Sklearn Simple Dimensionality Reduction Guide.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of dimensions in X is 4\n"
     ]
    }
   ],
   "source": [
    "# Load the data and scale it\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X = StandardScaler().fit_transform(X)\n",
    "print(f'The number of dimensions in X is {X.shape[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of reduced dimensions is 2\n"
     ]
    }
   ],
   "source": [
    "# Load PCA and specify the number of dimensions aka components\n",
    "pca = PCA(n_components=2)\n",
    "pc = pca.fit_transform(X)\n",
    "print(f'The number of reduced dimensions is {pc.shape[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put the data into a pandas data frame\n",
    "df = pd.DataFrame(data = pc, columns = ['pc_1', 'pc_2'])\n",
    "df['target'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc_1</th>\n",
       "      <th>pc_2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264703</td>\n",
       "      <td>0.480027</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.080961</td>\n",
       "      <td>-0.674134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.364229</td>\n",
       "      <td>-0.341908</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.299384</td>\n",
       "      <td>-0.597395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.389842</td>\n",
       "      <td>0.646835</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pc_1      pc_2  target\n",
       "0 -2.264703  0.480027       0\n",
       "1 -2.080961 -0.674134       0\n",
       "2 -2.364229 -0.341908       0\n",
       "3 -2.299384 -0.597395       0\n",
       "4 -2.389842  0.646835       0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc_1</th>\n",
       "      <th>pc_2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264703</td>\n",
       "      <td>0.480027</td>\n",
       "      <td>iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.080961</td>\n",
       "      <td>-0.674134</td>\n",
       "      <td>iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.364229</td>\n",
       "      <td>-0.341908</td>\n",
       "      <td>iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.299384</td>\n",
       "      <td>-0.597395</td>\n",
       "      <td>iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.389842</td>\n",
       "      <td>0.646835</td>\n",
       "      <td>iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pc_1      pc_2       target\n",
       "0 -2.264703  0.480027  iris-setosa\n",
       "1 -2.080961 -0.674134  iris-setosa\n",
       "2 -2.364229 -0.341908  iris-setosa\n",
       "3 -2.299384 -0.597395  iris-setosa\n",
       "4 -2.389842  0.646835  iris-setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recode the numerical data to categorical\n",
    "def recoding(data):\n",
    "    if data == 0:\n",
    "        return 'iris-setosa'\n",
    "    elif data == 1:\n",
    "        return 'iris-versicolor'\n",
    "    else:\n",
    "        return 'iris-virginica'\n",
    "    \n",
    "df['target'] = df['target'].apply(recoding)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "fig = plt.figure(figsize = (12,10))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 17)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 17)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = ['iris-setosa', 'iris-versicolor', 'iris-virginica']\n",
    "colors = ['r', 'g', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = df['target'] == target\n",
    "    ax.scatter(df.loc[indicesToKeep, 'pc_1'],\n",
    "               df.loc[indicesToKeep, 'pc_2'],\n",
    "               c = color,\n",
    "               s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}