FANN chipper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Simple method to convert from liblinear format to fann format\n",
    "def convert_liblinear_file(ll_filepath, fann_filepath):\n",
    "    with open(ll_filepath, 'r') as ll_file:\n",
    "        data = []\n",
    "        for line in ll_file.readlines():\n",
    "            fields = line.strip().split(\" \")\n",
    "            row = [int(fields[0])]\n",
    "            for (i, field) in enumerate(fields[1:]):\n",
    "                (colnum, value) = field.split(\":\")\n",
    "                assert(int(colnum) == (i + 1))\n",
    "                row.append(float(value))\n",
    "            data.append(row)\n",
    "    with open(fann_filepath, 'w') as fann_file:\n",
    "        rows = len(data)\n",
    "        cols = len(data[0]) -1\n",
    "        fann_file.write(\"%s %s %s\\n\" % (rows, cols, 1))\n",
    "        for row in data:\n",
    "            is_cleaved = row[0]\n",
    "            for feature in row[1:]:\n",
    "                fann_file.write(\"%.12f \" % (feature))\n",
    "            fann_file.write(\"\\n%d\\n\" % (is_cleaved))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fann2 import libfann\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = \"/workspace/chipper_data/chipper-0.3.0-data/\"\n",
    "train_data_path = data_dir + \"train_data.fann\"\n",
    "test_data_path = data_dir + \"test_data.fann\"\n",
    "neural_network_save_file = data_dir + \"neural.net\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "convert_liblinear_file(data_dir + \"testing_data.ll\", test_data_path)\n",
    "convert_liblinear_file(data_dir + \"training_data.ll\", train_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from fann2 import libfann\n",
    "\n",
    "train_data = libfann.training_data()\n",
    "train_data.read_train_from_file(train_data_path)\n",
    "\n",
    "num_input = train_data.num_input_train_data()\n",
    "num_output = train_data.num_output_train_data()\n",
    "\n",
    "alpha = 5.0 # Range of 2 to 10, below 5 might cause overfitting\n",
    "num_hidden = train_data.length_train_data() / (alpha * (num_input  + num_output))\n",
    "\n",
    "ann = libfann.neural_net()\n",
    "ann.create_sparse_array(connection_rate=0.5, layers=(num_input, num_output))\n",
    "ann.set_scaling_params(data=train_data, new_input_min=0, new_input_max=1, new_output_min=0, new_output_max=1)\n",
    "ann.scale_train(data=train_data)\n",
    "ann.set_learning_rate(0.7)\n",
    "#ann.set_train_error_function(libfann.ERRORFUNC_LINEAR)\n",
    "ann.set_activation_function_hidden(libfann.ELLIOT)\n",
    "# Steepness= 0.5 ELLIOT AUC=0.88\n",
    "ann.set_activation_steepness_output(0.5)\n",
    "ann.set_activation_function_output(libfann.ELLIOT)\n",
    "\n",
    "\n",
    "ann.train_on_data(data=train_data, desired_error=0.09, epochs_between_reports=5, max_epochs=500)\n",
    "ann.save(neural_network_save_file)\n",
    "ann.print_connections()\n",
    "ann.print_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE error on test data: 0.145478\n"
     ]
    }
   ],
   "source": [
    "from fann2 import libfann\n",
    "\n",
    "ann = libfann.neural_net()\n",
    "ann.create_from_file(neural_network_save_file)\n",
    "\n",
    "test_data = libfann.training_data()\n",
    "test_data.read_train_from_file(test_data_path)\n",
    "ann.scale_train(data=test_data)\n",
    "\n",
    "ann.reset_MSE()\n",
    "input=test_data.get_input()\n",
    "output=test_data.get_output()\n",
    "\n",
    "prediction_file = data_dir + \"neural_predictions.csv\"\n",
    "with open(prediction_file, 'w') as predict_file:\n",
    "    predict_file.write(\",actual,predicted\\n\")\n",
    "    for i in range(len(input)):\n",
    "        predict = ann.test(input[i], output[i])\n",
    "        predict_file.write(\"%d,%d,%f\\n\" % (i, int(output[i][0]), predict[0]))\n",
    "print \"MSE error on test data: %f\" % ann.get_MSE()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/anaconda2/lib/python2.7/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Cutoff= 0.37 (MCC=0.612) **\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.77      0.80       208\n",
      "          1       0.79      0.84      0.81       208\n",
      "\n",
      "avg / total       0.81      0.81      0.81       416\n",
      "\n",
      "NN: sensitivity(recall)=83.7, specificity=77.4, precision=78.7\n"
     ]
    },
    {
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
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       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
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       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
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       "                // Full images could contain transparency (where diff images\n",
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       "                // there is no ghosting.\n",
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       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "    this.header = titletext[0];\n",
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       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
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       "    var canvas_div = $('<div/>');\n",
