PCA IN NLP.ipynb

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      "source": [
        "import pandas as pd\n",
        "!pip install nlpia\n",
        "from nlpia.data.loaders import get_data\n",
        "pd.options.display.width = 120\n",
        "sms = get_data('sms-spam')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from nltk.tokenize.casual import casual_tokenize\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "sms"
      ],
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              "          spam                                               text\n",
              "sms0         0  Go until jurong point, crazy.. Available only ...\n",
              "sms1         0                      Ok lar... Joking wif u oni...\n",
              "sms2!        1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "sms3         0  U dun say so early hor... U c already then say...\n",
              "sms4         0  Nah I don't think he goes to usf, he lives aro...\n",
              "...        ...                                                ...\n",
              "sms4832!     1  This is the 2nd time we have tried 2 contact u...\n",
              "sms4833      0               Will ü b going to esplanade fr home?\n",
              "sms4834      0  Pity, * was in mood for that. So...any other s...\n",
              "sms4835      0  The guy did some bitching but I acted like i'd...\n",
              "sms4836      0                         Rofl. Its true to its name\n",
              "\n",
              "[4837 rows x 2 columns]"
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              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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              "      <td>U dun say so early hor... U c already then say...</td>\n",
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              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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              "      <td>Will ü b going to esplanade fr home?</td>\n",
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              "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
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              "      <td>0</td>\n",
              "      <td>Rofl. Its true to its name</td>\n",
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        "index = ['sms{}{}'.format(i, '!'*j) for (i,j) in zip(range(len(sms)), sms.spam)]\n",
        "sms.index = index\n",
        "sms"
      ],
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              "          spam                                               text\n",
              "sms0         0  Go until jurong point, crazy.. Available only ...\n",
              "sms1         0                      Ok lar... Joking wif u oni...\n",
              "sms2!        1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "sms3         0  U dun say so early hor... U c already then say...\n",
              "sms4         0  Nah I don't think he goes to usf, he lives aro...\n",
              "...        ...                                                ...\n",
              "sms4832!     1  This is the 2nd time we have tried 2 contact u...\n",
              "sms4833      0               Will ü b going to esplanade fr home?\n",
              "sms4834      0  Pity, * was in mood for that. So...any other s...\n",
              "sms4835      0  The guy did some bitching but I acted like i'd...\n",
              "sms4836      0                         Rofl. Its true to its name\n",
              "\n",
              "[4837 rows x 2 columns]"
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              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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              "      <td>Rofl. Its true to its name</td>\n",
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      "source": [
        "tfidf = TfidfVectorizer(tokenizer=casual_tokenize)\n",
        "tfidf_docs = tfidf.fit_transform(raw_documents=sms.text).toarray()\n",
        "\n",
        "len(tfidf.vocabulary_)"
      ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
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        "print(tfidf_docs)"
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            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n"
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      "source": [
        "tfidf_docs = pd.DataFrame(tfidf_docs)\n",
        "tfidf_docs = tfidf_docs - tfidf_docs.mean()\n",
        "tfidf_docs.shape"
