6_Topic_Modeling_+_evaluation_.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "6- Topic Modeling + evaluation .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPv564Z8muAFELTDSrpzGDd",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Malekkamoua/Medical-data-classifier/blob/main/6_Topic_Modeling_%2B_evaluation_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ej36tGBYDWli",
        "outputId": "82c514e5-31af-4c52-f336-f2eea98e1fe6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5PEDQBPEvaR"
      },
      "source": [
        "pip install pyLDAvis==2.1.2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRZWxbV5fwbt"
      },
      "source": [
        "pip install tmtoolkit"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpKJvf_dEjhq",
        "outputId": "d9ecfb46-1b9d-48bd-a6ba-4797505c6afa"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re, nltk, spacy, gensim\n",
        "# Sklearn \n",
        "from sklearn.decomposition import LatentDirichletAllocation, TruncatedSVD\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from pprint import pprint\n",
        "# Plotting tools\n",
        "import pyLDAvis\n",
        "import pyLDAvis.sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "  from collections import Iterable\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YrCidYQDloJ",
        "outputId": "7a457274-0a61-4333-bfd8-2ce960fe76bd"
      },
      "source": [
        "# load clean & summerized data\n",
        "df = pd.read_csv('/content/gdrive/MyDrive/PFA DATA/20000_data.csv')\n",
        "doc = np.array(df['SUMMARY'].values)\n",
        "print(len(doc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "15592\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdsRDgngE76S",
        "outputId": "aeadff3e-2fe3-49ba-b199-90d70f4a35e9"
      },
      "source": [
        "#Creating the Document-Word matrix\n",
        "#The LDA topic model algorithm requires a document word matrix as the main input.\n",
        "\n",
        "# minimum reqd occurences of a word \n",
        "vectorizer = CountVectorizer(analyzer='word',min_df=10)\n",
        "\n",
        "data_vectorized = vectorizer.fit_transform(doc)\n",
        "\n",
        "data_vectorized"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<15592x4397 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 769173 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ugveG9DvGUHx",
        "outputId": "3c7cdef5-ea2f-4861-df28-c9ff5e0bf81a"
      },
      "source": [
        "# Build LDA Model\n",
        "lda_model = LatentDirichletAllocation(n_components=7,\n",
        "                                      max_iter=10,\n",
        "                                      learning_method='online',   \n",
        "                                      random_state=100,\n",
        "                                      batch_size=128,\n",
        "                                      evaluate_every = -1,\n",
        "                                      n_jobs = -1,\n",
        "                                     )\n",
        "lda_output = lda_model.fit_transform(data_vectorized)\n",
        "print(lda_model) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
            "                          evaluate_every=-1, learning_decay=0.7,\n",
            "                          learning_method='online', learning_offset=10.0,\n",
            "                          max_doc_update_iter=100, max_iter=10,\n",
            "                          mean_change_tol=0.001, n_components=7, n_jobs=-1,\n",
            "                          perp_tol=0.1, random_state=100, topic_word_prior=None,\n",
            "                          total_samples=1000000.0, verbose=0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bj8geQhzHbIp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14c894d9-dbd9-41f1-ce2c-f30c92ebbbf4"
      },
      "source": [
        "#Searching the optimal number of topics. \n",
        "#Used 6, 7, 8, 10, 12 same numbers used in kmeans clustering ( initial values from elbow method, then optimised using silhouette)\n",
        "\n",
        "# Define Search Param\n",
        "search_params = {'n_components': [6, 7, 8, 9, 10, 11, 12, 13], 'learning_decay': [.5, .7, .9]}\n",
        "# Init the Model\n",
        "lda = LatentDirichletAllocation(max_iter=5, learning_method='online', learning_offset=50.,random_state=0)\n",
        "# Init Grid Search Class\n",
        "model = GridSearchCV(lda, param_grid=search_params)\n",
        "# Do the Grid Search \n",
        "model.fit(data_vectorized)   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=LatentDirichletAllocation(batch_size=128,\n",
              "                                                 doc_topic_prior=None,\n",
              "                                                 evaluate_every=-1,\n",
              "                                                 learning_decay=0.7,\n",
              "                                                 learning_method='online',\n",
              "                                                 learning_offset=50.0,\n",
              "                                                 max_doc_update_iter=100,\n",
              "                                                 max_iter=5,\n",
              "                                                 mean_change_tol=0.001,\n",
              "                                                 n_components=10, n_jobs=None,\n",
              "                                                 perp_tol=0.1, random_state=0,\n",
              "                                                 topic_word_prior=None,\n",
              "                                                 total_samples=1000000.0,\n",
              "                                                 verbose=0),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'learning_decay': [0.5, 0.7, 0.9],\n",
              "                         'n_components': [6, 7, 8, 9, 10, 11, 12, 13]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9KkCer3Jygh"
      },
      "source": [
        "GridSearchCV(cv=None, error_score='raise',\n",
        "       estimator=LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
        "             evaluate_every=-1, learning_decay=0.7, learning_method=None,\n",
        "             learning_offset=10.0, max_doc_update_iter=100, max_iter=10,\n",
        "             mean_change_tol=0.001, n_components=10, n_jobs=1, perp_tol=0.1, random_state=None,\n",
        "             topic_word_prior=None, total_samples=1000000.0, verbose=0),\n",
        "      iid=True, n_jobs=1,\n",
        "       param_grid={'n_topics':  [6, 7, 8, 9, 10, 11, 12, 13], 'learning_decay': [0.5, 0.7, 0.9]},\n",
        "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
        "       scoring=None, verbose=0) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DECYYKLUB2p_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe78f4ac-dec6-4a55-fdd5-11f6d3ee2a33"
      },
      "source": [
        "# Best Model\n",
        "best_lda_model = model.best_estimator_\n",
        "# Model Parameters\n",
        "print(\"Best Model's Params: \", model.best_params_)\n",
        "# Log Likelihood Score\n",
        "print(\"Best Log Likelihood Score: \", model.best_score_)\n",
        "# Perplexity\n",
        "print(\"Model Perplexity: \", best_lda_model.perplexity(data_vectorized))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Model's Params:  {'learning_decay': 0.7, 'n_components': 12}\n",
            "Best Log Likelihood Score:  -1586936.182127888\n",
            "Model Perplexity:  457.1943483259522\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96jNpIvZYFUR"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO2ROl4SFPU4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bcbf5620-83ef-4786-d5d7-87fe1609e27b"
      },
      "source": [
        "cv_results_df = pd.DataFrame(model.cv_results_)\n",
        "cv_results_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_learning_decay</th>\n",
