2-6 PLOS Meeting.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from nltk.tokenize import RegexpTokenizer\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from stop_words import get_stop_words\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "import gensim\n",
        "from gensim import corpora, models, similarities\n",
        "from gensim.models.doc2vec import TaggedDocument,TaggedLineDocument\n",
        "from gensim.models import Doc2Vec\n",
        "import gensim.models.doc2vec\n",
        "from gensim.test.test_doc2vec import ConcatenatedDoc2Vec\n",
        "from collections import OrderedDict\n",
        "import multiprocessing\n",
        "from collections import defaultdict\n",
        "from nltk.tokenize.moses import MosesDetokenizer\n",
        "from IPython.display import Image\n",
        "import requests\n",
        "from random import randint \n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.cluster import AffinityPropagation\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-07T03:38:06.210425Z",
          "start_time": "2018-02-07T03:38:03.049564Z"
        },
        "collapsed": true,
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PLOS Capstone Progress Update \n",
        "### 2/6/18"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "Image('dataflow.png')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
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              "BQjYQm/KHhFAAAEEEEAAAQQQQAABBKJQQEO2O++8Ux599FFCtig8/27ssh2u7dq1SyZMmCBTpkxx\n",
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              "iQ7x99BDD8nRRx8dQb2kK6EWKCoqknvuuUeys7PF4/HIsccea8LaM888M9SHYn8IIIAAAggggAAC\n",
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              "nQcIIIAAAggggAAC4SVAwBZe54vWIoAAAggggAACCCCAAAIIIBBUYPv27SbMmT9/vrn/+eeffeu1\n",
              "adNGBg0aZMK2Cy64QNq2bet7jwehEVi5cqUsWLDA3JYtWya7du3y7bh79+4mVPvzn/8shx9+uO91\n",
              "HiCAAAIIIIAAAgiErwABW/ieO1qOAAIIIIAAAggggAACCCCAQFCBnTt3yocffijz5s0TDdzWrVvn\n",
              "W2///feXbt26yamnniqnnXaauT/55JMZTtInVP+DiooK+fzzz0WDtKVLl8qnn34qmzZt8m144IEH\n",
              "mmEfNdRMTk6W448/3vceDxBAAAEEEEAAAQQiQ4CALTLOI71AAAEEEEAAAQQQQAABBBBAoE4BDYPe\n",
              "eustefvtt0UrrYItp59+upxyyikmcOvVq5d07do12GpR+dq3334ry5cvlyVLlpgw7V//+lcthxNO\n",
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              "38sPP/xgHut9QUGBeR6s/TpnnVb19e3bVzRM69evn6gFCwIIIIAAAggggED0ChCwRe+5p+cIIIAA\n",
              "AggggAACCCCAAAII1CmggZuGbXqvc4zp/Z4sWuVmB26dO3eWNm3aiM5JFhcX57u3H9uv28+d9w0J\n",
              "sNavX2/mQNu8ebP8+OOP8tNPP8mWLVtk48aN5nV9XlxcbJ7X14fu3bub4TH1PikpyVSqHX300fVt\n",
              "xvsIIIAAAggggAACUSZAwBZlJ5zuIoAAAggggAACCCCAAAIIILC3AhpSrVu3zlR92fdaDWY/1nDL\n",
              "7Ut8fLypwNPQTANAHdrRfqxz0LEggAACCCCAAAIIILAnAgRse6LEOggggAACCCCAAAIIIIAAAggg\n",
              "sEcCRUVFohVlOuyiBm9bt26VyspKqaio2KN7e93t27eb42mVW8uWLaVFixbmXh/bN31N39dqNx3G\n",
              "UW/t27eX1q1b+57br2mQ1qVLlz3qAyshgAACCCCAAAIIIFCfAAFbfUK8jwACCCCAAAIIIIAAAggg\n",
              "gAACCCCAAAIIIIAAAggggIBDgIDNgcFDBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOoT\n",
              "IGCrT4j3EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHAIELA5MHiIAAIIIIAAAggggAAC\n",
              "CCCAAAIIIIAAAggggAACCCCAQH0CBGz1CfE+AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAA\n",
              "Ag4BAjYHBg8RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqE+AgK0+Id5HAAEEEEAAAQQQ\n",
              "QAABBBBAAAEEEEAAAQQQQAABBBBAwCFAwObA4CECCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg\n",
              "gAAC9QkQsNUnxPsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIOAQI2BwYPEQAAQQQQAAB\n",
              "BBBAAAEEEEAAAQQQQAABBBBAAAEEEECgPgECtvqEeB8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\n",
              "AQQQQAABhwABmwODhwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjUJ0DAVp8Q7yOAAAII\n",
              "IIAAAggggAACCCCAAAIIIIAAAggggAACCCDgECBgc2DwEAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\n",
              "QAABBBBAAIH6BAjY6hPifQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQcAgRsDgweIoAA\n",
