Getting Insights into Quantum-Chemical Reactions.ipynb

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   "source": [
    "<center>\n",
    "    <hr width='75%' style='height:1px'>\n",
    "    <h3><b>Getting Insights into Quantum-Chemical Relations</b></h3>\n",
    "    <hr width=75% style='height:1px'>\n",
    "</center>\n",
    "<br>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Goal:\n",
    "\n",
    "To extract quantum-chemical insights from the '[QM7 dataset](http://quantum-machine.org/data/qm7.mat)' of molecules."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. The QM7 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:22:54.932202Z",
     "start_time": "2023-09-19T22:22:51.750217Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import io\n",
    "from scipy.spatial.distance import cdist, euclidean\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.manifold import TSNE\n",
    "import itertools\n",
    "import mplcursors\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:22:55.440845Z",
     "start_time": "2023-09-19T22:22:54.958134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'X', 'R', 'Z', 'T', 'P'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = io.loadmat('data/qm7.mat')\n",
    "data.keys()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The QM7 dataset consists of 7165 organic molecules, each of which is composed of up to 23 atoms. The 3d\n",
    "coordinates of each atom in each molecule are available in the variable R. It is an array of size 7165×23×3\n",
    "containing for each molecule and atom a triplet representing the 3d coordinates. The variable Z is an array\n",
    "of size 7165×23 which gives for each molecule and atom of the molecule the corresponding atomic number.\n",
    "An atomic number of 1 corresponds to a hydrogen atom (H), the number 6 corresponds to carbon (C), the\n",
    "numbers 7 and 8 to nitrogen (N) and oxygen (O) respectively, and finally, the number 16 corresponds to\n",
    "sulfur (S). If the number is zero, then it indicates that there is no atom at this index, and the corresponding\n",
    "3d coordinate is ignored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:22:57.015706Z",
     "start_time": "2023-09-19T22:22:57.007727Z"
    }
   },
   "outputs": [],
   "source": [
    "coords = np.array(data['R']) # shape (7165, 23, 3)\n",
    "atomic_nums = np.array(data['Z']) # shape (7165, 23)\n",
    "atomization_energies = np.array(data['T']) # shape (1, 7165)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:22:58.448769Z",
     "start_time": "2023-09-19T22:22:58.439793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7165, 23, 3), (7165, 23), (1, 7165))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords.shape, atomic_nums.shape, atomization_energies.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.1 Visualizing molecules"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function visualizes the molecules (at random) in the data using both a 2D scatter plot and a 3D one.\n",
    "\n",
    "The number of molecules to be visualized can be set using the `n_molecules` parameter whose default value is 5.\n",
    "\n",
    "Specific moelcules can also be plotted by passing a list of indices to the `indices` parameter.\n",
    "\n",
    "The `THRESHOLD` parameter is used to plot the bonds between atoms and is set to 3.0 by default.\n",
    "\n",
    "To make the 3D plot interactive, the `iteractive` parameter is set to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
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   "source": [
    "# Setting different colors for different atoms\n",
    "COLOR_MAPPING = {\n",
    "    1: [1, 0, 0],  # Hydrogen = red\n",
    "    6: [0, 1, 0],  # Carbon   = green\n",
    "    7: [1, 0, 1],  # Nitrogen = purple\n",
    "    8: [0, 0, 1],  # Oxygen   = blue\n",
    "    16: [1, 1, 0]  # Sulfur   = yellow\n",
    "}\n",
    "\n",
    "def plot_molecules(coords, atomic_nums, n_molecules=5, molecule=-1, THRESHOLD=3.0, interactive=False):\n",
    "    if interactive:\n",
    "        %matplotlib notebook\n",
    "    else:\n",
    "        %matplotlib inline\n",
    "\n",
    "    print('H: 🔴,  C: 🟢, N: 🟣, O: 🔵, S: 🟡')\n",
    "    \n",
    "    # if molecule are given explicitly\n",
    "    if molecule != -1:\n",
    "        indices = molecule\n",
    "        if isinstance(molecule, int):\n",
    "            indices = [molecule]\n",
    "        n_molecules = len(indices)    \n",
    "    else:\n",
    "        # Generate random indices for selecting molecule\n",
    "        indices = np.random.choice(range(len(coords)), size=n_molecules, replace=False)\n",
    " \n",
    "    if interactive:\n",
    "        n_molecules = 1\n",
    "        fig = plt.figure(figsize=(6,6))\n",
    "        ax = fig.add_subplot(projection='3d')\n",
    "        ax2 = [ax] \n",
    "    else:\n",
    "        # Create subplots for each molecule\n",
    "        fig, axes = plt.subplots(2, n_molecules, figsize=(4*n_molecules, 5), subplot_kw={'projection': '3d'}, \n",
    "                                 gridspec_kw={ 'height_ratios': [1, 1.5], 'hspace': 0.5})\n",
    "    \n",
    "        if n_molecules == 1:\n",
    "            ax1 = [axes[1]]\n",
    "            ax2 = [axes[0]]\n",
    "        else:\n",
    "            ax1 = axes[0]\n",
    "            ax2 = axes[1]\n",
    "            \n",
    "        # Loop over the molecules for the 2d-plot\n",
    "        for i, ax in enumerate(ax1):\n",
    "            # Remove the existing axis\n",
    "            ax.remove()\n",
    "            # Create a new axis with 2D projection\n",
    "            ax = fig.add_subplot(2, n_molecules, i+1, aspect='equal')\n",
    "            # Get the molecule data for the current index\n",
    "            molecule_index = indices[i]\n",
    "            ith_molecule = coords[molecule_index][atomic_nums[molecule_index] != 0]\n",
    "            ith_molecule_config = [int(item) for item in atomic_nums[molecule_index][atomic_nums[molecule_index] != 0]]\n",
    "            ith_molecule_colors = [COLOR_MAPPING[atom] for atom in ith_molecule_config]\n",
    "            # Plotting atoms\n",
    "            ax.scatter(ith_molecule[:, 0], ith_molecule[:, 1], c=ith_molecule_colors)\n",
    "            # Plotting bonds\n",
    "            for atom1 in ith_molecule:\n",
    "                for atom2 in ith_molecule:\n",
    "                    if np.linalg.norm(atom1 - atom2) < THRESHOLD:\n",
    "                        ax.plot((atom1[0], atom2[0]), (atom1[1], atom2[1]), color='black', linewidth=0.8, alpha=0.6)\n",
    "            ax.set_title(f'Molecule {molecule_index}')\n",
    "            # Add padding inside the 2D scatter plot\n",
    "            x_min, x_max = ax.get_xlim()\n",
    "            y_min, y_max = ax.get_ylim()\n",
    "            x_padding = 0.1 * (x_max - x_min)\n",
    "            y_padding = 0.1 * (y_max - y_min)\n",
    "            ax.set_xlim(x_min - x_padding, x_max + x_padding)\n",
    "            ax.set_ylim(y_min - y_padding, y_max + y_padding)\n",
    "    \n",
    "    # Loop over the molecules for the 3d-plot\n",
    "    for i, ax in enumerate(ax2):\n",
    "        # Get the molecule data for the current index\n",
    "        molecule_index = indices[i]\n",
    "        ith_molecule = coords[molecule_index][atomic_nums[molecule_index] != 0]\n",
