ch04 - Features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from io import StringIO\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.base import clone\n",
    "from itertools import combinations\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      A     B     C    D\n",
      "0   1.0   2.0   3.0  4.0\n",
      "1   5.0   6.0   NaN  8.0\n",
      "2  10.0  11.0  12.0  NaN\n"
     ]
    }
   ],
   "source": [
    "csv_data = '''A,B,C,D\n",
    "1.0,2.0,3.0,4.0\n",
    "5.0,6.0,,8.0\n",
    "10.0,11.0,12.0,'''\n",
    "\n",
    "df = pd.read_csv(StringIO(csv_data))\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    0\n",
      "B    0\n",
      "C    1\n",
      "D    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A    B    C    D\n",
      "0  1.0  2.0  3.0  4.0\n"
     ]
    }
   ],
   "source": [
    "print(df.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      A     B\n",
      "0   1.0   2.0\n",
      "1   5.0   6.0\n",
      "2  10.0  11.0\n"
     ]
    }
   ],
   "source": [
    "print(df.dropna(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      A     B     C    D\n",
      "0   1.0   2.0   3.0  4.0\n",
      "1   5.0   6.0   NaN  8.0\n",
      "2  10.0  11.0  12.0  NaN\n"
     ]
    }
   ],
   "source": [
    "# only drop rows where all columns are NaN\n",
    "print(df.dropna(how='all'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A    B    C    D\n",
      "0  1.0  2.0  3.0  4.0\n"
     ]
    }
   ],
   "source": [
    "# drop rows that have not at least 4 non-NaN values\n",
    "print(df.dropna(thresh=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      A     B     C    D\n",
      "0   1.0   2.0   3.0  4.0\n",
      "2  10.0  11.0  12.0  NaN\n"
     ]
    }
   ],
   "source": [
    "# only drop rows where NaN appear in specific columns (here: 'C')\n",
    "print(df.dropna(subset=['C']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.    2.    3.    4. ]\n",
      " [  5.    6.    7.5   8. ]\n",
      " [ 10.   11.   12.    6. ]]\n"
     ]
    }
   ],
   "source": [
    "## Imputing missing values\n",
    "imr = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "imr = imr.fit(df)\n",
    "imputed_data = imr.transform(df.values)\n",
    "print(imputed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   color size  price classlabel\n",
      "0  green    M   10.1     class1\n",
      "1    red    L   13.5     class2\n",
      "2   blue   XL   15.3     class1\n"
     ]
    }
   ],
   "source": [
    "# Handling categorical data\n",
    "df = pd.DataFrame([['green', 'M', 10.1, 'class1'],\n",
    "                   ['red', 'L', 13.5, 'class2'],\n",
    "                   ['blue', 'XL', 15.3, 'class1']])\n",
    "\n",
    "df.columns = ['color', 'size', 'price', 'classlabel']\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   color  size  price classlabel\n",
      "0  green     1   10.1     class1\n",
      "1    red     2   13.5     class2\n",
      "2   blue     3   15.3     class1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     M\n",
       "1     L\n",
       "2    XL\n",
       "Name: size, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Mapping ordinal features\n",
    "size_mapping = {'XL': 3,\n",
    "                'L': 2,\n",
    "                'M': 1}\n",
    "\n",
    "df['size'] = df['size'].map(size_mapping)\n",
    "print(df)\n",
    "\n",
    "inv_size_mapping = {v: k for k, v in size_mapping.items()}\n",
    "df['size'].map(inv_size_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'class2': 1, 'class1': 0}\n"
     ]
    }
   ],
   "source": [
    "## Encoding class labels\n",
    "class_mapping = {label: idx for idx, label in enumerate(np.unique(df['classlabel']))}\n",
    "print(class_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   color  size  price  classlabel\n",
      "0  green     1   10.1           0\n",
      "1    red     2   13.5           1\n",
      "2   blue     3   15.3           0\n"
     ]
    }
   ],
   "source": [
    "df['classlabel'] = df['classlabel'].map(class_mapping)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   color  size  price classlabel\n",
      "0  green     1   10.1     class1\n",
      "1    red     2   13.5     class2\n",
      "2   blue     3   15.3     class1\n"
     ]
    }
   ],
   "source": [
    "inv_class_mapping = {v: k for k, v in class_mapping.items()}\n",
    "df['classlabel'] = df['classlabel'].map(inv_class_mapping)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0]\n"
     ]
    }
   ],
   "source": [
    "class_le = LabelEncoder()\n",
