mirror of
https://github.com/ArthurDanjou/ArtStudies.git
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209 lines
4.8 KiB
Plaintext
209 lines
4.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "7e95cb09",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"columns": [
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"name": "index",
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{
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"name": "X1",
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"type": "float"
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},
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{
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"name": "X2",
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"rawType": "float64",
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"type": "float"
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},
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{
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"name": "Y",
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"rawType": "float64",
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"type": "float"
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}
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"1",
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" <thead>\n",
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" <th></th>\n",
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" <th>X1</th>\n",
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" <th>X2</th>\n",
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" <th>Y</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0.402008</td>\n",
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" <th>2</th>\n",
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"text/plain": [
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" X1 X2 Y\n",
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"0 -0.836354 4.520502 -19.868094\n",
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"1 0.402008 3.252834 -10.465985\n",
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},
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"execution_count": 12,
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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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"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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}
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],
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