              "      <th>param_n_components</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.690297</td>\n",
              "      <td>0.560246</td>\n",
              "      <td>0.750163</td>\n",
              "      <td>0.052764</td>\n",
              "      <td>0.5</td>\n",
              "      <td>6</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 6}</td>\n",
              "      <td>-1.480056e+06</td>\n",
              "      <td>-1.518430e+06</td>\n",
              "      <td>-1.536038e+06</td>\n",
              "      <td>-1.824862e+06</td>\n",
              "      <td>-1.835378e+06</td>\n",
              "      <td>-1.638953e+06</td>\n",
              "      <td>157169.206018</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17.708857</td>\n",
              "      <td>0.842282</td>\n",
              "      <td>0.766846</td>\n",
              "      <td>0.068598</td>\n",
              "      <td>0.5</td>\n",
              "      <td>7</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 7}</td>\n",
              "      <td>-1.483287e+06</td>\n",
              "      <td>-1.517178e+06</td>\n",
              "      <td>-1.544628e+06</td>\n",
              "      <td>-1.855654e+06</td>\n",
              "      <td>-1.864716e+06</td>\n",
              "      <td>-1.653093e+06</td>\n",
              "      <td>170227.543349</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.909789</td>\n",
              "      <td>0.883553</td>\n",
              "      <td>0.753928</td>\n",
              "      <td>0.069529</td>\n",
              "      <td>0.5</td>\n",
              "      <td>8</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 8}</td>\n",
              "      <td>-1.472859e+06</td>\n",
              "      <td>-1.492872e+06</td>\n",
              "      <td>-1.531482e+06</td>\n",
              "      <td>-1.889327e+06</td>\n",
              "      <td>-1.898834e+06</td>\n",
              "      <td>-1.657075e+06</td>\n",
              "      <td>194453.207862</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18.645780</td>\n",
              "      <td>0.759135</td>\n",
              "      <td>0.794160</td>\n",
              "      <td>0.066239</td>\n",
              "      <td>0.5</td>\n",
              "      <td>9</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 9}</td>\n",
              "      <td>-1.469030e+06</td>\n",
              "      <td>-1.493373e+06</td>\n",
              "      <td>-1.530114e+06</td>\n",
              "      <td>-1.869853e+06</td>\n",
              "      <td>-1.882364e+06</td>\n",
              "      <td>-1.648947e+06</td>\n",
              "      <td>186535.692138</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.092335</td>\n",
              "      <td>0.609117</td>\n",
              "      <td>0.802330</td>\n",
              "      <td>0.084958</td>\n",
              "      <td>0.5</td>\n",
              "      <td>10</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 10}</td>\n",
              "      <td>-1.474572e+06</td>\n",
              "      <td>-1.486562e+06</td>\n",
              "      <td>-1.525250e+06</td>\n",
              "      <td>-1.875737e+06</td>\n",
              "      <td>-1.885416e+06</td>\n",
              "      <td>-1.649508e+06</td>\n",
              "      <td>189434.008024</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>18.675209</td>\n",
              "      <td>0.663912</td>\n",
              "      <td>0.798359</td>\n",
              "      <td>0.066612</td>\n",
              "      <td>0.5</td>\n",
              "      <td>11</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 11}</td>\n",
              "      <td>-1.461131e+06</td>\n",
              "      <td>-1.480208e+06</td>\n",
              "      <td>-1.518614e+06</td>\n",
              "      <td>-1.869169e+06</td>\n",
              "      <td>-1.878091e+06</td>\n",
              "      <td>-1.641443e+06</td>\n",
              "      <td>190503.106366</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>18.806960</td>\n",
              "      <td>0.572671</td>\n",
              "      <td>0.774609</td>\n",
              "      <td>0.067598</td>\n",
              "      <td>0.5</td>\n",
              "      <td>12</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 12}</td>\n",
              "      <td>-1.467628e+06</td>\n",
              "      <td>-1.472682e+06</td>\n",
              "      <td>-1.509602e+06</td>\n",
              "      <td>-1.855898e+06</td>\n",
              "      <td>-1.866016e+06</td>\n",
              "      <td>-1.634365e+06</td>\n",
              "      <td>185605.777330</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>19.429653</td>\n",
              "      <td>0.556083</td>\n",
              "      <td>0.807531</td>\n",
              "      <td>0.070200</td>\n",
              "      <td>0.5</td>\n",
              "      <td>13</td>\n",
              "      <td>{'learning_decay': 0.5, 'n_components': 13}</td>\n",
              "      <td>-1.464295e+06</td>\n",
              "      <td>-1.481413e+06</td>\n",
              "      <td>-1.513279e+06</td>\n",
              "      <td>-1.853198e+06</td>\n",
              "      <td>-1.864333e+06</td>\n",
              "      <td>-1.635304e+06</td>\n",
              "      <td>183165.771166</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>19.080521</td>\n",
              "      <td>0.958829</td>\n",
              "      <td>0.818778</td>\n",
              "      <td>0.099705</td>\n",
              "      <td>0.7</td>\n",
              "      <td>6</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 6}</td>\n",
              "      <td>-1.434281e+06</td>\n",
              "      <td>-1.471664e+06</td>\n",
              "      <td>-1.502813e+06</td>\n",
              "      <td>-1.848546e+06</td>\n",
              "      <td>-1.858045e+06</td>\n",
              "      <td>-1.623070e+06</td>\n",
              "      <td>189250.810093</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>19.696191</td>\n",
              "      <td>1.025693</td>\n",
              "      <td>0.872836</td>\n",
              "      <td>0.118141</td>\n",
              "      <td>0.7</td>\n",
              "      <td>7</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 7}</td>\n",
              "      <td>-1.433934e+06</td>\n",
              "      <td>-1.473974e+06</td>\n",
              "      <td>-1.506197e+06</td>\n",
              "      <td>-1.862613e+06</td>\n",
              "      <td>-1.871346e+06</td>\n",
              "      <td>-1.629613e+06</td>\n",
              "      <td>195176.573561</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>19.723186</td>\n",
              "      <td>0.843390</td>\n",
              "      <td>0.850272</td>\n",
              "      <td>0.118656</td>\n",
              "      <td>0.7</td>\n",
              "      <td>8</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 8}</td>\n",
              "      <td>-1.423965e+06</td>\n",
              "      <td>-1.448782e+06</td>\n",
              "      <td>-1.489874e+06</td>\n",
              "      <td>-1.856127e+06</td>\n",
              "      <td>-1.866797e+06</td>\n",
              "      <td>-1.617109e+06</td>\n",
              "      <td>200649.446254</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>21.096658</td>\n",
              "      <td>1.047291</td>\n",
              "      <td>0.861404</td>\n",
              "      <td>0.099332</td>\n",
              "      <td>0.7</td>\n",
              "      <td>9</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 9}</td>\n",
              "      <td>-1.416487e+06</td>\n",
              "      <td>-1.467200e+06</td>\n",
              "      <td>-1.491359e+06</td>\n",
              "      <td>-1.831557e+06</td>\n",
              "      <td>-1.839215e+06</td>\n",
              "      <td>-1.609164e+06</td>\n",
              "      <td>186299.924420</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>20.901983</td>\n",
