crosswords/data/data_tests.ipynb

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2021-06-13 21:06:55 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import re\n",
"import tqdm\n",
"from IPython.display import Markdown"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## prepare raw wikiextract"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"cat english.words| jq -s . > english.json"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
"with open('english.json', 'r') as f:\n",
" english_vocab_db = json.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## helper functions to filter senses, synonyms and antonyms"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [],
"source": [
"def filter_senses(senses_list, word):\n",
" filtered_senses = []\n",
" for entry in senses_list:\n",
" if 'glosses' in entry:\n",
" glosses = entry['glosses']\n",
" for gloss_entry in glosses:\n",
" for gloss in gloss_entry.split(';'):\n",
" s = re.sub('\\s+', ' ', re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", gloss).replace(\".\", \"\")).strip()\n",
" if len(s) > 0 and word not in s:\n",
" filtered_senses.append(s)\n",
" return filtered_senses"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"def filter_synonyms(synonyms_list):\n",
" filtered_synonyms = []\n",
" for entry in synonyms_list:\n",
" if 'word' in entry:\n",
" filtered_synonyms.append(entry['word'])\n",
" return filtered_synonyms"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [],
"source": [
"def filter_antonyms(antonym_list):\n",
" filtered_antonyms = []\n",
" for entry in antonym_list:\n",
" if 'word' in entry:\n",
" filtered_antonyms.append(entry['word'])\n",
" return filtered_antonyms"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"def add_wiktionary_item(item, db):\n",
" item = {}\n",
" if 'word' not in item.keys():\n",
" return\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## build a dictionary_database from the raw extract\n",
"using words as keys, enumerate those keys (e.g. `house`, `house1`, `house2` etc.) on duplicates"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"100%|██████████| 1023195/1023195 [00:00<00:00, 1368488.50it/s]\n"
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]
}
],
"source": [
"dictionary_database = {}\n",
"for item in tqdm.tqdm(english_vocab_db):\n",
" if 'word' in item:\n",
" word = item['word']\n",
" i = 0\n",
" while word in dictionary_database:\n",
" i += 1\n",
" word = f\"{item['word']}{i}\"\n",
" dictionary_database[word] = item\n",
" "
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [],
"source": [
"class NoDataException(Exception):\n",
" pass\n",
"\n",
"def get_attribute(word:str, attr:str, db = dictionary_database):\n",
" if word not in db:\n",
" raise NoDataException\n",
" item = db[word]\n",
" if attr not in item:\n",
" raise NoDataException\n",
" return item[attr]\n",
"\n",
"def get_senses(word: str):\n",
" \n",
" senses = get_attribute(word, 'senses')\n",
" \n",
" return filter_senses(senses, word)\n",
"\n",
"def get_synonyms(word: str):\n",
" syns = get_attribute(word, 'synonyms')\n",
" \n",
" return filter_synonyms(syns)\n",
"\n",
"def get_antonyms(word: str):\n",
" ants = get_attribute(word, 'antonyms')\n",
" \n",
" return filter_antonyms(ants)\n",
"\n",
"def display_info(word: str):\n",
" \n",
" if word not in dictionary_database:\n",
" raise NoDataException\n",
" \n",
" try:\n",
" senses = get_senses(word)\n",
" except NoDataException:\n",
" senses = []\n",
" \n",
" try:\n",
