{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8226e658",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e95cb09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "X1",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "X2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Y",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "018727a2-2342-424f-8395-021f40817c5a",
       "rows": [
        [
         "0",
         "-0.8363543",
         "4.520502",
         "-19.868094121443526"
        ],
        [
         "1",
         "0.4020083",
         "3.252834",
         "-10.46598545005849"
        ],
        [
         "2",
         "-0.2492138",
         "3.610425",
         "-12.91499193423918"
        ],
        [
         "3",
         "-0.6257167",
         "4.58877",
         "-20.67839639765537"
        ],
        [
         "4",
         "-0.9899948",
         "4.893924",
         "-22.99404413854238"
        ]
       ],
       "shape": {
        "columns": 3,
        "rows": 5
       }
      },
      "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.836354</td>\n",
       "      <td>4.520502</td>\n",
       "      <td>-19.868094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.402008</td>\n",
       "      <td>3.252834</td>\n",
       "      <td>-10.465985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.249214</td>\n",
       "      <td>3.610425</td>\n",
       "      <td>-12.914992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.625717</td>\n",
       "      <td>4.588770</td>\n",
       "      <td>-20.678396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.989995</td>\n",
       "      <td>4.893924</td>\n",
       "      <td>-22.994044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2          Y\n",
       "0 -0.836354  4.520502 -19.868094\n",
       "1  0.402008  3.252834 -10.465985\n",
       "2 -0.249214  3.610425 -12.914992\n",
       "3 -0.625717  4.588770 -20.678396\n",
       "4 -0.989995  4.893924 -22.994044"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel(\"./data/data_pdp.xlsx\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e9a9a97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def partial_dependant_function(data: pd.DataFrame, model: object, feature: str, grid_points: list) -> list:\n",
    "    \"\"\"Compute the Partial Dependence Plot (PDP) for a given feature.\"\"\"\n",
    "    pdp = []\n",
    "    for val in grid_points:\n",
    "        data_temp = data.copy()\n",
    "        data_temp[feature] = val\n",
    "        preds = model.predict(data_temp)\n",
    "        pdp.append(preds.mean())\n",
    "    return pdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9553a1d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "studies",
   "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": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}