LR_citation_recognition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import numpy as np\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1]T., V. and Quinney, D. 1987. An Introductio...</td>\n",
       "      <td>acm-sig-proceedings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1]Pope, S.T. et al. 1995. Touched by Machine?...</td>\n",
       "      <td>acm-sig-proceedings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1]Sugihara, M. and Murota, K. 1982. A Note on...</td>\n",
       "      <td>acm-sig-proceedings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1]Franklin, J.N. et al. 1965. Monte Carlo Met...</td>\n",
       "      <td>acm-sig-proceedings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1]Kitchen, J. and Denbaly, M. 1987. Commodity...</td>\n",
       "      <td>acm-sig-proceedings</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>50098</th>\n",
       "      <td>Freedman, Marcia, and James G. Scoville. “The ...</td>\n",
       "      <td>mla</td>\n",
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       "    <tr>\n",
       "      <th>50099</th>\n",
       "      <td>Smoliar, Stephen W., and Deryck Cooke. “The La...</td>\n",
       "      <td>mla</td>\n",
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       "    <tr>\n",
       "      <th>50100</th>\n",
       "      <td>L., Y. L., and F. M. Arscott. “Periodic Differ...</td>\n",
       "      <td>mla</td>\n",
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       "    <tr>\n",
       "      <th>50101</th>\n",
       "      <td>Katz, Lawrence F. “Some Recent Developments in...</td>\n",
       "      <td>mla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50102</th>\n",
       "      <td>Lee, Cheng F. “Abstract--Investment Horizon an...</td>\n",
       "      <td>mla</td>\n",
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      "text/plain": [
       "                                                    text                label\n",
       "0      [1]T., V. and Quinney, D. 1987. An Introductio...  acm-sig-proceedings\n",
       "1      [1]Pope, S.T. et al. 1995. Touched by Machine?...  acm-sig-proceedings\n",
       "2      [1]Sugihara, M. and Murota, K. 1982. A Note on...  acm-sig-proceedings\n",
       "3      [1]Franklin, J.N. et al. 1965. Monte Carlo Met...  acm-sig-proceedings\n",
       "4      [1]Kitchen, J. and Denbaly, M. 1987. Commodity...  acm-sig-proceedings\n",
       "...                                                  ...                  ...\n",
       "50098  Freedman, Marcia, and James G. Scoville. “The ...                  mla\n",
       "50099  Smoliar, Stephen W., and Deryck Cooke. “The La...                  mla\n",
       "50100  L., Y. L., and F. M. Arscott. “Periodic Differ...                  mla\n",
       "50101  Katz, Lawrence F. “Some Recent Developments in...                  mla\n",
       "50102  Lee, Cheng F. “Abstract--Investment Horizon an...                  mla\n",
       "\n",
       "[50103 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "path = 'dataset\\\\cstyle_dataset\\\\'\n",
    "all_files = glob.glob(os.path.join(path, \"*.txt\")) \n",
    "\n",
    "frames = []\n",
    "for f in all_files:\n",
    "    file_df = pd.read_csv(f, header=None, index_col=None, sep='\\n', names=['text'], engine='python')\n",
    "    labels = [f.replace(path+'jstor_', '').replace('.txt', '') for x in range(len(file_df))]\n",
    "    file_df['label'] = labels\n",
    "    frames.append(file_df)\n",
    "    \n",
    "df   = pd.concat(frames, axis=0, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['acm-sig-proceedings', 'american-chemical-society', 'apa',\n",
       "       'chicago-author-date', 'harvard3', 'ieee', 'mla'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Reduce word tokens to just the type of words/punctuation they are'''\n",
    "# Add more granularity to words and numbers -- months and years\n",