    "        ith_molecule_config = [int(item) for item in atomic_nums[molecule_index][atomic_nums[molecule_index] != 0]]\n",
    "        ith_molecule_colors = [COLOR_MAPPING[atom] for atom in ith_molecule_config]\n",
    "        # Plotting atoms\n",
    "        ax.scatter(ith_molecule[:, 0], ith_molecule[:, 1], ith_molecule[:, 2], c=ith_molecule_colors)\n",
    "        # Plotting bonds\n",
    "        for atom1 in ith_molecule:\n",
    "            for atom2 in ith_molecule:\n",
    "                if np.linalg.norm(atom1 - atom2) < THRESHOLD:\n",
    "                    ax.plot((atom1[0], atom2[0]), (atom1[1], atom2[1]), (atom1[2], atom2[2]), color='black', \n",
    "                            linewidth=0.8, alpha=0.6)\n",
    "                    # code to plot bond length\n",
    "                    # if interactive and np.linalg.norm(atom1 - atom2) != 0:\n",
    "                        # midpoint = (atom1 + atom2) / 2  # Compute midpoint between atoms\n",
    "                        # ax.text(midpoint[0], midpoint[1], midpoint[2], f'{np.linalg.norm(atom1 - atom2):.2f}', \n",
    "                        # color='red', ha='center', va='center', fontsize=10)\n",
    "        ax.set_title(f'Molecule {molecule_index}')   \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H: 🔴,  C: 🟢, N: 🟣, O: 🔵, S: 🟡\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aditiya\\AppData\\Local\\Temp\\ipykernel_13868\\963239370.py:93: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1440x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_molecules(coords, atomic_nums, n_molecules=5, THRESHOLD=3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:32:55.420503Z",
     "start_time": "2023-09-19T22:32:55.214053Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H: 🔴,  C: 🟢, N: 🟣, O: 🔵, S: 🟡\n"
     ]
    },
    {
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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(\n            '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\nmpl.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 = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(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 (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\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\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute('style', 'box-sizing: content-box;');\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            canvas.setAttribute(\n                'style',\n                'width: ' + width + 'px; height: ' + height + 'px;'\n            );\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        return function (event) {\n            return fig.mouse_event(event, name);\n        };\n    }\n\n    rubberband_canvas.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    rubberband_canvas.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband_canvas.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    rubberband_canvas.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\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\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\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            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\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                img.type = 'image/png';\n            }\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\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\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(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\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(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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    }\n    if (e.target) {\n        targ = e.target;\n    } else if (e.srcElement) {\n        targ = e.srcElement;\n    }\n    if (targ.nodeType === 3) {\n        // defeat Safari bug\n        targ = targ.parentNode;\n    }\n\n    // pageX,Y are the mouse positions relative to the document\n    var boundingRect = targ.getBoundingClientRect();\n    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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 * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    var canvas_pos = mpl.findpos(event);\n\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * this.ratio;\n    var y = canvas_pos.y * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        guiEvent: simpleKeys(event),\n    });\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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\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        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.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 = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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 / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.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 () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\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            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('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    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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\" 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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_molecules(coords, atomic_nums, molecule=1111, THRESHOLD=3.0, interactive=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Data Representation, ML model and Explanations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.1 Data Representation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let $M = (\\epsilon_1, . . . , \\epsilon_{|M|})$ be a molecule viewed as a collection of $|M|$ elements i.e. the collection of atoms froming the molecule.\n",
    "\n",
    "Let $\\phi$ be a fuction that maps each element into a vector space $\\phi(\\epsilon_i) \\in \\mathbb{R}^h$.\n",
    "\n",
    "The overall representation can be then built as:\n",
    "\n",
    "$$ \n",
    "\\begin{equation}\n",
    "x = \\sum_{i=1}^{|M|} \\phi(\\epsilon_i) \n",
    "\\end{equation}\n",
    "$$\n",
    "\n",
    "Such a representation is used for Section 3.1 (atom based) and for Section 3.2 (pair based).\n",
    "\n",
    "A notable property of such representation is that it is invariant to the order of the atoms in the molecule."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.2 Ridge Regression model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the simple linear regression model:\n",
    "\n",
    "$$ f(x) = w^\\intercal x $$\n",
    "\n",
    "where the weight vector $w \\in \\mathbb{R}^h$ is the parameter vector that needs to be learned.\n",
    "\n",
    "Learning this vector using Ridge Regression consists of minimizing the following objective function:\n",
    "\n",
    "$$ J(w) = \\mathbb{E}[{w^\\intercal x - t}^2 + \\lambda {\\|w\\|}^2] $$\n",
    "\n",
    "where $\\mathbb{E}$ is the expectation over all molecules of the training set, $x$ is our input vector and $t$ is the target value.\n",
    "\n",
    "After centering both the data and the target values, minimizing this objeective yeilds the following closed form solution:\n",
    "\n",
    "$$ w = {(\\Sigma_{xx} + \\lambda I)}^{-1} \\Sigma_{xt} $$\n",
    "\n",
    "where $\\Sigma_{xx} = \\mathbb{E}[x x^\\intercal]$ and $\\Sigma_{xt} = \\mathbb{E}[x t]$ are the auto and cross-covariance matrices respectively.\n",
