Gensim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Handelsblatt reported, earlier this week that Deutsche Telekom is having Board level discussions regarding an all-stock deal to merge T-Mobile with Sprint.\n",
      "\n"
     ]
    },
    {
     "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>Labels</th>\n",
       "      <th>Sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Handelsblatt reported, earlier this week that ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Handelsblatt a German publication reported, ea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>The WSJ reported, the ELM will be located in t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>The deal is expected to close by early 2Q17 an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>We believe, however, that before TMUS can shut...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Labels                                           Sentence\n",
       "0       0  Handelsblatt reported, earlier this week that ...\n",
       "1       0  Handelsblatt a German publication reported, ea...\n",
       "2       0  The WSJ reported, the ELM will be located in t...\n",
       "3       0  The deal is expected to close by early 2Q17 an...\n",
       "4       0  We believe, however, that before TMUS can shut..."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=open('C:\\\\Users\\jay.timbadia\\Downloads\\Multiutility_lrclassifier.txt', encoding='utf-8', errors='ignore')\n",
    "import pandas as pd\n",
    "import re\n",
    "p = c.readlines()\n",
    "# print(p)\n",
    "y = p[0].split('LRRetain ')\n",
    "print(y[1])\n",
    "# t = re.findall(r'(LRRetain|LRReview)', p)\n",
    "dataf = pd.DataFrame()\n",
    "for i in p:\n",
    "    t = re.findall(r'(LRRetain|LRReview)', i)\n",
    "    if t[0] == 'LRReview':\n",
    "        o = i.split('LRReview ')\n",
    "        sentence = o[1]\n",
    "        label = 'LRReview'\n",
    "        dataf = dataf.append({'Sentence': sentence, 'Labels': label}, ignore_index=True)\n",
    "        \n",
    "    elif t[0] == 'LRRetain':\n",
    "        o = i.split('LRRetain ')\n",
    "        sentence = o[1]\n",
    "        label = 'LRRetain'\n",
    "        dataf = dataf.append({'Sentence': sentence, 'Labels': label}, ignore_index=True)\n",
    "        \n",
    "dataf['Labels'] = dataf['Labels'].map({'LRReview':1, 'LRRetain':0})\n",
    "\n",
    "lab = [1, 0]\n",
    "dataf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Handelsblatt reported, earlier this week that Deutsche Telekom is having Board level discussions regarding an all-stock deal to merge T-Mobile with Sprint.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(dataf['Sentence'][0])\n",
    "pos = []\n",
    "for index, row in dataf.iterrows():\n",
    "    r = row['Sentence']\n",
    "    pos.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Before', 'IN'),\n",
       " ('TMUS', 'NNP'),\n",
       " ('can', 'MD'),\n",
       " ('shut', 'VB'),\n",
       " ('down', 'RP'),\n",
       " ('the', 'DT'),\n",
       " ('Sprint', 'NN'),\n",
       " ('network', 'NN'),\n",
       " ('it', 'PRP'),\n",
       " ('will', 'MD'),\n",
       " ('have', 'VB'),\n",
       " ('to', 'TO'),\n",
       " ('augment', 'VB'),\n",
       " ('its', 'PRP$'),\n",
       " ('own', 'JJ'),\n",
       " ('network', 'NN'),\n",
