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Last active October 11, 2021 19:18
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Notebook to accompany RAPIDS cuML HDBSCAN Blog
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{
"cells": [
{
"cell_type": "markdown",
"id": "9e99c9a2-5c77-452c-8473-fa7d8c2c1d3b",
"metadata": {},
"source": [
"## Basic Usage"
]
},
{
"cell_type": "markdown",
"id": "936a1479-0ec9-407e-901a-955d58054439",
"metadata": {},
"source": [
"Example of training an HDBSCAN model using the hdbscan Python package in Scikit-learn contrib:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3385aa15-40e3-4efc-a759-9c336126ee69",
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"from hdbscan import HDBSCAN\n",
"\n",
"X, y = datasets.make_moons(n_samples=50, noise=0.05)\n",
"\n",
"model = HDBSCAN(min_samples=5)\n",
"y_hat = model.fit_predict(X)"
]
},
{
"cell_type": "markdown",
"id": "ff9574e8-3600-4501-acfc-fb0f94b8fbad",
"metadata": {},
"source": [
"And the same code using the GPU-Accelerated HDBSCAN in cuML (spoiler alert: the only difference is the import)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6d008678-e2b9-4285-8a65-78a063ee995d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/share/workspace/cuml/python/cuml/internals/api_decorators.py:794: FutureWarning: Pass handle=None, verbose=False, output_type=None as keyword args. From version 21.06, passing these as positional arguments will result in an error\n",
" return func(**kwargs)\n"
]
}
],
"source": [
"from sklearn import datasets\n",
"from cuml.cluster import HDBSCAN\n",
"\n",
"X, y = datasets.make_moons(n_samples=50, noise=0.05)\n",
"\n",
"model = HDBSCAN(min_samples=5, gen_min_span_tree=True)\n",
"y_hat = model.fit_predict(X)"
]
},
{
"cell_type": "markdown",
"id": "dfcfd131-4ece-4423-b7c5-9ad78eb909fc",
"metadata": {},
"source": [
"## Plotting"
]
},
{
"cell_type": "markdown",
"id": "e6dc568c-6b1f-4da6-a29b-5beb9a7decb5",
"metadata": {},
"source": [
"We can plot the minimum spanning tree the same way we would for the original HDBSCAN implementation:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6cf3ad0a-bd6f-4666-a4e7-20bbf63e7be9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.minimum_spanning_tree_.plot()"
]
},
{
"cell_type": "markdown",
"id": "9bb7e76f-5c32-43d4-9cd8-21d56f874e78",
"metadata": {},
"source": [
"The single linkage dendrogram and condensed tree can be plotted as well:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "82073b1b-4fe0-4efb-afa8-7783712a1a2a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='distance'>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.single_linkage_tree_.plot()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f29e68cc-afbb-48d9-a0d4-a3cd09fb69b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='$\\\\lambda$ value'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.condensed_tree_.plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (cuml_2108_062421)",
"language": "python",
"name": "cuml_2108_062421"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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