              "AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFCfAAFbfUK8jwACCCCAAAIIIIAAAggggAACCCCA\n",
              "AAIIIIAAAggggIBDgIDNgcFDBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOoTIGCrT4j3\n",
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              "IIAAAggggAACCCCAQH0CBGz1CfE+AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAg4BAjYH\n",
              "Bg8RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqE+AgK0+Id5HAAEEEEAAAQQQQAABBBBA\n",
              "AAEEEEAAAQQQQAABBBBAwCFAwObA4CECCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC9QkQ\n",
              "sNUnxPsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIOAQI2BwYPEQAAQQQQAABBBBAAAEE\n",
              "EEAAAQQQQAABBBBAAAEEEECgPgECtvqEeB8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\n",
              "h8D/B8vCfLy81WhxAAAAAElFTkSuQmCC\n"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T14:46:01.989122Z",
          "start_time": "2018-02-06T14:46:01.966231Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Loading"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Establish db connection, load data\n",
        "conn = sqlite3.connect(\"/Users/jackprominski/Desktop/UVA DSI/Capstone/testDB.db\")\n",
        "df = pd.read_sql_query(\"select doi,title,abstract,body from trial_10000;\", conn)\n",
        "df['abstract']=df['abstract'].str.replace('\\d+', '') # for digits\n",
        "df['abstract']=df['abstract'].str.replace(r'(\\b\\w{1,2}\\b)', '') # for words\n",
        "df['abstract']=df['abstract'].str.replace('Background', '')\n",
        "df['body']=df['body'].str.replace('Introduction', '')\n",
        "df['title']=df['title'].str.strip(\"[]'\")"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-07T03:38:20.216066Z",
          "start_time": "2018-02-07T03:38:11.475709Z"
        },
        "collapsed": true,
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>doi</th>\n",
              "      <th>title</th>\n",
              "      <th>abstract</th>\n",
              "      <th>body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.1371/journal.pone.0000100</td>\n",
              "      <td>Factors Affecting Frequency Discrimination of ...</td>\n",
              "      <td>Measuring perceptual judgments about stimuli w...</td>\n",
              "      <td>Many investigations of sensory processing are ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.1371/journal.pone.0000008</td>\n",
              "      <td>Molecular Adaptation during Adaptive Radiation...</td>\n",
              "      <td>“Explosive” adaptive radiations  islands remai...</td>\n",
              "      <td>The most dramatic “bursts” of adaptive radiati...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.1371/journal.pone.0000061</td>\n",
              "      <td>Self-Correcting Maps of Molecular Pathways</td>\n",
              "      <td>Reliable and comprehensive maps  molecular pat...</td>\n",
              "      <td>Scientific innovation often proceeds through a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.1371/journal.pone.0000094</td>\n",
              "      <td>A Bayesian Network Driven Approach to Model th...</td>\n",
              "      <td>The transcriptional response  exogenously supp...</td>\n",
              "      <td>Nitric oxide (NO·) is a critical mediator of t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.1371/journal.pone.0000011</td>\n",
              "      <td>A Comparative Study on the Cost of New Antibio...</td>\n",
              "      <td>Drug treatment  becoming more expensive due  t...</td>\n",
              "      <td>The investment of societies around the world o...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            doi  \\\n",
              "0  10.1371/journal.pone.0000100   \n",
              "1  10.1371/journal.pone.0000008   \n",
              "2  10.1371/journal.pone.0000061   \n",
              "3  10.1371/journal.pone.0000094   \n",
              "4  10.1371/journal.pone.0000011   \n",
              "\n",
              "                                               title  \\\n",