       "\n",
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       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
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       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
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       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
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       "        resize: function(event, ui) {\n",
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       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
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       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
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       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
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       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
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       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
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       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
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       "\n",
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       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
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       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
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       "    }\n",
       "\n",
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       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
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       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
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       "    var x0 = msg['x0'];\n",
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       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x12b881d50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame.from_csv(prediction_file)\n",
    "real = df.actual.astype(\"int\")\n",
    "predicted = df.predicted\n",
    "\n",
    "from sklearn.metrics import classification_report, matthews_corrcoef, confusion_matrix\n",
    "\n",
    "classification_vector = lambda cutoff: [1 if pred >= cutoff else 0 for pred in predicted]\n",
    "\n",
    "def find_best_mcc():\n",
    "    best_mcc = 0.0\n",
    "    best_cutoff = 0.0\n",
    "    for i in range(1, 100):\n",
    "        pred_cutoff = i/100.0\n",
    "        mcc = matthews_corrcoef(real, classification_vector(pred_cutoff))\n",
    "        if (mcc > best_mcc):\n",
    "            best_mcc = mcc\n",
    "            best_cutoff = pred_cutoff\n",
    "    return (best_cutoff, best_mcc)\n",
    "\n",
    "(best_cutoff, best_mcc) = find_best_mcc()\n",
    "print \"** Cutoff= %.2f (MCC=%.3f) **\" % (best_cutoff, best_mcc)\n",
    "print classification_report(real, classification_vector(best_cutoff))\n",
    "\n",
    "def print_metrics(name, actual, predicted):\n",
    "    ((tn, fp), (fn, tp)) = confusion_matrix(actual, predicted)    \n",
    "    sensitivity = 100.0 * tp / (tp + fn)\n",
    "    specificity = 100.0 * tn / (tn + fp)\n",
    "    precision = 100.0 * tp / (tp + fp)\n",
    "    print \"%s: sensitivity(recall)=%.1f, specificity=%.1f, precision=%.1f\" % (name, sensitivity, specificity, precision)\n",
    "\n",
    "print_metrics(\"NN\", real, classification_vector(best_cutoff))\n",
    "\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, metrics\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(real, predicted, pos_label=1)\n",
    "roc_auc = metrics.roc_auc_score(real, predicted, average='macro', sample_weight=None)\n",
    "plt.title('ROC Curve')\n",
    "plt.plot(fpr, tpr, 'b', label='AUC = %0.2f'% (roc_auc))\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.2])\n",
    "plt.ylim([-0.1,1.2])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>connected_to_neuron</th>\n",
       "      <th>weight</th>\n",
       "      <th>abs_weight</th>\n",
       "      <th>position</th>\n",
       "      <th>measure</th>\n",
       "      <th>hydrophic</th>\n",
       "      <th>electronic</th>\n",
       "      <th>steric</th>\n",
       "      <th>perc_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>453</td>\n",
       "      <td>2.098767</td>\n",
       "      <td>2.098767</td>\n",
       "      <td>9</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.627395</td>\n",
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       "    <tr>\n",
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       "      <td>2.083853</td>\n",
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       "      <td>True</td>\n",
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       "      <td>459</td>\n",
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       "      <td>1.651490</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.280574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>308</td>\n",
       "      <td>1.444614</td>\n",
       "      <td>1.444614</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.120162</td>\n",
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       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>448</td>\n",
       "      <td>-1.354448</td>\n",
