17_word2vec_gensim.ipynb

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   "id": "1d6d2ef8-0ca7-4c01-bfc9-a6609708547f",
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    "jupyter": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gensim==3.8.0\n",
      "  Downloading gensim-3.8.0.tar.gz (23.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.4/23.4 MB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from gensim==3.8.0) (1.24.2)\n",
      "Requirement already satisfied: scipy>=0.18.1 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from gensim==3.8.0) (1.9.3)\n",
      "Requirement already satisfied: six>=1.5.0 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from gensim==3.8.0) (1.15.0)\n",
      "Requirement already satisfied: smart_open>=1.7.0 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from gensim==3.8.0) (6.3.0)\n",
      "Building wheels for collected packages: gensim\n",
      "  Building wheel for gensim (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for gensim: filename=gensim-3.8.0-cp39-cp39-macosx_11_0_arm64.whl size=23721372 sha256=484088efefbd820fc21012255abf8cf3f2709c7fe509a31929e74d2e775da570\n",
      "  Stored in directory: /Users/serhatderya/Library/Caches/pip/wheels/43/a9/56/a1e12d0655384810fef36ef3a45812bdf7d6a9efc51a91b350\n",
      "Successfully built gensim\n",
      "Installing collected packages: gensim\n",
      "Successfully installed gensim-3.8.0\n"
     ]
    }
   ],
   "source": [
    "!pip install gensim==3.8.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb1f6543-2574-4243-b6ac-2d5e2f35a556",
   "metadata": {
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     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: python-Levenshtein in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (0.20.9)\n",
      "Requirement already satisfied: Levenshtein==0.20.9 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from python-Levenshtein) (0.20.9)\n",
      "Requirement already satisfied: rapidfuzz<3.0.0,>=2.3.0 in /Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages (from Levenshtein==0.20.9->python-Levenshtein) (2.13.7)\n"
     ]
    }
   ],
   "source": [
    "!pip install python-Levenshtein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6e466a5-0baf-4609-922f-92513b7f8b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f30a7eb2-6565-4a4f-a7f4-51e626b7f17e",
   "metadata": {},
   "source": [
    "# Reading and Exploring the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34bf9167-0010-43d7-9938-b65e7b5c6fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>summary</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>reviewTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A30TL5EWN6DFXT</td>\n",
       "      <td>120401325X</td>\n",
       "      <td>christina</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>They look good and stick good! I just don't li...</td>\n",
       "      <td>4</td>\n",
       "      <td>Looks Good</td>\n",
       "      <td>1400630400</td>\n",
       "      <td>05 21, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASY55RVNIL0UD</td>\n",
       "      <td>120401325X</td>\n",
       "      <td>emily l.</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>These stickers work like the review says they ...</td>\n",
       "      <td>5</td>\n",
       "      <td>Really great product.</td>\n",
       "      <td>1389657600</td>\n",
       "      <td>01 14, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2TMXE2AFO7ONB</td>\n",
       "      <td>120401325X</td>\n",
       "      <td>Erica</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>These are awesome and make my phone look so st...</td>\n",
       "      <td>5</td>\n",
       "      <td>LOVE LOVE LOVE</td>\n",
       "      <td>1403740800</td>\n",
       "      <td>06 26, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AWJ0WZQYMYFQ4</td>\n",
       "      <td>120401325X</td>\n",
       "      <td>JM</td>\n",
       "      <td>[4, 4]</td>\n",
       "      <td>Item arrived in great time and was in perfect ...</td>\n",
       "      <td>4</td>\n",
       "      <td>Cute!</td>\n",
       "      <td>1382313600</td>\n",
       "      <td>10 21, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATX7CZYFXI1KW</td>\n",
       "      <td>120401325X</td>\n",
       "      <td>patrice m rogoza</td>\n",
       "      <td>[2, 3]</td>\n",
       "      <td>awesome! stays on, and looks great. can be use...</td>\n",
       "      <td>5</td>\n",
       "      <td>leopard home button sticker for iphone 4s</td>\n",
       "      <td>1359849600</td>\n",
       "      <td>02 3, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194434</th>\n",
       "      <td>A1YMNTFLNDYQ1F</td>\n",
       "      <td>B00LORXVUE</td>\n",
       "      <td>eyeused2loveher</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Works great just like my original one. I reall...</td>\n",
       "      <td>5</td>\n",
       "      <td>This works just perfect!</td>\n",
       "      <td>1405900800</td>\n",
       "      <td>07 21, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194435</th>\n",
       "      <td>A15TX8B2L8B20S</td>\n",
       "      <td>B00LORXVUE</td>\n",
       "      <td>Jon Davidson</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Great product. Great packaging. High quality a...</td>\n",