    "\n",
    "The performance of this model depends on the choice of the feature map $\\phi$ and the regularization parameteter $\\lambda$.\n",
    "\n",
    "For optimizing $\\lambda$, we make use of nested cross-validation.\n",
    "\n",
    "The code for the Ridge Regression framework and for cross-validation is as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:09.659038Z",
     "start_time": "2023-09-19T22:33:09.648067Z"
    }
   },
   "outputs": [],
   "source": [
    "class RidgeRegression():\n",
    "    def fit(self, X, t, lambd):\n",
    "        N, d = X.shape # Data must be of this shape :/\n",
    "        C_xx = (1/N) * np.dot(X.T, X)\n",
    "        C_xx_lambda = C_xx + (lambd * np.identity(d))\n",
    "        C_xt = (1/N) * np.dot(X.T, t)\n",
    "        self.weights = np.dot(np.linalg.inv(C_xx_lambda), C_xt)\n",
    "        \n",
    "    def predict(self, X):\n",
    "        return np.dot(X, self.weights)\n",
    "    \n",
    "    def get_weights(self):\n",
    "        return self.weights\n",
    "    \n",
    "    def get_pred_error(self, t, preds):\n",
    "        return mean_absolute_error(t, preds)   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The nested cross-validation is achieved using the following helper functions:\n",
    "\n",
    "- `get_selection_matrix(X, n_folds)` for selecting the data according to the current fold/split\n",
    "\n",
    "- `cross_validate(X, t, lambda, model, n_folds)` for performing cross-validation against a given value of $\\lambda$ for the particular model\n",
    "\n",
    "- `select_optimal(X, t, lambdas, ridge_model, n_folds)` to select the value of $\\lambda$, from a logarithmically spaced pool of values, for which the mean absolute error is the lowest\n",
    "\n",
    "Finally, the function `nested_cv()` combines the process of hyperparameter selection and evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:12.430993Z",
     "start_time": "2023-09-19T22:33:12.422980Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_selection_matrix(X, n_folds=10):\n",
    "    n = X.shape[0] // n_folds\n",
    "    selection_matrix = np.zeros((X.shape[0], n_folds))\n",
    "    for i in range(n_folds):\n",
    "        selection_matrix[i*n:(i+1)*n, i] = 1\n",
    "    return selection_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:14.769728Z",
     "start_time": "2023-09-19T22:33:14.758754Z"
    }
   },
   "outputs": [],
   "source": [
    "def cross_validate(X, t, lambd, model, n_folds=10):\n",
    "    # n_folds = X.shape[0] ideal, but requires too much time to compute\n",
    "    # I = np.eye(n_folds)\n",
    "    \n",
    "    # Split the data into n_folds\n",
    "    I = get_selection_matrix(X, n_folds)\n",
    "    \n",
    "    # Iterate over each fold and train a model on the remaining `n_folds - 1` folds\n",
    "    errors = []\n",
    "    for k in range(n_folds):\n",
    "        X_selection = X[I[:, k] == 0, :]\n",
    "        t_selection = t[I[:, k] == 0, :]\n",
    "        X_test = X[I[:, k] == 1, :]\n",
    "        t_test = t[I[:, k] == 1, :]\n",
    "        \n",
    "        model.fit(X_selection, t_selection, lambd) \n",
    "        preds = model.predict(X_test)\n",
    "        error = model.get_pred_error(t_test, preds)\n",
    "        errors.append(error)\n",
    "        \n",
    "    return np.mean(errors), np.std(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:17.132500Z",
     "start_time": "2023-09-19T22:33:17.122529Z"
    }
   },
   "outputs": [],
   "source": [
    "def select_optimal(X, t, lambdas, ridge_model, n_folds=10):\n",
    "    errors = []\n",
    "    for lambd in lambdas:\n",
    "        ridge_model = RidgeRegression()\n",
    "        pred_error, _ = cross_validate(X, t, lambd, ridge_model, n_folds)\n",
    "        errors.append(pred_error)\n",
    "        \n",
    "    idx = np.argmin(errors)\n",
    "    return lambdas[idx], errors[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:19.619696Z",
     "start_time": "2023-09-19T22:33:19.606731Z"
    }
   },
   "outputs": [],
   "source": [
    "def nested_cv(X, t, lambdas, ridge_model, n_folds=10):\n",
    "    I = get_selection_matrix(X, n_folds)\n",
    "    errors = []\n",
    "    vals = []\n",
    "    \n",
    "    for k in range(n_folds):\n",
    "        X_selection = X[I[:, k] == 0, :]\n",
    "        t_selection = t[I[:, k] == 0, :]\n",
    "        X_test = X[I[:, k] == 1, :]\n",
    "        t_test = t[I[:, k] == 1, :]\n",
    "        \n",
    "        # get the best lamda for our split\n",
    "        lamda, _ = select_optimal(X_selection, t_selection, lambdas, ridge_model, n_folds)\n",
    "        vals.append(lamda)\n",
    "        \n",
    "        ridge_model.fit(X_selection, t_selection, lamda)\n",
    "        preds = ridge_model.predict(X_test)\n",
    "        error = ridge_model.get_pred_error(t_test, preds)\n",
    "        errors.append(error)\n",
    "        \n",
    "    idx = np.argmin(errors)\n",
    "    opt_lambda = vals[idx]\n",
    "    \n",
    "    mean_err = np.mean(errors)\n",
    "    std_err = np.std(errors)\n",
    "    \n",
    "    return opt_lambda, mean_err, std_err    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3 Deeper Insights with Explanations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get an idea about *what* in the molecule has contributed to the energy prediction, we decompose the prediction into its components using the structure of our model:\n",
    "\n",
    "\n",
    "$$ f(x) = w^\\intercal x $$\n",
    "$$ f(x) = w^\\intercal \\left(\\sum_{i=1}^{|M|} \\phi(\\epsilon_i)\\right) $$\n",
    "$$ \n",
    "\\begin{equation}\n",
    "f(x) = \\sum_{i=1}^{|M|} \\underbrace{w^\\intercal \\phi(\\epsilon_i)}_{R_i} \\tag{2}\n",
    "\\end{equation}\n",
    "$$\n",
    "\n",
    "where the term $R_i$ is the contribution of the $i$-th element to the prediction.\n",
    "\n",
    "The explanations for the predictions resulting from different representations are listed under Section 3.1.3 and Section 3.2.3 respectively."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Experiments"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.1 Simple atom-based Representation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.1.1 Feature Map"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each molecule $M$ is decomposed into its set of atoms. Then for each atom $\\epsilon_i$ we compute a feature vector $\\phi(\\epsilon_i)$ using one-hot encoding as follows:\n",
    "\n",
    "$$ \\phi(\\epsilon_i) = \n",
    "\\begin{pmatrix} \n",
    "I(\\epsilon_i = \\textrm{H}) \\\\ \n",
    "I(\\epsilon_i = \\textrm{C}) \\\\\n",
    "I(\\epsilon_i = \\textrm{N}) \\\\\n",
    "I(\\epsilon_i = \\textrm{O}) \\\\\n",
    "I(\\epsilon_i = \\textrm{S}) \\\\\n",
    "\\end{pmatrix}\n",
    "\\in \\mathbb{R}^5 $$\n",
    "\n",
    "For example, a carbon atom is represented by the vector (0, 1, 0, 0, 0), and a sulfur atom is represented by\n",
    "the vector (0, 0, 0, 0, 1). This atom representation is now used to generate a feature vector for the molecule $\\textrm{CH}_4$ (methane) as $x = (4, 1, 0, 0, 0)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:23.893350Z",
     "start_time": "2023-09-19T22:33:23.229537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X-shape(N x d): (7165, 5)\n"
     ]
    }
   ],