" synonyms = get_synonyms(word)\n",
" except NoDataException:\n",
" synonyms = []\n",
" \n",
" try:\n",
" antonyms = get_antonyms(word)\n",
" except NoDataException:\n",
" antonyms = []\n",
" \n",
" senses_str = '\\n'.join([f\"* {sense}\" for sense in senses])\n",
" \n",
" synonyms_str = '\\n'.join([f\"* {syn}\" for syn in synonyms])\n",
" \n",
" antonyms_str = '\\n'.join([f\"* {ant}\" for ant in antonyms])\n",
" \n",
" markdown_string = f\"\"\"\n",
"## {word}\n",
"\n",
"### Senses:\n",
"\n",
"{senses_str}\n",
"\n",
"### Synonyms:\n",
"\n",
"{synonyms_str}\n",
"\n",
"### Antonyms:\n",
"\n",
"{antonyms_str}\n",
" \n",
" \"\"\"\n",
" \n",
" return Markdown(markdown_string)"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"100%|██████████| 1023195/1023195 [00:08<00:00, 125917.77it/s]\n"
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]
}
],
"source": [
"import random\n",
"list_words = []\n",
"for word in tqdm.tqdm(dictionary_database.keys()):\n",
" item = dictionary_database[word]\n",
" try:\n",
" senses = get_senses(word)\n",
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" if len(senses) > 4:\n",
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" list_words.append(word)\n",
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" continue\n",
" try:\n",
" if len(get_antonyms(word)) >= 1:\n",
" list_words.append(word)\n",
" continue\n",
" except NoDataException:\n",
" if len(get_synonyms(word)) >= 1:\n",
" list_words.append(word)\n",
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" \n",
" except NoDataException:\n",
" pass\n",
" "
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"## cockmonger\n",
"\n",
"### Senses:\n",
"\n",
"* A frequent consumer of cocks\n",
"* A procurer of cocks\n",
"* A person with a large penis\n",
"\n",
"### Synonyms:\n",
"\n",
"\n",
"\n",
"### Antonyms:\n",
"\n",
"\n",
" \n",
" "
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(display_info(\"cockmonger\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## algorithm to build a grid of words"
]
},
{
"cell_type": "code",
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"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 68842/68842 [00:01<00:00, 50914.78it/s]\n"
]
}
],
"source": [
"## store an optimized library version for production\n",
"optimized_lib = {}\n",
"for word in tqdm.tqdm(list_words):\n",
" item = {}\n",
" senses = None\n",
" antonyms = None\n",
" synonyms = None\n",
" try:\n",
" senses = get_senses(word)\n",
" except:\n",
" pass\n",
" try:\n",
" synonyms = get_synonyms(word)\n",
" except:\n",
" pass\n",
" try:\n",
" antonyms = get_antonyms(word)\n",
" except:\n",
" pass\n",
" \n",
" item['senses'] = senses\n",
" item['antonyms'] = antonyms\n",
" item['synonyms'] = synonyms\n",
" item['word'] = dictionary_database[word]['word']\n",
" optimized_lib[word] = item"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"with open(\"en.json\",'w') as f:\n",
" json.dump(optimized_lib, f, indent = 4)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[0;31mSignature:\u001b[0m\n",
"\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mskipkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mensure_ascii\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcheck_circular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
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"\u001b[0;34m\u001b[0m \u001b[0msort_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
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"\u001b[0;31mDocstring:\u001b[0m\n",