      ],
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          "metadata": {},
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    {
      "cell_type": "code",
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        "sms.spam.sum()"
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        "colab": {
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        "id": "uWijqafaf_x1",
        "outputId": "fe795377-4772-4749-e0ac-de126ce431e4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "638"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=16)\n",
        "pca = pca.fit(tfidf_docs)\n",
        "pca_topic_vectors = pca.transform(tfidf_docs)\n",
        "columns = ['topic{}'.format(i) for i in range(pca.n_components)]\n",
        "pca_topic_vectors = pd.DataFrame(pca_topic_vectors, columns=columns,\\\n",
        "index=index)\n",
        "pca_topic_vectors.round(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "c-vbHJdZh-Lu",
        "outputId": "64718461-b129-4447-f002-b469a0265159"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          topic0  topic1  topic2  topic3  topic4  topic5  topic6  topic7  topic8  topic9  topic10  topic11  topic12  \\\n",
              "sms0       0.201   0.003   0.037   0.011  -0.019  -0.053   0.039  -0.066   0.013  -0.082    0.005   -0.009   -0.019   \n",
              "sms1       0.404  -0.094  -0.078   0.051   0.100   0.047   0.023   0.065   0.023  -0.023   -0.002    0.038   -0.045   \n",
              "sms2!     -0.030  -0.048   0.090  -0.067   0.091  -0.043  -0.000  -0.002  -0.057   0.048    0.122    0.022   -0.035   \n",
              "sms3       0.329  -0.033  -0.035  -0.016   0.052   0.056  -0.166  -0.074   0.062  -0.105    0.021    0.031   -0.080   \n",
              "sms4       0.002   0.031   0.038   0.034  -0.075  -0.093  -0.044   0.061  -0.044   0.028    0.028   -0.014   -0.020   \n",
              "...          ...     ...     ...     ...     ...     ...     ...     ...     ...     ...      ...      ...      ...   \n",
              "sms4832!  -0.126  -0.160   0.091  -0.074   0.194   0.103  -0.161   0.021   0.095  -0.113   -0.050    0.042   -0.134   \n",
              "sms4833    0.070   0.078   0.003  -0.063  -0.031   0.046  -0.018  -0.079  -0.090   0.162    0.070    0.029    0.097   \n",
              "sms4834    0.077   0.043  -0.019   0.060   0.016  -0.009  -0.024  -0.022  -0.062  -0.071   -0.078   -0.097    0.005   \n",
              "sms4835   -0.029   0.007   0.001  -0.015  -0.066  -0.101  -0.028   0.033  -0.111   0.035    0.018    0.033   -0.015   \n",
              "sms4836   -0.038  -0.078   0.016  -0.064  -0.007   0.007   0.038  -0.007  -0.054  -0.004    0.049   -0.003   -0.045   \n",
              "\n",
              "          topic13  topic14  topic15  \n",
              "sms0       -0.019   -0.006    0.032  \n",
              "sms1       -0.016    0.046   -0.044  \n",
              "sms2!       0.012   -0.032    0.048  \n",
              "sms3       -0.028    0.018   -0.070  \n",
              "sms4        0.053   -0.074   -0.016  \n",
              "...           ...      ...      ...  \n",
              "sms4832!   -0.090    0.239    0.054  \n",
              "sms4833    -0.018   -0.017    0.205  \n",
              "sms4834     0.050    0.024   -0.024  \n",
              "sms4835    -0.027    0.031   -0.055  \n",
              "sms4836     0.000   -0.053   -0.006  \n",
              "\n",
              "[4837 rows x 16 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>topic0</th>\n",
              "      <th>topic1</th>\n",
              "      <th>topic2</th>\n",
              "      <th>topic3</th>\n",
              "      <th>topic4</th>\n",
              "      <th>topic5</th>\n",
              "      <th>topic6</th>\n",
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              "      <th>topic8</th>\n",
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              "      <th>topic15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sms0</th>\n",
              "      <td>0.201</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.037</td>\n",
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              "      <td>-0.019</td>\n",
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              "      <td>-0.019</td>\n",
              "      <td>-0.019</td>\n",
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              "      <td>0.032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms1</th>\n",
              "      <td>0.404</td>\n",
              "      <td>-0.094</td>\n",
              "      <td>-0.078</td>\n",
