
.. 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: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause








.. 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 = 0.12s
    [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.79, time = 0.12s
    [BernoulliRBM] Iteration 6, pseudo-likelihood = -20.96, time = 0.12s
    [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.12s
    [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.50, time = 0.12s
    [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.34, time = 0.12s
    [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.21, time = 0.12s


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    }
    </style><body><div id="sk-container-id-75" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100.0, solver=&#x27;newton-cg&#x27;, 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-312" type="checkbox" checked><label for="sk-estimator-id-312" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/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></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('penalty',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
                penalty
                <span class="param-doc-description">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('C',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
                C
                <span class="param-doc-description">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
            </a>
        </td>
                <td class="value">100.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('l1_ratio',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
                l1_ratio
                <span class="param-doc-description">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dual',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
                dual
                <span class="param-doc-description">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('tol',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
                tol
                <span class="param-doc-description">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
            </a>
        </td>
                <td class="value">1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fit_intercept',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
                fit_intercept
                <span class="param-doc-description">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('intercept_scaling',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
                intercept_scaling
                <span class="param-doc-description">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a "synthetic" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
            </a>
        </td>
                <td class="value">1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('class_weight',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
                class_weight
                <span class="param-doc-description">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
                random_state
                <span class="param-doc-description">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('solver',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
                solver
                <span class="param-doc-description">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>
            </a>
        </td>
                <td class="value">&#x27;newton-cg&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_iter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
                max_iter
                <span class="param-doc-description">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
            </a>
        </td>
                <td class="value">100</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
                verbose
                <span class="param-doc-description">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('warm_start',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
                warm_start
                <span class="param-doc-description">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
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.. 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:1833: 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:1833: 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:1833: 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:1833: 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:1833: 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:1833: 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 2.205 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/1.8.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/index.html?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>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_rbm_logistic_classification.zip <plot_rbm_logistic_classification.zip>`


.. include:: plot_rbm_logistic_classification.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
