thwiki_lm/word2vec_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thai2Vec Embeddings Examples\n",
    "\n",
    "We use the embeddings from `v0.1` since it was trained specifically for word2vec as opposed to latter versions which garner to classification. The `thai2vec.bin` 51,556 word embeddings of 300 dimensions, in descending order by their frequencies (See `thai2vec.vocab`). The files are in word2vec format readable by `gensim`. Most common applications include word vector visualization, word arithmetic, word grouping, cosine similarity and sentence or document vectors. For sample code, see `thwiki_lm/word2vec_examples.ipynb`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #uncomment if you are running from google colab\n",
    "# !pip install sklearn_crfsuite\n",
    "# !pip install https://github.com/PyThaiNLP/pythainlp/archive/dev.zip\n",
    "# !pip install fastai==1.0.46\n",
    "# !pip install emoji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T04:55:05.294165Z",
     "start_time": "2018-01-28T04:55:05.203078Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download: thai2fit_wv\n",
      "thai2fit_wv 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "thai2vec.bin?dl=1: 62.5MB [00:15, 4.09MB/s]                            \n"
     ]
    }
   ],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "from pythainlp.tokenize import word_tokenize\n",
    "from gensim.models import KeyedVectors\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "import dill as pickle\n",
    "import pandas as pd\n",
    "\n",
    "model_path = 'thwiki_data/models/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load into gensim\n",
    "# model = KeyedVectors.load_word2vec_format(f'{model_path}thai2vec.bin',binary=True)\n",
    "from pythainlp import word_vector\n",
    "model = word_vector.get_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-28T04:58:27.057401Z",
     "start_time": "2018-01-28T04:58:09.721798Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ที่</th>\n",
       "      <td>0.308956</td>\n",
       "      <td>-0.097699</td>\n",
       "      <td>0.116745</td>\n",
       "      <td>0.215612</td>\n",
       "      <td>0.015768</td>\n",
       "      <td>-0.064163</td>\n",
       "      <td>0.062168</td>\n",
       "      <td>0.039649</td>\n",
       "      <td>0.864940</td>\n",
       "      <td>0.846904</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.142418</td>\n",
       "      <td>0.033241</td>\n",
       "      <td>0.171581</td>\n",
       "      <td>-0.624864</td>\n",
       "      <td>-0.009358</td>\n",
       "      <td>0.449131</td>\n",
       "      <td>0.120130</td>\n",
       "      <td>-0.122195</td>\n",
       "      <td>-0.450617</td>\n",
       "      <td>-0.071318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>และ</th>\n",
       "      <td>0.010751</td>\n",
       "      <td>-0.618971</td>\n",
       "      <td>0.129665</td>\n",
       "      <td>0.035460</td>\n",
       "      <td>-0.007560</td>\n",
       "      <td>0.027607</td>\n",
       "      <td>0.397824</td>\n",
       "      <td>0.026543</td>\n",
       "      <td>0.254075</td>\n",
       "      <td>0.168328</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.105786</td>\n",
       "      <td>0.180930</td>\n",
       "      <td>-0.101630</td>\n",
       "      <td>0.070885</td>\n",
       "      <td>-0.037263</td>\n",
       "      <td>0.183606</td>\n",
       "      <td>-0.049088</td>\n",
       "      <td>-0.672288</td>\n",
       "      <td>-1.293044</td>\n",
       "      <td>0.592576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>เป็น</th>\n",
       "      <td>-0.015736</td>\n",
       "      <td>-0.258926</td>\n",
       "      <td>0.052953</td>\n",
       "      <td>0.153728</td>\n",
       "      <td>-0.005985</td>\n",
       "      <td>-0.021081</td>\n",
       "      <td>0.041088</td>\n",
       "      <td>0.057312</td>\n",
       "      <td>1.633230</td>\n",
       "      <td>0.442729</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009408</td>\n",
       "      <td>-0.252576</td>\n",
       "      <td>-0.305512</td>\n",
       "      <td>0.372542</td>\n",
       "      <td>0.049151</td>\n",
       "      <td>0.568470</td>\n",
       "      <td>0.266586</td>\n",
