data-mining-part-b-preprocess-data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import nltk\n",
    "import numpy\n",
    "import pandas\n",
    "import sklearn.feature_extraction.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_dataframe = pandas.read_csv('onion-or-not.csv', encoding='utf-8', nrows=500)\n",
    "input_dataframe = pandas.read_csv('onion-or-not.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_vector = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in input_dataframe.index:\n",
    "    tokenized_vector[i] = nltk.word_tokenize(input_dataframe.loc[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = nltk.PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = set(nltk.corpus.stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tokens in tokenized_vector:\n",
    "    for counter, token in enumerate(tokenized_vector[tokens]):\n",
    "        if stemmer.stem(tokenized_vector[tokens][counter]) not in stopwords:\n",
    "            tokenized_vector[tokens][counter] = stemmer.stem(tokenized_vector[tokens][counter])\n",
    "        else:\n",
    "            tokenized_vector[tokens].remove(tokenized_vector[tokens][counter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = sklearn.feature_extraction.text.TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessed_tokenized_vector = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in tokenized_vector:\n",
    "    preprocessed_tokenized_vector.append(' '.join(tokenized_vector[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vectorizer.fit_transform(preprocessed_tokenized_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_df = pandas.DataFrame(X.todense(), columns=vectorizer.get_feature_names(), dtype=numpy.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessed_data = pandas.concat([tf_idf_df, input_dataframe.iloc[:, 1:]], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size of dataframe:  1012.99 MB\n"
     ]
    }
   ],
   "source": [
    "print('Total size of dataframe: ', round(sys.getsizeof(preprocessed_data) / 2**20, 2), 'MB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessed_data.to_csv('preprocessed_onion_data.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "        00  000  000d6  000ft  000th  001  007  030   05   06  ...  이벤트로   있는  \\\n",
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       "\n",
       "       저렴하게   전국  제이제이닷컴  집근처  최대업소수로  한눈에  휴게텔  label  \n",
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       "\n",
       "[24000 rows x 22126 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas.DataFrame(preprocessed_data.columns[:-1].tolist(), columns=['tokens']).to_csv('cols.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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