       " ('to', 'TO'),\n",
       " ('accommodate', 'VB'),\n",
       " ('Sprint', 'NNP'),\n",
       " ('’', 'NNP'),\n",
       " ('s', 'NN'),\n",
       " ('customers', 'NNS'),\n",
       " ('over', 'IN'),\n",
       " ('a', 'DT'),\n",
       " ('multi-year', 'JJ'),\n",
       " ('timeframe', 'NN'),\n",
       " ('.', '.')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parts = []\n",
    "for i in pos:\n",
    "    t= nltk.pos_tag(word_tokenize(i))\n",
    "    parts.append(t)\n",
    "parts[25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "parts_of_speech = []\n",
    "counter = 0\n",
    "labels = []\n",
    "for j in parts:\n",
    "    p =[]\n",
    "    for word, pos in j:\n",
    "        p.append(pos)\n",
    "    counter += 1\n",
    "    parts_of_speech.append(p)\n",
    "    labels.append(counter)\n",
    "    \n",
    "final = []\n",
    "remove =[',', '.', ')', '(', ']', '[']\n",
    "for sent in parts_of_speech:\n",
    "    senten = []\n",
    "    for words in sent:\n",
    "        if words not in remove:\n",
    "            senten.append(words)\n",
    "    final.append(senten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LabeledLineSentence(object):\n",
    "    def __init__(self, doc_list, labels_list):\n",
    "        self.labels_list = labels_list\n",
    "        self.doc_list = doc_list\n",
    "    def __iter__(self):\n",
    "        for idx, doc in enumerate(self.doc_list):\n",
    "              yield gensim.models.doc2vec.LabeledSentence(doc,[self.labels_list[idx]])\n",
    "\n",
    "it = LabeledLineSentence(final, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jay.timbadia\\AppData\\Local\\Continuum\\anaconda3\\envs\\py35\\lib\\site-packages\\ipykernel_launcher.py:7: DeprecationWarning: Call to deprecated `LabeledSentence` (Class will be removed in 4.0.0, use TaggedDocument instead).\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "from gensim.models import Doc2Vec\n",
    "model = Doc2Vec(alpha=0.025, min_alpha=0.025)  # use fixed learning rate\n",
    "model.build_vocab(it)\n",
    "for epoch in range(10):\n",
    "    model.train(it,total_examples=model.corpus_count, epochs=100)\n",
    "    model.alpha -= 0.002  # decrease the learning rate\n",
    "    model.min_alpha = model.alpha  # fix the learning rate, no decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NNP', 'VBD', 'RBR', 'DT', 'NN', 'IN', 'NNP', 'NNP', 'VBZ', 'VBG', 'NNP', 'NN', 'NNS', 'VBG', 'DT', 'JJ', 'NN', 'TO', 'VB', 'NNP', 'IN', 'NNP']\n",
      "\n",
      "[(467, 0.23044851422309875), (39, 0.22927193343639374), (468, 0.2276013195514679), (86, 0.22418755292892456), (444, 0.2217487394809723)]\n",
      "New products gaining traction: Any material uptake of Naukri’s new products like Career Site management and Semantic Search will lead to an upside to our and consensus estimates.\n",
      "\n",
      "0\n",
      "['NNP', 'NNS', 'VBG', 'NN', ':', 'DT', 'JJ', 'NN', 'IN', 'NNP', 'NNP', 'VBD', 'JJ', 'NNS', 'IN', 'NNP', 'NNP', 'NN', 'CC', 'JJ', 'NNP', 'MD', 'VB', 'TO', 'DT', 'NN', 'TO', 'PRP$', 'CC', 'NN', 'NNS']\n",
      "\n",
      "Despite the dilution, the actions as positive as they would not only enlarge and diversify Yili’s shareholding base, but also secure its access to premium quality organic milk, paving the way for future organic product development under a vertically integrated mode.\n",
