III - Visualize anime embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zero.svd import MangakiSVD\n",
    "from zero.als import MangakiALS\n",
    "from zero.pca import MangakiPCA\n",
    "from zero.knn import MangakiKNN\n",
    "import numpy as np\n",
    "from mangaki.utils.values import rating_values\n",
    "from zero.dataset import Dataset\n",
    "from mangaki.utils.fit_algo import fit_algo, get_algo_backup\n",
    "\n",
    "# dataset = Dataset()\n",
    "# dataset.load_csv('/Users/jilljenn/code/mangaki/mangaki/data/ratings.csv', convert=lambda choice: rating_values[choice], title_filename='works.csv')\n",
    "# titles = dataset.titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.64552744 0.93618019 1.22621537 1.62690785 0.5        1.56809729]\n",
      "[-2.   2.  -2.  -0.5  2.   2. ]\n"
     ]
    }
   ],
   "source": [
    "triplets = Rating.objects.values_list('user_id', 'work_id', 'choice')\n",
    "dataset = Dataset()\n",
    "anonymized = dataset.make_anonymous_data(triplets)\n",
    "\n",
    "algo = MangakiALS(nb_iterations=10)\n",
    "algo.set_parameters(dataset.anonymized.nb_users, dataset.anonymized.nb_works)\n",
    "#algo.save('/tmp')\n",
    "algo.load('/tmp')\n",
    "#algo.fit(dataset.anonymized.X, dataset.anonymized.y)\n",
    "\n",
    "print(algo.predict(dataset.anonymized.X)[:6])\n",
    "print(dataset.anonymized.y[:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2221, 20)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo.U.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 11078)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo.VT.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find popular works and users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "NB = 50\n",
    "\n",
    "popular_users = Counter([user_id for user_id, _ in dataset.anonymized.X])\n",
    "popular_works = Counter([work_id for _, work_id in dataset.anonymized.X])\n",
    "\n",
    "popular_user_ids = [user_id for user_id, _ in popular_users.most_common(NB)]\n",
    "popular_work_ids = [work_id for work_id, _ in popular_works.most_common(NB)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(np.array(Work.objects.values_list('id', 'title')), columns=('work_id', 'title'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()\n",
    "df['work_id'] = df['work_id'].astype(np.int32)\n",
    "df['encoded_work_id'] = df['work_id'].map(dataset.encode_work)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['encoded_work_id'] = df['encoded_work_id'].astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_id</th>\n",
       "      <th>title</th>\n",
       "      <th>encoded_work_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>251</td>\n",
       "      <td>The Story of Saiunkoku</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12377</td>\n",
       "      <td>Yume Tsukai</td>\n",
       "      <td>9281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21370</td>\n",
       "      <td>Shijou Saikyou no Deshi Kenichi</td>\n",
       "      <td>8827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7247</td>\n",
       "      <td>Secret Girl</td>\n",
       "      <td>10647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11228</td>\n",
       "      <td>Gekiganger 3</td>\n",
       "      <td>9448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_id                            title  encoded_work_id\n",
       "0      251           The Story of Saiunkoku             2016\n",
       "1    12377                      Yume Tsukai             9281\n",
       "2    21370  Shijou Saikyou no Deshi Kenichi             8827\n",
       "3     7247                      Secret Girl            10647\n",
       "4    11228                     Gekiganger 3             9448"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.titles = dict(np.array(df[['encoded_work_id', 'title']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize anime embeddings using matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.pdf\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "svd = TruncatedSVD()\n",
    "\n",
    "class MangakiViz:\n",
    "    def __init__(self, title):\n",
    "        self.fig, self.ax = plt.subplots()\n",
    "        plt.xlim((-1, 1))\n",
    "        plt.ylim((-1, 1))\n",
    "        plt.title(title)\n",
    "        \n",
    "    def load_points(self, V, displayed_ids, marker='.', color='#FF6A00', titles=None):\n",
    "        V2 = svd.fit_transform(V)\n",
    "        self.ax.scatter(V2[displayed_ids, 0], V2[displayed_ids, 1], marker=marker, c=color, picker=True)\n",
    "        if titles is not None:\n",
    "            self.V2 = V2\n",
    "            self.displayed_ids = displayed_ids\n",
    "            self.title_of_point = dict([(tuple(self.V2[entity_id].tolist()), titles[entity_id] if entity_id != 3 else 'Princess Mononoke') for entity_id in self.displayed_ids])\n",
    "            self.fig.canvas.mpl_connect('pick_event', self.onpick)\n",
