{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emotion Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "This tutorial is available as an IPython notebook at [Malaya/example/emotion](https://github.com/huseinzol05/Malaya/tree/master/example/emotion).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "This module trained on both standard and local (included social media) language structures, so it is save to use for both.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6 s, sys: 1.23 s, total: 7.23 s\n",
      "Wall time: 8.33 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import malaya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "anger_text = 'babi la company ni, aku dah la penat datang dari jauh'\n",
    "fear_text = 'takut doh tengok cerita hantu tadi'\n",
    "happy_text = 'bestnya dapat tidur harini, tak payah pergi kerja'\n",
    "love_text = 'aku sayang sgt dia dah doh'\n",
    "sadness_text = 'kecewa tengok kerajaan baru ni, janji ape pun tak dapat'\n",
    "surprise_text = 'sakit jantung aku, terkejut dengan cerita hantu tadi'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger', 'fear', 'happy', 'love', 'sadness', 'surprise']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.emotion.label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All models follow same method as sklearn interface, `predict` to get batch of labels, `predict_proba` to get batch of probabilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load multinomial model\n",
    "\n",
    "```python\n",
    "def multinomial(**kwargs):\n",
    "    \"\"\"\n",
    "    Load multinomial emotion model.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result : malaya.model.ml.Bayes class\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = malaya.emotion.multinomial()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict batch of strings\n",
    "\n",
    "```python\n",
    "def predict(self, strings: List[str]):\n",
    "    \"\"\"\n",
    "    classify list of strings.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    strings: List[str]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: List[str]\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([anger_text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger', 'fear', 'happy', 'love', 'sadness', 'surprise']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(\n",
    "    [anger_text, fear_text, happy_text, love_text, sadness_text, surprise_text]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict batch of strings with probability\n",
    "\n",
    "```python\n",
    "def predict_proba(self, strings: List[str]):\n",
    "    \"\"\"\n",
    "    classify list of strings and return probability.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    strings: List[str]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: List[dict[str, float]]\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'anger': 0.32948272681734814,\n",
       "  'fear': 0.13959708810717708,\n",
       "  'happy': 0.14671455153216045,\n",
       "  'love': 0.12489192355631354,\n",
       "  'sadness': 0.1285972541671178,\n",
       "  'surprise': 0.13071645581988448},\n",
       " {'anger': 0.11379406005377896,\n",
       "  'fear': 0.4006934391283133,\n",
       "  'happy': 0.11389665647702245,\n",
       "  'love': 0.12481915233837086,\n",
       "  'sadness': 0.0991261507380643,\n",
       "  'surprise': 0.14767054126445014},\n",
       " {'anger': 0.14667998117610198,\n",
       "  'fear': 0.1422732633232615,\n",
       "  'happy': 0.29984520430807293,\n",
       "  'love': 0.1409005078277281,\n",
       "  'sadness': 0.13374705318404811,\n",
       "  'surprise': 0.13655399018078768},\n",
       " {'anger': 0.1590563839629243,\n",
       "  'fear': 0.14687344690114268,\n",
       "  'happy': 0.1419948160674701,\n",
       "  'love': 0.279550441361504,\n",
       "  'sadness': 0.1285927908584157,\n",
       "  'surprise': 0.14393212084854254},\n",
       " {'anger': 0.13425914937312508,\n",
       "  'fear': 0.12053328146716755,\n",
       "  'happy': 0.14923350911233682,\n",
       "  'love': 0.10289492749919464,\n",
       "  'sadness': 0.36961334597699913,\n",
       "  'surprise': 0.12346578657117815},\n",
       " {'anger': 0.06724850384395685,\n",
       "  'fear': 0.1283628050361525,\n",
       "  'happy': 0.05801958643852813,\n",
       "  'love': 0.06666524240157067,\n",
       "  'sadness': 0.06537667186293224,\n",
       "  'surprise': 0.6143271904168589}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(\n",
    "    [anger_text, fear_text, happy_text, love_text, sadness_text, surprise_text]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List available Transformer models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:tested on 20% test set.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size (MB)</th>\n",
       "      <th>Quantized Size (MB)</th>\n",
       "      <th>macro precision</th>\n",
       "      <th>macro recall</th>\n",
       "      <th>macro f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bert</th>\n",
       "      <td>425.6</td>\n",
       "      <td>111.00</td>\n",
       "      <td>0.99786</td>\n",