              "      <td>0.051</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.047</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.023</td>\n",
              "      <td>-0.023</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>0.038</td>\n",
              "      <td>-0.045</td>\n",
              "      <td>-0.016</td>\n",
              "      <td>0.046</td>\n",
              "      <td>-0.044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms2!</th>\n",
              "      <td>-0.030</td>\n",
              "      <td>-0.048</td>\n",
              "      <td>0.090</td>\n",
              "      <td>-0.067</td>\n",
              "      <td>0.091</td>\n",
              "      <td>-0.043</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>-0.002</td>\n",
              "      <td>-0.057</td>\n",
              "      <td>0.048</td>\n",
              "      <td>0.122</td>\n",
              "      <td>0.022</td>\n",
              "      <td>-0.035</td>\n",
              "      <td>0.012</td>\n",
              "      <td>-0.032</td>\n",
              "      <td>0.048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms3</th>\n",
              "      <td>0.329</td>\n",
              "      <td>-0.033</td>\n",
              "      <td>-0.035</td>\n",
              "      <td>-0.016</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.056</td>\n",
              "      <td>-0.166</td>\n",
              "      <td>-0.074</td>\n",
              "      <td>0.062</td>\n",
              "      <td>-0.105</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.031</td>\n",
              "      <td>-0.080</td>\n",
              "      <td>-0.028</td>\n",
              "      <td>0.018</td>\n",
              "      <td>-0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms4</th>\n",
              "      <td>0.002</td>\n",
              "      <td>0.031</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.034</td>\n",
              "      <td>-0.075</td>\n",
              "      <td>-0.093</td>\n",
              "      <td>-0.044</td>\n",
              "      <td>0.061</td>\n",
              "      <td>-0.044</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.028</td>\n",
              "      <td>-0.014</td>\n",
              "      <td>-0.020</td>\n",
              "      <td>0.053</td>\n",
              "      <td>-0.074</td>\n",
              "      <td>-0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>sms4832!</th>\n",
              "      <td>-0.126</td>\n",
              "      <td>-0.160</td>\n",
              "      <td>0.091</td>\n",
              "      <td>-0.074</td>\n",
              "      <td>0.194</td>\n",
              "      <td>0.103</td>\n",
              "      <td>-0.161</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.095</td>\n",
              "      <td>-0.113</td>\n",
              "      <td>-0.050</td>\n",
              "      <td>0.042</td>\n",
              "      <td>-0.134</td>\n",
              "      <td>-0.090</td>\n",
              "      <td>0.239</td>\n",
              "      <td>0.054</td>\n",
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              "    <tr>\n",
              "      <th>sms4833</th>\n",
              "      <td>0.070</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.003</td>\n",
              "      <td>-0.063</td>\n",
              "      <td>-0.031</td>\n",
              "      <td>0.046</td>\n",
              "      <td>-0.018</td>\n",
              "      <td>-0.079</td>\n",
              "      <td>-0.090</td>\n",
              "      <td>0.162</td>\n",
              "      <td>0.070</td>\n",
              "      <td>0.029</td>\n",
              "      <td>0.097</td>\n",
              "      <td>-0.018</td>\n",
              "      <td>-0.017</td>\n",
              "      <td>0.205</td>\n",
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              "    <tr>\n",
              "      <th>sms4834</th>\n",
              "      <td>0.077</td>\n",
              "      <td>0.043</td>\n",
              "      <td>-0.019</td>\n",
              "      <td>0.060</td>\n",
              "      <td>0.016</td>\n",
              "      <td>-0.009</td>\n",
              "      <td>-0.024</td>\n",
              "      <td>-0.022</td>\n",
              "      <td>-0.062</td>\n",
              "      <td>-0.071</td>\n",
              "      <td>-0.078</td>\n",
              "      <td>-0.097</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0.024</td>\n",
              "      <td>-0.024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms4835</th>\n",
