examples/numerical_data/multigranular_learner/multi_resolution_gfmm_general_use.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This example shows how to use the multi-resolution hierarchical granular representation based classifier using general fuzzy min-max neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Execute directly from the python file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the path to the this jupyter notebook file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\examples\\\\numerical_data\\\\multigranular_learner'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_notebook_dir = os.path.dirname(os.path.abspath(\"__file__\"))\n",
    "this_notebook_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the home folder of the Hyperbox-Brain project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/hyperbox-brain')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "project_dir = Path(this_notebook_dir).parent.parent.parent\n",
    "project_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the path to the Python file containing the implementation of the multi-resolution hierarchical granular representation based classifier using the general fuzzy min-max neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\hbbrain\\\\numerical_data\\\\multigranular_learner\\\\multi_resolution_gfmm.py'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_resolution_gfmm_file_path = os.path.join(project_dir, Path(\"hbbrain/numerical_data/multigranular_learner/multi_resolution_gfmm.py\"))\n",
    "multi_resolution_gfmm_file_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run the found file by showing the execution directions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: multi_resolution_gfmm.py [-h] -training_file TRAINING_FILE\n",
      "                                -testing_file TESTING_FILE\n",
      "                                [--val_file VAL_FILE]\n",
      "                                [--n_partitions N_PARTITIONS]\n",
      "                                [--granular_theta GRANULAR_THETA]\n",
      "                                [--gamma GAMMA]\n",
      "                                [--min_membership_aggregation MIN_MEMBERSHIP_AGGREGATION]\n",
      "\n",
      "The description of parameters\n",
      "\n",
      "required arguments:\n",
      "  -training_file TRAINING_FILE\n",
      "                        A required argument for the path to training data file\n",
      "                        (including file name)\n",
      "  -testing_file TESTING_FILE\n",
      "                        A required argument for the path to testing data file\n",
      "                        (including file name)\n",
      "\n",
      "optional arguments:\n",
      "  --val_file VAL_FILE   The path to validation data file (including file name)\n",
      "  --n_partitions N_PARTITIONS\n",
      "                        Number of disjoint partitions to train base learners\n",
      "                        (default: 4)\n",
      "  --granular_theta GRANULAR_THETA\n",
      "                        Granular maximum hyperbox sizes (default: [0.1, 0.2,\n",
      "                        0.3, 0.4, 0.5])\n",
      "  --gamma GAMMA         A sensitivity parameter describing the speed of\n",
      "                        decreasing of the membership function in each\n",
      "                        dimension (larger than 0) (default: 1)\n",
      "  --min_membership_aggregation MIN_MEMBERSHIP_AGGREGATION\n",
      "                        Minimum membership value for hyperbox aggregration at\n",
      "                        higher granular levels (in the range of [0, 1])\n",
      "                        (default: 0)\n"
     ]
    }
   ],
   "source": [
    "!python \"{multi_resolution_gfmm_file_path}\" -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the path to training and testing datasets stored in the dataset folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\dataset\\\\syn_num_train.csv'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_file = os.path.join(project_dir, Path(\"dataset/syn_num_train.csv\"))\n",
    "training_data_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\hyperbox-brain\\\\dataset\\\\syn_num_test.csv'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data_file = os.path.join(project_dir, Path(\"dataset/syn_num_test.csv\"))\n",
    "testing_data_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run a demo program"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If the argument 'validation_file' gets the value of validation file path, the pruning procedure will be used after merging all hyperboxes from base learners. Otherwise, the pruning procedure will not be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 3.058 (s)\n",
      "Testing accuracy (using voting from all granularity levels) =  86.70%\n",
      "Prediction of each base learner at a given partition:\n",
      "Partition 0 - Testing accuracy =  84.00% - No boxes = 27\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   4 | elapsed:    2.8s remaining:    2.8s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    2.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partition 1 - Testing accuracy =  87.80% - No boxes = 29\n",
