dev_nb/007a_imdb_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from nb_007 import *\n",
    "\n",
    "import pandas as pd, re, html, os\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n",
    "\n",
    "import spacy\n",
    "from spacy.symbols import ORTH\n",
    "from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMDB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path('../data/aclImdb/')\n",
    "CLAS_PATH = PATH/'clas'\n",
    "LM_PATH = PATH/'lm'\n",
    "os.makedirs(CLAS_PATH, exist_ok=True)\n",
    "os.makedirs(LM_PATH, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "BOS,FLD,UNK,PAD = 'xxbos','xxfld','xxunk','xxpad'\n",
    "TK_UP,TK_REP,TK_WREP = 'xxup','xxrep','xxwrep'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSES = ['neg', 'pos', 'unsup']\n",
    "\n",
    "def get_texts(path):\n",
    "    texts,labels = [],[]\n",
    "    for idx,label in enumerate(CLASSES):\n",
    "        for fname in (path/label).glob('*.*'):\n",
    "            texts.append(fname.open('r', encoding='utf8').read())\n",
    "            labels.append(idx)\n",
    "    return np.array(texts),np.array(labels)\n",
    "\n",
    "train_texts,train_labels = get_texts(PATH/'train')\n",
    "valid_texts,valid_labels = get_texts(PATH/'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_idx = np.random.permutation(len(train_texts))\n",
    "valid_idx = np.random.permutation(len(valid_texts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_texts,train_labels = train_texts[train_idx],train_labels[train_idx]\n",
    "valid_texts,valid_labels = valid_texts[valid_idx],valid_labels[valid_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.DataFrame({'text':train_texts, 'labels':train_labels}, columns=['labels','text'])\n",
    "valid_df = pd.DataFrame({'text':valid_texts, 'labels':valid_labels}, columns=['labels','text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We put aside the unsup labels for the classification but keep them to finetune the language model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[train_df['labels']!=2].to_csv(CLAS_PATH/'train.csv', header=False, index=False)\n",
    "valid_df.to_csv(CLAS_PATH/'valid.csv', header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_texts = np.concatenate([train_texts,valid_texts])\n",
    "idx = np.random.permutation(len(all_texts))\n",
    "cut = int(0.1 * len(idx))\n",
    "train_df = pd.DataFrame({'text':all_texts[idx[cut:]], 'labels':[0] * (len(all_texts)-cut)}, columns=['labels','text'])\n",
    "valid_df = pd.DataFrame({'text':all_texts[idx[:cut]], 'labels':[0] * cut}, columns=['labels','text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv(LM_PATH/'train.csv', header=False, index=False)\n",
    "valid_df.to_csv(LM_PATH/'valid.csv', header=False, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenization + Numericalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def partition(a:Collection, sz:int) -> List[Collection]: \n",
    "    \"Splits iterables a in equal parts of size sz\"\n",
    "    return [a[i:i+sz] for i in range(0, len(a), sz)]\n",
    "\n",
    "def partition_by_cores(a:Collection, n_cpus:int) -> List[Collection]:\n",
    "    \"Split data equally among CPU cores\"\n",
    "    return partition(a, len(a)//n_cpus + 1)\n",
    "\n",
    "def num_cpus() -> int:\n",
    "    \"Return the number of CPUs in the system\"\n",
    "    try:\n",
    "        return len(os.sched_getaffinity(0))\n",
    "    except AttributeError:\n",
    "        return os.cpu_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class BaseTokenizer():\n",
    "    \"Basic class for a tokenizer function.\"\n",
