docs/notebooks/arfs_boruta_borutaShap_comparison.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARFS vs Boruta and BorutaShap\n",
    "\n",
    "comparison with Leshy, which is BorutaPy implementation with:\n",
    "\n",
    " - categorical features handling\n",
    " - plot method\n",
    " - catboost and lightGBM handling\n",
    " - SHAP and permutation importance\n",
    " - sample weight\n",
    "\n",
    "The implementation is however quite close to the BorutaPy one. A PR has been opened on the official BorutaPy repo.\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 numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import gc\n",
    "import shap\n",
    "from boruta import BorutaPy as bp\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.base import clone\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sys import getsizeof, path\n",
    "\n",
    "from boruta import BorutaPy\n",
    "\n",
    "import arfs\n",
    "import arfs.feature_selection as arfsfs\n",
    "import arfs.feature_selection.allrelevant as arfsgroot\n",
    "from arfs.utils import LightForestClassifier, LightForestRegressor\n",
    "from arfs.benchmark import highlight_tick, compare_varimp, sklearn_pimp_bench\n",
    "from arfs.utils import load_data\n",
    "\n",
    "# plt.style.use('fivethirtyeight')\n",
    "rng = np.random.RandomState(seed=42)\n",
    "\n",
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.enable()\n",
    "gc.collect()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison\n",
    "\n",
    "I'll just remove the collinear predictors since they are actually harmful for the ARFS, see the [Collinearity notebook](./Collinearity.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>texture error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>random_num1</th>\n",
       "      <th>random_num2</th>\n",
       "      <th>genuine_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.38</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.496714</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.249340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.77</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>-0.138264</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.044410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.25</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0.647689</td>\n",
       "      <td>3</td>\n",
       "      <td>0.128395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.38</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>27.23</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>1.523030</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.079921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.34</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>-0.234153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.094302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean texture  mean area  mean symmetry  texture error  area error  \\\n",
       "0         10.38     1001.0         0.2419         0.9053      153.40   \n",
       "1         17.77     1326.0         0.1812         0.7339       74.08   \n",
       "2         21.25     1203.0         0.2069         0.7869       94.03   \n",
       "3         20.38      386.1         0.2597         1.1560       27.23   \n",
       "4         14.34     1297.0         0.1809         0.7813       94.44   \n",
       "\n",
       "   smoothness error  concavity error  symmetry error  fractal dimension error  \\\n",
       "0          0.006399          0.05373         0.03003                 0.006193   \n",
       "1          0.005225          0.01860         0.01389                 0.003532   \n",
       "2          0.006150          0.03832         0.02250                 0.004571   \n",
       "3          0.009110          0.05661         0.05963                 0.009208   \n",
       "4          0.011490          0.05688         0.01756                 0.005115   \n",
       "\n",
       "   worst smoothness  worst symmetry  worst fractal dimension  random_num1  \\\n",
       "0            0.1622          0.4601                  0.11890     0.496714   \n",
       "1            0.1238          0.2750                  0.08902    -0.138264   \n",
       "2            0.1444          0.3613                  0.08758     0.647689   \n",
       "3            0.2098          0.6638                  0.17300     1.523030   \n",
       "4            0.1374          0.2364                  0.07678    -0.234153   \n",
       "\n",
       "   random_num2  genuine_num  \n",
       "0            0    -0.249340  \n",
       "1            1    -0.044410  \n",
       "2            3     0.128395  \n",
       "3            0    -0.079921  \n",
       "4            0    -0.094302  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer = load_data(name=\"cancer\")\n",
    "X, y = cancer.data, cancer.target\n",
    "\n",
    "# basic feature selection\n",
    "basic_fs_pipeline = Pipeline(\n",
    "    [\n",
    "        (\"missing\", arfsfs.MissingValueThreshold(threshold=0.05)),\n",
    "        (\"unique\", arfsfs.UniqueValuesThreshold(threshold=1)),\n",
    "        (\"cardinality\", arfsfs.CardinalityThreshold(threshold=1000)),\n",
    "        (\"collinearity\", arfsfs.CollinearityThreshold(threshold=0.75)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "X_filtered = basic_fs_pipeline.fit_transform(\n",
    "    X=X, y=y\n",
    ")  #  , collinearity__sample_weight=w,\n",
    "X_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = basic_fs_pipeline.named_steps[\"collinearity\"].plot_association()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BorutaPy\n",
    "\n",
    "Boruta, in its \"official\" implementation uses gain/gini feature importance (which is known to be biased). Let's see what are the results on this data set\n",
    "\n",
    "**Unfortunately, Boruta does not work with newer version of numpy and python.** However, if you have an older version of numpy, you can run this comparison, it should returns the same results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'int'.\n`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:9\u001b[0m\n",