    "# separate short form and full spelling of months for possible differences in citation format\n",
    "\n",
    "\n",
    "def token_transformer(sentence, add_noise=False):\n",
    "    sentence = re.sub(r\"\\b[A-Z]\\b\", 'L', sentence) #uppercase letters\n",
    "    sentence = re.sub(r\"\\b[a-z]\\b\", 'l', sentence) #lowercase letters\n",
    "    if add_noise:\n",
    "        sentence = re.sub(r'\\b(January|February|March|April|May|June|July|August|September|October|November|December)\\b', random.choices(['M',''], weights=[3,1])[0], sentence)\n",
    "        sentence = re.sub(r'\\b(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\\b', random.choices(['m',''], weights=[3,1])[0], sentence)\n",
    "#         x = random.choice(['.', '\\\"', ',', '\\'', '(', ')', '[', ']'])\n",
    "#         print(x)\n",
    "#         sentence = re.sub(r\"[\"+\"{}]\".format(re.escape(x)), random.choices([x, ''], weights=[1,1])[0], sentence)\n",
    "    else:\n",
    "        sentence = re.sub(r'\\b(January|February|March|April|May|June|July|August|September|October|November|December)\\b', 'M', sentence) #random.choices(['M',''], weights=[3,1])[0], sentence)\n",
    "        sentence = re.sub(r'\\b(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\\b', 'm', sentence) #random.choices(['m',''], weights=[3,1])[0], sentence)\n",
    "    sentence = re.sub(r\"\\b[a-z][a-z]+\\b\", 'w', sentence) #lowercase words\n",
    "    sentence = re.sub(r\"\\b[A-Z][a-z]+\\b\", 'W', sentence) #uppercase words\n",
    "    sentence = re.sub(r'\\b(1\\d{3}|20[012]\\d)\\b', 'y', sentence)\n",
    "    sentence = re.sub(r\"\\b[0-9]+\\b\", 'n', sentence) #numbers\n",
    "    return sentence\n",
    "\n",
    "def token_punctuation(sentence, add_noise=False):\n",
    "    if add_noise:\n",
    "        words = sentence.split()\n",
    "        newSentence = []\n",
    "        for word in words: # for every word with punctuation, there is a 10% chance of omitting one type of punctuation.\n",
    "            newSentence.append(word.translate({ord(i):\" {} \".format(random.choices([i, ''], weights=[9,1])[0]) for i in ',.()[]:;\\'\\\"-'}))\n",
    "        sentence = \" \".join(newSentence)\n",
    "        return sentence\n",
    "    \n",
    "    else:\n",
    "        return sentence.translate({ord(i):\" {} \".format(i) for i in ',.()[]:;\\'\\\"-'})\n",
    "    \n",
    "\n",
    "''' Preprocess text column -- separate symbols with spaces to preserve punctuation as tokens'''\n",
    "def preprocess(df, add_noise=False): # text is a dataframe column\n",
    "    df.text = df.text.apply(token_punctuation, add_noise)\n",
    "    df.text = df.text.apply(token_transformer, add_noise)\n",
    "    return df\n",
    "df = preprocess(df, True) # boolean flag to add noise. For training purposes to be robust against noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]Pope, S.T. et al. 1995. Touched by Machine?: Composition and Performance in the Digital Age. Computer Music Journal. 19, 3 (1995), 13.\n",
      " [ 1 ] Pope ,  S . T .  et al .  1995 .  Touched by Machine? :  Composition and Performance in the Digital Age .  Computer Music Journal .  19 ,  3  ( 1995 )  ,  13 . \n",
      " [ n ] W ,  L . L .  w w .  y .  W w W? :  W w W w w W W .  W W W .  n ,  n  ( y )  ,  n . \n",
      "(1) Pope, S. T.; Rahn, J.; Cerana, C.; Katayose, H.; Pecquet, F.; Karpen, R. Computer Music Journal 1995, 19, 13.\n",
      " ( 1 )  Pope ,  S .  T .  ;  Rahn ,  J .  ;  Cerana ,  C .  ;  Katayose ,  H .  ;  Pecquet   F .    Karpen ,  R .  Computer Music Journal 1995 ,  19 ,  13 . \n",
      " ( n )  W ,  L .  L .  ;  W ,  L .  ;  W ,  L .  ;  W ,  L .  ;  W   L .    W ,  L .  W W W y ,  n ,  n . \n",
      "Pope, S. T., Rahn, J., Cerana, C., Katayose, H., Pecquet, F., & Karpen, R. (1995). Touched by Machine?: Composition and Performance in the Digital Age. Computer Music Journal, 19(3), 13. doi:10.2307/3680650\n",