    "    def __init__(self, lang:str):\n",
    "        self.lang = lang\n",
    "    \n",
    "    def tokenizer(self, t:spacy.tokens.doc.Doc) -> List[str]: raise NotImplementedError\n",
    "    def add_special_cases(self, toks:Collection[str]):        raise NotImplementedError   \n",
    "\n",
    "#export\n",
    "class SpacyTokenizer(BaseTokenizer):\n",
    "    \"Little wrapper around a `spacy` tokenizer\"\n",
    "    \n",
    "    def __init__(self, lang:str):\n",
    "        self.tok = spacy.load(lang)\n",
    "    \n",
    "    def tokenizer(self, t:spacy.tokens.doc.Doc) -> List[str]:\n",
    "        return [t.text for t in self.tok.tokenizer(t)]\n",
    "    \n",
    "    def add_special_cases(self, toks:Collection[str]):\n",
    "        for w in toks:\n",
    "            self.tok.tokenizer.add_special_case(w, [{ORTH: w}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(LM_PATH/'train.csv', header=None, chunksize=10)\n",
    "trn_df = next(train_df)\n",
    "test_tok = SpacyTokenizer('en')\n",
    "test_txt = trn_df.iloc[0][1]\n",
    "test_tok.tokenizer(test_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def sub_br(t:str) -> str:\n",
    "    \"Replaces the <br /> by \\n\"\n",
    "    re_br = re.compile(r'<\\s*br\\s*/?>', re.IGNORECASE)\n",
    "    return re_br.sub(\"\\n\", t)\n",
    "\n",
    "def spec_add_spaces(t:str) -> str:\n",
    "    \"Add spaces between special characters\"\n",
    "    return re.sub(r'([/#])', r' \\1 ', t)\n",
    "\n",
    "def rm_useless_spaces(t:str) -> str:\n",
    "    \"Remove multiple spaces\"\n",
    "    return re.sub(' {2,}', ' ', t)\n",
    "\n",
    "def replace_rep(t:str) -> str:\n",
    "    \"Replace repetitions at the character level\"\n",
    "    def _replace_rep(m:Collection[str]) -> str:\n",
    "        c,cc = m.groups()\n",
    "        return f' {TK_REP} {len(cc)+1} {c} '\n",
    "    re_rep = re.compile(r'(\\S)(\\1{3,})')\n",
    "    return re_rep.sub(_replace_rep, t)\n",
    "    \n",
    "def replace_wrep(t:str) -> str:\n",
    "    \"Replace word repetitions\"\n",
    "    def _replace_wrep(m:Collection[str]) -> str:\n",
    "        c,cc = m.groups()\n",
    "        return f' {TK_WREP} {len(cc.split())+1} {c} '\n",
    "    re_wrep = re.compile(r'(\\b\\w+\\W+)(\\1{3,})')\n",
    "    return re_wrep.sub(_replace_wrep, t)\n",
    "\n",
    "def deal_caps(t:str) -> str:\n",
    "    \"Replace words in all caps\"\n",
    "    res = []\n",
    "    for s in re.findall(r'\\w+|\\W+', t):\n",
    "        res += ([f' {TK_UP} ',s.lower()] if (s.isupper() and (len(s)>2)) else [s.lower()])\n",
    "    return ''.join(res)\n",
    "\n",
    "def fixup(x:str) -> str:\n",
    "    \"List of replacements from html strings\"\n",
    "    re1 = re.compile(r'  +')\n",
    "    x = x.replace('#39;', \"'\").replace('amp;', '&').replace('#146;', \"'\").replace(\n",
    "        'nbsp;', ' ').replace('#36;', '$').replace('\\\\n', \"\\n\").replace('quot;', \"'\").replace(\n",
    "        '<br />', \"\\n\").replace('\\\\\"', '\"').replace('<unk>',UNK).replace(' @.@ ','.').replace(\n",
    "        ' @-@ ','-').replace('\\\\', ' \\\\ ')\n",
    "    return re1.sub(' ', html.unescape(x))\n",
    "    \n",
    "default_rules = [fixup, replace_rep, replace_wrep, deal_caps, spec_add_spaces, rm_useless_spaces, sub_br]\n",
    "default_spec_tok = [BOS, FLD, UNK, PAD]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "\n",
    "ListRules = Collection[Callable[[str],str]]\n",
    "\n",
    "class Tokenizer():\n",
    "    \"Puts together rules, a tokenizer function and a language to process text with multiprocessing\"\n",