              "      <td>0.598419</td>\n",
              "      <td>0.889364</td>\n",
              "      <td>0.086639</td>\n",
              "      <td>0.7</td>\n",
              "      <td>10</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 10}</td>\n",
              "      <td>-1.425845e+06</td>\n",
              "      <td>-1.440929e+06</td>\n",
              "      <td>-1.484176e+06</td>\n",
              "      <td>-1.854148e+06</td>\n",
              "      <td>-1.861937e+06</td>\n",
              "      <td>-1.613407e+06</td>\n",
              "      <td>200674.888235</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>20.773990</td>\n",
              "      <td>0.459292</td>\n",
              "      <td>0.875913</td>\n",
              "      <td>0.080270</td>\n",
              "      <td>0.7</td>\n",
              "      <td>11</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 11}</td>\n",
              "      <td>-1.411514e+06</td>\n",
              "      <td>-1.434617e+06</td>\n",
              "      <td>-1.470205e+06</td>\n",
              "      <td>-1.841435e+06</td>\n",
              "      <td>-1.846885e+06</td>\n",
              "      <td>-1.600931e+06</td>\n",
              "      <td>199481.258142</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>20.981544</td>\n",
              "      <td>0.674928</td>\n",
              "      <td>0.841055</td>\n",
              "      <td>0.080462</td>\n",
              "      <td>0.7</td>\n",
              "      <td>12</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 12}</td>\n",
              "      <td>-1.400408e+06</td>\n",
              "      <td>-1.433850e+06</td>\n",
              "      <td>-1.468131e+06</td>\n",
              "      <td>-1.811501e+06</td>\n",
              "      <td>-1.820791e+06</td>\n",
              "      <td>-1.586936e+06</td>\n",
              "      <td>188393.349510</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>21.195303</td>\n",
              "      <td>0.856800</td>\n",
              "      <td>0.875086</td>\n",
              "      <td>0.091876</td>\n",
              "      <td>0.7</td>\n",
              "      <td>13</td>\n",
              "      <td>{'learning_decay': 0.7, 'n_components': 13}</td>\n",
              "      <td>-1.407575e+06</td>\n",
              "      <td>-1.437379e+06</td>\n",
              "      <td>-1.470066e+06</td>\n",
              "      <td>-1.823664e+06</td>\n",
              "      <td>-1.833049e+06</td>\n",
              "      <td>-1.594347e+06</td>\n",
              "      <td>192111.182968</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>20.538038</td>\n",
              "      <td>0.745086</td>\n",
              "      <td>0.889678</td>\n",
              "      <td>0.126946</td>\n",
              "      <td>0.9</td>\n",
              "      <td>6</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 6}</td>\n",
              "      <td>-1.418087e+06</td>\n",
              "      <td>-1.460462e+06</td>\n",
              "      <td>-1.499515e+06</td>\n",
              "      <td>-1.877760e+06</td>\n",
              "      <td>-1.886770e+06</td>\n",
              "      <td>-1.628519e+06</td>\n",
              "      <td>208797.464827</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>20.602751</td>\n",
              "      <td>0.778217</td>\n",
              "      <td>0.885194</td>\n",
              "      <td>0.112993</td>\n",
              "      <td>0.9</td>\n",
              "      <td>7</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 7}</td>\n",
              "      <td>-1.423410e+06</td>\n",
              "      <td>-1.457338e+06</td>\n",
              "      <td>-1.496463e+06</td>\n",
              "      <td>-1.875596e+06</td>\n",
              "      <td>-1.883034e+06</td>\n",
              "      <td>-1.627168e+06</td>\n",
              "      <td>207184.552607</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>20.862064</td>\n",
              "      <td>0.566961</td>\n",
              "      <td>0.880700</td>\n",
              "      <td>0.155983</td>\n",
              "      <td>0.9</td>\n",
              "      <td>8</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 8}</td>\n",
              "      <td>-1.418071e+06</td>\n",
              "      <td>-1.454701e+06</td>\n",
              "      <td>-1.492686e+06</td>\n",
              "      <td>-1.862901e+06</td>\n",
              "      <td>-1.870370e+06</td>\n",
              "      <td>-1.619746e+06</td>\n",
              "      <td>202974.658709</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>22.130920</td>\n",
              "      <td>1.196118</td>\n",
              "      <td>0.968474</td>\n",
              "      <td>0.177903</td>\n",
              "      <td>0.9</td>\n",
              "      <td>9</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 9}</td>\n",
              "      <td>-1.403327e+06</td>\n",
              "      <td>-1.455114e+06</td>\n",
              "      <td>-1.487679e+06</td>\n",
              "      <td>-1.856397e+06</td>\n",
              "      <td>-1.864429e+06</td>\n",
              "      <td>-1.613390e+06</td>\n",
              "      <td>203496.458252</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21.793787</td>\n",
              "      <td>0.708666</td>\n",
              "      <td>0.868020</td>\n",
              "      <td>0.117110</td>\n",
              "      <td>0.9</td>\n",
              "      <td>10</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 10}</td>\n",
              "      <td>-1.408743e+06</td>\n",
              "      <td>-1.440073e+06</td>\n",
              "      <td>-1.481737e+06</td>\n",
              "      <td>-1.867466e+06</td>\n",
              "      <td>-1.873867e+06</td>\n",
              "      <td>-1.614377e+06</td>\n",
              "      <td>210546.931840</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>21.537197</td>\n",
              "      <td>0.842918</td>\n",
              "      <td>0.901912</td>\n",
              "      <td>0.111983</td>\n",
              "      <td>0.9</td>\n",
              "      <td>11</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 11}</td>\n",
              "      <td>-1.401821e+06</td>\n",
              "      <td>-1.427582e+06</td>\n",
              "      <td>-1.469883e+06</td>\n",
              "      <td>-1.860617e+06</td>\n",
              "      <td>-1.865607e+06</td>\n",
              "      <td>-1.605102e+06</td>\n",
              "      <td>211788.231403</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>22.323240</td>\n",
              "      <td>0.802361</td>\n",
              "      <td>0.944992</td>\n",
              "      <td>0.093514</td>\n",
              "      <td>0.9</td>\n",
              "      <td>12</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 12}</td>\n",
              "      <td>-1.400101e+06</td>\n",
              "      <td>-1.435832e+06</td>\n",
              "      <td>-1.471224e+06</td>\n",
              "      <td>-1.823947e+06</td>\n",
              "      <td>-1.831277e+06</td>\n",
              "      <td>-1.592476e+06</td>\n",
              "      <td>193314.238114</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>22.298434</td>\n",
              "      <td>0.551472</td>\n",
              "      <td>0.957787</td>\n",
              "      <td>0.129492</td>\n",
              "      <td>0.9</td>\n",
              "      <td>13</td>\n",
              "      <td>{'learning_decay': 0.9, 'n_components': 13}</td>\n",
              "      <td>-1.414892e+06</td>\n",
              "      <td>-1.437104e+06</td>\n",
              "      <td>-1.471086e+06</td>\n",
              "      <td>-1.825501e+06</td>\n",
              "      <td>-1.832808e+06</td>\n",
              "      <td>-1.596278e+06</td>\n",
              "      <td>190997.052434</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
              "0       16.690297      0.560246  ...   157169.206018               19\n",
