scikit_machine_learning_Data_Compression_PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# http://www.bogotobogo.com/python/scikit-learn/scikit_machine_learning_Data_Compresssion_via_Dimensionality_Reduction_1_Principal_component_analysis _PCA.php"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_wine = pd.read_csv(\n",
    "    'https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', \n",
    "    header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0      1     2     3     4    5     6     7     8     9     10    11    12  \\\n",
       "0   1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29  5.64  1.04  3.92   \n",
       "1   1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
       "2   1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
       "3   1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
       "4   1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
       "\n",
       "     13  \n",
       "0  1065  \n",
       "1  1050  \n",
       "2  1185  \n",
       "3  1480  \n",
       "4   735  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "     train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "sc = StandardScaler()\n",
    "X_train_std = sc.fit_transform(X_train)\n",
    "X_test_std = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "covariant_matrix = np.cov(X_train_std.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00813008,  0.08797701,  0.23066952, -0.32868099,  0.2141631 ,\n",
       "         0.35576761,  0.2991246 , -0.16913744,  0.09649074,  0.56962271,\n",
       "        -0.04781543,  0.07403492,  0.63277882],\n",
       "       [ 0.35576761, -0.30124242,  0.12235533, -0.37018442,  0.16513295,\n",
       "         1.00813008,  0.88119961, -0.45396901,  0.6196806 , -0.06935051,\n",
       "         0.45718802,  0.72214462,  0.56326772],\n",
       "       [-0.04781543, -0.54992807, -0.10928021, -0.25313262,  0.05792599,\n",
       "         0.45718802,  0.58331869, -0.3178224 ,  0.32282167, -0.52395358,\n",
       "         1.00813008,  0.60022569,  0.2452794 ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covariant_matrix[0::5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "eigen_values, eigen_vectors = np.linalg.eig(covariant_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4.8923083 ,  2.46635032,  1.42809973,  1.01233462,  0.84906459,\n",
       "         0.60181514,  0.52251546,  0.08414846,  0.33051429,  0.29595018,\n",
       "         0.16831254,  0.21432212,  0.2399553 ]),\n",
       " array([[  1.46698114e-01,   5.04170789e-01,  -1.17235150e-01,\n",
       "           2.06254611e-01,  -1.87815947e-01,  -1.48851318e-01,\n",
       "          -1.79263662e-01,  -5.54687162e-02,  -4.03054922e-01,\n",
       "          -4.17197583e-01,   2.75660860e-01,   4.03567189e-01,\n",
       "           4.13320786e-04],\n",
       "        [  3.89344551e-01,   9.36399132e-02,   1.80804417e-01,\n",
       "           1.93179478e-01,   1.40645426e-01,   1.22248798e-02,\n",
       "           5.31455344e-02,  -4.21265116e-01,   1.35111456e-01,\n",
       "          -2.80985650e-01,   2.83897644e-01,  -6.18600153e-01,\n",
       "           9.45645138e-02],\n",
       "        [  3.00325353e-01,  -2.79243218e-01,   9.32387182e-02,\n",
       "           2.41740256e-02,  -3.72610811e-01,   2.16515349e-01,\n",
       "          -3.84654748e-01,  -1.05383688e-01,  -5.17259438e-01,\n",
       "           1.97814118e-01,  -1.98844532e-01,  -2.00456386e-01,\n",
       "          -3.02254353e-01]]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigen_values, eigen_vectors[::5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HKVOmRHVVzvLaq+D4Z5xxBj/++CMPPvhg4et5eXncf//9Icdz5JFH0qVLF+bP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\nMzOzyvw/FwWyyJ+OzTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f711aa20190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tot = sum(eigen_values)\n",
    "var_exp = [(i / tot) for i in sorted(eigen_values, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "\n",
    "bar(range(1,14), var_exp, alpha=0.5, align='center',\n",
    "                  label='individual explained variance')\n",
    "step(range(1,14), cum_var_exp, where='mid',\n",
    "                  label='cumulative explained variance')\n",
    "ylabel('Explained variance ratio')\n",
    "xlabel('Principal components')\n",
    "legend(loc='best')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "eigen_pairs = \\\n",
    "[(np.abs(eigen_values[i]),eigen_vectors[:,i]) for i in range(len(eigen_values))]\n",