              "0  Factors Affecting Frequency Discrimination of ...   \n",
              "1  Molecular Adaptation during Adaptive Radiation...   \n",
              "2         Self-Correcting Maps of Molecular Pathways   \n",
              "3  A Bayesian Network Driven Approach to Model th...   \n",
              "4  A Comparative Study on the Cost of New Antibio...   \n",
              "\n",
              "                                            abstract  \\\n",
              "0  Measuring perceptual judgments about stimuli w...   \n",
              "1  “Explosive” adaptive radiations  islands remai...   \n",
              "2  Reliable and comprehensive maps  molecular pat...   \n",
              "3  The transcriptional response  exogenously supp...   \n",
              "4  Drug treatment  becoming more expensive due  t...   \n",
              "\n",
              "                                                body  \n",
              "0  Many investigations of sensory processing are ...  \n",
              "1  The most dramatic “bursts” of adaptive radiati...  \n",
              "2  Scientific innovation often proceeds through a...  \n",
              "3  Nitric oxide (NO·) is a critical mediator of t...  \n",
              "4  The investment of societies around the world o...  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T14:49:29.517539Z",
          "start_time": "2018-02-06T14:49:29.504574Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load saved model files\n",
        "lsi = models.LsiModel.load('models/model.lsi')\n",
        "lda = models.LdaModel.load('models/model.lda')\n",
        "tfidf = models.TfidfModel.load('models/model.tfidf')\n",
        "index = similarities.MatrixSimilarity.load('models/similarity.index')\n",
        "corpus = corpora.MmCorpus('models/corpus.mm')\n",
        "dictionary = corpora.Dictionary.load('models/dictionary.dict')"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-07T03:38:20.867153Z",
          "start_time": "2018-02-07T03:38:20.260332Z"
        },
        "collapsed": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform corpus\n",
        "corpus_tfidf = tfidf[corpus]\n",
        "corpus_lsi = lsi[corpus_tfidf]\n",
        "corpus_lda = lda[corpus_tfidf]"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-07T03:38:20.897262Z",
          "start_time": "2018-02-07T03:38:20.893331Z"
        },
        "collapsed": true
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Topic Modeling"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Topic modeling is a technique for the discovery of hidden (or \"latent\") semantic structures in a corpus. Each document is described in terms of one or more \"topics,\" which are made up of the most common words in that topic. These unobserved semantic relationships can be used to ascertain similarity between documents. Topic weights can also be used as inputs for other analysis methods, like association rule learning."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('LSI Topics')\n",
        "print()\n",
        "for i in range(0,6):\n",
        "    x = randint(0,200)\n",
        "    print('Topic ' + str(x) + ': ' + lsi.print_topic(x))\n",
        "    print()\n",
        "\n",
        "print('---------')\n",
        "print()\n",
        "print('LDA Topics')\n",
        "print()\n",
        "for i in range(0,6):\n",
        "    x = randint(0,200)\n",
        "    print('Topic ' + str(x) + ': ' + lda.print_topic(x))\n",
        "    print()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LSI Topics\n",
            "\n",
            "Topic 88: 0.236*\"bone\" + -0.222*\"tlr\" + 0.184*\"may\" + 0.180*\"identifi\" + 0.164*\"process\" + -0.160*\"high\" + 0.156*\"individu\" + 0.151*\"posit\" + -0.147*\"two\" + -0.145*\"predict\"\n",
            "\n",
            "Topic 172: 0.272*\"includ\" + 0.152*\"splice\" + -0.145*\"sensit\" + 0.136*\"visual\" + -0.125*\"day\" + -0.122*\"howev\" + -0.112*\"variat\" + 0.109*\"local\" + -0.107*\"inform\" + -0.105*\"myc\"\n",
            "\n",