              "1       17.708857      0.842282  ...   170227.543349               23\n",
              "2       17.909789      0.883553  ...   194453.207862               24\n",
              "3       18.645780      0.759135  ...   186535.692138               21\n",
              "4       19.092335      0.609117  ...   189434.008024               22\n",
              "5       18.675209      0.663912  ...   190503.106366               20\n",
              "6       18.806960      0.572671  ...   185605.777330               17\n",
              "7       19.429653      0.556083  ...   183165.771166               18\n",
              "8       19.080521      0.958829  ...   189250.810093               13\n",
              "9       19.696191      1.025693  ...   195176.573561               16\n",
              "10      19.723186      0.843390  ...   200649.446254               11\n",
              "11      21.096658      1.047291  ...   186299.924420                7\n",
              "12      20.901983      0.598419  ...   200674.888235                9\n",
              "13      20.773990      0.459292  ...   199481.258142                5\n",
              "14      20.981544      0.674928  ...   188393.349510                1\n",
              "15      21.195303      0.856800  ...   192111.182968                3\n",
              "16      20.538038      0.745086  ...   208797.464827               15\n",
              "17      20.602751      0.778217  ...   207184.552607               14\n",
              "18      20.862064      0.566961  ...   202974.658709               12\n",
              "19      22.130920      1.196118  ...   203496.458252                8\n",
              "20      21.793787      0.708666  ...   210546.931840               10\n",
              "21      21.537197      0.842918  ...   211788.231403                6\n",
              "22      22.323240      0.802361  ...   193314.238114                2\n",
              "23      22.298434      0.551472  ...   190997.052434                4\n",
              "\n",
              "[24 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kzf9__amFRZJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "f8c51691-5f5d-4951-faa0-cb26acd12caa"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set(rc={\"figure.dpi\":150, 'savefig.dpi':150})\n",
        "sns.pointplot(x=\"param_n_components\",\n",
        "              y=\"mean_test_score\",\n",
        "              hue=\"param_learning_decay\",\n",
        "              data=cv_results_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='param_n_components', ylabel='mean_test_score'>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "4cDl7fh2LYeO",
        "outputId": "c3d35f3a-1701-4513-9ba1-0cbfc8334eee"
      },
      "source": [
        "# Show top n keywords for each topic\n",
        "def show_topics(vectorizer=vectorizer, lda_model=lda_model, n_words=20):\n",
        "    keywords = np.array(vectorizer.get_feature_names())\n",
        "    topic_keywords = []\n",
        "    for topic_weights in lda_model.components_:\n",
        "        top_keyword_locs = (-topic_weights).argsort()[:n_words]\n",
        "        topic_keywords.append(keywords.take(top_keyword_locs))\n",
        "    return topic_keywords\n",
        "topic_keywords = show_topics(vectorizer=vectorizer, lda_model=best_lda_model, n_words=15)\n",
        "# Topic - Keywords Dataframe\n",
        "df_topic_keywords = pd.DataFrame(topic_keywords)\n",
        "df_topic_keywords.columns = ['Word '+str(i) for i in range(df_topic_keywords.shape[1])]\n",
        "df_topic_keywords.index = ['Topic '+str(i) for i in range(df_topic_keywords.shape[0])]\n",
        "df_topic_keywords"
      ],
      "execution_count": null,
      "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>Word 0</th>\n",
              "      <th>Word 1</th>\n",
              "      <th>Word 2</th>\n",
              "      <th>Word 3</th>\n",
              "      <th>Word 4</th>\n",
              "      <th>Word 5</th>\n",
              "      <th>Word 6</th>\n",
              "      <th>Word 7</th>\n",
              "      <th>Word 8</th>\n",
              "      <th>Word 9</th>\n",
              "      <th>Word 10</th>\n",
              "      <th>Word 11</th>\n",
              "      <th>Word 12</th>\n",
              "      <th>Word 13</th>\n",
              "      <th>Word 14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Topic 0</th>\n",
              "      <td>hearing</td>\n",
              "      <td>open</td>\n",
              "      <td>receive</td>\n",
              "      <td>back</td>\n",
              "      <td>level</td>\n",
              "      <td>help</td>\n",
              "      <td>health</td>\n",
              "      <td>work</td>\n",
              "      <td>offer</td>\n",
              "      <td>loss</td>\n",
              "      <td>close</td>\n",
              "      <td>next</td>\n",
              "      <td>cancel</td>\n",
              "      <td>thank</td>\n",
              "      <td>join</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 1</th>\n",
              "      <td>dental</td>\n",
              "      <td>health</td>\n",
              "      <td>access</td>\n",
              "      <td>pediatric</td>\n",
              "      <td>education</td>\n",
              "      <td>news</td>\n",
              "      <td>practice</td>\n",
              "      <td>pet</td>\n",
              "      <td>care</td>\n",
              "      <td>dentistry</td>\n",
              "      <td>member</td>\n",
              "      <td>oral</td>\n",
              "      <td>diabetes</td>\n",
              "      <td>center</td>\n",
              "      <td>dentist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 2</th>\n",
              "      <td>news</td>\n",
              "      <td>privacy</td>\n",
              "      <td>said</td>\n",
              "      <td>policy</td>\n",
              "      <td>would</td>\n",
              "      <td>live</td>\n",
              "      <td>view</td>\n",
              "      <td>world</td>\n",
              "      <td>week</td>\n",
              "      <td>new</td>\n",
              "      <td>camp</td>\n",
              "      <td>good</td>\n",
              "      <td>contact</td>\n",
              "      <td>morning</td>\n",
              "      <td>use</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 3</th>\n",
              "      <td>test</td>\n",
              "      <td>blood</td>\n",
              "      <td>skin</td>\n",
              "      <td>diabetic</td>\n",
              "      <td>care</td>\n",
              "      <td>cancer</td>\n",
              "      <td>testing</td>\n",
              "      <td>medical</td>\n",
              "      <td>pressure</td>\n",
              "      <td>pain</td>\n",
              "      <td>insulin</td>\n",
              "      <td>feeding</td>\n",
              "      <td>food</td>\n",
              "      <td>screening</td>\n",
              "      <td>glucose</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 4</th>\n",
              "      <td>treatment</td>\n",
              "      <td>addiction</td>\n",
              "      <td>drug</td>\n",
              "      <td>alcohol</td>\n",
              "      <td>abuse</td>\n",
              "      <td>health</td>\n",
              "      <td>mental</td>\n",
              "      <td>substance</td>\n",
              "      <td>intake</td>\n",
              "      <td>dial</td>\n",
              "      <td>para</td>\n",
              "      <td>city</td>\n",
              "      <td>general</td>\n",
              "      <td>therapy</td>\n",
              "      <td>new</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 5</th>\n",
              "      <td>printing</td>\n",
              "      <td>beau</td>\n",
              "      <td>printed</td>\n",
              "      <td>data</td>\n",
              "      <td>medical</td>\n",
              "      <td>industry</td>\n",