    "    def __init__(self, tok_fn:Callable=SpacyTokenizer, lang:str='en', rules:ListRules=None, \n",
    "                 special_cases:Collection[str]=None, n_cpus:int=None):\n",
    "        self.tok_fn,self.lang,self.special_cases = tok_fn,lang,special_cases\n",
    "        self.rules = rules if rules else default_rules\n",
    "        self.special_cases = special_cases if special_cases else default_spec_tok\n",
    "        self.n_cpus = n_cpus or num_cpus()//2\n",
    "    \n",
    "    def __repr__(self) -> str:\n",
    "        res = f'Tokenizer {self.tok_fn.__name__} in {self.lang} with the following rules:\\n'\n",
    "        for rule in self.rules: res += f' - {rule.__name__}\\n'\n",
    "        return res\n",
    "    \n",
    "    def proc_text(self, t:str, tok:BaseTokenizer) -> List[str]:\n",
    "        \"Processes one text\"\n",
    "        for rule in self.rules: t = rule(t)\n",
    "        return tok.tokenizer(t)\n",
    "    \n",
    "    def process_all_1(self, texts:Collection[str]) -> List[List[str]]:\n",
    "        \"Processes a list of texts in one process\"\n",
    "        tok = self.tok_fn(self.lang)\n",
    "        if self.special_cases: tok.add_special_cases(self.special_cases)\n",
    "        return [self.proc_text(t, tok) for t in texts]\n",
    "\n",
    "    def process_all(self, texts:Collection[str]) -> List[List[str]]:\n",
    "        \"Processes a list of texts in several processes\"\n",
    "        if self.n_cpus <= 1: return self.process_all_1(texts)\n",
    "        with ProcessPoolExecutor(self.n_cpus) as e:\n",
    "            return sum(e.map(self.process_all_1, partition_by_cores(texts, self.n_cpus)), [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(rules=default_rules, special_cases=[BOS, FLD, 'xxunk', 'xxpad'], n_cpus=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert sub_br('end <br /><br /> begins again')=='end \\n\\n begins again'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert spec_add_spaces('\\#%') == '\\\\ # %'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert rm_useless_spaces('this             is') == 'this is'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert replace_rep('ffffffive .') == ' xxrep 6 f ive .'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert replace_wrep('five five five five .') == ' xxwrep 4 five  .'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert deal_caps('ANGRY') == ' xxup angry'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def get_chunk_length(csv_name:PathOrStr, chunksize:int) -> int:\n",
    "    \"Reads the number of chunks in a pandas `DataFrame`\"\n",
    "    dfs = pd.read_csv(csv_name, header=None, chunksize=chunksize)\n",
    "    l = 0\n",
    "    for _ in dfs: l+=1\n",
    "    return l\n",
    "\n",
    "def get_total_length(csv_name:PathOrStr, chunksize:int) -> int:\n",
    "    \"Reads the the total length of a pandas `DataFrame`\"\n",
    "    dfs = pd.read_csv(csv_name, header=None, chunksize=chunksize)\n",
    "    l = 0\n",
    "    for df in dfs: l+=len(df)\n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def maybe_copy(old_fnames:Collection[PathOrStr], new_fnames:Collection[PathOrStr]):\n",
    "    \"Copies the `old_fnames` to `new_fnames` location if new_fnames don't exist or are less recent.\"\n",
    "    os.makedirs(os.path.dirname(new_fnames[0]), exist_ok=True)\n",
    "    for old_fname,new_fname in zip(old_fnames, new_fnames):    \n",
    "        if not os.path.isfile(new_fname) or os.path.getmtime(new_fname) < os.path.getmtime(old_fname):\n",
    "            shutil.copyfile(old_fname, new_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import hashlib\n",
    "\n",