            "Topic 103: 0.245*\"tlr\" + -0.198*\"process\" + 0.196*\"predict\" + 0.190*\"gener\" + 0.171*\"analysi\" + 0.165*\"complex\" + -0.155*\"channel\" + 0.150*\"mitochondri\" + -0.148*\"treatment\" + -0.142*\"methyl\"\n",
            "\n",
            "Topic 186: -0.173*\"import\" + -0.167*\"day\" + -0.156*\"replic\" + -0.140*\"myc\" + 0.136*\"anim\" + -0.127*\"size\" + -0.120*\"phenotyp\" + -0.119*\"vivo\" + -0.119*\"involv\" + -0.116*\"macrophag\"\n",
            "\n",
            "Topic 114: -0.249*\"non\" + -0.202*\"also\" + 0.184*\"mechan\" + -0.181*\"drug\" + -0.176*\"bone\" + -0.172*\"promot\" + -0.162*\"process\" + 0.139*\"can\" + 0.135*\"treatment\" + -0.133*\"sleep\"\n",
            "\n",
            "Topic 7: -0.441*\"activ\" + 0.382*\"express\" + 0.258*\"mice\" + -0.244*\"hiv\" + 0.223*\"vaccin\" + -0.194*\"patient\" + -0.179*\"associ\" + 0.161*\"respons\" + -0.151*\"cancer\" + -0.150*\"gene\"\n",
            "\n",
            "---------\n",
            "\n",
            "LDA Topics\n",
            "\n",
            "Topic 126: 0.055*\"diamet\" + 0.053*\"retina\" + 0.050*\"inner\" + 0.041*\"slca\" + 0.031*\"photoreceptor\" + 0.030*\"tpa\" + 0.026*\"particl\" + 0.025*\"bad\" + 0.022*\"icd\" + 0.019*\"müller\"\n",
            "\n",
            "Topic 137: 0.074*\"male\" + 0.058*\"femal\" + 0.024*\"sex\" + 0.019*\"mate\" + 0.014*\"sexual\" + 0.013*\"parent\" + 0.012*\"differ\" + 0.011*\"reproduct\" + 0.010*\"evolutionari\" + 0.010*\"social\"\n",
            "\n",
            "Topic 145: 0.084*\"vaccin\" + 0.067*\"protect\" + 0.051*\"immun\" + 0.042*\"challeng\" + 0.021*\"respons\" + 0.018*\"induc\" + 0.014*\"trap\" + 0.013*\"volunt\" + 0.012*\"effect\" + 0.012*\"dose\"\n",
            "\n",
            "Topic 146: 0.047*\"phosphoryl\" + 0.047*\"mutant\" + 0.024*\"age\" + 0.024*\"ser\" + 0.023*\"brucei\" + 0.016*\"matur\" + 0.015*\"sulfat\" + 0.014*\"wnta\" + 0.013*\"trypanosom\" + 0.013*\"hpv\"\n",
            "\n",
            "Topic 177: 0.038*\"receptor\" + 0.022*\"ligand\" + 0.021*\"cell\" + 0.014*\"express\" + 0.008*\"bind\" + 0.008*\"scd\" + 0.008*\"basal\" + 0.007*\"use\" + 0.007*\"vascular\" + 0.007*\"model\"\n",
            "\n",
            "Topic 72: 0.049*\"acid\" + 0.026*\"amino\" + 0.023*\"protein\" + 0.019*\"ribosom\" + 0.018*\"synthesi\" + 0.017*\"translat\" + 0.013*\"nerv\" + 0.012*\"ngf\" + 0.012*\"analog\" + 0.010*\"residu\"\n",
            "\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T11:25:18.155425Z",
          "start_time": "2018-02-06T11:25:18.032080Z"
        },
        "inputHidden": false,
        "outputHidden": false,
        "scrolled": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a DOI\n",
        "x = randint(0,200)\n",
        "df.loc[x]['doi']"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": [
              "'10.1371/journal.pone.0000115'"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T14:48:54.200099Z",
          "start_time": "2018-02-06T14:48:54.194275Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## SHOW TOPIC WEIGHTS OF A GIVEN DOCUMENT\n",
        "\n",
        "doi_in = input('Enter a DOI: ')\n",
        "qu_doi = df.loc[df['doi']==doi_in].index[0]\n",
        "print('Title:')\n",
        "print(df.loc[qu_doi]['title'])\n",
        "print()\n",
        "topics = lda.get_document_topics(corpus[qu_doi],minimum_probability=.05)\n",
        "print('Topic Weights:')\n",
        "print(sorted(topics,key=lambda x: x[1],reverse=True))\n",
        "print()\n",
        "for i in range(3):\n",
        "    print('Topic ' + str(topics[i][0]) + ': ')\n",
        "    [print(x[0]) for x in lda.show_topic(topics[i][0])]\n",
        "    print()\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a DOI: 10.1371/journal.pone.0000020\n",
            "Title:\n",
            "Societal Learning in Epidemics: Intervention Effectiveness during the 2003 SARS Outbreak in Singapore\n",
            "\n",
            "Topic Weights:\n",
            "[(74, 0.2701141), (171, 0.110299125), (176, 0.10901061), (97, 0.08740212), (76, 0.06643704)]\n",
            "\n",
            "Topic 74: \n",
            "influenza\n",
            "season\n",
            "temperatur\n",