              "      <td>new</td>\n",
              "      <td>education</td>\n",
              "      <td>additive</td>\n",
              "      <td>part</td>\n",
              "      <td>item</td>\n",
              "      <td>metal</td>\n",
              "      <td>job</td>\n",
              "      <td>business</td>\n",
              "      <td>market</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 6</th>\n",
              "      <td>center</td>\n",
              "      <td>member</td>\n",
              "      <td>health</td>\n",
              "      <td>news</td>\n",
              "      <td>policy</td>\n",
              "      <td>research</td>\n",
              "      <td>public</td>\n",
              "      <td>education</td>\n",
              "      <td>join</td>\n",
              "      <td>practice</td>\n",
              "      <td>volunteer</td>\n",
              "      <td>action</td>\n",
              "      <td>contact</td>\n",
              "      <td>find</td>\n",
              "      <td>login</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 7</th>\n",
              "      <td>medicine</td>\n",
              "      <td>client</td>\n",
              "      <td>banner</td>\n",
              "      <td>new</td>\n",
              "      <td>center</td>\n",
              "      <td>lake</td>\n",
              "      <td>ago</td>\n",
              "      <td>search</td>\n",
              "      <td>east</td>\n",
              "      <td>job</td>\n",
              "      <td>park</td>\n",
              "      <td>home</td>\n",
              "      <td>site</td>\n",
              "      <td>west</td>\n",
              "      <td>hill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 8</th>\n",
              "      <td>sleep</td>\n",
              "      <td>search</td>\n",
              "      <td>skip</td>\n",
              "      <td>home</td>\n",
              "      <td>drug</td>\n",
              "      <td>medical</td>\n",
              "      <td>food</td>\n",
              "      <td>education</td>\n",
              "      <td>allergy</td>\n",
              "      <td>program</td>\n",
              "      <td>practice</td>\n",
              "      <td>advocacy</td>\n",
              "      <td>medicine</td>\n",
              "      <td>clinical</td>\n",
              "      <td>page</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 9</th>\n",
              "      <td>family</td>\n",
              "      <td>medicine</td>\n",
              "      <td>care</td>\n",
              "      <td>health</td>\n",
              "      <td>medical</td>\n",
              "      <td>primary</td>\n",
              "      <td>practice</td>\n",
              "      <td>news</td>\n",
              "      <td>member</td>\n",
              "      <td>course</td>\n",
              "      <td>home</td>\n",
              "      <td>academy</td>\n",
              "      <td>join</td>\n",
              "      <td>advocacy</td>\n",
              "      <td>may</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 10</th>\n",
              "      <td>contact</td>\n",
              "      <td>weather</td>\n",
              "      <td>valley</td>\n",
              "      <td>watch</td>\n",
              "      <td>news</td>\n",
              "      <td>traffic</td>\n",
              "      <td>live</td>\n",
              "      <td>joe</td>\n",
              "      <td>smart</td>\n",
              "      <td>shopper</td>\n",
              "      <td>sports</td>\n",
              "      <td>sign</td>\n",
              "      <td>health</td>\n",
              "      <td>safe</td>\n",
              "      <td>operation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 11</th>\n",
              "      <td>one</td>\n",
              "      <td>test</td>\n",
              "      <td>people</td>\n",
              "      <td>negative</td>\n",
              "      <td>also</td>\n",
              "      <td>known</td>\n",
              "      <td>get</td>\n",
              "      <td>many</td>\n",
              "      <td>lab</td>\n",
              "      <td>best</td>\n",
              "      <td>days</td>\n",
              "      <td>better</td>\n",
              "      <td>may</td>\n",
              "      <td>three</td>\n",
              "      <td>blood</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Word 0     Word 1   Word 2  ...   Word 12    Word 13    Word 14\n",
              "Topic 0     hearing       open  receive  ...    cancel      thank       join\n",
              "Topic 1      dental     health   access  ...  diabetes     center    dentist\n",
              "Topic 2        news    privacy     said  ...   contact    morning        use\n",
              "Topic 3        test      blood     skin  ...      food  screening    glucose\n",
              "Topic 4   treatment  addiction     drug  ...   general    therapy        new\n",
              "Topic 5    printing       beau  printed  ...       job   business     market\n",
              "Topic 6      center     member   health  ...   contact       find      login\n",
              "Topic 7    medicine     client   banner  ...      site       west       hill\n",
              "Topic 8       sleep     search     skip  ...  medicine   clinical       page\n",
              "Topic 9      family   medicine     care  ...      join   advocacy        may\n",
              "Topic 10    contact    weather   valley  ...    health       safe  operation\n",
              "Topic 11        one       test   people  ...       may      three      blood\n",
              "\n",
              "[12 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjUyCzoOI5wF",
        "outputId": "25eb2833-15f8-481a-eff9-e75871941c90"
      },
      "source": [
        "print(topic_keywords)\n",
        "# print out first 8 topics\n",
        "for i, topic in enumerate(topic_keywords.print_topics(8)):\n",
        "    print ('%d: %s\\n'%(i+1, topic))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array(['hearing', 'open', 'receive', 'back', 'level', 'help', 'health',\n",
            "       'work', 'offer', 'loss', 'close', 'next', 'cancel', 'thank',\n",
            "       'join'], dtype='<U9'), array(['dental', 'health', 'access', 'pediatric', 'education', 'news',\n",
            "       'practice', 'pet', 'care', 'dentistry', 'member', 'oral',\n",
            "       'diabetes', 'center', 'dentist'], dtype='<U9'), array(['news', 'privacy', 'said', 'policy', 'would', 'live', 'view',\n",
            "       'world', 'week', 'new', 'camp', 'good', 'contact', 'morning',\n",
            "       'use'], dtype='<U9'), array(['test', 'blood', 'skin', 'diabetic', 'care', 'cancer', 'testing',\n",
            "       'medical', 'pressure', 'pain', 'insulin', 'feeding', 'food',\n",
            "       'screening', 'glucose'], dtype='<U9'), array(['treatment', 'addiction', 'drug', 'alcohol', 'abuse', 'health',\n",
            "       'mental', 'substance', 'intake', 'dial', 'para', 'city', 'general',\n",
            "       'therapy', 'new'], dtype='<U9'), array(['printing', 'beau', 'printed', 'data', 'medical', 'industry',\n",
            "       'new', 'education', 'additive', 'part', 'item', 'metal', 'job',\n",
            "       'business', 'market'], dtype='<U9'), array(['center', 'member', 'health', 'news', 'policy', 'research',\n",
            "       'public', 'education', 'join', 'practice', 'volunteer', 'action',\n",
            "       'contact', 'find', 'login'], dtype='<U9'), array(['medicine', 'client', 'banner', 'new', 'center', 'lake', 'ago',\n",
            "       'search', 'east', 'job', 'park', 'home', 'site', 'west', 'hill'],\n",
            "      dtype='<U9'), array(['sleep', 'search', 'skip', 'home', 'drug', 'medical', 'food',\n",
            "       'education', 'allergy', 'program', 'practice', 'advocacy',\n",
            "       'medicine', 'clinical', 'page'], dtype='<U9'), array(['family', 'medicine', 'care', 'health', 'medical', 'primary',\n",
