transfix-mt/rapidfuzztest.ipynb

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      "text/plain": "                                    source  \\\nsource_lem                                   \naaofi                                aaofi   \naca                                    aca   \nacca                                  acca   \nabacus                              abacus   \nabandonment cost         abandonment costs   \n...                                    ...   \nytd                                    ytd   \nyear-end                          year-end   \nyear-to-date                  year-to-date   \nzog                                    zog   \nzero overhead growth  zero overhead growth   \n\n                                                                 result  \nsource_lem                                                               \naaofi                 organizacja rachunkowości i audytu dla islamsk...  \naca                   członek stowarzyszenia dyplomowanych biegłych ...  \nacca                   stowarzyszenie dyplomowanych biegłych rewidentów  \nabacus                                                         liczydło  \nabandonment cost                                     koszty zaniechania  \n...                                                                 ...  \nytd                                                    od początku roku  \nyear-end                                                    koniec roku  \nyear-to-date                                           od początku roku  \nzog                                       zero wzrostu kosztów ogólnych  \nzero overhead growth                      zero wzrostu kosztów ogólnych  \n\n[1197 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source</th>\n      <th>result</th>\n    </tr>\n    <tr>\n      <th>source_lem</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>aaofi</th>\n      <td>aaofi</td>\n      <td>organizacja rachunkowości i audytu dla islamsk...</td>\n    </tr>\n    <tr>\n      <th>aca</th>\n      <td>aca</td>\n      <td>członek stowarzyszenia dyplomowanych biegłych ...</td>\n    </tr>\n    <tr>\n      <th>acca</th>\n      <td>acca</td>\n      <td>stowarzyszenie dyplomowanych biegłych rewidentów</td>\n    </tr>\n    <tr>\n      <th>abacus</th>\n      <td>abacus</td>\n      <td>liczydło</td>\n    </tr>\n    <tr>\n      <th>abandonment cost</th>\n      <td>abandonment costs</td>\n      <td>koszty zaniechania</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>ytd</th>\n      <td>ytd</td>\n      <td>od początku roku</td>\n    </tr>\n    <tr>\n      <th>year-end</th>\n      <td>year-end</td>\n      <td>koniec roku</td>\n    </tr>\n    <tr>\n      <th>year-to-date</th>\n      <td>year-to-date</td>\n      <td>od początku roku</td>\n    </tr>\n    <tr>\n      <th>zog</th>\n      <td>zog</td>\n      <td>zero wzrostu kosztów ogólnych</td>\n    </tr>\n    <tr>\n      <th>zero overhead growth</th>\n      <td>zero overhead growth</td>\n      <td>zero wzrostu kosztów ogólnych</td>\n    </tr>\n  </tbody>\n</table>\n<p>1197 rows × 2 columns</p>\n</div>"
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     "execution_count": 2,
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   "source": [
    "\n",
    "import nltk\n",
    "import pandas as pd\n",
    "import rapidfuzz\n",
    "import time\n",
    "\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from rapidfuzz.fuzz import  partial_ratio\n",
    "from rapidfuzz.utils import default_process\n",
    "\n",
    "nltk.download('wordnet')\n",
    "\n",
    "\n",
    "wl = WordNetLemmatizer()\n",
    "\n",
    "glossary = pd.read_csv('mt-summit-corpora/glossary.tsv', sep='\\t', header=None, names=['source', 'result'])\n",
    "\n",
    "source_lemmatized = []\n",
    "for word in glossary['source']:\n",
    "    word = nltk.word_tokenize(word)\n",
    "    source_lemmatized.append(' '.join([wl.lemmatize(x) for x in word]))\n",
    "\n",
    "glossary['source_lem'] = source_lemmatized\n",
    "glossary = glossary[['source', 'source_lem', 'result']]\n",
    "glossary.set_index('source_lem')\n",
    "\n"
   ],
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   "execution_count": 12,
   "outputs": [
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     "name": "stdout",
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     "text": [
      "0.187306436\n"
     ]
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   ],
   "source": [
    "# train_in_path = 'mt-summit-corpora/train/in.tsv'\n",
    "# train_expected_path = 'mt-summit-corpora/train/expected.tsv'\n",
    "\n",
    "train_in_path = 'mt-summit-corpora/dev-0/in.tsv'\n",