"Serialize ``obj`` as a JSON formatted stream to ``fp`` (a\n",
"``.write()``-supporting file-like object).\n",
"\n",
"If ``skipkeys`` is true then ``dict`` keys that are not basic types\n",
"(``str``, ``int``, ``float``, ``bool``, ``None``) will be skipped\n",
"instead of raising a ``TypeError``.\n",
"\n",
"If ``ensure_ascii`` is false, then the strings written to ``fp`` can\n",
"contain non-ASCII characters if they appear in strings contained in\n",
"``obj``. Otherwise, all such characters are escaped in JSON strings.\n",
"\n",
"If ``check_circular`` is false, then the circular reference check\n",
"for container types will be skipped and a circular reference will\n",
"result in an ``OverflowError`` (or worse).\n",
"\n",
"If ``allow_nan`` is false, then it will be a ``ValueError`` to\n",
"serialize out of range ``float`` values (``nan``, ``inf``, ``-inf``)\n",
"in strict compliance of the JSON specification, instead of using the\n",
"JavaScript equivalents (``NaN``, ``Infinity``, ``-Infinity``).\n",
"\n",
"If ``indent`` is a non-negative integer, then JSON array elements and\n",
"object members will be pretty-printed with that indent level. An indent\n",
"level of 0 will only insert newlines. ``None`` is the most compact\n",
"representation.\n",
"\n",
"If specified, ``separators`` should be an ``(item_separator, key_separator)``\n",
"tuple. The default is ``(', ', ': ')`` if *indent* is ``None`` and\n",
"``(',', ': ')`` otherwise. To get the most compact JSON representation,\n",
"you should specify ``(',', ':')`` to eliminate whitespace.\n",
"\n",
"``default(obj)`` is a function that should return a serializable version\n",
"of obj or raise TypeError. The default simply raises TypeError.\n",
"\n",
"If *sort_keys* is true (default: ``False``), then the output of\n",
"dictionaries will be sorted by key.\n",
"\n",
"To use a custom ``JSONEncoder`` subclass (e.g. one that overrides the\n",
"``.default()`` method to serialize additional types), specify it with\n",
"the ``cls`` kwarg; otherwise ``JSONEncoder`` is used.\n",
"\u001b[0;31mFile:\u001b[0m /usr/lib/python3.9/json/__init__.py\n",
"\u001b[0;31mType:\u001b[0m function\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"?json.dump"
]
},
{
"cell_type": "code",
"execution_count": 28,
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import random\n",
"from string import digits\n",
"\n",
"class WordInfo(object):\n",
" def __init__(self, word:str, y:int, x:int, is_vertical: bool, database=dictionary_database):\n",
" self._dictionary_database = dictionary_database\n",
" self._y = y\n",
" self._x = x\n",
" self._word = word\n",
" self._hint = None\n",
" self._is_vertical = is_vertical\n",
" \n",
" self.choose_info()\n",
" \n",
" def get_best_antonym(self) -> str:\n",
" antonyms = get_antonyms(self._word)\n",
" return random.choice(antonyms)\n",
" \n",
" def get_best_synonym(self) -> str:\n",
" synonyms = get_synonyms(self._word)\n",
" return random.choice(synonyms)\n",
" \n",
" def get_best_sense(self) -> str:\n",
" senses = get_senses(self._word)\n",
" return random.choice(senses)\n",
" \n",
" def choose_info(self):\n",
" # first choose antonyms, then synonyms, then senses\n",
" \n",
" try:\n",
" self._hint = f\"opposite of {self.get_best_antonym()}\"\n",
" except NoDataException:\n",
" pass\n",
" \n",
" try:\n",
" self._hint = f\"other word for {self.get_best_synonym()}\"\n",
" except NoDataException:\n",
" pass\n",
" \n",
" self._hint = self.get_best_sense()\n",
" \n",
