t_SNE_(1).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Bady9898/R_Python_libraries/blob/main/t_SNE_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ij9Bp9Ak2Oug"
      },
      "source": [
        "!pip install bioinfokit"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzuvU2uJ1i7U"
      },
      "source": [
        "from pandas import read_csv\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.manifold import TSNE\n",
        "from bioinfokit.visuz import cluster"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XV1fMmqK1i7a"
      },
      "source": [
        "# load data\n",
        "filename = 'TSNE_data.csv'\n",
        "dataframe = pd.read_csv(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBVMFwzA1i7e"
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      "source": [
        "# Split-out validation dataset\n",
        "array = dataframe.values\n",
        "# separate array into input and output components\n",
        "X = array[:,1:]\n",
        "Y = array[:,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fveh_yh1i7i"
      },
      "source": [
        "#TSNE visualization\n",
        "from bioinfokit.visuz import cluster\n",
        "\n",
        "data_tsne = TSNE(n_components=2).fit_transform(X)\n",
        "cluster.tsneplot(score=data_tsne)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ulqq1yFO1i7m"
      },
      "source": [
        "# get a list of categories\n",
        "color_class = dataframe['diagnosis'].to_numpy()\n",
        "cluster.tsneplot(score=data_tsne, colorlist=color_class, legendpos='upper right',legendanchor=(1.15, 1))\n",
        "\n",
        "#Plot will be stored in the default directory"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nT5egfAq1i7p"
      },
      "source": [
        "data_tsne"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGkiTFXN1i7w"
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      "source": [
        ""
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      "execution_count": null,
      "outputs": []
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