    "Tokens = Collection[Collection[str]]\n",
    "\n",
    "class Vocab():\n",
    "    \"Contains the correspondance between numbers and tokens and numericalizes\"\n",
    "    \n",
    "    def __init__(self, path:PathOrStr):\n",
    "        self.itos = pickle.load(open(path/'itos.pkl', 'rb'))\n",
    "        self.stoi = collections.defaultdict(int,{v:k for k,v in enumerate(self.itos)})\n",
    "    \n",
    "    def numericalize(self, t:Collection[str]) -> List[int]:\n",
    "        \"Converts a list of tokens to their ids\"\n",
    "        return [self.stoi[w] for w in t]\n",
    "    \n",
    "    def textify(self, nums:Collection[int]) -> List[str]:\n",
    "        \"Converts a list of ids to their tokens\"\n",
    "        return ' '.join([self.itos[i] for i in nums])\n",
    "    \n",
    "    @classmethod\n",
    "    def create(cls, path:PathOrStr, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab':\n",
    "        \"Create a vocabulary from a set of tokens.\"\n",
    "        freq = Counter(p for o in tokens for p in o)\n",
    "        itos = [o for o,c in freq.most_common(max_vocab) if c > min_freq]\n",
    "        itos.insert(0, PAD)\n",
    "        if UNK in itos: itos.remove(UNK)\n",
    "        itos.insert(0, UNK)\n",
    "        pickle.dump(itos, open(path/'itos.pkl', 'wb'))\n",
    "        h = hashlib.sha1(np.array(itos))\n",
    "        with open(path/'numericalize.log','w') as f: f.write(h.hexdigest())\n",
    "        return cls(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "TextMtd = IntEnum('TextMtd', 'CSV TOK IDS')\n",
    "\n",
    "import shutil\n",
    "\n",
    "class TextDataset():\n",
    "    \"Basic dataset for NLP tasks.\"\n",
    "    \n",
    "    def __init__(self, path:PathOrStr, tokenizer:Tokenizer, vocab:Vocab=None, max_vocab:int=60000, chunksize:int=10000, \n",
    "                 name:str='train', min_freq:int=2, n_labels:int=1, create_mtd:TextMtd=TextMtd.CSV, classes:Classes=None):\n",
    "        self.path,self.tokenizer,self.max_vocab,self.min_freq = Path(path/'tmp'),tokenizer,max_vocab,min_freq\n",
    "        self.chunksize,self.name,self.n_labels,self.create_mtd = chunksize,name,n_labels,create_mtd\n",
    "        self.vocab=vocab\n",
    "        os.makedirs(self.path, exist_ok=True)\n",
    "        if not self.check_toks(): self.tokenize()\n",
    "        if not self.check_ids():  self.numericalize()\n",
    "        \n",
    "        if self.vocab is None: self.vocab = Vocab(self.path)\n",
    "        self.ids = np.load(self.path/f'{self.name}_ids.npy')\n",
    "        if os.path.isfile(self.path/f'{self.name}_lbl.npy'):\n",
    "            self.labels = np.load(self.path/f'{self.name}_lbl.npy')\n",
    "        else: self.labels = np.zeros((len(self.ids),), dtype=np.int64)\n",
    "        self.classes = classes if classes else np.unique(self.labels)\n",
    "        \n",
    "    def __getitem__(self, idx:int) -> Tuple[int,int]: return self.ids[idx],self.labels[idx]\n",
    "    def __len__(self) -> int: return len(self.ids)\n",
    "    \n",
    "    def general_check(self, pre_files:Collection[PathOrStr], post_files:Collection[PathOrStr]):\n",
    "        \"Checks that post_files exist and were modified after all the prefiles.\"\n",
    "        if not np.all([os.path.isfile(fname) for fname in post_files]): return False\n",
    "        for pre_file in pre_files:\n",
    "            if os.path.getmtime(pre_file) > os.path.getmtime(post_files[0]): return False\n",
    "        return True\n",
    "    \n",
    "    def check_ids(self) -> bool:\n",
    "        \"Checks if a new numericalization is needed.\"\n",