" def get_hint(self) -> str:\n",
" return self._hint\n",
" \n",
" def get_hint_location(self):\n",
" x = self._x if self._is_vertical else self._x - 1\n",
" y = self._y if self._is_vertical else self._y - 1\n",
" return (y, x)\n",
" \n",
" def is_vertical(self):\n",
" return self._is_vertical\n",
"\n",
"def create_word_grid(w: int, h: int, word_list = list_words, target_density = 0.5):\n",
" grid = np.full(shape=(h,w), dtype=np.unicode, fill_value = ' ')\n",
" \n",
" locations = {}\n",
" \n",
" word_hints = {}\n",
" \n",
" def store_location(char: str, y: int, x: int):\n",
" assert len(char) == 1\n",
" \n",
" if char not in locations:\n",
" locations[char] = []\n",
" \n",
" locations[char].append([y,x])\n",
" \n",
" remove_digits = str.maketrans('', '', digits)\n",
" n_words = len(list_words)\n",
" \n",
" def get_word(max_length: int, min_length = 0):\n",
" assert max_length > 1\n",
" \n",
" index = random.randint(0,n_words-1)\n",
" word = list_words[index][:]\n",
" \n",
" while len(word) >= max_length or not word.isalnum() or len(word) <= min_length:\n",
" index = random.randint(0,n_words-1)\n",
" word = list_words[index][:]\n",
" \n",
" return word\n",
" \n",
" def normalize_word(word:str):\n",
" word = word.translate(remove_digits)\n",
" return word.lower()\n",
" \n",
" def place_word(word:str, y: int, x:int, vertical:bool = False):\n",
" normalized_word = normalize_word(word)\n",
" n = len(normalized_word)\n",
" if vertical:\n",
" assert grid.shape[0] - n >= y\n",
" for i, char in enumerate(normalized_word):\n",
" grid[y + i,x] = char\n",
" store_location(char, y+i, x)\n",
" else:\n",
" assert grid.shape[1] - n >= x\n",
" for i, char in enumerate(normalized_word):\n",
" grid[y,x + i] = char\n",
" store_location(char, y, x+i)\n",
" \n",
" word_hints[normalized_word] = WordInfo(word, y, x, vertical)\n",
" \n",
" \n",
" \n",
" def density():\n",
" return 1 - (grid == \" \").sum() / (w * h)\n",
" \n",
" \n",
" \n",
" def check_if_fits(word:str, y:int, x:int, vertical:bool):\n",
" n = len(word)\n",
" if vertical:\n",
" \n",
" # check if there is space before and after\n",
" if y - 1 >= 0 and grid[y - 1, x] != \" \":\n",
" return False\n",
" if y + n < grid.shape[0] - 1 and grid[y+n,x] != \" \":\n",
" return False\n",
" \n",
" if grid.shape[0] - n < y or y < 0:\n",
" #print(\"over board\")\n",
" return False\n",
" \n",
" for i, char in enumerate(word):\n",
" char_x = x\n",
" char_y = y + i\n",
" \n",
" if not (grid[char_y, char_x] == \" \" or grid[char_y, char_x] == char):\n",
" #print(\"not matching\")\n",
" return False\n",
" \n",
" if grid[char_y, char_x] == \" \":\n",
" # check for horizonatal neighbors:\n",
" if char_x - 1 >= 0 and grid[char_y, char_x - 1] != \" \":\n",
" #print(\"3\")\n",
" return False\n",
" if char_x + 1 < grid.shape[1] and grid[char_y, char_x + 1] != \" \":\n",
" #print(\"4\")\n",
" return False\n",
" \n",
" else:\n",
" \n",
" # check if there is space before and after\n",
" if x - 1 >= 0 and grid[y, x - 1] != \" \":\n",
" return False\n",
" if x + n < grid.shape[1] - 1 and grid[y,x + n] != \" \":\n",
" return False\n",
" \n",
" if grid.shape[1] - n < x or x < 0:\n",
" #print(\"over board\")\n",
" return False\n",
" \n",
" for i, char in enumerate(word):\n",
" char_x = x + i\n",
" char_y = y\n",
" \n",
" if not (grid[char_y, char_x] == \" \" or grid[char_y, char_x] == char):\n",
" #print(\"not matching\")\n",
" return False\n",
" \n",
" if grid[char_y, char_x] == \" \":\n",
" # check for vertical neighbors:\n",