    "y = class_le.fit_transform(df['classlabel'].values)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['class1' 'class2' 'class1']\n"
     ]
    }
   ],
   "source": [
    "print(class_le.inverse_transform(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 10.1]\n",
      " [2 2 13.5]\n",
      " [0 3 15.3]]\n"
     ]
    }
   ],
   "source": [
    "## Performing one-hot encoding on nominal features\n",
    "X = df[['color', 'size', 'price']].values\n",
    "color_le = LabelEncoder()\n",
    "X[:, 0] = color_le.fit_transform(X[:, 0])\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0. ,   1. ,   0. ,   1. ,  10.1],\n",
       "       [  0. ,   0. ,   1. ,   2. ,  13.5],\n",
       "       [  1. ,   0. ,   0. ,   3. ,  15.3]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder(categorical_features=[0])\n",
    "ohe.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>price</th>\n",
       "      <th>size</th>\n",
       "      <th>color_blue</th>\n",
       "      <th>color_green</th>\n",
       "      <th>color_red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  size  color_blue  color_green  color_red\n",
       "0   10.1     1         0.0          1.0        0.0\n",
       "1   13.5     2         0.0          0.0        1.0\n",
       "2   15.3     3         1.0          0.0        0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df[['price', 'color', 'size']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class labels [1 2 3]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class label</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>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": [
       "   Class label  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0            1    14.23        1.71  2.43               15.6        127   \n",
       "1            1    13.20        1.78  2.14               11.2        100   \n",
       "2            1    13.16        2.36  2.67               18.6        101   \n",
       "3            1    14.37        1.95  2.50               16.8        113   \n",
       "4            1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partitioning a dataset in training and test sets\n",
    "df_wine = pd.read_csv('https://archive.ics.uci.edu/'\n",
    "                      'ml/machine-learning-databases/wine/wine.data',\n",
    "                      header=None)\n",
    "\n",
    "df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',\n",
    "                   'Alcalinity of ash', 'Magnesium', 'Total phenols',\n",
    "                   'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',\n",
    "                   'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',\n",
    "                   'Proline']\n",
    "\n",
    "print('Class labels', np.unique(df_wine['Class label']))\n",
    "df_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.72849462  0.16386555  0.47849462  0.29896907  0.52173913  0.53956835\n",
      "  0.74311927  0.13461538  0.37974684  0.4364852   0.32478632  0.70695971\n",
      "  0.60566802]\n"
     ]
    }
   ],
   "source": [
    "# Bringing features onto the same scale\n",
    "mms = MinMaxScaler()\n",
    "X_train_norm = mms.fit_transform(X_train)\n",
    "X_test_norm = mms.transform(X_test)\n",
    "print(X_test_norm[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.94841977 -0.63042822 -0.4110476  -0.87677804  1.22679643  0.57147571\n",
      "  0.95620726 -1.24682729  0.01258759  0.36299992 -0.15283642  0.83141032\n",
      "  1.01828164]\n"
     ]
    }
   ],
   "source": [
    "stdsc = StandardScaler()\n",
    "X_train_std = stdsc.fit_transform(X_train)\n",
    "X_test_std = stdsc.transform(X_test)\n",
    "print(X_test_std[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   input  standardized  normalized\n",
      "0      0      -1.46385         0.0\n",
      "1      1      -0.87831         0.2\n",
      "2      2      -0.29277         0.4\n",
      "3      3       0.29277         0.6\n",
      "4      4       0.87831         0.8\n",
      "5      5       1.46385         1.0\n"
     ]
    }
   ],
   "source": [
    "ex = pd.DataFrame([0, 1, 2, 3, 4, 5])\n",
    "# standardize\n",
    "ex[1] = (ex[0] - ex[0].mean()) / ex[0].std(ddof=0)\n",
    "\n",
    "# Please note that pandas uses ddof=1 (sample standard deviation)\n",
    "# by default, whereas NumPy's std method and the StandardScaler\n",
    "# uses ddof=0 (population standard deviation)\n",
    "\n",
    "# normalize\n",
    "ex[2] = (ex[0] - ex[0].min()) / (ex[0].max() - ex[0].min())\n",
    "ex.columns = ['input', 'standardized', 'normalized']\n",
    "print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.983870967742\n",
