Part 1.4: Word2Vec_gensim_t_SNE.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_Basics/blob/main/Part%201.4%3A%20Word2Vec_gensim_t_SNE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Arxiv : https://arxiv.org/pdf/1301.3781.pdf\n",
        "\n",
        "Paper : https://papers.nips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf\n",
        "\n",
        "Code walkthrough video : https://www.youtube.com/watch?v=MtM9QrCjuK4\n"
      ]
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        "outputId": "ba1e4f44-9bf7-4688-e727-bfc868a0278e"
      },
      "source": [
        "import gensim.downloader as api\n",
        "wv = api.load('word2vec-google-news-300')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
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      },
      "source": [
        "for i, word in enumerate(wv.vocab):\n",
        "    if i == 10:\n",
        "        break\n",
        "    print(word)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "</s>\n",
            "in\n",
            "for\n",
            "that\n",
            "is\n",
            "on\n",
            "##\n",
            "The\n",
            "with\n",
            "said\n"
          ],
          "name": "stdout"
        }
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        "outputId": "d83b0918-4e8a-4886-add4-7d7125d756b5"
      },
      "source": [
        "vec_king = wv['king']\n",
        "print(vec_king)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
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            "[ 1.25976562e-01  2.97851562e-02  8.60595703e-03  1.39648438e-01\n",
            " -2.56347656e-02 -3.61328125e-02  1.11816406e-01 -1.98242188e-01\n",
            "  5.12695312e-02  3.63281250e-01 -2.42187500e-01 -3.02734375e-01\n",
            " -1.77734375e-01 -2.49023438e-02 -1.67968750e-01 -1.69921875e-01\n",
            "  3.46679688e-02  5.21850586e-03  4.63867188e-02  1.28906250e-01\n",
            "  1.36718750e-01  1.12792969e-01  5.95703125e-02  1.36718750e-01\n",
            "  1.01074219e-01 -1.76757812e-01 -2.51953125e-01  5.98144531e-02\n",
            "  3.41796875e-01 -3.11279297e-02  1.04492188e-01  6.17675781e-02\n",
            "  1.24511719e-01  4.00390625e-01 -3.22265625e-01  8.39843750e-02\n",
            "  3.90625000e-02  5.85937500e-03  7.03125000e-02  1.72851562e-01\n",
            "  1.38671875e-01 -2.31445312e-01  2.83203125e-01  1.42578125e-01\n",
            "  3.41796875e-01 -2.39257812e-02 -1.09863281e-01  3.32031250e-02\n",
            " -5.46875000e-02  1.53198242e-02 -1.62109375e-01  1.58203125e-01\n",
            " -2.59765625e-01  2.01416016e-02 -1.63085938e-01  1.35803223e-03\n",
            " -1.44531250e-01 -5.68847656e-02  4.29687500e-02 -2.46582031e-02\n",
            "  1.85546875e-01  4.47265625e-01  9.58251953e-03  1.31835938e-01\n",
            "  9.86328125e-02 -1.85546875e-01 -1.00097656e-01 -1.33789062e-01\n",
            " -1.25000000e-01  2.83203125e-01  1.23046875e-01  5.32226562e-02\n",
            " -1.77734375e-01  8.59375000e-02 -2.18505859e-02  2.05078125e-02\n",
            " -1.39648438e-01  2.51464844e-02  1.38671875e-01 -1.05468750e-01\n",
            "  1.38671875e-01  8.88671875e-02 -7.51953125e-02 -2.13623047e-02\n",
            "  1.72851562e-01  4.63867188e-02 -2.65625000e-01  8.91113281e-03\n",
            "  1.49414062e-01  3.78417969e-02  2.38281250e-01 -1.24511719e-01\n",
            " -2.17773438e-01 -1.81640625e-01  2.97851562e-02  5.71289062e-02\n",
            " -2.89306641e-02  1.24511719e-02  9.66796875e-02 -2.31445312e-01\n",
            "  5.81054688e-02  6.68945312e-02  7.08007812e-02 -3.08593750e-01\n",
            " -2.14843750e-01  1.45507812e-01 -4.27734375e-01 -9.39941406e-03\n",
            "  1.54296875e-01 -7.66601562e-02  2.89062500e-01  2.77343750e-01\n",
            " -4.86373901e-04 -1.36718750e-01  3.24218750e-01 -2.46093750e-01\n",
            " -3.03649902e-03 -2.11914062e-01  1.25000000e-01  2.69531250e-01\n",
            "  2.04101562e-01  8.25195312e-02 -2.01171875e-01 -1.60156250e-01\n",
            " -3.78417969e-02 -1.20117188e-01  1.15234375e-01 -4.10156250e-02\n",
            " -3.95507812e-02 -8.98437500e-02  6.34765625e-03  2.03125000e-01\n",
            "  1.86523438e-01  2.73437500e-01  6.29882812e-02  1.41601562e-01\n",
            " -9.81445312e-02  1.38671875e-01  1.82617188e-01  1.73828125e-01\n",
