docs/notebooks/mrmr_fs_VS_arfs.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARFS vs MRmr\n",
    "\n",
    "Maximal relevance minimal redundancy feature selection is, theoretically, a subset of all relevant feature selection. However, contrary to the provided ARFS method, there is no criterion for the number of features to select. This is a hyper-parameter, found by HPO or set by the User. \n",
    "On the other hand, ARFS methods are negatively impacted in presence of collinearity while those collinear features are penalized in the MRmr (redundancy part).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.core.display import display, HTML\n",
    "# display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
    "import gc\n",
    "import arfs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sys import getsizeof, path\n",
    "from sklearn.pipeline import Pipeline\n",
    "from arfs.utils import load_data\n",
    "from arfs.feature_selection import (\n",
    "    MinRedundancyMaxRelevance,\n",
    "    GrootCV,\n",
    "    MissingValueThreshold,\n",
    "    UniqueValuesThreshold,\n",
    "    CollinearityThreshold,\n",
    "    make_fs_summary,\n",
    ")\n",
    "from arfs.benchmark import highlight_tick\n",
    "\n",
    "# plt.style.use('fivethirtyeight')\n",
    "rng = np.random.RandomState(seed=42)\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with ARFS 2.3.1\n"
     ]
    }
   ],
   "source": [
    "print(f\"Run with ARFS {arfs.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.enable()\n",
    "gc.collect()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MRmr\n",
    "\n",
    "convenient, handles collinearity but does not automatically detect the number of features to select (let as a hyper-param)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_data(name=\"Boston\")\n",
    "X, y = boston.data, boston.target\n",
    "y.name = \"target\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "1    21.6\n",
       "2    34.7\n",
       "3    33.4\n",
       "4    36.2\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM             float64\n",
       "ZN               float64\n",
       "INDUS            float64\n",
       "CHAS            category\n",
       "NOX              float64\n",
       "RM               float64\n",
       "AGE              float64\n",
       "DIS              float64\n",
       "RAD             category\n",
       "TAX              float64\n",
       "PTRATIO          float64\n",
       "B                float64\n",
       "LSTAT            float64\n",
       "random_num1      float64\n",
       "random_num2        int32\n",
       "random_cat      category\n",
       "random_cat_2    category\n",
       "genuine_num      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>random_num1</th>\n",
       "      <th>random_num2</th>\n",
       "      <th>random_cat</th>\n",
       "      <th>random_cat_2</th>\n",
       "      <th>genuine_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>0.496714</td>\n",
       "      <td>0</td>\n",
       "      <td>cat_3517</td>\n",
       "      <td>Platist</td>\n",
       "      <td>7.080332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>-0.138264</td>\n",
       "      <td>0</td>\n",
       "      <td>cat_2397</td>\n",
       "      <td>MarkZ</td>\n",
       "      <td>5.245384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.647689</td>\n",
       "      <td>0</td>\n",
       "      <td>cat_3735</td>\n",
       "      <td>Dracula</td>\n",
       "      <td>6.375795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.523030</td>\n",
       "      <td>0</td>\n",
       "      <td>cat_2870</td>\n",
       "      <td>Bejita</td>\n",
       "      <td>6.725118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>-0.234153</td>\n",
       "      <td>4</td>\n",
       "      <td>cat_1160</td>\n",
       "      <td>Variance</td>\n",
       "      <td>7.867781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS CHAS    NOX     RM   AGE     DIS  RAD    TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0     15.3   \n",
       "1  0.02731   0.0   7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0     17.8   \n",
       "2  0.02729   0.0   7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0     17.8   \n",
       "3  0.03237   0.0   2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0     18.7   \n",
       "4  0.06905   0.0   2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0     18.7   \n",
       "\n",
       "        B  LSTAT  random_num1  random_num2 random_cat random_cat_2  \\\n",
       "0  396.90   4.98     0.496714            0   cat_3517      Platist   \n",
       "1  396.90   9.14    -0.138264            0   cat_2397        MarkZ   \n",
       "2  392.83   4.03     0.647689            0   cat_3735      Dracula   \n",
       "3  394.63   2.94     1.523030            0   cat_2870       Bejita   \n",