            "       'practice', 'news', 'member', 'course', 'home', 'academy', 'join',\n",
            "       'advocacy', 'may'], dtype='<U9'), array(['contact', 'weather', 'valley', 'watch', 'news', 'traffic', 'live',\n",
            "       'joe', 'smart', 'shopper', 'sports', 'sign', 'health', 'safe',\n",
            "       'operation'], dtype='<U9'), array(['one', 'test', 'people', 'negative', 'also', 'known', 'get',\n",
            "       'many', 'lab', 'best', 'days', 'better', 'may', 'three', 'blood'],\n",
            "      dtype='<U9')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "T4KoFF-op6ea",
        "outputId": "928bd8b8-f6b7-4d3f-9ae2-512b3469fc34"
      },
      "source": [
        "Topics = [\"Work health\",\"Dentistry\",\"New/Newcast\",\"Diabetes\",\"Addiction\", \"Industrialization\",\n",
        "          \"Health voluntarisme\", \"Environment\",\"Nutrition\", \"Family Medecine\",\"Topic 11\", \"Blood tests\"]\n",
        "df_topic_keywords[\"Topics\"]=Topics\n",
        "df_topic_keywords"
      ],
      "execution_count": null,
      "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>Word 0</th>\n",
              "      <th>Word 1</th>\n",
              "      <th>Word 2</th>\n",
              "      <th>Word 3</th>\n",
              "      <th>Word 4</th>\n",
              "      <th>Word 5</th>\n",
              "      <th>Word 6</th>\n",
              "      <th>Word 7</th>\n",
              "      <th>Word 8</th>\n",
              "      <th>Word 9</th>\n",
              "      <th>Word 10</th>\n",
              "      <th>Word 11</th>\n",
              "      <th>Word 12</th>\n",
              "      <th>Word 13</th>\n",
              "      <th>Word 14</th>\n",
              "      <th>Topics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Topic 0</th>\n",
              "      <td>hearing</td>\n",
              "      <td>open</td>\n",
              "      <td>receive</td>\n",
              "      <td>back</td>\n",
              "      <td>level</td>\n",
              "      <td>help</td>\n",
              "      <td>health</td>\n",
              "      <td>work</td>\n",
              "      <td>offer</td>\n",
              "      <td>loss</td>\n",
              "      <td>close</td>\n",
              "      <td>next</td>\n",
              "      <td>cancel</td>\n",
              "      <td>thank</td>\n",
              "      <td>join</td>\n",
              "      <td>Work health</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 1</th>\n",
              "      <td>dental</td>\n",
              "      <td>health</td>\n",
              "      <td>access</td>\n",
              "      <td>pediatric</td>\n",
              "      <td>education</td>\n",
              "      <td>news</td>\n",
              "      <td>practice</td>\n",
              "      <td>pet</td>\n",
              "      <td>care</td>\n",
              "      <td>dentistry</td>\n",
              "      <td>member</td>\n",
              "      <td>oral</td>\n",
              "      <td>diabetes</td>\n",
              "      <td>center</td>\n",
              "      <td>dentist</td>\n",
              "      <td>Dentistry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 2</th>\n",
              "      <td>news</td>\n",
              "      <td>privacy</td>\n",
              "      <td>said</td>\n",
              "      <td>policy</td>\n",
              "      <td>would</td>\n",
              "      <td>live</td>\n",
              "      <td>view</td>\n",
              "      <td>world</td>\n",
              "      <td>week</td>\n",
              "      <td>new</td>\n",
              "      <td>camp</td>\n",
              "      <td>good</td>\n",
              "      <td>contact</td>\n",
              "      <td>morning</td>\n",
              "      <td>use</td>\n",
              "      <td>New/Newcast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 3</th>\n",
              "      <td>test</td>\n",
              "      <td>blood</td>\n",
              "      <td>skin</td>\n",
              "      <td>diabetic</td>\n",
              "      <td>care</td>\n",
              "      <td>cancer</td>\n",
              "      <td>testing</td>\n",
              "      <td>medical</td>\n",
              "      <td>pressure</td>\n",
              "      <td>pain</td>\n",
              "      <td>insulin</td>\n",
              "      <td>feeding</td>\n",
              "      <td>food</td>\n",
              "      <td>screening</td>\n",
              "      <td>glucose</td>\n",
              "      <td>Diabetes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 4</th>\n",
              "      <td>treatment</td>\n",
              "      <td>addiction</td>\n",
              "      <td>drug</td>\n",
              "      <td>alcohol</td>\n",
              "      <td>abuse</td>\n",
              "      <td>health</td>\n",
              "      <td>mental</td>\n",
              "      <td>substance</td>\n",
              "      <td>intake</td>\n",
              "      <td>dial</td>\n",
              "      <td>para</td>\n",
              "      <td>city</td>\n",
              "      <td>general</td>\n",
              "      <td>therapy</td>\n",
              "      <td>new</td>\n",
              "      <td>Addiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 5</th>\n",
              "      <td>printing</td>\n",
              "      <td>beau</td>\n",
              "      <td>printed</td>\n",
              "      <td>data</td>\n",
              "      <td>medical</td>\n",
              "      <td>industry</td>\n",
              "      <td>new</td>\n",
              "      <td>education</td>\n",
              "      <td>additive</td>\n",
              "      <td>part</td>\n",
              "      <td>item</td>\n",
              "      <td>metal</td>\n",
              "      <td>job</td>\n",
              "      <td>business</td>\n",
              "      <td>market</td>\n",
              "      <td>Industrialization</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 6</th>\n",
              "      <td>center</td>\n",
              "      <td>member</td>\n",
              "      <td>health</td>\n",
              "      <td>news</td>\n",
              "      <td>policy</td>\n",
              "      <td>research</td>\n",
              "      <td>public</td>\n",
              "      <td>education</td>\n",
              "      <td>join</td>\n",
              "      <td>practice</td>\n",
              "      <td>volunteer</td>\n",
              "      <td>action</td>\n",
              "      <td>contact</td>\n",
              "      <td>find</td>\n",
              "      <td>login</td>\n",
              "      <td>Health voluntarisme</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 7</th>\n",
              "      <td>medicine</td>\n",
              "      <td>client</td>\n",
              "      <td>banner</td>\n",
              "      <td>new</td>\n",
              "      <td>center</td>\n",
              "      <td>lake</td>\n",
              "      <td>ago</td>\n",
              "      <td>search</td>\n",
              "      <td>east</td>\n",
              "      <td>job</td>\n",
              "      <td>park</td>\n",
              "      <td>home</td>\n",
              "      <td>site</td>\n",
              "      <td>west</td>\n",
              "      <td>hill</td>\n",
              "      <td>Environment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 8</th>\n",
              "      <td>sleep</td>\n",
              "      <td>search</td>\n",
              "      <td>skip</td>\n",
              "      <td>home</td>\n",
              "      <td>drug</td>\n",
              "      <td>medical</td>\n",
              "      <td>food</td>\n",
              "      <td>education</td>\n",
              "      <td>allergy</td>\n",
              "      <td>program</td>\n",
              "      <td>practice</td>\n",
              "      <td>advocacy</td>\n",
              "      <td>medicine</td>\n",
              "      <td>clinical</td>\n",
