PCA Lesson.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "## https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get access to Python\n",
    "\n",
    "# https://jupyter.org/try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get access to Python\n",
    "\n",
    "# https://repl.it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading dataset into Pandas DataFrame\n",
    "df = pd.read_csv(url, \n",
    "                 names=['sepal length','sepal width','petal length','petal width','target'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width       target\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['sepal length', 'sepal width', 'petal length', 'petal width']\n",
    "x = df.loc[:, features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.loc[:,['target']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.032057</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.337848</td>\n",
       "      <td>-1.398138</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.106445</td>\n",
       "      <td>-1.284407</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.263460</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width\n",
       "0     -0.900681     1.032057     -1.341272    -1.312977\n",
       "1     -1.143017    -0.124958     -1.341272    -1.312977\n",
       "2     -1.385353     0.337848     -1.398138    -1.312977\n",
       "3     -1.506521     0.106445     -1.284407    -1.312977\n",
       "4     -1.021849     1.263460     -1.341272    -1.312977"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data = x, columns = features).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "principalComponents = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.52237162, -0.26335492,  0.58125401,  0.56561105],\n",
       "       [ 0.37231836,  0.92555649,  0.02109478,  0.06541577]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  From the website\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html\n",
    "\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalDf = pd.concat([principalDf, df[['target']]], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-2.264542</td>\n",
       "      <td>0.505704</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-2.086426</td>\n",
       "      <td>-0.655405</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-2.367950</td>\n",
       "      <td>-0.318477</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-2.304197</td>\n",
       "      <td>-0.575368</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-2.388777</td>\n",
       "      <td>0.674767</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2       target\n",
       "0              -2.264542               0.505704  Iris-setosa\n",
       "1              -2.086426              -0.655405  Iris-setosa\n",
       "2              -2.367950              -0.318477  Iris-setosa\n",
       "3              -2.304197              -0.575368  Iris-setosa\n",
       "4              -2.388777               0.674767  Iris-setosa"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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 = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n",
    "colors = ['r', 'g', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf['target'] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
    "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.72770452, 0.23030523])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}