{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continous Learner for Emoji classifier 🤓\n",
    "**usage:**\n",
    "run all cells, then go to the [user interface](#User-Interface)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /home/jonas/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import simple_twitter_learning as stl\n",
    "import glob\n",
    "import sys\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## user interface area:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* UI helper functions and global states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output, Markdown, Math\n",
    "import ipywidgets as widgets\n",
    "\n",
    "out_areas = {}\n",
    "shown_widgets = {}\n",
    "tab_manager = widgets.Tab()\n",
    "\n",
    "def mp(obj):\n",
    "    display(Markdown(obj))\n",
    "\n",
    "def set_widget_visibility(widget_names, visible=True):\n",
    "    for w in widget_names:\n",
    "        shown_widgets[w].disabled = not visible\n",
    "\n",
    "def create_area(area_name:str, list_widgets:list, out_name:str, tab=tab_manager):\n",
    "    \"\"\"\n",
    "    creates a table of widgets with corresponding output area below\n",
    "    \n",
    "    @param area_name: title of the area\n",
    "    @param list_widgets: list of tuples: (widget, name:str)\n",
    "    @param out_name: name for the output area\n",
    "    \"\"\"\n",
    "    if out_name is not None:\n",
    "        out = widgets.Output()\n",
    "        out_areas[out_name] = out\n",
    "    h_box_widgets = []\n",
    "    v_box_widgets = []\n",
    "    for v in list_widgets:\n",
    "        for h in v:\n",
    "            if 'description' in h[0].__dir__() and h[1] is not None:\n",
    "                h[0].description = h[1]\n",
    "            if h[1] is not None:\n",
    "                shown_widgets[h[1]] = h[0]\n",
    "            h_box_widgets.append(h[0])\n",
    "        v_box_widgets.append(widgets.HBox(h_box_widgets))\n",
    "        h_box_widgets = []\n",
    "    \n",
    "    if out_name is not None:\n",
    "        v_box_widgets += [out]\n",
    "    tab.children = list(tab.children) + [widgets.VBox(v_box_widgets)]\n",
    "    tab.set_title(len(tab.children) - 1, area_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* build UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "----"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## User Interface"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9035abacb17b41e4ac3875663fb23014",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(VBox(children=(HBox(children=(HTML(value='<b> Data Root Folder: </b> <br> setup the folder conta…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp(\"----\")\n",
    "mp(\"## User Interface\")\n",
    "# create widgets\n",
    "create_area(\"load dataset 💾\",\n",
    "           [\n",
    "               [\n",
    "                   (widgets.HTML(\"<b> Data Root Folder: </b> <br> setup the folder containing *.json train data \"), None)\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Text(value=\"./data_en/\"), \"root_path\"),\n",
    "                   (widgets.Button(), \"set_path\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.HTML(\"<b> Loading and preprocessing options: </b> <br> setup the range of files to load. Only_emoticons will filter out 'non-smiley' emojis, min_words is the minimum amount of words for one document. Also you can setup top-emoji filtering or only load samples containing a custom emoji set\"), None)\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.IntRangeSlider(disabled=True, min=0, max=0), \"file_range\"),\n",
    "                   (widgets.Checkbox(value=True,disabled=True), \"only_emoticons\"),\n",
    "                   (widgets.Checkbox(value=False,disabled=True), \"apply_lemmatization_and_stemming\"),\n",
    "                   (widgets.BoundedIntText(value=5,min=0, max=10), \"min_words\")\n",
    "               ],\n",
    "               [\n",
    "                   #(widgets.BoundedIntText(value=-1,disabled=True,min=-1, max=10), \"k_means_cluster\")\n",
    "                   (widgets.BoundedIntText(value=20,disabled=True,min=-1, max=100), \"n_top_emojis\"),\n",
    "                   (widgets.Dropdown(options=[\"latest\", \"mean\"], value=\"latest\"), \"label_criteria\"),\n",
    "                   (widgets.Text(value=\"\"), \"custom_emojis\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Button(disabled=True),\"load_data\")\n",