      "Pope ,  S .  T .  ,  Rahn ,  J .    Cerana ,  C .  ,  Katayose ,  H .  ,  Pecquet ,  F .  ,  & Karpen ,  R .   ( 1995 )  .  Touched by Machine? :  Composition and Performance in the Digital Age .  Computer Music Journal ,  19 ( 3 )  ,  13 .  doi : 10 . 2307/3680650\n",
      "W ,  L .  L .  ,  W ,  L .    W ,  L .  ,  W ,  L .  ,  W ,  L .  ,  & W ,  L .   ( y )  .  W w W? :  W w W w w W W .  W W W ,  n ( n )  ,  n .  w : n . n/n\n",
      "Pope, Stephen Travis, John Rahn, Carlos Cerana, Haruhiro Katayose, Frank Pecquet, and Richard Karpen. 1995. “Touched by Machine?: Composition and Performance in the Digital Age.” Computer Music Journal 19 (3): 13. doi:10.2307/3680650. http://dx.doi.org/10.2307/3680650.\n",
      "Pope ,  Stephen Travis ,  John Rahn ,  Carlos Cerana ,  Haruhiro Katayose   Frank Pecquet   and Richard Karpen .  1995 .  “Touched by Machine? :  Composition and Performance in the Digital Age . ” Computer Music Journal 19  ( 3 )  :  13 .  doi : 10 . 2307/3680650 .  http : //dx . doi . org/10 . 2307/3680650 . \n",
      "W ,  W W ,  W W ,  W W ,  W W   W W   w W W .  y .  “W w W? :  W w W w w W W . ” W W W n  ( n )  :  n .  w : n . n/n .  w : //w . w . w/n . n/n . \n",
      "Pope, S.T. et al., 1995. Touched by Machine?: Composition and Performance in the Digital Age. Computer Music Journal, 19(3), p.13. Available at: http://dx.doi.org/10.2307/3680650.\n",
      "Pope ,  S . T .  et al .  ,  1995 .  Touched by Machine? :  Composition and Performance in the Digital Age   Computer Music Journal ,  19 ( 3 )  ,  p . 13 .  Available at :  http  //dx . doi . org/10 . 2307/3680650 . \n",
      "W ,  L . L .  w w .  ,  y .  W w W? :  W w W w w W W   W W W ,  n ( n )  ,  l . n .  W w :  w  //w . w . w/n . n/n . \n",
      "[1]S. T. Pope, J. Rahn, C. Cerana, H. Katayose, F. Pecquet, and R. Karpen, “Touched by Machine?: Composition and Performance in the Digital Age,” Computer Music Journal, vol. 19, no. 3, p. 13, 1995.\n",
      " [ 1 ] S .  T .  Pope ,  J .  Rahn   C   Cerana   H .  Katayose ,  F .  Pecquet ,  and R .  Karpen ,  “Touched by Machine? :  Composition and Performance in the Digital Age , ” Computer Music Journal ,  vol .  19 ,  no   3 ,  p .  13 ,  1995  \n",
      " [ n ] L .  L .  W ,  L .  W   L   W   L .  W ,  L .  W ,  w L .  W ,  “W w W? :  W w W w w W W , ” W W W ,  w .  n ,  w   n ,  l .  n ,  y  \n",
      "Pope, Stephen Travis et al. “Touched by Machine?: Composition and Performance in the Digital Age.” Computer Music Journal 19.3 (1995): 13. Crossref. Web.\n",
      "Pope   Stephen Travis et al .  “Touched by Machine? :  Composition and Performance in the Digital Age . ” Computer Music Journal 19 . 3  ( 1995 )  :  13 .  Crossref .  Web . \n",
      "W   W W w w .  “W w W? :  W w W w w W W . ” W W W n . n  ( y )  :  n .  W .  W . \n"
     ]
    }
   ],
   "source": [
    "'''Testing the noise adding function. random punctuations and months are omitted'''\n",
    "for frame in frames:\n",
    "    mutator = token_punctuation(frame.iloc[1][0], True)\n",
    "    print(frame.iloc[1][0])\n",
    "    print(mutator)\n",
    "    print(token_transformer(mutator))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Create dataset with \"unknown\" label by mixing up the tokens'''\n",
    "def token_scrambler(sentence):\n",
    "    ss = sentence.split()\n",
    "    sentence = ' '.join(random.sample(ss, len(ss)))\n",
    "    return sentence\n",
    "\n",
    "'''Returns original dataframe with the new unknown data'''\n",
    "def create_unknown(df):\n",
    "    df_unknown = df.sample(frac=0.25)\n",
    "    df_unknown.label = 'unknown'\n",
    "    df_unknown.text = df_unknown.text.apply(token_scrambler)\n",
    "    frames = [df, df_unknown]\n",
    "    df_extended = pd.concat(frames, axis=0, ignore_index=True)\n",
    "    return df_extended\n",
    "\n",
    "    \n",
    "df_extended = create_unknown(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>[ n ] L .  ,  L .  w W ,  L .  y .  W W w w W...</td>\n",
