Creating fit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from docopt import docopt\n",
    "import logging\n",
    "import os\n",
    "import hashlib\n",
    "import glob\n",
    "import getpass\n",
    "import socket\n",
    "import datetime\n",
    "import re\n",
    "import requests\n",
    "from slimit.lexer import Lexer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn import manifold\n",
    "from sklearn import cross_validation\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "class Database():\n",
    "    \"\"\"A directory structure containing JavaScript samples and associated metadata\"\"\"\n",
    "    def __init__(self, path):\n",
    "        \"\"\"Load or create the Database at path\"\"\"\n",
    "        self.path = path\n",
    "        self.subdirs = [self.path+'/'+sdir for sdir in ['malicious', 'legitimate', 'unknown']]\n",
    "        if not self.check():\n",
    "            self.create()\n",
    "\n",
    "    def check(self):\n",
    "        \"\"\"Check if the database exists and contains the needed subdirs\"\"\"\n",
    "        if not os.path.isdir(self.path):\n",
    "            return False\n",
    "        if not all(map(os.path.isdir, self.subdirs)):\n",
    "            return False\n",
    "        return True\n",
    "\n",
    "    def create(self):\n",
    "        \"\"\"Create the directory structure needed to host a database\"\"\"\n",
    "        for sdir in self.subdirs:\n",
    "            os.makedirs(sdir)\n",
    "\n",
    "    def lookup(self, sha1):\n",
    "        \"\"\"Return the snippet with the provided sha1, if it exists in the database\"\"\"\n",
    "        logging.debug('Looking for snippet '+sha1)\n",
    "        files=sum([glob.glob(sdir+'/*.js') for sdir in self.subdirs], [])\n",
    "        matching_files = [f for f in files if os.path.basename(f)[:-3] == sha1]\n",
    "        assert len(matching_files) <= 1, \"Multiple snippets of the same hash in the DB\"\n",
    "        if matching_files:\n",
    "            fname = matching_files[0]\n",
    "            logging.debug('Found')\n",
    "            return Snippet(filename=matching_files[0])\n",
    "        logging.debug('Not found')\n",
    "        return None\n",
    "\n",
    "    def add_new(self, snippet, message):\n",
    "        \"\"\"Add to the database a snippet that wasn't there before, return the newly created snippet\"\"\"\n",
    "        fname = self.path+'/'+snippet.status+'/'+snippet.sha1+'.js'\n",
    "        with open(fname, 'wb') as f:\n",
    "            f.write(snippet.data)\n",
    "        snippet.filename = fname\n",
    "        snippet.log(event='created', message=message)\n",
    "        return snippet\n",
    "\n",
    "    def move(self, snippet, status, message):\n",
    "        \"\"\"Moves an existing snippet from its status to another\"\"\"\n",
    "        path = snippet.filename.split('/')\n",
    "        new_name = '/'.join(path[:-2]+[status, path[-1]])\n",
    "        os.rename(snippet.filename, new_name)\n",
    "        #Now moving the logs\n",
    "        os.rename(snippet.filename[:-2]+'log', new_name[:-2]+'log')\n",
    "        snippet.filename = new_name\n",
    "        old_status = snippet.status\n",
    "        snippet.status = status\n",
    "        snippet.log(event='moved from '+old_status, message=message)\n",
    "        \n",
    "    def legitimate_snippets(self):\n",
    "        \"\"\"The legitimate snippets in the DB\"\"\"\n",
    "        return [Snippet(filename=fname) for fname in sorted(glob.glob(self.path+'/legitimate/*.js'))]\n",
    "    \n",
    "    def malicious_snippets(self):\n",
    "        \"\"\"The malicious snippets in the DB\"\"\"\n",
    "        return [Snippet(filename=fname) for fname in sorted(glob.glob(self.path+'/malicious/*.js'))]\n",
    "    \n",
    "        \n",
    "class Snippet():\n",
    "    \"\"\"A JavaScript snippet\"\"\"\n",
    "\n",
    "    log_template = \"{date} {severity} hostname={hostname} user={user} sha1={sha1} status={status} event={event} message={message}\\n\"\n",