      "Partition 2 - Testing accuracy =  85.40% - No boxes = 27\n",
      "Partition 3 - Testing accuracy =  87.10% - No boxes = 26\n",
      "Prediction for each granularity level:\n",
      "Level 1 - Testing accuracy =  85.10% - No boxes = 101\n",
      "Level 2 - Testing accuracy =  88.20% - No boxes = 38\n",
      "Level 3 - Testing accuracy =  87.10% - No boxes = 27\n",
      "Level 4 - Testing accuracy =  86.10% - No boxes = 20\n",
      "Level 5 - Testing accuracy =  86.20% - No boxes = 14\n",
      "Level 6 - Testing accuracy =  82.60% - No boxes = 10\n"
     ]
    }
   ],
   "source": [
    "!python \"{multi_resolution_gfmm_file_path}\" -training_file \"{training_data_file}\" -testing_file \"{testing_data_file}\" --n_partitions 4 --granular_theta \"[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]\" --gamma 1 --min_membership_aggregation 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Using the multi-resolution hierarchical granular representation based classifier using general fuzzy min-max neural network through init, fit, and predict functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hbbrain.numerical_data.multigranular_learner.multi_resolution_gfmm import MultiGranularGFMM\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create training and testing data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(training_data_file, header=None)\n",
    "df_test = pd.read_csv(testing_data_file, header=None)\n",
    "\n",
    "Xy_train = df_train.to_numpy()\n",
    "Xy_test = df_test.to_numpy()\n",
    "\n",
    "Xtr = Xy_train[:, :-1]\n",
    "ytr = Xy_train[:, -1]\n",
    "\n",
    "Xtest = Xy_test[:, :-1]\n",
    "ytest = Xy_test[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initializing parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of disjoint partitions to build base learners\n",
    "n_partitions = 4\n",
    "# a list of maximum hyperbox sizes for granularity levels\n",
    "granular_theta = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]\n",
    "# minimum membership values between two hyperboxes aggregated at higher abstraction levels\n",
    "min_membership_aggregation = 0.1\n",
    "# the speed of decreasing of membership values\n",
    "gamma = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   4 | elapsed:    2.7s remaining:    2.7s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    2.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MultiGranularGFMM(granular_theta=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6],\n",
       "                  min_membership_aggregation=0.1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hbbrain.constants import HETEROGENEOUS_CLASS_LEARNING\n",
    "multi_granular_gfmm_clf = MultiGranularGFMM(n_partitions=n_partitions, granular_theta=granular_theta, gamma=gamma, min_membership_aggregation=min_membership_aggregation)\n",
    "# Training using the heterogeneous model for class labels.\n",
    "multi_granular_gfmm_clf.fit(Xtr, ytr, learning_type=HETEROGENEOUS_CLASS_LEARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The code below shows how to display decision boundaries among classes at a given granularity level if input data are 2-dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "\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"
    },
    {
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\" width=\"588\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing hyperboxes and boundaries at the first granularity level (level 1). Note that the order of the level starts from 0.\n",
    "multi_granular_gfmm_clf.draw_2D_hyperbox_and_boundary_granular_level(window_name=\"Hyperbox-based classifier and its decision boundaries at level 1\", level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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=\"612.6666666666666\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing hyperboxes and boundaries at the last granularity level (level 6)\n",
    "multi_granular_gfmm_clf.draw_2D_hyperbox_and_boundary_granular_level(window_name=\"Hyperbox-based classifier and its decision boundaries at level 6\", level = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of hyperboxes at all granularity levels = 210\n"
     ]
    }
   ],
   "source": [
    "# Get total number of hyperboxes at all granularity levels\n",
    "print(\"Total number of hyperboxes at all granularity levels = %d\"%multi_granular_gfmm_clf.get_n_hyperboxes(level=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of hyperboxes at the first granularity levels = 101\n",
      "Total number of hyperboxes at the last granularity levels = 10\n"
     ]
    }
   ],
   "source": [
    "# Get number of hyperboxes at a given granularity level\n",
    "print(\"Total number of hyperboxes at the first granularity levels = %d\"%multi_granular_gfmm_clf.get_n_hyperboxes(level=0))\n",