       "4  396.90   5.33    -0.234153            4   cat_1160     Variance   \n",
       "\n",
       "   genuine_num  \n",
       "0     7.080332  \n",
       "1     5.245384  \n",
       "2     6.375795  \n",
       "3     6.725118  \n",
       "4     7.867781  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinRedundancyMaxRelevance(n_features_to_select=10,\n",
       "                          redundancy_func=functools.partial(&lt;function association_series at 0x7dd0785d0720&gt;, n_jobs=1, normalize=True),\n",
       "                          relevance_func=functools.partial(&lt;function f_stat_regression_parallel at 0x7dd0785d0c20&gt;, n_jobs=1))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MinRedundancyMaxRelevance<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MinRedundancyMaxRelevance(n_features_to_select=10,\n",
       "                          redundancy_func=functools.partial(&lt;function association_series at 0x7dd0785d0720&gt;, n_jobs=1, normalize=True),\n",
       "                          relevance_func=functools.partial(&lt;function f_stat_regression_parallel at 0x7dd0785d0c20&gt;, n_jobs=1))</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MinRedundancyMaxRelevance(n_features_to_select=10,\n",
       "                          redundancy_func=functools.partial(<function association_series at 0x7dd0785d0720>, n_jobs=1, normalize=True),\n",
       "                          relevance_func=functools.partial(<function f_stat_regression_parallel at 0x7dd0785d0c20>, n_jobs=1))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of features is a hyper-param\n",
    "\n",
    "fs_mrmr = MinRedundancyMaxRelevance(\n",
    "    n_features_to_select=10,\n",
    "    relevance_func=None,\n",
    "    redundancy_func=None,\n",
    "    task=\"regression\",  # \"classification\",\n",
    "    denominator_func=np.mean,\n",
    "    only_same_domain=False,\n",
    "    return_scores=False,\n",
    "    show_progress=True,\n",
    "    n_jobs=1,\n",
    ")\n",
    "\n",
    "# fs_mrmr.fit(X=X, y=y.astype(str), sample_weight=None)\n",
    "fs_mrmr.fit(X=X, y=y, sample_weight=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>LSTAT</th>\n",
       "      <th>genuine_num</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>RM</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>NOX</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>PTRATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.98</td>\n",
       "      <td>7.080332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.575</td>\n",
       "      <td>2.31</td>\n",
       "      <td>296.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.14</td>\n",
       "      <td>5.245384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.421</td>\n",
       "      <td>7.07</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.03</td>\n",
       "      <td>6.375795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.185</td>\n",
       "      <td>7.07</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.94</td>\n",
       "      <td>6.725118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.998</td>\n",
       "      <td>2.18</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.33</td>\n",
       "      <td>7.867781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.147</td>\n",
       "      <td>2.18</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LSTAT  genuine_num CHAS  RAD     RM  INDUS    TAX    NOX     CRIM  PTRATIO\n",
       "0   4.98     7.080332  0.0  1.0  6.575   2.31  296.0  0.538  0.00632     15.3\n",
       "1   9.14     5.245384  0.0  2.0  6.421   7.07  242.0  0.469  0.02731     17.8\n",
       "2   4.03     6.375795  0.0  2.0  7.185   7.07  242.0  0.469  0.02729     17.8\n",
       "3   2.94     6.725118  0.0  3.0  6.998   2.18  222.0  0.458  0.03237     18.7\n",
       "4   5.33     7.867781  0.0  3.0  7.147   2.18  222.0  0.458  0.06905     18.7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trans = fs_mrmr.transform(X)\n",
    "X_trans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT', 'random_num1', 'random_num2',\n",
       "       'random_cat', 'random_cat_2', 'genuine_num'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_mrmr.feature_names_in_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True,  True,  True,  True, False, False,  True,\n",
       "        True,  True, False,  True, False, False, False, False,  True])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_mrmr.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'INDUS', 'CHAS', 'NOX', 'RM', 'RAD', 'TAX', 'PTRATIO',\n",
       "       'LSTAT', 'genuine_num'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_mrmr.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>mrmr</th>\n",
       "      <th>relevance</th>\n",
       "      <th>redundancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>inf</td>\n",
       "      <td>3.026693</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genuine_num</th>\n",