    "\n",
    "    def __init__(self, filename=None, status='unknown', sha1=None, data=None):\n",
    "        \"\"\"Create a new snippet instance, either from data and status, or from a file in a DB\"\"\"\n",
    "        self.data = data\n",
    "        if filename:\n",
    "            self.loadFromFile(filename)\n",
    "            return\n",
    "        if not sha1:\n",
    "            self.sha1 = hashlib.sha1(self.data).hexdigest()\n",
    "        else:\n",
    "            self.sha1 = sha1\n",
    "        self.status = status\n",
    "\n",
    "    def loadFromFile(self, fname):\n",
    "        \"\"\"Load a snippet from a file in a DB\"\"\"\n",
    "        self.filename = fname\n",
    "        self.__init__(data=open(fname, 'r').read(), status=fname.split('/')[-2], sha1=fname.split('/')[-1][:-3])\n",
    "\n",
    "    def log(self, event, message):\n",
    "        \"\"\"Append a line to the snippet's log file\"\"\"\n",
    "        if not self.filename:\n",
    "            raise ValueError(\"We don't know which DB we belong to.\")\n",
    "        severity='ERROR:' if 'bad move' in event else 'WARNING:' if 'moved' in event else 'INFO:'\n",
    "        log_message = self.log_template.format(date=datetime.datetime.now().isoformat(),\n",
    "                                               severity=severity,\n",
    "                                               hostname=socket.gethostname(),\n",
    "                                               user=getpass.getuser(),\n",
    "                                               sha1=self.sha1,\n",
    "                                               status=self.status,\n",
    "                                               event=event,\n",
    "                                               message=message if message else '')\n",
    "        log_fname = self.filename[:-3]+'.log'\n",
    "        with open(log_fname, 'a') as f:\n",
    "            f.write(log_message)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db = Database('sekoia_db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_from_js_tokens(corpus):\n",
    "    tokens_corpus = []\n",
    "    for t in corpus:\n",
    "        print('INFO: Parsing '+t.filename)\n",
    "        try:\n",
    "            lexer = Lexer()\n",
    "            lexer.input(t.data)\n",
    "            tokens_corpus.append(' '.join([token.type for token in lexer]))\n",
    "        except TypeError as e:\n",
    "            print('Error lexing '+t.filename+' : '+str(e))\n",
    "    return train_tfidf(tokens_corpus)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_tfidf(corpus):\n",
    "    '''Return the tf_idf transformer trained on the supplied corpus\n",
    "\n",
    "    Args:\n",
    "        corpus (list): List of text elements'''\n",
    "    count_vect = CountVectorizer()\n",
    "    train_counts = count_vect.fit_transform(corpus)\n",
    "    tfidf_transformer = TfidfTransformer().fit(train_counts)\n",
    "    def text2tfidf(text_list):\n",
    "        '''Transform a list of text into a tfidf matrix'''\n",
    "        return tfidf_transformer.transform(count_vect.transform(text_list)).toarray()\n",
    "    return text2tfidf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Parsing sekoia_db/legitimate/0034c7dacfeb469c20f9fb8bcad407a6dc82937b.js\n",
      "INFO: Parsing sekoia_db/legitimate/0a7e9f4c559853b7d1bb10d045b93e207150283a.js\n",
      "INFO: Parsing sekoia_db/legitimate/12717789ff0f8512728ec437724b90b4698cf718.js\n",
      "INFO: Parsing sekoia_db/legitimate/1852661bd11a09ca9b9cb63d1aa6ff390fffaf4e.js\n",
      "INFO: Parsing sekoia_db/legitimate/2674a226732a96f20320ae4b3233bdf9476ff218.js\n",
      "INFO: Parsing sekoia_db/legitimate/27aa8ddb0c9457d659c51806103741b163f4650a.js\n",
      "INFO: Parsing sekoia_db/legitimate/2e1a413df833abd6e09088d1e7f4f7507b156995.js\n",
      "INFO: Parsing sekoia_db/legitimate/32ea2acd3d51248716d70e26b047efe35099d0ce.js\n",
      "INFO: Parsing sekoia_db/legitimate/33dbfc1abb4f98ede991f25fb9882f286b900e4d.js\n",