              "      <td>page</td>\n",
              "      <td>Nutrition</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 9</th>\n",
              "      <td>family</td>\n",
              "      <td>medicine</td>\n",
              "      <td>care</td>\n",
              "      <td>health</td>\n",
              "      <td>medical</td>\n",
              "      <td>primary</td>\n",
              "      <td>practice</td>\n",
              "      <td>news</td>\n",
              "      <td>member</td>\n",
              "      <td>course</td>\n",
              "      <td>home</td>\n",
              "      <td>academy</td>\n",
              "      <td>join</td>\n",
              "      <td>advocacy</td>\n",
              "      <td>may</td>\n",
              "      <td>Family Medecine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 10</th>\n",
              "      <td>contact</td>\n",
              "      <td>weather</td>\n",
              "      <td>valley</td>\n",
              "      <td>watch</td>\n",
              "      <td>news</td>\n",
              "      <td>traffic</td>\n",
              "      <td>live</td>\n",
              "      <td>joe</td>\n",
              "      <td>smart</td>\n",
              "      <td>shopper</td>\n",
              "      <td>sports</td>\n",
              "      <td>sign</td>\n",
              "      <td>health</td>\n",
              "      <td>safe</td>\n",
              "      <td>operation</td>\n",
              "      <td>Topic 11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 11</th>\n",
              "      <td>one</td>\n",
              "      <td>test</td>\n",
              "      <td>people</td>\n",
              "      <td>negative</td>\n",
              "      <td>also</td>\n",
              "      <td>known</td>\n",
              "      <td>get</td>\n",
              "      <td>many</td>\n",
              "      <td>lab</td>\n",
              "      <td>best</td>\n",
              "      <td>days</td>\n",
              "      <td>better</td>\n",
              "      <td>may</td>\n",
              "      <td>three</td>\n",
              "      <td>blood</td>\n",
              "      <td>Blood tests</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Word 0     Word 1  ...    Word 14               Topics\n",
              "Topic 0     hearing       open  ...       join          Work health\n",
              "Topic 1      dental     health  ...    dentist            Dentistry\n",
              "Topic 2        news    privacy  ...        use          New/Newcast\n",
              "Topic 3        test      blood  ...    glucose             Diabetes\n",
              "Topic 4   treatment  addiction  ...        new            Addiction\n",
              "Topic 5    printing       beau  ...     market    Industrialization\n",
              "Topic 6      center     member  ...      login  Health voluntarisme\n",
              "Topic 7    medicine     client  ...       hill          Environment\n",
              "Topic 8       sleep     search  ...       page            Nutrition\n",
              "Topic 9      family   medicine  ...        may      Family Medecine\n",
              "Topic 10    contact    weather  ...  operation             Topic 11\n",
              "Topic 11        one       test  ...      blood          Blood tests\n",
              "\n",
              "[12 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "y0SJO34CCJf-",
        "outputId": "96d5eec7-9d6f-4468-a070-63894f5da84f"
      },
      "source": [
        "# Create Document — Topic Matrix\n",
        "lda_output = best_lda_model.transform(data_vectorized)\n",
        "# column names\n",
        "topicnames = [\"Topic\" + str(i) for i in range(best_lda_model.n_components)]\n",
        "# index names\n",
        "docnames = [\"Doc\" + str(i) for i in range(len(doc))]\n",
        "# Make the pandas dataframe\n",
        "df_document_topic = pd.DataFrame(np.round(lda_output, 2), columns=topicnames, index=docnames)\n",
        "# Get dominant topic for each document\n",
        "dominant_topic = np.argmax(df_document_topic.values, axis=1)\n",
        "df_document_topic[\"dominant_topic\"] = dominant_topic\n",
        "# Styling\n",
        "def color_green(val):\n",
        " color = \"green\" if val > .1 else \"black\"\n",
        " return \"color: {col}\".format(col=color)\n",
        "def make_bold(val):\n",
        " weight = 700 if val > .1 else 400\n",
        " return \"font-weight: {weight}\".format(weight=weight)\n",
        "# Apply Style\n",
        "\n",
        "# df_document_topics.to_excel('final.xlsx', index=False) \n",
        "\n",
        "df_document_topics"
      ],
      "execution_count": null,
      "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>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",
              "      <th>Topic7</th>\n",
              "      <th>Topic8</th>\n",
              "      <th>Topic9</th>\n",
              "      <th>Topic10</th>\n",
              "      <th>Topic11</th>\n",
              "      <th>dominant_topic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Doc0</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc1</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc2</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.35</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc3</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.64</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc4</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.32</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc5</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc6</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc7</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc8</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc9</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc10</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc11</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc12</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc13</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.39</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Doc14</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.51</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Topic0  Topic1  Topic2  Topic3  ...  Topic9  Topic10  Topic11  dominant_topic\n",
              "Doc0     0.00    0.00    0.00    0.21  ...    0.29     0.00     0.11               9\n",
              "Doc1     0.01    0.93    0.01    0.01  ...    0.01     0.01     0.01               1\n",
              "Doc2     0.00    0.28    0.18    0.00  ...    0.00     0.00     0.35              11\n",
              "Doc3     0.05    0.00    0.15    0.00  ...    0.14     0.00     0.64              11\n",
              "Doc4     0.00    0.21    0.07    0.22  ...    0.00     0.00     0.32              11\n",
              "Doc5     0.11    0.00    0.00    0.00  ...    0.00     0.00     0.09               4\n",
              "Doc6     0.01    0.59    0.01    0.01  ...    0.01     0.01     0.16               1\n",
              "Doc7     0.00    0.00    0.00    0.11  ...    0.87     0.00     0.00               9\n",
              "Doc8     0.01    0.59    0.01    0.01  ...    0.01     0.01     0.01               1\n",
              "Doc9     0.00    0.00    0.00    0.00  ...    0.79     0.00     0.00               9\n",
              "Doc10    0.00    0.00    0.58    0.00  ...    0.14     0.00     0.02               2\n",
              "Doc11    0.20    0.00    0.00    0.00  ...    0.11     0.00     0.66              11\n",
              "Doc12    0.75    0.00    0.03    0.00  ...    0.15     0.00     0.00               0\n",
              "Doc13    0.02    0.06    0.00    0.04  ...    0.08     0.00     0.39              11\n",
              "Doc14    0.00    0.29    0.00    0.12  ...    0.00     0.00     0.51              11\n",
              "\n",
              "[15 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0exd2axGMCm"
      },
      "source": [