    "               ]\n",
    "           ],\n",
    "           \"load\")\n",
    "\n",
    "classifier_tab = widgets.Tab()\n",
    "\n",
    "create_area(\"keras\",\n",
    "           [\n",
    "               [\n",
    "                   (widgets.IntSlider(min=0, max=10), \"n_keras_layer\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.HBox([]), \"n_keras_neurons\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.HBox([]), \"keras_funcs\")\n",
    "               ]\n",
    "           ],\n",
    "           None,\n",
    "           classifier_tab)\n",
    "\n",
    "create_area(\"create/save/load classifier\",\n",
    "           [\n",
    "               [\n",
    "                   (classifier_tab, \"classifier_tab\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.HTML(\"<b> Create new Classifier: </b> <br> create a new keras classifier with layer options from above. Also a vectorizer will be trained on loaded sample data. If doc2vec is disabled, TFIDF is used\"), None)\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Checkbox(value=True),\"use_doc2vec\"),\n",
    "                   (widgets.Checkbox(value=True),\"d2v_use_pretrained\"),\n",
    "                   (widgets.IntText(value=100),\"d2v_size\"),\n",
    "                   (widgets.IntText(value=8), \"d2v_window\"),\n",
    "                   (widgets.IntSlider(value=5, min=0, max=32), \"d2v_min_count\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Button(), \"create_classifier\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.HTML(\"<b> Save Classifier: </b>\"), None)\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Text(), \"classifier_name\"),\n",
    "                   (widgets.Button(), \"save_classifier\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.HTML(\"<b> Load Classifier: </b>\"), None)\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Select(options=sorted(glob.glob(\"./*.pipeline\"))), \"clf_file_selector\"),\n",
    "                   (widgets.Text(), \"clf_file\"),\n",
    "                   (widgets.Button(), \"load_classifier\")\n",
    "               ]\n",
    "           ],\n",
    "           \"create\")\n",
    "\n",
    "create_area(\"train classifier 🎓\", \n",
    "            [\n",
    "                [\n",
    "                    (widgets.HTML(\"<b> Custom Batch Settings: </b> <br> (Ignored if batch_size is 0)\"), None)\n",
    "                ],\n",
    "                [\n",
    "                    (widgets.IntSlider(value=0,min=0,max=0), \"batch_size\"),\n",
    "                    (widgets.FloatSlider(value=0.15, min=0, max=1), \"val_split\")\n",
    "                ],\n",
    "                [\n",
    "                    (widgets.HTML(\"<b> Train: </b>\"), None)\n",
    "                ],\n",
    "                [\n",
    "                    (widgets.IntText(value=1), \"n_epochs\"),\n",
    "                    (widgets.Button(),\"train\")\n",
    "                ]\n",
    "            ], \n",
    "            \"train\" )\n",
    "create_area(\"playground 😎\",\n",
    "           [\n",
    "               [\n",
    "                   (widgets.HTML(\"<b> predict single sentence </b> <br> (uses min distance to given emojis in prediction_ground_set)\"), None)\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Text(),\"test_input\"),\n",
    "                   (widgets.Text(value=\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\"),\"prediction_ground_set\"),\n",
    "                   (widgets.HTML(\"<h1>∅</h1>\"),\"prediction\"),\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Checkbox(),\"show_sorted_list\"),\n",
    "                   (widgets.Button(),\"show_plot\")\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.HTML(\"<b> Test on loaded validation set: </b> <br> (performs prediction plot on all validation samples that are labeled with given emojis)\"), None)\n",
    "               ],\n",
    "               [\n",
    "                   (widgets.Text(value=\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\"), \"validation_emojis\"),\n",
    "                   (widgets.Button(),\"show_validation_plot\")\n",
    "               ]\n",
    "           ],\n",
    "           \"playground\")\n",
    "\n",
    "tab_manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## global variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdm = None\n",
    "pm = None\n",
    "tr = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pretty jupyter print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import traceback\n",
