.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/sphinx_gallery_examples/stream/plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_generated_sphinx_gallery_examples_stream_plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.py>`
        to download the full example code.

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

.. _sphx_glr_generated_sphinx_gallery_examples_stream_plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.py:


Cognitive Dual-Query Strategy with Fixed-Uncertainty
====================================================

.. GENERATED FROM PYTHON SOURCE LINES 10-23

.. note::
   The generated animation can be found at the bottom of the page.

| **Google Colab Note**: If the notebook fails to run after installing the
  needed packages, try to restart the runtime (Ctrl + M) under
  Runtime -> Restart session.

.. image:: https://colab.research.google.com/assets/colab-badge.svg
   :target: https://colab.research.google.com/github/scikit-activeml/scikit-activeml.github.io/blob/gh-pages/latest/generated/sphinx_gallery_notebooks//stream/plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.ipynb

| **Notebook Dependencies**
| Uncomment the following cell to install all dependencies for this
  tutorial.

.. GENERATED FROM PYTHON SOURCE LINES 23-26

.. code-block:: Python


    # !pip install scikit-activeml








.. GENERATED FROM PYTHON SOURCE LINES 27-31

.. raw:: html

  <hr style="border-style: solid; border-top: 1px solid; border-right: 0; border-bottom: 0; border-left: 0;">


.. GENERATED FROM PYTHON SOURCE LINES 33-150

.. code-block:: Python

    import numpy as np
    from matplotlib import pyplot as plt, animation
    from sklearn.datasets import make_blobs

    from skactiveml.utils import MISSING_LABEL
    from skactiveml.visualization import (
        plot_stream_training_data,
        plot_stream_decision_boundary,
    )

    from skactiveml.classifier import ParzenWindowClassifier
    from skactiveml.stream import CognitiveDualQueryStrategyFixUn

    # Set a fixed random state for reproducibility.
    random_state = np.random.RandomState(0)

    # Initial training set size.
    init_size = 0  # e.g. 0 or any integer value

    # Build a dataset.
    X, y_true = make_blobs(
        n_samples=200 + init_size,
        n_features=1,
        centers=[[0], [-3], [1], [2], [-0.5]],
        cluster_std=0.7,
        random_state=random_state,
    )
    y_true = y_true % 2  # Convert labels to binary (0, 1)

    # Split the data into initial training and streaming parts.
    X_init = X[:init_size]
    y_init = y_true[:init_size]
    X_stream = X[init_size:]
    y_stream = y_true[init_size:]

    # Initialise the classifier.
    clf = ParzenWindowClassifier(classes=[0, 1], random_state=random_state)

    # Initialise the query strategy.
    qs = CognitiveDualQueryStrategyFixUn(budget=0.2, classes=[0, 1])
    plot_step = 5

    # Initialize training data with initial examples.
    X_train = list(X_init)
    y_train = list(y_init)
    classes = np.unique(y_true)

    # Preparation for plotting.
    fig, ax = plt.subplots()
    feature_bound = [[0, len(X)], [min(X), max(X)]]
    ax.set_xlim(0, len(X))
    ax.set_ylim(bottom=min(X), top=max(X))
    artists = []  # List to store frames for the animation

    # List to track whether each sample was queried (True) or not (False).
    queried_indices = [True] * len(y_init)
    # List to store decision boundary predictions over time.
    predictions_list = []

    # Process each streaming sample.
    for t_x, (x_t, y_t) in enumerate(zip(X_stream, y_stream)):

        X_cand = x_t.reshape([1, -1])
        y_cand = y_t

        # Fit the classifier with current training data.
        clf.fit(X_train, y_train)

        # Check whether to query the current sample or not.
        sampled_indices, utilities = qs.query(
            candidates=X_cand, clf=clf, return_utilities=True
        )
        budget_manager_param_dict = {"utilities": utilities}
        # Update the query strategy and budget manager to calculate the right budget.
        qs.update(candidates=X_cand, queried_indices=sampled_indices)

        # Label the sample based on whether it was queried.
        X_train.append(x_t)
        y_train.append(y_t if len(sampled_indices) else MISSING_LABEL)

        queried_indices.append(len(sampled_indices) > 0)

        # Plot the current state at intervals defined by plot_step.
        if t_x % plot_step == 0:
            # Save current plot elements to determine what is new.
            coll_old = list(ax.collections)
            ax, predictions_list = plot_stream_decision_boundary(
                ax, t_x, plot_step, clf, X, predictions_list, res=25
            )
            data_lines = plot_stream_training_data(
                ax, X_train, y_train, queried_indices, classes, feature_bound
            )

            title_string = (
                f"Decision boundary after {t_x} new samples\n"
                f"Utility: {utilities[0]:.4f} | "
                f"Budget: {sum(queried_indices) / (t_x + 1):.4f}"
            )
            title = ax.text(
                x=0.5,
                y=1.05,
                s=title_string,
                size=plt.rcParams["axes.titlesize"],
                ha="center",
                transform=ax.transAxes,
            )
            coll_new = list(ax.collections)
            coll_new.append(title)

