Repository: scikit-activeml/scikit-activeml (language: python)

Issue:
Include notebooks of stream and multi-annot scenarios in documentation
- [ ] stream
- [ ] multi-annot


Discussion / hints:

Fix patch:
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index c3a76f84..5ccd848c 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -49,9 +49,7 @@ jobs:
         pip install pybtex>=0.23.0
         pip install pydata_sphinx_theme>=0.7.1
         sphinx-build -b html docs docs/_build
-    - name: Deploy documentation to Github Pages
-      uses: JamesIves/github-pages-deploy-action@4.1.5
+    - name: Trigger documentation for scikit-activeml-docs
       if: github.ref == 'refs/heads/master'
-      with:
-        branch: gh-pages
-        folder: docs/_build
+      run: |
+        curl -H "Authorization: token ${{ secrets.DOCS_PAT }}" --request POST https://api.github.com/repos/scikit-activeml/scikit-activeml-docs/dispatches -d "{\"event_type\":\"build_docs\"}"
diff --git a/README.rst b/README.rst
index bd4b56e4..e2edbfbc 100644
--- a/README.rst
+++ b/README.rst
@@ -3,7 +3,7 @@
 |Doc|_ |Codecov|_ |PythonVersion|_ |PyPi|_ |Paper|_
 
 .. |Doc| image:: https://img.shields.io/badge/docs-latest-green
-.. _Doc: https://scikit-activeml.github.io/scikit-activeml/
+.. _Doc: https://scikit-activeml.github.io/scikit-activeml-docs/
 
 .. |Codecov| image:: https://codecov.io/gh/scikit-activeml/scikit-activeml/branch/master/graph/badge.svg
 .. _Codecov: https://app.codecov.io/gh/scikit-activeml/scikit-activeml
@@ -46,8 +46,10 @@ The easiest way of installing scikit-activeml is using ``pip``   ::
 Example
 =======
 
-The following code implements an active learning cycle with 20 iterations using a logistic regression classifier and uncertainty sampling. To use other classifiers, you can simply wrap classifiers from ``scikit-learn`` or use classifiers provided by ``scikit-activeml``. Note that the main difficulty using active learning with ``scikit-learn`` is the ability to handle unlabeled data, which we denote as a specific value (``MISSING_LABEL``) in the label vector ``y``. More query strategies can be found in the documentation.     ::
+The following code implements an active learning cycle with 20 iterations using a logistic regression classifier and uncertainty sampling. To use other classifiers, you can simply wrap classifiers from ``scikit-learn`` or use classifiers provided by ``scikit-activeml``. Note that the main difficulty using active learning with ``scikit-learn`` is the ability to handle unlabeled data, which we denote as a specific value (``MISSING_LABEL``) in the label vector ``y``. More query strategies can be found in the documentation.
 
+.. code-block:: python
+    
     import numpy as np
     from sklearn.linear_model import LogisticRegression
     from sklearn.datasets import make_classification
@@ -59,6 +61,11 @@ The following code implements an active learning cycle with 20 iterations using
     X, y_true = make_classification(random_state=0)
     y = np.full(shape=y_true.shape, fill_value=MISSING_LABEL)
 
+    # LogisticRegression needs initial training data otherwise a warning will 
+    # be raised by SklearnClassifier. Therfore, the first 10 instances are used as
+    # training data.
+    y[:10] = y_true[:10]
+
     # Create classifier and query strategy.
     clf = SklearnClassifier(LogisticRegression(), classes=np.unique(y_true))
     qs = UncertaintySampling(method='entropy')
@@ -66,11 +73,11 @@ The following code implements an active learning cycle with 20 iterations using
     # Execute active learning cycle.
     n_cycles = 20
     for c in range(n_cycles):
-         clf.fit(X, y)
-         unlbld_idx = unlabeled_indices(y)
-         X_cand = X[unlbld_idx]
-         query_idx = unlbld_idx[qs.query(X_cand=X_cand, clf=clf)]
-         y[query_idx] = y_true[query_idx]
+        clf.fit(X, y)
+        unlbld_idx = unlabeled_indices(y)
+        X_cand = X[unlbld_idx]
+        query_idx = unlbld_idx[qs.query(X_cand=X_cand, clf=clf)]
+        y[query_idx] = y_true[query_idx]
     print(f'Accuracy: {clf.fit(X, y).score(X, y_true)}')
 
 .. examples_end
diff --git a/docs/api/stream.rst b/docs/api/stream.rst
deleted file mode 100644
index 18c331ae..00000000
--- a/docs/api/stream.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-module: stream
---------------
-
-.. toctree::
-
-.. automodule:: skactiveml.stream
-   :members:
-   :undoc-members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
index f6e6c70a..7662f227 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -19,7 +19,7 @@
 from skactiveml import pool, __version__
 
 from docs.generate import generate_strategy_overview_rst, \
-    generate_api_reference_rst, generate_examples
+    generate_api_reference_rst, generate_examples, generate_tutorials
 
 # -- Project information -----------------------------------------------------
 
@@ -74,7 +74,7 @@
 # You can specify multiple suffix as a list of string:
 #
 # source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+source_suffix = ['.rst']
 
 # The master toctree document.
 master_doc = 'index'
@@ -228,3 +228,8 @@
     gen_path=os.path.abspath('generated'),
     examples_data=examples_data
 )
+
+generate_tutorials(
+    src_path=os.path.abspath('../tutorials/'),
+    dst_path=os.path.abspath('generated/tutorials/'),
+)
diff --git a/docs/examples/pool/template.py b/docs/examples/pool/template.py
index 1b9e47d3..82ff2daf 100644
--- a/docs/examples/pool/template.py
+++ b/docs/examples/pool/template.py
@@ -1,3 +1,4 @@
+import os
 import numpy as np
 from matplotlib import pyplot as plt, animation
 from sklearn.datasets import make_blobs
@@ -27,7 +28,7 @@
 artists = []
 
 # The active learning cycle:
-n_cycles = 25
+n_cycles = "#_n_cycles"
 for c in range(n_cycles):
     # Fit the classifier.
     clf.fit(X, y)
diff --git a/docs/generate.py b/docs/generate.py
index 38a6ee46..2b1734bd 100644
--- a/docs/generate.py
+++ b/docs/generate.py
@@ -7,6 +7,7 @@
 
 import skactiveml
 import warnings
+import distutils.dir_util
 
 warnings.filterwarnings("ignore")
 
@@ -545,6 +546,10 @@ def format_plot(data, template_path):
                     line = line[:start] + '{' + \
                            dict_to_str(data['query_params'], allocator=': ',
                                        key_as_string=True) + '}\n'
+                elif '"#_n_cycles"' in line:
+                    start = line.find('"#_n_cycles"')
+                    n_cycles = os.getenv('N_CYCLES', default='2')
+                    line = line[:start] + n_cycles + '\n'
                 elif '#_bp' in line:
                     try:
                         s = line.find('#_')
@@ -634,3 +639,18 @@ def dict_to_str(d, idx=None, allocator='=', key_as_string=False):
                 key = '"' + key + '"'
         dd_str += str(key) + allocator + value + ", "
     return dd_str[0:-2]
+
+
+def generate_tutorials(src_path, dst_path):
+    """Includes the tutorials folder from the git root, such that tutorials are
+    included in the documentation. Effectively this function copies all 
+    contents from src_path to dst_path.
+    Parameters
+    ----------
+    src_path: string
+        The path where the notebooks are found.
+    dst_path: string
+        The path where the notebooks are saved, such that tutorials.rst can
+        find them.
+    """
+    distutils.dir_util.copy_tree(src=src_path, dst=dst_path)
diff --git a/docs/index.rst b/docs/index.rst
index 2a74ad71..165ea7d4 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -5,7 +5,8 @@ Welcome to scikit-activeml's documentation!
    :maxdepth: 2
    :hidden:
 