    "from pprint import pprint as pp\n",
    "\n",
    "def jupyter_print(obj, cell_w = 10, headers=None, p_type=True, ret_mdown=False, index_offset=0, list_horizontal=False):\n",
    "    \"\"\"\n",
    "    pretty hacky function to convert arrays, lists and matrices into\n",
    "    nice readable markdown code and render that in jupyter. if that is not possible\n",
    "    it will use pretty print instead\n",
    "    \"\"\"\n",
    "    try:\n",
    "        ts = \"**Type:** \" + str(type(obj)).strip(\"<>\") + \"\\n\\n\"\n",
    "        if type(obj) == str:\n",
    "            display(Markdown(obj))\n",
    "        elif isinstance(obj, collections.Iterable):\n",
    "            if isinstance(obj[0], collections.Iterable) and type(obj[0]) is not str:\n",
    "                # we have a table\n",
    "                \n",
    "                if headers is None:\n",
    "                    headers = [str(i) for i in range(len(obj[0]))]\n",
    "                \n",
    "                if len(headers) < len(obj[0]):\n",
    "                    headers += [\" \" for i in range(len(obj[0]) - len(headers))]\n",
    "                \n",
    "                s = \"|\" + \" \" * cell_w + \"|\"\n",
    "                \n",
    "                for h in headers:\n",
    "                    s += str(h) + \" \" * (cell_w - len(h)) + \"|\"\n",
    "                s += \"\\n|\" + \"-\" * (len(headers) + (len(headers) + 1) * cell_w) + \"|\\n\"\n",
    "                \n",
    "                #s = (\"|\" + (\" \" * (cell_w))) * len(obj[0]) + \"|\\n\" + \"|\" + (\"-\" * (cell_w + 1)) * len(obj[0])\n",
    "                #s += '|\\n'\n",
    "                \n",
    "                row = index_offset\n",
    "                \n",
    "                for o in obj:\n",
    "                    s += \"|**\" + str(row) + \"**\" + \" \" * (cell_w - (len(str(row))+4))\n",
    "                    row += 1\n",
    "                    for i in o:\n",
    "                        s += \"|\" + str(i) + \" \" * (cell_w - len(str(i)))\n",
    "                    s+=\"|\" + '\\n'\n",
    "                s += ts\n",
    "                display(Markdown(s))\n",
    "                return s if ret_mdown else None\n",
    "            else:\n",
    "                # we have a list\n",
    "                \n",
    "            \n",
    "                if headers is None:\n",
    "                    headers = [\"index\",\"value\"]\n",
    "                    \n",
    "                index_title = headers[0]\n",
    "                value_title = headers[1]\n",
    "                \n",
    "                s = \"|\" + index_title + \" \" * (cell_w - len(value_title)) + \"|\" + value_title + \" \" * (cell_w - len(value_title)) + \"|\" + '\\n'\n",
    "                s += \"|\" + \"-\" * (1 + 2 * cell_w) + '|\\n'\n",
    "                i = index_offset\n",
    "                for o in obj:\n",
    "                    s_i = str(i)\n",
    "                    s_o = str(o)\n",
    "                    s += \"|\" + s_i + \" \" * (cell_w - len(s_i)) + \"|\" + s_o + \" \" * (cell_w - len(s_o)) + \"|\" + '\\n'\n",
    "                    i+=1\n",
    "                s += ts\n",
    "                #print(s)\n",
    "                display(Markdown(s))\n",
    "                return s if ret_mdown else None\n",
    "        else:\n",
    "            jupyter_print([obj])\n",
    "    except Exception as e:\n",
    "        print(ts)\n",
    "        pp(obj) \n",
    "\n",
    "jp = jupyter_print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## output progress printing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class progress_indicator(object):\n",
    "    \n",
    "    def __init__(self, description=\"progress\"):\n",
    "        self.w = widgets.FloatProgress(value=0, min=0,max=1, description = description)\n",
    "        display(self.w)\n",
    "    def update(self, val):\n",
    "        self.w.value = val\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## load datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_path(b):\n",
    "    with out_areas[\"load\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "        files = sorted(glob.glob(shown_widgets[\"root_path\"].value + \"/*.json\"))\n",
    "        \n",
    "        if len(files) == 0:\n",
    "            sys.stderr.write(\"ERROR: no json files available in \" + shown_widgets[\"root_path\"].value + \"\\n\")\n",