            # Collect new artists (plot elements) to animate.
            artists.append(
                [x for x in coll_new if (x not in coll_old)] + data_lines
            )

    # Create an animation from the collected frames.
    ani = animation.ArtistAnimation(fig, artists, interval=500, blit=True)




.. container:: sphx-glr-animation

    .. raw:: html

        
     <link rel="stylesheet"
     href="https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css">
     <script language="javascript">
       function isInternetExplorer() {
         ua = navigator.userAgent;
         /* MSIE used to detect old browsers and Trident used to newer ones*/
         return ua.indexOf("MSIE ") > -1 || ua.indexOf("Trident/") > -1;
       }

       /* Define the Animation class */
       function Animation(frames, img_id, slider_id, interval, loop_select_id){
         this.img_id = img_id;
         this.slider_id = slider_id;
         this.loop_select_id = loop_select_id;
         this.interval = interval;
         this.current_frame = 0;
         this.direction = 0;
         this.timer = null;
         this.frames = new Array(frames.length);

         for (var i=0; i<frames.length; i++)
         {
          this.frames[i] = new Image();
          this.frames[i].src = frames[i];
         }
         var slider = document.getElementById(this.slider_id);
         slider.max = this.frames.length - 1;
         if (isInternetExplorer()) {
             // switch from oninput to onchange because IE <= 11 does not conform
             // with W3C specification. It ignores oninput and onchange behaves
             // like oninput. In contrast, Microsoft Edge behaves correctly.
             slider.setAttribute('onchange', slider.getAttribute('oninput'));
             slider.setAttribute('oninput', null);
         }
         this.set_frame(this.current_frame);
       }

       Animation.prototype.get_loop_state = function(){
         var button_group = document[this.loop_select_id].state;
         for (var i = 0; i < button_group.length; i++) {
             var button = button_group[i];
             if (button.checked) {
                 return button.value;
             }
         }
         return undefined;
       }

       Animation.prototype.set_frame = function(frame){
         this.current_frame = frame;
         document.getElementById(this.img_id).src =
                 this.frames[this.current_frame].src;
         document.getElementById(this.slider_id).value = this.current_frame;
       }

       Animation.prototype.next_frame = function()
       {
         this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));
       }

       Animation.prototype.previous_frame = function()
       {
         this.set_frame(Math.max(0, this.current_frame - 1));
       }

       Animation.prototype.first_frame = function()
       {
         this.set_frame(0);
       }

       Animation.prototype.last_frame = function()
       {
         this.set_frame(this.frames.length - 1);
       }

       Animation.prototype.slower = function()
       {
         this.interval /= 0.7;
         if(this.direction > 0){this.play_animation();}
         else if(this.direction < 0){this.reverse_animation();}
       }

       Animation.prototype.faster = function()
       {
         this.interval *= 0.7;
         if(this.direction > 0){this.play_animation();}
         else if(this.direction < 0){this.reverse_animation();}
       }

       Animation.prototype.anim_step_forward = function()
       {
         this.current_frame += 1;
         if(this.current_frame < this.frames.length){
           this.set_frame(this.current_frame);
         }else{
           var loop_state = this.get_loop_state();
           if(loop_state == "loop"){
             this.first_frame();
           }else if(loop_state == "reflect"){
             this.last_frame();
             this.reverse_animation();
           }else{
             this.pause_animation();
             this.last_frame();
           }
         }
       }

       Animation.prototype.anim_step_reverse = function()
       {
         this.current_frame -= 1;
         if(this.current_frame >= 0){
           this.set_frame(this.current_frame);
         }else{
           var loop_state = this.get_loop_state();
           if(loop_state == "loop"){
             this.last_frame();
           }else if(loop_state == "reflect"){
             this.first_frame();
             this.play_animation();
           }else{
             this.pause_animation();
             this.first_frame();
           }
         }
       }

       Animation.prototype.pause_animation = function()
       {
         this.direction = 0;
         if (this.timer){
           clearInterval(this.timer);
           this.timer = null;
         }
       }

       Animation.prototype.play_animation = function()
       {
         this.pause_animation();
         this.direction = 1;
         var t = this;
         if (!this.timer) this.timer = setInterval(function() {
             t.anim_step_forward();
         }, this.interval);
       }

       Animation.prototype.reverse_animation = function()
       {
         this.pause_animation();
         this.direction = -1;
         var t = this;
         if (!this.timer) this.timer = setInterval(function() {
             t.anim_step_reverse();
         }, this.interval);
       }
     </script>