       "      <td>5</td>\n",
       "      <td>Great replacement cable. Apple certified</td>\n",
       "      <td>1405900800</td>\n",
       "      <td>07 21, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194436</th>\n",
       "      <td>A3JI7QRZO1QG8X</td>\n",
       "      <td>B00LORXVUE</td>\n",
       "      <td>Joyce M. Davidson</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>This is a great cable, just as good as the mor...</td>\n",
       "      <td>5</td>\n",
       "      <td>Real quality</td>\n",
       "      <td>1405900800</td>\n",
       "      <td>07 21, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194437</th>\n",
       "      <td>A1NHB2VC68YQNM</td>\n",
       "      <td>B00LORXVUE</td>\n",
       "      <td>Nurse Farrugia</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>I really like it becasue it works well with my...</td>\n",
       "      <td>5</td>\n",
       "      <td>I really like it becasue it works well with my...</td>\n",
       "      <td>1405814400</td>\n",
       "      <td>07 20, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194438</th>\n",
       "      <td>A1AG6U022WHXBF</td>\n",
       "      <td>B00LORXVUE</td>\n",
       "      <td>Trisha Crocker</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>product as described, I have wasted a lot of m...</td>\n",
       "      <td>5</td>\n",
       "      <td>I have wasted a lot of money on cords</td>\n",
       "      <td>1405900800</td>\n",
       "      <td>07 21, 2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>194439 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "            reviewerID        asin       reviewerName helpful  \\\n",
       "0       A30TL5EWN6DFXT  120401325X          christina  [0, 0]   \n",
       "1        ASY55RVNIL0UD  120401325X           emily l.  [0, 0]   \n",
       "2       A2TMXE2AFO7ONB  120401325X              Erica  [0, 0]   \n",
       "3        AWJ0WZQYMYFQ4  120401325X                 JM  [4, 4]   \n",
       "4        ATX7CZYFXI1KW  120401325X   patrice m rogoza  [2, 3]   \n",
       "...                ...         ...                ...     ...   \n",
       "194434  A1YMNTFLNDYQ1F  B00LORXVUE    eyeused2loveher  [0, 0]   \n",
       "194435  A15TX8B2L8B20S  B00LORXVUE       Jon Davidson  [0, 0]   \n",
       "194436  A3JI7QRZO1QG8X  B00LORXVUE  Joyce M. Davidson  [0, 0]   \n",
       "194437  A1NHB2VC68YQNM  B00LORXVUE     Nurse Farrugia  [0, 0]   \n",
       "194438  A1AG6U022WHXBF  B00LORXVUE     Trisha Crocker  [0, 0]   \n",
       "\n",
       "                                               reviewText  overall  \\\n",
       "0       They look good and stick good! I just don't li...        4   \n",
       "1       These stickers work like the review says they ...        5   \n",
       "2       These are awesome and make my phone look so st...        5   \n",
       "3       Item arrived in great time and was in perfect ...        4   \n",
       "4       awesome! stays on, and looks great. can be use...        5   \n",
       "...                                                   ...      ...   \n",
       "194434  Works great just like my original one. I reall...        5   \n",
       "194435  Great product. Great packaging. High quality a...        5   \n",
       "194436  This is a great cable, just as good as the mor...        5   \n",
       "194437  I really like it becasue it works well with my...        5   \n",
       "194438  product as described, I have wasted a lot of m...        5   \n",
       "\n",
       "                                                  summary  unixReviewTime  \\\n",
       "0                                              Looks Good      1400630400   \n",
       "1                                   Really great product.      1389657600   \n",
       "2                                          LOVE LOVE LOVE      1403740800   \n",
       "3                                                   Cute!      1382313600   \n",
       "4               leopard home button sticker for iphone 4s      1359849600   \n",
       "...                                                   ...             ...   \n",
       "194434                           This works just perfect!      1405900800   \n",
       "194435           Great replacement cable. Apple certified      1405900800   \n",
       "194436                                       Real quality      1405900800   \n",
       "194437  I really like it becasue it works well with my...      1405814400   \n",
       "194438              I have wasted a lot of money on cords      1405900800   \n",
       "\n",
       "         reviewTime  \n",
       "0       05 21, 2014  \n",
       "1       01 14, 2014  \n",
       "2       06 26, 2014  \n",
       "3       10 21, 2013  \n",
       "4        02 3, 2013  \n",
       "...             ...  \n",
       "194434  07 21, 2014  \n",
       "194435  07 21, 2014  \n",
       "194436  07 21, 2014  \n",
       "194437  07 20, 2014  \n",
       "194438  07 21, 2014  \n",
       "\n",
       "[194439 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json(\"Cell_Phones_and_Accessories_5.json\", lines=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "132c928e-ae83-4c41-a6ae-03f8560106dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(194439, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4cff722-bb52-4d7c-9b47-e4bfda33cda5",