      "Test accuracy: 0.981481481481\n",
      "lr.intercept: [-0.38379899 -0.15807827 -0.70044603]\n",
      "lr.coef: [[ 0.27992659  0.          0.         -0.02796699  0.          0.\n",
      "   0.70990449  0.          0.          0.          0.          0.\n",
      "   1.23691923]\n",
      " [-0.64394169 -0.06877714 -0.05720455  0.          0.          0.          0.\n",
      "   0.          0.         -0.92685282  0.06015438  0.         -0.3710419 ]\n",
      " [ 0.          0.06158088  0.          0.          0.          0.\n",
      "  -0.63529936  0.          0.          0.49774492 -0.35847421 -0.57174624\n",
      "   0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Selecting meaningful features\n",
    "## Sparse solutions with L1-regularization\n",
    "lr = LogisticRegression(penalty='l1', C=0.1)\n",
    "lr.fit(X_train_std, y_train)\n",
    "print('Training accuracy:', lr.score(X_train_std, y_train))\n",
    "print('Test accuracy:', lr.score(X_test_std, y_test))\n",
    "print('lr.intercept:', lr.intercept_)\n",
    "print('lr.coef:', lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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J6KHsKlrPOzP35QBWW/PUxuZw1lIcxGCAIH+IjdRDWqC/PqLWCaUU+/bt48sv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uw5l8SljBuY0hgQDvdsgbRTsPFuSD5SfkEKcYtRSvH666+TlZXF9OnTi62fnZ3NlClTqFevHg4O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\nhLSzlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa743c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "colors = ['blue', 'green', 'red', 'cyan',\n",
    "          'magenta', 'yellow', 'black',\n",
    "          'pink', 'lightgreen', 'lightblue',\n",
    "          'gray', 'indigo', 'orange']\n",
    "\n",
    "weights, params = [], []\n",
    "for c in np.arange(-4., 6.):\n",
    "    lr = LogisticRegression(penalty='l1', C=10. ** c, random_state=0)\n",
    "    lr.fit(X_train_std, y_train)\n",
    "    weights.append(lr.coef_[1])\n",
    "    params.append(10. ** c)\n",
    "\n",
    "weights = np.array(weights)\n",
    "\n",
    "for column, color in zip(range(weights.shape[1]), colors):\n",
    "    plt.plot(params, weights[:, column],\n",
    "             label=df_wine.columns[column + 1],\n",
    "             color=color)\n",
    "plt.axhline(0, color='black', linestyle='--', linewidth=3)\n",
    "plt.xlim([10 ** (-5), 10 ** 5])\n",
    "plt.ylabel('weight coefficient')\n",
    "plt.xlabel('C')\n",
    "plt.xscale('log')\n",
    "plt.legend(loc='upper left')\n",
    "ax.legend(loc='upper center',\n",
    "          bbox_to_anchor=(1.38, 1.03),\n",
    "          ncol=1, fancybox=True)\n",
    "# plt.savefig('./figures/l1_path.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zzcU1DqCzxfH+KWbKJ0z5hMWZT8Ptq37wg2j85+c+F92+KvfzsFevk+nb93jG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\nmZmZWSEHJzMzM7NC/wMKx5VB/3lYgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb587ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class SBS():\n",
    "    def __init__(self, estimator, k_features, scoring=accuracy_score,\n",
    "                 test_size=0.25, random_state=1):\n",
    "        self.scoring = scoring\n",
    "        self.estimator = clone(estimator)\n",
    "        self.k_features = k_features\n",
    "        self.test_size = test_size\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def fit(self, X, y):\n",
    "\n",
    "        X_train, X_test, y_train, y_test = \\\n",
    "            train_test_split(X, y, test_size=self.test_size,\n",
    "                             random_state=self.random_state)\n",
    "\n",
    "        dim = X_train.shape[1]\n",
    "        self.indices_ = tuple(range(dim))\n",
    "        self.subsets_ = [self.indices_]\n",
    "        score = self._calc_score(X_train, y_train,\n",
    "                                 X_test, y_test, self.indices_)\n",
    "        self.scores_ = [score]\n",
    "\n",
    "        while dim > self.k_features:\n",
    "            scores = []\n",
    "            subsets = []\n",
    "\n",
    "            for p in combinations(self.indices_, r=dim - 1):\n",
    "                score = self._calc_score(X_train, y_train,\n",
    "                                         X_test, y_test, p)\n",
    "                scores.append(score)\n",
    "                subsets.append(p)\n",
    "\n",
    "            best = np.argmax(scores)\n",
    "            self.indices_ = subsets[best]\n",
    "            self.subsets_.append(self.indices_)\n",
    "            dim -= 1\n",
    "\n",
    "            self.scores_.append(scores[best])\n",