    "    \n",
    "    def display_title(self, x, y):\n",
    "        plt.annotate(self.title_of_point[(x, y)], xy=(x, y), xycoords='data',\n",
    "            xytext=(-30, -30), textcoords='offset points',\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc3,rad=.2\")\n",
    "            )\n",
    "    \n",
    "    def onpick(self, event):\n",
    "        ind = self.displayed_ids[event.ind[0]]\n",
    "        x, y = self.V2[ind]\n",
    "        self.display_title(x, y)\n",
    "    \n",
    "    def show_popular(self):\n",
    "        for famous_pos in [0, 3, 5]:\n",
    "            x, y = self.V2[self.displayed_ids[famous_pos]]\n",
    "            self.display_title(x, y)\n",
    "    \n",
    "    def save(self, filename):\n",
    "        print(filename)\n",
    "        plt.show()\n",
    "        # self.fig.savefig(filename, format='pdf')\n",
    "        # plt.close()\n",
    "\n",
    "U = algo.U\n",
    "V = algo.VT.T\n",
    "titles = dataset.titles\n",
    "viz = MangakiViz('Visualizing anime embeddings')\n",
    "viz.load_points(U, popular_user_ids)\n",
    "viz.load_points(V, popular_work_ids, '+', 'tab:cyan', titles)\n",
    "viz.show_popular()\n",
    "viz.save('test.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This below was just to generate the slides for Hacker News Kansai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "V_ids = np.arange(20).repeat(2)\n",
    "U_ids = V_ids.copy()[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0 Start with random points\n",
      "1 1 0 Iteration 1: learn users fix works (RMSE: 1.068)\n",
      "2 1 1 Iteration 1: learn works fix users (RMSE: 1.020)\n",
      "3 2 1 Iteration 2: learn users fix works (RMSE: 0.977)\n",
      "4 2 2 Iteration 2: learn works fix users (RMSE: 0.963)\n",
      "5 3 2 Iteration 3: learn users fix works (RMSE: 0.945)\n",
      "6 3 3 Iteration 3: learn works fix users (RMSE: 0.940)\n",
      "7 4 3 Iteration 4: learn users fix works (RMSE: 0.931)\n",
      "8 4 4 Iteration 4: learn works fix users (RMSE: 0.928)\n",
      "9 5 4 Iteration 5: learn users fix works (RMSE: 0.923)\n",
      "10 5 5 Iteration 5: learn works fix users (RMSE: 0.922)\n",
      "11 6 5 Iteration 6: learn users fix works (RMSE: 0.918)\n",
      "12 6 6 Iteration 6: learn works fix users (RMSE: 0.917)\n",
      "13 7 6 Iteration 7: learn users fix works (RMSE: 0.915)\n",
      "14 7 7 Iteration 7: learn works fix users (RMSE: 0.914)\n",
      "15 8 7 Iteration 8: learn users fix works (RMSE: 0.913)\n",
      "16 8 8 Iteration 8: learn works fix users (RMSE: 0.912)\n",
      "17 9 8 Iteration 9: learn users fix works (RMSE: 0.911)\n",
      "18 9 9 Iteration 9: learn works fix users (RMSE: 0.910)\n",
      "19 10 9 Iteration 10: learn users fix works (RMSE: 0.910)\n",
      "20 10 10 Iteration 10: learn works fix users (RMSE: 0.909)\n",
      "21 11 10 Iteration 11: learn users fix works (RMSE: 0.909)\n",
      "22 11 11 Iteration 11: learn works fix users (RMSE: 0.908)\n",
      "23 12 11 Iteration 12: learn users fix works (RMSE: 0.908)\n",
      "24 12 12 Iteration 12: learn works fix users (RMSE: 0.908)\n",
      "25 13 12 Iteration 13: learn users fix works (RMSE: 0.907)\n",
      "26 13 13 Iteration 13: learn works fix users (RMSE: 0.907)\n",
      "27 14 13 Iteration 14: learn users fix works (RMSE: 0.907)\n",
      "28 14 14 Iteration 14: learn works fix users (RMSE: 0.906)\n",
      "29 15 14 Iteration 15: learn users fix works (RMSE: 0.906)\n",
      "30 15 15 Iteration 15: learn works fix users (RMSE: 0.906)\n",
      "31 16 15 Iteration 16: learn users fix works (RMSE: 0.906)\n",
      "32 16 16 Iteration 16: learn works fix users (RMSE: 0.906)\n",
      "33 17 16 Iteration 17: learn users fix works (RMSE: 0.905)\n",
      "34 17 17 Iteration 17: learn works fix users (RMSE: 0.905)\n",
      "35 18 17 Iteration 18: learn users fix works (RMSE: 0.905)\n",
      "36 18 18 Iteration 18: learn works fix users (RMSE: 0.905)\n",
      "37 19 18 Iteration 19: learn users fix works (RMSE: 0.905)\n",
      "38 19 19 Iteration 19: learn works fix users (RMSE: 0.905)\n"
     ]
    }
   ],
   "source": [
    "with open('/tmp/log.txt') as f:\n",
    "    plot_titles = ['Start with random points'] + f.read().splitlines()\n",
    "\n",
    "for t, (uid_t, vid_t) in enumerate(zip(U_ids, V_ids)):\n",
    "    print(t, uid_t, vid_t, plot_titles[t])\n",
    "    viz = MangakiViz(plot_titles[t])\n",
    "    U = np.load('U{:d}.npy'.format(uid_t))\n",
    "    V = np.load('V{:d}.npy'.format(vid_t))\n",
    "    viz.load_points(U, popular_user_ids)\n",
    "    viz.load_points(V, popular_work_ids, '+', 'tab:cyan', titles)\n",
    "    viz.show_popular()\n",
    "    viz.save('/Users/jilljenn/Desktop/hnkansai/figures/embed{:d}.pdf'.format(t))"
   ]
  }
 ],
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   "display_name": "Django Shell-Plus",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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