" if char_y - 1 >= 0 and grid[char_y - 1, char_x] != \" \":\n",
" #print(\"1\")\n",
" return False\n",
" if char_y + 1 < grid.shape[0] and grid[char_y + 1, char_x] != \" \":\n",
" #print(\"2\")\n",
" return False\n",
" \n",
" return True\n",
" \n",
" \n",
" def get_crossover(word: str):\n",
" # returns Tuple of: (y,x, is_vertical?) or None\n",
" \n",
" shuffled_order = list(range(len(word)))\n",
" random.shuffle(shuffled_order)\n",
" \n",
" for index in shuffled_order:\n",
" # check for existing locations\n",
" char = word[index]\n",
" if char in locations:\n",
" char_locations = locations[char]\n",
" \n",
" for char_loc in char_locations:\n",
" # test vertical\n",
" y = char_loc[0] - index\n",
" x = char_loc[1]\n",
" \n",
" if check_if_fits(word, y, x, vertical=True):\n",
" return (y,x,True)\n",
" \n",
" # test horizontal\n",
" y = char_loc[0]\n",
" x = char_loc[1] - index\n",
" \n",
" if check_if_fits(word, y, x, vertical=False):\n",
" return (y,x,False)\n",
" \n",
" return None\n",
" \n",
" min_shape = min(w,h,30)\n",
" \n",
" # place first word:\n",
" first_word = get_word(max_length=min_shape, min_length=min(10,grid.shape[1] - 2))\n",
" \n",
" # find random place:\n",
" x = random.randint(0, grid.shape[1] - len(first_word) - 1)\n",
" y = random.randint(0, grid.shape[0] - 1)\n",
" \n",
" place_word(first_word, y, x, vertical=False)\n",
" \n",
" i = 0\n",
" \n",
" \n",
" current_density = density()\n",
" \n",
" while current_density < target_density:\n",
" word = get_word(max_length=(1 - current_density ** 0.4) * min_shape,\n",
" min_length=max(min(10, 0.5 * (1 - current_density ** 0.3) * min_shape), 2))\n",
" \n",
" normalized_word = normalize_word(word)\n",
" \n",
" if normalized_word in word_hints:\n",
" continue\n",
" \n",
" # check if matching characters exist:\n",
" crossover = get_crossover(normalized_word)\n",
" \n",
" i += 1\n",
" if i % 100000 == 0:\n",
" print(i)\n",
" if i > 100000:\n",
" break\n",
" \n",
" if crossover == None:\n",
" current_density = density()\n",
" continue\n",
" \n",
" y,x,is_vertical = crossover\n",
" \n",
" place_word(word, y,x, is_vertical)\n",
" \n",
" current_density = density()\n",
" \n",
" print(i)\n",
" return grid, word_hints\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
2021-06-17 16:02:50 +02:00
"execution_count": 24,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name '__file__' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-358d5687b810>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m__file__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined"
]
}
],
"source": [
"__file__\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
2021-06-13 21:06:55 +02:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2021-06-17 16:02:50 +02:00
"762\n"
2021-06-13 21:06:55 +02:00
]
},
{
"data": {
"text/markdown": [
"| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n",
"|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
2021-06-17 16:02:50 +02:00
"| | | | | |**d**|**e**|**n**|**s**|**i**|**t**|**o**|**m**|**e**|**t**|**e**|**r**| | |**h**| | |**z**| | | |**h**|**a**|**m**|**m**|**e**|**r**|**s**| | |**p**|**p**|**l**| | | | |**d**| |**h**|**a**|**w**|**k**|\n",
"| | | |**v**| | | |**o**| | | | | |**x**| | |**m**| |**w**|**o**|**r**|**k**|**e**|**r**|**y**| | |**u**| | | | | | |**d**| |**e**| |**t**|**e**|**n**|**o**|**u**|**s**| |**m**| | |\n",
"| | |**m**|**a**|**s**|**l**|**e**|**n**|**i**|**t**|**s**|**a**| | | | | | | |**i**| | |**k**| | | |**f**|**r**|**e**|**e**| |**c**|**u**|**m**|**m**|**e**|**r**| | |**c**| | |**f**| | |**i**| |**m**|\n",