              "      <td>-0.029</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.001</td>\n",
              "      <td>-0.015</td>\n",
              "      <td>-0.066</td>\n",
              "      <td>-0.101</td>\n",
              "      <td>-0.028</td>\n",
              "      <td>0.033</td>\n",
              "      <td>-0.111</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.033</td>\n",
              "      <td>-0.015</td>\n",
              "      <td>-0.027</td>\n",
              "      <td>0.031</td>\n",
              "      <td>-0.055</td>\n",
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              "    <tr>\n",
              "      <th>sms4836</th>\n",
              "      <td>-0.038</td>\n",
              "      <td>-0.078</td>\n",
              "      <td>0.016</td>\n",
              "      <td>-0.064</td>\n",
              "      <td>-0.007</td>\n",
              "      <td>0.007</td>\n",
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              "      <td>-0.004</td>\n",
              "      <td>0.049</td>\n",
              "      <td>-0.003</td>\n",
              "      <td>-0.045</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.053</td>\n",
              "      <td>-0.006</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4837 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ce04e56-ce44-423f-9c0c-c2116e0cca46')\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8ce04e56-ce44-423f-9c0c-c2116e0cca46');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf.vocabulary_"
      ],
      "metadata": {
        "id": "oyqBcaTrki99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b518507-38bc-4ee1-fd5e-a2c72d941e9d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'go': 3807,\n",
              " 'until': 8487,\n",
              " 'jurong': 4675,\n",
              " 'point': 6296,\n",
              " ',': 13,\n",
              " 'crazy': 2549,\n",
              " '..': 21,\n",
              " 'available': 1531,\n",
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              " 'in': 4396,\n",
              " 'bugis': 1973,\n",
              " 'n': 5594,\n",
              " 'great': 3894,\n",
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              " 'fa': 3328,\n",
              " 'cup': 2608,\n",
              " 'final': 3450,\n",
              " 'tkts': 8180,\n",
              " '21st': 497,\n",
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              " '.': 15,\n",
              " 'text': 8020,\n",
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              " 'std': 7651,\n",
              " 'txt': 8379,\n",
              " 'rate': 6628,\n",
              " ')': 10,\n",
              " 't': 7889,\n",
              " '&': 7,\n",
              " \"c's\": 2020,\n",
              " 'apply': 1383,\n",
              " '08452810075': 115,\n",
              " 'over': 6003,\n",
              " '18': 438,\n",
              " \"'\": 8,\n",
              " 's': 6959,\n",
              " 'dun': 3041,\n",
              " 'say': 7034,\n",
              " 'so': 7438,\n",
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              " 'c': 2019,\n",
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              " 'nah': 5606,\n",
              " 'i': 4311,\n",
              " \"don't\": 2948,\n",
              " 'think': 8092,\n",
              " 'he': 4048,\n",
              " 'goes': 3819,\n",
              " 'usf': 8537,\n",
              " 'lives': 5004,\n",
              " 'around': 1435,\n",
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              " 'though': 8111,\n",
              " 'freemsg': 3613,\n",
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              " 'darling': 2666,\n",
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              " '3': 591,\n",
              " \"week's\": 8788,\n",
              " 'now': 5784,\n",
              " 'and': 1310,\n",
              " 'no': 5732,\n",
              " 'word': 8967,\n",
              " 'back': 1584,\n",
              " '!': 0,\n",
              " \"i'd\": 4312,\n",
              " 'like': 4954,\n",
              " 'some': 7454,\n",
              " 'fun': 3677,\n",
              " 'you': 9158,\n",
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              " 'password': 6102,\n",
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              " 'usher': 8538,\n",
              " 'britney': 1932,\n",
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              " 'telugu': 7991,\n",
              " 'movie': 5516,\n",
