04.10-Arbitrary-Value-Imputation-Sklearn.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arbitrary value imputation with Scikit-learn ==> SimpleImputer\n",
    "\n",
    "Scikit-learn provides a class to make most of the most common data imputation techniques.\n",
    "\n",
    "The **SimpleImputer** class provides basic strategies for imputing missing values, including:\n",
    "\n",
    "- Mean and median imputation for numerical variables\n",
    "- Most frequent category imputation for categorical variables\n",
    "- Arbitrary value imputation for both categorical and numerical variables\n",
    "\n",
    "### Advantages\n",
    "\n",
    "- Simple to use if applied to the entire dataframe\n",
    "- Maintained by the scikit-learn developers: good quality code\n",
    "- Fast computation (it uses numpy for calculations)\n",
    "- Allows for grid search over the various imputation techniques\n",
    "- Allows for different missing values encodings (you can indicate if the missing values are np.nan, or zeroes, etc)\n",
    "\n",
    "### Limitations\n",
    "\n",
    "- Returns a numpy array instead of a pandas dataframe, inconvenient for data analysis\n",
    "- Needs to use additional classes to select which features to impute  ==>\n",
    "    - requires more lines of code\n",
    "    - additional classes still in beta (may change without warning)\n",
    "    - not so straightforward to use anymore.\n",
    "\n",
    "### More details about the transformers\n",
    "\n",
    "- [SimpleImputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer)\n",
    "- [ColumnTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html)\n",
    "- [Stackoverflow](https://stackoverflow.com/questions/54160370/how-to-use-sklearn-column-transformer)\n",
    "\n",
    "\n",
    "## In this demo:\n",
    "\n",
    "We will do **arbitrary value imputation with Scikit-learn** using the Ames House Price Dataset.\n",
    "\n",
    "- To download the dataset please refer to the lecture **Datasets** in **Section 1** of this course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# these are the objects we need to impute missing data\n",
    "# with sklearn\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# to split the datasets\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we use only the following variables for the demo:\n",
    "# 3 of which contain NA\n",
    "\n",
    "cols_to_use = [\n",
    "    'OverallQual', 'TotalBsmtSF', '1stFlrSF', 'GrLivArea', 'WoodDeckSF',\n",
    "    'BsmtUnfSF', 'LotFrontage', 'MasVnrArea', 'GarageYrBlt', 'SalePrice'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 10)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\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>LotFrontage</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>7</td>\n",
       "      <td>196.0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>856</td>\n",
       "      <td>1710</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>0</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>298</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>7</td>\n",
       "      <td>162.0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>920</td>\n",
       "      <td>1786</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>961</td>\n",
       "      <td>1717</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145</td>\n",
       "      <td>2198</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>192</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage  OverallQual  MasVnrArea  BsmtUnfSF  TotalBsmtSF  1stFlrSF  \\\n",
       "0         65.0            7       196.0        150          856       856   \n",
       "1         80.0            6         0.0        284         1262      1262   \n",
       "2         68.0            7       162.0        434          920       920   \n",
       "3         60.0            7         0.0        540          756       961   \n",
       "4         84.0            8       350.0        490         1145      1145   \n",
       "\n",
       "   GrLivArea  GarageYrBlt  WoodDeckSF  SalePrice  \n",
       "0       1710       2003.0           0     208500  \n",
       "1       1262       1976.0         298     181500  \n",
       "2       1786       2001.0           0     223500  \n",
       "3       1717       1998.0           0     140000  \n",
       "4       2198       2000.0         192     250000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's load the House Prices dataset\n",
    "\n",
    "data = pd.read_csv('../houseprice.csv', usecols=cols_to_use)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    0.177397\n",
       "OverallQual    0.000000\n",
       "MasVnrArea     0.005479\n",
       "BsmtUnfSF      0.000000\n",
       "TotalBsmtSF    0.000000\n",
       "1stFlrSF       0.000000\n",
       "GrLivArea      0.000000\n",
       "GarageYrBlt    0.055479\n",
       "WoodDeckSF     0.000000\n",
       "SalePrice      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check the null values\n",
    "data.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables LotFrontage, MasVnrArea and GarageYrBlt contain missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 9), (438, 9))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "# first let's remove the target from the features\n",
    "cols_to_use.remove('SalePrice')\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[cols_to_use], # just the features\n",
    "                                                    data['SalePrice'], # the target\n",
    "                                                    test_size=0.3, # the percentage of obs in the test set\n",
    "                                                    random_state=0) # for reproducibility\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OverallQual    0.000000\n",
       "TotalBsmtSF    0.000000\n",
       "1stFlrSF       0.000000\n",
       "GrLivArea      0.000000\n",
       "WoodDeckSF     0.000000\n",
       "BsmtUnfSF      0.000000\n",
       "LotFrontage    0.184932\n",
       "MasVnrArea     0.004892\n",
       "GarageYrBlt    0.052838\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check the misssing data again\n",
