Assignment_8.ipynb

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      "name": "Assignment 8.ipynb",
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      "authorship_tag": "ABX9TyNT3naFhnG+Sf0rM0K0bOVu",
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/GuptaNavdeep1983/CS688/blob/main/Assignment_8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "#References\n",
        "\n",
        " https://www.scikityb.org/en/latest/api/text/index.html\n",
        "\n",
        "https://cran.r-project.org/web/packages/umap/vignettes/umap.html\n",
        "\n",
        "https://www.r-bloggers.com/2019/05/quick-and-easy-t-sne-analysis-in-r/"
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pip install tidytext"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tidytext in /usr/local/lib/python3.6/dist-packages (0.0.1)\n",
            "Requirement already satisfied: siuba in /usr/local/lib/python3.6/dist-packages (from tidytext) (0.0.24)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from siuba->tidytext) (1.18.5)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from siuba->tidytext) (1.1.4)\n",
            "Requirement already satisfied: SQLAlchemy>=1.1.18 in /usr/local/lib/python3.6/dist-packages (from siuba->tidytext) (1.3.20)\n",
            "Requirement already satisfied: PyYAML>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from siuba->tidytext) (3.13)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->siuba->tidytext) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->siuba->tidytext) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->siuba->tidytext) (1.15.0)\n"
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          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "pip install pymed"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pymed in /usr/local/lib/python3.6/dist-packages (0.8.9)\n",
            "Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from pymed) (2.23.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->pymed) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->pymed) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->pymed) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->pymed) (3.0.4)\n"
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          "name": "stdout"
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        "id": "C3Z4HIRA_0Ji",
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      "source": [
        "from pymed import PubMed\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os.path\n",
        "from os import path\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import re\n",
        "import gensim\n",
        "from gensim import corpora\n",
        "from gensim.models import LsiModel, LdaModel\n",
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "from gensim.test.utils import common_texts\n",
        "from gensim.parsing.preprocessing import remove_stopwords, strip_punctuation, strip_punctuation2, strip_short, strip_numeric, stem_text\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.parsing.preprocessing import STOPWORDS\n",
        "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
        "from nltk.stem.porter import *\n",
        "import nltk\n",
        "nltk.download('wordnet')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiBwh8CHAa-L"
      },
      "source": [
        "def download_pubmed_results(years, search_text, max_results_count, file_name):\n",
        "    pubmed = PubMed(tool=\"PubMedSearcher\", email=\"my@email.address\")\n",
        "    # create dataframe for search results\n",
        "    df = pd.DataFrame([], columns=['Title', 'Year'])\n",
        "    # iterate through data frame\n",
        "    index = 0\n",
        "    # iterate through years\n",
        "    for year in years:\n",
        "        # filter the query by search text and year\n",
        "        results = pubmed.query(f'({search_text}) AND ((\"{year}\"[Date - Create]))', \\\n",
        "            max_results=max_results_count)\n",
        "        # iterate through the results as per the query\n",
        "        for article in results:\n",
        "            article_dict = article.toDict()\n",
        "            df.loc[index, \"Title\"] = article_dict['title']\n",
        "            df.loc[index, \"Year\"] = year\n",
        "            index = index + 1\n",
        "    # export the results to a CSV\n",
        "    df.to_csv(file_name, index=False)\n",
        "def lemmatize_stemming(text):\n",
        "    stemmer = SnowballStemmer(\"english\")\n",
        "    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))\n",
        "\n",
        "# Tokenize and lemmatize\n",
        "def preprocess(text):\n",
        "    result=[]\n",
        "    for token in gensim.utils.simple_preprocess(text) :\n",
        "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
        "            result.append(lemmatize_stemming(token))\n",
        "            \n",