       "      <td>1.354448</td>\n",
       "      <td>8</td>\n",
       "      <td>48</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.050246</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>148</td>\n",
       "      <td>1.289342</td>\n",
       "      <td>1.289342</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.999763</td>\n",
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       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>337</td>\n",
       "      <td>1.256546</td>\n",
       "      <td>1.256546</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.974332</td>\n",
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       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>363</td>\n",
       "      <td>-1.216270</td>\n",
       "      <td>1.216270</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.943102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>499</td>\n",
       "      <td>1.169001</td>\n",
       "      <td>1.169001</td>\n",
       "      <td>9</td>\n",
       "      <td>49</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.906450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>259</td>\n",
       "      <td>1.031897</td>\n",
       "      <td>1.031897</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.800139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>303</td>\n",
       "      <td>-1.015071</td>\n",
       "      <td>1.015071</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.787092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>236</td>\n",
       "      <td>-0.993563</td>\n",
       "      <td>0.993563</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.770414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>413</td>\n",
       "      <td>-0.922397</td>\n",
       "      <td>0.922397</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.715231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>613</td>\n",
       "      <td>0.909673</td>\n",
       "      <td>0.909673</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.705366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>490</td>\n",
       "      <td>-0.848689</td>\n",
       "      <td>0.848689</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.658078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>342</td>\n",
       "      <td>-0.827520</td>\n",
       "      <td>0.827520</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.641663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>253</td>\n",
       "      <td>-0.815750</td>\n",
       "      <td>0.815750</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.632537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>455</td>\n",
       "      <td>-0.787811</td>\n",
       "      <td>0.787811</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.610873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>397</td>\n",
       "      <td>-0.777451</td>\n",
       "      <td>0.777451</td>\n",
       "      <td>7</td>\n",
       "      <td>47</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.602840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>338</td>\n",
       "      <td>0.756024</td>\n",
       "      <td>0.756024</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.586225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>248</td>\n",
       "      <td>0.747059</td>\n",
       "      <td>0.747059</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.579273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>520</td>\n",
       "      <td>-0.727990</td>\n",
       "      <td>0.727990</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.564488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>439</td>\n",
       "      <td>0.720371</td>\n",
       "      <td>0.720371</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.558579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>693</td>\n",
       "      <td>0.703738</td>\n",
       "      <td>0.703738</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.545682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>543</td>\n",
       "      <td>-0.687252</td>\n",
       "      <td>0.687252</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.532899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>198</td>\n",
       "      <td>0.673047</td>\n",
       "      <td>0.673047</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.521884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>349</td>\n",
       "      <td>0.668894</td>\n",
       "      <td>0.668894</td>\n",
       "      <td>6</td>\n",
       "      <td>49</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.518664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>437</td>\n",
       "      <td>-0.667054</td>\n",
       "      <td>0.667054</td>\n",
       "      <td>8</td>\n",
       "      <td>37</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.517237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>570</td>\n",
       "      <td>-0.664693</td>\n",
       "      <td>0.664693</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.515406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>389</td>\n",
       "      <td>0.663404</td>\n",
       "      <td>0.663404</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.514407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>623</td>\n",