       "      <td>1131.029947</td>\n",
       "      <td>1.131030</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>1.787263</td>\n",
       "      <td>0.731266</td>\n",
       "      <td>0.409154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>2.262116</td>\n",
       "      <td>0.990593</td>\n",
       "      <td>0.437905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>0.571875</td>\n",
       "      <td>0.145600</td>\n",
       "      <td>0.254601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>-0.324591</td>\n",
       "      <td>-0.096957</td>\n",
       "      <td>0.298705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>-0.358787</td>\n",
       "      <td>-0.154209</td>\n",
       "      <td>0.429805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>-0.328722</td>\n",
       "      <td>-0.153521</td>\n",
       "      <td>0.467023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>-0.324877</td>\n",
       "      <td>-0.166310</td>\n",
       "      <td>0.511916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>-0.381959</td>\n",
       "      <td>-0.176410</td>\n",
       "      <td>0.461857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mrmr  relevance  redundancy\n",
       "LSTAT                inf   3.026693    0.000000\n",
       "genuine_num  1131.029947   1.131030    0.001000\n",
       "CHAS            1.787263   0.731266    0.409154\n",
       "RAD             2.262116   0.990593    0.437905\n",
       "RM              0.571875   0.145600    0.254601\n",
       "INDUS          -0.324591  -0.096957    0.298705\n",
       "TAX            -0.358787  -0.154209    0.429805\n",
       "NOX            -0.328722  -0.153521    0.467023\n",
       "CRIM           -0.324877  -0.166310    0.511916\n",
       "PTRATIO        -0.381959  -0.176410    0.461857"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_mrmr.ranking_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARFS - GrootCV\n",
    "\n",
    "Cross-validated, using a stopping criterion for the features to select, does not play well with high collinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_data(name=\"Boston\")\n",
    "X, y = boston.data, boston.target\n",
    "y.name = \"target\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09c47614c95e4b48a687e76f58ba11da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Cross Validation:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The selected features: ['CRIM' 'NOX' 'RM' 'AGE' 'DIS' 'TAX' 'PTRATIO' 'LSTAT' 'genuine_num']\n",
      "The agnostic ranking: [2 1 1 1 2 2 2 2 1 2 2 1 2 1 1 1 1 2]\n",
      "The naive ranking: ['LSTAT', 'RM', 'PTRATIO', 'DIS', 'CRIM', 'NOX', 'AGE', 'B', 'ShadowVar1', 'ShadowVar13', 'INDUS', 'ShadowVar14', 'ShadowVar12', 'ShadowVar10', 'ShadowVar11', 'RAD', 'ShadowVar15', 'CHAS']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 53.2 s, sys: 655 ms, total: 53.9 s\n",
      "Wall time: 20.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# GrootCV\n",
    "feat_selector = GrootCV(objective=\"rmse\", cutoff=1, n_folds=5, n_iter=5, silent=True)\n",
    "feat_selector.fit(X, y, sample_weight=None)\n",
    "print(f\"The selected features: {feat_selector.get_feature_names_out()}\")\n",
    "print(f\"The agnostic ranking: {feat_selector.ranking_}\")\n",
    "print(f\"The naive ranking: {feat_selector.ranking_absolutes_}\")\n",
    "fig = feat_selector.plot_importance(n_feat_per_inch=5)\n",
    "\n",
    "# highlight synthetic random variable\n",
    "fig = highlight_tick(figure=fig, str_match=\"random\")\n",
    "fig = highlight_tick(figure=fig, str_match=\"genuine\", color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a pipeline for filtering out collinear features and improving the stability of the selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26f5bb4ff0834c8793c86085c99f18f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Cross Validation:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_selector = GrootCV(objective=\"rmse\", cutoff=1, n_folds=5, n_iter=5, silent=True)\n",
    "\n",
    "arfs_fs_pipeline = Pipeline(\n",
    "    [\n",
    "        (\"missing\", MissingValueThreshold(threshold=0.05)),\n",
    "        (\"unique\", UniqueValuesThreshold(threshold=1)),\n",
    "        (\"collinearity\", CollinearityThreshold(threshold=0.85)),\n",
    "        (\"arfs\", feat_selector),\n",
    "    ]\n",
    ")\n",
    "\n",
    "X_trans = arfs_fs_pipeline.fit(X=X, y=y).transform(X=X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'random_num1', 'random_num2',\n",
       "       'random_cat', 'random_cat_2', 'genuine_num'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arfs_fs_pipeline.named_steps[\"collinearity\"].get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = arfs_fs_pipeline.named_steps[\"collinearity\"].plot_association(figsize=(8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x340 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = arfs_fs_pipeline.named_steps[\"arfs\"].plot_importance()\n",