       "      <td>0.400800</td>\n",
       "      <td>-0.784650</td>\n",
       "      <td>0.197369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ของ</th>\n",
       "      <td>-0.189711</td>\n",
       "      <td>-0.174774</td>\n",
       "      <td>0.171124</td>\n",
       "      <td>-0.186771</td>\n",
       "      <td>0.054294</td>\n",
       "      <td>-0.114150</td>\n",
       "      <td>-1.109456</td>\n",
       "      <td>-0.094466</td>\n",
       "      <td>-0.447015</td>\n",
       "      <td>0.042377</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.168676</td>\n",
       "      <td>-0.148738</td>\n",
       "      <td>0.680404</td>\n",
       "      <td>0.097702</td>\n",
       "      <td>0.020270</td>\n",
       "      <td>0.182967</td>\n",
       "      <td>-0.083949</td>\n",
       "      <td>0.006287</td>\n",
       "      <td>-0.707434</td>\n",
       "      <td>-0.070234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>มี</th>\n",
       "      <td>-0.156962</td>\n",
       "      <td>-0.231863</td>\n",
       "      <td>0.080312</td>\n",
       "      <td>0.323157</td>\n",
       "      <td>0.215695</td>\n",
       "      <td>0.055145</td>\n",
       "      <td>0.420794</td>\n",
       "      <td>0.016842</td>\n",
       "      <td>0.256759</td>\n",
       "      <td>0.832864</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044267</td>\n",
       "      <td>-0.147186</td>\n",
       "      <td>-0.105424</td>\n",
       "      <td>0.907078</td>\n",
       "      <td>0.009299</td>\n",
       "      <td>0.550953</td>\n",
       "      <td>0.139337</td>\n",
       "      <td>0.031696</td>\n",
       "      <td>-0.670379</td>\n",
       "      <td>-0.008048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ได้</th>\n",
       "      <td>-0.428813</td>\n",
       "      <td>-0.031194</td>\n",
       "      <td>0.041922</td>\n",
       "      <td>-0.036608</td>\n",
       "      <td>-0.008106</td>\n",
       "      <td>0.076470</td>\n",
       "      <td>-0.782270</td>\n",
       "      <td>0.033361</td>\n",
       "      <td>0.606864</td>\n",
       "      <td>0.440520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024458</td>\n",
       "      <td>-0.025031</td>\n",
       "      <td>0.103389</td>\n",
       "      <td>-0.078255</td>\n",
       "      <td>0.034323</td>\n",
       "      <td>0.459774</td>\n",
       "      <td>-0.748643</td>\n",
       "      <td>0.337775</td>\n",
       "      <td>-0.487408</td>\n",
       "      <td>-0.511535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"\"\"\"</th>\n",
       "      <td>-0.287710</td>\n",
       "      <td>0.064193</td>\n",
       "      <td>0.205076</td>\n",
       "      <td>0.146356</td>\n",
       "      <td>-0.071343</td>\n",
       "      <td>-0.039451</td>\n",
       "      <td>-1.845461</td>\n",
       "      <td>0.163763</td>\n",
       "      <td>1.018096</td>\n",
       "      <td>0.272786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051024</td>\n",
       "      <td>-0.532856</td>\n",
       "      <td>-0.131856</td>\n",
       "      <td>-0.090323</td>\n",
       "      <td>-0.058895</td>\n",
       "      <td>0.151262</td>\n",
       "      <td>-0.420358</td>\n",
       "      <td>0.055971</td>\n",
       "      <td>-0.930814</td>\n",
       "      <td>0.163908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>การ</th>\n",
       "      <td>0.239587</td>\n",
       "      <td>-0.303620</td>\n",
       "      <td>0.079953</td>\n",
       "      <td>-0.453045</td>\n",
       "      <td>-0.528826</td>\n",
       "      <td>-0.161692</td>\n",
       "      <td>0.235725</td>\n",
       "      <td>-0.099673</td>\n",
       "      <td>0.691668</td>\n",
       "      <td>0.536159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110436</td>\n",
       "      <td>-0.297495</td>\n",
       "      <td>-0.217414</td>\n",
       "      <td>0.045158</td>\n",
       "      <td>0.066647</td>\n",
       "      <td>0.190095</td>\n",
       "      <td>-0.304333</td>\n",
       "      <td>-0.724927</td>\n",
       "      <td>-0.995488</td>\n",
       "      <td>-0.716609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(</th>\n",
       "      <td>-0.120522</td>\n",
       "      <td>-0.355783</td>\n",
       "      <td>0.168180</td>\n",