       "      <td>0.99773</td>\n",
       "      <td>0.99779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiny-bert</th>\n",
       "      <td>57.4</td>\n",
       "      <td>15.40</td>\n",
       "      <td>0.99692</td>\n",
       "      <td>0.99696</td>\n",
       "      <td>0.99694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>albert</th>\n",
       "      <td>48.6</td>\n",
       "      <td>12.80</td>\n",
       "      <td>0.99740</td>\n",
       "      <td>0.99773</td>\n",
       "      <td>0.99757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiny-albert</th>\n",
       "      <td>22.4</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.99325</td>\n",
       "      <td>0.99378</td>\n",
       "      <td>0.99351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xlnet</th>\n",
       "      <td>446.5</td>\n",
       "      <td>118.00</td>\n",
       "      <td>0.99773</td>\n",
       "      <td>0.99775</td>\n",
       "      <td>0.99774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alxlnet</th>\n",
       "      <td>46.8</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.99663</td>\n",
       "      <td>0.99697</td>\n",
       "      <td>0.99680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastformer</th>\n",
       "      <td>446.0</td>\n",
       "      <td>113.00</td>\n",
       "      <td>0.99197</td>\n",
       "      <td>0.99194</td>\n",
       "      <td>0.99195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiny-fastformer</th>\n",
       "      <td>77.2</td>\n",
       "      <td>19.60</td>\n",
       "      <td>0.98926</td>\n",
       "      <td>0.98783</td>\n",
       "      <td>0.98853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Size (MB)  Quantized Size (MB)  macro precision  \\\n",
       "bert                 425.6               111.00          0.99786   \n",
       "tiny-bert             57.4                15.40          0.99692   \n",
       "albert                48.6                12.80          0.99740   \n",
       "tiny-albert           22.4                 5.98          0.99325   \n",
       "xlnet                446.5               118.00          0.99773   \n",
       "alxlnet               46.8                13.30          0.99663   \n",
       "fastformer           446.0               113.00          0.99197   \n",
       "tiny-fastformer       77.2                19.60          0.98926   \n",
       "\n",
       "                 macro recall  macro f1-score  \n",
       "bert                  0.99773         0.99779  \n",
       "tiny-bert             0.99696         0.99694  \n",
       "albert                0.99773         0.99757  \n",
       "tiny-albert           0.99378         0.99351  \n",
       "xlnet                 0.99775         0.99774  \n",
       "alxlnet               0.99697         0.99680  \n",
       "fastformer            0.99194         0.99195  \n",
       "tiny-fastformer       0.98783         0.98853  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.emotion.available_transformer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure you can check accuracy chart from here first before select a model, https://malaya.readthedocs.io/en/latest/Accuracy.html#emotion-analysis\n",
    "\n",
    "**You might want to use Tiny-Albert, a very small size, 22.4MB, but the accuracy is still on the top notch.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Transformer model\n",
    "\n",
    "```python\n",
    "def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):\n",
    "    \"\"\"\n",
    "    Load Transformer emotion model.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model : str, optional (default='bert')\n",
    "        Model architecture supported. Allowed values:\n",
    "\n",
    "        * ``'bert'`` - Google BERT BASE parameters.\n",
    "        * ``'tiny-bert'`` - Google BERT TINY parameters.\n",
    "        * ``'albert'`` - Google ALBERT BASE parameters.\n",
    "        * ``'tiny-albert'`` - Google ALBERT TINY parameters.\n",
    "        * ``'xlnet'`` - Google XLNET BASE parameters.\n",
    "        * ``'alxlnet'`` - Malaya ALXLNET BASE parameters.\n",
    "        * ``'fastformer'`` - FastFormer BASE parameters.\n",
    "        * ``'tiny-fastformer'`` - FastFormer TINY parameters.\n",
    "\n",
    "    quantized : bool, optional (default=False)\n",
    "        if True, will load 8-bit quantized model.\n",
    "        Quantized model not necessary faster, totally depends on the machine.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: model\n",
    "        List of model classes:\n",
    "\n",
    "        * if `bert` in model, will return `malaya.model.bert.MulticlassBERT`.\n",
    "        * if `xlnet` in model, will return `malaya.model.xlnet.MulticlassXLNET`.\n",
    "        * if `fastformer` in model, will return `malaya.model.fastformer.MulticlassFastFormer`.\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = malaya.emotion.transformer(model = 'albert')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Quantized model\n",
    "\n",
    "To load 8-bit quantized model, simply pass `quantized = True`, default is `False`.\n",
    "\n",