       "      <td>-0.146371</td>\n",
       "      <td>0.146371</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.113497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>605</td>\n",
       "      <td>-0.146086</td>\n",
       "      <td>0.146086</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.113276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>501</td>\n",
       "      <td>0.145884</td>\n",
       "      <td>0.145884</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.113119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>504</td>\n",
       "      <td>0.144971</td>\n",
       "      <td>0.144971</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.112411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>181</td>\n",
       "      <td>0.144398</td>\n",
       "      <td>0.144398</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.111967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>949</td>\n",
       "      <td>0.143513</td>\n",
       "      <td>0.143513</td>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.111280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>601</td>\n",
       "      <td>0.143477</td>\n",
       "      <td>0.143477</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.111253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>355</td>\n",
       "      <td>0.143098</td>\n",
       "      <td>0.143098</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.110959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>717</td>\n",
       "      <td>0.142541</td>\n",
       "      <td>0.142541</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.110527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>625</td>\n",
       "      <td>-0.142198</td>\n",
       "      <td>0.142198</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.110261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>467</td>\n",
       "      <td>-0.142100</td>\n",
       "      <td>0.142100</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.110185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>619</td>\n",
       "      <td>-0.140747</td>\n",
       "      <td>0.140747</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.109136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>486</td>\n",
       "      <td>0.140268</td>\n",
       "      <td>0.140268</td>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.108765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>682</td>\n",
       "      <td>0.139436</td>\n",
       "      <td>0.139436</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.108120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>724</td>\n",
       "      <td>-0.137877</td>\n",
       "      <td>0.137877</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.106911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>114</td>\n",
       "      <td>-0.137534</td>\n",
       "      <td>0.137534</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.106645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>502</td>\n",
       "      <td>-0.137099</td>\n",
       "      <td>0.137099</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.106308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>983</td>\n",
       "      <td>-0.136961</td>\n",
       "      <td>0.136961</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.106200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>283</td>\n",
       "      <td>0.136811</td>\n",
       "      <td>0.136811</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.106084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>640</td>\n",
       "      <td>0.136426</td>\n",
       "      <td>0.136426</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.105786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>471</td>\n",
       "      <td>0.135166</td>\n",
       "      <td>0.135166</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.104808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>899</td>\n",
       "      <td>-0.134755</td>\n",
       "      <td>0.134755</td>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.104490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>73</td>\n",
       "      <td>0.133886</td>\n",
       "      <td>0.133886</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.103816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>320</td>\n",
       "      <td>0.132825</td>\n",
       "      <td>0.132825</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.102993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>188</td>\n",
       "      <td>0.132576</td>\n",
       "      <td>0.132576</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.102800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20</td>\n",
       "      <td>-0.131978</td>\n",
       "      <td>0.131978</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0.102337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>295</td>\n",
       "      <td>-0.131114</td>\n",
       "      <td>0.131114</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.101667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>736</td>\n",
       "      <td>-0.130869</td>\n",
