.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\sklearn-gridsearchcv-replacement.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_sklearn-gridsearchcv-replacement.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_sklearn-gridsearchcv-replacement.py:


==========================================
Scikit-learn hyperparameter search wrapper
==========================================

Iaroslav Shcherbatyi, Tim Head and Gilles Louppe. June 2017.
Reformatted by Holger Nahrstaedt 2020

.. currentmodule:: skopt

Introduction
============

This example assumes basic familiarity with
`scikit-learn <http://scikit-learn.org/stable/index.html>`_.

Search for parameters of machine learning models that results in best
cross-validation performance is necessary in almost all practical
cases to get a model with best generalization estimate.
A standard approach in scikit-learn is to use
:obj:`sklearn.model_selection.GridSearchCV` class, which enumerates
all combinations of hyperparameters values given as input.
This search complexity grows exponentially with the number of parameters.
A more scalable approach is to use
:obj:`sklearn.model_selection.RandomizedSearchCV`, which however does not
take advantage of the structure of a search space.
Scikit-optimize provides a drop-in replacement for these two scikit-learn
methods. The hyperparameter search is achieved by Bayesian Optimization
At each step of the optimization, a surrogate model infers the objective
function using observed evluation results as priors. An acquisition function
utilizes these predictions to navigate between exploration (sampling
unexplored areas) and exploitation (focusing on regions likely containing
the global optimum). By balancing these two strategies, Bayesian Optimization
identifies probable optimal areas while ensuring comprehensive search
coverage.
In practice, this method often leads to quicker and better results.

Note: for a manual hyperparameter optimization example, see
"Hyperparameter Optimization" notebook.

.. GENERATED FROM PYTHON SOURCE LINES 42-54

.. code-block:: Python


    print(__doc__)
    import numpy as np

    np.random.seed(123)
    import matplotlib.pyplot as plt
    from sklearn.datasets import load_digits
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC

    from skopt import BayesSearchCV








.. GENERATED FROM PYTHON SOURCE LINES 55-59

Minimal example
===============

A minimal example of optimizing hyperparameters of SVC (Support Vector machine Classifier) is given below.

.. GENERATED FROM PYTHON SOURCE LINES 59-84

.. code-block:: Python



    X, y = load_digits(n_class=10, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, train_size=0.75, test_size=0.25, random_state=0
    )

    # log-uniform: understand as search over p = exp(x) by varying x
    opt = BayesSearchCV(
        SVC(),
        {
            'C': (1e-6, 1e6, 'log-uniform'),
            'gamma': (1e-6, 1e1, 'log-uniform'),
            'degree': (1, 8),  # integer valued parameter
            'kernel': ['linear', 'poly', 'rbf'],  # categorical parameter
        },
        n_iter=32,
        cv=3,
    )

    opt.fit(X_train, y_train)

    print("val. score: %s" % opt.best_score_)
    print("test score: %s" % opt.score(X_test, y_test))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    val. score: 0.9866369710467705
    test score: 0.9844444444444445




.. GENERATED FROM PYTHON SOURCE LINES 85-92

Advanced example
================

In practice, one wants to enumerate over multiple predictive model classes,
with different search spaces and number of evaluations per class. An
example of such search over parameters of Linear SVM, Kernel SVM, and
decision trees is given below.

.. GENERATED FROM PYTHON SOURCE LINES 92-140

.. code-block:: Python


    from sklearn.datasets import load_digits
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import Pipeline
    from sklearn.svm import SVC, LinearSVC

    from skopt import BayesSearchCV
    from skopt.plots import plot_histogram, plot_objective
    from skopt.space import Categorical, Integer, Real

    X, y = load_digits(n_class=10, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    # pipeline class is used as estimator to enable
    # search over different model types
    pipe = Pipeline([('model', SVC())])

    # single categorical value of 'model' parameter is
    # sets the model class
    # We will get ConvergenceWarnings because the problem is not well-conditioned.
    # But that's fine, this is just an example.
    linsvc_search = {
        'model': [LinearSVC(max_iter=1000, dual="auto")],
        'model__C': (1e-6, 1e6, 'log-uniform'),
    }

