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rearranging, cleaning, adding adapters example
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Nathaniel Saul committed Nov 8, 2018
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10 changes: 9 additions & 1 deletion docs/index.rst
Expand Up @@ -32,8 +32,16 @@ Contents
:maxdepth: 1
:caption: Tutorials

KeplerMapper Newsgroup20 Pipeline
Adapters
Plotly Demo
KeplerMapper usage in Jupyter Notebook

.. toctree::
:maxdepth: 1
:caption: Advanced Tutorials

KeplerMapper Newsgroup20 Pipeline
TOR-XGB-TDA
Confidence Graphs
self-guessing

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120 changes: 120 additions & 0 deletions notebooks/Adapters.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adapters example\n",
"\n",
"Demonstration using the networkx adapter. This example requires installing the `networkx` library before continuing:\n",
"```\n",
"pip install networkx\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import kmapper\n",
"from sklearn import datasets\n",
"import networkx as nx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Construct data and a simple Mapper"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = datasets.make_circles(n_samples=1000)[0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"km = kmapper.KeplerMapper()\n",
"lens = km.project(data)\n",
"graph = km.map(X=data, lens=lens)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert Mapper to a networkx graph\n",
"\n",
"We can easily convert the graph to a networkx graph representation. This enables us to use many of the commonly provided algorithms and visualization methods."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"nx_graph = kmapper.adapter.to_nx(graph)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nx.draw(nx_graph)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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