      "File \u001b[0;32m~/mambaforge-pypy3/envs/arfs/lib/python3.10/site-packages/boruta/boruta_py.py:201\u001b[0m, in \u001b[0;36mBorutaPy.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y):\n\u001b[1;32m    189\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    190\u001b[0m \u001b[38;5;124;03m    Fits the Boruta feature selection with the provided estimator.\u001b[39;00m\n\u001b[1;32m    191\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;124;03m        The target values.\u001b[39;00m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 201\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge-pypy3/envs/arfs/lib/python3.10/site-packages/boruta/boruta_py.py:260\u001b[0m, in \u001b[0;36mBorutaPy._fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    255\u001b[0m _iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    256\u001b[0m \u001b[38;5;66;03m# holds the decision about each feature:\u001b[39;00m\n\u001b[1;32m    257\u001b[0m \u001b[38;5;66;03m# 0  - default state = tentative in original code\u001b[39;00m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;66;03m# 1  - accepted in original code\u001b[39;00m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;66;03m# -1 - rejected in original code\u001b[39;00m\n\u001b[0;32m--> 260\u001b[0m dec_reg \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(n_feat, dtype\u001b[38;5;241m=\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mint\u001b[49m)\n\u001b[1;32m    261\u001b[0m \u001b[38;5;66;03m# counts how many times a given feature was more important than\u001b[39;00m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;66;03m# the best of the shadow features\u001b[39;00m\n\u001b[1;32m    263\u001b[0m hit_reg \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(n_feat, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mint)\n",
      "File \u001b[0;32m~/mambaforge-pypy3/envs/arfs/lib/python3.10/site-packages/numpy/__init__.py:324\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    319\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    320\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn the future `np.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` will be defined as the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorresponding NumPy scalar.\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mFutureWarning\u001b[39;00m, stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m __former_attrs__:\n\u001b[0;32m--> 324\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(__former_attrs__[attr])\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtesting\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    327\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtesting\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'int'.\n`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# define random forest classifier, with utilising all cores and\n",
    "# sampling in proportion to y labels\n",
    "rf = RandomForestClassifier(n_jobs=-1, class_weight=\"balanced\", max_depth=5)\n",
    "\n",
    "# define Boruta feature selection method\n",
    "bp_feat_selector = BorutaPy(rf, n_estimators=\"auto\", verbose=1, random_state=1)\n",
    "\n",
    "# find all relevant features - 5 features should be selected\n",
    "bp_feat_selector.fit(X_filtered.values, y.values)\n",
    "\n",
    "# check selected features - first 5 features are selected\n",
    "print(\"\\n\")\n",
    "print(list(X_filtered.columns[bp_feat_selector.support_]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leshy\n",
    "\n",
    "Let's compare to the official python implementation, using the same setting and the gini/gain feature importance. We should have the same results (btw, you can check the unit tests, BorutaPy is used as baseline)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52e7c3e202f54f28890206ffb200c83c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Leshy iteration:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Leshy finished running using native var. imp.\n",
      "\n",
      "Iteration: \t1 / 100\n",
      "Confirmed: \t13\n",
      "Tentative: \t0\n",
      "Rejected: \t2\n",
      "All relevant predictors selected in 00:00:03.43\n",
      "The selected features: ['mean texture' 'mean area' 'mean symmetry' 'texture error' 'area error'\n",
      " 'smoothness error' 'concavity error' 'symmetry error'\n",
      " 'fractal dimension error' 'worst smoothness' 'worst symmetry'\n",
      " 'worst fractal dimension' 'genuine_num']\n",
      "The agnostic ranking: [1 1 1 1 1 1 1 1 1 1 1 1 2 3 1]\n",
      "The naive ranking: ['mean area', 'area error', 'concavity error', 'mean texture', 'worst smoothness', 'worst symmetry', 'genuine_num', 'worst fractal dimension', 'mean symmetry', 'symmetry error', 'fractal dimension error', 'smoothness error', 'texture error', 'random_num1', 'random_num2']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.94 s, sys: 383 ms, total: 5.32 s\n",
      "Wall time: 3.97 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Leshy, all the predictors, no-preprocessing\n",
    "model = clone(rf)\n",
    "\n",
    "leshy_feat_selector = arfsgroot.Leshy(\n",
    "    rf,\n",
    "    n_estimators=\"auto\",\n",
    "    verbose=1,\n",
    "    max_iter=100,\n",
    "    random_state=1,\n",
    "    importance=\"native\",\n",
    ")\n",
    "leshy_feat_selector.fit(X_filtered, y, sample_weight=None)\n",
    "print(f\"The selected features: {leshy_feat_selector.get_feature_names_out()}\")\n",
    "print(f\"The agnostic ranking: {leshy_feat_selector.ranking_}\")\n",
    "print(f\"The naive ranking: {leshy_feat_selector.ranking_absolutes_}\")\n",
    "fig = leshy_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": [
    "### Same Results?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_list_equal(L1, L2):\n",
    "    return len(L1) == len(L2) and sorted(L1) == sorted(L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'BorutaPy' object has no attribute 'support_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m check_list_equal(\n\u001b[1;32m      2\u001b[0m     leshy_feat_selector\u001b[38;5;241m.\u001b[39mget_feature_names_out(),\n\u001b[0;32m----> 3\u001b[0m     \u001b[38;5;28mlist\u001b[39m(X_filtered\u001b[38;5;241m.\u001b[39mcolumns[\u001b[43mbp_feat_selector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msupport_\u001b[49m]),\n\u001b[1;32m      4\u001b[0m )\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'BorutaPy' object has no attribute 'support_'"
