LSA_IN_NLP.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eDcVO7-fwcOa"
      },
      "outputs": [],
      "source": [
        "#Given an m x n matrix A, the SVD factorizes it into three matrices: U, Σ, and V, such that:\n",
        "#A = UΣV^T\n",
        "#where U is an m x m orthogonal matrix, Σ is an m x n diagonal matrix with non-negative real numbers on the diagonal, and V is an n x n orthogonal matrix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4REEtCrzBKw",
        "outputId": "0b1ebe41-f42f-419a-b90b-24cfaadd24d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: ptyprocess in /usr/local/lib/python3.9/dist-packages (from terminado>=0.8.3->notebook->jupyter->nlpia) (0.7.0)\n",
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            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.9/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->nlpia) (22.2.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.9/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->nlpia) (0.19.3)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.9/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow->nlpia) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow->nlpia) (3.2.2)\n",
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          ]
        }
      ],
      "source": [
        "!pip install nlpia\n",
        "from nlpia.book.examples.ch04_catdog_lsa_sorted import lsa_models, prettify_tdm\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "xFcda0DAzF4x",
        "outputId": "3c2df81a-bf61-4ebf-a468-6d4ce429033d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 263/263 [00:00<00:00, 208565.32it/s]\n"
          ]
        },
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            "text/plain": [
              "   cat dog apple lion nyc love                                             text\n",
              "0              1        1                                 NYC is the Big Apple.\n",
              "1              1        1                        NYC is known as the Big Apple.\n",
              "2                       1    1                                      I love NYC!\n",
              "3              1        1           I wore a hat to the Big Apple party in NYC.\n",
              "4              1        1                       Come to NYC. See the Big Apple!\n",
              "5              1                             Manhattan is called the Big Apple.\n",
              "6    1                                  New York is a big city for a small cat.\n",
              "7    1              1           The lion, a big cat, is the king of the jungle.\n",
              "8    1                       1                               I love my pet cat.\n",
              "9                       1    1                      I love New York City (NYC).\n",
              "10   1   1                                              Your dog chased my cat."
            ]
          },
          "execution_count": 9,
          "metadata": {},
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      ],
      "source": [
        "bow_svd, tfidf_svd = lsa_models()\n",
        "\n",
        "prettify_tdm(**bow_svd)\n",
        "#SVD will find combinations of words that belong together. SVD\n",
        "#finds those co-occurring words by calculating the correlation between the columns\n",
        "#(terms) of your term-document matrix.\n"
      ]
    },
    {
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        "id": "DQ7ty1arzhZf",
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              "          const element = document.querySelector('#df-69acb4e0-6eb7-45d6-9bbb-cd9531811ae4');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       0   1   2   3   4   5   6   7   8   9   10\n",
              "cat     0   0   0   0   0   0   1   1   1   0   1\n",
              "dog     0   0   0   0   0   0   0   0   0   0   1\n",
              "apple   1   1   0   1   1   1   0   0   0   0   0\n",
              "lion    0   0   0   0   0   0   0   1   0   0   0\n",
              "nyc     1   1   1   1   1   0   0   0   0   1   0\n",
              "love    0   0   1   0   0   0   0   0   1   1   0"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tdm = bow_svd['tdm']\n",
        "tdm\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
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        "id": "3uVKSQvq0y8H",
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      "outputs": [
        {
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              "          0     1     2     3     4     5\n",
              "cat   -0.04  0.83 -0.38 -0.00  0.11 -0.38\n",
              "dog   -0.00  0.21 -0.18 -0.71 -0.39  0.52\n",
              "apple -0.62 -0.21 -0.51  0.00  0.49  0.27\n",
              "lion  -0.00  0.21 -0.18  0.71 -0.39  0.52\n",
              "nyc   -0.75 -0.00  0.24 -0.00 -0.52 -0.32\n",
