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Merge pull request #183 from devendragovil/tpm3
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Fixes #169, Thumbnails Updated
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ninamiolane authored Sep 13, 2023
2 parents efc1d1c + 6ed555e commit 1ece602
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25 changes: 24 additions & 1 deletion docs/tutorials/index.rst
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Tutorials
=========

---------------------------------------------------------
Tutorials for Topological Neural Nets on Cellular Complex
---------------------------------------------------------

.. nbgallery::
:maxdepth: 1
:glob:

../notebooks/cell/*


----------------------------------------------------
Tutorials for Topological Neural Nets on Hypergraphs
----------------------------------------------------

.. nbgallery::
:maxdepth: 1
:glob:

../notebooks/hypergraph/*
../notebooks/simplicial/*

-----------------------------------------------------------
Tutorials for Topological Neural Nets on Simplicial Complex
-----------------------------------------------------------

.. nbgallery::
:maxdepth: 1
:glob:

../notebooks/simplicial/*
16 changes: 16 additions & 0 deletions tutorials/README.md
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@@ -0,0 +1,16 @@

# Tutorials

This section contains tutorials for a variety of topological neural networks.

## Contribution

To add an additional tutorial follow the following:
1. Prepare a well documented and self-explanatory .ipynb file using various modules of the library.
2. Place your notebook in the right directory depending on the data structure used. e.g. cell for Cellular Complex
3. The notebook title (H1 heading) will be displayed as the title of the notebook in the Sphinx Notebook Gallery, so please chose that wisely.
4. Make sure the notebook is able to test pytest for the notebook.
5. Prepare a thumbnail (ideally a tensor diagram) for the notebook.
6. Place the thumbnail in the docs/_thumbnails under the correct directory. "cell" directory in our example as used above.
7. Make sure to change the conf.py file present within docs to include the thumbnail to be included in Sphinx generated Gallery.

9 changes: 7 additions & 2 deletions tutorials/cell/ccxn_train.ipynb
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Expand Up @@ -634,7 +634,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.6 64-bit",
"language": "python",
"name": "python3"
},
Expand All @@ -648,7 +648,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.9.6"
},
"vscode": {
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}
}
},
"nbformat": 4,
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9 changes: 7 additions & 2 deletions tutorials/cell/cwn_train.ipynb
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Expand Up @@ -635,7 +635,7 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.6 64-bit",
"language": "python",
"name": "python3"
},
Expand All @@ -649,7 +649,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.9.6"
},
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},
"nbformat": 4,
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11 changes: 8 additions & 3 deletions tutorials/hypergraph/allset_train.ipynb
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Expand Up @@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a Hypergraph Neural Network\n",
"# Train an All-Set TNN\n",
"\n",
"In this notebook, we will create and train a two-step message passing network named AllSet (Chien et al., [2021](https://arxiv.org/abs/2106.13264)) in the hypergraph domain. We will use a benchmark dataset, shrec16, a collection of 3D meshes, to train the model to perform classification at the level of the hypergraph. \n",
"\n",
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.6 64-bit",
"language": "python",
"name": "python3"
},
Expand All @@ -514,7 +514,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.9.6"
},
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}
}
},
"nbformat": 4,
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11 changes: 8 additions & 3 deletions tutorials/hypergraph/allset_transformer_train.ipynb
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Expand Up @@ -7,7 +7,7 @@
"source": [
"\n",
"\n",
"# Train a Hypergraph Neural Network\n",
"# Train an All-Set-Transformer TNN\n",
"\n",
"In this notebook, we will create and train a two-step message passing network named AllSetTransformer (Chien et al., [2021](https://arxiv.org/abs/2106.13264)) in the hypergraph domain. We will use a benchmark dataset, shrec16, a collection of 3D meshes, to train the model to perform classification at the level of the hypergraph. \n",
"\n",
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.6 64-bit",
"language": "python",
"name": "python3"
},
Expand All @@ -526,7 +526,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.9.6"
},
"vscode": {
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}
}
},
"nbformat": 4,
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11 changes: 8 additions & 3 deletions tutorials/hypergraph/dhgcn_train.ipynb
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Expand Up @@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a Hypergraph Neural Network\n",
"# Train a DHGCN TNN\n",
"\n",
"In this notebook, we will create and train a two-step message passing network in the hypergraph domain. We will use a benchmark dataset, shrec16, a collection of 3D meshes, to train the model to perform classification at the level of the hypergraph. "
]
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.6 64-bit",
"language": "python",
"name": "python3"
},
Expand All @@ -480,7 +480,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.9.6"
},
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}
}
},
"nbformat": 4,
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13 changes: 9 additions & 4 deletions tutorials/hypergraph/hypersage_train.ipynb
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Expand Up @@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a Hypergraph Neural Network\n",
"# Train a Hypersage TNN\n",
"\n",
"In this notebook, we will create and train HyperSAGE layer (Arya et al., [2020](https://arxiv.org/abs/2010.04558)) - two-levels message passing strategy for hypergraphs learning. We will use a benchmark dataset, shrec16, a collection of 3D meshes, to train the model to perform classification at the level of the hypergraph.\n",
"\n",
Expand Down Expand Up @@ -389,7 +389,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "torchg",
"display_name": "Python 3.9.6 64-bit",
"language": "python",
"name": "python3"
},
Expand All @@ -403,9 +403,14 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.9.6"
},
"orig_nbformat": 4
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},
"nbformat": 4,
"nbformat_minor": 2
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11 changes: 8 additions & 3 deletions tutorials/hypergraph/template_train.ipynb
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Expand Up @@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a Hypergraph Neural Network\n",
"# Train a (template) Hypergraph Neural Network\n",
"\n",
"In this notebook, we will create and train a two-step message passing network in the hypergraph domain. We will use a benchmark dataset, shrec16, a collection of 3D meshes, to train the model to perform classification at the level of the hypergraph. "
]
Expand Down Expand Up @@ -467,7 +467,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.6 64-bit",
"language": "python",
"name": "python3"
},
Expand All @@ -481,7 +481,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.9.6"
},
"vscode": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
}
}
},
"nbformat": 4,
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8 changes: 8 additions & 0 deletions tutorials/hypergraph/unigcn_train.ipynb
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Expand Up @@ -22,6 +22,14 @@
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"id": "ca204a1d",
"metadata": {},
"source": [
"# Train a UNIGCN TNN"
]
},
{
"cell_type": "markdown",
"id": "40b0ed66",
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7 changes: 7 additions & 0 deletions tutorials/hypergraph/unigin_train.ipynb
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Expand Up @@ -15,6 +15,13 @@
"from topomodelx.nn.hypergraph.unigin_layer import UniGINLayer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a UNIGIN TNN"
]
},
{
"attachments": {},
"cell_type": "markdown",
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8 changes: 8 additions & 0 deletions tutorials/hypergraph/unisage_train.ipynb
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Expand Up @@ -31,6 +31,14 @@
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"id": "b7251cc4",
"metadata": {},
"source": [
"# Train a Uni-sage TNN"
]
},
{
"cell_type": "markdown",
"id": "35742145",
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