       "      <td>-0.377733</td>\n",
       "      <td>-0.158624</td>\n",
       "      <td>-0.047249</td>\n",
       "      <td>0.361140</td>\n",
       "      <td>0.161460</td>\n",
       "      <td>0.913314</td>\n",
       "      <td>0.345037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116285</td>\n",
       "      <td>-0.318218</td>\n",
       "      <td>-0.356664</td>\n",
       "      <td>0.519889</td>\n",
       "      <td>0.130475</td>\n",
       "      <td>0.125772</td>\n",
       "      <td>0.101328</td>\n",
       "      <td>-0.382658</td>\n",
       "      <td>-1.205359</td>\n",
       "      <td>0.340139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>)</th>\n",
       "      <td>-0.086848</td>\n",
       "      <td>-0.155231</td>\n",
       "      <td>0.133015</td>\n",
       "      <td>-0.039913</td>\n",
       "      <td>0.183761</td>\n",
       "      <td>0.115142</td>\n",
       "      <td>-1.940854</td>\n",
       "      <td>-0.066565</td>\n",
       "      <td>-2.399744</td>\n",
       "      <td>0.146722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019406</td>\n",
       "      <td>-0.181474</td>\n",
       "      <td>0.099863</td>\n",
       "      <td>0.516092</td>\n",
       "      <td>0.201697</td>\n",
       "      <td>0.249139</td>\n",
       "      <td>0.252957</td>\n",
       "      <td>1.138815</td>\n",
       "      <td>-0.018209</td>\n",
       "      <td>0.232265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6    \\\n",
       "ที่   0.308956 -0.097699  0.116745  0.215612  0.015768 -0.064163  0.062168   \n",
       "และ   0.010751 -0.618971  0.129665  0.035460 -0.007560  0.027607  0.397824   \n",
       "เป็น -0.015736 -0.258926  0.052953  0.153728 -0.005985 -0.021081  0.041088   \n",
       "ของ  -0.189711 -0.174774  0.171124 -0.186771  0.054294 -0.114150 -1.109456   \n",
       "มี   -0.156962 -0.231863  0.080312  0.323157  0.215695  0.055145  0.420794   \n",
       "ได้  -0.428813 -0.031194  0.041922 -0.036608 -0.008106  0.076470 -0.782270   \n",
       "\"\"\"\" -0.287710  0.064193  0.205076  0.146356 -0.071343 -0.039451 -1.845461   \n",
       "การ   0.239587 -0.303620  0.079953 -0.453045 -0.528826 -0.161692  0.235725   \n",
       "(    -0.120522 -0.355783  0.168180 -0.377733 -0.158624 -0.047249  0.361140   \n",
       ")    -0.086848 -0.155231  0.133015 -0.039913  0.183761  0.115142 -1.940854   \n",
       "\n",
       "           7         8         9    ...       290       291       292  \\\n",
       "ที่   0.039649  0.864940  0.846904  ... -0.142418  0.033241  0.171581   \n",
       "และ   0.026543  0.254075  0.168328  ... -0.105786  0.180930 -0.101630   \n",
       "เป็น  0.057312  1.633230  0.442729  ... -0.009408 -0.252576 -0.305512   \n",
       "ของ  -0.094466 -0.447015  0.042377  ... -0.168676 -0.148738  0.680404   \n",
       "มี    0.016842  0.256759  0.832864  ... -0.044267 -0.147186 -0.105424   \n",
       "ได้   0.033361  0.606864  0.440520  ...  0.024458 -0.025031  0.103389   \n",
       "\"\"\"\"  0.163763  1.018096  0.272786  ...  0.051024 -0.532856 -0.131856   \n",
       "การ  -0.099673  0.691668  0.536159  ... -0.110436 -0.297495 -0.217414   \n",
       "(     0.161460  0.913314  0.345037  ...  0.116285 -0.318218 -0.356664   \n",
       ")    -0.066565 -2.399744  0.146722  ...  0.019406 -0.181474  0.099863   \n",
       "\n",
       "           293       294       295       296       297       298       299  \n",
       "ที่  -0.624864 -0.009358  0.449131  0.120130 -0.122195 -0.450617 -0.071318  \n",
       "และ   0.070885 -0.037263  0.183606 -0.049088 -0.672288 -1.293044  0.592576  \n",
       "เป็น  0.372542  0.049151  0.568470  0.266586  0.400800 -0.784650  0.197369  \n",
       "ของ   0.097702  0.020270  0.182967 -0.083949  0.006287 -0.707434 -0.070234  \n",
       "มี    0.907078  0.009299  0.550953  0.139337  0.031696 -0.670379 -0.008048  \n",
       "ได้  -0.078255  0.034323  0.459774 -0.748643  0.337775 -0.487408 -0.511535  \n",
       "\"\"\"\" -0.090323 -0.058895  0.151262 -0.420358  0.055971 -0.930814  0.163908  \n",
       "การ   0.045158  0.066647  0.190095 -0.304333 -0.724927 -0.995488 -0.716609  \n",
       "(     0.519889  0.130475  0.125772  0.101328 -0.382658 -1.205359  0.340139  \n",