   "metadata": {},
   "source": [
    "# Simple Processing & Tokenization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72a9ad8d-6031-49df-8751-f3c6791203e1",
   "metadata": {},
   "source": [
    "#### The first thing to do for any data science task is to clean the data. For NLP, we apply various processing like converting all the words to lower case, trimming spaces, removing punctuations. This is something we will do over here too.\n",
    "\n",
    "#### Additionally, we can also remove stop words like 'and', 'or', 'is', 'the', 'a', 'an' and convert words to their root forms like 'running' to 'run'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5836b0f5-f69e-4403-8a6e-784ab563ccc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "review_text = df.reviewText.apply(gensim.utils.simple_preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "462643c1-3a1d-42e4-b5fe-2da7c7a83333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         [they, look, good, and, stick, good, just, don...\n",
       "1         [these, stickers, work, like, the, review, say...\n",
       "2         [these, are, awesome, and, make, my, phone, lo...\n",
       "3         [item, arrived, in, great, time, and, was, in,...\n",
       "4         [awesome, stays, on, and, looks, great, can, b...\n",
       "                                ...                        \n",
       "194434    [works, great, just, like, my, original, one, ...\n",
       "194435    [great, product, great, packaging, high, quali...\n",
       "194436    [this, is, great, cable, just, as, good, as, t...\n",
       "194437    [really, like, it, becasue, it, works, well, w...\n",
       "194438    [product, as, described, have, wasted, lot, of...\n",
       "Name: reviewText, Length: 194439, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95a598c6-ab3b-4a18-b8d3-4b2a7172b012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['they',\n",
       " 'look',\n",
       " 'good',\n",
       " 'and',\n",
       " 'stick',\n",
       " 'good',\n",
       " 'just',\n",
       " 'don',\n",
       " 'like',\n",
       " 'the',\n",
       " 'rounded',\n",
       " 'shape',\n",
       " 'because',\n",
       " 'was',\n",
       " 'always',\n",
       " 'bumping',\n",
       " 'it',\n",
       " 'and',\n",
       " 'siri',\n",
       " 'kept',\n",
       " 'popping',\n",
       " 'up',\n",
       " 'and',\n",
       " 'it',\n",
       " 'was',\n",
       " 'irritating',\n",
       " 'just',\n",
       " 'won',\n",
       " 'buy',\n",
       " 'product',\n",
       " 'like',\n",
       " 'this',\n",
       " 'again']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_text.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e5488ba-cd3a-4f42-9e67-b74830b9df2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"They look good and stick good! I just don't like the rounded shape because I was always bumping it and Siri kept popping up and it was irritating. I just won't buy a product like this again\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reviewText.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6518b48a-04d2-444c-b70b-f033c4d24727",
   "metadata": {},
   "source": [
    "# Training the Word2Vec Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a1e0ba-c88e-46ac-9c93-a77c4ee976fa",
   "metadata": {},
   "source": [
    "#### Train the model for reviews. Use a window of size 10 i.e. 10 words before the present word and 10 words ahead. A sentence with at least 2 words should only be considered, configure this using min_count parameter.\n",
    "\n",
    "#### Workers define how many CPU threads to be used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082181f6-58c6-4930-b0aa-6c81ea6f822e",
   "metadata": {},
   "source": [
    "## Initialize the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f3da5be-55a2-453f-b0a2-40c07ac8f9f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/serhatderya/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages/gensim/models/base_any2vec.py:742: UserWarning: C extension not loaded, training will be slow. Install a C compiler and reinstall gensim for fast training.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model = gensim.models.Word2Vec(\n",
    "    window=10,\n",
    "    min_count=2,\n",
    "    workers=4\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf8f27f-ea12-4c76-b50f-b6e5d47817cc",
   "metadata": {},
   "source": [
    "## Build Vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fde8e34a-669a-456c-8468-d3e40573beec",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.build_vocab(review_text, progress_per=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14586ea3-4c32-446f-bafa-83a6107f0bf1",
   "metadata": {},
   "source": [
