Training_Distributed/4.3a-Model_evaluate_ROC_and_CM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inclusile classifier model performance metrics with tf.Keras using data in TFRecord format\n",
    "\n",
    "This notebook loads a trained model for the dense neural network using the Inclusive Classifier.\n",
    "This uses as input the list of particles + the High Level Features. Data is prepared from Parquet using Apache Spark, into TFRecord format and used with tf.Data and tf.io in tf.keras.\n",
    "Training is done in a separate notebook and/or distributed code.\n",
    "\n",
    "To run this notebook we used the following configuration:\n",
    "* *Software stack*: TensorFlow 2.0.0-rc0\n",
    "* *Platform*: CentOS 7, Python 3.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0-rc0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import Sequential, Input, Model\n",
    "from tensorflow.keras.layers import Masking, Dense, Activation, GRU, Dropout, concatenate\n",
    "\n",
    "tf.version.VERSION\n",
    "# only needed for TensorFlow 1.x\n",
    "# tf.enable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = \"./mymodel.tf\"\n",
    "\n",
    "model = tf.keras.models.load_model(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load test data in TFRecord format, using tf.data and tf.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test dataset \n",
    "PATH = \"/home/canali/Data/testUndersampled.tfrecord\"\n",
    "files_test_dataset = tf.data.Dataset.list_files(PATH+\"/part-r-*\", shuffle=False)\n",
    "#files_test_dataset = tf.data.Dataset.list_files(PATH+\"/part-r-0000*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tunable\n",
    "num_parallel_reads=8\n",
    "\n",
    "test_dataset = files_test_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE).interleave(\n",
    "    tf.data.TFRecordDataset, \n",
    "    cycle_length=num_parallel_reads,\n",
    "    num_parallel_calls=tf.data.experimental.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to decode TF records into the required features and labels\n",
    "def decode(serialized_example):\n",
    "    deser_features = tf.io.parse_single_example(\n",
    "      serialized_example,\n",
    "      # Defaults are not specified since both keys are required.\n",
    "      features={\n",
    "          'HLF_input': tf.io.FixedLenFeature((14), tf.float32),\n",
    "          'GRU_input': tf.io.FixedLenFeature((801,19), tf.float32),\n",
    "          'encoded_label': tf.io.FixedLenFeature((3), tf.float32),\n",
    "          })\n",
    "    return((deser_features['GRU_input'], deser_features['HLF_input']), deser_features['encoded_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "parsed_test_dataset=test_dataset.map(decode, num_parallel_calls=tf.data.experimental.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tunable\n",
    "test_batch_size = 10240\n",
    "\n",
    "test=parsed_test_dataset.batch(test_batch_size)\n",
    "test=test.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
    "test=test.repeat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_test_samples=856090 # there are 856090 samples in the test dataset\n",
    "\n",
    "validation_steps=num_test_samples//test_batch_size  \n",
    "validation_steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83/83 [==============================] - 42s 505ms/step - loss: 0.1128 - accuracy: 0.9596\n",
      "CPU times: user 3min 11s, sys: 44.2 s, total: 3min 55s\n",
      "Wall time: 42 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.11281443306480546, 0.95959854]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model.evaluate(test, steps=validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 7s, sys: 40.1 s, total: 3min 47s\n",
      "Wall time: 39.5 s\n"
     ]
    }
   ],
   "source": [
    "%time y_pred = model.predict(test, steps=validation_steps)\n",
    "num_entries=y_pred[:,0].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 2s, sys: 47 s, total: 5min 49s\n",
      "Wall time: 2min 34s\n"
     ]
    }
   ],
   "source": [
    "%time y_test_all = [labels.numpy() for features,labels in parsed_test_dataset.__iter__()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = np.stack(y_test_all[:num_entries])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the HLF classifier: 0.9596\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Accuracy of the HLF classifier: {:.4f}'.format(\n",
    "    accuracy_score(np.argmax(y_true, axis=1),np.argmax(y_pred, axis=1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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       "mpl.figure.prototype._init_header = function() {\n",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
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       "\n",
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       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
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       "\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",
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       "\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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
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       "    var rubberband = $('<canvas/>');\n",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "\n",
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       "        tooltip_span.addClass('ui-button-text');\n",
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       "\n",
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       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
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       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
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       "\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    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",
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       "}\n",
       "\n",
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       "    if (!this.waiting) {\n",
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       "\n",
       "\n",
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       "    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'] / mpl.ratio;\n",
       "    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 overridden (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",
       "        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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt \n",
    "plt.style.use('seaborn-darkgrid')\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "labels_name = ['qcd', 'tt', 'wjets']\n",
    "labels = [0,1,2]\n",
    "\n",
    "cm = confusion_matrix(np.argmax(y_true, axis=1), np.argmax(y_pred, axis=1), labels=labels)\n",
    "\n",
    "## Normalize CM\n",
    "cm = cm / cm.astype(np.float).sum(axis=1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax = sns.heatmap(cm, annot=True, fmt='g')\n",
    "ax.xaxis.set_ticklabels(labels_name)\n",
    "ax.yaxis.set_ticklabels(labels_name)\n",
    "plt.xlabel('True labels')\n",
    "plt.ylabel('Predicted labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example test label and model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.9958092e-01, 2.4495419e-04, 1.7413442e-04],\n",
       "       [4.8228692e-05, 9.9961072e-01, 3.4099948e-04],\n",
       "       [6.6918891e-04, 9.9788254e-01, 1.4482477e-03],\n",
       "       [4.7982630e-05, 9.9947387e-01, 4.7808807e-04]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC and AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "\n",
    "for i in range(3):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_true[:, i], y_pred[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.9956209434429777, 1: 0.9972418860089601, 2: 0.9952398930182931}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary containign ROC-AUC for the three classes \n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n",
       "    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 overridden (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",
       "        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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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "# Plot roc curve \n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr[0], tpr[0], lw=2, \n",
    "         label='HLF classifier (AUC) = %0.4f' % roc_auc[0])\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Background Contamination (FPR)')\n",
    "plt.ylabel('Signal Efficiency (TPR)')\n",
    "plt.title('$tt$ selector')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.9"
  },
  "sparkconnect": {
   "bundled_options": [],
   "list_of_options": [
    {
     "name": "spark.dynamicAllocation.enabled",
     "value": "false"
    },
    {
     "name": "spark.executor.memory",
     "value": "14G"
    },
    {
     "name": "spark.executor.cores",
     "value": "6"
    },
    {
     "name": "spark.executor.instances",
     "value": "6"
    },
    {
     "name": "spark.driver.memory",
     "value": "10G"
    },
    {
     "name": "spark.driver.maxResultSize",
     "value": "10G"
    }
   ]
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}