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ripsnet #587

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352fce1
ripsnet first commit
hensel-f Feb 16, 2022
51eeb03
ripsnet typos fixed
hensel-f Feb 16, 2022
f879d1d
changed perm_op API and added documentation
hensel-f Feb 22, 2022
8ff14bf
added test
hensel-f Mar 1, 2022
9eb3231
added test
hensel-f Mar 1, 2022
8e5003f
updated test
hensel-f Mar 2, 2022
496a141
Update src/python/doc/ripsnet.inc
hensel-f Mar 7, 2022
fcf7edf
Fixed imports
hensel-f Mar 7, 2022
e23e929
Fixed imports
hensel-f Mar 7, 2022
25b842e
updated documentation
hensel-f Mar 7, 2022
f01e3cc
updated CMakeLists.txt
hensel-f Mar 7, 2022
9281794
updated documentation and bibliography
hensel-f Mar 7, 2022
6b9ecce
updated CMakeLists.txt
hensel-f Mar 10, 2022
ec19f2d
Update src/python/doc/ripsnet.inc
hensel-f Mar 10, 2022
aef67bd
documentation fix
hensel-f Mar 10, 2022
28d7320
increased error margin from 1e-7 to 1e-6
hensel-f Mar 10, 2022
32ba96b
fixed imports
hensel-f Mar 10, 2022
33424c8
fixed imports
hensel-f Mar 10, 2022
146b34e
fixed imports
hensel-f Mar 10, 2022
f29ca81
fixed testoutput check
hensel-f Mar 11, 2022
6edf471
removed print statement
hensel-f Mar 11, 2022
59dc256
update of gitignore
hensel-f Apr 26, 2022
db348e6
removede numpy import and added explanation
hensel-f Apr 26, 2022
dd5e94e
changed name of pop to perm_op
hensel-f Apr 26, 2022
25222ca
changed TFBlock to support regged inputs and commented DenseRaggedBlock
hensel-f Apr 26, 2022
63edfc2
changed to TFBlock in test_ripsnet.py
hensel-f Apr 26, 2022
1cca679
fixed __init__.py
hensel-f Apr 26, 2022
1122d11
fixed documentation
hensel-f Apr 26, 2022
a25d3f8
allowing user specified permop functions
hensel-f Jun 7, 2022
a05f825
updated comments in the documentation and changed imports
hensel-f Jun 7, 2022
67cb4d4
added get_config()
hensel-f Jun 20, 2022
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9 changes: 9 additions & 0 deletions .gitignore
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Expand Up @@ -18,3 +18,12 @@ data/points/human.off_sc.txt
.idea/
cmake-build-debug/

# Poetry files
*.lock
*.toml
./venv

# Ripsnet files
ripsnet_doc_test.py
src/python/gudhi/tensorflow/tutorial.ipynb

35 changes: 35 additions & 0 deletions biblio/bibliography.bib
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@@ -1,3 +1,38 @@
@article{RipsNet_arXiv,
author = {Thibault de Surrel and
Felix Hensel and
Mathieu Carri{\`{e}}re and
Th{\'{e}}o Lacombe and
Yuichi Ike and
Hiroaki Kurihara and
Marc Glisse and
Fr{\'{e}}d{\'{e}}ric Chazal},
title = {RipsNet: a general architecture for fast and robust estimation of
the persistent homology of point clouds},
journal = {CoRR},
volume = {abs/2202.01725},
year = {2022},
url = {https://arxiv.org/abs/2202.01725},
eprinttype = {arXiv},
eprint = {2202.01725},
timestamp = {Wed, 09 Feb 2022 15:43:35 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2202-01725.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DeepSets17,
author = {Zaheer, Manzil and Kottur, Satwik and Ravanbakhsh, Siamak and Poczos, Barnabas and Salakhutdinov, Russ R and Smola, Alexander J},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Deep Sets},
url = {https://proceedings.neurips.cc/paper/2017/file/f22e4747da1aa27e363d86d40ff442fe-Paper.pdf},
volume = {30},
year = {2017}
}


