Add a more relevant example inspired from https://dioscuri-tda.org/Paris_TDA_Tutorial_2021.html

1 files changed, 43 insertions, 23 deletions

diff --git a/src/python/doc/cubical_complex_user.rst b/src/python/doc/cubical_complex_user.rst
index 12971243..ebecb592 100644
--- a/src/python/doc/cubical_complex_user.rst
+++ b/src/python/doc/cubical_complex_user.rst
@@ -173,36 +173,56 @@ explains how to represent sublevels sets of functions using cubical complexes.
 Scikit-learn like interface example
 -----------------------------------
 
-.. plot::
-   :include-source:
+.. code-block:: python
 
     # Standard scientific Python imports
-    import matplotlib.pyplot as plt
-    from sklearn import datasets
+    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 cubical persistence computation scikit-learn interfaces
+    # Import TDA pipeline requirements
     from gudhi.sklearn.cubical_persistence import CubicalPersistence
-    # Import persistence representation
     from gudhi.representations import PersistenceImage, DiagramSelector
     
-    # Get the first 10 images from scikit-learn hand digits dataset
-    digits = datasets.load_digits().images[:10]
-    targets = datasets.load_digits().target[:10]
+    X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)
     
-    # TDA pipeline
-    cub = CubicalPersistence(persistence_dim = 0, n_jobs=-2)
-    diags = cub.fit_transform(digits)
+    # Target is: "is an eight ?"
+    y = (y == '8') * 1
+    print('There are', np.sum(y), 'eights out of', len(y), 'numbers.')
     
-    finite = DiagramSelector(use=True, point_type="finite")
-    finite_diags = finite.fit_transform(diags)
+    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(persistence_dim = 0, dimensions=[28,28], n_jobs=-2)),
+                     ('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())])
     
-    persim = PersistenceImage(im_range=[0,16,0,16], resolution=[16, 16])
-    pers_images = persim.fit_transform(finite_diags)
+    predicted = pipe.predict(X_test)
     
-    # Display persistence images
-    _, axes = plt.subplots(nrows=1, ncols=10, figsize=(15, 3))
-    for ax, image, label in zip(axes, pers_images, targets):
-        ax.set_axis_off()
-        ax.imshow(image.reshape(16, 16), cmap=plt.cm.gray_r, interpolation='nearest')
-        ax.set_title('Target: %i' % label)
-    plt.show()
+    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(dimensions=[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
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