-   notebooks/getting_started
+   Home <self>
+   Tutorials <tutorials>
    Strategy Overview <generated/strategy_overview/strategy_overview-Settles2012>
    generated/api_reference
    generated/sphinx_gallery_examples/index
diff --git a/docs/tutorials.rst b/docs/tutorials.rst
new file mode 100644
index 00000000..689d1464
--- /dev/null
+++ b/docs/tutorials.rst
@@ -0,0 +1,15 @@
+.. _tutorials:
+
+=========
+Tutorials
+=========
+
+.. toctree::
+    :maxdepth: 1
+    :glob:
+
+    generated/tutorials/*
+
+.. This is an overview of the Tutorials test
+
+.. :doc:`Pool getting started <tutorials/getting_started>`
diff --git a/examples/pool/al_cycle.ipynb b/examples/pool/al_cycle.ipynb
deleted file mode 100644
index 79a89de6..00000000
--- a/examples/pool/al_cycle.ipynb
+++ /dev/null
@@ -1,335 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../..')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pylab as plt\n",
-    "\n",
-    "import ipywidgets as widgets\n",
-    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from skactiveml.utils import is_labeled, is_unlabeled, MISSING_LABEL, initialize_class_with_kwargs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### DATASETS\n",
-    "\n",
-    "from sklearn.datasets import make_blobs\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "def create_2d_data_set(seed=42):\n",
-    "    X, y = make_blobs(n_samples=200, n_features=2, centers=12, cluster_std=1, random_state=seed)\n",
-    "    y = y % 2\n",
-    "    X = StandardScaler().fit_transform(X)\n",
-    "    return X, y\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### CLASSIFIER\n",
-    "from sklearn.naive_bayes import GaussianNB\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.svm import SVC\n",
-    "from skactiveml.classifier import PWC, SklearnClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clf_dict = {'GaussianNaiveBayes' : SklearnClassifier(GaussianNB(), missing_label=MISSING_LABEL),\n",
-    "            'DecisionTree' : SklearnClassifier(DecisionTreeClassifier(), missing_label=MISSING_LABEL),\n",
-    "            'LinearSVC' : SklearnClassifier(SVC(probability=True), missing_label=MISSING_LABEL),\n",
-    "            'PWC' : SklearnClassifier(PWC(classes=[0,1]), missing_label=MISSING_LABEL)}\n",
-    "\n",
-    "perf_est_dict = {'PWC' : PWC}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### SAMPLING STRATEGIES\n",
-    "import skactiveml.pool as skacmlp\n",
-    "\n",
-    "query_strategies = {}\n",
-    "for qs_name in skacmlp.__all__:\n",
-    "    query_strategies[qs_name] = getattr(skacmlp, qs_name)\n",
-    "\n",
-    "print(query_strategies.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### AL CYCLE\n",
-    "\n",
-    "def get_labels_with_selector(X, y, y_oracle, clf, selector, budget=30):  \n",
-    "    \n",
-    "    for b in range(budget):\n",
-    "        unlabeled = np.where (is_unlabeled(y))[0]\n",
-    "        clf.fit(X, y)\n",
-    "        unlabeled_id = selector.query(X[unlabeled], X=X, y=y, X_eval=X)\n",
-    "        sample_id = unlabeled[unlabeled_id]\n",
-    "        y[sample_id] = y_oracle[sample_id]\n",
-    "\n",
-    "    return y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# minimal example\n",
-    "\n",
-    "X,y_oracle = create_2d_data_set()\n",
-    "y = np.full(y_oracle.shape, MISSING_LABEL)\n",
-    "\n",
-    "clf = clf_dict['PWC']\n",
-    "\n",
-    "selector = initialize_class_with_kwargs(query_strategies['RandomSampler'], \n",
-    "                          clf=clf, perf_est=clf_dict['PWC'], classes = [0,1], random_state=1)\n",
-    "\n",
-    "y = get_labels_with_selector(X, y, y_oracle, clf, selector, budget=5)\n",
-    "\n",
-    "#print(y)\n",
-    "#print(is_labeled(y))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_scores_2d(X, y, y_oracle, clf, selector, res=21):\n",
-    "    # create mesh for plotting\n",
-    "    x_1_vec = np.linspace(min(X[:, 0]), max(X[:, 0]), res)\n",
-    "    x_2_vec = np.linspace(min(X[:, 1]), max(X[:, 1]), res)\n",
-    "    X_1_mesh, X_2_mesh = np.meshgrid(x_1_vec, x_2_vec)\n",
-    "    X_mesh = np.array([X_1_mesh.reshape(-1), X_2_mesh.reshape(-1)]).T\n",
-    "    \n",
-    "    # compute gains\n",
-    "    clf.fit(X, y)\n",
-    "    posteriors = clf.predict_proba(X_mesh)[:,0].reshape(X_1_mesh.shape)\n",
-    "    \n",
-    "    # compute gains\n",
-    "    _, scores = selector.query(X_mesh, X=X, y=y, X_eval=X, return_utilities=True)\n",
-    "    scores = scores.reshape(X_1_mesh.shape)\n",
-    "    \n",
-    "    # get indizes for plotting\n",
-    "    labeled_indices = np.where(is_labeled(y))[0]\n",
-    "    unlabeled_indices = np.where(is_unlabeled(y))[0]\n",
-    "    \n",
-    "    # setup figure\n",
-    "    fig = plt.figure(figsize=(10, 8))\n",
-    "    plt.xlim(min(X[:, 0]), max(X[:, 0]))\n",
-    "    plt.ylim(min(X[:, 1]), max(X[:, 1]))\n",
-    "    plt.xlabel(r'$x_1$')\n",
-    "    plt.ylabel(r'$x_2$')\n",
-    "    cmap = plt.get_cmap('coolwarm')\n",
-    "    \n",
-    "    plt.scatter(X[labeled_indices, 0], X[labeled_indices, 1], c=[[.2, .2, .2]], s=90, marker='o', zorder=3.8)\n",
-    "    plt.scatter(X[labeled_indices, 0], X[labeled_indices, 1], c=[[.8, .8, .8]], s=60, marker='o', zorder=4)\n",
-    "    for cl, marker in zip([0,1],['D','s']):\n",
-    "        cl_labeled_idx = labeled_indices[y[labeled_indices] == cl]\n",
-    "        cl_unlabeled_idx = unlabeled_indices[y_oracle[unlabeled_indices]==cl]\n",
-    "        plt.scatter(X[cl_labeled_idx, 0], X[cl_labeled_idx, 1], c=np.ones(len(cl_labeled_idx))*cl, marker=marker, vmin=-0.2, vmax=1.2, cmap='coolwarm', s=20, zorder=5)\n",
-    "        plt.scatter(X[cl_unlabeled_idx, 0], X[cl_unlabeled_idx, 1], c=np.ones(len(cl_unlabeled_idx)) * cl, marker=marker, vmin=-0.2, vmax=1.2, cmap='coolwarm', s=20, zorder=3)\n",
-    "        plt.scatter(X[cl_unlabeled_idx, 0], X[cl_unlabeled_idx, 1], c='k', marker=marker, vmin=-0.1, vmax=1.1, cmap='coolwarm', s=30, zorder=2.8)\n",
-    "    \n",
-    "    CS = plt.contourf(X_1_mesh, X_2_mesh, scores, cmap='Greens', alpha=.75)\n",
-    "    CS = plt.contour(X_1_mesh, X_2_mesh, posteriors, [.5], colors='k', linewidths=[2], zorder=1)\n",
-    "    CS = plt.contour(X_1_mesh, X_2_mesh, posteriors, [.25,.75], cmap='coolwarm_r', linewidths=[2,2], \n",
-    "                     zorder=1, linestyles='--', alpha=.9, vmin=.2, vmax=.8)\n",
-    "    \n",
-    "    fig.tight_layout()\n",
-    "    plt.show()\n",
-    "    \n",
-    "    return fig\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_gain_data_set_2d(al, clf, usefulness, perf_est, budget=15, seed=43, n_samples=250):\n",
-    "    \n",
-    "    # create data set\n",
-    "    X, y_oracle = create_2d_data_set(seed = seed)\n",
-    "    X = StandardScaler().fit_transform(X)\n",
-    "    y = np.full(y_oracle.shape, MISSING_LABEL)\n",
-    "    \n",
-    "    classes = np.unique(y_oracle)\n",
-    "    \n",
-    "    # classifier\n",
-    "    clf = clf_dict[clf]\n",
-    "    perf_est = initialize_class_with_kwargs(perf_est_dict[perf_est], n_classes=len(classes))\n",
-    "        \n",
-    "    # AL cycle\n",
-    "    al_qs = initialize_class_with_kwargs(query_strategies[al], \n",
-    "                          clf=clf, perf_est=perf_est, model=clf, classes=classes, random_state=seed)\n",
-    "    y = get_labels_with_selector(X, y, y_oracle, clf, al_qs, budget=budget)\n",
-    "    \n",
-    "    # plot gains and decision boundary\n",
-    "    ut_qs = initialize_class_with_kwargs(query_strategies[usefulness], \n",
-    "                          clf=clf, perf_est=perf_est, model=clf, classes=classes, random_state=seed)\n",
-    "    fig = plot_scores_2d(X, y, y_oracle, clf, ut_qs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#budget_slider = widgets.IntSlider(value=15, min=1, max=250, step=1)\n",
-    "clf_slider = widgets.Dropdown(options=clf_dict.keys())\n",
-    "al_slider = widgets.Dropdown(options=query_strategies.keys())\n",
-    "usefulness_slider = widgets.Dropdown(options=query_strategies.keys())\n",
-    "perf_slider = widgets.Dropdown(options=perf_est_dict.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "interact(plot_gain_data_set_2d, clf=clf_slider,\n",
-    "         al=al_slider, usefulness=usefulness_slider, perf_est=perf_slider)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/pool/al_cycle_random.ipynb b/examples/pool/al_cycle_random.ipynb
deleted file mode 100644
index 0342428f..00000000
--- a/examples/pool/al_cycle_random.ipynb
+++ /dev/null
@@ -1,418 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../..')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from skactiveml.pool import RandomSampler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 0])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "RandomSampler(batch_size=2, random_state=1).query([[1],[1],[1],[1],[1]],\n",
-    "                                                  return_utilities=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "trailing comma not allowed without surrounding parentheses (2995197618.py, line 11)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;36m  File \u001B[0;32m\"/tmp/ipykernel_260825/2995197618.py\"\u001B[0;36m, line \u001B[0;32m11\u001B[0m\n\u001B[0;31m    McPAL, RandomSampler, EpistemicUncertainty, FourDS,\u001B[0m\n\u001B[0m                                                       ^\u001B[0m\n\u001B[0;31mSyntaxError\u001B[0m\u001B[0;31m:\u001B[0m trailing comma not allowed without surrounding parentheses\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from time import time\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
-    "from sklearn.datasets import make_blobs\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from skactiveml.pool import UncertaintySampling, ExpectedErrorReduction, ALCE, \\\n",
-    "    McPAL, RandomSampler, EpistemicUncertainty, FourDS,\n",
-    "from skactiveml.utils import unlabeled_indices, MISSING_LABEL\n",
-    "from skactiveml.classifier import SklearnClassifier\n",
-    "\n",
-    "# Generate data set.\n",
-    "X, y_true = make_blobs(random_state=0, centers=5, shuffle=True)\n",
-    "X = StandardScaler().fit_transform(X)\n",
-    "y = np.full(shape=y_true.shape, fill_value=-1)\n",
-    "y[:20] = y_true[:20]\n",
-    "\n",
-    "# Create classifier and query strategy.\n",
-    "clf = SklearnClassifier(MLPClassifier(random_state=0, max_iter=10000),\n",
-    "                        classes=np.unique(y_true), missing_label=-1,\n",
-    "                        random_state=0)\n",
-    "qs = UncertaintySampling(random_state=0)\n",
-    "\n",
-    "# Execute active learning cycle.\n",
-    "n_cycles = 10\n",
-    "for c in range(n_cycles):\n",
-    "     clf.fit(X, y)\n",
-    "     unlbld_idx = unlabeled_indices(y, missing_label=-1)\n",
-    "     X_cand = X[unlbld_idx]\n",
-    "     t = time()\n",
-    "     query_idx = unlbld_idx[qs.query(X_cand=X_cand, clf=clf, batch_size=5)]\n",
-    "     print(time()-t)\n",
-    "     y[query_idx] = y_true[query_idx]\n",
-    "print(f'Accuracy: {clf.score(X, y_true)}')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.005366802215576172\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/marek/Projects/GitHub/scikit-activeml/skactiveml/classifier/_wrapper.py:259: UserWarning: The 'base_estimator' could not be fitted because of 'num_samples should be a positive integer value, but got num_samples=0'. Therefore, the class labels of the samples are counted and will be used to make predictions. The class label distribution is `_label_counts=[0, 0]`.\n",
-      "  warnings.warn(\"The 'base_estimator' could not be fitted because of\"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.005888223648071289\n",
-      "0.003514528274536133\n",
-      "0.0031244754791259766\n",
-      "0.0032498836517333984\n",
-      "0.006188869476318359\n",
-      "0.0041658878326416016\n",
-      "0.003576993942260742\n",
-      "0.0053863525390625\n",
-      "0.0036351680755615234\n",
-      "0.005046844482421875\n",
-      "0.0064313411712646484\n",
-      "0.004711627960205078\n",
-      "0.0038118362426757812\n",
-      "0.00393986701965332\n",
-      "0.004993438720703125\n",
-      "0.005129575729370117\n",
-      "0.003937721252441406\n",
-      "0.004674196243286133\n",
-      "0.004380941390991211\n",
-      "0.004061460494995117\n",
-      "0.0040204524993896484\n",
-      "0.0037920475006103516\n",
-      "0.004462718963623047\n",
-      "0.0037102699279785156\n",
-      "Accuracy: 0.79\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torch import nn\n",
-    "from skorch import NeuralNetClassifier\n",
-    "from skactiveml.classifier import PWC\n",
-    "\n",
-    "# Generate data set.\n",
-    "X, y_true = make_blobs(random_state=0, centers=5, shuffle=True)\n",
-    "y_true %= 2\n",
-    "X = StandardScaler().fit_transform(X)\n",
-    "y = np.full(shape=y_true.shape, fill_value=-1)\n",
-    "#y[:50] = y_true[:50]\n",
-    "\n",
-    "X = X.astype(np.float32)\n",
-    "y = y.astype(np.int64)\n",
-    "y_true = y_true.astype(np.int64)\n",
-    "\n",
-    "class MyModule(nn.Module):\n",
-    "    def __init__(self, num_units=100, nonlin=nn.ReLU()):\n",
-    "        super(MyModule, self).__init__()\n",
-    "\n",
-    "        self.dense0 = nn.Linear(2, num_units)\n",
-    "        self.nonlin = nonlin\n",
-    "        self.dense1 = nn.Linear(num_units, num_units)\n",
-    "        self.output = nn.Linear(num_units, 2)\n",
-    "        self.softmax = nn.Softmax(dim=-1)\n",
-    "\n",
-    "    def forward(self, X, **kwargs):\n",
-    "        X = self.nonlin(self.dense0(X))\n",
-    "        X = self.nonlin(self.dense1(X))\n",
-    "        X = self.softmax(self.output(X))\n",
-    "        return X\n",
-    "\n",
-    "\n",
-    "net = NeuralNetClassifier(\n",
-    "    MyModule,\n",
-    "    max_epochs=50,\n",
-    "    lr=0.1,\n",
-    "    # Shuffle training data on each epoch\n",
-    "    iterator_train__shuffle=True,\n",
-    "    verbose=False,\n",
-    "    train_split=False\n",
-    ")\n",
-    "\n",
-    "pwc = PWC(class_prior=0, classes=np.unique(y_true), missing_label=-1)\n",
-    "clf = SklearnClassifier(net, classes=np.unique(y_true), missing_label=-1,\n",
-    "                        random_state=0)\n",
-    "qs = UncertaintySampling(random_state=0)\n",
-    "\n",
-    "# Execute active learning cycle.\n",
-    "n_cycles = 25\n",
-    "for c in range(n_cycles):\n",
-    "     clf.fit(X, y)\n",
-    "     unlbld_idx = unlabeled_indices(y, missing_label=-1)\n",
-    "     X_cand = X[unlbld_idx]\n",
-    "     t = time()\n",
-    "     query_idx = unlbld_idx[qs.query(X_cand=X_cand, clf=pwc, X=X, y=y,\n",
-    "                                     batch_size=1)]\n",
-    "     print(time()-t)\n",
-    "     y[query_idx] = y_true[query_idx]\n",
-    "print(f'Accuracy: {clf.fit(X, y).score(X, y_true)}')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "float32\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.utils.validation import check_array\n",
-    "\n",
-    "X, y_true = make_blobs(random_state=0, centers=5, shuffle=True)\n",
-    "y_true %= 2\n",
-    "X = StandardScaler().fit_transform(X)\n",
-    "y = np.full(shape=y_true.shape, fill_value=-1)\n",
-    "y[:50] = y_true[:50]\n",
-    "\n",
-    "X = X.astype(np.float32)\n",
-    "y = y.astype(np.int64)\n",
-    "y_true = y_true.astype(np.int64)\n",
-    "\n",
-    "X = check_array(X)\n",
-    "print(X.dtype)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bandwidth: 0.2630154484476343\n",
-      "0.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.metrics.pairwise import pairwise_kernels, KERNEL_PARAMS\n",
-    "from sklearn.utils import check_array\n",
-    "from sklearn.utils.validation import check_is_fitted, check_scalar\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.datasets import make_friedman1, make_regression\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.stats import t\n",
-    "\n",
-    "from skactiveml.utils import MISSING_LABEL\n",
-    "\n",
-    "\n",
-    "class PWR:\n",
-    "    METRICS = list(KERNEL_PARAMS.keys()) + ['precomputed']\n",
-    "\n",
-    "    def __init__(self, n_neighbors=None, metric='rbf', metric_dict=None):\n",
-    "        self.metric = metric\n",
-    "        self.n_neighbors = n_neighbors\n",
-    "        self.metric_dict = {} if metric_dict is None else metric_dict\n",
-    "\n",
-    "    def fit(self, X, y):\n",
-    "        self.X_ = X.copy()\n",
-    "        self.y_ = y.copy()\n",
-    "\n",
-    "        return self\n",
-    "\n",
-    "    def predict_freq(self, X):\n",
-    "        K = pairwise_kernels(X, self.X_, metric=self.metric,\n",
-    "                             **self.metric_dict)\n",
-    "\n",
-    "\n",
-    "        # maximum likelihood\n",
-    "        N = np.sum(K, axis=1)\n",
-    "        mu_ml = K @ self.y_ / N\n",
-    "        sigma_ml = np.sqrt((K @ self.y_**2 / N) - mu_ml**2)\n",
-    "\n",
-    "        # normal\n",
-    "        mu_0 = 0\n",
-    "        sigma_0 = 1\n",
-    "        mu_N = ((sigma_ml**2) / (N * sigma_0**2 + sigma_ml**2)) * mu_0 + \\\n",
-    "               ((N*sigma_0**2)/(N*sigma_0**2 + sigma_ml**2)) * mu_ml\n",
-    "        sigma_N = ((1 / (sigma_0**2)) + (N/(sigma_ml**2)))**(-1)\n",
-    "\n",
-    "        # normal wishart\n",
-    "        mu_0 = 0\n",
-    "        lmbda_0 = 0.001\n",
-    "        alpha_0 = 1\n",
-    "        beta_0 = 0.0001\n",
-    "        mu_N = (lmbda_0 * mu_0 + N * mu_ml) / (lmbda_0 + N)\n",
-    "        lmbda_N = lmbda_0 + N\n",
-    "        # alpha and beta to variance\n",
-    "        alpha_N = alpha_0 + N/2\n",
-    "        beta_N = beta_0 + 0.5 * N*sigma_ml**2 \\\n",
-    "                 + 0.5 * (lmbda_0*N*(mu_ml-mu_0)**2)/(lmbda_0 + N)\n",
-    "        df = alpha_N\n",
-    "        loc = mu_N\n",
-    "        scale = (beta_N * (lmbda_N + 1)) / (alpha_N * lmbda_N)\n",
-    "        mean, var, skew, kurt = t.stats(df=5, loc=loc, scale=scale, moments='mvsk')\n",
-    "        print(np.abs(mean - mu_N).sum())\n",
-    "        #return mu_N, (alpha_N * beta_N**(-1))**(-1)\n",
-    "        return mean, np.sqrt(var)\n",
-    "\n",
-    "#y[200:400] += np.random.randn(200)\n",
-    "#rand_indices = np.arange(int(len(X)*0.4))\n",
-    "#idx = np.append(rand_indices, np.random.choice(np.arange(400, 800), replace=False,\n",
-    "#                size=int(10)))\n",
-    "x_axis = np.linspace(-5, 5, 100)\n",
-    "y = np.sin(x_axis) + np.random.rand(len(x_axis))*0.001\n",
-    "X = x_axis.reshape(-1, 1)\n",
-    "y = StandardScaler().fit_transform(y.reshape(-1, 1)).ravel()\n",
-    "idx = np.random.choice(np.arange(len(X)), replace=False, size=int(0.25*len(X)))\n",
-    "X_train = X[idx]\n",
-    "y_train = y[idx]\n",
-    "\n",
-    "sc = StandardScaler().fit(X_train)\n",
-    "X_train = sc.transform(X_train)\n",
-    "X = sc.transform(X)\n",
-    "x_axis = np.linspace(X.min(), X.max(), 1000)\n",
-    "\n",
-    "n_samples = len(X_train)\n",
-    "n_features = 1\n",
-    "nominator = 2 * n_samples * n_features\n",
-    "denominator = (n_samples - 1) * np.log((n_samples - 1) / ((np.sqrt(2) * 10 ** -6) ** 2))\n",
-    "bandwidth = np.sqrt(nominator / denominator)\n",
-    "print('bandwidth: {}'.format(str(bandwidth)))\n",
-    "gamma = (bandwidth ** (-2))\n",
-    "\n",
-    "pwr = PWR(metric_dict={'gamma': gamma}).fit(X_train, y_train)\n",
-    "y_pred, var_pred = pwr.predict_freq(x_axis.reshape(-1, 1))\n",
-    "\n",
-    "n_std = 1\n",
-    "plt.scatter(X_train.flatten(), y_train)\n",
-    "plt.plot(x_axis, y_pred)\n",
-    "plt.err(x_axis, y_pred+n_std*var_pred, y_pred-n_std*var_pred, alpha=0.35)\n",
-    "plt.ylim(-3, 3)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/examples/stream/al_cycle_random.ipynb b/examples/stream/al_cycle_random.ipynb
deleted file mode 100644
index 7fe16e4b..00000000
--- a/examples/stream/al_cycle_random.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stream active learning cycle\n",
-    "In this notebook, we will show how stream-based active learning strategies are used and compared them to one another. We showcase the methods available in the stream package. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('../..')\n",
-    "\n",
-    "import numpy as np\n",
-    "import sklearn\n",
-    "import sklearn.datasets\n",
-    "import matplotlib.pyplot as plt\n",
-    "from collections import deque\n",
-    "from scipy.ndimage import gaussian_filter1d\n",
-    "from skactiveml.classifier import PWC\n",
-    "from skactiveml.stream import RandomSampler, PeriodicSampler\n",
-    "from skactiveml.stream import FixedUncertainty, VariableUncertainty, Split, PALS\n",
-    "from skactiveml.utils import call_func"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize stream Parameters\n",
-    "Before the experiments can start, we need to construct a random data set. For this, we specify the necessary parameters in the cell below. To make the experiment repeatable, we will use the random_state object to generate all other random seeds, such that we only need to explicitly specify a single random seed. Furthermore, we specify the length of the data stream (stream_length) and the size of the sliding window that defines the available training data (training_size)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# random state that is used to generate random seeds\n",
-    "random_state = np.random.RandomState(0)\n",
-    "# number of instances that are provided to the classifier\n",
-    "init_train_length = 10\n",
-    "# the length of the data stream\n",
-    "stream_length = 10000\n",
-    "# the size of the sliding window that limits the training data\n",
-    "training_size = 1000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Random seed generation\n",
-    "The get_randomseed function simplifies the generation of a new random seed from a given random state object. For this notebook, this random state object will be always the random_state obejct defined above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_randomseed(random_state):\n",
-    "    return random_state.randint(2**31-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate and initialize data set\n",
-    "The next block initializes the tested data set. We use scikit-learn to generate a random dataset with our pre-defined stream length. The data set consists of multiple parts. X represents the location of the instance within the feature space. The class for each instance is denoted by y. \n",
-    "For models that need at least some initial training data, we generate samples to train an initial model. These are denoted by the suffix \"_init\", while all data used within the active learning cycle are denoted by the suffix \"_stream\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X, y = sklearn.datasets.make_classification(n_samples=init_train_length + stream_length, random_state=get_randomseed(random_state), shuffle=True)\n",
-    "X_init = X[:init_train_length, :]\n",
-    "y_init = y[:init_train_length]\n",
-    "X_stream = X[init_train_length:, :]\n",
-    "y_stream = y[init_train_length:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize query strategies\n",
-    "Next, we initialize the classifier and the base query strategies that we want to compare. To guarantee that the classifier is not affected by previous repetitions, we use factory functions to separate the classifier for each experiment run. Since all query strategies hav ea defaulr budget manag,,r we uset hat for the sake of simplicity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clf_factory = lambda: PWC(classes=[0,1], random_state=get_randomseed(random_state))\n",
-    "query_strategies = {\n",
-    "    'RandomSampler': RandomSampler(random_state=get_randomseed(random_state)),\n",
-    "    'PeriodicSampler': PeriodicSampler(random_state=get_randomseed(random_state)),\n",
-    "    'FixedUncertainty': FixedUncertainty(random_state=get_randomseed(random_state)),\n",
-    "    'VariableUncertainty': VariableUncertainty(random_state=get_randomseed(random_state)),\n",
-    "    'Split': Split(random_state=get_randomseed(random_state)),\n",
-    "    'PALS': PALS(random_state=get_randomseed(random_state))\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Start the active learning cycle\n",
-    "After all, variables are initialized, we can start the experiment. The experiment loop below goes through all query strategies defined in query_strategies. For each experiment run, the average accuracy of the selected query strategies will be displayed. Lastly, the accuracy over time will be plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for query_strategy_name, query_strategy in query_strategies.items():\n",
-    "    clf = clf_factory()\n",
-    "    # initializing the training data\n",
-    "    X_train = deque(maxlen=training_size)\n",
-    "    X_train.extend(X_init)\n",
-    "    y_train = deque(maxlen=training_size)\n",
-    "    y_train.extend(y_init)\n",
-    "    # train the model with the initially available data\n",
-    "    clf.fit(X_train, y_train)\n",
-    "    # initialize the list that stores the result of the classifier's prediction\n",
-    "    correct_classifications = []\n",
-    "    count = 0\n",
-    "    for t, (x_t, y_t) in enumerate(zip(X_stream, y_stream)):\n",
-    "        X_cand = x_t.reshape([1, -1])\n",
-    "        y_cand = y_t\n",
-    "        correct_classifications.append(clf.predict(X_cand)[0] == y_cand)\n",
-    "        # check whether to sample the instance or not\n",
-    "        # call_func is used since a classifier is not needed for RandomSampler and PeriodicSampler\n",
-    "        sampled_indices, utilities = call_func(query_strategy.query, X_cand=X_cand, clf=clf, return_utilities=True)\n",
-    "        # create budget_manager_param_dict for BIQF used by PALS\n",
-    "        budget_manager_param_dict = {\"utilities\": utilities}\n",
-    "        # update the query strategy and budget_manager to calculate the right budget\n",
-    "        query_strategy.update(X_cand, sampled_indices, budget_manager_param_dict)\n",
-    "        if len(sampled_indices):\n",
-    "            count += 1\n",
-    "        X_train.append(x_t)\n",
-    "        y_train.append(y_cand if len(sampled_indices) > 0 else clf.missing_label)\n",
-    "        # train the classifier\n",
-    "        clf.fit(X_train, y_train)\n",
-    "    # calculate and show the average accuracy \n",
-    "    print(\"Query Strategy: \", query_strategy_name, \", Avg Accuracy: \", np.mean(correct_classifications), \", Acquisation count:\", count)\n",
-    "    plt.plot(gaussian_filter1d(np.array(correct_classifications, dtype=float), 10), label=query_strategy_name)\n",
-    "plt.legend()"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "1292eb3755adf66b9290dde067207a19fd3b0f909d1a320c3e592979ed6b674f"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.8.11 64-bit ('al_forgetting_cd': conda)",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/skactiveml/pool/_expected_error.py b/skactiveml/pool/_expected_error.py
index 2f6b473d..78266b64 100644
--- a/skactiveml/pool/_expected_error.py
+++ b/skactiveml/pool/_expected_error.py
@@ -166,10 +166,10 @@ class `j` for a sample with the actual class `i`.
         In IJCAI (Vol. 5, pp. 1622-1623).
     """
     # Check if the classifier and its arguments are valid.
-    check_type(clf, SkactivemlClassifier, 'clf')
+    check_type(clf, 'clf', SkactivemlClassifier)
 