    "# highlight synthetic random variable\n",
    "fig = highlight_tick(figure=fig, str_match=\"random\")\n",
    "fig = highlight_tick(figure=fig, str_match=\"genuine\", color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c4fba_row0_col1, #T_c4fba_row0_col2, #T_c4fba_row0_col3, #T_c4fba_row0_col4, #T_c4fba_row1_col1, #T_c4fba_row1_col2, #T_c4fba_row1_col3, #T_c4fba_row2_col1, #T_c4fba_row2_col2, #T_c4fba_row2_col3, #T_c4fba_row3_col1, #T_c4fba_row3_col2, #T_c4fba_row3_col3, #T_c4fba_row4_col1, #T_c4fba_row4_col2, #T_c4fba_row5_col1, #T_c4fba_row5_col2, #T_c4fba_row5_col3, #T_c4fba_row5_col4, #T_c4fba_row6_col1, #T_c4fba_row6_col2, #T_c4fba_row6_col3, #T_c4fba_row6_col4, #T_c4fba_row7_col1, #T_c4fba_row7_col2, #T_c4fba_row7_col3, #T_c4fba_row7_col4, #T_c4fba_row8_col1, #T_c4fba_row8_col2, #T_c4fba_row8_col3, #T_c4fba_row9_col1, #T_c4fba_row9_col2, #T_c4fba_row9_col3, #T_c4fba_row9_col4, #T_c4fba_row10_col1, #T_c4fba_row10_col2, #T_c4fba_row10_col3, #T_c4fba_row10_col4, #T_c4fba_row11_col1, #T_c4fba_row11_col2, #T_c4fba_row11_col3, #T_c4fba_row12_col1, #T_c4fba_row12_col2, #T_c4fba_row12_col3, #T_c4fba_row12_col4, #T_c4fba_row13_col1, #T_c4fba_row13_col2, #T_c4fba_row13_col3, #T_c4fba_row14_col1, #T_c4fba_row14_col2, #T_c4fba_row14_col3, #T_c4fba_row15_col1, #T_c4fba_row15_col2, #T_c4fba_row15_col3, #T_c4fba_row16_col1, #T_c4fba_row16_col2, #T_c4fba_row16_col3, #T_c4fba_row17_col1, #T_c4fba_row17_col2, #T_c4fba_row17_col3, #T_c4fba_row17_col4 {\n",
       "  background-color: #0c8a30;\n",
       "}\n",
       "#T_c4fba_row1_col4, #T_c4fba_row2_col4, #T_c4fba_row3_col4, #T_c4fba_row4_col3, #T_c4fba_row8_col4, #T_c4fba_row11_col4, #T_c4fba_row13_col4, #T_c4fba_row14_col4, #T_c4fba_row15_col4, #T_c4fba_row16_col4 {\n",
       "  background-color: #ba0202;\n",
       "}\n",
       "#T_c4fba_row4_col4 {\n",
       "  background-color: #0c8a30;\n",
       "  background-color: #f57505;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c4fba\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c4fba_level0_col0\" class=\"col_heading level0 col0\" >predictor</th>\n",
       "      <th id=\"T_c4fba_level0_col1\" class=\"col_heading level0 col1\" >missing</th>\n",
       "      <th id=\"T_c4fba_level0_col2\" class=\"col_heading level0 col2\" >unique</th>\n",
       "      <th id=\"T_c4fba_level0_col3\" class=\"col_heading level0 col3\" >collinearity</th>\n",
       "      <th id=\"T_c4fba_level0_col4\" class=\"col_heading level0 col4\" >arfs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c4fba_row0_col0\" class=\"data row0 col0\" >CRIM</td>\n",
       "      <td id=\"T_c4fba_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row0_col3\" class=\"data row0 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_c4fba_row1_col0\" class=\"data row1 col0\" >ZN</td>\n",
       "      <td id=\"T_c4fba_row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row1_col3\" class=\"data row1 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_c4fba_row2_col0\" class=\"data row2 col0\" >INDUS</td>\n",
       "      <td id=\"T_c4fba_row2_col1\" class=\"data row2 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row2_col3\" class=\"data row2 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row2_col4\" class=\"data row2 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_c4fba_row3_col0\" class=\"data row3 col0\" >CHAS</td>\n",
       "      <td id=\"T_c4fba_row3_col1\" class=\"data row3 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row3_col2\" class=\"data row3 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row3_col4\" class=\"data row3 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_c4fba_row4_col0\" class=\"data row4 col0\" >NOX</td>\n",
       "      <td id=\"T_c4fba_row4_col1\" class=\"data row4 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row4_col2\" class=\"data row4 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row4_col3\" class=\"data row4 col3\" >0</td>\n",
       "      <td id=\"T_c4fba_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_c4fba_row5_col0\" class=\"data row5 col0\" >RM</td>\n",
       "      <td id=\"T_c4fba_row5_col1\" class=\"data row5 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row5_col2\" class=\"data row5 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row5_col3\" class=\"data row5 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row5_col4\" class=\"data row5 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_c4fba_row6_col0\" class=\"data row6 col0\" >AGE</td>\n",
       "      <td id=\"T_c4fba_row6_col1\" class=\"data row6 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row6_col2\" class=\"data row6 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row6_col3\" class=\"data row6 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row6_col4\" class=\"data row6 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_c4fba_row7_col0\" class=\"data row7 col0\" >DIS</td>\n",