   "source": [
    "# one-hot encode the molecules\n",
    "one_hot_dict = {0: -1, 1: 0, 6: 1, 7: 2, 8: 3, 16: 4}\n",
    "n = len(one_hot_dict)\n",
    "\n",
    "# Initialize an empty array to store the one-hot encodings\n",
    "one_hot_encodings = np.zeros((len(atomic_nums), atomic_nums.shape[1], n-1))\n",
    "\n",
    "# Iterate over each molecule\n",
    "for i, molecule_atom_types in enumerate(atomic_nums):\n",
    "    # Iterate over each atom in the molecule\n",
    "    for j, atom_type in enumerate(molecule_atom_types):\n",
    "        # Get the unique index for the atom type\n",
    "        atom_type_index = one_hot_dict[atom_type]\n",
    "        # Set the corresponding index in the one-hot encoding to 1\n",
    "        if atom_type_index != -1:\n",
    "            one_hot_encodings[i, j, atom_type_index] = 1 \n",
    "# Each molecule has a one-hot encoding for each atom, representing the atom type        \n",
    "\n",
    "#  build an overall feature representation by summing the representation of each element\n",
    "X = np.sum(one_hot_encodings, axis=1)\n",
    "print(f'X-shape(N x d): {X.shape}') # N x d"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.1.2 Hyperparameter Optimization, Cross-Validation, and Predictions\n",
    "\n",
    "The above obtained representation is used to train the Ridge Regression model after selecting the optimal value of $\\lambda$ using a 5-fold nested cross-validation and a mean absolute error of approximately 15 kcal/mol is obtained "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:26.182907Z",
     "start_time": "2023-09-19T22:33:26.174930Z"
    }
   },
   "outputs": [],
   "source": [
    "# Centering\n",
    "X_centered = X - np.mean(X, axis=0, keepdims=True)\n",
    "T = atomization_energies - np.mean(atomization_energies)\n",
    "T = T.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:29.102708Z",
     "start_time": "2023-09-19T22:33:28.373660Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal hyperparameter: 0.0001\n",
      "Error: 16.41 +/- 1.24 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "lambdas = np.logspace(-20 , 10, 31)\n",
    "\n",
    "ridge_model = RidgeRegression()\n",
    "optimal_lambda, mean_err, std_err = nested_cv(X_centered, T, lambdas, ridge_model, 5)\n",
    "print(f'Optimal hyperparameter: {optimal_lambda}')\n",
    "print(f'Error: {mean_err:.2f} +/- {std_err:.2f} kcal/mol')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model using the optimal value of $\\lambda$ and evaluating it on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:31.152212Z",
     "start_time": "2023-09-19T22:33:31.139247Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal lambda: 0.0001\n",
      "Mean absolute error: 15.24 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, t_train, t_test = train_test_split(X_centered, T, test_size=0.2)\n",
    "\n",
    "ridge_model.fit(X_train, t_train, optimal_lambda) # and our model is ready to predict\n",
    "preds = ridge_model.predict(X_test)\n",
    "mean_abs_err = ridge_model.get_pred_error(t_test, preds)\n",
    "print(f'Optimal lambda: {optimal_lambda}')\n",
    "print(f'Mean absolute error: {mean_abs_err:.2f} kcal/mol')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.1.3 Explanations\n",
    "\n",
    "We decompose $X$ into its elements and compute the contribution of each element to the prediction using equation no. (2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:33.466459Z",
     "start_time": "2023-09-19T22:33:33.458480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7165, 23, 5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encodings.shape\n",
    "# each row corresponds to phi(e_i)\n",
    "# Here, M = 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:35.987211Z",
     "start_time": "2023-09-19T22:33:35.965268Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: (7165, 1) \n",
      "Explanations: (7165, 23, 1)\n"
     ]
    }
   ],
   "source": [
    "# decompose X into its elements\n",
    "X_explained = one_hot_encodings \n",
    "weights = ridge_model.get_weights()\n",
    "explanations = np.dot(X_explained, weights)\n",
    "predictions = np.dot(X_centered, weights) # same as ridge_model.predict(X_centered)\n",
    "print(f'Predictions: {predictions.shape} ')\n",
    "print(f'Explanations: {explanations.shape}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, the prediction of each molecule is now decomposed into the contributions of its atoms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:41.908811Z",
     "start_time": "2023-09-19T22:33:41.895844Z"
    }
   },
   "outputs": [],
   "source": [
    "# Helper functions to get the contributions and encodings for a given molecule along with its atom labels\n",
    "\n",
    "reverse_one_hot_dict = {-1: 0, 0: 1, 1: 6, 2: 7, 3: 8, 4: 16}\n",
    "atomic_num_to_symbol = {1: 'H', 6: 'C', 7: 'N', 8: 'O', 16: 'S'}\n",
    "\n",
    "def get_contributions_and_encodings(molecule_idx):\n",
    "    contributions = explanations[molecule_idx]\n",
    "    encodings = one_hot_encodings[molecule_idx]\n",
    "\n",
    "    # remove the zero contributions\n",
    "    contributions = np.abs(contributions[np.nonzero(contributions)])\n",
    "    encodings = encodings[np.any(encodings==1, axis=1)]\n",
    "\n",
    "    return contributions, encodings\n",
    "\n",
    "def get_atom_labels(encodings):\n",
    "    atom_labels = [] \n",
    "    for encoding in encodings:\n",
    "        atom_type_index = np.argmax(encoding)\n",
    "        atom_type = reverse_one_hot_dict[atom_type_index]\n",
    "        atom_symbol = atomic_num_to_symbol[atom_type]\n",
    "        atom_labels.append(atom_symbol)\n",
    "    return atom_labels   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell shows how much each atom of molecule '0' i.e. $\\textrm{CH}_4$ has contributed to its prediction, as a bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:33:44.714414Z",
     "start_time": "2023-09-19T22:33:44.521854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "contributions, encodings = get_contributions_and_encodings(0)\n",
    "\n",
    "n = len(contributions)\n",
    "x = np.arange(n)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(x, contributions.flatten(), align='center', color='#FFC600', edgecolor='black', alpha=0.5)\n",
    "\n",
    "labels = get_atom_labels(encodings)\n",
    "        \n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)  \n",
    "ax.set_ylabel('contribution')\n",
    "ax.set_title('Contributions for CH4')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now visualize the contributions of each molecule interactively as the size of atoms it consists of (bigger atom size corresponds to higher contribution to the molecule's atomization energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:34:39.880513Z",
     "start_time": "2023-09-19T22:33:48.140004Z"
    }
   },
   "outputs": [],
   "source": [
    "tsne = TSNE() # use t-SNE to scatter plot the molecules according to their atomization energies\n",
    "embedded_data = tsne.fit_transform(X_centered)\n",
    "\n",
    "autumn = plt.cm.get_cmap('autumn').reversed()\n",
    "gray = plt.cm.get_cmap('gray').reversed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:35:26.067792Z",