              " 'abt': 1084,\n",
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              " 'loans': 5016,\n",
              " 'wk': 8928,\n",
              " 'hols': 4174,\n",
              " 'run': 6946,\n",
              " 'forgot': 3565,\n",
              " 'hairdressers': 3974,\n",
              " 'appointment': 1386,\n",
              " 'four': 3584,\n",
              " 'shower': 7266,\n",
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              " 'nothing': 5774,\n",
              " 'else': 3138,\n",
              " 'okay': 5877,\n",
              " 'price': 6431,\n",
              " 'long': 5042,\n",
              " 'legal': 4904,\n",
              " 'them': 8061,\n",
              " 'ave': 1536,\n",
              " 'ams': 1301,\n",
              " 'gone': 3832,\n",
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              " 'driving': 3007,\n",
              " 'test': 8014,\n",
              " 'yet': 9142,\n",
              " \"you're\": 9162,\n",
              " 'mean': 5287,\n",
              " 'guess': 3936,\n",
              " 'gave': 3729,\n",
              " 'boston': 1866,\n",
              " 'men': 5326,\n",
              " 'changed': 2174,\n",
              " 'search': 7080,\n",
              " 'location': 5019,\n",
              " 'nyc': 5819,\n",
              " 'cuz': 2631,\n",
              " 'signin': 7299,\n",
              " 'page': 6035,\n",
              " 'says': 7038,\n",
              " 'umma': 8423,\n",
              " 'life': 4940,\n",
              " 'vava': 8580,\n",
              " 'lot': 5066,\n",
              " 'dear': 2699,\n",
              " 'wishes': 8912,\n",
              " 'birthday': 1777,\n",
              " 'making': 5197,\n",
              " 'truly': 8335,\n",
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              " 'would': 8993,\n",
              " 'ip': 4501,\n",
              " 'address': 1141,\n",
              " 'considering': 2449,\n",
              " 'computer': 2412,\n",
              " \"isn't\": 4528,\n",
              " 'minecraft': 5380,\n",
              " 'server': 7149,\n",
              " 'grumpy': 3923,\n",
              " 'old': 5889,\n",
              " 'people': 6146,\n",
              " 'mom': 5462,\n",
              " 'better': 1738,\n",
              " 'lying': 5135,\n",
              " 'jokes': 4640,\n",
              " 'worry': 8983,\n",
              " 'busy': 1998,\n",
              " 'plural': 6277,\n",
              " 'noun': 5781,\n",
              " 'research': 6802,\n",
              " 'dinner.msg': 2859,\n",
              " 'cos': 2499,\n",
              " 'things': 8091,\n",
              " 'scared': 7044,\n",
              " 'mah': 5180,\n",
              " 'loud': 5076,\n",
              " 'gent': 3749,\n",
              " 'contact': 2454,\n",
              " 'last': 4855,\n",
              " 'weekends': 8793,\n",
              " 'draw': 2989,\n",
              " 'shows': 7272,\n",
              " '09064012160': 282,\n",
              " 'k52': 4691,\n",
              " '12hrs': 398,\n",
              " '150ppm': 419,\n",
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              " 'sentence': 7138,\n",
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              " 'anyway': 1360,\n",
              " 'juz': 4682,\n",
              " 'tt': 8348,\n",
              " 'eatin': 3083,\n",
              " 'puttin': 6554,\n",
              " 'weight': 8798,\n",
              " 'haha': 3968,\n",
              " 'anythin': 1355,\n",
              " 'happened': 4003,\n",
              " 'entered': 3185,\n",
              " 'cabin': 2025,\n",
              " \"b'day\": 1562,\n",
              " 'boss': 1865,\n",
              " 'felt': 3411,\n",
              " 'askd': 1464,\n",
              " 'invited': 4494,\n",
              " 'apartment': 1365,\n",
              " 'went': 8814,\n",
              " 'specially': 7536,\n",
              " 'holiday': 4171,\n",
              " 'flights': 3502,\n",
              " 'inc': 4399,\n",
              " 'operator': 5929,\n",
              " '08712778109': 166,\n",
              " '10p': 368,\n",
              " 'min': 5372,\n",
              " 'goodo': 3846,\n",
              " 'must': 5575,\n",
              " 'friday': 3626,\n",
              " 'egg-potato': 3112,\n",
              " 'ratio': 6631,\n",
              " 'tortilla': 8262,\n",
              " 'needed': 5656,\n",
              " 'hmm': 4153,\n",
              " 'uncle': 8433,\n",
              " 'informed': 4438,\n",
              " 'paying': 6124,\n",
              " 'school': 7050,\n",
              " 'directly': 2865,\n",