    "X_train.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SimpleImputer on the entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(copy=True, fill_value=999, missing_values=nan,\n",
       "       strategy='constant', verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we impute the missing values with SimpleImputer\n",
    "\n",
    "# create an instance of the simple imputer\n",
    "# we indicate that we want to impute with a constant\n",
    "# arbitrary value: 999\n",
    "imputer = SimpleImputer(strategy='constant', \n",
    "                       fill_value = 999)\n",
    "\n",
    "# we fit the imputer to the train set\n",
    "# the imputer assigns 999 to all variables\n",
    "imputer.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For arbitrary value imputation, we need to set the strategy to 'constant' and indicate the arbitrary value in the fill_value argument. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([999., 999., 999., 999., 999., 999., 999., 999., 999.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can look at the learnt imputation values like this:\n",
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   7., 1057., 1057., ...,  999.,  573., 1998.],\n",
       "       [   6., 1291., 1291., ...,  999.,    0., 1996.],\n",
       "       [   5.,  858.,  858., ...,   50.,    0.,  999.],\n",
       "       ...,\n",
       "       [   6.,    0., 1318., ...,   68.,    0., 1978.],\n",
       "       [   7., 1374., 1557., ...,  999.,   18., 2003.],\n",
       "       [   7., 1195., 1195., ...,   58.,   30., 1998.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now we impute the train and test set\n",
    "\n",
    "# NOTE: the data is returned as a numpy array!!!\n",
    "X_train = imputer.transform(X_train)\n",
    "X_test = imputer.transform(X_test)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OverallQual  TotalBsmtSF  1stFlrSF  GrLivArea  WoodDeckSF  BsmtUnfSF  \\\n",
       "0          7.0       1057.0    1057.0     2034.0       576.0      318.0   \n",
       "1          6.0       1291.0    1291.0     1291.0       307.0      288.0   \n",
       "2          5.0        858.0     858.0      858.0       117.0      162.0   \n",
       "3          6.0        560.0     698.0     1258.0         0.0      356.0   \n",
       "4          2.0        290.0     438.0      438.0         0.0        0.0   \n",
       "\n",
       "   LotFrontage  MasVnrArea  GarageYrBlt  \n",
       "0        999.0       573.0       1998.0  \n",
       "1        999.0         0.0       1996.0  \n",
       "2         50.0         0.0        999.0  \n",
       "3         60.0         0.0       1939.0  \n",
       "4         60.0         0.0       1930.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can transform it back to a dataframe:\n",
    "\n",
    "X_train = pd.DataFrame(X_train, columns=cols_to_use)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's explore the distributions after the imputation\n",
    "\n",
    "X_train.hist(bins=50, figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the tall bar at 999 in the LotFrontage's histogram, which appeared after the imputation. You can also see the imputation effect on GarageYrBlt, with the bar at the far left right at the 999 value.\n",
    "\n",
    "**Remember to be cautious with the order in which you pass the column names** as we discussed in the previous notebook.\n",
    "\n",
    "### SimpleImputer: different procedures on different features\n",
    "\n",
    "On occasions, we want to implement different imputation techniques to different features, how can we do this using the SimpleImputer from Scikit-learn?\n",
    "\n",
    "We need to use another class called ColumnTransformer. \n",
    "\n",
    "**Note** ColumnTransformer has been recently released by Scikit-learn and it is therefore in beta version. This means that the developers can change the way it is used, or remove it altogether any time. The implication is that the code you write today, may not be useful after new releases from Scikit-learn. That is usually what happens with any open-source package when the developers are trying new functionality.\n",
    "\n",
    "More details about the ColumnTransformer in the [Sklearn website](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 9), (438, 9))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[cols_to_use],\n",
    "                                                    data['SalePrice'],\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OverallQual    0.000000\n",
       "TotalBsmtSF    0.000000\n",
       "1stFlrSF       0.000000\n",
       "GrLivArea      0.000000\n",
       "WoodDeckSF     0.000000\n",
       "BsmtUnfSF      0.000000\n",
       "LotFrontage    0.184932\n",
       "MasVnrArea     0.004892\n",
       "GarageYrBlt    0.052838\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at the missing values\n",
    "X_train.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's look at the distributions of the variables to decide\n",
    "# which arbitrary value to use for each feature\n",
    "\n",
    "X_train.hist(bins=50, figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this demo, I will impute each of LotFrontage, MasVnrArea and GarageYrBlt with different arbitrary values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first we need to make lists, indicating which features\n",
    "# will be imputed with each value\n",
    "\n",
    "features_LotFrontAge = ['LotFrontage']\n",
    "features_MasVnrArea = ['MasVnrArea']\n",
    "features_GarageYrBlt = ['GarageYrBlt']\n",
    "\n",
    "# then we instantiate the imputers, within a pipeline\n",
    "# we create one imputer per feature\n",
    "# within the imputer I indicate the arbitrary value\n",
    "# which is differet for each variable\n",
    "\n",
    "imputer_LotFrontAge = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value = 999)),\n",
    "])\n",
    "\n",
    "imputer_MasVnrArea = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value = -10)),\n",
    "])\n",
    "\n",
    "imputer_GarageYrBlt = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value = 1700)),\n",
    "])\n",