    "        if self.create_mtd >= TextMtd.IDS: return True\n",
    "        if not self.general_check([self.tok_files[0],self.id_files[1]], self.id_files): return False\n",
    "        itos = pickle.load(open(self.id_files[1], 'rb'))\n",
    "        h = hashlib.sha1(np.array(itos))\n",
    "        with open(self.id_files[2]) as f:\n",
    "            if h.hexdigest() != f.read() or len(itos) > self.max_vocab + 2: return False\n",
    "        toks,ids = np.load(self.tok_files[0]),np.load(self.id_files[0])\n",
    "        if len(toks) != len(ids): return False\n",
    "        return True\n",
    "        \n",
    "    def check_toks(self) -> bool:\n",
    "        \"Checks if a new tokenization is needed.\"\n",
    "        if self.create_mtd >= TextMtd.TOK: return True\n",
    "        if not self.general_check([self.csv_file], self.tok_files): return False\n",
    "        with open(self.tok_files[1]) as f:\n",
    "            if repr(self.tokenizer) != f.read(): return False\n",
    "        return True\n",
    "    \n",
    "    def tokenize(self):\n",
    "        \"Tokenizes the texts in the csv file\"\n",
    "        print(f'Tokenizing {self.name}. This might take a while so you should grab a coffee.')\n",
    "        curr_len = get_chunk_length(self.csv_file, self.chunksize)\n",
    "        dfs = pd.read_csv(self.csv_file, header=None, chunksize=self.chunksize)\n",
    "        tokens,labels = [],[]\n",
    "        for _ in progress_bar(range(curr_len), leave=False):\n",
    "            df = next(dfs)\n",
    "            lbls = df.iloc[:,range(self.n_labels)].values.astype(np.int64)\n",
    "            texts = f'\\n{BOS} {FLD} 1 ' + df[self.n_labels].astype(str)\n",
    "            for i in range(self.n_labels+1, len(df.columns)): \n",
    "                texts += f' {FLD} {i-n_lbls} ' + df[i].astype(str)\n",
    "            toks = self.tokenizer.process_all(texts)\n",
    "            tokens += toks\n",
    "            labels += list(np.squeeze(lbls))\n",
    "        np.save(self.tok_files[0], np.array(tokens))\n",
    "        np.save(self.path/f'{self.name}_lbl.npy', np.array(labels))\n",
    "        with open(self.tok_files[1],'w') as f: f.write(repr(self.tokenizer))\n",
    "        \n",
    "    def numericalize(self):\n",
    "        \"Numericalizes the tokens in the token file\"\n",
    "        print(f'Numericalizing {self.name}.')\n",
    "        toks = np.load(self.tok_files[0])\n",
    "        if self.vocab is None: self.vocab = Vocab.create(self.path, toks, self.max_vocab, self.min_freq)\n",
    "        ids = np.array([self.vocab.numericalize(t) for t in toks])\n",
    "        np.save(self.id_files[0], ids)\n",
    "    \n",
    "    def clear(self):\n",
    "        \"Removes temporary files\"\n",
    "        files = [self.path/f'{self.name}_{suff}.npy' for suff in ['ids','tok','lbl']]\n",
    "        files.append(self.path/f'{self.name}.csv')\n",
    "        for file in files:\n",
    "            if os.path.isfile(file): os.remove(file)\n",
    "    \n",
    "    @property\n",
    "    def csv_file(self) -> Path: return self.path/f'{self.name}.csv'\n",
    "    @property\n",
    "    def tok_files(self) -> List[Path]: return [self.path/f'{self.name}_tok.npy', self.path/'tokenize.log']\n",
    "    @property\n",
    "    def id_files(self) -> List[Path]:\n",
    "        return [self.path/f'{self.name}_ids.npy', self.path/'itos.pkl', self.path/'numericalize.log']\n",
    "    \n",
    "    @classmethod\n",
    "    def from_ids(cls, folder:PathOrStr, name:str, id_suff:str='_ids', lbl_suff:str='_lbl', \n",
    "                 itos:str='itos.pkl', **kwargs) -> 'TextDataset':\n",