     # Check whether to use `fit` or `partial_fit`.
-    check_type(ignore_partial_fit, bool, 'ignore_partial_fit')
+    check_type(ignore_partial_fit, 'ignore_partial_fit', bool)
     use_fit = ignore_partial_fit or not hasattr(clf, 'partial_fit')
     if use_fit and (X is None or y is None):
         raise ValueError(
diff --git a/skactiveml/pool/_four_ds.py b/skactiveml/pool/_four_ds.py
index 1c98ddcc..40e2eca0 100644
--- a/skactiveml/pool/_four_ds.py
+++ b/skactiveml/pool/_four_ds.py
@@ -82,7 +82,7 @@ def query(self, X_cand, clf, X=None, y=None, sample_weight=None,
         y = column_or_1d(y)
 
         # Check classifier type.
-        check_type(clf, CMM, 'clf')
+        check_type(clf, 'clf', CMM)
 
         # Storage for query indices.
         query_indices = np.full(batch_size, fill_value=-1, dtype=int)
diff --git a/skactiveml/pool/_probal.py b/skactiveml/pool/_probal.py
index 71d8b8a3..7365bdff 100644
--- a/skactiveml/pool/_probal.py
+++ b/skactiveml/pool/_probal.py
@@ -79,7 +79,7 @@ def query(self, X_cand, clf, X=None, y=None, sample_weight=None,
                                 utility_weight, self.random_state, reset=True)
 
         # Check the classifier's type.
-        check_type(clf, ClassFrequencyEstimator, 'clf')
+        check_type(clf, 'clf', ClassFrequencyEstimator)
 
         # Fit the classifier and predict frequencies.
         clf = fit_if_not_fitted(clf, X, y, sample_weight)
diff --git a/skactiveml/pool/_qbc.py b/skactiveml/pool/_qbc.py
index 6652af5d..b37fecba 100644
--- a/skactiveml/pool/_qbc.py
+++ b/skactiveml/pool/_qbc.py
@@ -105,7 +105,7 @@ def query(self, X_cand, ensemble, X=None, y=None, sample_weight=None,
         elif _is_arraylike(ensemble):
             est_arr = deepcopy(ensemble)
             for i in range(len(est_arr)):
-                check_type(est_arr[i], SkactivemlClassifier, f'ensemble[{i}]')
+                check_type(est_arr[i], f'ensemble[{i}]', SkactivemlClassifier)
                 est_arr[i] = fit_if_not_fitted(
                     est_arr[i], X, y, sample_weight=sample_weight
                 )
diff --git a/skactiveml/pool/_uncertainty.py b/skactiveml/pool/_uncertainty.py
index 3352cea8..fce3628f 100644
--- a/skactiveml/pool/_uncertainty.py
+++ b/skactiveml/pool/_uncertainty.py
@@ -105,7 +105,7 @@ def query(self, X_cand, clf, X=None, y=None, sample_weight=None,
                                 self.random_state, reset=True)
 
         # Validate classifier type.
-        check_type(clf, SkactivemlClassifier, 'clf')
+        check_type(clf, 'clf', SkactivemlClassifier)
 
         # Validate method.
         if not isinstance(self.method, str):
diff --git a/skactiveml/pool/multi/_interval_estimation.py b/skactiveml/pool/multi/_interval_estimation.py
index 9016434f..94acfffb 100644
--- a/skactiveml/pool/multi/_interval_estimation.py
+++ b/skactiveml/pool/multi/_interval_estimation.py
@@ -252,7 +252,7 @@ def query(self, X_cand, clf, X=None, y=None, sample_weight=None,
                                    self.random_state, reset=True, adaptive=True)
 
         # Validate classifier type.
-        check_type(clf, SkactivemlClassifier, 'clf')
+        check_type(clf, 'clf', SkactivemlClassifier)
 
         # Check whether epsilon is float in [0, 1].
         check_scalar(x=self.epsilon, target_type=float, name='epsilon',
diff --git a/skactiveml/pool/multi/_wrapper.py b/skactiveml/pool/multi/_wrapper.py
index a6bf0c86..2590b341 100644
--- a/skactiveml/pool/multi/_wrapper.py
+++ b/skactiveml/pool/multi/_wrapper.py
@@ -132,19 +132,22 @@ def query(self, X_cand, query_params_dict=None, A_cand=None, batch_size=1,
                                    self.random_state, reset=True)
 
         # check strategy
-        check_type(self.strategy, SingleAnnotPoolBasedQueryStrategy,
-                   'self.strategy')
+        check_type(self.strategy, 'self.strategy',
+                   SingleAnnotPoolBasedQueryStrategy)
 
         # check query_params_dict
         if query_params_dict is None:
             query_params_dict = {}
 
-        check_type(query_params_dict, dict, 'query_params_dict')
+        check_type(query_params_dict, 'query_params_dict', dict)
 
         # aggregate y
         if 'y' in query_params_dict:
-            y_aggregate = majority_vote if self.y_aggregate is None \
-                else self.y_aggregate
+            if self.y_aggregate is None:
+                y_aggregate = lambda y: majority_vote(y,
+                                                      random_state=random_state)
+            else:
+                y_aggregate = self.y_aggregate
 
             if not callable(y_aggregate):
                 raise TypeError(
@@ -208,7 +211,7 @@ def query(self, X_cand, query_params_dict=None, A_cand=None, batch_size=1,
 
         # check A_perf and set annotator_utilities
         if A_perf is None:
-            annotator_utilities = random_state.rand(1, n_samples, n_annotators)\
+            annotator_utilities = random_state.rand(1, n_samples, n_annotators) \
                 .repeat(batch_size_sq, axis=0)
         elif sklearn.utils.validation._is_arraylike(A_perf):
             A_perf = check_array(A_perf, ensure_2d=False)
@@ -220,11 +223,11 @@ def query(self, X_cand, query_params_dict=None, A_cand=None, batch_size=1,
                 A_perf = np.zeros_like(A_perf, dtype=float)
 
             if A_perf.shape == (n_samples, n_annotators):
-                annotator_utilities = A_perf[np.newaxis, :, :]\
+                annotator_utilities = A_perf[np.newaxis, :, :] \
                     .repeat(batch_size_sq, axis=0)
             elif A_perf.shape == (n_annotators,):
-                annotator_utilities = A_perf[np.newaxis, np.newaxis, :]\
-                    .repeat(n_samples, axis=1)\
+                annotator_utilities = A_perf[np.newaxis, np.newaxis, :] \
+                    .repeat(n_samples, axis=1) \
                     .repeat(batch_size_sq, axis=0)
             else:
                 raise ValueError(
@@ -280,9 +283,8 @@ def _query_annotators(self, A_cand, batch_size, sample_utilities,
             query_indices[batch_index] = rand_argmax(utilities[batch_index],
                                                      random_state=random_state)
 
-            s_utilities[sample_index,
-                        query_indices[batch_index, 0],
-                        query_indices[batch_index, 1]] = np.nan
+            s_utilities[:, query_indices[batch_index, 0],
+            query_indices[batch_index, 1]] = np.nan
 
             batch_index += 1
             annotator_ps += 1
diff --git a/skactiveml/stream/_pals.py b/skactiveml/stream/_pals.py
index 3ba859c8..9a49e63c 100644
--- a/skactiveml/stream/_pals.py
+++ b/skactiveml/stream/_pals.py
@@ -297,7 +297,7 @@ def _validate_clf(self, clf, X, y, sample_weight):
             Checked model implementing the methods `fit` and `predict_freq`.
         """
         # Check if the classifier and its arguments are valid.
-        check_type(clf, SkactivemlClassifier, "clf")
+        check_type(clf, "clf", SkactivemlClassifier)
         return fit_if_not_fitted(clf, X, y, sample_weight)
 
     def _validate_utility_weight(self, utility_weight, X_cand):
diff --git a/skactiveml/stream/_uncertainty.py b/skactiveml/stream/_uncertainty.py
index 4a5389df..b06c7f68 100644
--- a/skactiveml/stream/_uncertainty.py
+++ b/skactiveml/stream/_uncertainty.py
@@ -233,7 +233,7 @@ def _validate_clf(self, clf, X, y, sample_weight):
             Checked model implementing the methods `fit` and `predict_freq`.
         """
         # Check if the classifier and its arguments are valid.
-        check_type(clf, SkactivemlClassifier, "clf")
+        check_type(clf, "clf", SkactivemlClassifier)
         return fit_if_not_fitted(clf, X, y, sample_weight)
 
 
@@ -465,7 +465,7 @@ def _validate_clf(self, clf, X, y, sample_weight):
             Checked model implementing the methods `fit` and `predict_freq`.
         """
         # Check if the classifier and its arguments are valid.
-        check_type(clf, SkactivemlClassifier, "clf")
+        check_type(clf, "clf", SkactivemlClassifier)
         return fit_if_not_fitted(clf, X, y, sample_weight)
 
 
@@ -694,7 +694,7 @@ def _validate_clf(self, clf, X, y, sample_weight):
             Checked model implementing the methods `fit` and `predict_freq`.
         """
         # Check if the classifier and its arguments are valid.
-        check_type(clf, SkactivemlClassifier, "clf")
+        check_type(clf, "clf", SkactivemlClassifier)
         return fit_if_not_fitted(clf, X, y, sample_weight)
 
 
diff --git a/skactiveml/utils/__init__.py b/skactiveml/utils/__init__.py
index 3ac3521f..35029d3f 100644
--- a/skactiveml/utils/__init__.py
+++ b/skactiveml/utils/__init__.py
@@ -10,8 +10,7 @@
 from ._selection import rand_argmax, rand_argmin
 from ._validation import check_classes, check_missing_label, check_scalar, \
     check_cost_matrix, check_classifier_params, check_X_y, MISSING_LABEL, \
-    check_random_state, check_class_prior, check_type
-
+    check_random_state, check_class_prior, check_type, check_bound
 
 __all__ = ['rand_argmax', 'rand_argmin', 'compute_vote_vectors',
            'majority_vote', 'is_unlabeled', 'is_labeled', 'ExtLabelEncoder',
@@ -19,4 +18,4 @@
            'check_scalar', 'check_classifier_params', 'check_X_y',
            'check_random_state', 'MISSING_LABEL', 'call_func', 'simple_batch',
            'check_class_prior', 'ext_confusion_matrix', 'fit_if_not_fitted',
-           'labeled_indices', 'unlabeled_indices', 'check_type']
+           'labeled_indices', 'unlabeled_indices', 'check_type', 'check_bound']
diff --git a/skactiveml/utils/_aggregation.py b/skactiveml/utils/_aggregation.py
index 795c665c..14b38615 100644
--- a/skactiveml/utils/_aggregation.py
+++ b/skactiveml/utils/_aggregation.py
@@ -1,5 +1,6 @@
 import numpy as np
 
+from ._selection import rand_argmax
 from ._label import ExtLabelEncoder, is_labeled
 from sklearn.utils import check_array, check_consistent_length
 
@@ -42,7 +43,7 @@ def compute_vote_vectors(y, w=None, classes=None, missing_label=np.nan):
 
     w = np.ones_like(y) if w is None else check_array(w, ensure_2d=False,
                                                       force_all_finite=False,
-                                                      dtype=None, copy=True)
+                                                      dtype=float, copy=True)
     w = w if w.ndim == 2 else w.reshape((-1, 1))
     check_consistent_length(y, w)
     check_consistent_length(y.T, w.T)
@@ -58,7 +59,8 @@ def compute_vote_vectors(y, w=None, classes=None, missing_label=np.nan):
     return v
 
 
-def majority_vote(y, w=None, classes=None, missing_label=np.nan):
+def majority_vote(y, w=None, classes=None, missing_label=np.nan,
+                  random_state=None):
     """ Assigns a label to each sample based on weighted voting.
     Samples with no labels are assigned with `missing_label`.
 
@@ -73,6 +75,9 @@ def majority_vote(y, w=None, classes=None, missing_label=np.nan):
         Holds the label for each class.
     missing_label : scalar|string|np.nan|None, default=np.nan
         Value to represent a missing label.
+    random_state : int, RandomState instance or None, optional (default=None)
+        Determines random number generation for shuffling the data. Pass an int
+        for reproducible results across multiple function calls.
 
     Returns
     -------
@@ -84,6 +89,9 @@ def majority_vote(y, w=None, classes=None, missing_label=np.nan):
     y = check_array(y, ensure_2d=False, dtype=None, force_all_finite=False)
     y = y if y.ndim == 2 else y.reshape((-1, 1))
     n_samples = y.shape[0]
+    w = np.ones_like(y) if w is None else check_array(w, ensure_2d=False,
+                                                      force_all_finite=False,
+                                                      dtype=None, copy=True)
 
     # extract labeled samples
     is_labeled_y = np.any(is_labeled(y, missing_label), axis=1)
@@ -91,7 +99,7 @@ def majority_vote(y, w=None, classes=None, missing_label=np.nan):
 
     # infer encoding
     le = ExtLabelEncoder(classes=classes, missing_label=missing_label)
-    le.fit(y_labeled)
+    le.fit(y)
     y_aggregated = np.full((n_samples,), missing_label, dtype=le._dtype)
 
     if np.any(is_labeled_y):
@@ -100,9 +108,11 @@ def majority_vote(y, w=None, classes=None, missing_label=np.nan):
         max_value_y_l_t = np.nanmax(y_labeled_transformed)
 
         # perform voting
-        vote_matrix = compute_vote_vectors(y_labeled_transformed, w,
+        vote_matrix = compute_vote_vectors(y_labeled_transformed,
+                                           w=w[is_labeled_y],
                                            classes=np.arange(max_value_y_l_t+1))
-        vote_vector = vote_matrix.argmax(axis=1)
+
+        vote_vector = rand_argmax(vote_matrix, random_state, axis=1)
 
         # inverse transform labels
         y_labeled_inverse_transformed = le.inverse_transform(vote_vector)
diff --git a/skactiveml/utils/_selection.py b/skactiveml/utils/_selection.py
index d79e9652..76d2579d 100644
--- a/skactiveml/utils/_selection.py
+++ b/skactiveml/utils/_selection.py
@@ -51,7 +51,7 @@ def rand_argmax(a, random_state=None, **argmax_kwargs):
         Determines random number generation for shuffling the data. Pass an int
         for reproducible results across multiple function calls.
     argmax_kwargs: dict-like
-        Keyword argument passed to numpy function argmin.
+        Keyword argument passed to numpy function argmax.
 