            "  1.73828125e-01 -2.37304688e-01  1.78710938e-01  6.34765625e-02\n",
            "  2.36328125e-01 -2.08984375e-01  8.74023438e-02 -1.66015625e-01\n",
            " -7.91015625e-02  2.43164062e-01 -8.88671875e-02  1.26953125e-01\n",
            " -2.16796875e-01 -1.73828125e-01 -3.59375000e-01 -8.25195312e-02\n",
            " -6.49414062e-02  5.07812500e-02  1.35742188e-01 -7.47070312e-02\n",
            " -1.64062500e-01  1.15356445e-02  4.45312500e-01 -2.15820312e-01\n",
            " -1.11328125e-01 -1.92382812e-01  1.70898438e-01 -1.25000000e-01\n",
            "  2.65502930e-03  1.92382812e-01 -1.74804688e-01  1.39648438e-01\n",
            "  2.92968750e-01  1.13281250e-01  5.95703125e-02 -6.39648438e-02\n",
            "  9.96093750e-02 -2.72216797e-02  1.96533203e-02  4.27246094e-02\n",
            " -2.46093750e-01  6.39648438e-02 -2.25585938e-01 -1.68945312e-01\n",
            "  2.89916992e-03  8.20312500e-02  3.41796875e-01  4.32128906e-02\n",
            "  1.32812500e-01  1.42578125e-01  7.61718750e-02  5.98144531e-02\n",
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            " -2.78320312e-02 -1.41601562e-01  2.07031250e-01 -1.58203125e-01\n",
            "  1.27929688e-01  1.49414062e-01 -2.24609375e-02 -8.44726562e-02\n",
            "  1.22558594e-01  2.15820312e-01 -2.13867188e-01 -3.12500000e-01\n",
            " -3.73046875e-01  4.08935547e-03  1.07421875e-01  1.06933594e-01\n",
            "  7.32421875e-02  8.97216797e-03 -3.88183594e-02 -1.29882812e-01\n",
            "  1.49414062e-01 -2.14843750e-01 -1.83868408e-03  9.91210938e-02\n",
            "  1.57226562e-01 -1.14257812e-01 -2.05078125e-01  9.91210938e-02\n",
            "  3.69140625e-01 -1.97265625e-01  3.54003906e-02  1.09375000e-01\n",
            "  1.31835938e-01  1.66992188e-01  2.35351562e-01  1.04980469e-01\n",
            " -4.96093750e-01 -1.64062500e-01 -1.56250000e-01 -5.22460938e-02\n",
            "  1.03027344e-01  2.43164062e-01 -1.88476562e-01  5.07812500e-02\n",
            " -9.37500000e-02 -6.68945312e-02  2.27050781e-02  7.61718750e-02\n",
            "  2.89062500e-01  3.10546875e-01 -5.37109375e-02  2.28515625e-01\n",
            "  2.51464844e-02  6.78710938e-02 -1.21093750e-01 -2.15820312e-01\n",
            " -2.73437500e-01 -3.07617188e-02 -3.37890625e-01  1.53320312e-01\n",
            "  2.33398438e-01 -2.08007812e-01  3.73046875e-01  8.20312500e-02\n",
            "  2.51953125e-01 -7.61718750e-02 -4.66308594e-02 -2.23388672e-02\n",
            "  2.99072266e-02 -5.93261719e-02 -4.66918945e-03 -2.44140625e-01\n",
            " -2.09960938e-01 -2.87109375e-01 -4.54101562e-02 -1.77734375e-01\n",
            " -2.79296875e-01 -8.59375000e-02  9.13085938e-02  2.51953125e-01]\n"
          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBuJa04I7ckx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51b9de4c-8846-46ad-db18-65191f9632b6"
      },
      "source": [
        "pairs = [\n",
        "    ('car', 'minivan'),   # a minivan is a kind of car\n",
        "    ('car', 'bicycle'),   # still a wheeled vehicle\n",
        "    ('car', 'airplane'),  # ok, no wheels, but still a vehicle\n",
        "    ('car', 'cereal'),    # ... and so on\n",
        "    ('car', 'communism'),\n",
        "]\n",
        "for w1, w2 in pairs:\n",
        "    print('%r\\t%r\\t%.2f' % (w1, w2, wv.similarity(w1, w2)))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'car'\t'minivan'\t0.69\n",
            "'car'\t'bicycle'\t0.54\n",
            "'car'\t'airplane'\t0.42\n",
            "'car'\t'cereal'\t0.14\n",
            "'car'\t'communism'\t0.06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0b1qUso7cky",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ccdeaef-764f-4464-f79c-8fb0722859ae"
      },
      "source": [
        "print(wv.most_similar(positive=['car', 'minivan'], topn=5))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('SUV', 0.853219211101532), ('vehicle', 0.8175784349441528), ('pickup_truck', 0.7763689160346985), ('Jeep', 0.7567334175109863), ('Ford_Explorer', 0.756571888923645)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTcEA-9Z7ckz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ae77a30-3fa8-4e9d-b900-646af81cd7b8"
      },
      "source": [
        "print(wv.doesnt_match(['fire', 'water', 'land', 'sea', 'air', 'car']))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "car\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py:895: 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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7jIcB2u7ck1"
