DATA602 Comparison LDA PCA Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Linear Discriminant Analysis (LDA) is a dimensionality reduction technique. As the name implies dimensionality reduction \n",
    "techniques reduce the number of dimensions (i.e. variables) in a dataset while retaining as much information as possible. \n",
    "For instance, suppose that we plotted the relationship between two variables where each color represent a different class.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=4)\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = load_wine()\n",
    "X = pd.DataFrame(wine.data, columns=wine.feature_names)\n",
    "y = pd.Categorical.from_codes(wine.target, wine.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</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>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</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>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</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>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</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>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</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>735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  \n",
       "3                          3.45   1480.0  \n",
       "4                          2.93    735.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = X.join(pd.Series(y, name='class'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_0</th>\n",
       "      <th>class_1</th>\n",
       "      <th>class_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>13.744746</td>\n",
       "      <td>12.278732</td>\n",
       "      <td>13.153750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malic_acid</th>\n",
       "      <td>2.010678</td>\n",
       "      <td>1.932676</td>\n",
       "      <td>3.333750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ash</th>\n",
       "      <td>2.455593</td>\n",
       "      <td>2.244789</td>\n",
       "      <td>2.437083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <td>17.037288</td>\n",
       "      <td>20.238028</td>\n",
       "      <td>21.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnesium</th>\n",
       "      <td>106.338983</td>\n",
       "      <td>94.549296</td>\n",
       "      <td>99.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_phenols</th>\n",
       "      <td>2.840169</td>\n",
       "      <td>2.258873</td>\n",
       "      <td>1.678750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flavanoids</th>\n",
       "      <td>2.982373</td>\n",
       "      <td>2.080845</td>\n",
       "      <td>0.781458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.363662</td>\n",
       "      <td>0.447500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proanthocyanins</th>\n",
       "      <td>1.899322</td>\n",
       "      <td>1.630282</td>\n",
       "      <td>1.153542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color_intensity</th>\n",
       "      <td>5.528305</td>\n",
       "      <td>3.086620</td>\n",
       "      <td>7.396250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hue</th>\n",
       "      <td>1.062034</td>\n",
       "      <td>1.056282</td>\n",
       "      <td>0.682708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <td>3.157797</td>\n",
       "      <td>2.785352</td>\n",
       "      <td>1.683542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proline</th>\n",
       "      <td>1115.711864</td>\n",
       "      <td>519.507042</td>\n",
       "      <td>629.895833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  class_0     class_1     class_2\n",
       "alcohol                         13.744746   12.278732   13.153750\n",
       "malic_acid                       2.010678    1.932676    3.333750\n",
       "ash                              2.455593    2.244789    2.437083\n",
       "alcalinity_of_ash               17.037288   20.238028   21.416667\n",
       "magnesium                      106.338983   94.549296   99.312500\n",
       "total_phenols                    2.840169    2.258873    1.678750\n",
       "flavanoids                       2.982373    2.080845    0.781458\n",
       "nonflavanoid_phenols             0.290000    0.363662    0.447500\n",
       "proanthocyanins                  1.899322    1.630282    1.153542\n",
       "color_intensity                  5.528305    3.086620    7.396250\n",
       "hue                              1.062034    1.056282    0.682708\n",
       "od280/od315_of_diluted_wines     3.157797    2.785352    1.683542\n",
       "proline                       1115.711864  519.507042  629.895833"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_feature_means = pd.DataFrame(columns=wine.target_names)\n",