@inproceedings{gudhilibrary_ICMS14,
author = {Cl\'ement Maria and Jean-Daniel Boissonnat and
Marc Glisse and Mariette Yvinec},
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7 changes: 7 additions & 0 deletions src/python/CMakeLists.txt
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Expand Up @@ -70,6 +70,7 @@ if(PYTHONINTERP_FOUND)
set(GUDHI_PYTHON_MODULES "${GUDHI_PYTHON_MODULES}'euclidean_strong_witness_complex', ")
# Modules that should not be auto-imported in __init__.py
set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'representations', ")
set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'tensorflow', ")
set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'wasserstein', ")
set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'point_cloud', ")
set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'weighted_rips_complex', ")
Expand Down Expand Up @@ -285,6 +286,7 @@ if(PYTHONINTERP_FOUND)
file(COPY "gudhi/dtm_rips_complex.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi")
file(COPY "gudhi/hera/__init__.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/hera")
file(COPY "gudhi/datasets" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi" FILES_MATCHING PATTERN "*.py")
file(COPY "gudhi/tensorflow" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi")


# Some files for pip package
Expand Down Expand Up @@ -551,6 +553,11 @@ if(PYTHONINTERP_FOUND)
add_gudhi_py_test(test_representations)
endif()

# Differentiation
if(TENSORFLOW_FOUND)
add_gudhi_py_test(test_ripsnet)
endif()

# Betti curves
if(SKLEARN_FOUND AND SCIPY_FOUND)
add_gudhi_py_test(test_betti_curve_representations)
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2 changes: 2 additions & 0 deletions src/python/doc/installation.rst
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Expand Up @@ -395,6 +395,8 @@ TensorFlow

`TensorFlow <https://www.tensorflow.org>`_ is currently only used in some automatic differentiation tests.

:doc:`RipsNet <ripsnet>` module requires `TensorFlow <https://https://www.tensorflow.org/install/>`_.

Bug reports and contributions
*****************************

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15 changes: 15 additions & 0 deletions src/python/doc/ripsnet.inc
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.. table::
:widths: 30 40 30

+----------------------------------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+
| | RipsNet is a general architecture for fast and robust estimation of the | :Author: Felix Hensel, Mathieu Carrière |
| | persistent homology of point clouds. | |
| | | :Since: GUDHI |
| | | |
| | | :License: MIT |
| | | |
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| | | :Requires: `TensorFlow <installation.html#tensorflow>`_ |
| | | |
+----------------------------------------------------------------+-------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+
| * :doc:`ripsnet` | |
+----------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
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102 changes: 102 additions & 0 deletions src/python/doc/ripsnet.rst
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:orphan:

.. To get rid of WARNING: document isn't included in any toctree

RipsNet user manual
=========================
Definition
----------

.. include:: ripsnet.inc

:class:`~gudhi.ripsnet` constructs a Tensorflow model for fast and robust estimation of persistent homology of
point clouds.
RipsNet is based on a Deep Sets architecture :cite:`DeepSets17`, for details see the paper RipsNet :cite:`RipsNet_arXiv`.

Example
-------------------

This example instantiates a RipsNet model which can then be trained as any tensorflow model.

.. testcode::

import gudhi.tensorflow as gtf
import tensorflow as tf
from tensorflow.keras import regularizers, layers

ragged_layers_size = [20, 10]
dense_layers_size = [10, 20]
output_units = 25
activation_fct = 'gelu'
output_activation = 'sigmoid'
dropout = 0
kernel_regularization = 0
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Perhaps it may be worth to comment (not in detail) what are these hyper-parameters ; in particular how one is supposed to chose ragged_layers_size and dense_layers_size.