     Returns
     -------
diff --git a/skactiveml/utils/_validation.py b/skactiveml/utils/_validation.py
index 48606f12..73420167 100644
--- a/skactiveml/utils/_validation.py
+++ b/skactiveml/utils/_validation.py
@@ -459,9 +459,86 @@ def check_random_state(random_state, seed_multiplier=None):
     return np.random.RandomState(seed)
 
 
-def check_type(obj, target_type, name):
-    if not isinstance(obj, target_type):
-        raise TypeError(
-            f'`{name}` has type `{type(obj)}` but must have type '
-            f'`{target_type}`.'
-        )
+def check_type(obj, name, *target_types):
+    """Check if obj is one of the given types.
+
+    Parameters
+    ----------
+    obj: object
+        The object to be checked.
+    name: str
+        The variable name of the object.
+    target_types : iterable of types
+        The possible types.
+    """
+    if all(not isinstance(obj, target_type) for target_type in target_types):
+        if len(target_types) == 1:
+            raise TypeError(
+                f'`{name}` has type `{type(obj)}` but must have type '
+                f'`{target_types[0]}`.')
+        elif len(target_types) <= 3:
+            error_str = f'`{name}` has type `{type(obj)}` but must have type '
+            for i in range(len(target_types) - 1):
+                error_str += f'`{target_types[i]}`,'
+            error_str = error_str[:-1]
+            error_str += f'or `{target_types[len(target_types) - 1]}`.'
+            raise TypeError(error_str)
+        else:
+            raise TypeError(
+                f'`{name}` has type `{type(obj)}` but must be one of the '
+                f'following types: {target_types}.')
+
+
+def check_bound(bound=None, X=None, ndim=2, epsilon=0,
+                bound_must_be_given=False):
+    """ Validates bound and returns the bound of X if bound is None.
+    `bound` and `X` must not be None.
+
+    Parameters
+    ----------
+    bound: array-like, shape (2, ndim), optional (default=None)
+        The given bound of shape
+        [[x1_min, x2_min, ..., xndim_min], [x1_max, x2_max, ..., xndim_max]]
+    X: matrix-like, shape (n_samples, ndim), optional (default=None)
+        The sample matrix X is the feature matrix representing samples.
+    ndim: int, optional (default=2)
+        The number of dimensions.
+    epsilon: float, optional (default=0)
+        The minimal distance between the returned bound and the values of `X`,
+        if `bound` is not specified.
+    bound_must_be_given: bool, optional (default=False)
+        Whether it is allowed for the bound to be `None` and to be inferred by
+        `X`.
+
+    Returns
+    -------
+    bound: array-like, shape (2, ndim), optional (default=None)
+        The given bound or bound of X.
+    """
+
+    if X is not None:
+        X = check_array(X)
+        if X.shape[1] != ndim:
+            raise ValueError(f"`X` along axis 1 must be of length {ndim}. "
+                             f"`X` along axis 1 is of length {X.shape[1]}.")
+    if bound is not None:
+        bound = check_array(bound)
+        if bound.shape != (2, ndim):
+            raise ValueError(f"Shape of `bound` must be (2, {ndim}). "
+                             f"Shape of `bound` is {bound.shape}.")
+    elif bound_must_be_given:
+        raise ValueError("`bound` must not be `None`.")
+
+    if bound is None and X is not None:
+        minima = np.nanmin(X, axis=0) - epsilon
+        maxima = np.nanmax(X, axis=0) + epsilon
+        bound = np.append(minima.reshape(1, -1), maxima.reshape(1, -1), axis=0)
+        return bound
+    elif bound is not None and X is not None:
+        if np.any(np.logical_or(bound[0] > X, X > bound[1])):
+            warnings.warn("`X` contains values not within range of `bound`.")
+        return bound
+    elif bound is not None:
+        return bound
+    else:
+        raise ValueError("`X` or `bound` must not be None.")
diff --git a/skactiveml/visualization/__init__.py b/skactiveml/visualization/__init__.py
index 8bd859ea..f4fd3e84 100644
--- a/skactiveml/visualization/__init__.py
+++ b/skactiveml/visualization/__init__.py
@@ -2,6 +2,11 @@
 The :mod:`skactiveml.visualization` module includes various tools for
 visualization.
 """
+from ._auxiliary_functions import mesh
 from ._feature_space import plot_utility, plot_decision_boundary
 
-__all__ = ['plot_utility', 'plot_decision_boundary']
+__all__ = ['plot_utility', 'plot_decision_boundary', 'plot_ma_current_state',
+           'plot_ma_utility', 'plot_ma_decision_boundary', 'mesh']
+
+from .multi import plot_ma_current_state, plot_ma_utility, \
+    plot_ma_decision_boundary
diff --git a/skactiveml/visualization/_auxiliary_functions.py b/skactiveml/visualization/_auxiliary_functions.py
new file mode 100644
index 00000000..be188780
--- /dev/null
+++ b/skactiveml/visualization/_auxiliary_functions.py
@@ -0,0 +1,112 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.colors import Colormap
+from matplotlib.figure import Figure
+
+from ..utils import check_scalar, check_type, check_bound
+
+
+def mesh(bound, res):
+    """
+    Function to get instances of a mesh grid as well as x-mesh and y-mesh
+    with given resolution in the specified bounds.
+
+    Parameters
+    ----------
+    bound: array-like, [[xmin, ymin], [xmax, ymax]]
+        The bounds of the mesh grid.
+    res: int, optional (default=21)
+        The resolution of the plot.
+
+    Returns
+    -------
+    X_mesh: np.ndarray, shape (res, res)
+        mesh grid over x
+    Y_mesh: np.ndarray, shape (res, res)
+        mesh grid over y
+    mesh_instances: np.ndarray, shape (res*res,)
+        instances of the mesh grid
+    """
+
+    check_scalar(res, 'res', int, min_val=1)
+    check_bound(bound=bound, bound_must_be_given=True)
+
+    xmin, ymin, xmax, ymax = np.ravel(bound)
+
+    x_vec = np.linspace(xmin, xmax, res)
+    y_vec = np.linspace(ymin, ymax, res)
+    X_mesh, Y_mesh = np.meshgrid(x_vec, y_vec)
+    mesh_instances = np.array([X_mesh.reshape(-1), Y_mesh.reshape(-1)]).T
+    return X_mesh, Y_mesh, mesh_instances
+
+
+def _get_cmap(cmap):
+    if isinstance(cmap, str):
+        cmap = plt.cm.get_cmap(cmap)
+    check_type(cmap, 'cmap', Colormap, str)
+    return cmap
+
+
+def _get_boundary_args(boundary_dict):
+    boundary_args = {'colors': 'k', 'linewidths': [2], 'zorder': 1}
+    if boundary_dict is not None:
+        check_type(boundary_dict, 'boundary_dict', dict)
+        boundary_args.update(boundary_dict)
+    return boundary_args
+
+
+def _get_confidence_args(confidence_dict):
+    confidence_args = {'linewidths': [2, 2], 'linestyles': '--', 'alpha': 0.9,
+                       'vmin': 0.2, 'vmax': 0.8, 'zorder': 1}
+    if confidence_dict is not None:
+        check_type(confidence_dict, 'confidence_dict', dict)
+        confidence_args.update(confidence_dict)
+    return confidence_args
+
+
+def _get_contour_args(contour_dict):
+    contour_args = {'cmap': 'Greens', 'alpha': 0.75}
+    if contour_dict is not None:
+        check_type(contour_dict, 'contour_dict', dict)
+        contour_args.update(contour_dict)
+    return contour_args
+
+
+def _get_legend_args(legend_dict, fontsize):
+    legend_args = {'fontsize': fontsize, 'loc': 'lower left',
+                   'bbox_to_anchor': (0.0, -0.5)}
+    if legend_dict is not None:
+        check_type(legend_dict, 'legend_dict', dict)
+        legend_args.update(legend_dict)
+    return legend_args
+
+
+def _get_tick_args(tick_dict):
+    tick_args = {'axis': 'both', 'which': 'both', 'direction': 'in',
+                 'labelbottom': False, 'labelleft': False}
+    if tick_dict is not None:
+        check_type(tick_dict, 'tick_dict', dict)
+        tick_args.update(tick_dict)
+    return tick_args
+
+
+def _get_figure_for_ma(fig, fig_size, title, fontsize, n_annotators,
+                       tick_args):
+    if fig is None:
+        if fig_size is None:
+            fig_size = (8, 1.5)
+        fig, _ = plt.subplots(nrows=1, ncols=n_annotators, figsize=fig_size,
+                              sharex='all', sharey='all')
+        axes = fig.get_axes()
+        if title is not None:
+            fig.suptitle(title, fontsize=fontsize)
+        axes[0].set_ylabel(r'feature $x_2$', fontsize=fontsize, color='k')
+        for a, ax in enumerate(axes):
+            ax.set_title(fr'annotator $a_{a + 1}$', fontsize=fontsize)
+            ax.set_xlabel(r'feature $x_1$', fontsize=fontsize, color='k')
+            ax.tick_params(**tick_args)
+        return fig
+    elif not isinstance(fig, Figure):
+        check_type(fig, 'fig', Figure)
+    else:
+        return fig
diff --git a/skactiveml/visualization/_feature_space.py b/skactiveml/visualization/_feature_space.py
index d56362dc..58d01c6c 100644
--- a/skactiveml/visualization/_feature_space.py
+++ b/skactiveml/visualization/_feature_space.py
@@ -4,13 +4,14 @@
 
 from matplotlib import pyplot as plt
 from matplotlib.axes import Axes
-from matplotlib.colors import Colormap
 from sklearn.base import ClassifierMixin
 from sklearn.neighbors import KNeighborsRegressor
-from sklearn.utils import check_array
 
 from ..base import QueryStrategy
 from ..utils import check_scalar
+from ..utils._validation import check_type
+from ._auxiliary_functions import mesh, check_bound, _get_boundary_args, \
+    _get_confidence_args, _get_contour_args, _get_cmap
 
 
 def plot_decision_boundary(clf, feature_bound, ax=None, res=21,
@@ -25,8 +26,9 @@ def plot_decision_boundary(clf, feature_bound, ax=None, res=21,
         is not None, the classifier must implement the predict_proba function.
     feature_bound: array-like, [[xmin, ymin], [xmax, ymax]]
         Determines the area in which the boundary is plotted.
-    ax: matplotlib.axes.Axes, optional (default=None)
-        The axis on which the decision boundary is plotted.
+    ax: matplotlib.axes.Axes or List, optional (default=None)
+        The axis on which the decision boundary is plotted. If ax is a List,
+        each entry has to be an `matplotlib.axes.Axes`.
     res: int, optional (default=21)
         The resolution of the plot.
     boundary_dict: dict, optional (default=None)
@@ -43,51 +45,41 @@ def plot_decision_boundary(clf, feature_bound, ax=None, res=21,
 
     Returns
     -------
-    matplotlib.axes.Axes: The axis on which the boundary was plotted.
+    ax: matplotlib.axes.Axes or List
+        The axis on which the boundary was plotted or the list of axis if ax
+        was a list.
     """
-    if not isinstance(clf, ClassifierMixin):
-        raise TypeError("'clf' must be an Sklearn classifier.")
+    check_type(clf, 'clf', ClassifierMixin)
     check_scalar(res, 'res', int, min_val=1)
     if ax is None:
         ax = plt.gca()
-    if not isinstance(ax, Axes):
-        raise TypeError("ax must be a matplotlib.axes.Axes.")
-    check_array(feature_bound)
-    xmin, ymin, xmax, ymax = np.ravel(feature_bound)
+    check_type(ax, 'ax', Axes, list)
+    if isinstance(ax, list):
+        for ax_item in ax:
+            check_type(ax_item, 'one item of ax', Axes)
+        axs = ax
+    else:
+        axs = [ax, ]
+    feature_bound = check_bound(bound=feature_bound)
 
     # Check and convert the colormap
-    if isinstance(cmap, str):
-        cmap = plt.cm.get_cmap(cmap)
-    if not isinstance(cmap, Colormap):
-        raise TypeError("'cmap' must be a string or a Colormap.")
+    cmap = _get_cmap(cmap)
 
     if confidence is not None:
         check_scalar(confidence, 'confidence', float, min_inclusive=False,
                      max_inclusive=False, min_val=0.5, max_val=1)
 
     # Update additional arguments
-    boundary_args = {'colors': 'k', 'linewidths': [2], 'zorder': 1}
-    if boundary_dict is not None:
-        if not isinstance(boundary_dict, dict):
-            raise TypeError("boundary_dict' must be a dictionary.")
-        boundary_args.update(boundary_dict)
-    confidence_args = {'linewidths': [2, 2], 'linestyles': '--', 'alpha': 0.9,
-                       'vmin': 0.2, 'vmax': 0.8, 'zorder': 1}
-    if confidence_dict is not None:
-        if not isinstance(confidence_dict, dict):
-            raise TypeError("confidence_dict' must be a dictionary.")
-        confidence_args.update(confidence_dict)
+    boundary_args = _get_boundary_args(boundary_dict)
+    confidence_args = _get_confidence_args(confidence_dict)
 
     # Create mesh for plotting
-    x_vec = np.linspace(xmin, xmax, res)
-    y_vec = np.linspace(ymin, ymax, res)
-    X_mesh, Y_mesh = np.meshgrid(x_vec, y_vec)
-    mesh_instances = np.array([X_mesh.reshape(-1), Y_mesh.reshape(-1)]).T
+    X_mesh, Y_mesh, mesh_instances = mesh(feature_bound, res)
 
     # Calculate predictions
     if hasattr(clf, 'predict_proba'):
         predictions = clf.predict_proba(mesh_instances)
-        classes = np.array(range(predictions.shape[1]))
+        classes = np.arange(predictions.shape[1])
     elif hasattr(clf, 'predict'):
         if confidence is not None:
             warnings.warn("The given classifier does not implement "
@@ -95,7 +87,7 @@ def plot_decision_boundary(clf, feature_bound, ax=None, res=21,
                           "plotted.")
             confidence = None
         predicted_classes = clf.predict(mesh_instances)
-        classes = np.array(range(len(np.unique(predicted_classes))))
+        classes = np.arange(len(np.unique(predicted_classes)))
         predictions = np.zeros((len(predicted_classes), len(classes)))
         for idx, y in enumerate(predicted_classes):
             predictions[idx, y] = 1
@@ -119,11 +111,12 @@ def plot_decision_boundary(clf, feature_bound, ax=None, res=21,
                                                   posterior_list[y2]], axis=0)
 
         posteriors = posteriors / (posteriors + posteriors_best_alternative)
-        ax.contour(X_mesh, Y_mesh, posteriors, [.5], **boundary_args)
+        for ax_item in axs:
+            ax_item.contour(X_mesh, Y_mesh, posteriors, [.5], **boundary_args)
         if confidence is not None:
-            ax.contour(X_mesh, Y_mesh, posteriors, [confidence],
-                       colors=[cmap(norm(y))], **confidence_args)
-
+            for ax_item in axs:
+                ax_item.contour(X_mesh, Y_mesh, posteriors, [confidence],
+                                colors=[cmap(norm(y))], **confidence_args)
     return ax
 
 
@@ -156,40 +149,21 @@ def plot_utility(qs, qs_dict, X_cand=None, feature_bound=None, ax=None, res=21,
     -------
     matplotlib.axes.Axes: The axis on which the utility was plotted.
     """
-    if not isinstance(qs, QueryStrategy):
-        raise TypeError("'qs' must be a query strategy.")
-    if not isinstance(qs_dict, dict):
-        raise TypeError("'qs_dict' must be a dictionary.")
+    check_type(qs, 'qs', QueryStrategy)
+    check_type(qs_dict, 'qs_dict', dict)
     if 'X_cand' in qs_dict.keys():
         raise ValueError("'X_cand' must be given as separate argument.")
 
-    if feature_bound is not None:
-        check_array(feature_bound)
-        xmin, ymin, xmax, ymax = np.ravel(feature_bound)
-    elif X_cand is not None:
-        xmin = min(X_cand[:, 0])
-        xmax = max(X_cand[:, 0])
-        ymin = min(X_cand[:, 1])
-        ymax = max(X_cand[:, 1])
-    else:
-        raise ValueError("If 'X_cand' is None, 'bound' must be given.")
+    feature_bound = check_bound(bound=feature_bound, X=X_cand)
 
     if ax is None:
         ax = plt.gca()
-    if not isinstance(ax, Axes):
-        raise TypeError("ax must be a matplotlib.axes.Axes.")
+    check_type(ax, 'ax', Axes)
     check_scalar(res, 'res', int, min_val=1)
 
-    x_vec = np.linspace(xmin, xmax, res)
-    y_vec = np.linspace(ymin, ymax, res)
-    X_mesh, Y_mesh = np.meshgrid(x_vec, y_vec)
-    mesh_instances = np.array([X_mesh.reshape(-1), Y_mesh.reshape(-1)]).T
+    X_mesh, Y_mesh, mesh_instances = mesh(feature_bound, res)
 