    "        \"Creates a dataset from an id, a dictionary and label file.\"\n",
    "        orig = [Path(folder/file) for file in [f'{name}{id_suff}.npy', f'{name}{lbl_suff}.npy', itos]]\n",
    "        dest = [Path(folder)/'tmp'/file for file in [f'{name}_ids.npy', f'{name}_lbl.npy', 'itos.pkl']]\n",
    "        maybe_copy(orig, dest)\n",
    "        return cls(folder, None, name=name, create_mtd=TextMtd.IDS, **kwargs)\n",
    "    \n",
    "    @classmethod\n",
    "    def from_tokens(cls, folder:PathOrStr, name:str, tok_suff:str='_tok', lbl_suff:str='_lbl', \n",
    "                    **kwargs) -> 'TextDataset':\n",
    "        \"Creates a dataset from a token and label file.\"\n",
    "        orig = [Path(folder/file) for file in [f'{name}{tok_suff}.npy', f'{name}{lbl_suff}.npy']]\n",
    "        dest = [Path(folder)/'tmp'/file for file in [f'{name}_tok.npy', f'{name}_lbl.npy']]\n",
    "        maybe_copy(orig, dest)\n",
    "        return cls(folder, None, name=name, create_mtd=TextMtd.TOK, **kwargs)\n",
    "    \n",
    "    @classmethod\n",
    "    def from_csv(cls, folder:PathOrStr, tokenizer:Tokenizer, name:str, **kwargs) -> 'TextDataset':\n",
    "        \"Creates a dataset from texts in a csv file.\"\n",
    "        orig = [Path(folder)/f'{name}.csv']\n",
    "        dest = [Path(folder)/'tmp'/f'{name}.csv']\n",
    "        maybe_copy(orig, dest)\n",
    "        return cls(folder, tokenizer, name=name, **kwargs)\n",
    "    \n",
    "    @classmethod\n",
    "    def from_folder(cls, folder:PathOrStr, tokenizer:Tokenizer, name:str, classes:Classes=None, \n",
    "                    shuffle:bool=True, **kwargs) -> 'TextDataset':\n",
    "        \"Creates a dataset from a folder\"\n",
    "        path = Path(folder)/'tmp'\n",
    "        os.makedirs(path, exist_ok=True)\n",
    "        if classes is None: classes = [cls.name for cls in find_classes(Path(folder)/name)]\n",
    "        texts,labels = [],[]\n",
    "        for idx,label in enumerate(classes):\n",
    "            for fname in (Path(folder)/name/label).glob('*.*'):\n",
    "                texts.append(fname.open('r', encoding='utf8').read())\n",
    "                labels.append(idx)\n",
    "        texts,labels = np.array(texts),np.array(labels)\n",
    "        if shuffle:\n",
    "            idx = np.random.permutation(len(texts))\n",
    "            texts,labels = texts[idx],labels[idx]\n",
    "        df = pd.DataFrame({'text':texts, 'labels':labels}, columns=['labels','text'])\n",
    "        if os.path.isfile(path/f'{name}.csv'):\n",
    "            if get_total_length(path/f'{name}.csv', 10000) != len(df):\n",
    "                df.to_csv(path/f'{name}.csv', index=False, header=False)\n",
    "        else: df.to_csv(path/f'{name}.csv', index=False, header=False)\n",
    "        return cls(folder, tokenizer, name=name, classes=classes, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def extract_kwargs(names:Collection[str], kwargs:KWArgs):\n",
    "    \"Extracts the keys in names from the kwargs.\"\n",
    "    new_kwargs = {}\n",
    "    for arg_name in names:\n",
    "        if arg_name in kwargs:\n",
    "            arg_val = kwargs.pop(arg_name)\n",
    "            new_kwargs[arg_name] = arg_val\n",
    "    return new_kwargs, kwargs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class LanguageModelLoader():\n",
    "    \"Creates a dataloader with bptt slightly changing.\"\n",
    "    def __init__(self, dataset:TextDataset, bs:int=64, bptt:int=70, backwards:bool=False):\n",
    "        self.dataset,self.bs,self.bptt,self.backwards = dataset,bs,bptt,backwards\n",