     "start_time": "2023-09-19T22:35:25.637162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Click on a point in the t-SNE representation to view its structure on the right (the size of atom indicates its contribution to the Atomization Energy of the molecule)\n",
      "H: 🔴,  C: 🟢, N: 🟣, O: 🔵, S: 🟡\n"
     ]
    },
    {
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' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.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 = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(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 (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\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\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute('style', 'box-sizing: content-box;');\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            canvas.setAttribute(\n                'style',\n                'width: ' + width + 'px; height: ' + height + 'px;'\n            );\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        return function (event) {\n            return fig.mouse_event(event, name);\n        };\n    }\n\n    rubberband_canvas.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    rubberband_canvas.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband_canvas.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    rubberband_canvas.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\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\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\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            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\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                img.type = 'image/png';\n            }\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\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\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(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\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(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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    }\n    if (e.target) {\n        targ = e.target;\n    } else if (e.srcElement) {\n        targ = e.srcElement;\n    }\n    if (targ.nodeType === 3) {\n        // defeat Safari bug\n        targ = targ.parentNode;\n    }\n\n    // pageX,Y are the mouse positions relative to the document\n    var boundingRect = targ.getBoundingClientRect();\n    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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 * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    var canvas_pos = mpl.findpos(event);\n\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * this.ratio;\n    var y = canvas_pos.y * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        guiEvent: simpleKeys(event),\n    });\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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\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        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.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 = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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 / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.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 () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\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            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('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    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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\" width=\"864\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making it interactive\n",
    "%matplotlib notebook \n",
    "\n",
    "COLOR_MAPPING = {\n",
    "    1: [1, 0, 0],  # Hydrogen = red\n",
    "    6: [0, 1, 0],  # Carbon   = green\n",
    "    7: [1, 0, 1],  # Nitrogen = purple\n",
    "    8: [0, 0, 1],  # Oxygen   = blue\n",
    "    16: [1, 1, 0]  # Sulfur   = yellow\n",
    "}\n",
    "print('Click on a point in the t-SNE representation to view its structure on the right (the size of atom indicates its contribution to the Atomization Energy of the molecule)')\n",
    "print('H: 🔴,  C: 🟢, N: 🟣, O: 🔵, S: 🟡')\n",
    "\n",
    "# Visualize the t-SNE representation with the molecule's structure on the side\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "ax1 = axes[0]\n",
    "ax1.remove()\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "scatter = ax1.scatter(embedded_data[:, 0], embedded_data[:, 1], c=atomization_energies[0, :], cmap=autumn, alpha=0.5)\n",
    "ax1.set_title('t-SNE representation')\n",
    "\n",
    "contributions, encodings = get_contributions_and_encodings(0)\n",
    "molecule = coords[0][atomic_nums[0] != 0]\n",
    "molecule_config = [int(item) for item in atomic_nums[0][atomic_nums[0] != 0]]\n",
    "molecule_colors = [COLOR_MAPPING[atom] for atom in molecule_config]\n",
    "labels = get_atom_labels(encodings)\n",
    "\n",
    "ax2 = axes[1]\n",
    "ax2.remove()\n",
    "ax2 = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "scatter_plot = ax2.scatter(molecule[:, 0], molecule[:, 1], molecule[:, 2], c=molecule_colors, s=contributions*1.1)\n",
    "ax2.set_title(f'CH4')\n",
    "for i, atom1 in enumerate(molecule):\n",
    "    for atom2 in molecule:\n",
    "        if np.linalg.norm(atom1 - atom2) < 3.0:\n",
    "            ax2.plot((atom1[0], atom2[0]), (atom1[1], atom2[1]), (atom1[2], atom2[2]), color='black', linewidth=0.8, alpha=0.6)\n",
    "    offset = atom1 + 0.16\n",
    "    ax2.text(offset[0], offset[1], offset[2], f'{labels[i]}', color='black', ha='center', va='center', fontsize=10, alpha=0.8)\n",
    "\n",
    "# Function to update the molecule's structure based on the clicked point\n",
    "def update_scatter_plot(cursor):\n",
    "    index = cursor.target.index\n",
    "    idx = int(index)\n",
    "    contributions, encodings = get_contributions_and_encodings(idx)\n",
    "    molecule = coords[idx][atomic_nums[idx] != 0]\n",
    "    molecule_config = [int(item) for item in atomic_nums[idx][atomic_nums[idx] != 0]]\n",
    "    molecule_colors = [COLOR_MAPPING[atom] for atom in molecule_config]\n",
    "\n",
    "    ax2.clear()\n",
    "    scatter_plot = ax2.scatter(molecule[:, 0], molecule[:, 1], molecule[:, 2], c=molecule_colors, s=contributions*1.1)\n",
    "    \n",
    "    labels = get_atom_labels(encodings)\n",
    "    \n",
    "    for i, atom1 in enumerate(molecule):\n",
    "        for atom2 in molecule:\n",
    "            if np.linalg.norm(atom1 - atom2) < 3.0:\n",
    "                ax2.plot((atom1[0], atom2[0]), (atom1[1], atom2[1]), (atom1[2], atom2[2]), color='black', \n",
    "                         linewidth=0.7, alpha=0.5)\n",
    "        offset = atom1 + 0.16\n",
    "        ax2.text(offset[0], offset[1], offset[2], f'{labels[i]}', color='black', ha='center', va='center', \n",
    "                 fontsize=10, alpha=0.8)\n",
    "    \n",
    "    element_counts = Counter(labels)\n",
    "    unique_elements = sorted(set(labels))\n",
    "    name = ''.join([element + str(element_counts[element]) for element in unique_elements])\n",
    "    \n",
    "    ax2.set_title(f'{name}')\n",
    "    \n",
    "    txt = f'molecule {idx}: {name} \\nAtomization Energy = {(atomization_energies[0, idx]):.2f} kcal/mol'\n",
    "    ann = cursor.annotation\n",
    "    ann.set_text(txt)\n",