      },
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "import string\n",
        "import time\n",
        "from fastai import *\n",
        "from fastai.vision import *"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "t-PPmb6YCKMV",
        "outputId": "56db0cde-1822-470b-c5c8-e26a77cbf5db"
      },
      "source": [
        "data = pd.read_csv('https://raw.githubusercontent.com/gulabpatel/NLP_Basics/master/Dataset/Amazon_cell_phone_reviews.csv')\n",
        "data"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>asin</th>\n",
              "      <th>name</th>\n",
              "      <th>rating</th>\n",
              "      <th>date</th>\n",
              "      <th>verified</th>\n",
              "      <th>title</th>\n",
              "      <th>body</th>\n",
              "      <th>helpfulVotes</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Janet</td>\n",
              "      <td>3</td>\n",
              "      <td>October 11, 2005</td>\n",
              "      <td>False</td>\n",
              "      <td>Def not best, but not worst</td>\n",
              "      <td>I had the Samsung A600 for awhile which is abs...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B0000SX2UC</td>\n",
              "      <td>Luke Wyatt</td>\n",
              "      <td>1</td>\n",
              "      <td>January 7, 2004</td>\n",
              "      <td>False</td>\n",
              "      <td>Text Messaging Doesn't Work</td>\n",
              "      <td>Due to a software issue between Nokia and Spri...</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Brooke</td>\n",
              "      <td>5</td>\n",
              "      <td>December 30, 2003</td>\n",
              "      <td>False</td>\n",
              "      <td>Love This Phone</td>\n",
              "      <td>This is a great, reliable phone. I also purcha...</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>B0000SX2UC</td>\n",
              "      <td>amy m. teague</td>\n",
              "      <td>3</td>\n",
              "      <td>March 18, 2004</td>\n",
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              "      <td>Love the Phone, BUT...!</td>\n",
              "      <td>I love the phone and all, because I really did...</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>tristazbimmer</td>\n",
              "      <td>4</td>\n",
              "      <td>August 28, 2005</td>\n",
              "      <td>False</td>\n",
              "      <td>Great phone service and options, lousy case!</td>\n",
              "      <td>The phone has been great for every purpose it ...</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>67981</th>\n",
              "      <td>B081H6STQQ</td>\n",
              "      <td>jande</td>\n",
              "      <td>5</td>\n",
              "      <td>August 16, 2019</td>\n",
              "      <td>False</td>\n",
              "      <td>Awesome Phone, but finger scanner is a big mis...</td>\n",
              "      <td>I love the camera on this phone. The screen is...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67982</th>\n",
              "      <td>B081H6STQQ</td>\n",
              "      <td>2cool4u</td>\n",
              "      <td>5</td>\n",
              "      <td>September 14, 2019</td>\n",
              "      <td>False</td>\n",
              "      <td>Simply Amazing!</td>\n",
              "      <td>I've been an Xperia user for several years and...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67983</th>\n",
              "      <td>B081H6STQQ</td>\n",
              "      <td>simon</td>\n",
              "      <td>5</td>\n",
              "      <td>July 14, 2019</td>\n",
              "      <td>False</td>\n",
              "      <td>great phon3, but many bugs need to fix. still ...</td>\n",
              "      <td>buy one more for my cousin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <th>67984</th>\n",
              "      <td>B081TJFVCJ</td>\n",
              "      <td>Tobiasz Jedrysiak</td>\n",
              "      <td>5</td>\n",
              "      <td>December 24, 2019</td>\n",
              "      <td>True</td>\n",
              "      <td>Phone is like new</td>\n",
              "      <td>Product looks and works like new. Very much re...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67985</th>\n",
