8000 DOC add examples in docstring for decomposition by yuanx749 · Pull Request #28131 · scikit-learn/scikit-learn · GitHub
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merged 4 commits into from
Jan 15, 2024
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DOC add examples in docstring for decomposition #28131

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DOC add examples in docstring for decomposition
yuanx749 Jan 15, 2024
bdbb891
Merge remote-tracking branch 'upstream/main' into decomposition-docst…
yuanx749 Jan 15, 2024
d66de7d
Merge branch 'main' into decomposition-docstrings
glemaitre Jan 15, 2024
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glemaitre Jan 15, 2024
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67 changes: 67 additions & 0 deletions sklearn/decomposition/_dict_learning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -338,6 +338,23 @@ def sparse_encode(
sklearn.linear_model.Lasso : Train Linear Model with L1 prior as regularizer.
SparseCoder : Find a sparse representation of data from a fixed precomputed
dictionary.

Examples
--------
>>> import numpy as np
>>> from sklearn.decomposition import sparse_encode
>>> X = np.array([[-1, -1, -1], [0, 0, 3]])
>>> dictionary = np.array(
... [[0, 1, 0],
... [-1, -1, 2],
... [1, 1, 1],
... [0, 1, 1],
... [0, 2, 1]],
... dtype=np.float64
... )
>>> sparse_encode(X, dictionary, alpha=1e-10)
array([[ 0., 0., -1., 0., 0.],
[ 0., 1., 1., 0., 0.]])
"""
if check_input:
if algorithm == "lasso_cd":
Expand Down Expand Up @@ -804,6 +821,32 @@ def dict_learning_online(
learning algorithm.
SparsePCA : Sparse Principal Components Analysis.
MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.

Examples
--------
>>> import numpy as np
>>> from sklearn.datasets import make_sparse_coded_signal
>>> from sklearn.decomposition import dict_learning_online
>>> X, _, _ = make_sparse_coded_signal(
... n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10,
... random_state=42,
... )
>>> U, V = dict_learning_online(
... X, n_components=15, alpha=0.2, max_iter=20, batch_size=3, random_state=42
... )

We can check the level of sparsity of `U`:

>>> np.mean(U == 0)
0.53...

We can compare the average squared euclidean norm of the reconstruction
error of the sparse coded signal relative to the squared euclidean norm of
the original signal:

>>> X_hat = U @ V
>>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1))
0.05...
"""
# TODO(1.6): remove in 1.6
if max_iter is None:
Expand Down Expand Up @@ -982,6 +1025,30 @@ def dict_learning(
of the dictionary learning algorithm.
SparsePCA : Sparse Principal Components Analysis.
MiniBatchSparsePCA : Mini-batch Sparse Principal Components Analysis.

Examples
--------
>>> import numpy as np
>>> from sklearn.datasets import make_sparse_coded_signal
>>> from sklearn.decomposition import dict_learning
>>> X, _, _ = make_sparse_coded_signal(
... n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10,
... random_state=42,
... )
>>> U, V, errors = dict_learning(X, n_components=15, alpha=0.1, random_state=42)

We can check the level of sparsity of `U`:

>>> np.mean(U == 0)
0.6...

We can compare the average squared euclidean norm of the reconstruction
error of the sparse coded signal relative to the squared euclidean norm of
the original signal:

>>> X_hat = U @ V
>>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1))
0.01...
"""
estimator = DictionaryLearning(
n_components=n_components,
Expand Down
0