    "print(\"Total number of hyperboxes at the last granularity levels = %d\"%multi_granular_gfmm_clf.get_n_hyperboxes(level=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using all GFMM models from all granularity level to make the final prediction using majority voting, in which each granularity level contributes one predicted result for each input pattern and the final predicted result is the class getting most of votes from the models at all granularity levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (majority voting) =  86.70%\n"
     ]
    }
   ],
   "source": [
    "y_pred = multi_granular_gfmm_clf.predict(Xtest, level=-1)\n",
    "acc = accuracy_score(ytest, y_pred)\n",
    "print(f'Accuracy (majority voting) = {acc * 100: .2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use a certain granularity level to make prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction for each granularity level:\n",
      "Level 1 - Testing accuracy =  85.10% - No hyperboxes = 101\n",
      "Level 2 - Testing accuracy =  88.20% - No hyperboxes = 38\n",
      "Level 3 - Testing accuracy =  87.10% - No hyperboxes = 27\n",
      "Level 4 - Testing accuracy =  86.10% - No hyperboxes = 20\n",
      "Level 5 - Testing accuracy =  86.20% - No hyperboxes = 14\n",
      "Level 6 - Testing accuracy =  82.60% - No hyperboxes = 10\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction for each granularity level:\")\n",
    "for i in range(len(granular_theta)):\n",
    "    y_pred_lv = multi_granular_gfmm_clf.predict(Xtest, level=i)\n",
    "    acc_lv = accuracy_score(ytest, y_pred_lv)\n",
    "    n_boxes = multi_granular_gfmm_clf.get_n_hyperboxes(i)\n",
    "    print(f'Level {i + 1} - Testing accuracy = {acc_lv * 100: .2f}% - No hyperboxes = {n_boxes}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaining the predicted result for the input sample by showing membership values and hyperboxes for each class using the model at a given granularity level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_need_explain = 0\n",
    "# Using the trained model at the sixth granularity level to make prediction and explanation. Note that the value for the level parameter starts from 0. \n",
    "level_explain = 5\n",
    "y_pred_input_0, mem_val_classes, min_points_classes, max_points_classes = multi_granular_gfmm_clf.get_sample_explanation_granular_level(Xtest[sample_need_explain], Xtest[sample_need_explain], level_explain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class for sample X = [0.752930, 0.385920] is 1 and real class is 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted class for sample X = [%f, %f] is %d and real class is %d\" % (Xtest[sample_need_explain, 0], Xtest[sample_need_explain, 1], y_pred_input_0, ytest[sample_need_explain]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Membership values:\n",
      "Class 1 has the maximum membership value = 0.989125\n",
      "Class 2 has the maximum membership value = 0.915132\n",
      "Class 1 has the representative hyperbox: V = [0.763805 0.369765] and W = [0.91185 0.48598]\n",
      "Class 2 has the representative hyperbox: V = [0.563065 0.17003 ] and W = [0.6680625 0.65662  ]\n"
     ]
    }
   ],
   "source": [
    "print(\"Membership values:\")\n",
    "for key, val in mem_val_classes.items():\n",
    "    print(\"Class %d has the maximum membership value = %f\" % (key, val))\n",
    "    \n",
    "for key in min_points_classes:\n",
    "    print(\"Class %d has the representative hyperbox: V = %s and W = %s\" % (key, min_points_classes[key], max_points_classes[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show input sample and hyperboxes belonging to each class. In 2D, we can show rectangles or use parallel coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using rectangles to show explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
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       "        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>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"556.6666666666666\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multi_granular_gfmm_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, \"2D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using parallel coordinates to show explanations. This method can be used for any number of dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the parallel coordinates graph\n",
    "multi_granular_gfmm_clf.show_sample_explanation(Xtest[sample_need_explain], Xtest[sample_need_explain], min_points_classes, max_points_classes, y_pred_input_0, file_path=\"iframe_figures/multi_resolution_gfmm_par_cord.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"820\"\n",
       "            height=\"500\"\n",
       "            src=\"iframe_figures/multi_resolution_gfmm_par_cord.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x232626bfbe0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the result to show on the notebook\n",
    "from IPython.display import IFrame\n",
    "IFrame('iframe_figures/multi_resolution_gfmm_par_cord.html', width=820, height=500)"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}