    "        self.data = self.batchify(np.concatenate(dataset.ids))\n",
    "        self.first,self.i,self.iter = True,0,0\n",
    "        self.n = len(self.data)\n",
    "\n",
    "    def __iter__(self):\n",
    "        self.i,self.iter = 0,0\n",
    "        while self.i < self.n-1 and self.iter<len(self):\n",
    "            if self.first and self.i == 0: self.first,seq_len = False,self.bptt + 25\n",
    "            else:\n",
    "                bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.\n",
    "                seq_len = max(5, int(np.random.normal(bptt, 5)))\n",
    "            res = self.get_batch(self.i, seq_len)\n",
    "            self.i += seq_len\n",
    "            self.iter += 1\n",
    "            yield res\n",
    "\n",
    "    def __len__(self) -> int: return (self.n-1) // self.bptt\n",
    "\n",
    "    def batchify(self, data:np.ndarray) -> LongTensor:\n",
    "        \"Splits the corpus in batches.\"\n",
    "        nb = data.shape[0] // self.bs\n",
    "        data = np.array(data[:nb*self.bs]).reshape(self.bs, -1).T\n",
    "        if self.backwards: data=data[::-1]\n",
    "        return LongTensor(data)\n",
    "\n",
    "    def get_batch(self, i:int, seq_len:int) -> Tuple[LongTensor, LongTensor]:\n",
    "        \"Creates a batch of a given seq_len\"\n",
    "        seq_len = min(seq_len, len(self.data) - 1 - i)\n",
    "        return self.data[i:i+seq_len], self.data[i+1:i+1+seq_len].contiguous().view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def standard_data(datasets:Collection[DatasetBase], path:PathOrStr, **kwargs) -> DataBunch:\n",
    "    \"Simply creates a `DataBunch` from the `datasets`\"\n",
    "    return DataBunch.create(*datasets, path=path, **kwargs)\n",
    "\n",
    "def lm_data(datasets:Collection[TextDataset], path:PathOrStr, **kwargs) -> DataBunch:\n",
    "    \"Creates a `DataBunch` from the `datasets` for language modelling\"\n",
    "    dataloaders = [LanguageModelLoader(ds, **kwargs) for ds in datasets]\n",
    "    return DataBunch(*dataloaders, path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "DataFunc = Callable[[Collection[DatasetBase], PathOrStr, KWArgs], DataBunch]\n",
    "\n",
    "def data_from_textids(path:PathOrStr, train:str='train', valid:str='valid', test:Optional[str]=None, \n",
    "                      data_func:DataFunc=standard_data, itos:str='itos.pkl', **kwargs) -> DataBunch:\n",
    "    \"Creates a `DataBunch` from ids, labels and a dictionary.\"\n",
    "    path=Path(path)\n",
    "    txt_kwargs, kwargs = extract_kwargs(['max_vocab', 'chunksize', 'min_freq', 'n_labels', 'id_suff', 'lbl_suff'], kwargs)\n",
    "    train_ds = TextDataset.from_ids(path, train, itos=itos, **txt_kwargs)\n",
    "    datasets = [train_ds, TextDataset.from_ids(path, valid, itos=itos, **txt_kwargs)]\n",
    "    if test: datasets.append(TextDataset.from_ids(path, test, itos=itos, **txt_kwargs))\n",
    "    return data_func(datasets, path, **kwargs)\n",
    "\n",
    "def data_from_texttokens(path:PathOrStr, train:str='train', valid:str='valid', test:Optional[str]=None, \n",
    "                         data_func:DataFunc=standard_data, vocab:Vocab=None, **kwargs) -> DataBunch:\n",
    "    \"Creates a `DataBunch` from tokens and labels.\"\n",
    "    path=Path(path)\n",
    "    txt_kwargs, kwargs = extract_kwargs(['max_vocab', 'chunksize', 'min_freq', 'n_labels', 'tok_suff', 'lbl_suff'], kwargs)\n",
    "    train_ds = TextDataset.from_tokens(path, train, vocab=vocab, **txt_kwargs)\n",
    "    datasets = [train_ds, TextDataset.from_tokens(path, valid, vocab=train_ds.vocab, **txt_kwargs)]\n",
