promise_similarity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from promise_similarity.executor import Executor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ex = Executor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading promises\n"
     ]
    }
   ],
   "source": [
    "ex.read_promises()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'body': 'Ap vil ha et inntektspolitisk samarbeid og en økonomisk politikk som sikrer arbeid til alle.',\n",
       " 'category_names': 'SYSSELSETTING;LØNN',\n",
       " 'id': 1,\n",
       " 'index': 0,\n",
       " 'period': '2009-2013',\n",
       " 'promisor': 'Arbeiderpartiet',\n",
       " 'promisor_type': 'Party',\n",
       " 'source': 'Partiprogram'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex.promises[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tfidf = vectorizer.fit_transform([promise['body'] for promise in ex.promises])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = linear_kernel(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13162, 13162)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.06764038,  0.0477894 , ...,  0.07312682,\n",
       "         0.00329616,  0.01320929],\n",
       "       [ 0.06764038,  1.        ,  0.03896515, ...,  0.03241755,\n",
       "         0.02488248,  0.        ],\n",
       "       [ 0.0477894 ,  0.03896515,  1.        , ...,  0.03991542,\n",
       "         0.0030371 ,  0.0121711 ],\n",
       "       ..., \n",
       "       [ 0.07312682,  0.03241755,  0.03991542, ...,  1.        ,\n",
       "         0.08053732,  0.10300367],\n",
       "       [ 0.00329616,  0.02488248,  0.0030371 , ...,  0.08053732,\n",
       "         1.        ,  0.14721518],\n",
       "       [ 0.01320929,  0.        ,  0.0121711 , ...,  0.10300367,\n",
       "         0.14721518,  1.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hits = np.nonzero(scores[1] > 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1, 9198])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "if 0: \n",
    "    print(1)\n",
    "else: \n",
    "    print(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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