Feature_Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning for Engineers: [FeatureEngineering](https://www.apmonitor.com/pds/index.php/Main/FeatureEngineering)\n",
    "- [Feature Engineering](https://www.apmonitor.com/pds/index.php/Main/FeatureEngineering)\n",
    " - Source Blocks: 9\n",
    " - Description: Feature engineering is the process of selecting and creating the input descriptors for machine learning.\n",
    "- [Course Overview](https://apmonitor.com/pds)\n",
    "- [Course Schedule](https://apmonitor.com/pds/index.php/Main/CourseSchedule)\n",
    "\n",
    "<img width=500px align=left src='https://apmonitor.com/pds/uploads/Main/feature_engineering.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-340d6ab342a4>:4: RuntimeWarning: divide by zero encountered in log\n",
      "  data['log_time'] = np.log(data['Time'].values)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>log_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.06</td>\n",
       "      <td>16.00</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.06</td>\n",
       "      <td>15.97</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.06</td>\n",
       "      <td>16.03</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>15.94</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time   Q1   Q2     T1     T2  log_time\n",
       "0   0.0  0.0  0.0  16.06  16.00      -inf\n",
       "1   1.0  0.0  0.0  16.06  15.97  0.000000\n",
       "2   2.0  0.0  0.0  16.06  16.03  0.693147\n",
       "3   3.0  0.0  0.0  16.03  16.00  1.098612\n",
       "4   4.0  0.0  0.0  16.03  15.94  1.386294"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = pd.read_csv('http://apmonitor.com/pds/uploads/Main/tclab_data6.txt')\n",
    "data['log_time'] = np.log(data['Time'].values)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.set_index('Time',inplace=True)\n",
    "data['log_time'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time', ylabel='Temperature (degC)'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['T1_mean']  = data['T1'].rolling(window=100,center=True).mean()\n",
    "data['T1_stdev'] = data['T1'].rolling(window=100,center=True).std()\n",
    "data['T1_skew']  = data['T1'].rolling(window=100,center=True).skew()\n",
    "data[['T1','T1_mean']].plot(figsize=(8,4),ylabel='Temperature (degC)')"
   ]
  },
  {
   "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",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Weight</th>\n",
       "      <th>Color</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.35</td>\n",
       "      <td>Gray</td>\n",
       "      <td>Cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.52</td>\n",
       "      <td>White</td>\n",
       "      <td>Dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.73</td>\n",
       "      <td>White</td>\n",
       "      <td>Dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.20</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.30</td>\n",
       "      <td>Gray</td>\n",
       "      <td>Cat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight   Color Type\n",
       "0    0.35    Gray  Cat\n",
       "1    0.52   White  Dog\n",
       "2    0.73   White  Dog\n",
       "3    1.20  Orange  Cat\n",
       "4    1.30    Gray  Cat"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = 'http://apmonitor.com/pds/uploads/Main/animals.txt'\n",
    "data = pd.read_csv(url)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'Cat': 0, 'Dog': 1}\n",
    "data['Label'] = data['Type'].replace(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ordinal number encoding\n",
    "data['CNumber'] = pd.factorize(data['Color'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Weight</th>\n",
       "      <th>Color</th>\n",
       "      <th>Type</th>\n",
       "      <th>Label</th>\n",
       "      <th>CNumber</th>\n",
       "      <th>Brown</th>\n",
       "      <th>Gray</th>\n",
       "      <th>Orange</th>\n",
       "      <th>Tan</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.35</td>\n",
       "      <td>Gray</td>\n",
       "      <td>Cat</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.52</td>\n",
       "      <td>White</td>\n",
       "      <td>Dog</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.73</td>\n",
       "      <td>White</td>\n",
       "      <td>Dog</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.20</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Cat</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.30</td>\n",
       "      <td>Gray</td>\n",
       "      <td>Cat</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight   Color Type  Label  CNumber  Brown  Gray  Orange  Tan  White\n",
       "0    0.35    Gray  Cat      0        0      0     1       0    0      0\n",
       "1    0.52   White  Dog      1        1      0     0       0    0      1\n",
       "2    0.73   White  Dog      1        1      0     0       0    0      1\n",
       "3    1.20  Orange  Cat      0        2      0     0       1    0      0\n",
       "4    1.30    Gray  Cat      0        0      0     1       0    0      0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.join(pd.get_dummies(data['Color']))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Weight   Color Type  Label  CNumber  Brown  Gray  Orange  Tan  White  \\\n",
      "0    0.35    Gray  Cat      0        0      0     1       0    0      0   \n",
      "1    0.52   White  Dog      1        1      0     0       0    0      1   \n",
      "2    0.73   White  Dog      1        1      0     0       0    0      1   \n",
      "3    1.20  Orange  Cat      0        2      0     0       1    0      0   \n",
      "4    1.30    Gray  Cat      0        0      0     1       0    0      0   \n",
      "\n",
      "   Color_0  Color_1  \n",
      "0      2.0      2.0  \n",
      "1     -1.0     -2.0  \n",
      "2     -1.0     -2.0  \n",
      "3      0.0      2.0  \n",
      "4      2.0      2.0  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_extraction import FeatureHasher\n",
    "fh = FeatureHasher(n_features=2, input_type='string')\n",
    "ht = fh.fit_transform(data['Color'].apply(lambda x: [x])).toarray()\n",
    "nc = pd.DataFrame(ht)\n",
    "nc.columns = ['Color_'+str(i) for i in range(fh.n_features)]\n",
    "data = data.join(nc)\n",
    "print(data.head())\n",
    "\n",
    "# create plot to show new features\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8,3))\n",
    "plt.rcParams['axes.facecolor'] = 'black'\n",
    "for i in range(len(data)):\n",
    "    plt.plot([0,data['Color_0'][i]],\\\n",
    "             [0,data['Color_1'][i]],\\\n",
    "             color=data['Color'][i],\\\n",
    "             marker='o',linestyle='-',lw=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "bestfeatures = SelectKBest(score_func=chi2, k='all')\n",
    "features = ['Brown','Gray','Orange','Tan','White','CNumber']\n",
    "X = data[features]\n",
    "y = data['Label']\n",
    "fit = bestfeatures.fit(X,y)\n",
    "dfscores = pd.DataFrame(fit.scores_)\n",
    "dfcolumns = pd.DataFrame(X.columns)\n",
    "scores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "scores.columns = ['Specs','Score']\n",
    "scores.index = features\n",
    "scores.plot(kind='bar',figsize=(8,2))\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}