    "train_expected_path = 'mt-summit-corpora/dev-0/expected.tsv'\n",
    "\n",
    "\n",
    "start_time = time.time_ns()\n",
    "file_lemmatized = []\n",
    "with open(train_in_path, 'r') as file:\n",
    "    for line in file:\n",
    "        if len(file_lemmatized) % 50000 == 0:\n",
    "            print(len(file_lemmatized), end='\\r')\n",
    "        line = nltk.word_tokenize(line)\n",
    "        file_lemmatized.append(' '.join([wl.lemmatize(x) for x in line]))\n",
    "\n",
    "stop = time.time_ns()\n",
    "timex = (stop - start_time) / 1000000000\n",
    "print(timex)\n"
   ],
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    }
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  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.592824061\n"
     ]
    }
   ],
   "source": [
    "\n",
    "THRESHOLD = 70\n",
    "\n",
    "\n",
    "def is_injectable(sentence_pl, sequence):\n",
    "    sen = sentence_pl.split()\n",
    "    window_size = len(sequence.split())\n",
    "    maxx = 0\n",
    "    for i in range(len(sen) - window_size + 1):\n",
    "        current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)\n",
    "        if current > maxx:\n",
    "            maxx = current\n",
    "    return maxx >= THRESHOLD\n",
    "\n",
    "def get_injected(sentence, sentence_en, sequence, inject):\n",
    "    sen = sentence.split()\n",
    "    sen_en = sentence_en.split()\n",
    "    window_size = len(sequence.split())\n",
    "    maxx = 0\n",
    "    maxx_prv = 0\n",
    "    maxxi = 0\n",
    "    for i in range(len(sen) - window_size + 1):\n",
    "        current = rapidfuzz.fuzz.ratio(' '.join(sen[i:i + window_size]), sequence)\n",
    "        if current >= maxx:\n",
    "            maxx_prv = maxx\n",
    "            maxx = current\n",
    "            maxxi = i\n",
    "    if maxx_prv != maxx:\n",
    "        return ' '.join(sen_en[:maxxi + window_size]) + ' $' + inject + '$ ' + ' '.join(sen_en[maxxi + window_size:])\n",
    "    return sentence_en\n",
    "\n",
    "glossary['source_lem'] = [str(default_process(x)) for x in glossary['source_lem']]\n",
    "file_pl = pd.read_csv(train_expected_path, sep='\\t', header=None, names=['text'])\n",
    "file_pl['text'] = [default_process(text) for text in file_pl['text'].values.tolist()]\n",
    "file_en= pd.read_csv(train_in_path, sep='\\t', header=None, names=['text'])\n",
    "file_en['text'] = [default_process(text) for text in file_en['text'].values.tolist()]\n",
    "\n",
    "start_time = time.time_ns()\n",
    "en = []\n",
    "translation_line_counts = []\n",
    "for line, line_en, line_pl in zip(file_lemmatized, file_en['text'].values.tolist(), file_pl['text'].values.tolist()):\n",
    "    line = default_process(line)\n",
    "    matchez = rapidfuzz.process.extract(query=line, choices=glossary['source_lem'], limit=5, score_cutoff=THRESHOLD, scorer=partial_ratio)\n",
    "    if len(matchez) > 0:\n",
    "        lines_added = 0\n",
    "        for match in matchez:\n",
    "            polish_translation = glossary.loc[lambda df: df['source_lem'] == match[0]]['result'].astype(str).values.flatten()[0]\n",
    "            if is_injectable(line_pl, polish_translation):\n",
    "                en.append(get_injected(line, line_en, match[0], polish_translation))\n",
    "                lines_added += 1\n",
    "        if lines_added == 0:\n",
    "            en.append(line_en)\n",
    "            lines_added = 1\n",
    "        translation_line_counts.append(lines_added)\n",
    "    else:\n",
    "        translation_line_counts.append(1)\n",
    "        en.append(line_en)\n",
    "\n",
    "\n",
    "stop = time.time_ns()\n",
    "timex = (stop - start_time) / 1000000000\n",
    "print(timex)\n"
   ],
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  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "\n",
    "def full_strip(line):\n",
    "    return ' '.join(line.split())\n",
    "\n",
    "with open(train_expected_path + '.injected', 'w') as file_pl_write:\n",
    "    for line, translation_line_ct in zip(file_pl['text'].values.tolist(), translation_line_counts):\n",
    "        for i in range(translation_line_ct):\n",
    "            file_pl_write.write(full_strip(line) + '\\n')\n",
    "\n",
    "\n",
    "with open(train_in_path + '.injected', 'w') as file_en_write:\n",
    "    for e in en:\n",
    "        file_en_write.write(e + '\\n')"
   ],
   "metadata": {
    "collapsed": false,
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     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
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