       "      <td>0.130869</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.101476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>341</td>\n",
       "      <td>0.130757</td>\n",
       "      <td>0.130757</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.101390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>252</td>\n",
       "      <td>0.129204</td>\n",
       "      <td>0.129204</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.100185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     connected_to_neuron    weight  abs_weight  position  measure hydrophic  \\\n",
       "454                  453  2.098767    2.098767         9        3      True   \n",
       "488                  487 -2.083853    2.083853         9       37     False   \n",
       "460                  459 -1.651490    1.651490         9        9      True   \n",
       "309                  308  1.444614    1.444614         6        8      True   \n",
       "449                  448 -1.354448    1.354448         8       48     False   \n",
       "149                  148  1.289342    1.289342         2       48     False   \n",
       "338                  337  1.256546    1.256546         6       37     False   \n",
       "364                  363 -1.216270    1.216270         7       13      True   \n",
       "500                  499  1.169001    1.169001         9       49     False   \n",
       "260                  259  1.031897    1.031897         5        9      True   \n",
       "304                  303 -1.015071    1.015071         6        3      True   \n",
       "237                  236 -0.993563    0.993563         4       36     False   \n",
       "414                  413 -0.922397    0.922397         8       13      True   \n",
       "614                  613  0.909673    0.909673        12       13      True   \n",
       "491                  490 -0.848689    0.848689         9       40     False   \n",
       "343                  342 -0.827520    0.827520         6       42     False   \n",
       "254                  253 -0.815750    0.815750         5        3      True   \n",
       "456                  455 -0.787811    0.787811         9        5      True   \n",
       "398                  397 -0.777451    0.777451         7       47     False   \n",
       "339                  338  0.756024    0.756024         6       38     False   \n",
       "249                  248  0.747059    0.747059         4       48     False   \n",
       "521                  520 -0.727990    0.727990        10       20     False   \n",
       "440                  439  0.720371    0.720371         8       39     False   \n",
       "694                  693  0.703738    0.703738        13       43     False   \n",
       "544                  543 -0.687252    0.687252        10       43     False   \n",
       "199                  198  0.673047    0.673047         3       48     False   \n",
       "350                  349  0.668894    0.668894         6       49     False   \n",
       "438                  437 -0.667054    0.667054         8       37     False   \n",
       "571                  570 -0.664693    0.664693        11       20     False   \n",
       "390                  389  0.663404    0.663404         7       39     False   \n",
       "..                   ...       ...         ...       ...      ...       ...   \n",
       "624                  623 -0.146371    0.146371        12       23     False   \n",
       "606                  605 -0.146086    0.146086        12        5      True   \n",
       "502                  501  0.145884    0.145884        10        1      True   \n",
       "505                  504  0.144971    0.144971        10        4      True   \n",
       "182                  181  0.144398    0.144398         3       31     False   \n",
       "950                  949  0.143513    0.143513        18       49     False   \n",
       "602                  601  0.143477    0.143477        12        1      True   \n",
       "356                  355  0.143098    0.143098         7        5      True   \n",
       "718                  717  0.142541    0.142541        14       17      True   \n",
       "626                  625 -0.142198    0.142198        12       25     False   \n",
       "468                  467 -0.142100    0.142100         9       17      True   \n",
       "620                  619 -0.140747    0.140747        12       19     False   \n",
       "487                  486  0.140268    0.140268         9       36     False   \n",
       "683                  682  0.139436    0.139436        13       32     False   \n",
       "725                  724 -0.137877    0.137877        14       24     False   \n",
       "115                  114 -0.137534    0.137534         2       14      True   \n",
       "503                  502 -0.137099    0.137099        10        2      True   \n",
       "984                  983 -0.136961    0.136961        19       33     False   \n",
       "284                  283  0.136811    0.136811         5       33     False   \n",
       "641                  640  0.136426    0.136426        12       40     False   \n",
       "472                  471  0.135166    0.135166         9       21     False   \n",
       "900                  899 -0.134755    0.134755        17       49     False   \n",