      "\n",
      "0\n",
      "['PDT', 'DT', 'VBD', 'PRP', 'VBP', 'NNP', 'NNP', 'JJ', 'NN', 'CC', 'JJ', 'NN', 'JJ', 'NNP', 'VBP', 'DT', 'JJ', 'NNS', 'TO', 'VBG', 'DT', 'NN']\n",
      "\n",
      "New products gaining traction: Any material uptake of Naukri’s new products like Career Site management and Semantic Search should lead to an upside to our and consensus estimates.\n",
      "\n",
      "1\n",
      "['CC', 'RB', 'NNS', 'IN', 'JJR', 'NN', 'NNS', 'MD', 'VB', 'JJR', 'IN', 'DT', 'NNS', 'IN', 'JJR', 'NN', 'JJ', 'TO', 'DT', 'NN']\n",
      "\n",
      "Despite the dilution, the actions are positive as they would not only enlarge and diversify Yili’s shareholding base, but also secure its access to premium quality organic milk, paving the way for future organic product development under a vertically integrated model.\n",
      "\n",
      "0\n",
      "['NNP', 'VBZ', 'TO', 'VB', 'CD', 'JJ', 'NNS', 'CC', 'CD', 'NN', 'NN', 'IN', 'JJ', 'NN', 'NN', 'IN', 'NNP', 'IN', 'JJ', 'NNS', 'IN', 'NNP']\n",
      "\n",
      "We believe further funding raised by Zomato/Policy Bazaar or any other business at a higher valuation may lead to value unlocking and hence potential upside to our PO for Info Edge.\n",
      "\n",
      "1\n",
      "['DT', 'JJ', 'NN', 'VBD', 'NNP', 'NNS', 'TO', 'VB', 'IN', 'NNP', 'IN', 'NNP', 'TO', 'NNP', 'IN', 'NNP', 'IN', 'VBG', 'IN', 'NNP']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(final[0])\n",
    "print()\n",
    "new_sentence = \"Handelsblatt reported, earlier this week that Deutsche Telekom is having Board level discussions regarding an all-stock deal to merge T-Mobile with Sprint.\".split(\" \")  \n",
    "t= model.docvecs.most_similar(positive=[model.infer_vector(new_sentence)],topn=5)\n",
    "print(t)\n",
    "for idx, _ in t:\n",
    "    print(dataf['Sentence'][idx-1])\n",
    "    print(dataf['Labels'][idx])\n",
    "    print(final[idx])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Tree.subtrees of Tree('S', [('Handelsblatt', 'NNP'), ('reported,', 'NN'), ('earlier', 'RBR'), ('this', 'DT'), ('week', 'NN'), ('that', 'IN'), ('Deutsche', 'NNP'), ('Telekom', 'NNP'), ('is', 'VBZ'), ('having', 'VBG'), ('Board', 'NNP'), ('level', 'NN'), ('discussions', 'NNS'), ('regarding', 'VBG'), ('an', 'DT'), ('all-stock', 'JJ'), ('deal', 'NN'), ('to', 'TO'), ('merge', 'VB'), ('T-Mobile', 'NNP'), ('with', 'IN'), ('Sprint.', 'NNP')])>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagged=nltk.pos_tag(dataf['Sentence'][0].split())\n",
    "irule= '''INDIRECT:{<DT>?<NN.*>+<.*><NN.*>(<VBD>|(<VBZ>|<VBP><RB>?)<VBN>)(<,>|<DT|IN>) | <,>?<DT>?<NN.*>+(<VBD>|(<VBZ>|<VBP><RB>?)<VBN>)(<,>|<DT|IN>) | <PRP.><NN><VBZ><IN> | <NNP><,><DT><JJ>?<NN>*<,><VBN> | <,><NN.*><VBD><.> | <,><DT><NN.*>*<VBD> | <VBG><TO><DT><NN.*>+ | <,>?<IN>(<VBN>|<NN>)<IN><DT>?<NN.*><NN.*><,> | <,><IN>(<VBN>|<NN>)<IN><DT>?<NN.*><,> }'''\n",
    "cp=nltk.RegexpParser(irule) \n",
    "iresult=cp.parse(tagged)\n",
    "ilist=[]\n",
    "for subtree in iresult.subtrees(filter=lambda t:t.label()==\"INDIRECT\"):\n",
    "     ichunked=\" \".join([a for (a,b) in subtree.leaves()])\n",
    "     if \"announced\" not in ichunked:\n",
    "            ilist.append(ichunked)\n",
    "iresult"
   ]
  }
 ],
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