              " 'food': 3542,\n",
              " 'private': 6447,\n",
              " '2004': 486,\n",
              " 'account': 1107,\n",
              " 'statement': 7641,\n",
              " '07742676969': 64,\n",
              " '786': 864,\n",
              " 'unredeemed': 8477,\n",
              " 'points': 6297,\n",
              " '08719180248': 213,\n",
              " 'identifier': 4344,\n",
              " '45239': 707,\n",
              " 'expires': 3307,\n",
              " '2000': 484,\n",
              " 'caller': 2045,\n",
              " '5/9': 752,\n",
              " '03': 46,\n",
              " 'landline': 4835,\n",
              " '09064019788': 288,\n",
              " '42wr29c': 690,\n",
              " 'apples': 1381,\n",
              " 'pairs': 6044,\n",
              " 'malarky': 5199,\n",
              " 'todays': 8205,\n",
              " 'voda': 8645,\n",
              " 'numbers': 5804,\n",
              " 'ending': 3160,\n",
              " '7548': 856,\n",
              " '350': 624,\n",
              " 'award': 1548,\n",
              " 'match': 5251,\n",
              " '08712300220': 149,\n",
              " 'quoting': 6589,\n",
              " '4041': 674,\n",
              " 'standard': 7621,\n",
              " 'rates': 6629,\n",
              " 'app': 1375,\n",
              " 'sao': 7004,\n",
              " 'mu': 5542,\n",
              " 'predict': 6392,\n",
              " \"ü'll\": 9222,\n",
              " 'buying': 2007,\n",
              " 'yetunde': 9144,\n",
              " \"hasn't\": 4024,\n",
              " 'sent': 7137,\n",
              " 'bother': 1869,\n",
              " 'sending': 7129,\n",
              " 'involve': 4498,\n",
              " \"shouldn't\": 7259,\n",
              " 'imposed': 4386,\n",
              " 'apologise': 1372,\n",
              " 'girl': 3782,\n",
              " 'del': 2740,\n",
              " 'bak': 1597,\n",
              " 'lucyxx': 5118,\n",
              " 'tmorrow.pls': 8185,\n",
              " 'accomodate': 1102,\n",
              " 'answer': 1335,\n",
              " 'sunshine': 7812,\n",
              " 'quiz': 6584,\n",
              " 'q': 6559,\n",
              " 'top': 8253,\n",
              " 'sony': 7480,\n",
              " 'dvd': 3051,\n",
              " 'player': 6257,\n",
              " 'country': 2518,\n",
              " 'algarve': 1245,\n",
              " 'ansr': 1334,\n",
              " '82277': 907,\n",
              " 'sp': 7516,\n",
              " 'tyrone': 8394,\n",
              " 'laid': 4827,\n",
              " 'dogging': 2932,\n",
              " 'locations': 5020,\n",
              " 'direct': 2864,\n",
              " 'join': 4631,\n",
              " \"uk's\": 8416,\n",
              " 'largest': 4852,\n",
              " 'bt': 1958,\n",
              " 'txting': 8383,\n",
              " 'gravel': 3888,\n",
              " '69888': 822,\n",
              " 'nt': 5791,\n",
              " 'ec2a': 3086,\n",
              " '31p': 611,\n",
              " '@150p': 1039,\n",
              " 'haf': 3967,\n",
              " 'msn': 5534,\n",
              " 'yijue@hotmail.com': 9149,\n",
              " 'him': 4132,\n",
              " 'rooms': 6911,\n",
              " 'befor': 1699,\n",
              " 'activities': 1129,\n",
              " \"you'll\": 9161,\n",
              " 'msgs': 5533,\n",
              " 'chat': 2195,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_nums, terms = zip(*sorted(zip(tfidf.vocabulary_.values(),\\\n",
        "tfidf.vocabulary_.keys())))"
      ],
      "metadata": {
        "id": "yOeH8BOeSK9O"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weights = pd.DataFrame(pca.components_, columns=terms,index=['topic{}'.format(i) for i in range(16)])"
      ],
      "metadata": {
        "id": "vTRyzXIUSvHR"
      },
      "execution_count": 12,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "pd.options.display.max_columns = 8\n",
        "weights.head(4).round(3)"
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        "id": "BEnBCOppS1ZU",
        "outputId": "bdf2cb58-226e-47ae-dd54-9cc5548eb28e"
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        "pd.options.display.max_columns = 10\n",
        "deals = weights['! ;) :) half off free crazy deal only $ 80 %'.split()].round(3) * 100\n",
        "deals.head(5)"
      ],
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              "      <td>0.2</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>topic3</th>\n",
