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": [],
   "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 2.3.1\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": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ed1e1a3c48c47fe930bf96d96a159f6",
       "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": [
      "n_jobs = 0, Execution time: 17.664 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "058731724aa941a4bdc622408ef03778",
       "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": [
      "n_jobs = 1, Execution time: 20.042 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e4051927c6143e19f8a02a50222da2e",
       "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": [
      "n_jobs = 2, Execution time: 12.272 seconds\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cdd308736b924b88a24a14d91682898f",
       "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": [
      "n_jobs = 3, Execution time: 15.318 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": "e957f22a655a488aa6799ca6ff504179",
       "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' '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', 'PTRATIO', 'DIS', 'NOX', 'CRIM', 'AGE', 'B', 'INDUS', 'ShadowVar14', 'ShadowVar1', 'ShadowVar12', 'ShadowVar13', 'ShadowVar11', 'ShadowVar10', 'RAD', 'ShadowVar15', '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": "a6d47216d2254999ad58f6c0806caf6f",
       "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', 'NOX', 'CRIM', 'AGE', 'B', 'ShadowVar1', 'ShadowVar13', 'INDUS', 'ShadowVar12', 'ShadowVar14', 'ShadowVar11', 'ShadowVar10', 'RAD', 'ShadowVar15', '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": "d1435597c99745799385b4a0d85975d3",
       "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' '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', 'RM', 'PTRATIO', 'CRIM', 'DIS', 'B', 'AGE', 'ShadowVar11', 'ShadowVar12', 'ShadowVar14', 'NOX', 'ShadowVar1', 'ShadowVar13', 'ShadowVar10', 'RAD', 'ShadowVar15', 'INDUS', '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": "3.10.14"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}