              "      <td>B0825BB7SG</td>\n",
              "      <td>Owen Gonzalez</td>\n",
              "      <td>5</td>\n",
              "      <td>December 11, 2019</td>\n",
              "      <td>False</td>\n",
              "      <td>Outstanding phone for the price</td>\n",
              "      <td>I love the size and style of this phone. It is...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67986 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             asin  ... helpfulVotes\n",
              "0      B0000SX2UC  ...          1.0\n",
              "1      B0000SX2UC  ...         17.0\n",
              "2      B0000SX2UC  ...          5.0\n",
              "3      B0000SX2UC  ...          1.0\n",
              "4      B0000SX2UC  ...          1.0\n",
              "...           ...  ...          ...\n",
              "67981  B081H6STQQ  ...          1.0\n",
              "67982  B081H6STQQ  ...          1.0\n",
              "67983  B081H6STQQ  ...          NaN\n",
              "67984  B081TJFVCJ  ...          NaN\n",
              "67985  B0825BB7SG  ...          NaN\n",
              "\n",
              "[67986 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3DhpzsV7ck4"
      },
      "source": [
        "df=data\n",
        "df = df.drop(columns=['name', 'helpfulVotes','date','title','verified'])\n",
        "df1 = df.rename(columns = {'body': 'reviewText', 'asin': 'productID'}, inplace = False)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "380rtZGL7ck5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cf86145b-761a-4e41-90da-92150e8358f0"
      },
      "source": [
        "df1.dropna(axis = 0, how ='any',inplace=True) \n",
        "df1.drop_duplicates(subset=['rating','reviewText'],keep='first',inplace=True)\n",
        "df1.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>productID</th>\n",
              "      <th>rating</th>\n",
              "      <th>reviewText</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>B0000SX2UC</td>\n",
              "      <td>3</td>\n",
              "      <td>I had the Samsung A600 for awhile which is abs...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B0000SX2UC</td>\n",
              "      <td>1</td>\n",
              "      <td>Due to a software issue between Nokia and Spri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>B0000SX2UC</td>\n",
              "      <td>5</td>\n",
              "      <td>This is a great, reliable phone. I also purcha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>B0000SX2UC</td>\n",
              "      <td>3</td>\n",
              "      <td>I love the phone and all, because I really did...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B0000SX2UC</td>\n",
              "      <td>4</td>\n",
              "      <td>The phone has been great for every purpose it ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    productID  rating                                         reviewText\n",
              "0  B0000SX2UC       3  I had the Samsung A600 for awhile which is abs...\n",
              "1  B0000SX2UC       1  Due to a software issue between Nokia and Spri...\n",
              "2  B0000SX2UC       5  This is a great, reliable phone. I also purcha...\n",
              "3  B0000SX2UC       3  I love the phone and all, because I really did...\n",
              "4  B0000SX2UC       4  The phone has been great for every purpose it ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3IPtujk7ck6"
      },
      "source": [
        "def clean_text(text ): \n",
        "    delete_dict = {sp_character: ' ' for sp_character in string.punctuation} \n",
        "    delete_dict[' '] = ' ' \n",
        "    table = str.maketrans(delete_dict)\n",
        "    text1 = text.translate(table)\n",
        "    #print('cleaned:'+text1)\n",
        "    textArr= text1.split()\n",
        "    text2 = ' '.join([w for w in textArr if ( not w.isdigit() and  ( not w.isdigit() and len(w)>2))]) \n",
        "    \n",
        "    return text2.lower().split(' ')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DnuD34r7ck7"
      },
      "source": [
        "df2 = df1.sample(n=20000)\n",
        "df2['reviewText']= df2['reviewText'].apply(clean_text)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j72ot4mV7ck7"
      },
      "source": [