    "    plt.draw()\n",
    "\n",
    "# Connect the cursor event to the update_scatter_plot function\n",
    "cursor1 = mplcursors.cursor(scatter, hover=False)\n",
    "cursor1.connect(\"add\", update_scatter_plot)\n",
    "\n",
    "plt.colorbar(scatter, ax=ax1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-18T10:38:19.911652Z",
     "start_time": "2023-06-18T10:38:19.908136Z"
    }
   },
   "source": [
    "From above, one can observe that atoms with higher electronegativity(such as C, N, and O) contribute more to the atomization energy of the molecule. \n",
    "\n",
    "Also, as carbon atoms are typically stable, their contribution to the molecule's atomization energy is more pronounced. These insights are consistent with the prevailing chemical context. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.2 Models with pairs of atoms"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each molecule is now decomposed into its set of pair of atoms, i.e. $\\epsilon_i$ now denotes a pair of atoms, and a molecule contains $\\frac{\\#atoms \\cdot (\\#atoms - 1)}{2}$ such pairs.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.1 Feature Maps\n",
    "\n",
    "To generate one-hot encodings for each pair, we bin them into multiple intervals $[\\theta_1, \\theta_2], [\\theta_2, \\theta_3], [\\theta_{m-1}, \\theta_m]$ and get the vector:\n",
    "\n",
    "$$\n",
    "\\phi^A(\\epsilon_i) = \n",
    "\\begin{pmatrix} \n",
    "I(\\theta_1 \\le dist(\\epsilon_i) \\lt \\theta_2) \\\\ \n",
    "I(\\theta_2 \\le dist(\\epsilon_i) \\lt \\theta_3) \\\\\n",
    "\\dots \\\\\n",
    "I(\\theta_{m-1} \\le dist(\\epsilon_i) \\lt \\theta_m) \\\\\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "\n",
    "And to avoid introducing unnatural discontinuities into the model, the hard indicator function $I$ is replaced by a soft indicator function i.e. a Gaussian function with mean at the center of the interval and fixed variance.\n",
    "\n",
    "The size of the intervals and the variance are chosen heuristically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:38:50.627705Z",
     "start_time": "2023-09-19T22:38:50.615702Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_feature_map_A(distance_pairs, interval, variance=0.08):\n",
    "    feature_map_A = []\n",
    "\n",
    "    for row in distance_pairs:\n",
    "        encoding = []\n",
    "        for dist in row:\n",
    "            enc = np.zeros((len(interval) - 1))\n",
    "            for i in range(len(interval)-1):\n",
    "                # Hard indicator function\n",
    "                # if interval[i] <= dist < interval[i+1]:\n",
    "                    # enc[i] = 1\n",
    "                # Soft indicator function\n",
    "                enc[i] = np.exp(-((dist - (interval[i] + interval[i+1])/2)**2) / variance)\n",
    "            encoding.append(enc)\n",
    "        feature_map_A.append(encoding)\n",
    "\n",
    "    return np.array(feature_map_A, dtype=object)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "In order to incorporate atom types, another feature representation is generated as follows:\n",
    "\n",
    "$$\n",
    "\\phi^B(\\epsilon_i) = \n",
    "\\begin{pmatrix} \n",
    "I(type(\\epsilon_i) = \\textrm{HH}) \\\\ \n",
    "I(type(\\epsilon_i) = \\textrm{HC}) \\\\\n",
    "I(type(\\epsilon_i) = \\textrm{HN}) \\\\\n",
    "\\dots \\\\\n",
    "I(type(\\epsilon_i) = \\textrm{SS}) \\\\\n",
    "\\end{pmatrix}\n",
    "\\in \\mathbb{R}^{15}\n",
    "$$\n",
    "\n",
    "Unordered pairs are considered to implement invariance to indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:38:53.080138Z",
     "start_time": "2023-09-19T22:38:53.069169Z"
    }
   },
   "outputs": [],
   "source": [
    "atoms = ['C', 'H', 'N', 'O', 'S']\n",
    "combinations = list(itertools.combinations_with_replacement(atoms, 2)) # 15 combinations, unique\n",
    "\n",
    "def get_feature_map_B(atom_pairs, combinations):    \n",
    "    feature_map_B = []\n",
    "\n",
    "    for row in atom_pairs:\n",
    "        encoding = []\n",
    "        for pair in row:\n",
    "            enc = np.zeros((len(combinations)))\n",
    "            for i in range(len(combinations)):\n",
    "                if combinations[i][0] == pair[0] and combinations[i][1] == pair[1]:\n",
    "                    enc[i] = 1\n",
    "            encoding.append(enc)\n",
    "        feature_map_B.append(encoding)\n",
    "    return np.array(feature_map_B, dtype=object)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the feature maps $\\phi_A$ and $\\phi_B$ are combined into one single feature map $\\phi$ consisting of all products between elements of the two feature maps.\n",
    "\n",
    "$$ [\\phi(\\epsilon_i)]_{jk} = \\phi_j^A(\\epsilon_i) \\cdot \\phi_k^B(\\epsilon_i) \\quad \\forall j,k$$\n",
    "\n",
    "The matrix $\\phi(\\epsilon_i)$ is then flattened into a vector for subsequent processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:38:56.229569Z",
     "start_time": "2023-09-19T22:38:56.216594Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_phi(coords, feature_map_A, feature_map_B):   \n",
    "    phi = []\n",
    "    phi_explained = []\n",
    "    for i in range(coords.shape[0]):\n",
    "        t = []\n",
    "        for j in range(len(feature_map_A[i])):\n",
    "            A = np.array(feature_map_A[i][j])\n",
    "            B = np.array(feature_map_B[i][j])\n",
    "            temp = np.zeros((len(A), len(B)))\n",
    "            for l in range(len(A)):\n",
    "                for m in range(len(B)):\n",
    "                    temp[l][m] = A[l] * B[m]\n",
    "            temp = temp.flatten()\n",
    "            t.append(temp)\n",
    "        phi_explained.append(np.array(t)) # useful for the explanations part\n",
    "        phi.append(np.sum(t, axis=0))\n",
    "        \n",
    "    return np.array(phi), np.array(phi_explained, dtype=object)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`itertools.combinations_with_replacement` is used to generate all possible pairs of atoms for a molecule and its related distances are also calculated and stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:39:09.262786Z",
     "start_time": "2023-09-19T22:38:58.071482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atom pairs' shape: (7165,)\n",
      "Distance pairs' shape: (7165,)\n"
     ]
    }
   ],
   "source": [
    "# map the atomic numbers to their symbols\n",
    "molecules = [[atomic_num_to_symbol[x] for x in y if x != 0] for y in atomic_nums]\n",
    "\n",
    "atom_pairs = []\n",
    "distance_pairs = []\n",
    "\n",
    "for i in range(len(molecules)):\n",
    "    # Generate all combinations of two atoms\n",
    "    pair = list(itertools.combinations_with_replacement(molecules[i], 2))\n",
    "    atom_pairs.append(pair)\n",
    "    # Generate all combinations of atom-atom distances\n",
    "    distances = coords[i][atomic_nums[i] != 0]\n",
    "    dist_pair = list(itertools.combinations_with_replacement(distances, 2))\n",
    "    d = []\n",
    "    for p in dist_pair:\n",
    "        d.append(np.linalg.norm(p[0] - p[1]))\n",
    "    distance_pairs.append(d)\n",
    "    \n",
    "atom_pairs = np.array(atom_pairs, dtype=object)\n",
    "distance_pairs = np.array(distance_pairs, dtype=object)\n",
    "\n",
    "print(f'Atom pairs\\' shape: {atom_pairs.shape}')\n",
