.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neural_networks/plot_rbm_logistic_classification.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_neural_networks_plot_rbm_logistic_classification.py>`
to download the full example code or to run this example in your browser via JupyterLite or Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py:
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten digit recognition, the
Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM
<sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear
feature extraction.
.. GENERATED FROM PYTHON SOURCE LINES 13-17
.. code-block:: Python
# Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve
# License: BSD
.. GENERATED FROM PYTHON SOURCE LINES 18-24
Generate data
-------------
In order to learn good latent representations from a small dataset, we
artificially generate more labeled data by perturbing the training data with
linear shifts of 1 pixel in each direction.
.. GENERATED FROM PYTHON SOURCE LINES 24-62
.. code-block:: Python
import numpy as np
from scipy.ndimage import convolve
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import minmax_scale
def nudge_dataset(X, Y):
"""
This produces a dataset 5 times bigger than the original one,
by moving the 8x8 images in X around by 1px to left, right, down, up
"""
direction_vectors = [
[[0, 1, 0], [0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [1, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 1], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [0, 1, 0]],
]
def shift(x, w):
return convolve(x.reshape((8, 8)), mode="constant", weights=w).ravel()
X = np.concatenate(
[X] + [np.apply_along_axis(shift, 1, X, vector) for vector in direction_vectors]
)
Y = np.concatenate([Y for _ in range(5)], axis=0)
return X, Y
X, y = datasets.load_digits(return_X_y=True)
X = np.asarray(X, "float32")
X, Y = nudge_dataset(X, y)
X = minmax_scale(X, feature_range=(0, 1)) # 0-1 scaling
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
.. GENERATED FROM PYTHON SOURCE LINES 63-69
Models definition
-----------------
We build a classification pipeline with a BernoulliRBM feature extractor and
a :class:`LogisticRegression <sklearn.linear_model.LogisticRegression>`
classifier.
.. GENERATED FROM PYTHON SOURCE LINES 69-79
.. code-block:: Python
from sklearn import linear_model
from sklearn.neural_network import BernoulliRBM
from sklearn.pipeline import Pipeline
logistic = linear_model.LogisticRegression(solver="newton-cg", tol=1)
rbm = BernoulliRBM(random_state=0, verbose=True)
rbm_features_classifier = Pipeline(steps=[("rbm", rbm), ("logistic", logistic)])
.. GENERATED FROM PYTHON SOURCE LINES 80-86
Training
--------
The hyperparameters of the entire model (learning rate, hidden layer size,
regularization) were optimized by grid search, but the search is not
reproduced here because of runtime constraints.
.. GENERATED FROM PYTHON SOURCE LINES 86-108
.. code-block:: Python
from sklearn.base import clone
# Hyper-parameters. These were set by cross-validation,
# using a GridSearchCV. Here we are not performing cross-validation to
# save time.
rbm.learning_rate = 0.06
rbm.n_iter = 10
# More components tend to give better prediction performance, but larger
# fitting time
rbm.n_components = 100
logistic.C = 6000
# Training RBM-Logistic Pipeline
rbm_features_classifier.fit(X_train, Y_train)
# Training the Logistic regression classifier directly on the pixel
raw_pixel_classifier = clone(logistic)
raw_pixel_classifier.C = 100.0
raw_pixel_classifier.fit(X_train, Y_train)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
[BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.09s
[BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.13s
[BernoulliRBM] Iteration 3, pseudo-likelihood = -22.88, time = 0.13s
[BernoulliRBM] Iteration 4, pseudo-likelihood = -21.91, time = 0.13s
[BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.13s
[BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.13s
[BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.13s
[BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.14s
[BernoulliRBM] Iteration 9, pseudo-likelihood = -20.34, time = 0.12s
[BernoulliRBM] Iteration 10, pseudo-likelihood = -20.21, time = 0.12s
.. raw:: html
<div class="output_subarea output_html rendered_html output_result">
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clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
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</style><div id="sk-container-id-59" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100.0, solver='newton-cg', tol=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-243" type="checkbox" checked><label for="sk-estimator-id-243" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> LogisticRegression<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(C=100.0, solver='newton-cg', tol=1)</pre></div> </div></div></div></div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 109-111
Evaluation
----------
.. GENERATED FROM PYTHON SOURCE LINES 111-120
.. code-block:: Python
from sklearn import metrics
Y_pred = rbm_features_classifier.predict(X_test)
print(
"Logistic regression using RBM features:\n%s\n"
% (metrics.classification_report(Y_test, Y_pred))
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
/home/circleci/project/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning:
Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
/home/circleci/project/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning:
Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
/home/circleci/project/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning:
Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
Logistic regression using RBM features:
precision recall f1-score support
0 0.10 1.00 0.18 174
1 0.00 0.00 0.00 184
2 0.00 0.00 0.00 166
3 0.00 0.00 0.00 194
4 0.00 0.00 0.00 186
5 0.00 0.00 0.00 181
6 0.00 0.00 0.00 207
7 0.00 0.00 0.00 154
8 0.00 0.00 0.00 182
9 0.00 0.00 0.00 169
accuracy 0.10 1797
macro avg 0.01 0.10 0.02 1797
weighted avg 0.01 0.10 0.02 1797
.. GENERATED FROM PYTHON SOURCE LINES 121-127
.. code-block:: Python
Y_pred = raw_pixel_classifier.predict(X_test)
print(
"Logistic regression using raw pixel features:\n%s\n"
% (metrics.classification_report(Y_test, Y_pred))
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
/home/circleci/project/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning:
Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
/home/circleci/project/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning:
Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
/home/circleci/project/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning:
Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
Logistic regression using raw pixel features:
precision recall f1-score support
0 0.10 1.00 0.18 174
1 0.00 0.00 0.00 184
2 0.00 0.00 0.00 166
3 0.00 0.00 0.00 194
4 0.00 0.00 0.00 186
5 0.00 0.00 0.00 181
6 0.00 0.00 0.00 207
7 0.00 0.00 0.00 154
8 0.00 0.00 0.00 182
9 0.00 0.00 0.00 169
accuracy 0.10 1797
macro avg 0.01 0.10 0.02 1797
weighted avg 0.01 0.10 0.02 1797
.. GENERATED FROM PYTHON SOURCE LINES 128-130
The features extracted by the BernoulliRBM help improve the classification
accuracy with respect to the logistic regression on raw pixels.
.. GENERATED FROM PYTHON SOURCE LINES 132-134
Plotting
--------
.. GENERATED FROM PYTHON SOURCE LINES 134-147
.. code-block:: Python
import matplotlib.pyplot as plt
plt.figure(figsize=(4.2, 4))
for i, comp in enumerate(rbm.components_):
plt.subplot(10, 10, i + 1)
plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r, interpolation="nearest")
plt.xticks(())
plt.yticks(())
plt.suptitle("100 components extracted by RBM", fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
plt.show()
.. image-sg:: /auto_examples/neural_networks/images/sphx_glr_plot_rbm_logistic_classification_001.png
:alt: 100 components extracted by RBM
:srcset: /auto_examples/neural_networks/images/sphx_glr_plot_rbm_logistic_classification_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 2.779 seconds)
.. _sphx_glr_download_auto_examples_neural_networks_plot_rbm_logistic_classification.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/?path=auto_examples/neural_networks/plot_rbm_logistic_classification.ipynb
:alt: Launch JupyterLite
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_rbm_logistic_classification.ipynb <plot_rbm_logistic_classification.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_rbm_logistic_classification.py <plot_rbm_logistic_classification.py>`
.. include:: plot_rbm_logistic_classification.recommendations
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_