ROC random forest

roc.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "X, y = make_classification(n_samples=1000, n_informative=10, n_clusters_per_class=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ms_arr = np.arange(1, 50, 10)\n",
    "\n",
    "gcv = GridSearchCV(clf, {'min_samples_leaf': ms_arr}, scoring='roc_auc', cv=5, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n",
      "[CV] min_samples_leaf=1 ..............................................\n",
      "[CV] ............................... min_samples_leaf=1, total=   0.3s\n",
      "[CV] min_samples_leaf=1 ..............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................... min_samples_leaf=1, total=   0.3s\n",
      "[CV] min_samples_leaf=1 ..............................................\n",
      "[CV] ............................... min_samples_leaf=1, total=   0.3s\n",
      "[CV] min_samples_leaf=1 ..............................................\n",
      "[CV] ............................... min_samples_leaf=1, total=   0.3s\n",
      "[CV] min_samples_leaf=1 ..............................................\n",
      "[CV] ............................... min_samples_leaf=1, total=   0.3s\n",
      "[CV] min_samples_leaf=11 .............................................\n",
      "[CV] .............................. min_samples_leaf=11, total=   0.2s\n",
      "[CV] min_samples_leaf=11 .............................................\n",
      "[CV] .............................. min_samples_leaf=11, total=   0.3s\n",
      "[CV] min_samples_leaf=11 .............................................\n",
      "[CV] .............................. min_samples_leaf=11, total=   0.3s\n",
      "[CV] min_samples_leaf=11 .............................................\n",
      "[CV] .............................. min_samples_leaf=11, total=   0.3s\n",
      "[CV] min_samples_leaf=11 .............................................\n",
      "[CV] .............................. min_samples_leaf=11, total=   0.2s\n",
      "[CV] min_samples_leaf=21 .............................................\n",
      "[CV] .............................. min_samples_leaf=21, total=   0.2s\n",
      "[CV] min_samples_leaf=21 .............................................\n",
      "[CV] .............................. min_samples_leaf=21, total=   0.3s\n",
      "[CV] min_samples_leaf=21 .............................................\n",
      "[CV] .............................. min_samples_leaf=21, total=   0.3s\n",
      "[CV] min_samples_leaf=21 .............................................\n",
      "[CV] .............................. min_samples_leaf=21, total=   0.2s\n",
      "[CV] min_samples_leaf=21 .............................................\n",
      "[CV] .............................. min_samples_leaf=21, total=   0.3s\n",
      "[CV] min_samples_leaf=31 .............................................\n",
      "[CV] .............................. min_samples_leaf=31, total=   0.2s\n",
      "[CV] min_samples_leaf=31 .............................................\n",
      "[CV] .............................. min_samples_leaf=31, total=   0.2s\n",
      "[CV] min_samples_leaf=31 .............................................\n",
      "[CV] .............................. min_samples_leaf=31, total=   0.2s\n",
      "[CV] min_samples_leaf=31 .............................................\n",
      "[CV] .............................. min_samples_leaf=31, total=   0.2s\n",
      "[CV] min_samples_leaf=31 .............................................\n",
      "[CV] .............................. min_samples_leaf=31, total=   0.2s\n",
      "[CV] min_samples_leaf=41 .............................................\n",
      "[CV] .............................. min_samples_leaf=41, total=   0.3s\n",
      "[CV] min_samples_leaf=41 .............................................\n",
      "[CV] .............................. min_samples_leaf=41, total=   0.3s\n",
      "[CV] min_samples_leaf=41 .............................................\n",
      "[CV] .............................. min_samples_leaf=41, total=   0.3s\n",
      "[CV] min_samples_leaf=41 .............................................\n",
      "[CV] .............................. min_samples_leaf=41, total=   0.2s\n",
      "[CV] min_samples_leaf=41 .............................................\n",
      "[CV] .............................. min_samples_leaf=41, total=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    7.0s finished\n"
     ]
    }
   ],
   "source": [
    "gcv.fit(X_train, y_train)\n",
    "clf_opt = gcv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "def roc(y_true, pred_probs, n_points=100, ax=None, **kwargs):\n",
    "    cm = lambda x: confusion_matrix(y_true, pred_probs > x)\n",
    "    tpr = np.zeros(n_points)\n",
    "    fpr = np.zeros(n_points)\n",
    "    \n",
    "    for i, p in enumerate(np.linspace(0, 1, n_points)):\n",
    "        tn, fp, fn, tp = cm(p).ravel()\n",
    "        tpr[i] = tp / (tp + fn)\n",
    "        fpr[i] = fp / (fp + tn)\n",
    "        \n",
    "    if ax is None:\n",
    "        plt.plot(fpr, tpr, **kwargs)\n",
    "        plt.xlabel('FPR')\n",
    "        plt.ylabel('TPR')\n",
    "        plt.title('ROC')\n",
    "    else:\n",
    "        ax.plot(fpr, tpr, **kwargs)\n",
    "        ax.set_xlabel('FPR')\n",
    "        ax.set_ylabel('TPR')\n",
    "        ax.set_title('ROC')\n",
    "\n",
    "def plot_sklearn_roc(y_true, pred_probs, ax=None, **kwargs):\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, pred_probs)\n",
    "    \n",
    "    if ax is None:\n",
    "        plt.plot(fpr, tpr, **kwargs)\n",
    "        plt.xlabel('FPR')\n",
    "        plt.ylabel('TPR')\n",
    "        plt.title('ROC sklearn')\n",
    "    else:\n",
    "        ax.plot(fpr, tpr, **kwargs)\n",
    "        ax.set_xlabel('FPR')\n",
    "        ax.set_ylabel('TPR')\n",
    "        ax.set_title('ROC sklearn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "probs1 = clf.predict_proba(X_test)[:,1]\n",
    "\n",
    "clf_opt.fit(X_train, y_train)\n",
    "probs2 = clf_opt.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x123d29320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f, ax = plt.subplots(ncols=2, figsize=(10,4))\n",
    "\n",
    "roc(y_test, probs1, label='rf', ax=ax[0])\n",
    "roc(y_test, probs2, label='rf tuned', ax=ax[0])\n",
    "plot_sklearn_roc(y_test, probs1, label='rf', ax=ax[1])\n",
    "plot_sklearn_roc(y_test, probs2, label='rf tuned', ax=ax[1])\n",
    "ax[0].legend()\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}