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Ajout de fichiers pour le calcul des graphiques de dépendance partielle : ajout de TP1.ipynb et data/data_pdp.xlsx ; mise à jour des dépendances dans pyproject.toml et uv.lock
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208
M2/Advanced Machine Learning/TP1.ipynb
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208
M2/Advanced Machine Learning/TP1.ipynb
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{
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"id": "8226e658",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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"execution_count": 12,
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"<style scoped>\n",
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" X1 X2 Y\n",
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = pd.read_excel(\"./data/data_pdp.xlsx\")\n",
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"data.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "4e9a9a97",
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"metadata": {},
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"outputs": [],
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"source": [
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"def partial_dependant_function(data: pd.DataFrame, model: object, feature: str, grid_points: list) -> list:\n",
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" \"\"\"Compute the Partial Dependence Plot (PDP) for a given feature.\"\"\"\n",
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" pdp = []\n",
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" for val in grid_points:\n",
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" data_temp = data.copy()\n",
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" data_temp[feature] = val\n",
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" preds = model.predict(data_temp)\n",
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" pdp.append(preds.mean())\n",
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" return pdp"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9553a1d8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "studies",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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BIN
M2/Advanced Machine Learning/data/data_pdp.xlsx
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BIN
M2/Advanced Machine Learning/data/data_pdp.xlsx
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