    "            set_widget_visibility([\"file_range\",\n",
    "                               \"only_emoticons\",\n",
    "                               \"n_top_emojis\",\n",
    "                               \"apply_lemmatization_and_stemming\",\n",
    "                               \"load_data\"], False)\n",
    "            return\n",
    "            \n",
    "        mp(\"**available files:**\")\n",
    "        jp(files, headers=[\"fileindex\",\"filepath\"])\n",
    "        set_widget_visibility([\"file_range\",\n",
    "                               \"only_emoticons\",\n",
    "                               \"n_top_emojis\",\n",
    "                               \"apply_lemmatization_and_stemming\",\n",
    "                               \"load_data\"], True)\n",
    "        shown_widgets[\"file_range\"].min=0\n",
    "        shown_widgets[\"file_range\"].max=len(files) -1\n",
    "\n",
    "def load_data(b):\n",
    "    global sdm\n",
    "    with out_areas[\"load\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "        \n",
    "        r = shown_widgets[\"file_range\"].value\n",
    "        r = (r[0], r[1] + 1) # range has to be exclusive according to the last element!\n",
    "        \n",
    "        p_r = progress_indicator(\"reading progress\")\n",
    "        \n",
    "        lemm_and_stemm = shown_widgets[\"apply_lemmatization_and_stemming\"].value\n",
    "        \n",
    "        if lemm_and_stemm:\n",
    "            p_s = progress_indicator(\"stemming progress\")\n",
    "        \n",
    "        emoji_mean = shown_widgets[\"label_criteria\"].value == \"mean\"\n",
    "        \n",
    "        custom_emojis = list(shown_widgets[\"custom_emojis\"].value)\n",
    "        \n",
    "        min_words = shown_widgets[\"min_words\"].value\n",
    "        \n",
    "        sdm = stl.sample_data_manager.generate_and_read(path=shown_widgets[\"root_path\"].value,\n",
    "                                                    n_top_emojis=shown_widgets[\"n_top_emojis\"].value,\n",
    "                                                    file_range=range(r[0], r[1]),\n",
    "                                                    n_kmeans_cluster=-1,\n",
    "                                                    read_progress_callback=p_r.update,\n",
    "                                                    stem_progress_callback=p_s.update if lemm_and_stemm else None,\n",
    "                                                    apply_stemming = lemm_and_stemm,\n",
    "                                                    emoji_mean=emoji_mean,\n",
    "                                                    custom_target_emojis=custom_emojis if len(custom_emojis) > 0 else None,\n",
    "                                                    min_words=min_words)\n",
    "        shown_widgets[\"batch_size\"].max = len(sdm.labels)\n",
    "        \n",
    "        \n",
    "# linking functions with buttons:\n",
    "shown_widgets[\"set_path\"].on_click(set_path)\n",
    "shown_widgets[\"load_data\"].on_click(load_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    with out_areas[\"train\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "        if sdm is None or pm is None:\n",
    "            sys.stderr.write(\"ERROR: sample data and/or classifier missing!\\n\")\n",
    "            return\n",
    "    \n",
    "        batch_size = shown_widgets[\"batch_size\"].value\n",
    "        val_split = shown_widgets[\"val_split\"].value\n",
    "        n_epochs = shown_widgets[\"n_epochs\"].value\n",
    "        \n",
    "        print(\"update train test split:\")\n",
    "        sdm.create_train_test_split(split=val_split)\n",
    "        \n",
    "        print(\"fit\")\n",
    "        \n",
    "        p = progress_indicator()\n",
    "        \n",
    "        tr = stl.trainer(sdm=sdm, pm=pm)\n",
    "        tr.fit(progress_callback=p.update, batch_size=batch_size if batch_size > 0 else None, n_epochs=n_epochs)\n",
    "        \n",
    "\n",
    "# linking:\n",
    "shown_widgets[\"train\"].on_click(train)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras_acivations = [\n",
    "    \"softmax\",\n",
    "    \"elu\",\n",
    "    \"selu\",\n",
    "    \"softplus\",\n",
    "    \"softsign\",\n",
    "    \"relu\",\n",
    "    \"tanh\",\n",
    "    \"sigmoid\",\n",
    "    \"hard_sigmoid\",\n",
    "    \"linear\",\n",
    "    \"None\"\n",