    "## Train the Word2Vec Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99213248-c74f-47a3-ae04-f1387a12d6e8",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreview_text\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorpus_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages/gensim/models/word2vec.py:907\u001b[0m, in \u001b[0;36mWord2Vec.train\u001b[0;34m(self, sentences, corpus_file, total_examples, total_words, epochs, start_alpha, end_alpha, word_count, queue_factor, report_delay, compute_loss, callbacks)\u001b[0m\n\u001b[1;32m    833\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(\u001b[38;5;28mself\u001b[39m, sentences\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, corpus_file\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, total_examples\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, total_words\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    834\u001b[0m           epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, start_alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, end_alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, word_count\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m    835\u001b[0m           queue_factor\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, report_delay\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m, compute_loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, callbacks\u001b[38;5;241m=\u001b[39m()):\n\u001b[1;32m    836\u001b[0m     \u001b[38;5;124;03m\"\"\"Update the model's neural weights from a sequence of sentences.\u001b[39;00m\n\u001b[1;32m    837\u001b[0m \n\u001b[1;32m    838\u001b[0m \u001b[38;5;124;03m    Notes\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    905\u001b[0m \n\u001b[1;32m    906\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 907\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mWord2Vec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    908\u001b[0m \u001b[43m        \u001b[49m\u001b[43msentences\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msentences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcorpus_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcorpus_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_examples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_words\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_words\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    909\u001b[0m \u001b[43m        \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_alpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_alpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mword_count\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mword_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    910\u001b[0m \u001b[43m        \u001b[49m\u001b[43mqueue_factor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mqueue_factor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreport_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreport_delay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompute_loss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages/gensim/models/base_any2vec.py:1077\u001b[0m, in \u001b[0;36mBaseWordEmbeddingsModel.train\u001b[0;34m(self, sentences, corpus_file, total_examples, total_words, epochs, start_alpha, end_alpha, word_count, queue_factor, report_delay, compute_loss, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m   1075\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_loss \u001b[38;5;241m=\u001b[39m compute_loss\n\u001b[1;32m   1076\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunning_training_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[0;32m-> 1077\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mBaseWordEmbeddingsModel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1078\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_iterable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msentences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcorpus_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcorpus_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_examples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1079\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtotal_words\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_words\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_alpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_alpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_alpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mword_count\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mword_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1080\u001b[0m \u001b[43m    \u001b[49m\u001b[43mqueue_factor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mqueue_factor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreport_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreport_delay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompute_loss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1081\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages/gensim/models/base_any2vec.py:551\u001b[0m, in \u001b[0;36mBaseAny2VecModel.train\u001b[0;34m(self, data_iterable, corpus_file, epochs, total_examples, total_words, queue_factor, report_delay, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    548\u001b[0m     callback\u001b[38;5;241m.