        "    return ' '.join(result)\n",
        "def create_documents(df):\n",
        "  documents = []\n",
        "  for index, row in df.iterrows():\n",
        "      all_content = preprocess(row['Title'])\n",
        "      documents.append(all_content)\n",
        "  return documents\n",
        "\n",
        "def create_document_term_matrix(documents):\n",
        "  df = pd.DataFrame(documents, columns=['content'])\n",
        "  vectorizer = CountVectorizer(input='content',stop_words='english', analyzer='word', token_pattern=r'\\b[a-zA-Z]{2,}\\b',max_features=100)\n",
        "  return vectorizer.fit_transform(df['content'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EG1Q2VB5AgLr"
      },
      "source": [
        "download_pubmed_results(years=['2020'], search_text='Obesity', max_results_count=100, file_name='obesity_results.csv')\n",
        "download_pubmed_results(years=['2020'], search_text='Cancer', max_results_count=100, file_name='cancer_results.csv')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OE-QYCFQBJMO",
        "outputId": "6559888d-a8ce-4529-d607-1238ccd076bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df_obesity_results = pd.read_csv(\"obesity_results.csv\")\n",
        "df_obesity_results.dropna(inplace=True)\n",
        "df_cancer_results = pd.read_csv(\"cancer_results.csv\")\n",
        "df_cancer_results.dropna(inplace=True)\n",
        "final_df = pd.concat([df_obesity_results, df_cancer_results], ignore_index=True)\n",
        "print(final_df)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                                 Title  Year\n",
            "0    Gestational weight gain disparities in South C...  2020\n",
            "1    Complementary feeding and future health in Mex...  2020\n",
            "2    Maternal-infant nutrition and development prog...  2020\n",
            "3    Evaluating the pathways linking complementary ...  2020\n",
            "4    Intervention during the first 1000 days in Mex...  2020\n",
            "..                                                 ...   ...\n",
            "194  Identification of Prognostic Model and Biomark...  2020\n",
            "195  Integrative Models of Histopathological Image ...  2020\n",
            "196  Detachment Activated CyPA/CD147 Induces Cancer...  2020\n",
            "197  Hsa_circ_0088233 Alleviates Proliferation, Mig...  2020\n",
            "198  HGF/c-Met Axis: The Advanced Development in Di...  2020\n",
            "\n",
            "[199 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LV2XJWrFG3bZ"
      },
      "source": [
        "documents = create_documents(final_df)\n",
        "\n",
        "dtm = create_document_term_matrix(documents)\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E61G8uKiL-jQ"
      },
      "source": [
        "\n",
        "df_doc_term_matrix = pd.DataFrame(dtm.toarray())"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEQLvD42QZZY",
        "outputId": "aa6e302f-e6a6-4d2b-b525-fa7831593b16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df_doc_term_matrix"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>199 rows × 100 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     0   1   2   3   4   5   6   7   8   ...  91  92  93  94  95  96  97  98  99\n",
              "0     0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   1   0\n",
              "1     0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "2     0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "3     0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "4     0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "..   ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..\n",
              "194   0   0   0   0   0   1   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "195   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "196   1   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "197   0   0   0   0   0   0   0   0   0  ...   0   1   0   0   0   0   0   0   0\n",
              "198   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   0   0\n",
              "\n",
              "[199 rows x 100 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krGayAf3WE5H"
      },
      "source": [
        "##PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mY45fTaPM_Fh"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.decomposition import IncrementalPCA\n",
        "ipca = IncrementalPCA(n_components=2)\n",
        "principalComponents = ipca.fit_transform(df_doc_term_matrix)\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MM4J6CIRAbT"
      },
      "source": [
        "principalDf = pd.DataFrame(data = principalComponents, columns = ['PCA1', 'PCA2'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2w_tM8WRXyl"
      },
      "source": [
        "principalDf['label'] = np.where(principalDf.index > 99, 'cancer', 'obesity')\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VcstwrcUAij",