            "pandem\n",
            "diseas\n",
            "epidem\n",
            "malaria\n",
            "outbreak\n",
            "mortal\n",
            "use\n",
            "\n",
            "Topic 76: \n",
            "cost\n",
            "durat\n",
            "life\n",
            "potenc\n",
            "longer\n",
            "effect\n",
            "shorter\n",
            "cation\n",
            "conjunct\n",
            "expect\n",
            "\n",
            "Topic 97: \n",
            "motor\n",
            "learn\n",
            "languag\n",
            "odor\n",
            "control\n",
            "olfactori\n",
            "skill\n",
            "tail\n",
            "acc\n",
            "associ\n",
            "\n"
          ]
        }
      ],
      "execution_count": 60,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T11:52:31.525599Z",
          "start_time": "2018-02-06T11:52:29.662900Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Search treats the query as another document -- which links it to topics and the hidden semantic\n",
        "# structures of the text. Then returns similarly topic weighted results. \n",
        "\n",
        "doc = input('Enter a search query: ')\n",
        "\n",
        "vec_bow = dictionary.doc2bow(doc.lower().split())\n",
        "vec_lsi = lsi[vec_bow] # convert the query to LSI space\n",
        "lsi_sims = sorted(enumerate(index[vec_lsi]), key=lambda item: -item[1])\n",
        "simdocs_lsi = [x[0] for x in lsi_sims[0:10]]\n",
        "\n",
        "for i in range(0,len(simdocs_lsi)):\n",
        "    print(str(i+1) + '. ' + df.loc[simdocs_lsi[i],'title'])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a search query: HIV\n",
            "1. Regional Differences in Prevalence of HIV-1 Discordance in Africa and Enrollment of HIV-1 Discordant Couples into an HIV-1 Prevention Trial\n",
            "2. Characteristics of HIV-1 Discordant Couples Enrolled in a Trial of HSV-2 Suppression to Reduce HIV-1 Transmission: The Partners Study\n",
            "3. Late HIV Diagnosis and Determinants of Progression to AIDS or Death after HIV Diagnosis among Injection Drug Users, 33 US States, 1996–2004\n",
            "4. Genital Herpes Has Played a More Important Role than Any Other Sexually Transmitted Infection in Driving HIV Prevalence in Africa\n",
            "5. Empirical Evidence for Synchrony in the Evolution of TB Cases and HIV+ Contacts among the San Francisco Homeless\n",
            "6. HIV Aspartyl Peptidase Inhibitors Interfere with Cellular Proliferation, Ultrastructure and Macrophage Infection of \n",
            "7. Vaccinia Scars Associated with Improved Survival among Adults in Rural Guinea-Bissau\n",
            "8. HIV Transmission in a State Prison System, 1988–2005\n",
            "9. HCV Coinfection Associated with Slower Disease Progression in HIV-Infected Former Plasma Donors Naïve to ART\n",
            "10. Sexual Risk Behaviors for HIV/AIDS in Chuuk State, Micronesia: The Case for HIV Prevention in Vulnerable Remote Populations\n"
          ]
        }
      ],
      "execution_count": 19,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T16:15:00.477944Z",
          "start_time": "2018-02-06T16:14:57.134863Z"
        },
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Most similar documents given doi\n",
        "query_doi = input('Enter a DOI: ')\n",
        "query_doc = df.loc[df['doi']==query_doi].index[0]\n",
        "doc_bow = corpus[query_doc]\n",
        "\n",
        "vec_lsi = lsi[doc_bow]\n",
        "lsi_sims = sorted(enumerate(index[vec_lsi]), key=lambda item: -item[1])\n",
        "simdocs_lsi = [x[0] for x in lsi_sims[1:11]]\n",
        "\n",
        "vec_lda = lda[doc_bow]\n",
        "lda_sims = sorted(enumerate(index[vec_lda]), key=lambda item: -item[1])\n",
        "simdocs_lda = [x[0] for x in lda_sims[0:10]]\n",
        "\n",
        "print('Target: ' + df.loc[query_doc,'title'])\n",
        "print('LSI Most Similar Documents:')\n",
        "for i in range(0,len(simdocs_lsi)):\n",