       ")     0.516092  0.201697  0.249139  0.252957  1.138815 -0.018209  0.232265  \n",
       "\n",
       "[10 rows x 300 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dataframe\n",
    "thai2dict = {}\n",
    "for word in model.index2word:\n",
    "    thai2dict[word] = model[word]\n",
    "thai2vec = pd.DataFrame.from_dict(thai2dict,orient='index')\n",
    "thai2vec.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using t-SNE, we can compress the 300 dimensions of each word into a 2D plane and plot their relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-26T02:35:36.291285Z",
     "start_time": "2018-01-26T02:35:36.264520Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = model.index2word\n",
    "\n",
    "# #tnse\n",
    "# tsne = TSNE(n_components=2, init='pca', n_iter=1000)\n",
    "# thai2plot = tsne.fit_transform(thai2vec)\n",
    "# pickle.dump(thai2plot,open(f'{model_path}thai2plot.pkl','wb'))\n",
    "\n",
    "thai2plot = pickle.load(open(f'{model_path}thai2plot.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-26T02:35:39.610920Z",
     "start_time": "2018-01-26T02:35:38.469383Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stolen from https://blog.manash.me/how-to-use-pre-trained-word-vectors-from-facebooks-fasttext-a71e6d55f27\n",
    "def plot_with_labels(low_dim_embs, labels, filename=None, figsize=(10,10),\n",
    "                    axis_lims = None):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
    "    plt.figure(figsize=figsize)  # in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i, :]\n",
    "        plt.scatter(x, y)\n",
    "        prop = fm.FontProperties(fname=f'THSarabunNew.ttf',size=20)\n",
    "        plt.annotate(label,\n",
    "                 fontproperties=prop,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',\n",
    "                 va='bottom')\n",
    "    if axis_lims is not None: plt.axis(axis_lims)\n",
    "    if filename: plt.savefig(filename)\n",
    "    \n",
    "plot_with_labels(thai2plot[200:500],labels[200:500],axis_lims = [0,30,0,30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Arithmetic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do simple \"arithmetic\" with words based on the word vectors such as:\n",
    "* ผู้หญิง + พระราชา - ผู้ชาย = พระราชินี\n",
    "* นายกรัฐมนตรี - อำนาจ = ประธานาธิบดี\n",
    "* กิ้งก่า + โบราณ = ไดโนเสาร์"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:48:16.434798Z",
     "start_time": "2018-01-25T08:48:16.312043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('พระราชินี', 0.7954867482185364),\n",
       " ('กษัตริย์', 0.7382755279541016),\n",
       " ('พระเจ้า', 0.7046602368354797),\n",
       " ('เจ้าชาย', 0.6979373097419739),\n",
       " ('พระมหากษัตริย์', 0.6972416639328003),\n",
       " ('เจ้าฟ้าหญิง', 0.6871017217636108),\n",
       " ('พระเจ้าแผ่นดิน', 0.6827988624572754),\n",
       " ('พระพุทธเจ้า', 0.671796977519989),\n",
       " ('มกุฎราชกุมาร', 0.6711805462837219),\n",
       " ('นายพล', 0.6694187521934509)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#word arithmetic\n",
    "model.most_similar_cosmul(positive=['พระราชา','ผู้หญิง'], negative=['ผู้ชาย'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:48:18.493511Z",
     "start_time": "2018-01-25T08:48:18.104736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_words = ['ผู้หญิง','พระราชา','ผู้ชาย','พระราชินี']\n",
    "sample_idx = []\n",
    "for word in sample_words:\n",
    "    sample_idx.append(labels.index(word))\n",
    "sample_plot = thai2plot[sample_idx]\n",
    "plot_with_labels(sample_plot,sample_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:48:50.851735Z",
     "start_time": "2018-01-25T08:48:50.731913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('รองนายกรัฐมนตรี', 1.4945054054260254),\n",
       " ('รัฐมนตรี', 1.400755763053894),\n",
       " ('ประธานาธิบดี', 1.3626699447631836),\n",
       " ('พันเอก', 1.3437265157699585),\n",
       " ('ผู้บัญชาการทหารบก', 1.3405414819717407),\n",
       " ('ผู้กำกับภาพยนตร์', 1.3339321613311768),\n",
       " ('นักฟุตบอล', 1.331659197807312),\n",
       " ('เอกอัครราชทูต', 1.3306005001068115),\n",
       " ('แห้ง', 1.3243674039840698),\n",
       " ('สุภาพสตรี', 1.3231494426727295)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar_cosmul(positive=['นายกรัฐมนตรี'],negative=['อำนาจ'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:48:51.481402Z",