        "sentences = df2['reviewText'].tolist()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUuku1jf7ck8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5ecebe3-524a-4818-eacf-9db1268f7c4c"
      },
      "source": [
        "print(len(sentences))\n",
        "print(sentences[1])\n",
        "print(sentences[200])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20000\n",
            "['phone', 'works', 'well', 'but', 'was', 'very', 'very', 'disappointed', 'packing', 'and', 'low', 'quality', 'charger', 'phone', 'was', 'not', 'cleaned', 'properly', 'was', 'having', 'dust', 'near', 'home', 'button']\n",
            "['this', 'phone', 'will', 'not', 'work', 'with', 'sprint', 'boost', 'mobile', 'phone', 'and', 'will', 'not', 'work', 'with', 'sprint', 'post', 'paid', 'accounts', 'even', 'contacted', 'the', 'seller', 'about', 'the', 'phone', 'had', 'confused', 'the', 'type', 'phone', 'was', 'selling', 'very', 'disappointed', 'ordered', 'for', 'son', 'picked', 'saturday', 'from', 'his', 'apartment', 'comples', 'just', 'arrived', 'home', 'today', 'and', 'found', 'out', 'boost', 'mobile', 'phone', 'which', 'will', 'not', 'work', 'with', 'sorint', 'accounts', 'very', 'disappointed', 'much', 'for', 'the', 'marketplace']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpdLfmb87ck8"
      },
      "source": [
        "import gensim"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BIyeGoH7ck9"
      },
      "source": [
        "from gensim.models.callbacks import CallbackAny2Vec\n",
        "from gensim.models import Word2Vec\n",
        "\n",
        "# init callback class\n",
        "class callback(CallbackAny2Vec):\n",
        "    \"\"\"\n",
        "    Callback to print loss after each epoch\n",
        "    \"\"\"\n",
        "    def __init__(self):\n",
        "        self.epoch = 0\n",
        "\n",
        "    def on_epoch_end(self, model):\n",
        "        loss = model.get_latest_training_loss()\n",
        "        \n",
        "        if self.epoch == 0:\n",
        "            print('Loss after epoch {}: {}'.format(self.epoch, loss))\n",
        "        elif self.epoch % 100 == 0:\n",
        "            print('Loss after epoch {}: {}'.format(self.epoch, loss- self.loss_previous_step))\n",
        "        \n",
        "        \n",
        "        self.epoch += 1\n",
        "        self.loss_previous_step = loss"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jt3IyPDq7ck-"
      },
      "source": [
        "# init word2vec class\n",
        "w2v_model = Word2Vec(size = 300,\n",
        "                     window = 15,\n",
        "                     min_count = 2,\n",
        "                     workers = 20,\n",
        "                     sg = 1,\n",
        "                     negative = 5,\n",
        "                     sample = 1e-5)\n",
        "# build vovab\n",
        "\n",
        "\n",
        "w2v_model.build_vocab(sentences)\n",
        "\n",
        "  \n",
        "# train the w2v model\n",
        "start = time.time()\n",
        "w2v_model.train(sentences, \n",
        "                total_examples=w2v_model.corpus_count, \n",
        "                epochs=1001, \n",
        "                report_delay=1,\n",
        "                compute_loss = True, # set compute_loss = True\n",
        "                callbacks=[callback()]) # add the callback class\n",
        "end = time.time()\n",
        "\n",
        "\n",
        "print(\"elapsedtime in seconds :\"+ str(end - start))\n",
        "# save the word2vec model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFOhnETnW7Ew"
      },
      "source": [
        "w2v_model.save('word2vec.model')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oy7ZXpL7clA"
      },
      "source": [
        "***\n",
        "Let us reload our word2vec model and perform operations using it\n",
        "***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSpP3NyA7clA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b76578b2-cfc5-4f70-b288-c41f2bcd3355"
      },
      "source": [
        "reloaded_w2v_model = Word2Vec.load('/content/word2vec.model')\n",
        "words = list(reloaded_w2v_model.wv.vocab)\n",
        "print('Vocab size: '+str(len(words)))\n",
        "w1 = 'cancellation'\n",
        "print(\"Top 3 words similar to cancellation:\",\\\n",
        "      reloaded_w2v_model.wv.most_similar(positive = w1,topn =3))\n",
        "w1 = 'poor'\n",
        "print(\"Top 3 words similar to poor:\",\\\n",
