{
 "metadata": {
  "name": "",
  "signature": "sha256:335876f4c7ce4b35357ada350e35c1201e78e3cb51723e1656f1912ddde7818c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the [previous version of this notebook][prev] attempted to run the CPAN module for InterologWalk.\n",
      "There were problems installing this and getting it to run locally.\n",
      "It turned out that it had already been run over a large set of proteins at Edinburgh and that the output file was available, which makes this task much easier.\n",
      "\n",
      "Looking at this file and loading it:\n",
      "\n",
      "[prev]: http://nbviewer.ipython.org/github/ggray1729/opencast-bio/blob/5a37db51e897f54ca3eaecd1d3d7ab22531adaea/notebooks/Extracting%20InterologWalk%20features.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd ../../InterologWalk/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/gavin/Documents/MRes/InterologWalk\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[0m\u001b[40m\u001b[m\u001b[00mIW_entrez.csv\u001b[0m\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head IW_entrez.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5610\t3667\r",
        "\r\n",
        "5610\t3667\r",
        "\r\n",
        "197\t265\r",
        "\r\n",
        "197\t266\r",
        "\r\n",
        "197\t10117\r",
        "\r\n",
        "57326\t1147\r",
        "\r\n",
        "57326\t1147\r",
        "\r\n",
        "335\t3778\r",
        "\r\n",
        "335\t3778\r",
        "\r\n",
        "335\t3778\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Creating dictionary and feature object\n",
      "\n",
      "As was done with the [STRING notebook][string] we will create a `ocbio.ppipred.features` object to store the dictionary of interactions.\n",
      "This can then be pickled and loaded when assembling feature vectors.\n",
      "\n",
      "[string]: http://nbviewer.ipython.org/github/ggray1729/opencast-bio/blob/master/notebooks/Extracting%20STRING%20results-based%20features.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"IW_entrez.csv\")\n",
      "featuredict = {}\n",
      "for line in csv.reader(f,delimiter=\"\\t\"):\n",
      "    featuredict[frozenset(line)] = ['1']\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sys.path.append(\"../opencast-bio/\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ocbio.ppipred"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = ocbio.ppipred.features(featuredict,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Testing\n",
      "\n",
      "Testing with arbitrary keys:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "realkey = featuredict.keys()[0]\n",
      "fakekey = frozenset([\"1234\",\"4321\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features[realkey]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "['1']"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features[fakekey]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "['0']"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Pickling\n",
      "\n",
      "Finally, we pickle this instance so that it can be accessed by the assembler to create feature vectors:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"human.interologwalk.features.pickle\",\"wb\")\n",
      "pickle.dump(features,f)\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}