docs/notebooks/arfs_grootcv_custom_params.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARFS - Customizing GrootCV\n",
    "\n",
    "GrootCV allows users to customize certain parameters when using the lightGBM algorithm in Python. However, it's essential to be aware that some of these parameters will be automatically adjusted by the algorithm itself. Specifically, the overridden parameters are objective, verbose, is_balanced, and n_iterations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n"
     ]
    }
   ],
   "source": [
    "# from IPython.core.display import display, HTML\n",
    "# display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import arfs\n",
    "from arfs.feature_selection import GrootCV\n",
    "from arfs.utils import load_data\n",
    "from arfs.benchmark import highlight_tick\n",
    "\n",
    "rng = np.random.RandomState(seed=42)\n",
    "\n",
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of physical cores: 4\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import multiprocessing\n",
    "\n",
    "\n",
    "def get_physical_cores():\n",
    "    if os.name == \"posix\":  # For Unix-based systems (e.g., Linux, macOS)\n",
    "        try:\n",
    "            return os.sysconf(\"SC_NPROCESSORS_ONLN\")\n",
    "        except ValueError:\n",
    "            pass\n",
    "    elif os.name == \"nt\":  # For Windows\n",
    "        try:\n",
    "            return int(os.environ[\"NUMBER_OF_PROCESSORS\"])\n",
    "        except (ValueError, KeyError):\n",
    "            pass\n",
    "    return multiprocessing.cpu_count()\n",
    "\n",
    "\n",
    "num_physical_cores = get_physical_cores()\n",
    "print(f\"Number of physical cores: {num_physical_cores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with ARFS 2.0.5\n"
     ]
    }
   ],
   "source": [
    "print(f\"Run with ARFS {arfs.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_data(name=\"Boston\")\n",
    "X, y = boston.data, boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of processes\n",
    "\n",
    "For optimal performance on CPUs, set the `n_jobs` parameter to match the number of **physical** cores available on your machine. Keep in mind that using `n_jobs=0` will use the default number of threads in OpenMP, and running multiple processes might not always yield faster results due to the overhead involved in initiating those processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in cast\n",
      "invalid value encountered in cast\n",
      "invalid value encountered in cast\n",
      "invalid value encountered in cast\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "402eb968027e428abdb8abf3a172ab7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_jobs = 0, Execution time: 9.602 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42dbfc420a744904813bb2ae5f47d868",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_jobs = 1, Execution time: 8.959 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb6ea1bea1f9454bb486fda5b4282c93",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_jobs = 2, Execution time: 7.147 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b5e7b21961846d297e994f6e0b37d87",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_jobs = 3, Execution time: 9.987 seconds\n"
     ]
    }
   ],
   "source": [
    "for n_jobs in range(num_physical_cores):\n",
    "    start_time = time.time()\n",
    "    feat_selector = GrootCV(\n",
    "        objective=\"rmse\",\n",
    "        cutoff=1,\n",
    "        n_folds=5,\n",
    "        n_iter=5,\n",
    "        silent=True,\n",
    "        fastshap=False,\n",
    "        n_jobs=n_jobs,\n",
    "    )\n",
    "    feat_selector.fit(X, y, sample_weight=None)\n",
    "    end_time = time.time()\n",
    "    execution_time = end_time - start_time\n",
    "    print(f\"n_jobs = {n_jobs}, Execution time: {execution_time:.3f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization\n",
    "\n",
    "By default, GrootCV is using crossvalidation and early-stopping to prevent overfitting and to ensure the robustness of the feature selection. However, if you want to prevent overfitting even more, you can use the dedicated `lightgbm` parameters such as `min_data_in_leaf`.\n",
    "\n",
    "Let's illustrate:\n",
    " - less regularization than default\n",
    " - default regularization (`{\"min_data_in_leaf\": 20}`)\n",
    " - larger regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f21bf3eff4d84d64adf7096263e9de2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The selected features: ['CRIM' 'NOX' 'RM' 'AGE' 'DIS' 'TAX' 'PTRATIO' 'B' 'LSTAT' 'genuine_num']\n",
      "The agnostic ranking: [2 1 1 1 2 2 2 2 1 2 2 2 2 1 1 1 1 2]\n",
      "The naive ranking: ['LSTAT', 'RM', 'genuine_num', 'PTRATIO', 'DIS', 'NOX', 'CRIM', 'AGE', 'TAX', 'B', 'random_num1', 'INDUS', 'random_cat', 'random_cat_2', 'RAD', 'random_num2', 'ZN', 'CHAS']\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GrootCV with less regularization\n",
    "feat_selector = GrootCV(\n",
    "    objective=\"rmse\",\n",
    "    cutoff=1,\n",
    "    n_folds=5,\n",
    "    n_iter=5,\n",
    "    silent=True,\n",
    "    fastshap=True,\n",
    "    n_jobs=0,\n",
    "    lgbm_params={\"min_data_in_leaf\": 10},\n",
    ")\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ae098d74d9b45f3923a84abf005b430",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Repeated k-fold:   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', 'genuine_num', 'PTRATIO', 'DIS', 'NOX', 'CRIM', 'AGE', 'TAX', 'B', 'random_num1', 'INDUS', 'random_cat', 'random_cat_2', 'RAD', 'ZN', 'random_num2', 'CHAS']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GrootCV with default regularization\n",
    "feat_selector = GrootCV(\n",
    "    objective=\"rmse\",\n",
    "    cutoff=1,\n",
    "    n_folds=5,\n",
    "    n_iter=5,\n",
    "    silent=True,\n",
    "    fastshap=True,\n",
    "    n_jobs=0,\n",
    "    lgbm_params=None,\n",
    ")\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef32b9aba9834348b2f1e323604cf6ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Repeated k-fold:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The selected features: ['CRIM' 'RM' 'DIS' 'TAX' 'PTRATIO' 'LSTAT' 'genuine_num']\n",
      "The agnostic ranking: [2 1 1 1 1 2 1 2 1 2 2 1 2 1 1 1 1 2]\n",
      "The naive ranking: ['LSTAT', 'genuine_num', 'RM', 'PTRATIO', 'TAX', 'CRIM', 'DIS', 'B', 'random_num1', 'AGE', 'NOX', 'random_cat', 'RAD', 'random_num2', 'random_cat_2', 'INDUS', 'ZN', 'CHAS']\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GrootCV with larger regularization\n",
    "feat_selector = GrootCV(\n",
    "    objective=\"rmse\",\n",
    "    cutoff=1,\n",
    "    n_folds=5,\n",
    "    n_iter=5,\n",
    "    silent=True,\n",
    "    fastshap=True,\n",
    "    n_jobs=0,\n",
    "    lgbm_params={\"min_data_in_leaf\": 100},\n",
    ")\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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "arfs",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}