-    contour_args = {'cmap': 'Greens', 'alpha': 0.75}
-    if contour_dict is not None:
-        if not isinstance(contour_dict, dict):
-            raise TypeError("contour_dict' must be a dictionary.")
-        contour_args.update(contour_dict)
+    contour_args = _get_contour_args(contour_dict)
 
     if X_cand is None:
         _, utilities = qs.query(mesh_instances, **qs_dict,
diff --git a/skactiveml/visualization/multi/__init__.py b/skactiveml/visualization/multi/__init__.py
new file mode 100644
index 00000000..4bbec007
--- /dev/null
+++ b/skactiveml/visualization/multi/__init__.py
@@ -0,0 +1,5 @@
+from ...visualization.multi._multi_annot_feature_space import plot_ma_current_state, \
+    plot_ma_data_set, plot_ma_utility, plot_ma_decision_boundary
+
+__all__ = ['plot_ma_current_state', 'plot_ma_data_set', 'plot_ma_utility',
+           'plot_ma_decision_boundary']
diff --git a/skactiveml/visualization/multi/_multi_annot_feature_space.py b/skactiveml/visualization/multi/_multi_annot_feature_space.py
new file mode 100644
index 00000000..3675bf37
--- /dev/null
+++ b/skactiveml/visualization/multi/_multi_annot_feature_space.py
@@ -0,0 +1,390 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.lines import Line2D
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.utils import check_array, check_consistent_length
+
+from skactiveml.base import MultiAnnotPoolBasedQueryStrategy
+from skactiveml.utils import is_labeled, check_scalar
+from .._feature_space import plot_decision_boundary
+from ...utils._validation import check_type
+from .._auxiliary_functions import mesh, check_bound, _get_contour_args, \
+    _get_tick_args, _get_legend_args, _get_cmap, _get_figure_for_ma
+
+
+def plot_ma_current_state(X, y, y_true, ma_qs, clf, ma_qs_arg_dict,
+                          bound=None, epsilon=1, title=None, fontsize=15,
+                          fig_size=None, plot_legend=True, legend_dict=None,
+                          contour_dict=None, boundary_dict=None,
+                          confidence_dict=None, tick_dict=None):
+    """Shows the annotations from the different annotators, the decision
+    boundary of the given classifier and the utilities expected of querying
+    a sample from a given region based on the query strategy.
+
+    Parameters
+    ----------
+    X : matrix-like, shape (n_samples, 2)
+        The sample matrix X is the feature matrix representing the samples.
+        The feature space must be two dimensional.
+    y : array-like, shape (n_samples, n_annotators)
+        It contains the annotated values for each sample.
+        The number of class labels may be variable for the samples, where
+        missing labels are represented the attribute 'missing_label'.
+    y_true : array-like, shape (n_samples,)
+        The correct labels
+    ma_qs: MultiAnnotPoolBasedQueryStrategy
+        The multi-annotator query strategy.
+    clf: sklearn classifier
+        The classifier whose decision boundary is plotted.
+    ma_qs_arg_dict: dict
+        The argument dictionary for the multiple annotator query strategy.
+    bound: array-like, [[xmin, ymin], [xmax, ymax]]
+        Determines the area in which the boundary is plotted.
+    epsilon: float, optional (default=1)
+        The minimal distance between the returned bound and the values of `X`,
+        if `bound` is not specified.
+    title : str, optional
+        The title for the figure.
+    fontsize: int
+        The fontsize of the labels.
+    fig_size: tuple, shape (width, height) (default=None)
+        The size of the figure in inches. If `fig_size` is None, the size
+        of the figure is set to 8 x 5 inches.
+    plot_legend: bool
+        Whether to plot the legend.
+    legend_dict: dict, optional (default=None)
+        Additional parameters for the legend.
+    contour_dict: dict, optional (default=None)
+        Additional parameters for the utility contour.
+    boundary_dict: dict, optional (default=None)
+        Additional parameters for the boundary contour.
+    confidence_dict: dict, optional (default=None)
+        Additional parameters for the confidence contour. Must not contain a
+        colormap because cmap is used.
+    tick_dict: dict, optional (default=None):
+        Additional parameters for the ticks of the plots.
+
+    Returns
+    ----------
+    fig: matplotlib.figure.Figure
+        The figure onto which the current state is plotted
+    """
+
+    bound = check_bound(bound, X, epsilon=epsilon)
+
+    fig = plot_ma_utility(fig_size=fig_size, ma_qs=ma_qs,
+                          ma_qs_arg_dict=ma_qs_arg_dict, feature_bound=bound,
+                          title=title, fontsize=fontsize, res=5,
+                          contour_dict=contour_dict, tick_dict=tick_dict)
+    plot_ma_data_set(fig=fig, X=X, y=y, y_true=y_true, feature_bound=bound,
+                     plot_legend=plot_legend, legend_dict=legend_dict,
+                     tick_dict=tick_dict)
+    plot_ma_decision_boundary(clf, fig=fig, feature_bound=bound,
+                              boundary_dict=boundary_dict,
+                              confidence_dict=confidence_dict,
+                              tick_dict=tick_dict)
+
+    return fig
+
+
+def plot_ma_data_set(X, y, y_true, fig=None, feature_bound=None, title=None,
+                     fontsize=15, fig_size=None, plot_legend=True,
+                     cmap='coolwarm', legend_dict=None, tick_dict=None,
+                     marker_size=10):
+    """Plots the annotations of a binary classification problem, differentiating
+    between correctly and incorrectly labeled data.
+
+    Parameters
+    ----------
+    X : array-like, shape (n_samples, 2)
+        The sample matrix X is the feature matrix representing the samples.
+        The feature space must be two dimensional.
+    y_true : array-like, shape (n_samples,)
+        The correct labels
+    y : array-like, shape (n_samples, n_annotators)
+        It contains the annotated values for each sample.
+        The number of class labels may be variable for the samples, where
+        missing labels are represented the attribute 'missing_label'.
+    fig: matplotlib.figure.Figure, optional (default=None)
+        The figure to which axes the utilities will be plotted.
+    fig_size: tuple, shape (width, height) (default=None)
+        The size of the figure in inches. If `fig_size` is None, the size
+        of the figure is set to 8 x 5 inches.
+    feature_bound: array-like, [[xmin, ymin], [xmax, ymax]]
+        Determines the area in which the boundary is plotted.
+    title : str, optional
+        The title for the figure.
+    fontsize: int
+        The fontsize of the labels.
+    plot_legend: bool
+        Whether to plot the legend.
+    legend_dict: dict, optional (default=None)
+        Additional parameters for the legend.
+    tick_dict: dict, optional (default=None)
+        Additional parameters for the ticks.
+    cmap: str | matplotlib.colors.Colormap, optional (default='coolwarm_r')
+        The colormap for the confidence levels.
+    marker_size: int
+        The size of the markers on the plot.
+
+    Returns
+    ----------
+    fig: matplotlib.figure.Figure
+        The figure onto which the data set is plotted
+    """
+
+    # check input values
+
+    X = check_array(X)
+    if X.shape[1] != 2:
+        raise ValueError(f"`X` along axis 1 must be of length two."
+                         f"`X` along axis 1 is of length {X.shape[1]}.")
+
+    y_true = check_array(y_true, ensure_2d=False)
+    if y_true.ndim != 1:
+        raise ValueError(f"`y_true` must be one dimensional."
+                         f"`y_true` is {y_true.ndim} dimensional.")
+    check_consistent_length(X, y_true)
+
+    y = check_array(y, force_all_finite='allow-nan')
+    check_consistent_length(y_true, y)
+
+    n_annotators = y.shape[1]
+    legend_args = _get_legend_args(legend_dict, fontsize)
+    tick_args = _get_tick_args(tick_dict)
+    fig = _get_figure_for_ma(fig, fig_size=fig_size, title=title,
+                             fontsize=fontsize, n_annotators=n_annotators,
+                             tick_args=tick_args)
+    feature_bound = check_bound(feature_bound, X)
+    check_scalar(plot_legend, 'plot_legend', bool)
+    cmap = _get_cmap(cmap)
+    labeled_indices = is_labeled(y)
+
+    n_classes = len(np.unique(np.append(y[labeled_indices].flatten(), y_true,
+                                        axis=0)))
+    classes = np.arange(n_classes)
+
+    norm = plt.Normalize(vmin=min(classes), vmax=max(classes))
+
+    # plot data set
+    for a, ax in enumerate(fig.get_axes()):
+        ax.scatter(X[~labeled_indices[:, a], 0], X[~labeled_indices[:, a], 1],
+                   c='gray', marker='o', s=marker_size)
+        ax.set_xlim(feature_bound[:, 0])
+        ax.set_ylim(feature_bound[:, 1])
+
+        for cl, color in zip(classes, cmap(norm(classes))):
+            for is_true, marker in zip([False, True], ['x', 's']):
+
+                cl_is_true = np.logical_xor(y_true != cl, is_true)
+                cl_current = np.logical_and(y[:, a] == cl, cl_is_true)
+
+                cl_labeled = np.logical_and(cl_current, labeled_indices[:, a])
+
+                ax.scatter(X[cl_labeled, 0], X[cl_labeled, 1], color=color,
+                           marker=marker, s=marker_size)
+
+    patch = Line2D([0], [0], marker='o', markerfacecolor='grey',
+                   markersize=20, alpha=0.8, color='w')
+    true_patches = (Line2D([0], [0], marker='s', markerfacecolor='b',
+                           markersize=15, color='w'),
+                    Line2D([0], [0], marker='s', markerfacecolor='r',
+                           markersize=15, color='w'))
+    false_patches = (Line2D([0], [0], marker='x', markerfacecolor='b',
+                            markeredgecolor='b', markersize=15, color='w'),
+                     Line2D([0], [0], marker='x', markerfacecolor='r',
+                            markeredgecolor='r', markersize=15, color='w'))
+
+    handles = [true_patches, false_patches]
+    labels = ['true annotation', 'false annotation']
+
+    if not(np.all(is_labeled(y))):
+        handles = [patch, ] + handles
+        labels = ['not acquired annotation', ] + labels
+
+    if plot_legend:
+        fig.legend(handles, labels, **legend_args)
+
+    return fig
+
+
+def plot_ma_utility(ma_qs, ma_qs_arg_dict, X_cand=None, A_cand=None, fig=None,
+                    fig_size=None, feature_bound=None, title=None, res=21,
+                    fontsize=15, contour_dict=None, tick_dict=None):
+    """Plots the utilities for the different annotators of the given
+    multi-annotator query strategy.
+
+    Parameters
+    ----------
+    ma_qs: MultiAnnotPoolBasedQueryStrategy
+        The multi-annotator query strategy.
+    ma_qs_arg_dict: dict
+        The argument dictionary for the multiple annotator query strategy.
+    fig: matplotlib.figure.Figure, optional (default=None)
+        The figure to which axes the utilities will be plotted
+    fig_size: tuple, shape (width, height) (default=None)
+        The size of the figure in inches. If `fig_size` is None, the size
+        of the figure is set to 8 x 5 inches.
+    feature_bound: array-like, [[xmin, ymin], [xmax, ymax]]
+        Determines the area in which the boundary is plotted.
+    X_cand : array-like, shape (n_samples, n_features)
+        Candidate samples from which the strategy can select.
+    A_cand : array-like, shape (n_samples, n_annotators), optional
+             (default=None)
+        Boolean matrix where `A_cand[i,j] = True` indicates that
+        annotator `j` can be selected for annotating sample `X_cand[i]`,
+        while `A_cand[i,j] = False` indicates that annotator `j` cannot be
+        selected for annotating sample `X_cand[i]`. If A_cand=None, each
+        annotator is assumed to be available for labeling each sample.
+    title : str, optional
+        The title for the figure.
+    res: int
+        The resolution of the plot.
+    fontsize: int
+        The fontsize of the labels.
+    contour_dict: dict, optional (default=None)
+        Additional parameters for the utility contour.
+    tick_dict: dict, optional (default=None)
+        Additional parameters for the ticks.
+
+    Returns
+    ----------
+    fig: matplotlib.figure.Figure
+        The figure onto which the utilities are plotted
+    """
+
+    # check arguments
+    check_type(ma_qs, 'ma_qs', MultiAnnotPoolBasedQueryStrategy)
+    check_type(ma_qs_arg_dict, 'ma_qs_arg_dict', dict)
+    if 'X_cand' in ma_qs_arg_dict.keys():
+        raise ValueError("'X_cand' must be given as separate argument.")
+    if 'A_cand' in ma_qs_arg_dict.keys():
