notebooks/unsupervised/dimensionality_reduction/mds/dimensionality_reduction_mds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality Reduction with MDS"
   ]
  },
  {
   "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "mds = manifold.MDS(n_components=2)\n",
    "new_dim = mds.fit_transform(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>label</th>\n",
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      "text/plain": [
       "          X         Y  label\n",
       "0  1.736746  2.081557      0\n",
       "1  2.022486  1.832834      0\n",
       "2  2.083811  2.040216      0\n",
       "3  2.107119  1.806914      0\n",
       "4  1.749478  2.141774      0"
      ]
     },
     "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": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
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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('MDS', 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))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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