    # explicit dimension classes can be specified like this
    svc_search = {
        'model': Categorical([SVC()]),
        'model__C': Real(1e-6, 1e6, prior='log-uniform'),
        'model__gamma': Real(1e-6, 1e1, prior='log-uniform'),
        'model__degree': Integer(1, 8),
        'model__kernel': Categorical(['linear', 'poly', 'rbf']),
    }

    opt = BayesSearchCV(
        pipe,
        # (parameter space, # of evaluations)
        [(svc_search, 40), (linsvc_search, 16)],
        cv=3,
    )

    opt.fit(X_train, y_train)

    print("val. score: %s" % opt.best_score_)
    print("test score: %s" % opt.score(X_test, y_test))
    print("best params: %s" % str(opt.best_params_))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    val. score: 0.9881217520415739
    test score: 0.9888888888888889
    best params: OrderedDict([('model', SVC()), ('model__C', 4.580543393203649), ('model__degree', 3), ('model__gamma', 0.0002585937465230229), ('model__kernel', 'poly')])




.. GENERATED FROM PYTHON SOURCE LINES 141-143

Partial Dependence plot of the objective function for SVC


.. GENERATED FROM PYTHON SOURCE LINES 143-150

.. code-block:: Python

    _ = plot_objective(
        opt.optimizer_results_[0],
        dimensions=["C", "degree", "gamma", "kernel"],
        n_minimum_search=int(1e8),
    )
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_001.png
   :alt: sklearn gridsearchcv replacement
   :srcset: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 151-153

Plot of the histogram for LinearSVC


.. GENERATED FROM PYTHON SOURCE LINES 153-156

.. code-block:: Python

    _ = plot_histogram(opt.optimizer_results_[1], 1)
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_002.png
   :alt: sklearn gridsearchcv replacement
   :srcset: /auto_examples/images/sphx_glr_sklearn-gridsearchcv-replacement_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 157-171

Progress monitoring and control using `callback` argument of `fit` method
=========================================================================

It is possible to monitor the progress of :class:`BayesSearchCV` with an event
handler that is called on every step of subspace exploration. For single job
mode, this is called on every evaluation of model configuration, and for
parallel mode, this is called when n_jobs model configurations are evaluated
in parallel.

Additionally, exploration can be stopped if the callback returns `True`.
This can be used to stop the exploration early, for instance when the
accuracy that you get is sufficiently high.

An example usage is shown below.

.. GENERATED FROM PYTHON SOURCE LINES 171-198

.. code-block:: Python


    from sklearn.datasets import load_iris
    from sklearn.svm import SVC

    from skopt import BayesSearchCV

    X, y = load_iris(return_X_y=True)

    searchcv = BayesSearchCV(
        SVC(gamma='scale'),
        search_spaces={'C': (0.01, 100.0, 'log-uniform')},
        n_iter=10,
        cv=3,
    )


    # callback handler
    def on_step(optim_result):
        score = -optim_result['fun']
        print("best score: %s" % score)
        if score >= 0.98:
            print('Interrupting!')
            return True


    searchcv.fit(X, y, callback=on_step)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    best score: 0.98
    Interrupting!


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-1 {
      /* Definition of color scheme common for light and dark mode */
      --sklearn-color-text: black;
      --sklearn-color-line: gray;
      /* Definition of color scheme for unfitted estimators */
      --sklearn-color-unfitted-level-0: #fff5e6;
      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
      --sklearn-color-fitted-level-0: #f0f8ff;
      --sklearn-color-fitted-level-1: #d4ebff;
      --sklearn-color-fitted-level-2: #b3dbfd;
      --sklearn-color-fitted-level-3: cornflowerblue;

      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-icon: #696969;

      @media (prefers-color-scheme: dark) {
        /* Redefinition of color scheme for dark theme */
        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-icon: #878787;
      }
    }