      "INFO: Parsing sekoia_db/legitimate/3a9910396919f0542104a28fb3e7edbaa19bc317.js\n",
      "INFO: Parsing sekoia_db/legitimate/3f36e7886f2e5d26b3537cc04d5bebdec3762f59.js\n",
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      "INFO: Parsing sekoia_db/legitimate/44fd3f7462bfc6d8e3b21cbadeffd14433c368f1.js\n",
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      "INFO: Parsing sekoia_db/legitimate/6919012e72e3ff6c90f853888ee81d8db5f2211f.js\n",
      "INFO: Parsing sekoia_db/legitimate/6e17aac23eee73d0d46a46ec66d332ea3c7f4429.js\n",
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      "INFO: Parsing sekoia_db/legitimate/7cb076e63cd15b63b72477a3f2ad069d7fd31b3b.js\n",
      "INFO: Parsing sekoia_db/legitimate/7e928d526cddff5ec6fdd832308db0e185d3c895.js\n",
      "INFO: Parsing sekoia_db/legitimate/979143a6b10532413aefba462844d59df29fb2c1.js\n",
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      "INFO: Parsing sekoia_db/legitimate/e2d0c34e25ebce60edc4814ed99c570bb6a803f7.js\n",
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      "INFO: Parsing sekoia_db/legitimate/fa9eebd174f8272bb3a68c96db6f19188aa9eea2.js\n",
      "INFO: Parsing sekoia_db/malicious/00493276ff45f6c942e304bccdb677247931c5c8.js\n",
      "INFO: Parsing sekoia_db/malicious/05107d9b05e03bc1d7163e5bf5f9c604858f1975.js\n",
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      "INFO: Parsing sekoia_db/malicious/46896658e9e2f0d3c1d64dd08abbf72b963f1a24.js\n",
      "INFO: Parsing sekoia_db/malicious/50cb0dd7c2bcda6596c972c95a929f6ba435d670.js\n",
      "INFO: Parsing sekoia_db/malicious/50deec2f718f46483aae4ae0fb5c90693a820689.js\n",
      "INFO: Parsing sekoia_db/malicious/519f04888204f277cba0e925634a3dd9958d9fb2.js\n",
      "INFO: Parsing sekoia_db/malicious/548a25b51845dec6720c800528a835fbd9977fab.js\n",
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      "INFO: Parsing sekoia_db/malicious/abe0507e4841e2176645bd5cf0ce49cfb53e143d.js\n",
      "INFO: Parsing sekoia_db/malicious/aceba980a1d602d78c4313c585b504d61f3d0db6.js\n",
      "INFO: Parsing sekoia_db/malicious/b81a665e11f75767e536b14fde540c9c96c7273c.js\n",
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      "INFO: Parsing sekoia_db/malicious/c735542798670583d2e492be4d11a2863a0ef195.js\n",
      "INFO: Parsing sekoia_db/malicious/c7bac1a640f74bf1e5128ec465b25f20145766f0.js\n",
      "INFO: Parsing sekoia_db/malicious/cdd9aebd5c7e84949bc90593b45d77f7ac383d33.js\n",
      "INFO: Parsing sekoia_db/malicious/cf4713e1ad5068267bfcac8c9227d9fad1fc0b09.js\n",
      "INFO: Parsing sekoia_db/malicious/d01177b92831b2544dc28b61467d61e620711bcc.js\n",
      "INFO: Parsing sekoia_db/malicious/d0202fd0e179a6cd8f3a8e2498dc7b5061a9db33.js\n",
      "INFO: Parsing sekoia_db/malicious/d346b132f82427951672fbdf5ddffc9bb0148d78.js\n",
      "INFO: Parsing sekoia_db/malicious/d97e9c3bf9508248ed7e6e81152029205162f9ee.js\n",
      "INFO: Parsing sekoia_db/malicious/e2c0b62964ebfc6b0ebc83f5b5cff8ac393bb3e5.js\n",
      "INFO: Parsing sekoia_db/malicious/e35d002c12f2b3411b6be81e7bec061f80a90112.js\n",
      "INFO: Parsing sekoia_db/malicious/e8b73f1cf84eb9f0f0ded0525bf8c4e4bed3d6ae.js\n",
      "INFO: Parsing sekoia_db/malicious/ec3e5487c526cf9f4a0c20e3317ddf046aac5f45.js\n",
      "INFO: Parsing sekoia_db/malicious/f8322b51104c8ee6906e8fca968341fd8921baf2.js\n",
      "INFO: Parsing sekoia_db/malicious/ffabce78341dc27fc7993efadd46fa54fbcb55dc.js\n"
     ]
    }
   ],
   "source": [
    "corpus = db.legitimate_snippets()+db.malicious_snippets()\n",
    "tfidf_transformer = train_from_js_tokens(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_0 = tfidf_transformer([s.data for s in db.legitimate_snippets()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_1 = tfidf_transformer([s.data for s in db.malicious_snippets()])\n",
    "X = np.vstack([X_0, X_1])\n",
    "Y =np.zeros(len(X))\n",
    "Y[-len(X_1):] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def clf_eval(clf, X, Y):\n",
    "    loo = cross_validation.LeaveOneOut(len(Y))\n",
    "    Y_true = []\n",