    "eigen_pairs.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(4.8923083032737509,\n",
       "  array([ 0.14669811, -0.24224554, -0.02993442, -0.25519002,  0.12079772,\n",
       "          0.38934455,  0.42326486, -0.30634956,  0.30572219, -0.09869191,\n",
       "          0.30032535,  0.36821154,  0.29259713])),\n",
       " (2.4663503157592306,\n",
       "  array([ 0.50417079,  0.24216889,  0.28698484, -0.06468718,  0.22995385,\n",
       "          0.09363991,  0.01088622,  0.01870216,  0.03040352,  0.54527081,\n",
       "         -0.27924322, -0.174365  ,  0.36315461])),\n",
       " (1.4280997275048455,\n",
       "  array([-0.11723515,  0.14994658,  0.65639439,  0.58428234,  0.08226275,\n",
       "          0.18080442,  0.14295933,  0.17223475,  0.1583621 , -0.14242171,\n",
       "          0.09323872,  0.19607741, -0.09731711])),\n",
       " (1.0123346209044966,\n",
       "  array([ 0.20625461,  0.1304893 ,  0.01515363, -0.09042209, -0.83912835,\n",
       "          0.19317948,  0.14045955,  0.33733262, -0.1147529 ,  0.07878571,\n",
       "          0.02417403,  0.18402864,  0.05676778])),\n",
       " (0.8490645933450266,\n",
       "  array([-0.18781595,  0.56863978, -0.29920943, -0.04124995, -0.02719713,\n",
       "          0.14064543,  0.09268665, -0.08584168,  0.56510524,  0.01323461,\n",
       "         -0.37261081,  0.08937967, -0.21752948]))]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigen_pairs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w= np.hstack((eigen_pairs[0][1][:, np.newaxis], eigen_pairs[1][1][:, np.newaxis]))\n",
    "w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.14669811,  0.50417079],\n",
       "       [-0.24224554,  0.24216889],\n",
       "       [-0.02993442,  0.28698484],\n",
       "       [-0.25519002, -0.06468718],\n",
       "       [ 0.12079772,  0.22995385],\n",
       "       [ 0.38934455,  0.09363991],\n",
       "       [ 0.42326486,  0.01088622],\n",
       "       [-0.30634956,  0.01870216],\n",
       "       [ 0.30572219,  0.03040352],\n",
       "       [-0.09869191,  0.54527081],\n",
       "       [ 0.30032535, -0.27924322],\n",
       "       [ 0.36821154, -0.174365  ],\n",
       "       [ 0.29259713,  0.36315461]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.91083058, -0.46259897, -0.01142613, -0.82067872,  0.06241693,\n",
       "        0.58820446,  0.93565436, -0.7619138 ,  0.13007174, -0.51238741,\n",
       "        0.65706596,  1.94354495,  0.93700997])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_std[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.59891628,  0.00484089])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_std[0].dot(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((124, 13), (13, 2), (124, 2))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca = X_train_std.dot(w)\n",
    "X_train_std.shape, w.shape, X_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0Py/3exlaofe20PGMzHs9cmRl73lQ5xEJWkdHh8PrTGh0Pq/VfuZgGHCLme3O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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f711a8d1490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'b', 'g']\n",
    "markers = ['s', 'x', 'o']\n",
    "for l, c, m in zip(np.unique(y_train), colors, markers):\n",
    "    scatter(X_train_pca[y_train==l, 0], X_train_pca[y_train==l, 1],\n",
    "            c=c, label=l, marker=m)\n",
    "xlabel('PC 1')\n",
    "ylabel('PC 2')\n",
    "legend(loc='lower left')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "    \n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "    np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    xlim(xx1.min(), xx1.max())\n",
    "    ylim(xx2.min(), xx2.max())\n",
    "\n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8, c=cmap(idx),\n",
    "                marker=markers[idx], label=cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hXlKXaKAwwOPGmPDTRDsAfzDGVIUuWGu/mY7BtToIYy4GfgtcDSwGbgDeMMYc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\n5rVgcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f711861c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "lr = LogisticRegression()\n",
    "X_train_pca = pca.fit_transform(X_train_std)\n",
    "X_test_pca = pca.transform(X_test_std)\n",
    "lr.fit(X_train_pca, y_train)\n",
    "plot_decision_regions(X_train_pca, y_train, classifier=lr)\n",
    "xlabel('PC1')\n",
    "ylabel('PC2')\n",
    "legend(loc='lower left')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}