              "love  -0.22  0.42  0.69  0.00  0.41  0.37"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "U, s, Vt = np.linalg.svd(tdm)\n",
        "pd.DataFrame(U, index=tdm.index).round(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqdhLzbQ5jUa",
        "outputId": "10de549c-71ac-44e9-d042-f132ec337fae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "u===> [[-3.97556178e-02  8.34057656e-01 -3.81851767e-01 -2.10618042e-16\n",
            "   1.12956969e-01 -3.79733154e-01]\n",
            " [-4.47829266e-03  2.08514414e-01 -1.78460107e-01 -7.07106781e-01\n",
            "  -3.90367865e-01  5.21791725e-01]\n",
            " [-6.17385624e-01 -2.08514414e-01 -5.13475593e-01  1.14930253e-16\n",
            "   4.89451262e-01  2.68583336e-01]\n",
            " [-4.47829266e-03  2.08514414e-01 -1.78460107e-01  7.07106781e-01\n",
            "  -3.90367865e-01  5.21791725e-01]\n",
            " [-7.52809940e-01 -1.94289029e-16  2.38804176e-01 -2.22044605e-16\n",
            "  -5.24858205e-01 -3.17448617e-01]\n",
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            "   4.09179557e-01  3.71966250e-01]]\n",
            " [3.14283392 2.23606798 1.77192093 1.         0.84299447 0.52177724]\n",
            "vt [[-4.35974537e-01 -4.35974537e-01 -3.11029233e-01 -4.35974537e-01\n",
            "  -4.35974537e-01 -1.96442332e-01 -1.26496082e-02 -1.40745301e-02\n",
            "  -8.41466359e-02 -3.11029233e-01 -1.40745301e-02]\n",
            " [-9.32504808e-02 -9.32504808e-02  1.86500962e-01 -9.32504808e-02\n",
            "  -9.32504808e-02 -9.32504808e-02  3.73001923e-01  4.66252404e-01\n",
            "   5.59502885e-01  1.86500962e-01  4.66252404e-01]\n",
            " [-1.55013360e-01 -1.55013360e-01  5.21597778e-01 -1.55013360e-01\n",
            "  -1.55013360e-01 -2.89784710e-01 -2.15501584e-01 -3.16217200e-01\n",
            "   1.71324845e-01  5.21597778e-01 -3.16217200e-01]\n",
            " [ 5.16084809e-18 -5.39766116e-17 -7.62774250e-17  8.99823045e-18\n",
            "   2.60575505e-17  2.73673584e-16 -1.56033100e-16  7.07106781e-01\n",
            "   4.85350121e-18 -8.67294318e-17 -7.07106781e-01]\n",
            " [-4.20013941e-02 -4.20013941e-02 -1.37223495e-01 -4.20013941e-02\n",
            "  -4.20013941e-02  5.80610286e-01  1.33994911e-01 -3.29077953e-01\n",
            "   6.19383095e-01 -1.37223495e-01 -3.29077953e-01]\n",
            " [-9.36516154e-02 -9.36516154e-02  1.04484500e-01 -9.36516154e-02\n",
            "  -9.36516154e-02  5.14747132e-01 -7.27768725e-01  2.72259044e-01\n",
            "  -1.48854780e-02  1.04484500e-01  2.72259044e-01]\n",
            " [-5.74389562e-01  2.14285573e-01  1.05975331e-01  3.33858729e-01\n",
            "  -3.14218850e-01  3.40464110e-01  3.40464110e-01  0.00000000e+00\n",
            "  -3.40464110e-01  2.34488779e-01  5.55111512e-17]\n",
            " [-3.17591636e-01  4.71083499e-01  2.47106547e-01 -6.34088217e-01\n",
            "   4.09838364e-01  7.07579893e-02  7.07579893e-02  8.32667268e-17\n",
            "  -7.07579893e-02 -1.76348558e-01  1.38777878e-16]\n",
            " [-4.95506781e-01  2.93168354e-01 -2.00663115e-01  4.12741510e-01\n",
            "   1.57174502e-01 -3.67577585e-01 -3.67577585e-01 -8.32667268e-17\n",
            "   3.67577585e-01 -1.66914470e-01  8.32667268e-17]\n",
            " [-1.54949763e-01 -1.54949763e-01 -5.88430944e-01 -1.54949763e-01\n",
            "   4.22574006e-01  4.22752846e-02  4.22752846e-02 -1.38777878e-17\n",
            "  -4.22752846e-02  6.30706229e-01 -5.55111512e-17]\n",
            " [-2.57570286e-01 -6.23595690e-01  3.28416791e-01  2.42429714e-01\n",
            "   5.44695403e-01  9.40408582e-02  9.40408582e-02  0.00000000e+00\n",
            "  -9.40408582e-02 -2.34375933e-01 -2.22044605e-16]]\n"
          ]
        }
      ],
      "source": [
        "print(\"u===>\",U)\n",
        "print(\"\",s)\n",
        "print(\"vt\",Vt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "SXV9AmFJ5voK",
        "outputId": "ff38f3bb-a797-4f21-86a1-24237c4aed92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-f0a3dc0e8d4d>:2: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead.\n",
            "  pd.np.fill_diagonal(S, s)\n"
          ]
        },
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              "    0    1    2    3    4    5    6    7    8    9    10\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5a7a3f4-a154-4899-ba63-f2ca983a711a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c5a7a3f4-a154-4899-ba63-f2ca983a711a 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-c5a7a3f4-a154-4899-ba63-f2ca983a711a');\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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              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "S = np.zeros((len(U), len(Vt)))\n",
        "pd.np.fill_diagonal(S, s)\n",
        "pd.DataFrame(S).round(1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ucc8gr1JT1YB"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}