    "    Y_pred = []\n",
    "    for train_index, test_index in loo:\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        Y_train, Y_test = Y[train_index], Y[test_index]\n",
    "        clf.fit(X_train, Y_train)\n",
    "        Y_pred.append(clf.predict(X_test))\n",
    "        Y_true.append(Y_test)\n",
    "    Y_true = np.array(Y_true)\n",
    "    Y_pred = np.array(Y_pred)\n",
    "    cm =  metrics.confusion_matrix(Y_true, Y_pred)\n",
    "    print(\"Overall accuracy : \"+str(sum(Y_true == Y_pred)/len(Y_true)))\n",
    "    return cm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating LinearSVC...\n",
      "Overall accuracy : [ 0.97297297]\n",
      "Evaluating KNeighborsClassifier...\n",
      "Overall accuracy : [ 0.93243243]\n",
      "Evaluating SVC...\n",
      "Overall accuracy : [ 0.59459459]\n",
      "Evaluating BaggingClassifier...\n",
      "Overall accuracy : [ 0.95945946]\n",
      "Evaluating RandomForestClassifier...\n",
      "Overall accuracy : [ 1.]\n",
      "Evaluating ExtraTreesClassifier...\n",
      "Overall accuracy : [ 0.97297297]\n",
      "Evaluating AdaBoostClassifier...\n",
      "Overall accuracy : [ 0.94594595]\n",
      "Evaluating GradientBoostingClassifier...\n",
      "Overall accuracy : [ 0.94594595]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "results = {}\n",
    "for str_clf in ['LinearSVC', 'KNeighborsClassifier', 'SVC', 'BaggingClassifier', 'RandomForestClassifier',\n",
    "                'ExtraTreesClassifier', 'AdaBoostClassifier', 'GradientBoostingClassifier']:\n",
    "    clf = eval(str_clf+'()')\n",
    "    print(\"Evaluating \"+str_clf+\"...\")\n",
    "    results[str_clf] = clf_eval(clf, X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11621b358>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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b8NbaOcC9wHb3X2tr7azcDkxERETOXXZa8FhrfwV+zeVYRERE5DzRvehFRER8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116eed588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_fpfn = {}\n",
    "results_fpfn['Yes'] = [len(X_0),0]\n",
    "results_fpfn['No'] = [0, len(X_1)]\n",
    "for k in results:\n",
    "    results_fpfn[k] = [results[k][0,1], results[k][1,0]]\n",
    "for k in results_fpfn:\n",
    "    plt.scatter(results_fpfn[k][0], results_fpfn[k][1])\n",
    "    plt.text(results_fpfn[k][0], results_fpfn[k][1], k)\n",
    "plt.title(\"Performance of different classification methods over the current database\")\n",
    "plt.xlabel(\"# of False Positives\")\n",
    "plt.ylabel(\"# of False Negatives\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def single_projection(X, s, l, color, labels=None):\n",
    "    Y = l.fit_transform(X)\n",
    "    plt.title(s)\n",
    "    plt.scatter(Y[:, 0], Y[:, 1], c=color, alpha=0.7)\n",
    "    plt.axis('tight')\n",
    "    if labels:\n",
    "        place_labels(labels, Y)\n",
    "\n",
    "def project_on_plane(X, color, n_neighbors = 10, n_components = 2, title='2D projection', unique=None, labels=None):\n",
    "    '''Give multiple 2D representations of a high-dimenstional dataset\n",
    "\n",
    "    See http://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html'''\n",
    "    fig = plt.figure(figsize=(15,8))\n",
    "    learners = [['Isomap', manifold.Isomap(n_neighbors, n_components)],\n",
    "                ['LLE', manifold.LocallyLinearEmbedding(n_neighbors, n_components, eigen_solver='auto')],\n",
    "                ['LTSA', manifold.LocallyLinearEmbedding(n_neighbors, n_components, eigen_solver='auto', method='ltsa')],\n",
    "                ['Hessian', manifold.LocallyLinearEmbedding(n_neighbors, n_components, eigen_solver='auto', method='hessian')],\n",
    "                [\"Modified\", manifold.LocallyLinearEmbedding(n_neighbors, n_components, eigen_solver='auto', method='hessian')],\n",
    "                [\"MDS\", manifold.MDS(n_components, max_iter=100, n_init=1)],\n",
    "                [\"Spectral Embedding\", manifold.SpectralEmbedding(n_components=n_components,n_neighbors=n_neighbors)],\n",