    "for c, rows in df.groupby('class'):\n",
    "    class_feature_means[c] = rows.mean()\n",
    "class_feature_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "within_class_scatter_matrix = np.zeros((13,13))\n",
    "for c, rows in df.groupby('class'):\n",
    "    rows = rows.drop(['class'], axis=1)\n",
    "    \n",
    "    s = np.zeros((13,13))\n",
    "for index, row in rows.iterrows():\n",
    "        x, mc = row.values.reshape(13,1), class_feature_means[c].values.reshape(13,1)\n",
    "        \n",
    "        s += (x - mc).dot((x - mc).T)\n",
    "    \n",
    "        within_class_scatter_matrix += s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_means = df.mean()\n",
    "between_class_scatter_matrix = np.zeros((13,13))\n",
    "for c in class_feature_means:    \n",
    "    n = len(df.loc[df['class'] == c].index)\n",
    "    \n",
    "    mc, m = class_feature_means[c].values.reshape(13,1), feature_means.values.reshape(13,1)\n",
    "    \n",
    "    between_class_scatter_matrix += n * (mc - m).dot((mc - m).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "eigen_values, eigen_vectors = np.linalg.eig(np.linalg.inv(within_class_scatter_matrix).dot(between_class_scatter_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.884546449232959\n",
      "2.903361061716059\n",
      "7.073384257475847e-16\n",
      "6.285476601801284e-16\n",
      "5.249529811277027e-16\n",
      "5.249529811277027e-16\n",
      "4.440892098500626e-16\n",
      "3.5302502135786647e-16\n",
      "1.9683436955150517e-16\n",
      "1.9683436955150517e-16\n",
      "1.584762720604532e-16\n",
      "1.2884190733441732e-16\n",
      "1.4959057737957277e-17\n"
     ]
    }
   ],
   "source": [
    "pairs = [(np.abs(eigen_values[i]), eigen_vectors[:,i]) for i in range(len(eigen_values))]\n",
    "pairs = sorted(pairs, key=lambda x: x[0], reverse=True)\n",
    "for pair in pairs:\n",
    "    print(pair[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance\n",
      "Eigenvector 0: 0.772960426932225\n",
      "Eigenvector 1: 0.22703957306777506\n",
      "Eigenvector 2: 5.531307019087845e-17\n",
      "Eigenvector 3: 4.915172084580416e-17\n",
      "Eigenvector 4: 4.105073333367774e-17\n",
      "Eigenvector 5: 4.105073333367774e-17\n",
      "Eigenvector 6: 3.472727727111203e-17\n",
      "Eigenvector 7: 2.7606160042650148e-17\n",
      "Eigenvector 8: 1.53922265533259e-17\n",
      "Eigenvector 9: 1.53922265533259e-17\n",
      "Eigenvector 10: 1.2392666425274478e-17\n",
      "Eigenvector 11: 1.0075292398236597e-17\n",
      "Eigenvector 12: 1.1697815084406358e-18\n"
     ]
    }
   ],
   "source": [
    "eigen_value_sums = sum(eigen_values)\n",
    "print('Explained Variance')\n",
    "for i, pair in enumerate(pairs):\n",
    "    print('Eigenvector {}: {}'.format(i, (pair[0]/eigen_value_sums).real))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_matrix = np.hstack((pairs[0][1].reshape(13,1), pairs[1][1].reshape(13,1))).real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_lda = np.array(X.dot(w_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "y = le.fit_transform(df['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231fc9fb588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('LD1')\n",
    "plt.ylabel('LD2')\n",
    "plt.scatter(\n",
    "    X_lda[:,0],\n",
    "    X_lda[:,1],\n",
    "    c=y,\n",
    "    cmap='rainbow',\n",
    "    alpha=0.7,\n",
    "    edgecolors='b'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rather than implementing the Linear Discriminant Analysis algorithm from scratch every time, we can use the predefined \n",
    "# LinearDiscriminantAnalysis class made available to us by the scikit-learn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "X_lda = lda.fit_transform(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.6875, 0.3125])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231fcc320f0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('LD1')\n",
    "plt.ylabel('LD2')\n",
    "plt.scatter(\n",
    "    X_lda[:,0],\n",
    "    X_lda[:,1],\n",
    "    c=y,\n",
    "    cmap='rainbow',\n",
    "    alpha=0.7,\n",
    "    edgecolors='b'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9981, 0.0017])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x231fcce16d8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.scatter(\n",
    "    X_pca[:,0],\n",
    "    X_pca[:,1],\n",
    "    c=y,\n",
    "    cmap='rainbow',\n",
    "    alpha=0.7,\n",
    "    edgecolors='b'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_lda, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18,  0,  0],\n",
       "       [ 0, 17,  0],\n",
       "       [ 0,  0, 10]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "y_pred = dt.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}