machine learning spaCy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np # linear algebra\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import base64\n",
    "import string\n",
    "import re\n",
    "from collections import Counter\n",
    "from nltk.corpus import stopwords\n",
    "stopwords = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Title</th>\n",
       "      <th>Conference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Innovation in Database Management: Computer Sc...</td>\n",
       "      <td>VLDB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High performance prime field multiplication fo...</td>\n",
       "      <td>ISCAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>enchanted scissors: a scissor interface for su...</td>\n",
       "      <td>SIGGRAPH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Detection of channel degradation attack by Int...</td>\n",
       "      <td>INFOCOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pinning a Complex Network through the Betweenn...</td>\n",
       "      <td>ISCAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title Conference\n",
       "0  Innovation in Database Management: Computer Sc...       VLDB\n",
       "1  High performance prime field multiplication fo...      ISCAS\n",
       "2  enchanted scissors: a scissor interface for su...   SIGGRAPH\n",
       "3  Detection of channel degradation attack by Int...    INFOCOM\n",
       "4  Pinning a Complex Network through the Betweenn...      ISCAS"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('research_paper.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2507, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title         0\n",
       "Conference    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Conference'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(df, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Research title sample: Cooperating with Smartness: Using Heterogeneous Smart Antennas in Ad-Hoc Networks.\n",
      "Conference of this paper: INFOCOM\n",
      "Training Data Shape: (1679, 2)\n",
      "Testing Data Shape: (828, 2)\n"
     ]
    }
   ],
   "source": [
    "print('Research title sample:', train['Title'].iloc[0])\n",
    "print('Conference of this paper:', train['Conference'].iloc[0])\n",
    "print('Training Data Shape:', train.shape)\n",
    "print('Testing Data Shape:', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/AzisPT8M+NJI+6vb1fR7A7dPHMqXJEnTt1lfK05yKvDvga2TrALeBbwPOD3JEcBPgENa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\nkjRABrwkSQP0/wEhod3mBFN4kQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c726940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4))\n",
    "sns.barplot(x = train['Conference'].unique(), y=train['Conference'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "punctuations = string.punctuation\n",
    "\n",
    "# Define function to cleanup text by removing personal pronouns, stopwords, and puncuation\n",
    "def cleanup_text(docs, logging=False):\n",
    "    texts = []\n",
    "    counter = 1\n",
    "    for doc in docs:\n",
    "        if counter % 1000 == 0 and logging:\n",
    "            print(\"Processed %d out of %d documents.\" % (counter, len(docs)))\n",
    "        counter += 1\n",
    "        doc = nlp(doc, disable=['parser', 'ner'])\n",
    "        tokens = [tok.lemma_.lower().strip() for tok in doc if tok.lemma_ != '-PRON-']\n",
    "        tokens = [tok for tok in tokens if tok not in stopwords and tok not in punctuations]\n",
    "        tokens = ' '.join(tokens)\n",
    "        texts.append(tokens)\n",
    "    return pd.Series(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "INFO_text = [text for text in train[train['Conference'] == 'INFOCOM']['Title']]\n",
    "\n",
    "IS_text = [text for text in train[train['Conference'] == 'ISCAS']['Title']]\n",
    "\n",
    "INFO_clean = cleanup_text(INFO_text)\n",
    "INFO_clean = ' '.join(INFO_clean).split()\n",
    "\n",
    "IS_clean = cleanup_text(IS_text)\n",
    "IS_clean = ' '.join(IS_clean).split()\n",
    "\n",
    "INFO_counts = Counter(INFO_clean)\n",
    "IS_counts = Counter(IS_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ADmrFJEJr7ezW2qfG5+8kOSnJ3knuk+TwMdnhSe67XkECAAAAG2+r/jpDVe2b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\nOEkCau8kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14a537b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "INFO_common_words = [word[0] for word in INFO_counts.most_common(20)]\n",
    "INFO_common_counts = [word[1] for word in INFO_counts.most_common(20)]\n",
    "\n",
    "fig = plt.figure(figsize=(18,6))\n",
    "sns.barplot(x=INFO_common_words, y=INFO_common_counts)\n",
    "plt.title('Most Common Words used in the research papers for conference INFOCOM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f5P2j+4mZtk9bqFw/Sb+Qmpvf747veyd586grOyQ5Y4HpvSLJk9K3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25ba7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "IS_common_words = [word[0] for word in IS_counts.most_common(20)]\n",
    "IS_common_counts = [word[1] for word in IS_counts.most_common(20)]\n",
    "\n",
    "fig = plt.figure(figsize=(18,6))\n",
    "sns.barplot(x=IS_common_words, y=IS_common_counts)\n",
    "plt.title('Most Common Words used in the research papers for conference ISCAS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Title</th>\n",
       "      <th>Conference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>Architectural Issues in Distributed Data Base ...</td>\n",
       "      <td>VLDB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Compressive sampling of EMG bio-signals.</td>\n",
       "      <td>ISCAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>User-Centered Modeling of Interactive Web Sites.</td>\n",