        "# Get topic weights and dominant topics ------------\n",
        "from sklearn.manifold import TSNE\n",
        "from bokeh.plotting import figure, output_file, show\n",
        "from bokeh.models import Label\n",
        "from bokeh.io import output_notebook\n",
        "\n",
        "# Get topic weights\n",
        "# topic_weights = []\n",
        "# for i in enumerate(df_topic_keywords.print_topics(8)):\n",
        "#     topic_weights.append([w for i, w in row_list[0]])\n",
        "topic_weights = topic_keywords\n",
        "# Array of topic weights    \n",
        "arr = pd.DataFrame(topic_weights).fillna(0).values\n",
        "\n",
        "# Keep the well separated points (optional)\n",
        "arr = arr[np.amax(arr, axis=1) > 0.35]\n",
        "\n",
        "# Dominant topic number in each doc\n",
        "topic_num = np.argmax(arr, axis=1)\n",
        "\n",
        "# tSNE Dimension Reduction\n",
        "tsne_model = TSNE(n_components=2, verbose=1, random_state=0, angle=.99, init='pca')\n",
        "tsne_lda = tsne_model.fit_transform(arr)\n",
        "\n",
        "# Plot the Topic Clusters using Bokeh\n",
        "output_notebook()\n",
        "n_topics = 4\n",
        "mycolors = np.array([color for name, color in mcolors.TABLEAU_COLORS.items()])\n",
        "plot = figure(title=\"t-SNE Clustering of {} LDA Topics\".format(n_topics), \n",
        "              plot_width=900, plot_height=700)\n",
        "plot.scatter(x=tsne_lda[:,0], y=tsne_lda[:,1], color=mycolors[topic_num])\n",
        "show(plot)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "QSrcP9vq8mM3",
        "outputId": "c57f38b0-70c9-4755-ee7f-605e6ab257dc"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# fig = plt.figure(figsize=(10,4))\n",
        "plt.rcParams[\"figure.figsize\"] = (20,4)\n",
        "\n",
        "topic_frequency = df.iloc[:, :2].groupby('topic').count()\n",
        "ax = topic_frequency.plot.bar( legend=False)\n",
        "plt.title(\"Frequency of Topics\", size=12)\n",
        "# plt.xticks(rotation=45)\n",
        "ax.tick_params(axis='x', which='minor', labelsize='small', labelcolor='m', rotation=30)\n",
        "\n",
        "\n",
        "for p in ax.patches:\n",
        "    ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 3000x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kd5t89Bvl5Wb"
      },
      "source": [
        "*************** MODEL EVALUATION  ****************"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NBOr17LfnR3"
      },
      "source": [
        "import tmtoolkit\n",
        "from tmtoolkit.topicmod.evaluate import metric_coherence_gensim\n",
        "def topic_model_coherence_generator(topic_num_start=4,\n",
        "                                    topic_num_end=15,\n",
        "                                    norm_corpus='',\n",
        "                                    cv_matrix='',\n",
        "                                    cv=''):\n",
        "    norm_corpus_tokens = [doc.split() for doc in norm_corpus]\n",
        "    models = []\n",
        "    coherence_scores = []\n",
        "\n",
        "    for i in range(topic_num_start, topic_num_end):\n",
        "        print(i)\n",
        "        cur_lda = LatentDirichletAllocation(n_components=i,\n",
        "                                            max_iter=10,\n",
        "                                            random_state=0)\n",
        "        cur_lda.fit_transform(cv_matrix)\n",
        "        cur_coherence_score = metric_coherence_gensim(\n",
        "            measure='c_v',\n",
        "            top_n=5,\n",
        "            topic_word_distrib=cur_lda.components_,\n",
        "            dtm=cv.fit_transform(norm_corpus), \n",
        "            vocab=np.array(cv.get_feature_names()),\n",
        "            texts=norm_corpus_tokens)\n",
        "        models.append(cur_lda)\n",
        "        coherence_scores.append(np.mean(cur_coherence_score))\n",
        "    return models, coherence_scores"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnJSCpMkgFBH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56d19c85-47a7-4c5d-eb2b-acb9cf03a340"
      },
      "source": [
        "ts = 4\n",
        "te = 15\n",
        "models, coherence_scores = topic_model_coherence_generator(\n",
        "    ts, te, norm_corpus=doc, cv=vectorizer, cv_matrix=data_vectorized) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHY7I5IUbMv7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98fc73c3-d245-4fdd-a3ea-2206e2b3eb51"
      },
      "source": [
        "coherence_scores"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6020700229237035,\n",
              " 0.7135723253165116,\n",
              " 0.6305497570844043,\n",
              " 0.6421290215506257,\n",
              " 0.6653589784038626,\n",
              " 0.6105732408948166,\n",
              " 0.6369827316295271,\n",
              " 0.6439776021277063,\n",
              " 0.6285444648184056,\n",
              " 0.5908978993627623,\n",
              " 0.608560371513164]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DWemua7bQdo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "1f5a737f-e0a8-423e-d4bb-f7612bc5e50c"
      },
      "source": [
        "coherence_df = pd.DataFrame({\n",
        "    'TOPIC_NUMBER': [str(i) for i in range(ts, te)],\n",
        "    'COHERENCE_SCORE': np.round(coherence_scores, 4)\n",
        "})\n",
        "\n",
        "coherence_df.sort_values(by=[\"COHERENCE_SCORE\"], ascending=False)"
      ],
      "execution_count": null,
      "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>TOPIC_NUMBER</th>\n",
              "      <th>COHERENCE_SCORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>0.7136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>0.6654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11</td>\n",
              "      <td>0.6440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>0.6421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10</td>\n",
              "      <td>0.6370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>0.6305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>12</td>\n",
              "      <td>0.6285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9</td>\n",
              "      <td>0.6106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>14</td>\n",
              "      <td>0.6086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>0.6021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>13</td>\n",
              "      <td>0.5909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   TOPIC_NUMBER  COHERENCE_SCORE\n",
              "1             5           0.7136\n",
              "4             8           0.6654\n",
              "7            11           0.6440\n",
              "3             7           0.6421\n",
              "6            10           0.6370\n",
              "2             6           0.6305\n",
              "8            12           0.6285\n",
              "5             9           0.6106\n",
              "10           14           0.6086\n",
              "0             4           0.6021\n",
              "9            13           0.5909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onl5vTDIbSbw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "outputId": "6d28ccda-bfa5-4707-f52c-c0adce44f8ce"
      },
      "source": [
        "import plotnine\n",
        "from plotnine import ggplot, aes, geom_point, geom_line, labs\n",
        "plotnine.options.dpi = 150\n",
        "\n",
        "g = (ggplot(coherence_df) + aes(x=\"TOPIC_NUMBER\", y=\"COHERENCE_SCORE\") +\n",
        "     geom_point(stat=\"identity\") + geom_line(group=1, color=\"lightgrey\") +\n",
        "     labs(x=\"Number of Topics\", y=\"Average Coherence Score\"))\n",
        "g"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  if pdtypes.is_categorical(arr):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8741654248297)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    }
  ]
}