Add new data files and update dependencies

- Added TP_ACE.pdf to the project.
- Introduced perf_circle.csv containing performance circle data.
- Added test_lissage.csv with test data for smoothing.
- Updated pyproject.toml to include statsmodels as a dependency.
- Modified uv.lock to reflect the addition of statsmodels and its dependencies.

M2/Advanced Machine Learning/TP1.ipynb

@@ -1,208 +0,0 @@
-{
- "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"
-        ],
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-         "0.4020083",
-         "3.252834",
-         "-10.46598545005849"
-        ],
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-         "3.610425",
-         "-12.91499193423918"
-        ],
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-         "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
-}

M2/Advanced Machine Learning/TP2/perf_circle.csv

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M2/Advanced Machine Learning/TP2/test_lissage.csv

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