    "]\n",
    "\n",
    "def populate_keras_options(b):\n",
    "    n_layers = shown_widgets[\"n_keras_layer\"].value\n",
    "    hbox_neurons = shown_widgets[\"n_keras_neurons\"]\n",
    "    hbox_funcs = shown_widgets[\"keras_funcs\"]\n",
    "    \n",
    "    hbox_neurons.children = [widgets.IntText(description = str(i)) for i in range(n_layers)]\n",
    "    hbox_funcs.children = [widgets.Dropdown(options=keras_acivations,description = str(i)) for i in range(n_layers)]\n",
    "    \n",
    "    #hbox_neurons.children[-1].disabled = True\n",
    "\n",
    "def create_classifier(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    with out_areas[\"create\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "        if sdm is None:\n",
    "            sys.stderr.write(\"load a dataset first!\\n\")\n",
    "            return\n",
    "        \n",
    "        chosen_classifier = classifier_tab.get_title(classifier_tab.selected_index)\n",
    "        \n",
    "        mp(\"**chosen classifier**: `\" + chosen_classifier + \"`\")\n",
    "        \n",
    "        # creating the vectorizer\n",
    "        vectorizer = None\n",
    "        if shown_widgets[\"use_doc2vec\"].value:\n",
    "            if shown_widgets[\"d2v_use_pretrained\"].value:\n",
    "                vectorizer = pickle.load( open( \"doc2VecModel.p\", \"rb\" ) )\n",
    "            else:\n",
    "                vectorizer = stl.skd2v.Doc2VecTransformer(size=shown_widgets[\"d2v_size\"].value,\n",
    "                                                         window=shown_widgets[\"d2v_window\"].value,\n",
    "                                                         min_count=shown_widgets[\"d2v_min_count\"].value)\n",
    "        else:\n",
    "            vectorizer=TfidfVectorizer(stop_words='english')\n",
    "        \n",
    "        # TODO: add more classifier options here:\n",
    "        if chosen_classifier is 'keras':\n",
    "            sdm.create_train_test_split()\n",
    "            \n",
    "            n_layers = shown_widgets[\"n_keras_layer\"].value\n",
    "            hbox_neurons = shown_widgets[\"n_keras_neurons\"]\n",
    "            hbox_funcs = shown_widgets[\"keras_funcs\"]\n",
    "\n",
    "            layers = []\n",
    "            for i in range(n_layers):\n",
    "                func = hbox_funcs.children[i].value\n",
    "                if func == 'None':\n",
    "                    func = None\n",
    "                layers.append((hbox_neurons.children[i].value, func))\n",
    "            \n",
    "            # modify last layer:\n",
    "            layers[-1] = (sdm.y.shape[1], layers[-1][1])\n",
    "            \n",
    "            mp(\"**layers:** \")\n",
    "            jp(layers, headers=['#neurons', 'activation_func'])\n",
    "\n",
    "            pm = stl.pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer, layers=layers, sdm=sdm, fit_vectorizer=not shown_widgets[\"d2v_use_pretrained\"].value)\n",
    "\n",
    "def save_classifier(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    with out_areas[\"create\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "        if pm is None:\n",
    "            sys.stderr.write(\"ERROR: create classifier first\")\n",
    "            return\n",
    "        \n",
    "        pm.save(shown_widgets[\"classifier_name\"].value)\n",
    "\n",
    "def load_classifier(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    with out_areas[\"create\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "\n",
    "def update_file_selector(b):\n",
    "    shown_widgets[\"clf_file_selector\"].options = sorted(glob.glob(\"./*.pipeline\"))\n",
    "\n",
    "def clf_file_selector(b):\n",
    "    shown_widgets[\"clf_file\"].value = shown_widgets[\"clf_file_selector\"].value\n",
    "    update_file_selector(b)\n",
    "\n",
    "def load_classifier(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    with out_areas[\"create\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "        clf_file = shown_widgets[\"clf_file\"].value\n",
    "        pm = stl.pipeline_manager.load_from_pipeline_file(clf_file)\n",
    "    \n",
    "\n",
    "# link\n",
    "shown_widgets[\"n_keras_layer\"].observe(populate_keras_options)\n",
    "shown_widgets[\"create_classifier\"].on_click(create_classifier)\n",
    "shown_widgets[\"save_classifier\"].on_click(save_classifier)\n",
    "shown_widgets[\"load_classifier\"].on_click(load_classifier)\n",