    "                [\"t-SNE\", manifold.TSNE(n_components=n_components, init='pca', random_state=0)]\n",
    "                ]\n",
    "    fig_num = 331\n",
    "    if unique:\n",
    "        s,l = [x for x in learners if x[0] == unique][0]\n",
    "        single_projection(X, s, l, color, labels)\n",
    "    else:\n",
    "        for s, l in learners:\n",
    "            ax = fig.add_subplot(fig_num)\n",
    "            fig_num += 1\n",
    "            single_projection(X, s, l, color, labels)\n",
    "    plt.savefig(title+'.pdf')\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GrgeuqNhore1McNP2XgQDKXxkrX0L6Al87pw7NoTMIiIiIglJdCCS44A859y+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\nExERERERyTAVbSIiIiIiIhn2/wFjRc3zLZMT+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f054e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "project_on_plane(X, ['r' if c == 1 else 'g' for c in Y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SzS82bz/QIdyRYjO/2MwvNvOLzfxiM7/YzC82bz8gSZIkSSOBRU6SOkhKaXJKaZuUkp+s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161c2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "project_on_plane(X, ['r' if c == 1 else 'g' for c in Y], unique='Isomap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8Yl5U8Ghl4fu+6sjcq64qmX0Pj5RUpLE7wYRVG5C3hClFDtQvjedNqwobwFZM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1173a5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "project_on_plane(X, ['r' if c == 1 else 'g' for c in Y], unique='MDS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1179601d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TNEnMBMnLlfeyEB0p+oEmDeCcuwxY7L1/2Dk3GZhB5pzB1d77LRGOl7Q1LZK4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117562940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y2d = manifold.Isomap(10, 2).fit_transform(X)\n",
    "plt.scatter(Y2d[:, 0], Y2d[:, 1], c=['r' if c == 1 else 'g' for c in Y], alpha=0.7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x119098be0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sc3NzzZH3RzwtLe01gLRybp+QSkVFv4owxmqeO3du8SeffFIbAJycnPrFxcV9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\nST+EEGrkP7vPu5IpSf+iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180b84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = RandomForestClassifier().fit(X,Y)\n",
    "isomap = manifold.Isomap(10, 2)\n",
    "Y2d = isomap.fit_transform(X)\n",
    "if hasattr(clf, \"decision_function\"):\n",
    "    Z = clf.decision_function(X)\n",
    "else:\n",
    "    Z = clf.predict_proba(X)[:, 1]\n",
    "plt.scatter(Y2d[:, 0], Y2d[:, 1], c=Z, alpha=0.7)\n",
    "x_min = X.min(0)\n",
    "x_span = X.max(0)-X.min(0)\n",
    "rand_points = []\n",
    "for i in range(0,100):\n",
    "    r = np.random.random(len(x_min))*x_span - x_min\n",
    "    rand_points.append(r)\n",
    "rand_points = np.array(rand_points)\n",
    "if hasattr(clf, \"decision_function\"):\n",
    "    rand_values = clf.decision_function(rand_points)\n",
    "else:\n",
    "    rand_values = clf.predict_proba(rand_points)[:, 1]\n",
    "Ybis2d = isomap.transform(rand_points)\n",
    "plt.scatter(Ybis2d[:,0], Ybis2d[:,1], c=rand_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zi[:,:,0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MkflWO509Ly2Ap4YYziIiIpKFhm92OlS/hwUcbjqMGFEb/+pkRNqDJJEKuCzz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117559518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "from scipy.interpolate import griddata\n",
    "_Y = np.vstack([Y2d, Ybis2d])\n",
    "_Z = np.vstack([Z[:,None], rand_values[:,None]])\n",
    "xi = np.linspace(_Y[:,0].min(), _Y[:,0].max(), 500)\n",
    "yi = np.linspace(_Y[:,1].min(), _Y[:,1].max(), 500)\n",
    "zi = griddata(_Y, _Z, (xi[None,:], yi[:,None]), method='nearest')\n",
    "CS = plt.contour(xi,yi,zi[:,:,0],15,linewidths=0.5,colors='k')\n",
    "CS = plt.contourf(xi,yi,zi[:,:,0],15,cmap=plt.cm.jet)\n",
    "plt.scatter(Y2d[:, 0], Y2d[:, 1], c=Z)\n",
    "plt.savefig('Picasso.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}