       "      <td>WWW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>A Decomposition Method for Transmission Schedu...</td>\n",
       "      <td>INFOCOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>ASCENT: Adaptive Self-Configuring sEnsor Netwo...</td>\n",
       "      <td>INFOCOM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title Conference\n",
       "2121  Architectural Issues in Distributed Data Base ...       VLDB\n",
       "56             Compressive sampling of EMG bio-signals.      ISCAS\n",
       "2479   User-Centered Modeling of Interactive Web Sites.        WWW\n",
       "1292  A Decomposition Method for Transmission Schedu...    INFOCOM\n",
       "1599  ASCENT: Adaptive Self-Configuring sEnsor Netwo...    INFOCOM"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS\n",
    "from sklearn.metrics import accuracy_score\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "import re\n",
    "import spacy\n",
    "spacy.load('en')\n",
    "from spacy.lang.en import English\n",
    "parser = English()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "STOPLIST = set(stopwords.words('english') + list(ENGLISH_STOP_WORDS))\n",
    "SYMBOLS = \" \".join(string.punctuation).split(\" \") + [\"-\", \"...\", \"”\", \"”\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CleanTextTransformer(TransformerMixin):\n",
    "    def transform(self, X, **transform_params):\n",
    "        return [cleanText(text) for text in X]\n",
    "    def fit(self, X, y=None, **fit_params):\n",
    "        return self\n",
    "    def get_params(self, deep=True):\n",
    "        return {}\n",
    "    \n",
    "def cleanText(text):\n",
    "    text = text.strip().replace(\"\\n\", \" \").replace(\"\\r\", \" \")\n",
    "    text = text.lower()\n",
    "    return text\n",
    "\n",
    "def tokenizeText(sample):\n",
    "    tokens = parser(sample)\n",
    "    lemmas = []\n",
    "    for tok in tokens:\n",
    "        lemmas.append(tok.lemma_.lower().strip() if tok.lemma_ != \"-PRON-\" else tok.lower_)\n",
    "    tokens = lemmas\n",
    "    tokens = [tok for tok in tokens if tok not in STOPLIST]\n",
    "    tokens = [tok for tok in tokens if tok not in SYMBOLS]\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7463768115942029\n",
      "Top 10 features used to predict: \n",
      "Class 1 best: \n",
      "(-0.9286016613168848, 'database')\n",
      "(-0.8479546379060897, 'chip')\n",
      "(-0.7676015086504997, 'wimax')\n",
      "(-0.693351007368828, 'object')\n",
      "(-0.6728544568458809, 'functional')\n",
      "(-0.6625154615527803, 'multihop')\n",
      "(-0.6410221284923994, 'amplifier')\n",
      "(-0.6396379083504244, 'chaotic')\n",
      "(-0.6175858508425204, 'receiver')\n",
      "(-0.6016681457116699, 'web')\n",
      "Class 2 best: \n",
      "(1.1835969416744359, 'speccast')\n",
      "(1.0752019094716938, 'manets')\n",
      "(0.9490175663889364, 'gossip')\n",
      "(0.8468381854216707, 'node')\n",
      "(0.8433092579925067, 'packet')\n",
      "(0.8370511942836825, 'schedule')\n",
      "(0.8344155723237219, 'multicast')\n",
      "(0.8332219451078251, 'queue')\n",
      "(0.8255453091313409, 'qos')\n",
      "(0.8182440953133643, 'location')\n"
     ]
    }
   ],
   "source": [
    "def printNMostInformative(vectorizer, clf, N):\n",
    "    feature_names = vectorizer.get_feature_names()\n",
    "    coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))\n",
    "    topClass1 = coefs_with_fns[:N]\n",
    "    topClass2 = coefs_with_fns[:-(N + 1):-1]\n",
    "    print(\"Class 1 best: \")\n",
    "    for feat in topClass1:\n",
    "        print(feat)\n",
    "    print(\"Class 2 best: \")\n",
    "    for feat in topClass2:\n",
    "        print(feat)\n",
    "\n",
    "vectorizer = CountVectorizer(tokenizer=tokenizeText, ngram_range=(1,1))\n",
    "clf = LinearSVC()\n",
    "pipe = Pipeline([('cleanText', CleanTextTransformer()), ('vectorizer', vectorizer), ('clf', clf)])\n",
    "\n",
    "# data\n",
    "train1 = train['Title'].tolist()\n",
    "labelsTrain1 = train['Conference'].tolist()\n",
    "\n",
    "test1 = test['Title'].tolist()\n",
    "labelsTest1 = test['Conference'].tolist()\n",
    "# train\n",
    "pipe.fit(train1, labelsTrain1)\n",
    "\n",
    "# test\n",
    "preds = pipe.predict(test1)\n",
    "print(\"accuracy:\", accuracy_score(labelsTest1, preds))\n",
    "print(\"Top 10 features used to predict: \")\n",
    "\n",
    "printNMostInformative(vectorizer, clf, 10)\n",
    "\n",
    "pipe = Pipeline([('cleanText', CleanTextTransformer()), ('vectorizer', vectorizer)])\n",
    "transform = pipe.fit_transform(train1, labelsTrain1)\n",
    "vocab = vectorizer.get_feature_names()\n",
    "\n",
    "for i in range(len(train1)):\n",
    "    s = \"\"\n",
    "    indexIntoVocab = transform.indices[transform.indptr[i]:transform.indptr[i+1]]\n",
    "    numOccurences = transform.data[transform.indptr[i]:transform.indptr[i+1]]\n",
    "    for idx, num in zip(indexIntoVocab, numOccurences):\n",
    "        s += str((vocab[idx], num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "       VLDB       0.75      0.77      0.76       159\n",
      "      ISCAS       0.90      0.84      0.87       299\n",
      "   SIGGRAPH       0.67      0.66      0.66       106\n",
      "    INFOCOM       0.62      0.69      0.65       139\n",
      "        WWW       0.62      0.62      0.62       125\n",
      "\n",
      "avg / total       0.75      0.75      0.75       828\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(labelsTest1, preds, \n",
    "                                    target_names=df['Conference'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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