challenging-america-word-gap-prediction-kenlm/.ipynb_checkpoints/run-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f73a28ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "KENLM_BUILD_PATH='/home/students/s434708/kenlm/build'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc5cda3",
   "metadata": {},
   "source": [
    "### Preprocessing danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d42ddd87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import regex as re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f84be210",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    text = text.lower().replace('-\\\\n', '').replace('\\\\n', ' ')\n",
    "    text = re.sub(r'\\p{P}', '', text)\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de0c12d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
    "train_labels = pd.read_csv('train/expected.tsv', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n",
    "\n",
    "train_data = train_data[[6, 7]]\n",
    "train_data = pd.concat([train_data, train_labels], axis=1)\n",
    "\n",
    "train_data['text'] = train_data[6] + train_data[0] + train_data[7]\n",
    "train_data = train_data[['text']]\n",
    "\n",
    "with open('processed_train.txt', 'w') as file:\n",
    "    for _, row in train_data.iterrows():\n",
    "        text = clean_text(str(row['text']))\n",
    "        file.write(text + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846b6b42",
   "metadata": {},
   "source": [
    "### Model kenLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c74d4be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 1/5 Counting and sorting n-grams ===\n",
      "Reading /home/students/s434708/Desktop/Modelowanie Języka/challenging-america-word-gap-prediction-kenlm/processed_train.txt\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "********************************Warning: <s> appears in the input.  All instances of <s>, </s>, and <unk> will be interpreted as whitespace.\n",
      "********************************************************************\n",
      "Unigram tokens 135911223 types 4381594\n",
      "=== 2/5 Calculating and sorting adjusted counts ===\n",
      "Chain sizes: 1:52579128 2:1295655936 3:2429355008 4:3886967808 5:5668495360\n",
      "Statistics:\n",
      "1 4381594 D1=0.841838 D2=1.01787 D3+=1.21057\n",
      "2 26800631 D1=0.836734 D2=1.01657 D3+=1.19437\n",
      "3 69811700 D1=0.878562 D2=1.11227 D3+=1.27889\n",
      "4 104063034 D1=0.931257 D2=1.23707 D3+=1.36664\n",
      "5 119487533 D1=0.938146 D2=1.3058 D3+=1.41614\n",
      "Memory estimate for binary LM:\n",
      "type      MB\n",
      "probing 6752 assuming -p 1.5\n",
      "probing 7917 assuming -r models -p 1.5\n",
      "trie    3572 without quantization\n",
      "trie    2120 assuming -q 8 -b 8 quantization \n",
      "trie    3104 assuming -a 22 array pointer compression\n",
      "trie    1652 assuming -a 22 -q 8 -b 8 array pointer compression and quantization\n",
      "=== 3/5 Calculating and sorting initial probabilities ===\n",
      "Chain sizes: 1:52579128 2:428810096 3:1396234000 4:2497512816 5:3345650924\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "####################################################################################################\n",
      "=== 4/5 Calculating and writing order-interpolated probabilities ===\n",
      "Chain sizes: 1:52579128 2:428810096 3:1396234000 4:2497512816 5:3345650924\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "####################################################################################################\n",
      "=== 5/5 Writing ARPA model ===\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "----------------------------------------------------------------------------------------------------Last input should have been poison.  The program should end soon with an error.  If it doesn't, there's a bug.\n",
      "terminate called after throwing an instance of 'util::FDException'\n",
      "  what():  /home/students/s434708/kenlm/util/file.cc:228 in void util::WriteOrThrow(int, const void*, std::size_t) threw FDException because `ret < 1'.\n",
      "No space left on device in /home/students/s434708/Desktop/Modelowanie Języka/challenging-america-word-gap-prediction-kenlm/model.arpa while writing 8189 bytes\n",
      "/bin/bash: line 1: 26725 Aborted                 /home/students/s434708/kenlm/build/bin/lmplz -o 5 --skip_symbols < processed_train.txt > model.arpa\n"
     ]
    }
   ],
   "source": [
    "!$KENLM_BUILD_PATH/bin/lmplz -o 5 --skip_symbols < processed_train.txt > model.arpa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc65780b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading model.arpa\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "****************************************************************************************************\n",
      "/home/students/s434708/kenlm/util/file.cc:86 in int util::CreateOrThrow(const char*) threw ErrnoException because `-1 == (ret = open(name, 0100 | 01000 | 02, 0400 | 0200 | (0400 >> 3) | ((0400 >> 3) >> 3)))'.\n",
      "No space left on device while creating model.binary Byte: 94\n",
      "ERROR\n"
     ]
    }
   ],
   "source": [
    "!$KENLM_BUILD_PATH/bin/build_binary model.arpa model.binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2087eb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm processed_train.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ba1e592",
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm model.arpa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41f7951",
   "metadata": {},
   "source": [
    "### Predykcje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6865301b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import kenlm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e32de662",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str = 'really good'\n",
    "\n",
    "model = kenlm.Model('model.binary')\n",
    "print(model.score(test_str, bos = True, eos = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a18b6ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in model.full_scores(test_str):\n",
    "    print(i)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}