     ]
    }
   ],
   "source": [
    "check_list_equal(\n",
    "    leshy_feat_selector.get_feature_names_out(),\n",
    "    list(X_filtered.columns[bp_feat_selector.support_]),\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BorutaShap with native importance\n",
    "\n",
    "BorutaShap, is an alternative implementation (heavy re-writting and new material) of Boruta with Shap feature importance. Let's see what are the results on this data set\n",
    "\n",
    "Unfortunately has also maintenance issues and some conflicts with newer version of its dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "\n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
      "File \u001b[0;32m~/mambaforge-pypy3/envs/arfs/lib/python3.10/site-packages/BorutaShap.py:2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mensemble\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RandomForestClassifier, RandomForestRegressor, IsolationForest\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_breast_cancer, load_boston\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmultitest\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m multipletests\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n",
      "File \u001b[0;32m~/mambaforge-pypy3/envs/arfs/lib/python3.10/site-packages/sklearn/datasets/__init__.py:160\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mload_boston\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    110\u001b[0m     msg \u001b[38;5;241m=\u001b[39m textwrap\u001b[38;5;241m.\u001b[39mdedent(\n\u001b[1;32m    111\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;124;03m        `load_boston` has been removed from scikit-learn since version 1.2.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[1;32m    159\u001b[0m     )\n\u001b[0;32m--> 160\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mglobals\u001b[39m()[name]\n",
      "\u001b[0;31mImportError\u001b[0m: \n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from BorutaShap import BorutaShap\n",
    "from arfs.preprocessing import OrdinalEncoderPandas\n",
    "\n",
    "# define random forest classifier, with utilising all cores and\n",
    "# sampling in proportion to y labels\n",
    "model = RandomForestClassifier(n_jobs=-1, class_weight=\"balanced\", max_depth=5)\n",
    "\n",
    "# define BorutaShap feature selection method (doesn't convert automatically cat feature)\n",
    "X_encoded = OrdinalEncoderPandas().fit_transform(X=X_filtered)\n",
    "bs_feat_selector = BorutaShap(\n",
    "    model=model, importance_measure=\"gini\", classification=True\n",
    ")\n",
    "\n",
    "# find all relevant features - 5 features should be selected\n",
    "bs_feat_selector.fit(X=X_encoded, y=y, n_trials=100, random_state=0)\n",
    "\n",
    "# Returns Boxplot of features\n",
    "bs_feat_selector.plot(X_size=12, figsize=(8, 6), y_scale=\"log\", which_features=\"all\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `boston` dataset is imported but removed from scikit-learn, from the version >= `1.2.0`.\n",
    "\n",
    "The `hasattr` is called before fitting so `feature_importances_` is not found. Let's use the default, which is also a random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "\n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
      "File \u001b[0;32m~/mambaforge-pypy3/envs/arfs/lib/python3.10/site-packages/BorutaShap.py:2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mensemble\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RandomForestClassifier, RandomForestRegressor, IsolationForest\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_breast_cancer, load_boston\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmultitest\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m multipletests\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n",
      "File \u001b[0;32m~/mambaforge-pypy3/envs/arfs/lib/python3.10/site-packages/sklearn/datasets/__init__.py:160\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mload_boston\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    110\u001b[0m     msg \u001b[38;5;241m=\u001b[39m textwrap\u001b[38;5;241m.\u001b[39mdedent(\n\u001b[1;32m    111\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;124;03m        `load_boston` has been removed from scikit-learn since version 1.2.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[1;32m    159\u001b[0m     )\n\u001b[0;32m--> 160\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mglobals\u001b[39m()[name]\n",
      "\u001b[0;31mImportError\u001b[0m: \n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from BorutaShap import BorutaShap\n",
    "\n",
    "bs_feat_selector = BorutaShap(importance_measure=\"gini\", classification=True)\n",
    "\n",
    "# find all relevant features - 5 features should be selected\n",
    "bs_feat_selector.fit(X=X_encoded, y=y, n_trials=100, random_state=0)\n",
    "\n",
    "# Returns Boxplot of features\n",
    "bs_feat_selector.plot(X_size=12, figsize=(12, 8), y_scale=\"log\", which_features=\"all\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison with Boruta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'BorutaPy' object has no attribute 'support_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m check_list_equal(\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28mlist\u001b[39m(X_filtered\u001b[38;5;241m.\u001b[39mcolumns[\u001b[43mbp_feat_selector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msupport_\u001b[49m]), \u001b[38;5;28mlist\u001b[39m(bs_feat_selector\u001b[38;5;241m.\u001b[39maccepted)\n\u001b[1;32m      3\u001b[0m )\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'BorutaPy' object has no attribute 'support_'"
     ]
    }
   ],
   "source": [
    "check_list_equal(\n",
    "    list(X_filtered.columns[bp_feat_selector.support_]), list(bs_feat_selector.accepted)\n",
    ")"
   ]
  }
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