              "      <td>-5.9</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-7.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-2.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>topic4</th>\n",
              "      <td>38.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-12.5</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8a2b161-91bf-4a7e-ad2a-b6cad01e2654')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8a2b161-91bf-4a7e-ad2a-b6cad01e2654');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deals.T.sum()\n",
        "#Topics 4, 8, and 9 appear to all contain positive “deal” topic sentiment. And topics 0, 3,\n",
        "#and 5 appear to be “anti-deal” topics, messages about stuff that’s the opposite of “deals”:\n",
        "#negative deals."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BtbRTaX6U4-2",
        "outputId": "43c16aee-b604-4ca8-e27e-4fa319144a01"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "topic0    -11.9\n",
              "topic1      7.5\n",
              "topic2     12.8\n",
              "topic3    -15.5\n",
              "topic4     38.3\n",
              "topic5    -33.9\n",
              "topic6      4.8\n",
              "topic7     -5.0\n",
              "topic8     40.6\n",
              "topic9     32.0\n",
              "topic10   -29.1\n",
              "topic11    48.3\n",
              "topic12     3.5\n",
              "topic13    47.5\n",
              "topic14    32.0\n",
              "topic15    -4.2\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dimension Reduce using SVD "
      ],
      "metadata": {
        "id": "taNTFuTsf2bI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svd = TruncatedSVD(n_components=16, n_iter=100)\n",
        "svd_topic_vectors = svd.fit_transform(tfidf_docs.values)\n",
        "svd_topic_vectors = pd.DataFrame(svd_topic_vectors, columns=columns,\\\n",
        "index=index)"
      ],
      "metadata": {
        "id": "_JwJMjhYYu8I"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svd_topic_vectors.round(3).head(6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "ASx9WNVlflq0",
        "outputId": "dcb1608e-efa2-48b6-f7d6-fafad621e906"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       topic0  topic1  topic2  topic3  topic4  ...  topic11  topic12  topic13  topic14  topic15\n",
              "sms0    0.201   0.003   0.037   0.011  -0.019  ...   -0.007    0.002   -0.036   -0.014    0.037\n",
              "sms1    0.404  -0.094  -0.078   0.051   0.100  ...    0.036    0.043   -0.021    0.051   -0.042\n",
              "sms2!  -0.030  -0.048   0.090  -0.067   0.091  ...    0.023    0.026   -0.020   -0.042    0.052\n",
              "sms3    0.329  -0.033  -0.035  -0.016   0.052  ...    0.023    0.073   -0.046    0.022   -0.070\n",
              "sms4    0.002   0.031   0.038   0.034  -0.075  ...   -0.009    0.027    0.034   -0.083   -0.021\n",
              "sms5!  -0.016   0.059   0.014  -0.006   0.122  ...    0.055   -0.037    0.075   -0.001    0.020\n",
              "\n",
              "[6 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-07162a0c-5c68-41a5-ae43-091a8d042718\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>topic0</th>\n",
              "      <th>topic1</th>\n",
              "      <th>topic2</th>\n",
              "      <th>topic3</th>\n",
              "      <th>topic4</th>\n",
              "      <th>...</th>\n",
              "      <th>topic11</th>\n",
              "      <th>topic12</th>\n",
              "      <th>topic13</th>\n",
              "      <th>topic14</th>\n",
              "      <th>topic15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sms0</th>\n",
              "      <td>0.201</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.011</td>\n",
              "      <td>-0.019</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007</td>\n",
              "      <td>0.002</td>\n",
              "      <td>-0.036</td>\n",
              "      <td>-0.014</td>\n",
              "      <td>0.037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms1</th>\n",
              "      <td>0.404</td>\n",
              "      <td>-0.094</td>\n",
              "      <td>-0.078</td>\n",
              "      <td>0.051</td>\n",
              "      <td>0.100</td>\n",
              "      <td>...</td>\n",
              "      <td>0.036</td>\n",
              "      <td>0.043</td>\n",
              "      <td>-0.021</td>\n",
              "      <td>0.051</td>\n",
              "      <td>-0.042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms2!</th>\n",
              "      <td>-0.030</td>\n",
              "      <td>-0.048</td>\n",