    "        self.k_score_ = self.scores_[-1]\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        return X[:, self.indices_]\n",
    "\n",
    "    def _calc_score(self, X_train, y_train, X_test, y_test, indices):\n",
    "        self.estimator.fit(X_train[:, indices], y_train)\n",
    "        y_pred = self.estimator.predict(X_test[:, indices])\n",
    "        score = self.scoring(y_test, y_pred)\n",
    "        return score\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    "\n",
    "# selecting features\n",
    "sbs = SBS(knn, k_features=1)\n",
    "sbs.fit(X_train_std, y_train)\n",
    "\n",
    "# plotting performance of feature subsets\n",
    "k_feat = [len(k) for k in sbs.subsets_]\n",
    "\n",
    "plt.plot(k_feat, sbs.scores_, marker='o')\n",
    "plt.ylim([0.7, 1.1])\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Number of features')\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./sbs.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Alcohol', 'Malic acid', 'Alcalinity of ash', 'Hue', 'Proline'], dtype='object')\n",
      "Training accuracy: 0.983870967742\n",
      "Test accuracy: 0.944444444444\n",
      "Training accuracy: 0.959677419355\n",
      "Test accuracy: 0.962962962963\n"
     ]
    }
   ],
   "source": [
    "k5 = list(sbs.subsets_[8])\n",
    "print(df_wine.columns[1:][k5])\n",
    "\n",
    "knn.fit(X_train_std, y_train)\n",
    "print('Training accuracy:', knn.score(X_train_std, y_train))\n",
    "print('Test accuracy:', knn.score(X_test_std, y_test))\n",
    "\n",
    "knn.fit(X_train_std[:, k5], y_train)\n",
    "print('Training accuracy:', knn.score(X_train_std[:, k5], y_train))\n",
    "print('Test accuracy:', knn.score(X_test_std[:, k5], y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1) Color intensity                0.182483\n",
      " 2) Proline                        0.158610\n",
      " 3) Flavanoids                     0.150948\n",
      " 4) OD280/OD315 of diluted wines   0.131987\n",
      " 5) Alcohol                        0.106589\n",
      " 6) Hue                            0.078243\n",
      " 7) Total phenols                  0.060718\n",
      " 8) Alcalinity of ash              0.032033\n",
      " 9) Malic acid                     0.025400\n",
      "10) Proanthocyanins                0.022351\n",
      "11) Magnesium                      0.022078\n",
      "12) Nonflavanoid phenols           0.014645\n",
      "13) Ash                            0.013916\n"
     ]
    },
    {
     "data": {
      "image/png": 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aIuJ5SdcBmwIXAEhS/vmYds7VG0mLAesAF/Z17NSpU5k4ceJgPbQNgBPUzcys6poNrMyc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\nUUjRFBERETEK/w+9Qng7JcWUYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd920400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1) Color intensity                0.182483\n",
      " 2) Proline                        0.158610\n",
      " 3) Flavanoids                     0.150948\n"
     ]
    }
   ],
   "source": [
    "# Assessing Feature Importances with Random Forests\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "feat_labels = df_wine.columns[1:]\n",
    "\n",
    "forest = RandomForestClassifier(n_estimators=10000,\n",
    "                                random_state=0,\n",
    "                                n_jobs=-1)\n",
    "\n",
    "forest.fit(X_train, y_train)\n",
    "importances = forest.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "for f in range(X_train.shape[1]):\n",
    "    print(\"%2d) %-*s %f\" % (f + 1, 30,\n",
    "                            feat_labels[indices[f]],\n",
    "                            importances[indices[f]]))\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.bar(range(X_train.shape[1]),\n",
    "        importances[indices],\n",
    "        color='lightblue',\n",
    "        align='center')\n",
    "\n",
    "plt.xticks(range(X_train.shape[1]),\n",
    "           feat_labels[indices], rotation=90)\n",
    "plt.xlim([-1, X_train.shape[1]])\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./random_forest.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "sfm = SelectFromModel(forest, threshold=0.15, prefit=True)\n",
    "X_selected = sfm.transform(X_train)\n",
    "\n",
    "X_selected.shape\n",
    "\n",
    "\n",
    "for f in range(X_selected.shape[1]):\n",
    "    print(\"%2d) %-*s %f\" % (f + 1, 30,\n",
    "                            feat_labels[indices[f]],\n",
    "                            importances[indices[f]]))"
   ]
  }
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