       "74                    73  0.133886    0.133886         1       23     False   \n",
       "321                  320  0.132825    0.132825         6       20     False   \n",
       "189                  188  0.132576    0.132576         3       38     False   \n",
       "21                    20 -0.131978    0.131978         0       20     False   \n",
       "296                  295 -0.131114    0.131114         5       45     False   \n",
       "737                  736 -0.130869    0.130869        14       36     False   \n",
       "342                  341  0.130757    0.130757         6       41     False   \n",
       "253                  252  0.129204    0.129204         5        2      True   \n",
       "\n",
       "    electronic steric  perc_weight  \n",
       "454      False  False     1.627395  \n",
       "488       True  False     1.615831  \n",
       "460      False  False     1.280574  \n",
       "309      False  False     1.120162  \n",
       "449       True  False     1.050246  \n",
       "149       True  False     0.999763  \n",
       "338       True  False     0.974332  \n",
       "364      False  False     0.943102  \n",
       "500       True  False     0.906450  \n",
       "260      False  False     0.800139  \n",
       "304      False  False     0.787092  \n",
       "237       True  False     0.770414  \n",
       "414      False  False     0.715231  \n",
       "614      False  False     0.705366  \n",
       "491       True  False     0.658078  \n",
       "343       True  False     0.641663  \n",
       "254      False  False     0.632537  \n",
       "456      False  False     0.610873  \n",
       "398       True  False     0.602840  \n",
       "339       True  False     0.586225  \n",
       "249       True  False     0.579273  \n",
       "521      False   True     0.564488  \n",
       "440       True  False     0.558579  \n",
       "694       True  False     0.545682  \n",
       "544       True  False     0.532899  \n",
       "199       True  False     0.521884  \n",
       "350       True  False     0.518664  \n",
       "438       True  False     0.517237  \n",
       "571      False   True     0.515406  \n",
       "390       True  False     0.514407  \n",
       "..         ...    ...          ...  \n",
       "624      False   True     0.113497  \n",
       "606      False  False     0.113276  \n",
       "502      False  False     0.113119  \n",
       "505      False  False     0.112411  \n",
       "182      False   True     0.111967  \n",
       "950       True  False     0.111280  \n",
       "602      False  False     0.111253  \n",
       "356      False  False     0.110959  \n",
       "718      False  False     0.110527  \n",
       "626      False   True     0.110261  \n",
       "468      False  False     0.110185  \n",
       "620      False   True     0.109136  \n",
       "487       True  False     0.108765  \n",
       "683      False   True     0.108120  \n",
       "725      False   True     0.106911  \n",
       "115      False  False     0.106645  \n",
       "503      False  False     0.106308  \n",
       "984      False   True     0.106200  \n",
       "284      False   True     0.106084  \n",
       "641       True  False     0.105786  \n",
       "472      False   True     0.104808  \n",
       "900       True  False     0.104490  \n",
       "74       False   True     0.103816  \n",
       "321      False   True     0.102993  \n",
       "189       True  False     0.102800  \n",
       "21       False   True     0.102337  \n",
       "296       True  False     0.101667  \n",
       "737       True  False     0.101476  \n",
       "342       True  False     0.101390  \n",
       "253      False  False     0.100185  \n",
       "\n",
       "[249 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fetch_neural_connections():\n",
    "    with open(neural_network_save_file, 'r') as network_file:\n",
    "        data = []\n",
    "        for line in network_file.readlines():\n",
    "            if line.startswith(\"connections\"):\n",
    "                (key, value) = line.split(\"=\")\n",
    "                for record in value.strip()[1:-1].split(\") (\"):\n",
    "                    (neuron, weight) = record.split(\", \")\n",
    "                    data.append((int(neuron), float(weight)))\n",
    "        df = pd.DataFrame(data)\n",
    "        df.columns = [\"connected_to_neuron\", \"weight\"]\n",
    "        return df\n",
    "                   \n",
    "df = fetch_neural_connections()\n",
    "df[\"abs_weight\"] = df.weight.abs()\n",
    "df[\"position\"] = df.connected_to_neuron.div(50).astype(\"int\")\n",
    "df[\"measure\"] = df.connected_to_neuron.mod(50)\n",
    "# 0 - 17 (18 values)\n",
    "df[\"hydrophic\"] = df.measure.le(17)\n",
    "# 36 - 49 (15 values)\n",
    "df[\"electronic\"] = df.measure.ge(36)\n",
    "# 18 - 35 (17 values)\n",
    "df[\"steric\"] = (df.hydrophic | df.electronic) == False\n",
    "\n",
    "total_weight = df.abs_weight.sum()\n",
    "df[\"perc_weight\"] = df.abs_weight / total_weight * 100\n",
    "df = df[df.perc_weight > 0.1]\n",
    "df.sort_values(by=[\"perc_weight\"], ascending=False)\n"
   ]
  }
 ],
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