3.3_Demo_Feature_Encoding.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import category_encoders as ce\n",
    "from feature_engineering import encoding\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp     Fare\n",
       "0         0       3    male  22.0      1   7.2500\n",
       "1         1       1  female  38.0      1  71.2833\n",
       "2         1       3  female  26.0      0   7.9250\n",
       "3         1       1  female  35.0      1  53.1000\n",
       "4         0       3    male  35.0      0   8.0500"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_cols = [\n",
    "    'Pclass', 'Sex', 'Age', 'Fare', 'SibSp',\n",
    "    'Survived'\n",
    "]\n",
    "\n",
    "data = pd.read_csv('./data/titanic.csv', usecols=use_cols)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 6), (268, 6))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, data.Survived, test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-hot encoding\n",
    "replace the categorical variable by different boolean variables (0/1) to indicate whether or not certain label is true for that observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1 = pd.get_dummies(data,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   Survived  Pclass   Age  SibSp     Fare  Sex_male\n",
       "0         0       3  22.0      1   7.2500         1\n",
       "1         1       1  38.0      1  71.2833         0\n",
       "2         1       3  26.0      0   7.9250         0\n",
       "3         1       1  35.0      1  53.1000         0\n",
       "4         0       3  35.0      0   8.0500         1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinal-encoding\n",
    "replace the labels by some ordinal number if ordinal is meaningful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ord_enc = ce.OrdinalEncoder(cols=['Sex']).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Survived  Pclass  Sex   Age  SibSp     Fare\n",
      "0         0       3    1  22.0      1   7.2500\n",
      "1         1       1    2  38.0      1  71.2833\n",
      "2         1       3    2  26.0      0   7.9250\n",
      "3         1       1    2  35.0      1  53.1000\n",
      "4         0       3    1  35.0      0   8.0500\n"
     ]
    }
   ],
   "source": [
    "data4 = ord_enc.transform(data)\n",
    "print(data4.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean encoding\n",
    "replace the label by the mean of the target for that label. \n",
    "(the target must be 0/1 valued or continuous)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.753488\n",
       "male      0.196078\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross check-- the mean of target group by Sex\n",
    "X_train['Survived'].groupby(data['Sex']).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_enc = encoding.MeanEncoding(cols=['Sex']).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Survived  Pclass       Sex   Age  SibSp     Fare\n",
      "0         0       3  0.196078  22.0      1   7.2500\n",
      "1         1       1  0.753488  38.0      1  71.2833\n",
      "2         1       3  0.753488  26.0      0   7.9250\n",
      "3         1       1  0.753488  35.0      1  53.1000\n",
      "4         0       3  0.196078  35.0      0   8.0500\n"
     ]
    }
   ],
   "source": [
    "data6 = mean_enc.transform(data)\n",
    "print(data6.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target-encoding\n",
    "Similar to mean encoding, but use both posterior probability and prior probability of the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the encoder and fit with our data\n",
    "target_enc = ce.TargetEncoder(cols=['Sex']).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# perform transformation\n",
    "# data.Survived.groupby(data['Sex']).agg(['mean'])\n",
    "data2 = target_enc.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>0</th>\n",
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       "      <td>7.2500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.753488</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.753488</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.753488</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>35.0</td>\n",
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      "text/plain": [
       "   Survived  Pclass       Sex   Age  SibSp     Fare\n",
       "0         0       3  0.196078  22.0      1   7.2500\n",
       "1         1       1  0.753488  38.0      1  71.2833\n",
       "2         1       3  0.753488  26.0      0   7.9250\n",
       "3         1       1  0.753488  35.0      1  53.1000\n",
       "4         0       3  0.196078  35.0      0   8.0500"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the result\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WOE-encoding\n",
    "replace the label  with Weight of Evidence of each label. WOE is computed from the basic odds ratio: \n",
    "\n",
    "ln( (Proportion of Good Outcomes) / (Proportion of Bad Outcomes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "woe_enc = ce.WOEEncoder(cols=['Sex']).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data3 = woe_enc.transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.555633</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.555633</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.555633</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.950742</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass       Sex   Age  SibSp     Fare\n",
       "0         0       3 -0.950742  22.0      1   7.2500\n",
       "1         1       1  1.555633  38.0      1  71.2833\n",
       "2         1       3  1.555633  26.0      0   7.9250\n",
       "3         1       1  1.555633  35.0      1  53.1000\n",
       "4         0       3 -0.950742  35.0      0   8.0500"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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