"| | | |**g**| | | |**i**| |**h**| | |**c**| | |**p**|**l**|**e**|**c**|**t**|**i**|**l**|**e**| | |**d**| |**e**| | |**p**| |**n**| |**v**| |**u**| |**s**|**t**|**a**|**m**|**f**|**o**|**r**|**d**| |**r**|\n",
"| | | |**u**|**l**|**c**|**e**|**r**|**o**|**u**|**s**| |**o**| |**i**| |**i**| |**o**| | | | |**c**| |**a**|**j**|**o**|**g**| |**a**| |**j**| | | |**s**| | |**h**| | | | | |**s**|**e**|**t**|\n",
"| |**d**|**u**|**e**| | | |**r**| |**s**| |**a**|**m**|**e**|**n**| |**q**| |**i**| |**p**|**i**|**l**|**l**|**a**|**r**| |**l**| | |**n**| |**o**| | |**r**|**a**|**d**|**c**|**l**|**i**|**f**|**f**|**e**| |**t**| | |\n",
"| |**e**| | | |**b**| |**i**| | |**h**| |**m**| |**c**| |**u**| |**n**| |**o**| | |**a**| |**t**| |**e**| | |**t**| |**c**| |**a**| |**l**| | |**i**| | |**a**| |**l**| |**i**| |\n",
"| |**c**|**o**|**n**|**s**|**u**|**l**|**t**|**i**|**v**|**e**| |**e**|**x**|**o**|**t**|**i**|**c**| |**b**|**l**|**n**| |**d**| |**o**| | |**v**| |**s**|**q**|**u**|**a**|**s**|**h**| |**k**|**i**|**p**| | |**n**| |**i**| |**n**| |\n",
"|**m**|**i**| | | |**t**| |**a**| | |**x**| |**a**| |**n**| |**d**| | | |**y**| |**c**| |**w**|**i**|**t**|**h**|**e**| | | |**l**| |**t**| | | | |**s**| |**s**|**t**|**e**|**m**| |**s**| |\n",
"| |**d**| |**u**| |**t**|**u**|**t**| |**o**| | |**s**| |**s**| |**a**| |**s**| |**s**| |**r**|**x**| |**d**| | |**g**|**g**| | |**a**| | |**e**| |**b**| |**i**| | |**o**| |**n**| |**u**| |\n",
"| |**a**| |**p**| |**r**| |**i**|**n**|**s**|**t**|**r**|**u**|**m**|**e**|**n**|**t**|**a**|**l**|**l**|**y**| |**y**| | | | | |**i**| |**p**|**a**|**r**|**v**|**o**|**v**|**i**|**r**|**u**|**s**| | |**d**| |**e**|**o**|**f**| |\n",
"| |**b**|**a**|**w**|**n**|**e**|**e**|**n**| |**t**| | |**r**| |**q**| |**i**| |**i**| |**n**| |**s**|**h**|**r**|**o**|**u**|**d**|**e**|**d**| |**m**| |**a**| |**p**| |**i**| | | |**m**| | |**m**| |**f**| |\n",
"| |**l**| |**i**| |**s**| |**g**|**a**|**r**|**b**| |**a**| |**u**| |**o**| |**c**|**a**|**o**| |**t**| |**f**| | | | | |**s**|**h**|**a**|**r**|**p**| |**i**|**n**|**a**|**c**|**c**|**e**|**s**|**s**|**i**|**b**|**l**|**e**|\n",
"| |**e**| |**n**| |**s**| | | |**a**| | |**b**|**e**|**e**| |**n**| |**k**| |**d**| |**a**| |**s**| |**c**| |**s**| | |**e**| | |**w**| | |**e**| |**s**| |**t**| | |**c**| |**a**| |\n",
"| | |**e**|**g**|**g**| | |**r**|**i**|**c**|**h**| |**l**| |**n**|**p**| |**p**|**i**| |**y**|**e**|**l**|**l**| |**j**|**o**|**i**|**n**|**d**|**e**|**r**| | | |**s**| | | |**s**| |**a**| |**c**| | |**t**| |\n",
"| |**t**| | | | |**f**| | |**i**| | |**e**| |**t**| |**b**| |**n**| | | |**l**| |**p**| |**n**| |**i**| | |**s**| |**o**| |**t**| | |**u**| |**a**|**l**|**p**|**h**|**a**|**b**|**e**|**t**|\n",
"| |**y**| | |**b**|**a**|**l**|**m**|**l**|**i**|**k**|**e**| |**a**| | |**u**|**n**|**g**|**l**|**a**|**m**|**o**|**r**|**o**|**u**|**s**| |**t**| | |**t**| |**s**|**o**|**f**|**a**| |**n**| | |**l**| |**i**| | | |**e**|\n",
"| |**r**| | |**u**| |**c**| | |**f**| |**v**| |**k**| | |**l**| | | | | |**g**| |**o**| |**o**| | |**c**| | | |**c**| |**u**| | |**c**|**l**|**a**|**y**| |**l**|**e**|**o**|**l**|**a**|\n",
"|**s**|**o**|**d**| |**r**| | |**g**|**e**|**o**|**d**|**e**|**t**|**i**|**c**|**a**|**l**| | | |**w**| |**e**| |**r**| |**l**| |**p**|**o**|**i**|**n**|**t**|**e**| | |**w**| |**u**| | | | |**i**| | |**o**| |\n",