\u001b[39mon_epoch_begin(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m    550\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_iterable \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 551\u001b[0m     trained_word_count_epoch, raw_word_count_epoch, job_tally_epoch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_train_epoch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    552\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata_iterable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcur_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcur_epoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_examples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    553\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtotal_words\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_words\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqueue_factor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mqueue_factor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreport_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreport_delay\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    554\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    555\u001b[0m     trained_word_count_epoch, raw_word_count_epoch, job_tally_epoch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_train_epoch_corpusfile(\n\u001b[1;32m    556\u001b[0m         corpus_file, cur_epoch\u001b[38;5;241m=\u001b[39mcur_epoch, total_examples\u001b[38;5;241m=\u001b[39mtotal_examples, total_words\u001b[38;5;241m=\u001b[39mtotal_words, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages/gensim/models/base_any2vec.py:487\u001b[0m, in \u001b[0;36mBaseAny2VecModel._train_epoch\u001b[0;34m(self, data_iterable, cur_epoch, total_examples, total_words, queue_factor, report_delay)\u001b[0m\n\u001b[1;32m    484\u001b[0m     thread\u001b[38;5;241m.\u001b[39mdaemon \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m  \u001b[38;5;66;03m# make interrupting the process with ctrl+c easier\u001b[39;00m\n\u001b[1;32m    485\u001b[0m     thread\u001b[38;5;241m.\u001b[39mstart()\n\u001b[0;32m--> 487\u001b[0m trained_word_count, raw_word_count, job_tally \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_epoch_progress\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprogress_queue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjob_queue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcur_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcur_epoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_examples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtotal_words\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_words\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreport_delay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreport_delay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_corpus_file_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trained_word_count, raw_word_count, job_tally\n",
      "File \u001b[0;32m~/miniconda3/envs/tfm1_arm64/lib/python3.9/site-packages/gensim/models/base_any2vec.py:346\u001b[0m, in \u001b[0;36mBaseAny2VecModel._log_epoch_progress\u001b[0;34m(self, progress_queue, job_queue, cur_epoch, total_examples, total_words, report_delay, is_corpus_file_mode)\u001b[0m\n\u001b[1;32m    343\u001b[0m unfinished_worker_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mworkers\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m unfinished_worker_count \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 346\u001b[0m     report \u001b[38;5;241m=\u001b[39m \u001b[43mprogress_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# blocks if workers too slow\u001b[39;00m\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m report \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# a thread reporting that it finished\u001b[39;00m\n\u001b[1;32m    348\u001b[0m         unfinished_worker_count \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/miniconda3/envs/tfm1_arm64/lib/python3.9/queue.py:171\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    170\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qsize():\n\u001b[0;32m--> 171\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnot_empty\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be a non-negative number\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/tfm1_arm64/lib/python3.9/threading.py:312\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:    \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    311\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m         \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    313\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    314\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.train(review_text, total_examples=model.corpus_count, epochs=model.epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6de2d2-a4f6-4761-918c-017b34844c6b",
   "metadata": {},
   "source": [
    "## Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea981ea3-3c57-4d25-bd13-5e57f46929fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"./word2vec-amazon-cell-accessories-reviews-short.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93268f46-0fff-4603-808c-ca6f7630b0eb",
   "metadata": {},
   "source": [
    "## Finding Similar Words and Similarity between Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0904cc6-3b46-4974-a7a8-60e886dbbbc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.wv.most_similar(\"bad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c3e7f98-3dde-4b98-90dd-ebb991fc4cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.wv.similarity(w1=\"cheap\", w2=\"inexpensive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b9bec9d-72e2-4686-912a-e4fb0ce3a626",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.wv.similarity(w1=\"great\", w2=\"good\")"
   ]
  }
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