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       "      <th>62625</th>\n",
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       "                                                    text                label\n",
       "0       [ n ] L .  ,  L .  w W ,  L .  y .  W W w w W...  acm-sig-proceedings\n",
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       "4       [ n ] W ,  L .  w W ,  L .  y .  W W ,  W W ,...  acm-sig-proceedings\n",
       "...                                                  ...                  ...\n",
       "62624  , w . . . y W W . W ) . y [ m L W W ] n , W n ...              unknown\n",
       "62625  + , w w , . //w n l l : W w . n L . ∞ W w - , ...              unknown\n",
       "62626  w w W n/n w W . ) . ( . L W W , W w W : , . n ...              unknown\n",
       "62627  , n , w ; y . W W W L L , , . ) ( . n W W W n w W              unknown\n",
       "62628  n . w “W w ] w W , w , w n n w w . . w . w w n...              unknown\n",
       "\n",
       "[62629 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_extended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Create training and test dataset'''\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.text, df.label, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mla                          5513\n",
      "chicago-author-date          5415\n",
      "american-chemical-society    5400\n",
      "ieee                         5361\n",
      "apa                          5342\n",
      "acm-sig-proceedings          5296\n",
      "harvard3                     5250\n",
      "Name: label, dtype: int64\n",
      "harvard3                     1842\n",
      "acm-sig-proceedings          1831\n",
      "mla                          1819\n",
      "apa                          1798\n",
      "ieee                         1771\n",
      "american-chemical-society    1740\n",
      "chicago-author-date          1725\n",
      "Name: label, dtype: int64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(y_train.value_counts())\n",
    "print(y_test.value_counts())\n",
    "print(type(y_test.value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' Will try a few approaches to categorising citation styles.\n",
    "    First will be a naive one -- take strings, get tfidf vectors, then feed into LR.\n",
    "    Second will like the one in crossref -- just use the orthographic features.\n",
    "    Open bracket, close bracket, open sqbracket, close sqbracket,\n",
    "    number, capitalised word, word, uppercase letter, \n",
    "    fullstop/dot, comma, colon, semicolon, quotation marks'''\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import f1_score, plot_confusion_matrix, confusion_matrix as cm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('features', TfidfVectorizer(lowercase=False, token_pattern=r\"\\S+\")),\n",
    "    ('scaler', StandardScaler(with_mean=False)),\n",
    "    ('classifier', LogisticRegression(class_weight='balanced'))\n",
    "])\n",
    "params = {'features__ngram_range': [(1,3)],\n",
    "    'classifier__C': [0.001],\n",
    "         'classifier__max_iter': [500],}\n",
    "gs = GridSearchCV(pipeline, params, refit=True, cv=2, scoring='f1_macro', verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 1 candidates, totalling 2 fits\n",
      "[CV 1/2; 1/1] START classifier__C=0.001, classifier__max_iter=500, features__ngram_range=(1, 3)\n",
      "[CV 1/2; 1/1] END classifier__C=0.001, classifier__max_iter=500, features__ngram_range=(1, 3); total time=  10.2s\n",
      "[CV 2/2; 1/1] START classifier__C=0.001, classifier__max_iter=500, features__ngram_range=(1, 3)\n",
      "[CV 2/2; 1/1] END classifier__C=0.001, classifier__max_iter=500, features__ngram_range=(1, 3); total time=   6.5s\n",
      "Test F1 score: 0.9963621014128475\n",
      "[[1822    0    8    0    0    0    1]\n",
      " [   3 1737    0    0    0    0    0]\n",
