cell_type": "markdown",
"id": "1e246fd0",
"metadata": {},
"source": [
"## Create German Database using OpenThesaurus\n",
"\n",
"* download data first:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "435d0b78",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenThesaurus available at .cache/openthesaurus.txt\n"
]
}
],
"source": [
"from multiplayer_crosswords.data_utils import download_openthesaurus\n",
"\n",
"openthesaurus_path = download_openthesaurus()"
]
},
{
"cell_type": "markdown",
"id": "0de30aac",
"metadata": {},
"source": [
"* parse the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07e4e97d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "be16b393",
"metadata": {},
"source": [
"### Save extracted databases\n",
"\n",
"Dump the db to disk as json"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "69b67091",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef2252adfaf240c981a18567f5d23b45",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/96407 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef2252adfaf240c981a18567f5d23b45",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/96407 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wrote 74357 entries to en.json\n"
]
}
],
"source": [
"EN_PATH = Path(\"./en.json\")\n",
"\n",
"import json\n",
"\n",
"# entries to include:\n",
"INCLUDED_SOURCES = {\n",
" #\"cryptics\",\n",
" \"wordnet\"\n",
"} \n",
"\n",
"with open(EN_PATH, \"w\") as f:\n",
" f.write(\"{\\n\")\n",
" i = 0\n",
" for key, value in tqdm(en_db.entries.items()):\n",
"\n",
" if value.source not in INCLUDED_SOURCES:\n",
" continue \n",
" \n",
" # dump json\n",
" if i > 0:\n",
" f.write(\",\\n\")\n",
" d_value = value.model_dump()\n",
" as_json = json.dumps(\n",
" d_value, indent=4\n",
" )\n",
" as_json = \"\\n \".join(as_json.split(\"\\n\"))\n",
" as_json = \" \\\"\" + key + \"\\\": \" + as_json\n",
" f.write(as_json)\n",
" i += 1\n",
" \n",
" f.write(\"\\n}\\n\")\n",
" print(f\"Wrote {i} entries to {EN_PATH}\" )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c3f8d049",
"metadata": {},
"outputs": [],
"source": [
"test_synset = wn.synsets(\"house\")[0]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "3870ea2c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_synset.a"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "7e7033cf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"60"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_word_str = \"table\"\n",
"\n",
"frequency_metric(wn.synsets(test_word_str)[0].lemmas()[0], test_word_str ) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f91aee6f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "multiplayer-crosswords-W02cfZ32-py3.12",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -0,0 +1,34 @@
import os
import requests
import zipfile
import time
from pathlib import Path
def download_openthesaurus(cache_dir = None) -> Path | None:
if cache_dir is None:
cache_dir = Path("./.cache")
else:
cache_dir = Path(cache_dir)
# Define paths
OPENTHESAURUS_URL = "https://www.openthesaurus.de/export/OpenThesaurus-Textversion.zip"
OPENTHESAURUS_ZIP = cache_dir / "OpenThesaurus-Textversion.zip"
OPENTHESAURUS_TXT = cache_dir / "openthesaurus.txt"
if not OPENTHESAURUS_TXT.exists():
if not OPENTHESAURUS_ZIP.exists():
print("Downloading OpenThesaurus...")
r = requests.get(OPENTHESAURUS_URL)
with open(OPENTHESAURUS_ZIP, "wb") as f:
f.write(r.content)
print("Extracting...")
with zipfile.ZipFile(OPENTHESAURUS_ZIP, 'r') as zip_ref:
zip_ref.extractall(cache_dir)
print(f"OpenThesaurus available at {OPENTHESAURUS_TXT}")
return OPENTHESAURUS_TXT

View File

@ -59,9 +59,16 @@ def load_dictionary(p: str | Path) -> Dictionary:
if len(hints) > 0: if len(hints) > 0:
difficulty = 1.0
word_freq = obj.get("word_frequency", -1)
if word_freq >= 0:
# Map 0-100 frequency to 1.0-0.0 difficulty
# Higher frequency -> Lower difficulty
difficulty = max(0.0, min(1.0, 1.0 - (word_freq / 100.0)))
w = Word(word=word, w = Word(word=word,
hints=hints, hints=hints,
difficulty=1) difficulty=difficulty)
dict_obj.add_word(w) dict_obj.add_word(w)
load_dictionary._cache[cache_key] = dict_obj load_dictionary._cache[cache_key] = dict_obj
return dict_obj return dict_obj
@ -70,4 +77,4 @@ def load_en_dictionary() -> Dictionary:
return load_dictionary(Path(__file__).parent.parent / "data" / "en.json") return load_dictionary(Path(__file__).parent.parent / "data" / "en.json")
def load_de_dictionary() -> Dictionary: def load_de_dictionary() -> Dictionary:
return load_dictionary(Path(__file__).parent.parent / "data" / "de.json") return load_dictionary(Path(__file__).parent.parent / "data" / "de.json")