     "start_time": "2018-01-25T08:48:51.158689Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_words = ['นายกรัฐมนตรี','อำนาจ','รองนายกรัฐมนตรี']\n",
    "sample_idx = []\n",
    "for word in sample_words:\n",
    "    sample_idx.append(labels.index(word))\n",
    "sample_plot = thai2plot[sample_idx]\n",
    "plot_with_labels(sample_plot,sample_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:49:11.465639Z",
     "start_time": "2018-01-25T08:49:11.336707Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('สิ่งมีชีวิต', 0.537461519241333),\n",
       " ('สัตว์เลี้ยงลูกด้วยนม', 0.5080005526542664),\n",
       " ('แมลง', 0.5048903226852417),\n",
       " ('ผลไม้', 0.4839756190776825),\n",
       " ('มนุษย์', 0.47641509771347046),\n",
       " ('ผัก', 0.46431201696395874),\n",
       " ('สัตว์น้ำ', 0.45941096544265747),\n",
       " ('ต้นไม้', 0.45185261964797974),\n",
       " ('พันธุ์ไม้', 0.4504697620868683),\n",
       " ('ไม้ยืนต้น', 0.44425833225250244)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#word arithmetic\n",
    "model.most_similar_cosmul(positive=['สัตว์','พืช'], negative=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:49:12.202620Z",
     "start_time": "2018-01-25T08:49:11.835817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_words = ['สัตว์','พืช','สิ่งมีชีวิต']\n",
    "sample_idx = []\n",
    "for word in sample_words:\n",
    "    sample_idx.append(labels.index(word))\n",
    "sample_plot = thai2plot[sample_idx]\n",
    "plot_with_labels(sample_plot,sample_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Doesn't Match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "It can also be used to do word groupings. For instance:\n",
    "* อาหารเช้า อาหารสัตว์ อาหารเย็น อาหารกลางวัน - อาหารสัตว์ is type of food whereas others are meals in the day\n",
    "* ลาก ดึง ดูด ดัน - ดัน is pushing while the rest is pulling.\n",
    "* กด กัด กิน เคี้ยว - กด is not verbs for the eating process\n",
    "Note that this could be relying on a different \"take\" than you would expect. For example, you could have answered ลูกเขย in the second example because it  is the one associated with male gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-26T02:35:24.619900Z",
     "start_time": "2018-01-26T02:35:24.505717Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/gensim/models/keyedvectors.py:858: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'อาหารสัตว์'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.doesnt_match(\"อาหารเช้า อาหารสัตว์ อาหารเย็น อาหารกลางวัน\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-26T02:35:46.163075Z",
     "start_time": "2018-01-26T02:35:45.838017Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_words = \"อาหารเช้า อาหารสัตว์ อาหารเย็น อาหารกลางวัน\".split()\n",
    "sample_idx = []\n",
    "for word in sample_words:\n",
    "    sample_idx.append(labels.index(word))\n",
    "sample_plot = thai2plot[sample_idx]\n",
    "plot_with_labels(sample_plot,sample_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T09:03:55.915881Z",
     "start_time": "2018-01-25T09:03:55.811980Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ดัน'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.doesnt_match(\"ลาก ดึง ดูด ดัน\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T09:03:57.111707Z",
     "start_time": "2018-01-25T09:03:56.766901Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_words = \"ลาก ดึง ดูด ดัน\".split()\n",
    "sample_idx = []\n",
    "for word in sample_words:\n",
    "    sample_idx.append(labels.index(word))\n",
    "sample_plot = thai2plot[sample_idx]\n",
    "plot_with_labels(sample_plot,sample_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T09:00:04.040459Z",
     "start_time": "2018-01-25T09:00:03.917290Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'หมอ'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.doesnt_match(\"แมว หมา หมู หมอ\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T09:00:23.186752Z",
     "start_time": "2018-01-25T09:00:22.875191Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_words = \"แมว หมา หมู หมอ\".split()\n",
    "sample_idx = []\n",
    "for word in sample_words:\n",