+        raise ValueError("'A_cand' must be given as separate argument.")
+
+    n_annotators = None
+    if fig is None:
+        if A_cand is not None:
+            check_array(A_cand)
+            n_annotators = A_cand.shape[1]
+        elif ma_qs.n_annotators is not None:
+            check_scalar(ma_qs.n_annotators, "ma_qs.n_annotators",
+                         target_type=int)
+            n_annotators = ma_qs.n_annotators
+        else:
+            raise ValueError("`A_cand`, `fig` or `n_annotators` must be set in "
+                             "the multi annotator query strategy, to determine "
+                             "the number of annotators.")
+    elif fig is not None and A_cand is not None:
+        check_array(A_cand)
+        if A_cand.shape[0] != len(fig.get_axes()):
+            raise ValueError(f"`A_cand.shape[0]` must equal "
+                             f"`len(fig.get_axes())`, but "
+                             f"`A_cand.shape[0] == {A_cand.shape[0]})` and "
+                             f"`len(fig.get_axes()) == {len(fig.get_axes())}`.")
+
+    elif fig is not None and ma_qs.n_annotators is not None:
+        check_scalar(ma_qs.n_annotators, "ma_qs.n_annotators", target_type=int)
+        qs_n_a = ma_qs.n_annotators
+        if qs_n_a != len(fig.get_axes()):
+            raise ValueError(f"`ma_qs.n_annotators` must equal "
+                             f"`len(fig.get_axes())`, but "
+                             f"`ma_qs.n_annotators == {qs_n_a})` and "
+                             f"`len(fig.get_axes()) == {len(fig.get_axes())}`.")
+
+    feature_bound = check_bound(feature_bound, X_cand)
+    contour_args = _get_contour_args(contour_dict)
+    tick_args = _get_tick_args(tick_dict)
+    fig = _get_figure_for_ma(fig, fig_size=fig_size, title=title,
+                             fontsize=fontsize, n_annotators=n_annotators,
+                             tick_args=tick_args)
+
+    # plot the utilities
+    X_mesh, Y_mesh, mesh_instances = mesh(feature_bound, res)
+
+    if X_cand is None:
+        _, utilities = ma_qs.query(mesh_instances, A_cand=A_cand,
+                                   **ma_qs_arg_dict, return_utilities=True,
+                                   batch_size=1)
+
+        for a, ax in enumerate(fig.get_axes()):
+            a_utilities = utilities[0, :, a]
+            a_utilities_mesh = a_utilities.reshape(X_mesh.shape)
+            ax.contourf(X_mesh, Y_mesh, a_utilities_mesh, **contour_args)
+    else:
+        _, utilities = ma_qs.query(X_cand, A_cand=A_cand, **ma_qs_arg_dict,
+                                   return_utilities=True, batch_size=1)
+
+        for a, ax in enumerate(fig.get_axes()):
+            utilities_a = utilities[0, :, a]
+            neighbors = KNeighborsRegressor(n_neighbors=1)
+            neighbors.fit(X_cand, utilities_a)
+            scores = neighbors.predict(mesh_instances).reshape(X_mesh.shape)
+            ax.contourf(X_mesh, Y_mesh, scores, **contour_args)
+
+    return fig
+
+
+def plot_ma_decision_boundary(clf, feature_bound, n_annotators=None, fig=None,
+                              boundary_dict=None, confidence=0.75, title=None,
+                              res=21, fig_size=None, fontsize=15,
+                              cmap='coolwarm', confidence_dict=None,
+                              tick_dict=None):
+    """Plot the decision boundary of the given classifier for each annotator.
+
+    Parameters
+    ----------
+    clf: sklearn classifier
+        The classifier whose decision boundary is plotted.
+    feature_bound: array-like, [[xmin, ymin], [xmax, ymax]]
+        Determines the area in which the boundary is plotted.
+    n_annotators: int, optional (default=None)
+        The number of annotators for which the decision boundary will be
+        plotted. `n_annotators` or `fig` have to be passed as an argument.
+    fig: matplotlib.figure.Figure, optional (default=None)
+        The figure to which axes the decision boundary will be plotted.
+        `n_annotators` or `fig` have to be passed as an argument.
+    confidence: scalar | None, optional (default=0.5)
+        The confidence interval plotted with dashed lines. It is not plotted if
+        confidence is None.
+    title : str, optional
+        The title for the figure.
+    res: int, optional (default=21)
+        The resolution of the plot.
+    fig_size: tuple, shape (width, height) (default=None)
+        The size of the figure in inches. If `fig_size` is None, the size
+        of the figure is set to 8 x 5 inches.
+    fontsize: int
+        The fontsize of the labels
+    cmap: str | matplotlib.colors.Colormap, optional (default='coolwarm_r')
+        The colormap for the confidence levels.
+    boundary_dict: dict, optional (default=None)
+        Additional parameters for the boundary contour.
+    confidence_dict: dict, optional (default=None)
+        Additional parameters for the confidence contour. Must not contain a
+        colormap because cmap is used.
+    tick_dict: dict, optional (default=None)
+        Additional parameters for the ticks.
+
+    Returns
+    ----------
+    fig: matplotlib.figure.Figure
+        The figure onto which the decision boundaries are plotted.
+    """
+
+    # check arguments
+    if n_annotators is None and fig is None:
+        raise ValueError("`n_annotators` or `fig` must not be `None`")
+
+    if n_annotators is not None:
+        check_scalar(n_annotators, name='n_annotators', target_type=int)
+
+    tick_args = _get_tick_args(tick_dict)
+    fig = _get_figure_for_ma(fig, fig_size=fig_size, title=title,
+                             fontsize=fontsize, n_annotators=n_annotators,
+                             tick_args=tick_args)
+
+    # plot decision boundary
+    plot_decision_boundary(clf, feature_bound, ax=fig.get_axes(), res=res,
+                           boundary_dict=boundary_dict, confidence=confidence,
+                           cmap=cmap, confidence_dict=confidence_dict)
+    return fig
diff --git a/tutorials/multiple_annotators_getting_started.ipynb b/tutorials/multiple_annotators_getting_started.ipynb
new file mode 100644
index 00000000..54048eb8
--- /dev/null
+++ b/tutorials/multiple_annotators_getting_started.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e1239f66",
+   "metadata": {},
+   "source": [
+    "# Multi-annotator Pool-based Active Learning - Getting Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "informed-teacher",
+   "metadata": {},
+   "source": [
+    "This Notebook gives an introduction for dealing with multiple annotators using `scikit-activeml`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "piano-tulsa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import make_classification\n",
+    "\n",
+    "from skactiveml.classifier import PWC\n",
+    "from skactiveml.pool import UncertaintySampling\n",
+    "from skactiveml.pool.multi._wrapper import MultiAnnotWrapper\n",
+    "from skactiveml.utils import is_unlabeled, MISSING_LABEL, majority_vote\n",
+    "from skactiveml.visualization.multi._multi_annot_feature_space import plot_ma_current_state, plot_ma_data_set\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "simplified-alcohol",
+   "metadata": {},
+   "source": [
+    "Suppose we have the following problem. We have 50 samples having 2 features\n",
+    "belonging to one of two classes. To generate the example problem, we\n",
+    "use the `make_classification` function by sklearn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "formal-sweet",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x576 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_samples = 50\n",
+    "X, y_true = make_classification(n_samples=n_samples, n_features=2, n_redundant=0, random_state=0)\n",
+    "fig = plot_ma_data_set(X, y_true.reshape(-1, 1), y_true, title='Data set', fig_size=(8, 8),\n",
+    "              plot_legend=False)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Further, suppose we have 5 annotators to label the samples. The annotators have\n",
+    "different accuracies for labeling the samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x216 with 5 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.default_rng(seed=0)\n",
+    "n_annotators = 5\n",
+    "y_annot = np.zeros(shape=(n_samples, n_annotators), dtype=int)\n",
+    "for i, p in enumerate(np.linspace(0.0, 0.5, num=n_annotators)):\n",
+    "    y_noise = rng.binomial(1, p, n_samples)\n",
+    "    y_annot[:, i] = y_noise ^ y_true\n",
+    "\n",
+    "fig = plot_ma_data_set(X, y_annot, y_true, title='Annotator predictions', fig_size=(10, 3),\n",
+    "                       legend_dict={'loc': 'lower left'})\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We want to label these samples using a Parzen window classifier. We query the\n",
+    "samples using uncertainty sampling, and the annotators at random using the\n",
+    "`MultiAnnotWrapper`.\n",
+    "To achieve this, we first pass the Parzen window classifier as an argument to\n",
+    "the single annotator query strategy uncertainty sampling. Then we pass the single annotator query\n",
+    "strategy as an argument to the wrapper, also specifying the number of annotators.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "hydraulic-biotechnology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = PWC(classes=np.unique(y_true))\n",
+    "sa_qs = UncertaintySampling(random_state=0)\n",
+    "ma_qs = MultiAnnotWrapper(sa_qs, random_state=0, n_annotators=n_annotators)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We loop through the process of querying samples and annotators over a total of\n",
+    "20 cycles. Each iteration, we query three annotators for one sample, setting the\n",
+    "batch size and the number of annotators per sample to three.\n",
+    "We set the candidate annotators for each sample to be those, who have not already\n",
+    "labeled the given sample. Further we fit our classifier using the majority votes\n",
+    "of the queried labels.\n",
+    "The results are displayed after the 5th, the 14th, and the 20th cycle.\n",
+    "The assigned utilities of the query strategy for labeling a sample annotator\n",
+    "pair are displayed by the saturation of the green color in the plot.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 720x180 with 5 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x180 with 5 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 720x180 with 5 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# function to be able to index via an array of indices\n",
+    "idx = lambda A: (A[:, 0], A[:, 1])\n",
+    "\n",
+    "n_cycle = 20\n",
+    "\n",
+    "# the already observed labels for each sample and annotator\n",
+    "y = np.full(shape=(n_samples, n_annotators), fill_value=MISSING_LABEL)\n",
+    "clf.fit(X, majority_vote(y))\n",
+    "for c in range(n_cycle):\n",
+    "    A_cand = is_unlabeled(y)\n",
+    "\n",
+    "    # the needed query parameters for the wrapped singe annotator query strategy\n",
+    "    query_params_dict = {'clf': clf}\n",
+    "\n",
+    "    query_idx = ma_qs.query(X_cand=X, A_cand=A_cand, batch_size=3,\n",
+    "                            n_annotators_per_sample=3,\n",
+    "                            query_params_dict=query_params_dict)\n",
+    "\n",
+    "    y[idx(query_idx)] = y_annot[idx(query_idx)]\n",
+    "    clf.fit(X, majority_vote(y, random_state=0))\n",
+    "    if c in [4, 13, 19]:\n",
+    "        ma_qs_arg_dict = {'query_params_dict': query_params_dict}\n",
+    "        fig = plot_ma_current_state(X, y, y_true, ma_qs, clf, epsilon=1, ma_qs_arg_dict=ma_qs_arg_dict,\n",
+    "                                    fig_size=(10, 2.5))\n",
+    "        fig.tight_layout()\n",
+    "        plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/notebooks/getting_started.ipynb b/tutorials/pool_getting_started.ipynb
similarity index 99%
rename from docs/notebooks/getting_started.ipynb
rename to tutorials/pool_getting_started.ipynb
index 1a68f7f2..4958b6dd 100644
--- a/docs/notebooks/getting_started.ipynb
+++ b/tutorials/pool_getting_started.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Getting Started"
+    "# Pool-based Active Learning - Getting Started"
    ]
   },
   {
@@ -41,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data set Generation\n",
+    "## Data Set Generation\n",
     "We generate a data set of 100 data points with two clusters from the `make_classification` method of `scikit-learn`. This method also returns the true labels of each data point. In practice, however, we do not know these labels unless we ask an oracle. The labels are stored in `y_true`, which acts as an oracle."
    ]
   },