       "      <td id=\"T_c4fba_row7_col1\" class=\"data row7 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row7_col2\" class=\"data row7 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row7_col3\" class=\"data row7 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row7_col4\" class=\"data row7 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_c4fba_row8_col0\" class=\"data row8 col0\" >RAD</td>\n",
       "      <td id=\"T_c4fba_row8_col1\" class=\"data row8 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row8_col2\" class=\"data row8 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row8_col3\" class=\"data row8 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row8_col4\" class=\"data row8 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_c4fba_row9_col0\" class=\"data row9 col0\" >TAX</td>\n",
       "      <td id=\"T_c4fba_row9_col1\" class=\"data row9 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row9_col2\" class=\"data row9 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row9_col3\" class=\"data row9 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row9_col4\" class=\"data row9 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_c4fba_row10_col0\" class=\"data row10 col0\" >PTRATIO</td>\n",
       "      <td id=\"T_c4fba_row10_col1\" class=\"data row10 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row10_col2\" class=\"data row10 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row10_col3\" class=\"data row10 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row10_col4\" class=\"data row10 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_c4fba_row11_col0\" class=\"data row11 col0\" >B</td>\n",
       "      <td id=\"T_c4fba_row11_col1\" class=\"data row11 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row11_col2\" class=\"data row11 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row11_col3\" class=\"data row11 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row11_col4\" class=\"data row11 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_c4fba_row12_col0\" class=\"data row12 col0\" >LSTAT</td>\n",
       "      <td id=\"T_c4fba_row12_col1\" class=\"data row12 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row12_col2\" class=\"data row12 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row12_col3\" class=\"data row12 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row12_col4\" class=\"data row12 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_c4fba_row13_col0\" class=\"data row13 col0\" >random_num1</td>\n",
       "      <td id=\"T_c4fba_row13_col1\" class=\"data row13 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row13_col2\" class=\"data row13 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row13_col3\" class=\"data row13 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row13_col4\" class=\"data row13 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_c4fba_row14_col0\" class=\"data row14 col0\" >random_num2</td>\n",
       "      <td id=\"T_c4fba_row14_col1\" class=\"data row14 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row14_col2\" class=\"data row14 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row14_col3\" class=\"data row14 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row14_col4\" class=\"data row14 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_c4fba_row15_col0\" class=\"data row15 col0\" >random_cat</td>\n",
       "      <td id=\"T_c4fba_row15_col1\" class=\"data row15 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row15_col2\" class=\"data row15 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row15_col3\" class=\"data row15 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row15_col4\" class=\"data row15 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_c4fba_row16_col0\" class=\"data row16 col0\" >random_cat_2</td>\n",
       "      <td id=\"T_c4fba_row16_col1\" class=\"data row16 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row16_col2\" class=\"data row16 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row16_col3\" class=\"data row16 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row16_col4\" class=\"data row16 col4\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c4fba_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_c4fba_row17_col0\" class=\"data row17 col0\" >genuine_num</td>\n",
       "      <td id=\"T_c4fba_row17_col1\" class=\"data row17 col1\" >1</td>\n",
       "      <td id=\"T_c4fba_row17_col2\" class=\"data row17 col2\" >1</td>\n",
       "      <td id=\"T_c4fba_row17_col3\" class=\"data row17 col3\" >1</td>\n",
       "      <td id=\"T_c4fba_row17_col4\" class=\"data row17 col4\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7dd076f9fbc0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_fs_summary(arfs_fs_pipeline)"
   ]
  }
 ],
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