ch02-missing-data-imputation/Recipe-11-Assembling-an-imputation-pipeline-with-Feature-Engine.ipynb

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    "## Replacing missing values by a value at the end of the distribution\n",
    "\n",
    "In this recipe, we will replace missing values by a value at the end of the distribution, estimated with the Gaussian approximation or the inter-quantal range proximity rule, using pandas and Feature-Engine, all open source Python libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "import feature_engine.missing_data_imputers as mdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8   A9  A10  A11 A12 A13    A14  A15  A16\n",
       "0  b  30.83  0.000  u  g  w  v  1.25    t    t    1   f   g  202.0    0    1\n",
       "1  a  58.67  4.460  u  g  q  h  3.04    t    t    6   f   g   43.0  560    1\n",
       "2  a  24.50    NaN  u  g  q  h   NaN  NaN  NaN    0   f   g  280.0  824    1\n",
       "3  b  27.83  1.540  u  g  w  v  3.75    t    t    5   t   g  100.0    3    1\n",
       "4  b  20.17  5.625  u  g  w  v  1.71    t    f    0   f   s  120.0    0    1"
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   ],
   "source": [
    "# load data\n",
    "data = pd.read_csv('creditApprovalUCI.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((483, 15), (207, 15))"
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     "execution_count": 3,
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   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop('A16', axis=1), data['A16'], test_size=0.3, random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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       "A12    0.000000\n",
       "A13    0.000000\n",
       "A4     0.008696\n",
       "A5     0.008696\n",
       "A6     0.013043\n",
       "A7     0.013043\n",
       "A1     0.017391\n",
       "A9     0.133333\n",
       "A10    0.133333\n",
       "dtype: float64"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# find categorical variables\n",
    "cat_cols = [c for c in data.columns if data[c].dtypes=='O']\n",
    "data[cat_cols].isnull().mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A11    0.000000\n",
       "A15    0.000000\n",
       "A16    0.000000\n",
       "A2     0.017391\n",
       "A14    0.018841\n",
       "A3     0.133333\n",
       "A8     0.133333\n",
       "dtype: float64"
      ]
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     "execution_count": 5,
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   ],
   "source": [
    "# find numerical variables\n",
    "num_cols = [c for c in data.columns if data[c].dtypes!='O']\n",
    "data[num_cols].isnull().mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # find the percentage of missing data within those variables\n",
    "\n",
    "# X_train.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first we need to make a list with the numerical vars\n",
    "features_num_arbitrary = ['A3', 'A8']\n",
    "features_num_median = ['A2', 'A14']\n",
    "\n",
    "features_cat_frequent = ['A4', 'A5', 'A6', 'A7']\n",
    "features_cat_missing = ['A1', 'A9', 'A10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we instantiate each imputer within a pipeline\n",
    "\n",
    "pipe = Pipeline(steps=[\n",
    "    ('imp_num_arbitrary', mdi.ArbitraryNumberImputer(variables = features_num_arbitrary)),\n",
    "    ('imp_num_median', mdi.MeanMedianImputer(imputation_method = 'median', variables=features_num_median)),\n",
    "    ('imp_cat_frequent', mdi.FrequentCategoryImputer(variables = features_cat_frequent)),\n",
    "    ('imp_cat_missing', mdi.CategoricalVariableImputer(variables=features_cat_missing))\n",
    "])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
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     "text": [
      "C:\\Users\\Sole\\Anaconda3\\envs\\feml\\lib\\site-packages\\sklearn\\utils\\validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n",
      "  \"argument is ignored.\", FutureWarning)\n"
     ]
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       "Pipeline(memory=None,\n",
       "         steps=[('imp_num_arbitrary',\n",
       "                 ArbitraryNumberImputer(arbitrary_number=-999,\n",
       "                                        variables=['A3', 'A8'])),\n",
       "                ('imp_num_median',\n",
       "                 MeanMedianImputer(imputation_method='median',\n",
       "                                   variables=['A2', 'A14'])),\n",
       "                ('imp_cat_frequent',\n",
       "                 FrequentCategoryImputer(variables=['A4', 'A5', 'A6', 'A7'])),\n",
       "                ('imp_cat_missing',\n",
       "                 CategoricalVariableImputer(variables=['A1', 'A9', 'A10']))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we fit the preprocessor\n",
    "pipe.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and now we impute the data\n",
    "X_train = pipe.transform(X_train)\n",
    "X_test = pipe.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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       "A1     0\n",
       "A2     0\n",
       "A3     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A8     0\n",
       "A9     0\n",
       "A10    0\n",
       "A11    0\n",
       "A12    0\n",
       "A13    0\n",
       "A14    0\n",
       "A15    0\n",
       "dtype: int64"
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     "execution_count": 11,
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   "source": [
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A2     0\n",
       "A3     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A8     0\n",
       "A9     0\n",
       "A10    0\n",
       "A11    0\n",
       "A12    0\n",
       "A13    0\n",
       "A14    0\n",
       "A15    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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