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@aryan-jadon
Created September 16, 2022 02:20
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{
"cells": [
{
"cell_type": "markdown",
"id": "7f91dbe9",
"metadata": {},
"source": [
"## Partial Dependency"
]
},
{
"cell_type": "markdown",
"id": "09dfb676",
"metadata": {},
"source": [
"Partial dependency plots are often used to interpret the model better (assuming independence of features). "
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "68c1e31e",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc0258560e264ef3a36b7dda8bcc3f7d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Predict: 0%| | 0/30 [00:00<?, ? batches/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dependency = best_tft.predict_dependency(\n",
" val_dataloader.dataset, \"discount_in_percent\", np.linspace(0, 30, 30), show_progress_bar=True, mode=\"dataframe\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "47a6052d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting median and 25% and 75% percentile\n",
"agg_dependency = dependency.groupby(\"discount_in_percent\").normalized_prediction.agg(\n",
" median=\"median\", q25=lambda x: x.quantile(0.25), q75=lambda x: x.quantile(0.75)\n",
")\n",
"ax = agg_dependency.plot(y=\"median\")\n",
"ax.fill_between(agg_dependency.index, agg_dependency.q25, agg_dependency.q75, alpha=0.3);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9 (pytorch)",
"language": "python",
"name": "pytorch"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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