    "print(f'Distance pairs\\' shape: {distance_pairs.shape}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the below two intervals are chosen for the distance bins along with the value of variance (both set heuristically) and the feature maps are obtained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T22:44:03.344966Z",
     "start_time": "2023-09-19T22:39:29.009321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature map A shape: (7165,)\n",
      "Feature map B shape: (7165,)\n",
      "Phi shape: (7165, 210)\n"
     ]
    }
   ],
   "source": [
    "example_interval1 = [0, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5, 2.7, 2.9, 3.1, 3.3, 3.5, 3.7, 3.9, 4.1]\n",
    "example_interval2 = [0, 1.4, 1.6, 1.8, 2.0, 2.4, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0]\n",
    "\n",
    "feature_map_A = get_feature_map_A(distance_pairs, example_interval2, variance=0.08)\n",
    "feature_map_B = get_feature_map_B(atom_pairs, combinations)\n",
    "phi, phi_decomposed = get_phi(coords, feature_map_A, feature_map_B)\n",
    "\n",
    "print(f'Feature map A shape: {feature_map_A.shape}')\n",
    "print(f'Feature map B shape: {feature_map_B.shape}')\n",
    "print(f'Phi shape: {phi.shape}')\n",
    "# print(f'Phi-decomposed shape: {phi_decomposed.shape}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second dimension of the feature maps A and B as well as of the distance-pairs and atom-pairs is different for each molecule as each molecule has different atoms and so different atom-pairs."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.2 Hyperparameter Optimization, Cross-Validation, and Predictions\n",
    "\n",
    "This new feature map is then used to train the Ridge Regression model after selecting the optimal value of $\\lambda$ using a 5-fold nested cross-validation and a mean absolute error of approximately 6 kcal/mol is obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T12:35:22.914196Z",
     "start_time": "2023-07-14T12:35:16.861488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal lambda: 2.51e-11\n",
      "Mean absolute error (phi1): 4.97 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "lambdas = np.logspace(-19, 9, 21)\n",
    "\n",
    "phi_centered = phi - np.mean(phi, axis=0, keepdims=True)\n",
    "ridger = RidgeRegression()\n",
    "optimal_lambda, mean_err, std_err = nested_cv(phi_centered, T, lambdas, ridger, 5)\n",
    "\n",
    "phi_train, phi_test, t_train, t_test = train_test_split(phi_centered, T, test_size=0.2)\n",
    "ridger.fit(phi_train, t_train, optimal_lambda)\n",
    "preds = ridger.predict(phi_test)\n",
    "err = ridger.get_pred_error(t_test, preds)\n",
    "weights = ridger.get_weights()\n",
    "print(f'Optimal lambda: {optimal_lambda:.2e}')\n",
    "print(f'Mean absolute error (phi1): {err:.2f} kcal/mol')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.3 Explanations\n",
    "\n",
    "For each molecule, we decompose its prediction into the contributions of its atom-pairs. Such a decomposition is readily avaialable in the matrix `phi_decomposed` that is calulated along with the feature map $\\phi$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T12:35:24.861186Z",
     "start_time": "2023-07-14T12:35:24.854711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi-decomposed shape: (7165,)\n"
     ]
    }
   ],
   "source": [
    "print(f'Phi-decomposed shape: {phi_decomposed.shape}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the contributions for each atom-pair, each row of `phi_decomposed` is multiplied with the weight vector '$w$' and the explanations are obtained as per equation no. (2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T12:35:29.402342Z",
     "start_time": "2023-07-14T12:35:29.212470Z"
    }
   },
   "outputs": [],
   "source": [
    "phi_explained = []\n",
    "for i in range(len(phi_decomposed)):\n",
    "    phi_explained.append(np.dot(phi_decomposed[i], weights))\n",
    "phi_explained = np.array(phi_explained, dtype=object)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the contributions of each atom-pair of molecule '0' i.e. $\\textrm{CH}_4$ as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T12:35:34.031815Z",
     "start_time": "2023-07-14T12:35:34.015019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molecule = CH4\n",
      "Pair: ('C', 'C') Distance: 0.0Å Contribution: -198.53\n",
      "Pair: ('C', 'H') Distance: 2.1Å Contribution: 19.68\n",
      "Pair: ('C', 'H') Distance: 2.1Å Contribution: 19.68\n",
      "Pair: ('C', 'H') Distance: 2.1Å Contribution: 19.67\n",
      "Pair: ('C', 'H') Distance: 2.1Å Contribution: 19.48\n",
      "Pair: ('H', 'H') Distance: 0.0Å Contribution: -71.40\n",
      "Pair: ('H', 'H') Distance: 3.4Å Contribution: -3.26\n",
      "Pair: ('H', 'H') Distance: 3.4Å Contribution: -3.26\n",
      "Pair: ('H', 'H') Distance: 3.4Å Contribution: -3.26\n",
      "Pair: ('H', 'H') Distance: 0.0Å Contribution: -71.40\n",
      "Pair: ('H', 'H') Distance: 3.4Å Contribution: -3.26\n",
      "Pair: ('H', 'H') Distance: 3.4Å Contribution: -3.26\n",
      "Pair: ('H', 'H') Distance: 0.0Å Contribution: -71.40\n",
      "Pair: ('H', 'H') Distance: 3.4Å Contribution: -3.26\n",
      "Pair: ('H', 'H') Distance: 0.0Å Contribution: -71.40\n"
     ]
    }
   ],
   "source": [
    "print(f'Molecule = CH4')\n",
    "for i in range(len(phi_explained[0])):\n",
    "    print(f'Pair: {atom_pairs[0][i]} Distance: {float(distance_pairs[0][i]):.1f}Å Contribution: {float(phi_explained[0][i]):.2f}')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand the explanations better, we visualize the molecule and show the contibutions as a function of the thickness of the bond. The thicker the bond, the higher the contribution of the corresponding atom-pair to the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-14T12:35:43.913966Z",
     "start_time": "2023-07-14T12:35:43.604785Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H: 🔴,  C: 🟢, N: 🟣, O: 🔵, S: 🟡\n",
      "The thickness of the bond shows the contribution of the pair to the atomization energy prediction.\n"
     ]
    },
    {
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      'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    rubberband_canvas.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\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\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\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            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\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                img.type = 'image/png';\n            }\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\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\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(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\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(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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    }\n    if (e.target) {\n        targ = e.target;\n    } else if (e.srcElement) {\n        targ = e.srcElement;\n    }\n    if (targ.nodeType === 3) {\n        // defeat Safari bug\n        targ = targ.parentNode;\n    }\n\n    // pageX,Y are the mouse positions relative to the document\n    var boundingRect = targ.getBoundingClientRect();\n    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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 * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    var canvas_pos = mpl.findpos(event);\n\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * this.ratio;\n    var y = canvas_pos.y * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        guiEvent: simpleKeys(event),\n    });\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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\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        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.