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:orphan:
.. To get rid of WARNING: document isn't included in any toctree
Cubical complex persistence scikit-learn like interface
#######################################################
.. list-table::
:width: 100%
:header-rows: 0
* - :Since: GUDHI 3.6.0
- :License: MIT
- :Requires: `Scikit-learn <installation.html#scikit-learn>`_
Cubical complex persistence scikit-learn like interface example
---------------------------------------------------------------
In this example, hand written digits are used as an input.
a TDA scikit-learn pipeline is constructed and is composed of:
#. :class:`~gudhi.sklearn.cubical_persistence.CubicalPersistence` that builds a cubical complex from the inputs and
returns its persistence diagrams
#. :class:`~gudhi.representations.preprocessing.DiagramSelector` that removes non-finite persistence diagrams values
#. :class:`~gudhi.representations.vector_methods.PersistenceImage` that builds the persistence images from persistence diagrams
#. `SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_ which is a scikit-learn support
vector classifier.
This ML pipeline is trained to detect if the hand written digit is an '8' or not, thanks to the fact that an '8' has
two holes in :math:`\mathbf{H}_1`, or, like in this example, three connected components in :math:`\mathbf{H}_0`.
.. code-block:: python
# Standard scientific Python imports
import numpy as np
# Standard scikit-learn imports
from sklearn.datasets import fetch_openml
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn import metrics
# Import TDA pipeline requirements
from gudhi.sklearn.cubical_persistence import CubicalPersistence
from gudhi.representations import PersistenceImage, DiagramSelector
X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False)
# Target is: "is an eight ?"
y = (y == "8") * 1
print("There are", np.sum(y), "eights out of", len(y), "numbers.")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
pipe = Pipeline(
[
("cub_pers", CubicalPersistence(homology_dimensions=0, newshape=[28, 28], n_jobs=-2)),
# Or for multiple persistence dimension computation
# ("cub_pers", CubicalPersistence(homology_dimensions=[0, 1], newshape=[28, 28], n_jobs=-2)),
# ("H0_diags", DimensionSelector(index=0), # where index is the index in homology_dimensions array
("finite_diags", DiagramSelector(use=True, point_type="finite")),
(
"pers_img",
PersistenceImage(bandwidth=50, weight=lambda x: x[1] ** 2, im_range=[0, 256, 0, 256], resolution=[20, 20]),
),
("svc", SVC()),
]
)
# Learn from the train subset
pipe.fit(X_train, y_train)
# Predict from the test subset
predicted = pipe.predict(X_test)
print(f"Classification report for TDA pipeline {pipe}:\n" f"{metrics.classification_report(y_test, predicted)}\n")
.. code-block:: none
There are 6825 eights out of 70000 numbers.
Classification report for TDA pipeline Pipeline(steps=[('cub_pers',
CubicalPersistence(newshape=[28, 28], n_jobs=-2)),
('finite_diags', DiagramSelector(use=True)),
('pers_img',
PersistenceImage(bandwidth=50, im_range=[0, 256, 0, 256],
weight=<function <lambda> at 0x7f3e54137ae8>)),
('svc', SVC())]):
precision recall f1-score support
0 0.97 0.99 0.98 25284
1 0.92 0.68 0.78 2716
accuracy 0.96 28000
macro avg 0.94 0.84 0.88 28000
weighted avg 0.96 0.96 0.96 28000
Cubical complex persistence scikit-learn like interface reference
-----------------------------------------------------------------
.. autoclass:: gudhi.sklearn.cubical_persistence.CubicalPersistence
:members:
:special-members: __init__
:show-inheritance:
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