"|**k**| |**o**| |**p**|**l**| | | |**r**| |**n**| | | |**p**| | |**b**|**g**|**a**| |**n**| | |**s**|**i**|**c**| |**n**| | | |**o**| | |**r**| |**t**| | |**d**|**o**|**a**|**t**|**i**|**n**|**g**|\n",
"|**i**| |**w**| | | | |**s**|**i**|**m**| | |**n**|**a**|**g**|**p**|**u**|**r**| | |**l**| | |**w**| | |**d**| | |**t**| |**w**|**i**|**l**|**d**|**f**|**i**|**r**|**e**| |**e**| | |**s**| | |**g**| |\n",
"|**r**| |**n**| | |**f**|**d**|**m**| | |**p**|**s**| | | |**e**| | | | |**d**|**e**|**v**|**a**|**s**|**t**|**a**|**t**|**i**|**o**|**n**| | |**a**| | |**g**| | |**b**|**l**|**o**|**o**|**t**|**h**| |**v**| |\n",
"|**r**|**e**|**s**|**e**|**t**| | |**e**| | |**i**| | | | |**r**|**o**|**u**|**p**| |**r**| | |**t**| | |**t**| | |**r**| | | | |**p**|**i**|**g**|**g**|**y**| |**s**| | | |**d**| |**i**| |\n",
"| | |**t**| |**i**| |**c**|**a**|**p**|**i**|**t**|**a**|**l**|**i**|**s**|**t**| | |**r**| |**o**| | |**t**| |**b**|**e**|**a**|**s**|**t**| | | |**h**| | |**l**| | | |**e**|**n**|**r**|**o**|**l**|**l**|**e**|**d**|\n",
"|**m**|**e**|**a**|**s**|**l**|**y**| |**t**| | |**k**| | |**d**| |**a**|**l**|**l**|**y**| |**n**|**c**| |**l**| |**e**| | |**o**| |**m**| |**w**|**a**|**n**|**t**|**e**|**d**| | | | |**a**| | | |**w**| |\n",
"|**e**| |**g**| |**l**| | |**o**| |**p**|**i**|**p**| |**p**| |**i**| | | | | | | |**e**| |**n**| | |**p**| |**s**| | |**n**| | |**r**| | | |**a**| |**c**| | | | | |\n",
"| |**k**|**e**|**y**|**p**|**o**|**i**|**n**|**t**| |**n**| |**g**|**r**|**a**|**n**|**d**|**f**|**a**|**t**|**h**|**e**|**r**|**l**|**y**| | |**w**|**a**|**r**|**b**|**i**|**r**|**d**| | | | | | |**c**|**l**|**e**|**a**|**n**| | | |\n"
2021-06-13 21:06:55 +02:00
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
2021-06-17 16:02:50 +02:00
"#%%prun\n",
2021-06-13 21:06:55 +02:00
"gr, infos = create_word_grid(16*3,9*3, target_density=0.55)\n",
"def grid2mdown(grid):\n",
" h,w = grid.shape\n",
" \n",
" md_str = \" \".join([\"|\"] * (w+1)) + \"\\n\"\n",
" md_str += \":---:\".join([\"|\"] * (w+1)) + \"\\n\"\n",
" \n",
" for y in range(h):\n",
" md_str += \"|\" + \"|\".join([(f\"**{grid[y,x]}**\" if grid[y,x] != \" \" else \" \") for x in range(w)]) + \"|\\n\"\n",
" \n",
" return Markdown(md_str)\n",
"\n",
"display(grid2mdown(gr))\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 279,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"edie -- surname\n"
]
}
],
"source": [
"i=220\n",
"\n",
"word = list(infos.keys())[i]\n",
"\n",
"print(word, \"--\", infos[word].get_hint())"
]
},
{
"cell_type": "code",
"execution_count": 360,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"## wagenboom\n",
"\n",
"### Senses:\n",
"\n",
"* A South African proteaceous tree\n",
"* The tough wood of this tree, used for making wagon wheels\n",
"\n",
"### Synonyms:\n",
"\n",
"* waboom\n",
"* wagon tree\n",
"\n",
"### Antonyms:\n",
"\n",
"\n",
" \n",
" "
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(display_info(\"wagenboom\"))"
]
},
{
"cell_type": "code",
2021-06-17 16:02:50 +02:00
"execution_count": 30,
2021-06-13 21:06:55 +02:00
"metadata": {},
"outputs": [],
"source": [
"from enum import Enum\n",
"\n",
"class HintOrientation(Enum):\n",
" VERTICAL = 0\n",
" HORIZONTAL = 1\n",
" BOTH = 2\n",
" \n",
"class FieldType(Enum):\n",
" EMPTY = 0\n",
" HINT = 1\n",
" LETTER = 2\n",
"\n",
"class Field(object):\n",
" def __init__(self, field_type: FieldType = FieldType.EMPTY):\n",
" self._field_type = field_type\n",
" \n",
" def get_type(self) -> FieldType:\n",
" return self._field_type\n",
" \n",
" def get_content(self) -> str:\n",
" return None\n",
"\n",