        "outputId": "623d588a-ad6d-4bdf-e05c-7872366e2056",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "source": [
        "fig = plt.figure(figsize = (8,8))\n",
        "ax = fig.add_subplot(1,1,1) \n",
        "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
        "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
        "ax.set_title('2 component PCA', fontsize = 20)\n",
        "targets = ['obesity', 'cancer']\n",
        "colors = ['r', 'g']\n",
        "for target, color in zip(targets,colors):\n",
        "    indicesToKeep = principalDf['label'] == target\n",
        "    ax.scatter(principalDf.loc[indicesToKeep, 'PCA1']\n",
        "               , principalDf.loc[indicesToKeep, 'PCA2']\n",
        "               , c = color\n",
        "               , s = 50)\n",
        "ax.legend(targets)\n",
        "ax.grid()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqBZHrLmaRn9"
      },
      "source": [
        "##t-SNE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKY6ydEzWHd-",
        "outputId": "ad7c8ab1-437a-4635-cfa0-f2c529a2eded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "from yellowbrick.text import TSNEVisualizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# create document vectors\n",
        "tfidf = TfidfVectorizer()\n",
        "\n",
        "X = tfidf.fit_transform(documents)\n",
        "y = principalDf['label']\n",
        "\n",
        "tsne = TSNEVisualizer()\n",
        "tsne.fit(X, y)\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TSNEVisualizer(alpha=0.7,\n",
              "               ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7fca2f9f7ba8>,\n",
              "               classes=None, colormap=None, colors=None, decompose=None,\n",
              "               decompose_by=None, labels=None, random_state=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2EiArS5CaUYf"
      },
      "source": [
        "##UMAP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Q5wNb51bRLM",
        "outputId": "e685dc82-fdf9-45ae-9c4d-c78408ed3f3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pip install git+https://github.com/DistrictDataLabs/yellowbrick.git@develop"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/DistrictDataLabs/yellowbrick.git@develop\n",
            "  Cloning https://github.com/DistrictDataLabs/yellowbrick.git (to revision develop) to /tmp/pip-req-build-by71hd1j\n",
            "  Running command git clone -q https://github.com/DistrictDataLabs/yellowbrick.git /tmp/pip-req-build-by71hd1j\n",
            "Requirement already satisfied (use --upgrade to upgrade): yellowbrick==1.2 from git+https://github.com/DistrictDataLabs/yellowbrick.git@develop in /usr/local/lib/python3.6/dist-packages\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from yellowbrick==1.2) (3.2.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from yellowbrick==1.2) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.6/dist-packages (from yellowbrick==1.2) (0.22.2.post1)\n",
            "Requirement already satisfied: numpy>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from yellowbrick==1.2) (1.18.5)\n",
            "Requirement already satisfied: cycler>=0.10.0 in /usr/local/lib/python3.6/dist-packages (from yellowbrick==1.2) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick==1.2) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick==1.2) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick==1.2) (2.8.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20->yellowbrick==1.2) (0.17.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10.0->yellowbrick==1.2) (1.15.0)\n",
            "Building wheels for collected packages: yellowbrick\n",
            "  Building wheel for yellowbrick (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yellowbrick: filename=yellowbrick-1.2-cp36-none-any.whl size=270245 sha256=9a2034b5f00ebae2f682a6187c59501982285efb248ac9160149d7823137b474\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-nohtm85g/wheels/19/7c/6e/b270407cdf07adc6080f597d76240341462cc12ed477693370\n",
            "Successfully built yellowbrick\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3akq7jl2aWKX",
        "outputId": "68f4bbee-9402-45e6-e95c-50fcbf7626c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from yellowbrick.datasets import load_hobbies\n",
        "from yellowbrick.text import UMAPVisualizer\n",
        "# import umap.umap_ as UMAP\n",
        "tfidf = TfidfVectorizer()\n",
        "docs = tfidf.fit_transform(documents)\n",
        "labels = principalDf['label']\n",
        "corpus = load_hobbies()\n",
        "print(corpus.labels)\n",
        "# Instantiate the text visualizer\n",
        "umap = UMAPVisualizer(metric='cosine')\n",
        "umap.fit(docs, labels)\n",
        "umap.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['sports', 'cooking', 'cinema', 'gaming', 'books']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fca2da5e128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    }
  ]
}