    "\n",
    "# then we put the features list and the transformers together\n",
    "# using the column transformer\n",
    "\n",
    "# in this example, I will use the default parameter of ColumnTransformer\n",
    "# remainder = drop, which means that only the imputed features will\n",
    "# be retained, and the rest dropped\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[\n",
    "    ('imputer_LotFrontAge', imputer_LotFrontAge, features_LotFrontAge),\n",
    "    ('imputer_MasVnrArea', imputer_MasVnrArea, features_MasVnrArea),\n",
    "    ('imputer_GarageYrBlt', imputer_GarageYrBlt, features_GarageYrBlt)\n",
    "],remainder = 'drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
       "         transformer_weights=None,\n",
       "         transformers=[('imputer_LotFrontAge', Pipeline(memory=None,\n",
       "     steps=[('imputer', SimpleImputer(copy=True, fill_value=999, missing_values=nan,\n",
       "       strategy='constant', verbose=0))]), ['LotFrontage']), ('imputer_MasVnrArea', Pipeline(memory=None,\n",
       "     steps=[('imputer', SimpleImputer(copy=True, ...e, fill_value=1700, missing_values=nan,\n",
       "       strategy='constant', verbose=0))]), ['GarageYrBlt'])])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we fit the preprocessor\n",
    "preprocessor.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('imputer_LotFrontAge', Pipeline(memory=None,\n",
       "       steps=[('imputer', SimpleImputer(copy=True, fill_value=999, missing_values=nan,\n",
       "         strategy='constant', verbose=0))]), ['LotFrontage']),\n",
       " ('imputer_MasVnrArea', Pipeline(memory=None,\n",
       "       steps=[('imputer', SimpleImputer(copy=True, fill_value=-10, missing_values=nan,\n",
       "         strategy='constant', verbose=0))]), ['MasVnrArea']),\n",
       " ('imputer_GarageYrBlt', Pipeline(memory=None,\n",
       "       steps=[('imputer', SimpleImputer(copy=True, fill_value=1700, missing_values=nan,\n",
       "         strategy='constant', verbose=0))]), ['GarageYrBlt'])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can explore the transformers like this:\n",
    "\n",
    "preprocessor.transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([999.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and we can look at the parameters learnt like this:\n",
    "\n",
    "# for LotFrontAge\n",
    "preprocessor.named_transformers_['imputer_LotFrontAge'].named_steps['imputer'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-10.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for MasVnrArea\n",
    "\n",
    "preprocessor.named_transformers_['imputer_MasVnrArea'].named_steps['imputer'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1700.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for GarageYrBlt\n",
    "\n",
    "preprocessor.named_transformers_['imputer_GarageYrBlt'].named_steps['imputer'].statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 999.,  573., 1998.],\n",
       "       [ 999.,    0., 1996.],\n",
       "       [  50.,    0., 1700.],\n",
       "       ...,\n",
       "       [  68.,    0., 1978.],\n",
       "       [ 999.,   18., 2003.],\n",
       "       [  58.,   30., 1998.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now we can impute the data\n",
    "X_train = preprocessor.transform(X_train)\n",
    "X_test = preprocessor.transform(X_test)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe how the returned data contains only 3 variables, the ones for which we specified imputation values. This is the effect of specifying remainder='drop' in the columnTransformer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANOTHER NOTE OF CAUTION**\n",
    "\n",
    "ColumnTransformer is in essence just slicing the dataframe in the required features. Then the SimpleImputer operates on the sliced dataframe. If different features are required by the different imputation methods, it will slice the dataframe on the required features for each imputation, and then concatenate them to produce the final output.\n",
    "\n",
    "For our specific example, what happens is the following:\n",
    "\n",
    "- ColumnTransformer first creates 3 single column dataframes, one with each of the indicated variables\n",
    "- The SimpleImputers learn the parameters in their respective sliced dataframes\n",
    "    - In this case they don't learn anything, because we hard code the arbitrary value\n",
    "- ColunmnTransformer contatenates back the 3 single column dataframes into the final result, which is a 3 column dataframe\n",
    "\n",
    "So note that you will not get all the variables from the original X_train, unless you create imputers for all the variables!!\n",
    "\n",
    "Also, there is a risk of messing up with the order of the features, if we do not use the transformers within a pipeline or we do not keep an eye on the order of the features in the train set, before they were converted to numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>LotFrontage</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>999.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage  MasVnrArea  GarageYrBlt\n",
       "0        999.0       573.0       1998.0\n",
       "1        999.0         0.0       1996.0\n",
       "2         50.0         0.0       1700.0\n",
       "3         60.0         0.0       1939.0\n",
       "4         60.0         0.0       1930.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see how the result of the imputation is a 3 column dataset\n",
    "pd.DataFrame(X_train,\n",
    "             columns=features_LotFrontAge + features_MasVnrArea +\n",
    "             features_GarageYrBlt).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = pd.DataFrame(X_train,\n",
    "             columns=features_LotFrontAge + features_MasVnrArea +\n",
    "             features_GarageYrBlt)\n",
    "\n",
    "X_train.hist(bins=50, figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the bars corresponding to the arbitrary values at 1700, 999 and -1 for each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "feml",
   "language": "python",
   "name": "feml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}