"class HintField(Field):\n",
" def __init__(self, horizontal_hint: str = None, vertical_hint: str = None):\n",
" super().__init__(field_type=FieldType.HINT)\n",
" \n",
" self._horizontal_hint = horizontal_hint\n",
" self._vertical_hint = vertical_hint\n",
" \n",
" def get_horizontal_hint(self) -> str:\n",
" return self._horizontal_hint\n",
" \n",
" def get_vertical_hint(self) -> str:\n",
" return self._vertical_hint\n",
" \n",
" def set_horizintal_hint(hint: str):\n",
" self._horizontal_hint = hint\n",
" \n",
" def set_vertical_hint(hint:str):\n",
" self._vertical_hint = hint\n",
"\n",
"class LetterField(Field):\n",
" def __init__(self, letter: str):\n",
" assert len(letter) <= 1\n",
" \n",
" super().__init__(field_type = FieldType.LETTER)\n",
" \n",
" self._letter = letter\n",
" self._revealed = False\n",
" \n",
" def get_content(self) -> str:\n",
" return self._letter\n",
" \n",
" def reveal(self):\n",
" self._revealed = True\n",
" \n",
" def is_revealed(self) -> bool:\n",
" return self._revealed\n",
"\n",
"class Grid(object):\n",
" def __init__(self, width: int, height: int, density = 0.5):\n",
" self._width = width\n",
" self._height = height\n",
" self._density = density\n",
" self._grid = []\n",
" self._build_grid()\n",
" \n",
" def _build_grid(self):\n",
" raw_grid, word_infos = create_word_grid(self._width - 1, self._height - 1, target_density=self._density)\n",
" \n",
" # note: we will append an additional row and column, to have enough space to place hint fields\n",
" \n",
" self._grid = [[Field()] * self._width] # initialize with empty row\n",
" \n",
" for y in range(self._height):\n",
" row = [Field()] # initialize row with empty column\n",
" for x in range(self._width):\n",
" raw_cell = raw_grid[y-1,x-1]\n",
" if raw_cell == \" \":\n",
" row.append(Field())\n",
" else:\n",
" row.append(LetterField(raw_cell))\n",
" \n",
" self._grid.append(row)\n",
" \n",
" # place hint fields:\n",
" for word, info in word_infos.items():\n",
" y,x = info.get_hint_location()\n",
" # correct offset\n",
" y += 1\n",
" x += 1\n",
" \n",
" cell = self._grid[y][x]\n",
" \n",
" # check if we already have a hint here:\n",
" if cell.get_type() == FieldType.HINT:\n",
" if info.is_vertical():\n",
" cell.set_vertical_hint(info.get_hint())\n",
" else:\n",
" cell.set_horizintal_hint(info.get_hint())\n",
" elif cell.get_type() == FieldType.LETTER:\n",
" # edge case: a word has \"eaten up\" another one, skipping that case\n",
" pass\n",
" \n",
" else:\n",
" if info.is_vertical():\n",
" self._grid[y][x] = HintField(vertical_hint=info.get_hint())\n",
" else:\n",
" self._grid[y][x] = HintField(horizontal_hint=info.get_hint())\n",
"\n",
" \n",
"class Crossword(object):\n",
" def __init__(self, width: int, height: int, database: dict):\n",
" self._width = width\n",
" self._height = height\n",
" self._database = database\n",
" self._grid = Grid(width, height)\n",
" \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
2021-06-17 16:02:50 +02:00
"execution_count": 31,
2021-06-13 21:06:55 +02:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2021-06-17 16:02:50 +02:00
"209\n"
2021-06-13 21:06:55 +02:00
]
}
],
"source": [
"cw = Crossword(16*3, 9*3, english_vocab_db)"
]
},
2021-06-17 16:02:50 +02:00
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[<__main__.HintField at 0x7f207d65a820>,\n",
" <__main__.Field at 0x7f207eadd460>,\n",
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2021-06-13 21:06:55 +02:00
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