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 = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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 / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.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 () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\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            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('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    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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\" width=\"897.6\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# molecules 66 and 976 are apt\n",
    "\n",
    "COLOR_MAPPING = {\n",
    "    1: [1, 0, 0],  # Hydrogen = red\n",
    "    6: [0, 1, 0],  # Carbon   = green\n",
    "    7: [1, 0, 1],  # Nitrogen = purple\n",
    "    8: [0, 0, 1],  # Oxygen   = blue\n",
    "    16: [1, 1, 0]  # Sulfur   = yellow\n",
    "}\n",
    "\n",
    "print('H: 🔴,  C: 🟢, N: 🟣, O: 🔵, S: 🟡')\n",
    "print('The thickness of the bond shows the contribution of the pair to the atomization energy prediction.')\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "ax1 = axes[0]\n",
    "ax1.remove()\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "scatter = ax1.scatter(embedded_data[:, 0], embedded_data[:, 1], c=atomization_energies[0, :], cmap=autumn, alpha=0.5)\n",
    "ax1.set_title('t-SNE representation')\n",
    "\n",
    "molecule = coords[0][atomic_nums[0] != 0]\n",
    "molecule_config = [int(item) for item in atomic_nums[0][atomic_nums[0] != 0]]\n",
    "molecule_colors = [COLOR_MAPPING[atom] for atom in molecule_config]\n",
    "atoms = atom_pairs[0]\n",
    "distances = distance_pairs[0]\n",
    "explanationz = phi_explained[0]\n",
    "contributions, encodings = get_contributions_and_encodings(0)\n",
    "labels = get_atom_labels(encodings)\n",
    "colors = (np.array(explanationz) - np.min(explanationz)) / (np.max(explanationz) - np.min(explanationz))\n",
    "\n",
    "ax2 = axes[1]\n",
    "ax2.remove()\n",
    "ax2 = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "scatter_plot = ax2.scatter(molecule[:, 0], molecule[:, 1], molecule[:, 2], c=molecule_colors, s=contributions*1.1)\n",
    "ax2.set_title(f'CH4')\n",
    "\n",
    "counter = 0\n",
    "for i, atom1 in enumerate(molecule):\n",
    "    offset = atom1 + 0.16\n",
    "    ax2.text(offset[0], offset[1], offset[2], f'{labels[i]}', color='black', alpha=0.8, ha='center', va='center')\n",
    "\n",
    "    for atom2 in molecule[i:]:\n",
    "        if counter == len(distances):\n",
    "            break\n",
    "\n",
    "        if 0 < distances[counter] <= 3.0:\n",
    "            ax2.plot((atom1[0], atom2[0]), (atom1[1], atom2[1]), (atom1[2], atom2[2]), c=gray(colors[counter]), \n",
    "                     linewidth=abs(explanationz[counter]/10)+1, alpha=0.3)\n",
    "            # show the contribution value/distance at the center of the bond\n",
    "            # midpoint = (atom1 + atom2) / 2\n",
    "            # ax.text(midpoint[0], midpoint[1], midpoint[2], f'{float(explanations[counter]):.2f}', color='red', \n",
    "            # ha='center', va='center', fontsize=10)\n",
    "            # ax.text(midpoint[0], midpoint[1], midpoint[2], f'{distances[counter]:.2f}', color='red', ha='center', \n",
    "            # va='center', fontsize=10)\n",
    "        counter += 1\n",
    "\n",
    "# Function to update the molecule's strucutre based on the clicked point\n",
    "def update_scatter_plot(cursor):\n",
    "    index = cursor.target.index\n",
    "    idx = int(index)\n",
    "    contributions, encodings = get_contributions_and_encodings(idx)\n",
    "    atoms = atom_pairs[idx]\n",
    "    distances = distance_pairs[idx]\n",
    "    explanationz = phi_explained[idx]\n",
    "    molecule = coords[idx][atomic_nums[idx] != 0]\n",
    "    molecule_config = [int(item) for item in atomic_nums[idx][atomic_nums[idx] != 0]]\n",
    "    molecule_colors = [COLOR_MAPPING[atom] for atom in molecule_config]\n",
    "    colors = (np.array(explanationz) - np.min(explanationz)) / (np.max(explanationz) - np.min(explanationz))\n",
    "    labels = get_atom_labels(encodings)\n",
    "\n",
    "    ax2.clear()\n",
    "    scatter_plot = ax2.scatter(molecule[:, 0], molecule[:, 1], molecule[:, 2], c=molecule_colors, s=contributions*1.1)\n",
    "    \n",
    "    counter = 0\n",
    "    for i, atom1 in enumerate(molecule):\n",
    "        offset = atom1 + 0.16\n",
    "        ax2.text(offset[0], offset[1], offset[2], f'{labels[i]}', color='black', alpha=0.8, ha='center', va='center')\n",
    "        \n",
    "        for atom2 in molecule[i:]:\n",
    "            if counter == len(distances):\n",
    "                break\n",
    "        \n",
    "            if 0 < distances[counter] <= 3.0:\n",
    "                ax2.plot((atom1[0], atom2[0]), (atom1[1], atom2[1]), (atom1[2], atom2[2]), c=gray(colors[counter]), \n",
    "                         linewidth=abs(explanationz[counter]/10)+1, alpha=0.3)\n",
    "                # show the contribution value/distance at the center of the bond\n",
    "                # midpoint = (atom1 + atom2) / 2\n",
    "                # ax.text(midpoint[0], midpoint[1], midpoint[2], f'{float(explanations[counter]):.2f}', color='red', \n",
    "                # ha='center', va='center', fontsize=10)\n",
    "                # ax.text(midpoint[0], midpoint[1], midpoint[2], f'{distances[counter]:.2f}', color='red', ha='center',\n",
    "                # va='center', fontsize=10)\n",
    "            counter += 1\n",
    "    \n",
    "    element_counts = Counter(labels)\n",
    "    unique_elements = sorted(set(labels))\n",
    "    name = ''.join([element + str(element_counts[element]) for element in unique_elements])\n",
    "    \n",
    "    ax2.set_title(f'{name}')\n",
    "    \n",
    "    txt = f'molecule {idx}: {name} \\nAtomization Energy = {(atomization_energies[0, idx]):.2f} kcal/mol'\n",
    "    ann = cursor.annotation\n",
    "    ann.set_text(txt)\n",
    "    plt.draw()\n",
    "\n",
    "# Connect the cursor event to the update_bar_plot function\n",
    "cursor2 = mplcursors.cursor(scatter, hover=False)\n",
    "cursor2.connect(\"add\", update_scatter_plot)\n",
    "\n",
    "plt.colorbar(scatter, ax=ax1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, one can see that the Carbon-Carbon triple-bond contributes heavily to the molecule's atomization energy as compared to the other single bonds due to the higher bond strength of triple as well as double bonds. \n",
    "\n",
    "The covalent bonds that carbon typically forms are stable and relatively strong, and as a result, they contribute significantly to the molecule's atomization energy."
   ]
  }
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