    "    sample_idx.append(labels.index(word))\n",
    "sample_plot = thai2plot[sample_idx]\n",
    "plot_with_labels(sample_plot,sample_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cosine Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:49:26.327610Z",
     "start_time": "2018-01-25T08:49:26.219002Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "China + Beijing: 0.3135956\n",
      "Italy + Rome: 0.42819628\n",
      "Beijing + Rome: 0.27347285\n",
      "Italy + Beijing: 0.17900795\n",
      "China + Rome: 0.02666693\n",
      "China + Italy: 0.24352394\n"
     ]
    }
   ],
   "source": [
    "print('China + Beijing:', model.similarity('ปักกิ่ง', 'จีน'))\n",
    "print('Italy + Rome:', model.similarity('โรม','อิตาลี'))\n",
    "print('Beijing + Rome:', model.similarity('โรม', 'ปักกิ่ง'))\n",
    "print('Italy + Beijing:', model.similarity('ปักกิ่ง', 'อิตาลี'))\n",
    "print('China + Rome:', model.similarity('โรม','จีน'))\n",
    "print('China + Italy:', model.similarity('อิตาลี','จีน'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-25T08:55:58.556210Z",
     "start_time": "2018-01-25T08:55:58.223988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_words = \"ปักกิ่ง จีน โรม อิตาลี โตเกียว ญี่ปุ่น\".split()\n",
    "sample_idx = []\n",
    "for word in sample_words:\n",
    "    sample_idx.append(labels.index(word))\n",
    "sample_plot = thai2plot[sample_idx]\n",
    "plot_with_labels(sample_plot,sample_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spellchecking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Originally contributed by [Sakares ATV](https://github.com/sakares), adapted from [Kaggle Spell Checker using Word2vec by CPMP](https://www.kaggle.com/cpmpml/spell-checker-using-word2vec)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = model.index2word\n",
    "\n",
    "w_rank = {}\n",
    "for i,word in enumerate(words):\n",
    "    w_rank[word] = i\n",
    "\n",
    "WORDS = w_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "thai_letters = 'กขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤฤๅลฦฦๅวศษสหฬอฮะัาำิีึืุูเแโใไ็่้๊๋์'\n",
    "\n",
    "def words(text): return re.findall(r'\\w+', text.lower())\n",
    "\n",
    "def P(word): \n",
    "    \"Probability of `word`.\"\n",
    "    # use inverse of rank as proxy\n",
    "    # returns 0 if the word isn't in the dictionary\n",
    "    return - WORDS.get(word, 0)\n",
    "\n",
    "def correction(word): \n",
    "    \"Most probable spelling correction for word.\"\n",
    "    return max(candidates(word), key=P)\n",
    "\n",
    "def candidates(word): \n",
    "    \"Generate possible spelling corrections for word.\"\n",
    "    return (known([word]) or known(edits1(word)) or known(edits2(word)) or [word])\n",
    "\n",
    "def known(words): \n",
    "    \"The subset of `words` that appear in the dictionary of WORDS.\"\n",
    "    return set(w for w in words if w in WORDS)\n",
    "\n",
    "def edits1(word):\n",
    "    \"All edits that are one edit away from `word`.\"\n",
    "    letters    = thai_letters\n",
    "    splits     = [(word[:i], word[i:])    for i in range(len(word) + 1)]\n",
    "    deletes    = [L + R[1:]               for L, R in splits if R]\n",
    "    transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1]\n",
    "    replaces   = [L + c + R[1:]           for L, R in splits if R for c in letters]\n",
    "    inserts    = [L + c + R               for L, R in splits for c in letters]\n",
    "    return set(deletes + transposes + replaces + inserts)\n",
    "\n",
    "def edits2(word): \n",
    "    \"All edits that are two edits away from `word`.\"\n",
    "    return (e2 for e1 in edits1(word) for e2 in edits1(e1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'พัฒนา'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correction('พัดนา')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'จริง'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correction('ขริง')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'จ้า'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correction('จย้า')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'นะคะ'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correction('นะค่ะ')"
   ]
  }
 ],
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