     <style>
     .animation {
         display: inline-block;
         text-align: center;
     }
     input[type=range].anim-slider {
         width: 374px;
         margin-left: auto;
         margin-right: auto;
     }
     .anim-buttons {
         margin: 8px 0px;
     }
     .anim-buttons button {
         padding: 0;
         width: 36px;
     }
     .anim-state label {
         margin-right: 8px;
     }
     .anim-state input {
         margin: 0;
         vertical-align: middle;
     }
     </style>

     <div class="animation">
       <img id="_anim_img99ff8b94b4ad40879d2914d9115f21e4">
       <div class="anim-controls">
         <input id="_anim_slider99ff8b94b4ad40879d2914d9115f21e4" type="range" class="anim-slider"
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                oninput="anim99ff8b94b4ad40879d2914d9115f21e4.set_frame(parseInt(this.value));">
         <div class="anim-buttons">
           <button title="Decrease speed" aria-label="Decrease speed" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.slower()">
               <i class="fa fa-minus"></i></button>
           <button title="First frame" aria-label="First frame" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.first_frame()">
             <i class="fa fa-fast-backward"></i></button>
           <button title="Previous frame" aria-label="Previous frame" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.previous_frame()">
               <i class="fa fa-step-backward"></i></button>
           <button title="Play backwards" aria-label="Play backwards" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.reverse_animation()">
               <i class="fa fa-play fa-flip-horizontal"></i></button>
           <button title="Pause" aria-label="Pause" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.pause_animation()">
               <i class="fa fa-pause"></i></button>
           <button title="Play" aria-label="Play" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.play_animation()">
               <i class="fa fa-play"></i></button>
           <button title="Next frame" aria-label="Next frame" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.next_frame()">
               <i class="fa fa-step-forward"></i></button>
           <button title="Last frame" aria-label="Last frame" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.last_frame()">
               <i class="fa fa-fast-forward"></i></button>
           <button title="Increase speed" aria-label="Increase speed" onclick="anim99ff8b94b4ad40879d2914d9115f21e4.faster()">
               <i class="fa fa-plus"></i></button>
         </div>
         <form title="Repetition mode" aria-label="Repetition mode" action="#n" name="_anim_loop_select99ff8b94b4ad40879d2914d9115f21e4"
               class="anim-state">
           <input type="radio" name="state" value="once" id="_anim_radio1_99ff8b94b4ad40879d2914d9115f21e4"
                  >
           <label for="_anim_radio1_99ff8b94b4ad40879d2914d9115f21e4">Once</label>
           <input type="radio" name="state" value="loop" id="_anim_radio2_99ff8b94b4ad40879d2914d9115f21e4"
                  checked>
           <label for="_anim_radio2_99ff8b94b4ad40879d2914d9115f21e4">Loop</label>
           <input type="radio" name="state" value="reflect" id="_anim_radio3_99ff8b94b4ad40879d2914d9115f21e4"
                  >
           <label for="_anim_radio3_99ff8b94b4ad40879d2914d9115f21e4">Reflect</label>
         </form>
       </div>
     </div>


     <script language="javascript">
       /* Instantiate the Animation class. */
       /* The IDs given should match those used in the template above. */
       (function() {
         var img_id = "_anim_img99ff8b94b4ad40879d2914d9115f21e4";
         var slider_id = "_anim_slider99ff8b94b4ad40879d2914d9115f21e4";
         var loop_select_id = "_anim_loop_select99ff8b94b4ad40879d2914d9115f21e4";
         var frames = new Array(40);
    
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         /* set a timeout to make sure all the above elements are created before
            the object is initialized. */
         setTimeout(function() {
             anim99ff8b94b4ad40879d2914d9115f21e4 = new Animation(frames, img_id, slider_id, 500.0,
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     </script>



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

 .. code-block:: none

    /home/runner/work/scikit-activeml.github.io/scikit-activeml.github.io/scikit-activeml/skactiveml/stream/_density_uncertainty.py:1013: UserWarning: force_full_budget is set to False. Therefore the full budget may not be utilised.
      warnings.warn(




.. GENERATED FROM PYTHON SOURCE LINES 151-152

.. image:: ../../examples/stream_classification_legend.png

.. GENERATED FROM PYTHON SOURCE LINES 154-159

.. rubric:: References:

The implementation of this strategy is based on :footcite:t:`liu2023active`.

.. footbibliography::


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

   **Total running time of the script:** (0 minutes 16.083 seconds)


.. _sphx_glr_download_generated_sphinx_gallery_examples_stream_plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

      :download:`Download Jupyter notebook: plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.ipynb <plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.ipynb>`

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

      :download:`Download Python source code: plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.py <plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.py>`

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

      :download:`Download zipped: plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.zip <plot-CognitiveDualQueryStrategyFixUn-Cognitive_Dual-Query_Strategy_with_Fixed-Uncertainty.zip>`


.. only:: html

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

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