EvalClassifiers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of shallow classifiers\n",
    "\n",
    "In this notebook we take some trained scikit-learn classifiers and evaluate their performance on the test set which has already been processed into feature data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('precision', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the inputs\n",
    "\n",
    "Retrieve our test set features, the serialized pre-trained classifiers, and the fitted data scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/global/cscratch1/sd/sfarrell/atlasdl/classifiers.pkl\r\n",
      "/global/cscratch1/sd/sfarrell/atlasdl/test_features.npz\r\n"
     ]
    }
   ],
   "source": [
    "ls $SCRATCH/atlasdl/test_features.npz $SCRATCH/atlasdl/classifiers.pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_file = '/global/cscratch1/sd/sfarrell/atlasdl/test_features.npz'\n",
    "classifiers_file = '/global/cscratch1/sd/sfarrell/atlasdl/classifiers.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load the classifiers\n",
    "with open(classifiers_file) as f:\n",
    "    classifiers_data = pickle.load(f)\n",
    "    scaler = classifiers_data['scaler']\n",
    "    classifiers = dict((key, val) for (key, val) in classifiers_data.items()\n",
    "                       if key != 'scaler')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'abdt': AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "           learning_rate=1.0, n_estimators=50, random_state=None),\n",
       " 'gbdt': GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "               learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "               max_features=None, max_leaf_nodes=None,\n",
       "               min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "               min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "               n_estimators=100, presort='auto', random_state=None,\n",
       "               subsample=1.0, verbose=0, warm_start=False),\n",
       " 'lr': LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "           penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "           verbose=0, warm_start=False),\n",
       " 'mlp': MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "        beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "        hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "        learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "        nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "        shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "        verbose=False, warm_start=False),\n",
       " 'rf': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "             max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "             min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "             verbose=0, warm_start=False)}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the classifiers that were trained\n",
    "classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the input feature data\n",
    "with np.load(features_file) as f:\n",
    "    X = f['X']\n",
    "    y = f['y']\n",
    "    w = f['weights']\n",
    "    passSR = f['passSR']    \n",
    "\n",
    "# Scale the data with the pre-fit standard scaler\n",
    "X = scaler.transform(X)\n",
    "\n",
    "# Scale the weights up to 14.8/fb\n",
    "w = w*14.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shapes\n",
      "X: (695446, 23)\n",
      "y: (695446,)\n",
      "w: (695446,)\n",
      "passSR: (695446,)\n"
     ]
    }
   ],
   "source": [
    "print('Data shapes')\n",
    "print('X:', X.shape)\n",
    "print('y:', y.shape)\n",
    "print('w:', w.shape)\n",
    "print('passSR:', passSR.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dict(RPV=y, QCD=1-y, Total=1), dtype=np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw counts\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>QCD</th>\n",
       "      <th>RPV</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>392625</td>\n",
       "      <td>302821</td>\n",
       "      <td>695446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>14019</td>\n",
       "      <td>126924</td>\n",
       "      <td>140943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        QCD     RPV   Total\n",
       "All  392625  302821  695446\n",
       "SR    14019  126924  140943"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Raw counts')\n",
    "pd.DataFrame(map(np.sum, [df, df.mul(passSR,0)]),\n",
    "             index=['All', 'SR'], dtype=np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted counts\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>QCD</th>\n",
       "      <th>RPV</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3.860e+06</td>\n",
       "      <td>201.119</td>\n",
       "      <td>3.860e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>1.309e+03</td>\n",
       "      <td>84.297</td>\n",
       "      <td>1.393e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           QCD      RPV      Total\n",
       "All  3.860e+06  201.119  3.860e+06\n",
       "SR   1.309e+03   84.297  1.393e+03"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Weighted counts')\n",
    "pd.DataFrame(map(np.sum, [df.mul(w,0), df.mul(passSR*w,0)]), index=['All', 'SR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convenience\n",
    "rpv, qcd = y, 1-y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate the TPR and FPR of the SR\n",
    "sr_tpr = (w*rpv*passSR).sum() / (w*rpv).sum()\n",
    "sr_fpr = (w*qcd*passSR).sum() / (w*qcd).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's draw the ROC curves\n",
    "clf_names = ['lr', 'gbdt', 'abdt', 'mlp']\n",
    "clf_labels = ['Logistic reg', 'Gradient BDT', 'Ada BDT', 'MLP']\n",
    "clfs = [classifiers[name] for name in clf_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test set probability scores\n",
    "clf_probs = [c.predict_proba(X)[:,1] for c in clfs]\n",
    "# ROC curves\n",
    "clf_rocs = [metrics.roc_curve(y, p, sample_weight=w) for p in clf_probs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_fprs = [roc[0] for roc in clf_rocs]\n",
    "clf_tprs = [roc[1] for roc in clf_rocs]\n",
    "clf_thrs = [roc[2] for roc in clf_rocs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2aaae66b9850>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the ROCs\n",
    "plt.figure()\n",
    "plt.plot(sr_fpr, sr_tpr, 's', label='Ana SR')\n",
    "for i in range(len(clfs)):\n",
    "    plt.plot(clf_fprs[i], clf_tprs[i], label=clf_labels[i])\n",
    "plt.xlim((0, 0.001))\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physics significance\n",
    "\n",
    "Quantify performance in terms of statistical significance of the classification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Approximate median significance\n",
    "def calc_ams(nsig, nbkg, reg=10):\n",
    "    s = float(nsig)\n",
    "    b = float(nbkg + reg)\n",
    "    return np.sqrt(2 * ((s + b) * np.log(1 + s/b) - s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Calculate AMS for the SR\n",
    "nsig = (w * rpv * passSR).sum()\n",
    "nbkg = (w * qcd * passSR).sum()\n",
    "sr_ams = calc_ams(nsig, nbkg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Calculate the AMS for each classifier for each threshold used in the ROC\n",
    "wrpv, wqcd = w*rpv, w*qcd\n",
    "clf_ams = [np.zeros_like(th) for th in clf_thrs]\n",
    "for i in range(len(clfs)):\n",
    "    print(clf_names[i])\n",
    "    for j in range(clf_thrs[i].shape[0]):\n",
    "        sel = clf_probs[i] > clf_thrs[i][j]\n",
    "        clf_ams[i][j] = calc_ams(wrpv.dot(sel), wqcd.dot(sel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2aaae3c5bbd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the AMS\n",
    "plt.figure()\n",
    "plt.plot(sr_fpr, sr_ams, 's', label='Ana SR')\n",
    "for i in range(len(clfs)):\n",
    "    plt.plot(clf_fprs[i], clf_ams[i], label=clf_labels[i])\n",
    "plt.xlim((0, 0.001))\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('Approximate median significance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "metrics_file = '/global/cscratch1/sd/sfarrell/atlasdl/sklearn_metrics_14p8ifb.npz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savez(metrics_file, fpr=clf_fprs, tpr=clf_tprs, thresholds=clf_thrs,\n",
    "         ams=clf_ams, names=clf_names)"
   ]
  }
 ],
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