notebooks/unsupervised/dimensionality_reduction/isomap/dimensionality_reduction_isomap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality Reduction with Isomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import manifold, datasets\n",
    "from matplotlib.colors import ListedColormap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "isomap = manifold.Isomap(n_components=2)\n",
    "new_dim = isomap.fit_transform(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>label</th>\n",
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      "text/plain": [
       "          X         Y  label\n",
       "0  0.118155  0.381038      0\n",
       "1  0.113729  0.323243      0\n",
       "2  0.113922  0.325955      0\n",
       "3  0.113904  0.325207      0\n",
       "4  0.117613  0.372992      0"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(new_dim, columns=['X', 'Y'])\n",
    "df['label'] = iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('Isomap', fontsize=14, fontweight='bold')\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plt.scatter(df[df.label == 0].X, df[df.label == 0].Y, color='red', label=iris.target_names[0])\n",
    "plt.scatter(df[df.label == 1].X, df[df.label == 1].Y, color='blue', label=iris.target_names[1])\n",
    "plt.scatter(df[df.label == 2].X, df[df.label == 2].Y, color='green', label=iris.target_names[2])\n",
    "\n",
    "_ = plt.legend(bbox_to_anchor=(1.25, 1))"
   ]
  }
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