    "    if test: datasets.append(TextDataset.from_tokens(path, test, vocab=train_ds.vocab, **txt_kwargs))\n",
    "    return data_func(datasets, path, **kwargs)\n",
    "\n",
    "def data_from_textcsv(path:PathOrStr, tokenizer:Tokenizer, train:str='train', valid:str='valid', test:Optional[str]=None, \n",
    "                      data_func:DataFunc=standard_data, vocab:Vocab=None, **kwargs) -> DataBunch:\n",
    "    \"Creates a `DataBunch` from texts in csv files.\"\n",
    "    path=Path(path)\n",
    "    txt_kwargs, kwargs = extract_kwargs(['max_vocab', 'chunksize', 'min_freq', 'n_labels'], kwargs)\n",
    "    train_ds = TextDataset.from_csv(path, tokenizer, train, vocab=vocab, **txt_kwargs)\n",
    "    datasets = [train_ds, TextDataset.from_csv(path, tokenizer, valid, vocab=train_ds.vocab, **txt_kwargs)]\n",
    "    if test: datasets.append(TextDataset.from_csv(path, tokenizer, test, vocab=train_ds.vocab, **txt_kwargs))\n",
    "    return data_func(datasets, path, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(rules=default_rules, special_cases=[BOS, FLD, 'xxunk', 'xxpad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_from_textcsv(LM_PATH, tokenizer, chunksize=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_from_texttokens(LM_PATH, chunksize=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_from_textids(LM_PATH, chunksize=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[0].clear()\n",
    "#data[1].clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_sample(dir_name='tst_folders'):\n",
    "    os.makedirs(PATH/dir_name, exist_ok=True)\n",
    "    PATH1 = PATH/dir_name\n",
    "    for name,name1 in zip(['train', 'valid'],['train', 'test']):\n",
    "        os.makedirs(PATH1/name, exist_ok=True)\n",
    "        for clas in ['neg', 'pos']:\n",
    "            os.makedirs(PATH1/name/clas, exist_ok=True)\n",
    "            fnames = list((PATH/name1/clas).glob('*.txt'))\n",
    "            for i in range(2000):\n",
    "                shutil.copy(fnames[i], PATH1/name/clas/fnames[i].name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make_sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = TextDataset.from_folder(PATH/'tst_folders', tokenizer, 'train', chunksize=1000)\n",
    "valid_ds = TextDataset.from_folder(PATH/'tst_folders', tokenizer, 'valid', chunksize=1000, vocab=train_ds.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def data_from_textfolder(path:PathOrStr, tokenizer:Tokenizer, train:str='train', valid:str='valid', test:Optional[str]=None, \n",
    "                         shuffle:bool=True, data_func:DataFunc=standard_data, vocab:Vocab=None, **kwargs):\n",
    "    \"Creates a `DataBunch` from text files in folders.\"\n",
    "    path=Path(path)\n",
    "    txt_kwargs, kwargs = extract_kwargs(['max_vocab', 'chunksize', 'min_freq', 'n_labels'], kwargs)\n",
    "    train_ds = TextDataset.from_folder(path, tokenizer, train, shuffle=shuffle, vocab=vocab, **txt_kwargs)\n",
    "    datasets = [train_ds, TextDataset.from_folder(path, tokenizer, valid, classes=train_ds.classes, \n",
    "                                        shuffle=shuffle, vocab=train_ds.vocab, **txt_kwargs)]\n",
    "    if test: datasets.append(TextDataset.from_folder(path, tokenizer, test, classes=train_ds.classes, \n",
    "                                        shuffle=shuffle, vocab=train_ds.vocab, **txt_kwargs))\n",
    "    return data_func(datasets, path, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_from_textfolder(PATH/'tst_folders', tokenizer, chunksize=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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