              "      <td>0.090</td>\n",
              "      <td>-0.067</td>\n",
              "      <td>0.091</td>\n",
              "      <td>...</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.026</td>\n",
              "      <td>-0.020</td>\n",
              "      <td>-0.042</td>\n",
              "      <td>0.052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms3</th>\n",
              "      <td>0.329</td>\n",
              "      <td>-0.033</td>\n",
              "      <td>-0.035</td>\n",
              "      <td>-0.016</td>\n",
              "      <td>0.052</td>\n",
              "      <td>...</td>\n",
              "      <td>0.023</td>\n",
              "      <td>0.073</td>\n",
              "      <td>-0.046</td>\n",
              "      <td>0.022</td>\n",
              "      <td>-0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms4</th>\n",
              "      <td>0.002</td>\n",
              "      <td>0.031</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.034</td>\n",
              "      <td>-0.075</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009</td>\n",
              "      <td>0.027</td>\n",
              "      <td>0.034</td>\n",
              "      <td>-0.083</td>\n",
              "      <td>-0.021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sms5!</th>\n",
              "      <td>-0.016</td>\n",
              "      <td>0.059</td>\n",
              "      <td>0.014</td>\n",
              "      <td>-0.006</td>\n",
              "      <td>0.122</td>\n",
              "      <td>...</td>\n",
              "      <td>0.055</td>\n",
              "      <td>-0.037</td>\n",
              "      <td>0.075</td>\n",
              "      <td>-0.001</td>\n",
              "      <td>0.020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6 rows × 16 columns</p>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-07162a0c-5c68-41a5-ae43-091a8d042718 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-07162a0c-5c68-41a5-ae43-091a8d042718');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svd_topic_vectors = (svd_topic_vectors.T / np.linalg.norm(\\\n",
        "svd_topic_vectors, axis=1)).T"
      ],
      "metadata": {
        "id": "4D7c3YImlPaq"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svd_topic_vectors.iloc[:20].dot(svd_topic_vectors.iloc[:20].T).round(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "b4ilwFIxlSAM",
        "outputId": "966493e8-54f8-47c2-ffae-010c873bb44b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sms0  sms1  sms2!  sms3  sms4  ...  sms15!  sms16  sms17  sms18  sms19!\n",
              "sms0     1.0   0.6   -0.1   0.6  -0.0  ...    -0.2    0.4    0.2   -0.1    -0.2\n",
              "sms1     0.6   1.0   -0.2   0.8  -0.2  ...    -0.2    0.3    0.4   -0.0    -0.2\n",
              "sms2!   -0.1  -0.2    1.0  -0.2   0.1  ...     0.3   -0.1   -0.4   -0.2     0.9\n",
              "sms3     0.6   0.8   -0.2   1.0  -0.2  ...    -0.3    0.2    0.7    0.1    -0.2\n",
              "sms4    -0.0  -0.2    0.1  -0.2   1.0  ...    -0.0   -0.1   -0.1   -0.2     0.2\n",
              "sms5!   -0.3   0.0    0.4  -0.3   0.2  ...     0.2   -0.2   -0.5   -0.1     0.4\n",
              "sms6    -0.3  -0.2    0.0  -0.1   0.0  ...    -0.2   -0.2    0.1    0.1     0.2\n",
              "sms7    -0.1  -0.2    0.3  -0.3   0.1  ...     0.5   -0.1   -0.1   -0.2     0.6\n",
              "sms8!   -0.3  -0.1    0.5  -0.2  -0.4  ...    -0.1   -0.2   -0.1   -0.2     0.4\n",
              "sms9!   -0.3  -0.1    0.4  -0.1  -0.2  ...     0.7   -0.2   -0.3    0.2     0.5\n",
              "sms10   -0.2  -0.3    0.1  -0.3   0.3  ...     0.1    0.4   -0.2   -0.2     0.0\n",
              "sms11!  -0.2  -0.2    0.8  -0.2   0.3  ...     0.3   -0.1   -0.5   -0.3     0.9\n",
              "sms12!  -0.1  -0.1    0.6  -0.2  -0.2  ...     0.2   -0.2   -0.4   -0.1     0.5\n",
              "sms13   -0.5  -0.4   -0.2  -0.5  -0.1  ...     0.1   -0.2    0.1    0.1    -0.0\n",
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              "      <td>-0.2</td>\n",
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              "<p>20 rows × 20 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32f5a845-5519-49a5-80e5-be02f881f645')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          document.querySelector('#df-32f5a845-5519-49a5-80e5-be02f881f645 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-32f5a845-5519-49a5-80e5-be02f881f645');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kTKXHp3ilqQV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}