    #sk-container-id-1 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-1 pre {
      padding: 0;
    }

    #sk-container-id-1 input.sk-hidden--visually {
      border: 0;
      clip: rect(1px 1px 1px 1px);
      clip: rect(1px, 1px, 1px, 1px);
      height: 1px;
      margin: -1px;
      overflow: hidden;
      padding: 0;
      position: absolute;
      width: 1px;
    }

    #sk-container-id-1 div.sk-dashed-wrapped {
      border: 1px dashed var(--sklearn-color-line);
      margin: 0 0.4em 0.5em 0.4em;
      box-sizing: border-box;
      padding-bottom: 0.4em;
      background-color: var(--sklearn-color-background);
    }

    #sk-container-id-1 div.sk-container {
      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    #sk-container-id-1 div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
      background-position: center center;
    }

    /* Parallel-specific style estimator block */

    #sk-container-id-1 div.sk-parallel-item::after {
      content: "";
      width: 100%;
      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
      flex-grow: 1;
    }

    #sk-container-id-1 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-1 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-1 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-1 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-1 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-1 div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
      padding-left: 1em;
    }


    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
    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
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-1 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    #sk-container-id-1 label.sk-toggleable__label {
      cursor: pointer;
      display: block;
      width: 100%;
      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
    }

    #sk-container-id-1 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    #sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-1 div.sk-toggleable__content {
      max-height: 0;
      max-width: 0;
      overflow: hidden;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-1 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-1 div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-1 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      max-height: 200px;
      max-width: 100%;
      overflow: auto;
    }

    #sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-1 div.sk-label label.sk-toggleable__label,
    #sk-container-id-1 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-1 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      display: inline-block;
      line-height: 1.2em;
    }

    #sk-container-id-1 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-1 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-1 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-1 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-1 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 1ex;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
      color: var(--sklearn-color-unfitted-level-1);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-1 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-1 a.estimator_doc_link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-1 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-1 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>BayesSearchCV(cv=3, estimator=SVC(), n_iter=10,
                  search_spaces={&#x27;C&#x27;: (0.01, 100.0, &#x27;log-uniform&#x27;)})</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 sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;BayesSearchCV<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>BayesSearchCV(cv=3, estimator=SVC(), n_iter=10,
                  search_spaces={&#x27;C&#x27;: (0.01, 100.0, &#x27;log-uniform&#x27;)})</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">estimator: SVC</label><div class="sk-toggleable__content fitted"><pre>SVC()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SVC<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></label><div class="sk-toggleable__content fitted"><pre>SVC()</pre></div> </div></div></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 199-207

Counting total iterations that will be used to explore all subspaces
====================================================================

Subspaces in previous examples can further increase in complexity if you add
new model subspaces or dimensions for feature extraction pipelines. For
monitoring of progress, you would like to know the total number of
iterations it will take to explore all subspaces. This can be
calculated with `total_iterations` property, as in the code below.

.. GENERATED FROM PYTHON SOURCE LINES 207-225

.. code-block:: Python


    from sklearn.datasets import load_iris
    from sklearn.svm import SVC

    from skopt import BayesSearchCV

    X, y = load_iris(return_X_y=True)

    searchcv = BayesSearchCV(
        SVC(),
        search_spaces=[
            ({'C': (0.1, 1.0)}, 19),  # 19 iterations for this subspace
            {'gamma': (0.1, 1.0)},
        ],
        n_iter=10,
    )

    print(searchcv.total_iterations)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    29





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (1 minutes 33.880 seconds)


.. _sphx_glr_download_auto_examples_sklearn-gridsearchcv-replacement.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/holgern/scikit-optimize/master?urlpath=lab/tree/notebooks/auto_examples/sklearn-gridsearchcv-replacement.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: sklearn-gridsearchcv-replacement.ipynb <sklearn-gridsearchcv-replacement.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: sklearn-gridsearchcv-replacement.py <sklearn-gridsearchcv-replacement.py>`


.. only:: html

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

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