      " [   5    0 1790    0    2    0    1]\n",
      " [   0    0    0 1720    0    0    5]\n",
      " [   5    0    5    0 1831    0    1]\n",
      " [   3    0    0    0    0 1767    1]\n",
      " [   1    0    0    5    0    0 1813]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "gs.fit(X_train, y_train)\n",
    "y_pred = gs.predict(X_test)\n",
    "score = f1_score(y_test, y_pred, average='macro')\n",
    "cMtx = cm(y_test, y_pred)\n",
    "print('Test F1 score: {}'.format(score))\n",
    "print(cMtx)\n",
    "plot_confusion_matrix(gs, X_test, y_test, normalize='true', cmap='Blues')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.get_params().keys() # For parameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models\\\\cstyle_LR_augmentedfeatures_3grams_noisyinput_noUnknown.pkl']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' load the saved model '''\n",
    "from joblib import dump\n",
    "dump(gs.best_estimator_, 'models\\\\cstyle_LR_augmentedfeatures_3grams_noisyinput_noUnknown.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Test with unseen texts from another subject matter'''\n",
    "\n",
    "fpath = 'dataset\\\\jstor\\\\'\n",
    "all_files = glob.glob(os.path.join(fpath, \"unseen1000jstor_*.txt\")) \n",
    "frames = []\n",
    "for f in all_files:\n",
    "    file_df = pd.read_csv(f, header=None, index_col=None, sep='\\n', names=['text'], engine='python')\n",
    "    labels = [f.replace(fpath+'unseen1000jstor_', '').replace('.txt', '') for x in range(len(file_df))]\n",
    "    file_df['label'] = labels\n",
    "    frames.append(file_df)\n",
    "\n",
    "testdf   = pd.concat(frames, axis=0, ignore_index=True)\n",
    "testdf = preprocess(testdf, True) # adding noise to test case. For actual use, might consider setting to false.\n",
    "testdf_extended = create_unknown(testdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test F1 score: 0.9979533815235956\n",
      "[[895   0   0   0   0   0  12]\n",
      " [  0 907   0   0   0   0   0]\n",
      " [  0   0 906   0   0   0   1]\n",
      " [  0   0   0 907   0   0   0]\n",
      " [  0   0   0   0 907   0   0]\n",
      " [  0   0   0   0   0 907   0]\n",
      " [  0   0   0   0   0   0 907]]\n"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from joblib import load\n",
    "\n",
    "# gs = load('models\\\\cstyle_LR_punctuations.pkl')\n",
    "y_unseen = gs.predict(testdf.text)\n",
    "score = f1_score(testdf.label, y_unseen, average='macro')\n",
    "cMtx = cm(testdf.label, y_unseen)\n",
    "print('Test F1 score: {}'.format(score))\n",
    "print(cMtx)\n",
    "plot_confusion_matrix(gs, testdf.text, testdf.label, normalize='true', cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "enc = LabelEncoder().fit(df_extended.label)\n",
    "y_transformed = enc.transform(df_extended.label)\n",
    "df_extended['labels_cat'] = y_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 22,
     "metadata": {},
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    }
   ],
   "source": [
    "df_extended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(handle_unknown='ignore')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# c_style = df_extended.label.unique()\n",
    "predictedlabels = gs.predict(testdf.text)\n",
    "pred = list(map(lambda x:[x], predictedlabels))\n",
    "categories = [['acm-sig-proceedings'], ['american-chemical-society'], ['apa'],\n",
    "       ['chicago-author-date'], ['harvard3'], ['ieee'], ['mla']]\n",
    "\n",
    "feature_style = OneHotEncoder(handle_unknown='ignore')\n",
    "feature_style.fit(pred)\n",
    "# feature_style = enc.transform(y_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pred = pd.DataFrame(predictedlabels)\n",
    "feat = df_pred.apply(lambda x: feature_style.transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat = feature_style.transform(pred)"
   ]
  }
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