    "shown_widgets[\"clf_file_selector\"].observe(clf_file_selector)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plotting stuff for testing area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_score(s):\n",
    "    #(pos, neg, neu)^T\n",
    "    return s[0] - s[1]\n",
    "\n",
    "def plot_sentiment_space(predicted_sentiment_vectors, top_sentiments, top_emojis):\n",
    "    # sentiment score axis\n",
    "    top_X = np.array([sentiment_score(x) for x in top_sentiments])\n",
    "    pred_X = np.array([sentiment_score(x) for x in predicted_sentiment_vectors])\n",
    "    \n",
    "    # neutral axis:\n",
    "    top_Y = np.array([x[2] for x in top_sentiments])\n",
    "    pred_Y = np.array([x[2] for x in predicted_sentiment_vectors])\n",
    "    \n",
    "    fig_1, ax_1 = plt.subplots()#figsize=(15,10))\n",
    "    plt.title(\"sentiment-score-plot\")\n",
    "    plt.xlabel(\"sentiment score\")\n",
    "    plt.ylabel(\"neutrality\")\n",
    "    plt.xlim([-1,1])\n",
    "    plt.ylim([0,1])\n",
    "    for i in range(len(top_X)):\n",
    "        plt.text(top_X[i], top_Y[i], top_emojis[i])\n",
    "    plt.plot(pred_X, pred_Y, 'bo')\n",
    "    #plt.savefig(title + \" -- sentiment-plot.png\", bbox_inches='tight')\n",
    "    \n",
    "    # sentiment score axis\n",
    "    top_X = np.array([x[0] for x in top_sentiments])\n",
    "    pred_X = np.array([x[0] for x in predicted_sentiment_vectors])\n",
    "    \n",
    "    # neutral axis:\n",
    "    top_Y = np.array([x[1] for x in top_sentiments])\n",
    "    pred_Y = np.array([x[1] for x in predicted_sentiment_vectors])\n",
    "    \n",
    "    fig_2, ax_2 = plt.subplots()#figsize=(15,10))\n",
    "    plt.title(\"positive-negative-plot\")\n",
    "    plt.xlabel(\"positive\")\n",
    "    plt.ylabel(\"negative\")\n",
    "    plt.xlim([0,1])\n",
    "    plt.ylim([0,1])\n",
    "    for i in range(len(top_X)):\n",
    "        plt.text(top_X[i], top_Y[i], top_emojis[i])\n",
    "    plt.plot(pred_X, pred_Y, 'bo')\n",
    "    #plt.savefig(title + \" -- positive-negative-plot.png\", bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## testing area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_20 = list(\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\")\n",
    "top_20_sents = stl.emoji2sent(top_20)\n",
    "\n",
    "pred = None\n",
    "\n",
    "def test_input(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    global pred\n",
    "    with out_areas[\"playground\"]:\n",
    "        clear_output()\n",
    "        mp(\"----\")\n",
    "        if pm is None:\n",
    "            sys.stderr.write(\"ERROR: load or create classifier first\")\n",
    "            return\n",
    "        X = shown_widgets[\"test_input\"].value\n",
    "        pred = pm.predict([X])\n",
    "        target_list=list(shown_widgets[\"prediction_ground_set\"].value)\n",
    "        shown_widgets[\"prediction\"].value = \"<h1> \" + str(stl.sent2emoji(pred,custom_target_emojis=target_list)[0]) + \"</h1>\"\n",
    "        if shown_widgets[\"show_sorted_list\"].value:\n",
    "            mp(\"## \" + \"\".join(stl.edist.sentiment_vector_to_emoji(pred, only_emoticons=True, n_results=100, custom_target_emojis=target_list)))\n",
    "        \n",
    "\n",
    "\n",
    "def plot_pred(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    global pred\n",
    "    with out_areas[\"playground\"]:\n",
    "        plot_sentiment_space(pred, top_20_sents, top_20)\n",
    "        \n",
    "        \n",
    "def plot_subset_pred(b):\n",
    "    global sdm\n",
    "    global pm\n",
    "    global tr\n",
    "    global pred\n",
    "    with out_areas[\"playground\"]:\n",
    "        clear_output()\n",
    "        \n",
    "        if sdm is None or pm is None:\n",
    "            sys.stderr.write(\"ERROR: sample data and/or classifier missing!\\n\")\n",
    "            return\n",
    "        \n",
    "        if tr is None:\n",
    "            tr = stl.trainer(sdm=sdm, pm=pm)\n",
    "        \n",
    "        pred, y = tr.test(emoji_subset=list(shown_widgets[\"validation_emojis\"].value))\n",
    "        print(len(pred))\n",
    "        plot_sentiment_space(pred, top_20_sents, top_20)\n",
    "\n",
    "#link\n",
    "shown_widgets[\"test_input\"].observe(test_input)\n",
    "shown_widgets[\"show_plot\"].on_click(plot_pred)\n",
    "shown_widgets[\"show_validation_plot\"].on_click(plot_subset_pred)"
   ]
  }
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