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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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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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99 changes: 99 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::

from gudhi.tensorflow import *
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It may be better to be explicit, i.e. naming the imported functions or to do import gudhi.tensorflow as gtf just to make clear to the user which functions below are indeed coming from the gudhi.tensorflow package.

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I agree, that makes it a bit clearer.

import tensorflow as tf
from tensorflow.keras import regularizers, layers
import numpy as np

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(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 = DenseRaggedBlock(ragged_layers)
perm_op = 'mean' # can also be 'sum'.
phi_2 = TFBlock(dense_layers)
input_dim = 2

RN = RipsNet(phi_1, phi_2, input_dim, perm_op=perm_op)
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data_test = [np.array([[-7.04493841, 9.60285858],
[-13.14389003, -13.21854157],
[-3.21137961, -1.28593644]]),
np.array([[10.40324933, -0.80540584],
[16.54752459, 0.70355361],
[6.410207, -10.63175183],
[2.96613799, -11.97463568]]),
np.array([[4.85041719, -2.93820024],
[2.15379915, -5.39669696],
[5.83968556, -5.67350982],
[5.25955172, -6.36860269]])]

tf_data_test = tf.ragged.constant([
[list(c) for c in list(data_test[i])] for i in range(len(data_test))], ragged_rank=1)
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Uh, so we build data_test using numpy (from lists), only to convert it again to lists here, and finally build a tensorflow object from those lists? Would it be possible to skip some of those conversions? We probably don't need to import numpy at all.

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Yes, you're right, thanks. I updated it.


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.

A possible output is:
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It isn't obvious what the example is computing. Maybe adding a comment or 2 would help (or an image). Is data_test a list of 3 point sets in 2D, and is the output some kind of vectorized persistence diagram? It becomes clear once we read the detailed doc, but I think some minimal comments in the example would still make sense.

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Good point, I added a sentence to describe it.


.. 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", "DenseRaggedBlock", "DenseRagged"]
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