        "    print(str(i+1) + '. ' + df.loc[simdocs_lsi[i],'title'])\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a DOI: 10.1371/journal.pone.0000094\n",
            "Target: A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in \n",
            "LSI Most Similar Documents:\n",
            "1. How to Turn a Genetic Circuit into a Synthetic Tunable Oscillator, or a Bistable Switch\n",
            "2. Computational Modelling of Genome-Side Transcription Assembly Networks Using a Fluidics Analogy\n",
            "3. Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence\n",
            "4. The Role of Input Noise in Transcriptional Regulation\n",
            "5. Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs\n",
            "6. Floral Morphogenesis: Stochastic Explorations of a Gene Network Epigenetic Landscape\n",
            "7. Boolean Network Model Predicts Cell Cycle Sequence of Fission Yeast\n",
            "8. Statecharts for Gene Network Modeling\n",
            "9. Non-Uniform Survival Rate of Heterodimerization Links in the Evolution of the Yeast Protein-Protein Interaction Network\n",
            "10. Optimal Signal Processing in Small Stochastic Biochemical Networks\n"
          ]
        }
      ],
      "execution_count": 17,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T14:49:51.794732Z",
          "start_time": "2018-02-06T14:49:50.513870Z"
        },
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Word Embedding"
      ],
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Word Embedding is a neural network model to represent words and documents as numerical vectors. The goal is to learn word representations that can predict a document given its surrounding words and documents. This allows context to play a much larger role in document grouping, and thus give better querying results."
      ],
      "metadata": {
        "collapsed": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "documents = [doc for doc in TaggedLineDocument('processed_docs.txt')]\n",
        "model = Doc2Vec(documents, size=200, window=5, min_count=5,workers=7)\n",
        "np.save('models/features-w2v-200.npy',model.docvecs.doctag_syn0)\n",
        "model.save('models/features-w2v-200.doc2vec')"
      ],
      "outputs": [],
      "execution_count": 70,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-06T12:02:15.244829Z",
          "start_time": "2018-02-06T12:02:07.005484Z"
        },
        "collapsed": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Doc2Vec.load('models/features-w2v-200.doc2vec')"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-07T03:39:28.614940Z",
          "start_time": "2018-02-07T03:39:28.254091Z"
        },
        "collapsed": true
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc_id = np.random.randint(model.docvecs.count) \n",
        "sims = model.docvecs.most_similar(doc_id, topn=model.docvecs.count)  # get *all* similar documents\n",
        "print(u'TARGET (%d): «%s»' % (doc_id, ''.join(df.loc[doc_id]['title'])))\n",
        "print(df.loc[sims[0][0]]['doi'])\n",
        "print()\n",
        "\n",
        "for label, index in [('MOST', 0), ('LEAST', len(sims) - 1)]:\n",
        "    print(u'%s %s: «%s»' % (label, sims[index], ''.join(df.loc[sims[index][0]]['title'])))\n",
        "    print(df.loc[sims[index][0]]['doi'])\n",
        "    print()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TARGET (6251): «\"Mental Rotation of Faces in Healthy Aging and Alzheimer's Disease\"»\n",
            "10.1371/journal.pone.0006460\n",
            "\n",
            "MOST (6588, 0.9276092052459717): «The Composite Task Reveals Stronger Holistic Processing in Children than Adults for Child Faces»\n",
            "10.1371/journal.pone.0006460\n",
            "\n",
            "LEAST (7128, -0.7308048009872437): «High Efficiency Lipid-Based siRNA Transfection of Adipocytes in Suspension»\n",
            "10.1371/journal.pone.0006940\n",
            "\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-02-07T03:40:41.178988Z",
          "start_time": "2018-02-07T03:40:41.147430Z"
        }
      }
    }
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