    "We can expect slightly accuracy drop from quantized model, and not necessary faster than normal 32-bit float model, totally depends on machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Load quantized model will cause accuracy drop.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loading sentence piece model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loading sentence piece model\n"
     ]
    }
   ],
   "source": [
    "quantized_model = malaya.emotion.transformer(model = 'albert', quantized = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict batch of strings\n",
    "\n",
    "```python\n",
    "def predict(self, strings: List[str]):\n",
    "    \"\"\"\n",
    "    classify list of strings.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    strings: List[str]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: List[str]\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger', 'fear', 'anger', 'love', 'sadness', 'surprise']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(\n",
    "    [anger_text, fear_text, happy_text, love_text, sadness_text, surprise_text]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger', 'fear', 'happy', 'love', 'sadness', 'surprise']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantized_model.predict(\n",
    "    [anger_text, fear_text, happy_text, love_text, sadness_text, surprise_text]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict batch of strings with probability\n",
    "\n",
    "```python\n",
    "def predict_proba(self, strings: List[str]):\n",
    "    \"\"\"\n",
    "    classify list of strings and return probability.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    strings : List[str]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: List[dict[str, float]]\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'anger': 0.9998901,\n",
       "  'fear': 3.2524113e-05,\n",
       "  'happy': 2.620931e-05,\n",
       "  'love': 2.2871463e-05,\n",
       "  'sadness': 9.782951e-06,\n",
       "  'surprise': 1.8502667e-05},\n",
       " {'anger': 1.6941378e-05,\n",
       "  'fear': 0.9999205,\n",
       "  'happy': 9.070281e-06,\n",
       "  'love': 2.044179e-05,\n",
       "  'sadness': 6.7731107e-06,\n",
       "  'surprise': 2.6314676e-05},\n",
       " {'anger': 0.15370166,\n",
       "  'fear': 0.0013852724,\n",
       "  'happy': 0.8268689,\n",
       "  'love': 0.011433229,\n",
       "  'sadness': 0.0011807577,\n",
       "  'surprise': 0.005430276},\n",
       " {'anger': 1.2597201e-05,\n",
       "  'fear': 1.7600481e-05,\n",
       "  'happy': 9.667115e-06,\n",
       "  'love': 0.9999331,\n",
       "  'sadness': 1.3735416e-05,\n",
       "  'surprise': 1.3399296e-05},\n",
       " {'anger': 1.9176923e-05,\n",
       "  'fear': 1.1163729e-05,\n",
       "  'happy': 6.353941e-06,\n",
       "  'love': 7.004002e-06,\n",
       "  'sadness': 0.99994576,\n",
       "  'surprise': 1.0511084e-05},\n",
       " {'anger': 5.8739704e-05,\n",
       "  'fear': 1.9771342e-05,\n",
       "  'happy': 1.8316741e-05,\n",
       "  'love': 2.2319455e-05,\n",
       "  'sadness': 3.646786e-05,\n",
       "  'surprise': 0.9998443}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(\n",
    "    [anger_text, fear_text, happy_text, love_text, sadness_text, surprise_text]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'anger': 0.99988353,\n",
       "  'fear': 3.5938003e-05,\n",
       "  'happy': 2.7778764e-05,\n",
       "  'love': 2.3541537e-05,\n",
       "  'sadness': 9.574292e-06,\n",
       "  'surprise': 1.9607493e-05},\n",
       " {'anger': 1.6855265e-05,\n",
       "  'fear': 0.9999219,\n",
       "  'happy': 9.185196e-06,\n",
       "  'love': 2.0216348e-05,\n",
       "  'sadness': 6.6679663e-06,\n",
       "  'surprise': 2.5186611e-05},\n",
       " {'anger': 0.22842072,\n",
       "  'fear': 0.001628682,\n",
       "  'happy': 0.7477462,\n",
       "  'love': 0.014303649,\n",
       "  'sadness': 0.0013838055,\n",
       "  'surprise': 0.00651699},\n",
       " {'anger': 1.28296715e-05,\n",
       "  'fear': 1.7833345e-05,\n",
       "  'happy': 9.577061e-06,\n",
       "  'love': 0.9999324,\n",
       "  'sadness': 1.3832815e-05,\n",
       "  'surprise': 1.34745715e-05},\n",
       " {'anger': 1.9776813e-05,\n",
       "  'fear': 1.1116885e-05,\n",
       "  'happy': 6.3422367e-06,\n",
       "  'love': 6.905633e-06,\n",
       "  'sadness': 0.9999455,\n",
       "  'surprise': 1.0316757e-05},\n",
       " {'anger': 5.8218586e-05,\n",
       "  'fear': 2.07504e-05,\n",
       "  'happy': 1.8061248e-05,\n",
       "  'love': 2.1852256e-05,\n",
       "  'sadness': 3.5944133e-05,\n",
       "  'surprise': 0.99984515}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantized_model.predict_proba(\n",
    "    [anger_text, fear_text, happy_text, love_text, sadness_text, surprise_text]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open emotion visualization dashboard\n",
    "\n",
    "```python\n",
    "def predict_words(\n",
    "    self, string: str, method: str = 'last', visualization: bool = True\n",
    "):\n",
    "    \"\"\"\n",
    "    classify words.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    string : str\n",
    "    method : str, optional (default='last')\n",