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Sure, I added a comment saying they should be tuned according to the specific dataset in order to reach a better performance.


ragged_layers = []
dense_layers = []

for n_units in ragged_layers_size:
ragged_layers.append(gtf.DenseRagged(units=n_units, use_bias=True, activation=activation_fct))

for n_units in dense_layers_size:
dense_layers.append(layers.Dense(n_units, activation=activation_fct,
kernel_regularizer=regularizers.l2(kernel_regularization)))
dense_layers.append(layers.Dropout(dropout))

dense_layers.append(layers.Dense(output_units, activation=output_activation))

phi_1 = gtf.TFBlock(ragged_layers)
perm_op = 'mean' # can also be 'sum' (or a user specified function).
phi_2 = gtf.TFBlock(dense_layers)
input_dim = 2

RN = gtf.RipsNet(phi_1, phi_2, input_dim, perm_op=perm_op)

data_test = [[[-7.04493841, 9.60285858],
[-13.14389003, -13.21854157],
[-3.21137961, -1.28593644]],
[[10.40324933, -0.80540584],
[16.54752459, 0.70355361],
[6.410207, -10.63175183],
[2.96613799, -11.97463568]],
[[4.85041719, -2.93820024],
[2.15379915, -5.39669696],
[5.83968556, -5.67350982],
[5.25955172, -6.36860269]]]

tf_data_test = tf.ragged.constant(data_test, ragged_rank=1)

RN.predict(tf_data_test)

Once RN is properly trained (which we skip in this documentation) it can be used to make predictions.
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Would it be cumbersome to provide a sort of minimal working example to train RipsNet ?

A user may not be familiar with tensorflow and have no clue on how to train the RipsNet model at this stage. Perhaps just setting the typical optimizer = ..., loss_function = ... , and do a single step of gradient descent here, would help to and not discourage the user not familiar with tf?

Another option is to write a Tutorial ( https://github.com/GUDHI/TDA-tutorial ) to reproduce, say, the synthetic experiment of the paper (multiple_circles) and to refer to it in this doc (I understand that we don't want this doc to be too long).

A final option (that requires more development) would be to provide a method train to RipsNet that does the job with some default parameters, so that one could get starting by simply going for something like

RN = ripsnet.RipsNet(...)
RN.train(train_data)
RN.predict(test_data)

Of course these are just suggestions.

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I think the best option is to link to the notebook containing the synthetic examples. This provides a very nice example where one can see the workflow.

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I've adapted Mathieu's original tutorial on the synthetic data so that it illustrates the use (including setup and training) of a RipsNet architecture. So, as @tlacombe suggested, I think it may be nice to include and link to this tutorial somewhere.

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I've opened a PR (here: GUDHI/TDA-tutorial#59) to include the tutorial notebook so that we can then link to it.

In this example RipsNet estimates persistence vectorizations (of output size 25) of a list of 3 point clouds
of 3 points each) in 2D.
It yields an output of shape 'nb_input_pointclouds x output_units'.
The 'ragged_layers_size' and 'dense_layers_size' define the architecture of the network.
To reach best performance, they should be tuned depending on the dataset.
A possible output is:

.. code-block::

[[0.58554363 0.6054868 0.44672886 0.5216672 0.5814481 0.48068565
0.49626726 0.5285395 0.4805212 0.37918684 0.49745193 0.49247316
0.4706078 0.5491477 0.47016636 0.55804974 0.46501246 0.4065692
0.5386659 0.5660226 0.52014357 0.5329493 0.52178216 0.5156043
0.48742113]
[0.9446074 0.99024785 0.1316272 0.3013248 0.98174655 0.52285945
0.33727515 0.997285 0.3711884 0.00388432 0.63181967 0.5377489
0.22074646 0.7681194 0.04337704 0.80116796 0.02139336 0.04605395
0.8911999 0.9570045 0.5789719 0.8221929 0.7742506 0.4596561
0.08529088]
[0.8230771 0.9320036 0.25120026 0.48027694 0.8988322 0.5789062
0.38307947 0.9252455 0.39485127 0.06090912 0.5786307 0.51115406
0.28706372 0.70552015 0.16929033 0.7028084 0.12379596 0.1867683
0.6969584 0.84437454 0.6172329 0.66728634 0.630455 0.47643042
0.27172992]]

Detailed documentation
----------------------
.. automodule:: gudhi.tensorflow.ripsnet
:members:
:special-members:
:show-inheritance:
3 changes: 3 additions & 0 deletions src/python/gudhi/tensorflow/__init__.py
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from .ripsnet import *

__all__ = ["RipsNet", "PermopRagged", "TFBlock", "DenseRagged"]
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