        "      reloaded_w2v_model.wv.most_similar(positive = w1,topn =3))\n",
        "print(\"Similarity between earphones and headphones:\"+\\\n",
        "      str(reloaded_w2v_model.wv.similarity(w1=\"earphones\",w2=\"headphones\")))\n",
        "print(\"Similarity between charger and charge:\"+\\\n",
        "      str(reloaded_w2v_model.wv.similarity(w1=\"charger\",w2=\"charge\")))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocab size: 11145\n",
            "Top 3 words similar to cancellation: [('declared', 0.5914719700813293), ('enhancing', 0.5022028088569641), ('wlan', 0.47891634702682495)]\n",
            "Top 3 words similar to poor: [('orlando', 0.36951470375061035), ('antena', 0.36393311619758606), ('quality', 0.3365371823310852)]\n",
            "Similarity between earphones and headphones:0.16372164\n",
            "Similarity between charger and charge:0.6349773\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnRciuH-7clB"
      },
      "source": [
        "***\n",
        "Let us use TSNE to do reduce features\n",
        "Code from: https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html\n",
        "***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYgN8Hyc7clD"
      },
      "source": [
        "from sklearn.manifold import TSNE                   # final reduction\n",
        "import numpy as np                                  # array handling\n",
        "\n",
        "\n",
        "def reduce_dimensions(model):\n",
        "    num_dimensions = 2  # final num dimensions (2D, 3D, etc)\n",
        "\n",
        "    vectors = [] # positions in vector space\n",
        "    labels = [] # keep track of words to label our data again later\n",
        "    for word in model.wv.vocab:\n",
        "        vectors.append(model.wv[word])\n",
        "        labels.append(word)\n",
        "\n",
        "    # convert both lists into numpy vectors for reduction\n",
        "    vectors = np.asarray(vectors)\n",
        "    #labels = np.asarray(labels)\n",
        "\n",
        "    # reduce using t-SNE\n",
        "    vectors = np.asarray(vectors)\n",
        "    tsne = TSNE(n_components=num_dimensions, random_state=0)\n",
        "    vectors = tsne.fit_transform(vectors)\n",
        "\n",
        "    x_vals = [v[0] for v in vectors]\n",
        "    y_vals = [v[1] for v in vectors]\n",
        "    return x_vals, y_vals, labels\n",
        "\n",
        "\n",
        "x_vals, y_vals, labels = reduce_dimensions(reloaded_w2v_model)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqAxFjtz7clF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "outputId": "d0808244-ff24-4609-f279-92841cc69d96"
      },
      "source": [
        "def plot_with_matplotlib(x_vals, y_vals, labels):\n",
        "    import matplotlib.pyplot as plt\n",
        "    import random\n",
        "\n",
        "    random.seed(0)\n",
        "\n",
        "    plt.figure(figsize=(12, 12))\n",
        "    plt.scatter(x_vals, y_vals)\n",
        "\n",
        "    #\n",
        "    # Label randomly subsampled 25 data points\n",
        "    #\n",
        "    \n",
        "    \n",
        "    indices = list(range(len(labels)))\n",
        "    #selected_indices = random.sample(indices, 25)\n",
        "    selected_indices=[]\n",
        "    index = labels.index(\"cell\")\n",
        "    selected_indices.append(index)\n",
        "    index = labels.index(\"phone\")\n",
        "    selected_indices.append(index)\n",
        "    index = labels.index(\"noise\")\n",
        "    selected_indices.append(index)\n",
        "    index = labels.index(\"cancellation\")\n",
        "    selected_indices.append(index)\n",
        "    index = labels.index(\"charger\")\n",
        "    selected_indices.append(index)\n",
        "    index = labels.index(\"charge\")\n",
        "    selected_indices.append(index)\n",
        "    index = labels.index(\"poor\")\n",
        "    selected_indices.append(index)\n",
        "    index = labels.index(\"bad\")\n",
        "    selected_indices.append(index)\n",
        "    \n",
        "    \n",
        "    for i in selected_indices:\n",
        "        plt.annotate(labels[i], (x_vals[i], y_vals[i]))\n",
        "\n",
        "\n",
        "\n",
        "plot_function = plot_with_matplotlib\n",
        "\n",
        "\n",
        "plot_function(x_vals, y_vals, labels)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}