    "        Attention layer supported. Allowed values:\n",
    "\n",
    "        * ``'last'`` - attention from last layer.\n",
    "        * ``'first'`` - attention from first layer.\n",
    "        * ``'mean'`` - average attentions from all layers.\n",
    "    visualization: bool, optional (default=True)\n",
    "        If True, it will open the visualization dashboard.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: dict\n",
    "    \"\"\"\n",
    "```\n",
    "\n",
    "Default when you call `predict_words` it will open a browser with visualization dashboard, you can disable by `visualization=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict_words(sadness_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import Image, display\n",
    "\n",
    "display(Image('emotion-dashboard.png', width=800))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorize\n",
    "\n",
    "Let say you want to visualize sentence / word level in lower dimension, you can use `model.vectorize`,\n",
    "\n",
    "```python\n",
    "def vectorize(self, strings: List[str], method: str = 'first'):\n",
    "    \"\"\"\n",
    "    vectorize list of strings.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    strings: List[str]\n",
    "    method : str, optional (default='first')\n",
    "        Vectorization layer supported. Allowed values:\n",
    "\n",
    "        * ``'last'`` - vector from last sequence.\n",
    "        * ``'first'`` - vector from first sequence.\n",
    "        * ``'mean'`` - average vectors from all sequences.\n",
    "        * ``'word'`` - average vectors based on tokens.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    result: np.array\n",
    "    \"\"\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentence level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = [anger_text, fear_text, happy_text, love_text, sadness_text, surprise_text]\n",
    "r = quantized_model.vectorize(texts, method = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tsne = TSNE().fit_transform(r)\n",
    "tsne.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7, 7))\n",
    "plt.scatter(tsne[:, 0], tsne[:, 1])\n",
    "labels = texts\n",
    "for label, x, y in zip(\n",
    "    labels, tsne[:, 0], tsne[:, 1]\n",
    "):\n",
    "    label = (\n",
    "        '%s, %.3f' % (label[0], label[1])\n",
    "        if isinstance(label, list)\n",
    "        else label\n",
    "    )\n",
    "    plt.annotate(\n",
    "        label,\n",
    "        xy = (x, y),\n",
    "        xytext = (0, 0),\n",
    "        textcoords = 'offset points',\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Word level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = quantized_model.vectorize(texts, method = 'word')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = [], []\n",
    "for row in r:\n",
    "    x.extend([i[0] for i in row])\n",
    "    y.extend([i[1] for i in row])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne = TSNE().fit_transform(y)\n",
    "tsne.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7, 7))\n",
    "plt.scatter(tsne[:, 0], tsne[:, 1])\n",
    "labels = x\n",
    "for label, x, y in zip(\n",
    "    labels, tsne[:, 0], tsne[:, 1]\n",
    "):\n",
    "    label = (\n",
    "        '%s, %.3f' % (label[0], label[1])\n",
    "        if isinstance(label, list)\n",
    "        else label\n",
    "    )\n",
    "    plt.annotate(\n",
    "        label,\n",
    "        xy = (x, y),\n",
    "        xytext = (0, 0),\n",
    "        textcoords = 'offset points',\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty good, the model able to know cluster top right as surprise emotion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking models\n",
    "\n",
    "More information, you can read at [https://malaya.readthedocs.io/en/latest/Stack.html](https://malaya.readthedocs.io/en/latest/Stack.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "multinomial = malaya.emotion.multinomial()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'anger': 0.5739743139312979,\n",
       "  'fear': 0.002130791264743306,\n",
       "  'happy': 0.0019609404077070573,\n",
       "  'love': 0.0016901068202818533,\n",
       "  'sadness': 0.001121633002361737,\n",
       "  'surprise': 0.0015551851123993595}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.stack.predict_stack([multinomial, model], [anger_text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'anger': 0.5739743139312979,\n",
       "  'fear': 0.002130791264743306,\n",
       "  'happy': 0.0019609404077070573,\n",
       "  'love': 0.0016901068202818533,\n",
       "  'sadness': 0.001121633002361737,\n",
       "  'surprise': 0.0015551858768478731},\n",
       " {'anger': 0.001604580129233267,\n",
       "  'fear': 0.0011600003908574707,\n",
       "  'happy': 0.0009737663531537643,\n",
       "  'love': 0.0008489265368074127,\n",
       "  'sadness': 0.6079418541812244,\n",
       "  'surprise': 0.001139192858067602}]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya.stack.predict_stack([multinomial, model], [anger_text, sadness_text])"
   ]
  }
 ],
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