@@ -52,8 +52,10 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
@@ -165,8 +167,10 @@
     },
     {
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
@@ -183,8 +187,10 @@
     },
     {
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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OPP2Jp4or8HhPHE9/4ileH2+ANVda2ZHYjYn5i44KHo4MHSj28TbLU6P7HBWGWGH2kuO+hle+EsPsRACCxKCrhLGPZ8AK+2HXaK5kYARgeKhY2dDWi64R9LxNDx8zhjAryUzT5zcuwhC7U/mFSgB6pqwFMeNDY3jp9Ot47NDhytfVObtzLDFSU5DFaRyRJNzN3MbG8DB27dnBE3cLrLlE3s3kCkOYn//BEGZvBaCrK8n1yg/DCD++Cdho//oeFsIDanOdLGKAIZTII3u/MilKoXtQsw7I9AnQZAmSUnnnoAXt9wTsWD27k8PpZHhpZQ2RiwWQ1iVcnYpDV0tXx5pCyJwdxfnpy3iADVhO/hlGL0IIgEDFTpZBrf5hEGM/PwshoAPmBCFiEAI6ere+BQDIp6vX7mtChDuHxqCVue1pARG3Dz/U8OlWi4Lcxu27sWYIS1FMzF/0OgyOA/BE3iHU47eSjQaQYjIEm1FsgUJSttvULJ/FWZzdWScDD6Wx7zfvoH8sjVAij3W7lnHgf7iF6CajpKJlW1edTn50P+Z2DkOTBORDAeiigHt7tuHGk7VtU8sxSyusw2T6HE45vLSyhsj0BhEa0CBndORzZVOCBIbBPXmkYT/ezYpGO1l6t2ax9x/cLTmWvG18gWj51tWTTBJx8bOPQV5KIzyXRGZdHEqTQwzMocu6EoSQykG+n4Syrgd6mJdaOJ0FT+RriNsPr8fth9dj/EwWZ74qQVMAgEAigxRk2P2ZDM5MWo93Y2AVK3KgtU4Wk/CA6bfinExf6YlA6WltCo0QzIF0FQ+99pfY+K3nwSQRpGmY/eh+TP/K0c4xYOF0PTyRdwjyjI5nFvcgsiAjL+qYTmSwELc2Sho+mEc4sYSfHQ8hdU/E+p15jD2TRbifAZMF9aaGEon+LFLYiJ66Bio3ygMbtuKJ555Db28vsqqKSZfsABqFRB3jmX+BdTffhqBqQMHYa+DE28gnopj72H5vA+Rw6oQn8g5AntERvcJAzJi2I2siNs8Z7ZWWyVxn6H9Qw5Hfs+/bthoYsaRlHfdfGdRi2BEYhNhnfEGEHbYDaAmdYeAn70Ao64IRFRXrj7/JEzmnY+CJvAMI32CgsqqEwAhD8+HSRK4zfOpPX0Uwlcf/+2ePgwmNlQZanQZkhVt2AE5AmlaRxE0kn42m43Cq4UjXChF9g4hmiOi8E+fjlCLYdO0FtLIfn0AQNAZBY5B94kvulh2AE7CAhGyftaozPbqhzdFwOM3jVPvhfwDwjEPn8iVHhg44ItNvBj1ofTwvVm4emrM7Qz4ZNOCGHYCTXH3i16CJwWLDJiOCJku4+/c+7GlcHE4jOJLIGWMvAfAmy9mQUVv39XCap0b3NeW3ktlGYGU/KZ0YphOVkveVkW/+WJFfozloKP3CcWoT1QmWHhrD2cf/DAtje5Db0IvFQ9tx5Y//LjJlK/KxxAj3W3EBU6bPaY223d8S0bMAngWATVtaF0hUw/RbWSsogwIAHXQ5iwir3rWSLUyKcWrkW6sDKMwOmc3JCOLROJKpZdyKp9q60VnNb0WQFSz178ClQ7+D2O732hYTh+MkbUvkjLGvAfgaADz8yHjnjGTxCcqggBdmLmAsMVL1eeaK3InSilMDKGbEZfzk+Pcxe/EmIht68ejvfbLl2JyiqO7kfiucDoZL9DuMmjJ9c0VuU1oZkzfiiLoNj6sP4oi6raqXSrWOk0YJ9hriHTXjj9q9iVCnA2KrDEc3tey34oZMn/utrA28bx3g1M2T44dq1mnv7uyHEpEwO9Jb8VhfUsZ47z4IzGhLrLXCdrLjJNRn9L1rOX9scpoIwfYZZ3E4buFU++HfAHgNwA4iukVEv+XEeTmNMzfSi589tgX3t1ZOvRmaDxeTuEm1FbaTHSfmEGa9oJ70C0I4g9DIFEKb+YYbp3NxZEXOGPtVJ87DcZeKvvMCditsK0+WZjtOwuuNLxamOee34gRiKIf4wxe8DoPDaQleWlljSDkVw+/OQtAZJh8tnSKRF3XImljxGrsVtpUnS7OyfXNFDgBqVoEUaryUQaqGLS9fwKbTVyCoGmZ3bsb1j+6HEg83fC4OZy3BE/kaQ1J0fPA/XUIuGsD8rm0Ymg8joAnIizqWwnn0p4SS8kqtFfZq2b7ZirhL3dBwUhckwZjJxoDMXBLx4cY3TPf8zUtIXJ2GWCjPDJ27hoErd/DG7/9CUxOCTNTFOLRsCPLAfZDkr9IPh1MPvGtljZGLSGAEZIY3YPNcFLImgkCQNRH9qSDuR3PI6ApY4X/lYh07zFbEViYIkWh83NJzyYb/XZH3F5C4tpLEAWPwspRRsOGtaw2fbzXJc/uw9MZBaCnn5rxyOO2EJ/IG8FKmXy9MFKBEApj54HjFxqbACH1pGRIJoML/ZEh1JWQnWhHFgHEDmL3feCKP37kPZuEPLuY19N6Yafh8q6E2jnzjcNyAJ/I206xMvxGysQBUm6k5ok4IUGlFrZ6E7EQrolgof2QXGrdPyCasv2g0SUB6fWWrZSO0c3Ynxxou02+NNZvIdyR2+9JvpR3kYjKkZLqh19RKyE60IgYKI9SUZKVHTC0Wt61Hri8KvcyalwkC7h7a3vD5ViMU1Z3V6+zcb8UdROJbda2yZhN5N5OLBTD46nkwVlr/1olBI2t3hFoJ2Qnzq0A0BABQmvH6JsK53/oo5rdvhC4K0EUByxv68PZvfbSumZ2m34rlqduk7uRw3IJ/Fa5BsjEZw+/exrWlTRD6N5V0rfSlZTCdgaj+zhXAmVbEYKFNMJ+xNlivZdCVj4bw7uc+AkFRIWga1LCNv2+DOKXulGd0hG8wCDnDejizjQqGZys8P/k2nhrd1/Q1TJn+kaEDLcVazsT8RexI7Hb0nJz2wRN5h2HK9KuZZ731qe1489NjgEAAFgEY8vzNc1FjA7SQwxkY8tBwhWbrSsitThCSC34rWrbSB6YRgy5dlqA7+NEl2YinFeOs4ji+wk2LmAOiVxgAvZjMh6ObXN8faYawFO3aMuRagSfyDqUvKZf0iK+2tdWlyoqZlTyfQNDB2mYpGy5sWGr5yjKOlyPh5PX30P+RF4sr82awGsdHunFcGWwxQA6nBjyRdyCbcwlsXowWE3PNYcxoXJ7vBpF1hkxfVyt716t1xTyuPujYMGgrhIAKBFoz87Ibx2d3nMNxEp7IHSAzlUbqwjL0jA4hLCC6J4bw1tobcM0ynt1k2SNuDmPuvbOMI39zCcsDYZz8jXEAgCYwSHplH3YeGo6o21qW4NdDuJDIwRh0XYcgrHy55KAihMquEUJ9To3toC8p45nFPYi8olXUwPWgUU4px25MH4fjJLxrpUUyU2kk31qCnjFWmXpGR/KtJWSmGmv/a4QIs67lmqtuJhISt5fRd9dIeH1JGYJFEtfBIEFoSa3ZCHKhawUAcoul749VV0w5zXqh14IxYOnN/Vh47VEwm5En5h5DlAVBWKmByzNGzJlthLLvVkNhu63yfedwnIavyJtgXrmPhNwPAEhdMLo5StCM426tytOkIMoql3rmMOby4RJD82EIsE4ogkVdeifb0JSfSj2QQGA6Q3p2qVgzByq7YoCV1fhq3CgFEQHKvfVgeQksHyhufq7Gco+hvAZe/iXA52A5yqULEzj54qtILiUR74nj2BNHsWvPDq/D8gV8Rd4g5W1f5kq8HLvjTnA+dAd6WT/46mHMSliCLhDkjApSddv6uN1a0RTwhxDATjaIo+pIXROF6kGQDPfFjIXfyoy4jFPSDfxUuuqoF3pdcdVQd9q9h2YNPHyDVbyfVDjOaZ1LFyZw4vjzSC4Zn5vkUhInjj+PSxcmPI7MH/BE3iJC2OYX3OY40LpM/1ZwHrcGUlBEDQwMiqjh1kBqZaNTIChRo94cTOWLK/Vy6kkxAgTIkBwrvYiyEVduofoq3wkBUiPUmt1p9x6aNXC+2ekuJ198Fapa+iWuqipOvviqRxH5izWdyNsh04/uiQHlFt9i4biLLMQVvLd1Ee+OzuO9rYsV3SrZVUOYpxOZihV8nqm4g8W63Q9NWq1TS6GC38pi9T2EGXEZEzSDLPJgYMgijwmacW2js9aK3Oo9ZMJKDdxuU5NvdtZPNb8VcyVe7/Fug9fIW8Ssg7eza6UecjEZQAqh5Tymh40ktbrv/IX58wj2x7CkZYt1aQbY1tJX00qdOhANArNAvg6ZfqsCpEZYSeTWfivmF6X5HupBKulayWyjEkEQUJroOdURSYLG7Mtm8Z64ZdKO98Qtnt198ETuAOGtEc8Tdzk3963H/c1xpHuNJeFCXClZtV9+/y7GMVYxOKJ8tJsVrdSpAzFDpq+kmvBbaRHTb+WxQ4crHqtVWgFW3sPL89fx5PihkseMhF5bou8Ubsj0/cyxJ47ixPHnS8orkiTh2BNHPYzKP/BE3qFcnr9eVaZ/5ehww+cs7xzJQ4MEoaSzpdU6dbDH+MJTs82rKN1A6l2CPDQDIdK4M6OJMijUVHEORzc54rfihi++n/1WzO4U3rViDU/kHYjpt+IG5eUMOyOrWgZXdoT6Cn4rucoWPy8JDd9FaPiu12F4Rif4rezas4Mnbht4Il+jBNJ5JO4sQ5VF3N/a0/R5rOrUjRhclRMeMGX6fDYmh+MUa7prpZtZf20RH/nLcxj/0XXLxx87dBjnpy83de5Wxr6ZfitM81d/NWOAlg1CS/lrr4PDqQeeyNcouUL7YXDZ+Vp0K2PfQn0rbZl+qpPr6Qju//gjWDz1qNehcDgNwxN5E3TCEOZcsY/c+Vp0K6pLQRKKktLU7JLt8wa1GI6o2xxTlNaCarQfcjh+hidyD3FzyIDptxJKOr/qbVV1KYgFmb5NIjdr8O0y8wIAklSAGJgqgdnI8Tkcv+LIJ5aIniGiCSK6QkR/6MQ51zqttJ/VgxoUoQUEiHkdYs7ZjcVWVZdCwEjk2Xnr57dSg28WolWioPzKqrwvKWPnVC8enkxg51Qv+pJ8rifHf7TctUJEIoB/D+CjAG4BOE1E32OMXWz13E6wI7EbE/MXEZaiXofSVvqWg7jy938OWjSMh+5quDuQtR060QytqC6lUABqRkFuwbrdrZUafCsIQQV6LgiWk4FQrnQ8HlYGeNwNJVyNg8NpFCdW5I8CuMIYu8YYUwB8C8CnHDgvp0nMBKTFIgARAkzC5rlow6tJt+rUUmFoci5pLb5x2/nQbpVNZX4rVta1AiM8omx1rY+/26nmt8Kxx4klzjCAm6v+fgtAhQaaiJ4F8CwAbNqyyYHLcuywS0DmBKF6aKVXvBbmgAnFxm/lGs1VWAU45Xz4kfWPYOO9EAJklHdkTcSW2SgiWQlLBR9yXTG+aOysa+2Oc1rD9FvhvuON07ZPJGPsa4yxg4yxg/0D/NbUTZxIQG7WqeUew29FzVh/qbjpfPgAG0CAStcvBMLAchDDW66h7+gpyOvvAbC3rrU7Xi+mTL8VEnI/Tk2fbekcfuT6e1Pcd7wJnEjktwFsWfX3zYVjHBd5cvwQLs9ft3zMiQTkZp062GvsV6hVZPqrh0yckm44Zl9rFz+BMJwFAgPzEAorcyvr2tUDPPwIs5tV1yG8c/I89x1vAicS+WkAY0Q0SkQygM8C+J4D5+U0iaV3NhiWwvX3lLtZpw4VRrzpijvTfqpRLf7yO5aFuFJ9gIePmH/pLM488Q/xypaP47WHfxk3/+I/g+mN3TmEpSgm5r3tUUjb7Jtw3/HqtLy8YoypRPS7AP47jBEL32CMXWg5Mk7TLMQVRLISBpJBEBm1cgKhPxVEOqTWlYjcrFNHBgwPaV1rv9/KNZrDLn1D8X1ZTV5gWL64EyTlEX3oKoBK+99WkGdWbG5jgXXIiGlH7I+XzlzExc//CfSsMY5InU9i6n/7T1CTKYz+s8+3fH6TyUs3cO7kO0gn04jEI9h/bC9Gd21z7PwAEImHLZM59x2vjiM1csbYccbYQ4yxBxljX3binJ2An9WdPZlARbIyNzxXY+e34madOrK+1/gDA/QGV42tMiMu453MDTBUlkxuh3Vkro4id7txC+BayDM6olcYxJwhbJXzIpJvLSEzVX1SUj3c+Nf/sZjETfRMDne+8V+hZZyZNTd56QZeP3Ea6aQRbzqZxusnTmPy0g1Hzm+y99g4JKl0fcl9x2vD3Q+b5MjQAV9vNtWz4fnYocN46fTrtudwa0LPsNyP5557Dr29vcioeVwX77s2ws2Kl9OXkNi6rmRi0nQig8Wgkdz1rPPz2cI3SqcHAQA0Y7JUq6vy9OUp6weIoMzcR3jbxpbODwDnTr4DrcyxUlM1nDv5jqOr8pGdW9En9/OulQbhidxjbqfuYDjqfDtmXtQha+XDRA12TvViOpGxLBk06zNeL8W2xj7jCyVCsmNtjY1gVTIhBkDQizJ9arE7ZTV2Q5j1TOvXiDy0Dcq0RcmLMciD/S2fH0BxJV7v8VbgvuON0zUNsX40zXdTpm+14QkYtXJToVguEGqHx4kX8vtqrBYH7brZi6GBaQCAng05eh27IcxCuPVfwW1f/HUI4dILCOEghp/9DMSwM3cXkbj1XYPdcU576cgVeaObLqZMfy3Sl5QrSgSrV5sPXBegRsOGmcgqrOrl1ZKsUyUWJ9saW717sJLg71x/C0yRkcsFIUadW21aDWeGCET3tP4l2fOBXdjzH/4U1/7k/0Bq4gYCiR5s/ke/guF/+OmWz22y/9hevH7idEl5RZRE7D+217FrcJqn4xK5uelifqDMTRcAju+g+53NuQQ2L1Z6gQArpQPpwiWoj1pvFJXX0dvhcZKDihAqrWIbbWt0QnlqpYAVBYYHN9zEu1mrKEt54fzpiiHMdpQPZ84HNAzsSzg2tLvv2D4c+NH/7si5rDB/t9zuWuE0R8cl8nZtunQC49lNNaX4b3x2F3ZO2dfLP5d4Are0RcyIy44l2Wo41dbY6t3DS6dfx8PrnrF8LBRQQEL1X42xxIitIMuO1cOZb6fex1Nbne+OcZPRXduq/o61oz2RY03H1cjbuenidyLM2gSrfKVdrV7eI4aLdfBWfcbrwWxrXEotgzGGxaWlptoaW7l7GB8aA1BFAStpCA7NNBRPPcgzOnpPa0i8omHH+XWOtB76pXPKqfZEkSRunNUEHbcij8Qjlkm7Gzdd0qQgyio3s1YnKFHRoGYX8H4kj4FsDwKaAEJZOaGwkj0l3QA0uNq1AhjJ/LXXfoqbr1xCIBbCsT/8pYbP0crdw6AWw+cSTyCgCWBgJe+HWxJ8s4/crJHLeRGLby4AQNPllYTc7xstA79T9paOS+RObLrMXLmH62duIresIBiTMXJwCwa3r3cjXFdhYyGoP1MhYaVsUp6Idrx0E3uPT+LSR7bi7Z9/EA9PWhuWmStZt3rHK67X15pMv9kSTbG2Lq68zhQHmZvF8zEFLC9BCDhXUrLqIxeY4EgfuR/gd8re0nGlldFd23D46UPFFXgkHsHhpw/V/a0/c+UeLr88iVxhKHFuWcHllycxc+WeazG7hTIo4M3wVFUvEHPkmzmE2a6c4GQdvB7CpkxfbU6m36zy1Kq2bq7IpxMZzDIZs9//OBZOHmkqLjvc7CN3mmY6vHh7ord03IocqL3pUo3rZ25C10p/eXRNx/UzNztyVX4rOI/wUK/t49m4ObtzxdFvdcsd4HwdvB6i63sAAExv3q2vmbuHau6HQ/Nh3N+QMawDXOgjFy2SuRN95E4SlqJNaS54e6K3dGQibwVzJV7v8VrMK/eRkJ1Rz7lBLmbUkUOFf5+5Wjd7z5NaFrekxabr4IwBd97owc2TfVAzIvoeSOOBj91HZKC606Lcs7JSU1LZ4rAJt7GrrQPGJjHJijGEOR8A0wkkOGMLa9VHrpOO+J4eR87vNbw90Vu6LpEHY7Jl0g7GGh+q63e/FWD1inzl31wuT3/p9FvFTo5GufqDAdw53Qs9b6wsZy/GMH8lgkO/dxOhPvtyjSAIhkiJMaTvLbYtkV+jOexiGyo2fAGj7ERUmN2ZDULPhiBGnNn4LO8j14PA7aEkhraunWlZrdwpc1rDX/d1LrIjsRsZNYWRg1sgiKX/bEEUMHJwi80r3eWp0X24nbrj2vnNGnkoqRjLZwfJpwXceWMliQMAGEFXBdw8aV/uMREk43WZ2fZ5Tc+Iy7iNxYoBDKs3iYWgUQPRc86aZymDAhYPiZj/kIjFQyIW+61H3XE4jdJ1K3KzDr4WulbqQZNF5IMiAjkNgayGfNi5H3lqRgZJDOX7pEwjLN4IW79oFaIsQc9ryMy3d2jAFWkWF+5fx5OJhyusDQBACGWBxZ6WE/lq/3E9aJRXjJU5h+MsXZfIASOZr9XEbcXLv/Uw8kEJasDZJBLqU8HUyhIFiCGyrvY0IikkI5/KIbfY/ha1y8pdbNy61fKx4oq8BTvb8r5xMQdErzAAOk/mHMfhn6guYGZ7AvNb4mCS84m874EMSCrtAhIkhq0fnq/5eqngzKfYjPdyGzsv9tDWW4gfOAd5sHpL6lhiBC+cP235mFXfOOnGcQ7HaXgi71BMufdnFh7BzqneCkvadrHns9MY3LMMEnWQyBDsy2PPr04jtrF2F5AcMzY4lVT7a8XVNncDiQWEhu+2tNFp1zdud5zDaYWuLK10OqW37VTheljOpguz2HRpDrf3rMPdXc76fosyw65fnsGxT6axOTeAvogEhdbjmlZb2h/sMeroasZ/w4xbxa5vvNyX/MybExiZSUDP6BDCAqJ7Yg0rPU9Nn8WRoQMtROs/7mZuY2O4s0zFvIQn8g7EWu5d6nq4mv5bSWx/9Q6yMdnxRA4UZO/SIESpMUtZU6av5WrX09uJrgSQvTUMIobwaHMzKa36xplgHDcZTQ0hMqVBZ8aT9IyO5FtLAOz9VzJTaaQuLJck/uzQ2up+EUmCxtqrNO50eCLvQOxuz+3mdGajBVFQsrmVb60BDnaWsrvYBjygDtgab4X7C4k835xM3y2YKiF1YReEUK7pRG7VN17etRK+wSCwsp9ZlTmemam0kegLb5eZ+IWHRWCoqTA5awSeyDsQu9t2Ox8VK1FQvdQzwKGa7L3a6jxiyvS1xvxGAqksBt+5Dnkpg8XRDbi/fSMgWHTPNMlKH7kMxiqGK9XNav9xy+s06L+SurBcTOJFNECf0ICDzcVoxdzVeXz33N9yhWYHwRN5B2It97a3Xy0m8iZsCOoZ4FBN9m71fJNIwTgLAFRFhSTX/jj2Xp/Bw3/9E5DOIKoa1NcnsLypH2//xlNgkvXwjEYhUQcF8oZMX5FBQXdq+I36r9gabDnY9DNz5R5uvHqn6EfUzRO4Oomu61rx4xDmRlEGBaS2E7QgwACkKFfheriaWivyxw4dxvnpy5aP1TPAwWogRT3nEaSVY5m5xaqvBwDoDLu/9RIkRYVYMGeSFBXx23PYdNo6/mZxS925msw2gl6+2VFljqetwVZt7VXdWJnKmb7iHP/SVYl8R2K34+c8MnTAE3P/1XLvH/ZesE3iQJlMv0Hs7G1XHy+3lG3kPFTYIE3fq63ujM4sQLTwLxfzGja8da3m6xtBCLmfyJVBAbe2JosJWggLiD/SY7vRGd0TA6xuOtY7V1ayM4/jvuL+pqXSChH9MoB/AWAXgEcZY2ecCKrbeGp0H56ffBvDUXcMlNSQiIVNMShhCaTqDQmD6h3gYFrKltfU7Z5vIgYkqKqC7P3aiZwRwWJinYGDNXLAGXVnPSz2Z7HuA1UK6asIb41AmVOQmyzrUrnNcPnkNczfXGjZdsLOVI77ivubVmvk5wF8BsBXHYiF4xZE+OE/qW/aezkz4nJD498afb4YDEDNKMgu1C55pQd7oUSDCC2oJd6FWkDE3YPbm/nn2SKEssVVuZ/IT1usmDVg+tLKjFFzWAqAhpP5yMEtuPzyZEl5hfuK+5+WEjlj7BIAULPb+hzH6EvKRY/xchOoVml0gEMjzw+EZeQWUsjVI9MnwoW/9zj2fePHIJ1B0HQwgXB/+ybcPfBg3fGZjA+N4aXTr+OxQ4crHovtnkBs90TNc5gy/SfHm/uibJR6Jwo1OyzFfP7k6SkoqTzvWukQ2ta1QkTPAngWADZtWTsezH5gcy6BzYsrU3/slJ6iogFkrGD9QqDgQ55frq/1YnlTP177g1/Euos3IacyWNw2iOTmdW6G6CuEsFB3Mm92WMrg9vWIj0Rc2VPiuEPNRE5EJ2AtN/gSY+y/1nshxtjXAHwNAB5+ZJw7BznIeHZTyeg2oFLp+YHv/Axjr9zGmV98CFeOOSd9zmcE3HgxgXvnYyCRYePBJWw5ugChziVCsDApKJ+uP+nosoSZ/aPNhNswrfSRu0F0T6xEFFSNZoalrB5MfjF+la/GO4Sav26MsafbEQineSLMekNutdIzFykd+eYEugqc/epmZOclsMK1brzQj8XrYez93N26zhHsM+4cVJ/J9LVUGAuvfhCCrCDx+CuuXWc4ugnPT76Np0b31fV8s6NltUw/MCQjN5UtSe7NDEsxB5PzHvLOgwuC1gD1KD2LszuXnEvk987HkFtaSeIAoOcFLEyGkbwTRHxT7c3CUMLomdYt2gq9hAIq9GwQTPNPGcokvDVS0aI423sPgStiS10r1XrI253IRZK4cVYDtNp++GkA/w7AegDfJ6JzjLGfq/U6u15jTnNkthFCP1MhrWoyLld61iMKeun06w3N7ly4EYauWLQyMgBTPTgyGKnZuWKqO3XNX34rFMgDAgPLG19UZGN/4BfEzSIePdiaAyLvIe9cWu1a+S6A7zoUC6dJlEEBF25O4RFlq23XSqYFmb4d4f48SNLB1LIZqAIw1hNHqIo/i0l0sDDbkwG6qhfneHoNESDIOejZEPRcsMKbfHWX0IMUBZvp/Mk/vIe8c/Hkk3f//Xl89+t/i8lLzTnLtYI5hHmtcSs4j/e2LuLdUeP/y1sPWzHOsmPokSSE8soDMYRDhLEHrf1ZTAa1GI6o2/CUtAvPPfccHn74YWQX6m9xbAd26s6+pIzNc1HImggCIcqCiF5hkGf8vWqvhdVgct5D3hl4toQwN1K8SObdSIlMnzlT2pJjGvZ9/jbC6xQIkjEhqGdLFp//PCBalJZNvxVT/RlCAARCX18fPvnJT2Ig466KslHs/FaG5sMVXUJrYYzb4Pb1GPvwaLHbRY4GcPjpQ3yjswPwdLPTq40UpzkydACnps8iIfe3dJ7bqTuOyfQrBUJpvPErO4yVOQPgUEtdz3AOh5+bQm5JBIkMclRHWN0GWLghmn4rVo6KsixjT/8ozuCOM4E5gJ1M3873vZExbvLMild5LLAOGTHd8GQgN1g9mDyjpjCa6OzfzW7B86Ie30gxqLf9rB7Kb/0NgVAM9/eM4M6edY77kgBAsEeDHDVKC1ZuiKv9VuwcFaOygzZ+DWA3hDm4cRrR3e8h0F86SNrO9718jJsd5qg+MWd8n8p5Ecm3lpCZ4r8LnObwPJHzjRTnsbr1NwVC7aDcDTGLPCZoprjRaeeEmEy3v0ZerUtHHpxF5MFJSD2lhl7TiQz0MvcuFVrJGLdqWI3qMycDcTjN4GlphW+kuIPdrX9AFbD7x9dxZ9cAFjbHLZ9Ta6xbvVTzW7FyVFQUBS+ffg3Rpx9o+FrtxtxIXl26ekuewthgfcZdjU4G4nBq4Vki52Y8znN5/jrGEiPIizpkCyELKTns/cEk8mHJMpGPyRtrjnVzgnKHxGQqhR//8L/jyp3rOOyjRK7nJSj3jHpxaFOpUnUhrpR0Bt2an0e9HfiNTgbicGrhSSLv35DAp3/7k15ces3y5PghvHDekFNPJzLYPBctKa/oxID7xkZicNlaDv/B6I6aY92cYvWK/eqpt3Dz3QtFAy2/wBQZyTf3Q4hkKhJ5K1iN6qs2GYjDqQVfAqxBFuIKbg2koIgaGBgUUcOtgRSyzOifDy9Z39vHRetEarc56RThfiOBaYq//FZWd6041LEJoHJUnxYEpjYvttS1kpD7cWr6rHNBcjoK7rWyRim/9QeAWE91UZBdScZuc9IpwqZMX/VXjZgkDSRpYKoIpkqggHPvgzIoQFk1GGgxlbV/ModTg65cka9VdWctMjXUndOJDPKsNFlVG9PmFNHBPuMPjEHX/ZXM2zGEmWPP3cxtr0PoCLoykXcrtWT6C3EFP0met20bdAs5tlLSUeqZFNRGuj2RT8xf9OzaIvGCQb148k4FBBkaUzvmB7XabL+VwbZek43LyAdFqEHJdmLCZeUuglL7N91IIDCdIT2ziFBvtO3Xt6ObE3lYinblnWsn0hmZ1EPKzfZbGWzrNZos4tv/82Neh2GJIInQFBXpuST663fSdQy72Z0UNO5eWN6dXxVTqp/IDWL2vRlE98R8IdXndBY8kdfAymzfarCtE34rT43uw/OTbzvmt9JJCLIETVGR88ABcXxoDOenL1s+Ftt9CbE9F0GC84ZYplTfaEMk6BndGOMGeJLMre484yP8S6UT4DXyGtiZ7Tc72NY3ONlP5wCBkFG/zy76y2+ERL3uJG728deLn6T65p2n+bk27zznrs7XeCXHD/BEXgO7AbbNDLb1A4/8f5fxmS+9jK1vzXgdSgmBqFGD9ttmZ72MJUYafo2fpPp2d563z77f9lg4jcMTeQ2szPabGWzbLi7PX6/6OOkMckZF2MEBE04QiBmGXvl0A16wbUBdjmD+5aNYfOMDjp/bzi3RC6m+3R2mkvKXSItjDa+R18Csg3dC18pqmb4dWVMUZDOEuZnZnU4Q6jVqsWrGX18wJDCoC70QQs4Ldvwk1bcb8yZHK33lOZVcujCBky++iuRSEvGeOI49cRS79uxo2/V5Iq+D1Wb7nU4mbiwDnRz55gShvoJMP+evFWCx/VAJ2nVsNo0x49McMMGQD+gY2JfwZKNz5OCWku4swLjzHD6woe2xdBqXLkzgxPHnoaqGmC65lMSJ488DQNuSOS+tdBnmijyc9FcJI7zOlOlrHkdSCok6SFIBncDyzq9OlUEBi4dEzH9IwsT4rGd+K+Vj3oIxGWMfHsXAg4mm4+kWTr74ajGJm6iqipMvvtq2GLp6RZ5RUwhL/hGftIOiTN+mtOIVkfW9AACm+6ubBgCEoAJNlaDnghBkf90xOInVnScXBNUmuZRs6LgbdO2KfEdit9cheEJRpu+z9slg78pKVEn7y0BqRd3ZmZ1KrRCWop7K9DuBeI/1kBa7427QtYm8W8nFAjj/sRG8+4x/BjgAgCAIxQJ0emax7dcfHxqznd3ZzTJ9rxFJ8r1x1rEnjkKSSosbkiTh2BNH2xZDV5dWuhEmCjj/zKjXYVgiSAL0vIbMXBJ9I/7ZZJM3zEAIZyFG/SVW4vgDc0OzY7tWiOgrAD4JQAFwFcBvMsYW6nntxvAw7mZud4xxFsd9xIBkJPJ5fw0hDm25DeA2+pIyhqZ6i3M6pxOZCs93Tneya8+OtibuclotrfwYwDhjbC+AnwH4Z62H1LkcGTqAeeV+S+d4anQfbqfuOBSRNf1TSxh94y5is/5aYUohoyskt+C/Dba+pIzNc1HImggCQdZEbJ6Loi+5UjcfS4w0LNPncJygpUTOGPsRY8VJBKcAbG49JI7bjJ28jcPfeg+DVxe8DqUEKWL4kvtNps9UERvKZqACgMAIQ/Nhj6LicFZwcrPz8wB+YPcgET1LRGeI6Mzc7KyDl+U0SraOFkQ7N0A3MQdMKD4be6Ym45B1ayVQQOP9AhzvqfkpJKITRHTe4r9PrXrOlwCoAL5pdx7G2NcYYwcZYwcH1q1zJnpOBU+OH6rpt5IpioLsZfpeEOwxVrd+k+kLwRyyeevWw7zor9F0nO6k5k4jY+zpao8T0W8A+HkATzHmM29UjiWZnoJM32eioKCPZfpX39+CXcOTEIWVxK0Tw3TCX2UgTnfS0n0hET0D4A8A/AJjzF87Zxxbas3u9IpwfyGR5/0n059J9eHS7VEoog4GBkXUcGsg5WjXynB0E56ffNux83G6h1Z7//4CQBDAj8kQc5xijH2h5ag4rlL0W1nyl99KUaaveVeusBv5JgQVvL+4Dkr/RUgx/3XVrObU9FkcGTrg2PlMdaephp68dAPnTr6DdDKNSDyC/cf2YnTXNseux2mcVrtWtjPGtjDG9hf+40m8AzD9VgJZf618IwMr9q2qolZ5pjtUs+7tFHVnK6MG62Hy0g28fuI00knjBjydTOP1E6cxeemGa9fsBHWn13S9GqcbjbPUkIT/8uUPQw2JXodSgrBK5pyZW0R844CH0ZRiDmH2eyJ3m3Mn34FW5lCpqRrOnXyHr8o9pKt7p7rVOAsA1LDkrLm2Q5BkfCTT99rnHFcPsT0X0f/0CwgOTXsdiqeYK/F6j3PaQ1cnco7/EAPGqjx731+JXAxnIYazdQ9iXqtE4tZ+6XbHOe2BJ3Kf4rZMf/ePr+Pjf/4GNr97z9XrNIoYNGT6WR/K9Othrcv09x/bC1EqLcmJkoj9x/Z6FBEH8DiRbwwPQ2Pt39RyE6f8VtwmtJxH73QK0Tl/9UEHwsZGrN9k+upiDxZPH0DqknfGSH5gdNc2HH76UHEFHolHcPjpQ7w+7jFdv9nZrZjqzmq95OenL7d9CHMgWpDpL1fK9Ae1GB5gAwhCQg4qrtEcZsT2OCUyTYQyvQF6YgHdtTVeyeiubTxx+wxeWlmD1CPTN0VBYRt1p2cy/bgh089nSnvcB7UYdrBBhBAAgRBCADvYIAa19kycN9sPWZd3rXD8CU/kXUrRb8VnMn25MPJNy5bK9B9gAxDLPq4iBDzA2tOiSLLZfth94944/ocn8i6l6LeS9Je6M9xvzDnU8qV7J0GbKqDdcachSQUEHUwTwVT/9N/r6Sz0NrlF8tmd/oUn8i4l69MVeWRdDwBAV0tl+jlYb4rbHXcaIkOmD7grCqrXb0W7ex/zn//XmP3QFzH74S/i/t//c6jX33ctLq9Fc1zdWR2eyLuUXCSAq4c34mcf2gz4yLQyvK4weZwx6PpKMr9Gc9BQmtw16LhGc45ev64hzIq35RWWVzH/63+O/NmrgKoBqgb13UnMf+4r0NPG6vzU9FlPY+S0F57Iu5S+VBDJDx4G7TiAnTf7SkaWeYlcmBIElI58mxGXMUEzyCIPBoYs8pigmbZ1rQCAvG4WwU13QaK3HjW5F9+BvpwBVn3RgTGwnILcD9903W+F4z94+2EXYs6fNEeXmfMnAfhimDAJBKYzpGeXijVzwEjmM/BuMHN01888u/ZqtFuzQM6ipJRRoN3yl8CL0x66fkW+I7EbGbUzVYTNMjQf9vX8SaGgHMzM+kum7xcCu7aAghZrsEgQ0s4t7Q+I4zldn8i7Ebs5k36ZP1mU6S96t/q2gukELR2Glq79heemTD/w6A6Io0OAvCqZB0SIg30IPrnftety/Is/fnPXIE7I9N3yW7GbM+mX+ZNSyEjkuUV/Oerl7g7h/vNP1JTpjyVGXI2DBAF9f/U/IfyrT4D646BEDKFf/DAS//c/BQX80xrJaR++qJFrTIVIvgjFEY4MHfB118B0IlNSIwfs5096JtO/t2Qp0/eSdrQf1osQCSH+xV9C/Iu/5HUoHB/g+Yp8Y3jY6xDWLHYy/YW4glsDKWh6HmAMQiZjOX/SK5m+HDM6V/Jpf4mVBNmcEuSPDh8Ox8TzRM5xhyfHD1V9fCGu4E7wDnb/xXcw9P2f+qJbxSRYkOmrGf/EBPhrRc7hrIYn8i7GnN1ZzQHRC0K9hhGWpuRrPLO9kKwABLB8AEz333QltzGHMHsFV3fawxN5F5OLy2CC4U1Oqj82OgEgVBjCrOf9NRyaCBBM8yyX1Z31yPQ5HJO1s8PIaRgmEI7/wWFk4zKY6J8VZnR9LwCA6f6xDjAhOQfkZDBFBkLu1PCHo5tcnxDFWVvwRN7lJAf9N2sx2Ldi0JTPKMWpQX4gvvc8QIAY9beILCH349T0WRwZOuB1KJw2wEsrHN8hCIJRxwCQvrfY9utXM84K9C8gkFgA+aTnnsMBeCLvekbfuIsnvnoOW87NeB1KCYJofDTTs0seR8Lh+B+eyNGdfism8XtpDE3MI37PXypKsSA/z877S6afv5/A8ru7kb29yetQOJwiLSVyIvqXRPQOEZ0joh8REf90O4zbm165WKEFcdlfrX6m34rfZPrqchSZ69uQv1d9xNxYYsRVvxUOZzWtrsi/whjbyxjbD+C/Afij1kNaGxwZOuCI34rb5KJGwgym7BP5+enLrsdRTiBiiG5yS/5K5O1qP+RwGqGlRM4YW13AjAJoul9MY+0Z2cUpZSWRW4uCvJLpB0yZfpvmUdYLV3dy/EjLNXIi+jIR3QTwa6iyIieiZ4noDBGdmZudLXmM+624w5Pjh2z9VkyKidxnpZVg3LCKzftNps/9VjyFqzutqZnIiegEEZ23+O9TAMAY+xJjbAuAbwL4XbvzMMa+xhg7yBg7OLBunXP/Ak5L5GK1SyteECr0kms5f8W1ekXuo1GnbcNrmT7HmpqCIMbY03We65sAjgP445Yi4rSVbFTGnd0DSPf6q1QQShT8VvI+K7mJGkjUwTQBTBNBkjs2AsPRTXh+8u227JNwOp+WlJ1ENMYYM3fCPgXgvdZD4rQTLSjipd/e63UYFUTW9QAAmOYv4Q0RIPUtgOkCoEqAS4ncKbi6sztoVaL/vxDRDgA6gBsAvtB6SBwOEB4oDF1mgK7qECT/SB76jlqrPv1GQu5vuXOK0xm0lMgZY7/oVCAc75BTeYSSOaT7QlBD/rDfkUIrm4mZ+WTRSIvD4VTin2UOxzOO/scL+MSfn8a66+33NakGFWT6mXveyPTt/FYAYxAz88mwag6HfxI5vm1BFCRjkHBmPtn2a1ebU5qaGMPs959B5tpoGyPicOzhiXwV3eq3Ysr0/daCKAaNMk9m3l8/F5KMTpp6esm5TJ/TDngiL7AjsduV8zoh03fdb6WwIg/5LJFLBR9yxW8y/WBBFFRDpj+WGGlDNBwOT+Su0iltX2Yil20S+WOHDnvityJHDJm+suwzmb7MZfpew9WdpfgqkXO/FXeoKdP3qbozYMr00+6MVGsWU93JuHGWJ4jkj84qP+GbRM79VtzhyfFDNZ9TLK0s+8vXJNRrjKFTs/6Ki4Ld7bfCZfr+wzeJnOMd88NxvPA/7scbf3en16GUYPqt6Iq/7tRWW9m66bdiyvQ5nFrwexQO8mEJ748lvA6jgnC/oe7UVX/J4ElgiO17F0JABRgB5F/3LD6EuTvgiZzjWyKDhpqT6f5LlOGtt7wOgcMpwksrHADAnh9dx6PfuoRAxj9lDLmw2Qn4r3OFw/ETfEXeRjJTaaQuLEPP6BDCAqJ7YghvjXgdFgBg5M1pxO9l8N6TW5EP++NjIQiCYTfIGNKzi5ALU4P8gDI7AHWhF/LgPUg97Veecjir4SvyNpGZSiP51hL0jGHLqmd0JN9aQmbKH2KXemZ3eoHpepiZ9VeyzN3ZiNSlHcjf99/eAqf74Im8TaQuLAPle3Za4bgPyJoyfZ/5rYhyQaa/0P73aXxozNY4a0UU1J0tiBx/wRN5GW75rZgr8XqPl+O2TF/x6YpcChpx5Rb8cediYsr0a4mCxhIj3G+F4zo8ka/CDb+VI0MHMK/chxC2fqvtjq+mHeO+sqa6sw2ioEEthiPqNjyuPogj6jYMajHb50oRQwavLGdcj6sRqNhLzmX6XsFl+ivwRN4montigFh2UCwc9wG1auRO+a0MajHsYIMIIQACIYQAdrBB22RubnD6rWtlZQgzL614AZfpl+K7RL5W/VbCWyOIP9JTXIELYQHxR3ra0rXy5Pihmn4rqUQIC5tiyMbdTUwPsAGIZR87EQIeYAOWz4+s64UgiZCj/ulYAQAhmIUYX4YY8VfJp11wmb6/IOamxtjuokT3YMz4rMY6ALNtCMctOjl+12LfuHHjB+weu3v37psOXYa/997QybEDnRH/NsbY+vKDniTyeiCiM4yxg17H0SydHH8nxw50dvw8du/o5Ph9V1rhcDgcTmPwRM7hcDgdjp8T+de8DqBFOjn+To4d6Oz4eeze0bHx+7ZGzuFwOJz68POKnMPhcDh1wBM5h8PhdDi+TuRE9C+J6B0iOkdEPyKiTV7H1AhE9BUieq/wb/guEfV5HVO9ENEvE9EFItKJqCNasojoGSKaIKIrRPSHXsfTCET0DSKaIaLzXsfSKES0hYheIKKLhc/M73sdU70QUYiI3iCitwux/4nXMTWDr2vkRNTDGFsq/PkfA9jNGPuCx2HVDRF9DMBPGGMqEf0ZADDG/qnHYdUFEe0CoAP4KoB/whg743FIVSEiEcDPAHwUwC0ApwH8KmOsI+SHRPQYgGUAf80YG/c6nkYgoo0ANjLGzhJRHMCbAP5OJ7z3REQAooyxZSIKAHgFwO8zxk55HFpD+HpFbibxAlEA/v3WsYAx9iPGip4DpwBs9jKeRmCMXWKMTXgdRwM8CuAKY+waY0wB8C0An/I4prphjL0E4L7XcTQDY+wuY+xs4c9JAJcADHsbVX0wA9MjOVD4r6PyDODzRA4ARPRlIroJ4NcA/JHX8bTA5wH8wOsg1jDDAG6u+vstdEgyWUsQ0QiARwBYG7n7ECISiegcgBkAP2aMdUzsJp4nciI6QUTnLf77FAAwxr7EGNsC4JsAftfbaCupFX/hOV8CoML4N/iGemLncOqFiGIAvg3gubK7aV/DGNMYY/th3DE/SkQdVdoCfDCzkzH2dJ1P/SaA4wD+2MVwGqZW/ET0GwB+HsBTzGcbEg28953AbQBbVv19c+EYpw0U6svfBvBNxth3vI6nGRhjC0T0AoBnAHTUprPnK/JqENHYqr9+CsB7XsXSDET0DIA/APALjLHu9DttH6cBjBHRKBHJAD4L4Hsex9QVFDYM/wrAJcbYv/E6nkYgovVmNxkRhWFslndUngH837XybQA7YHRP3ADwBcZYx6yyiOgKgCCAucKhU53SdUNEnwbw7wCsB7AA4Bxj7Oc8DaoGRPQJAP8WxgiPbzDGvuxtRPVDRH8D4AkYVqrvA/hjxthfeRpUnRDRhwC8DOBdGL+rAPDPGWPHvYuqPohoL4D/C8ZnRgDw/zDG/tTbqBrH14mcw+FwOLXxdWmFw+FwOLXhiZzD4XA6HJ7IORwOp8PhiZzD4XA6HJ7IORwOp8PhiZzD4XA6HJ7IORwOp8P5/wG1037WR7vzeAAAAABJRU5ErkJggg==\n"
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
@@ -201,8 +207,10 @@
     },
     {
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
@@ -257,4 +265,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
\ No newline at end of file
+}
diff --git a/tutorials/stream_getting_started.ipynb b/tutorials/stream_getting_started.ipynb
new file mode 100644
index 00000000..5679fde9
--- /dev/null
+++ b/tutorials/stream_getting_started.ipynb
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stream-based Active Learning - Getting Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will show how stream-based active learning strategies are used and compared them to one another. We showcase the methods available in the stream package. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "import numpy as np\n",
+    "import sklearn\n",
+    "import sklearn.datasets\n",
+    "import matplotlib.pyplot as plt\n",
+    "from collections import deque\n",
+    "from scipy.ndimage import gaussian_filter1d\n",
+    "from skactiveml.classifier import PWC\n",
+    "from skactiveml.stream import RandomSampler, PeriodicSampler\n",
+    "from skactiveml.stream import FixedUncertainty, VariableUncertainty, Split, PALS\n",
+    "from skactiveml.utils import call_func"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Stream Parameters\n",
+    "Before the experiments can start, we need to construct a random data set. For this, we specify the necessary parameters in the cell below. To make the experiment repeatable, we will use the random_state object to generate all other random seeds, such that we only need to explicitly specify a single random seed. Furthermore, we specify the length of the data stream (stream_length) and the size of the sliding window that defines the available training data (training_size)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# random state that is used to generate random seeds\n",
+    "random_state = np.random.RandomState(0)\n",
+    "# number of instances that are provided to the classifier\n",
+    "init_train_length = 10\n",
+    "# the length of the data stream\n",
+    "stream_length = 10000\n",
+    "# the size of the sliding window that limits the training data\n",
+    "training_size = 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Seed Generation\n",
+    "The get_randomseed function simplifies the generation of a new random seed from a given random state object. For this notebook, this random state object will be always the random_state obejct defined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_randomseed(random_state):\n",
+    "    return random_state.randint(2**31-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate And Initialize Data Set\n",
+    "The next block initializes the tested data set. We use scikit-learn to generate a random dataset with our pre-defined stream length. The data set consists of multiple parts. X represents the location of the instance within the feature space. The class for each instance is denoted by y. \n",
+    "For models that need at least some initial training data, we generate samples to train an initial model. These are denoted by the suffix \"_init\", while all data used within the active learning cycle are denoted by the suffix \"_stream\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = sklearn.datasets.make_classification(n_samples=init_train_length + stream_length, random_state=get_randomseed(random_state), shuffle=True)\n",
+    "X_init = X[:init_train_length, :]\n",
+    "y_init = y[:init_train_length]\n",
+    "X_stream = X[init_train_length:, :]\n",
+    "y_stream = y[init_train_length:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Query Strategies\n",
+    "Next, we initialize the classifier and the base query strategies that we want to compare. To guarantee that the classifier is not affected by previous repetitions, we use factory functions to separate the classifier for each experiment run. Since all query strategies hav ea defaulr budget manag,,r we uset hat for the sake of simplicity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf_factory = lambda: PWC(classes=[0,1], random_state=get_randomseed(random_state))\n",
+    "query_strategies = {\n",
+    "    'RandomSampler': RandomSampler(random_state=get_randomseed(random_state)),\n",
+    "    'PeriodicSampler': PeriodicSampler(random_state=get_randomseed(random_state)),\n",
+    "    'FixedUncertainty': FixedUncertainty(random_state=get_randomseed(random_state)),\n",
+    "    'VariableUncertainty': VariableUncertainty(random_state=get_randomseed(random_state)),\n",
+    "    'Split': Split(random_state=get_randomseed(random_state)),\n",
+    "    'PALS': PALS(random_state=get_randomseed(random_state))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start Active Learning Cycle\n",
+    "After all, variables are initialized, we can start the experiment. The experiment loop below goes through all query strategies defined in query_strategies. For each experiment run, the average accuracy of the selected query strategies will be displayed. Lastly, the accuracy over time will be plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query Strategy:  RandomSampler , Avg Accuracy:  0.7525 , Acquisation count: 994\n",
+      "Query Strategy:  PeriodicSampler , Avg Accuracy:  0.7687 , Acquisation count: 1000\n",
+      "Query Strategy:  FixedUncertainty , Avg Accuracy:  0.8235 , Acquisation count: 1040\n",
+      "Query Strategy:  VariableUncertainty , Avg Accuracy:  0.8044 , Acquisation count: 1039\n",
+      "Query Strategy:  Split , Avg Accuracy:  0.8267 , Acquisation count: 1037\n",
+      "Query Strategy:  PALS , Avg Accuracy:  0.8205 , Acquisation count: 999\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x280152cfa00>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for query_strategy_name, query_strategy in query_strategies.items():\n",
+    "    clf = clf_factory()\n",
+    "    # initializing the training data\n",
+    "    X_train = deque(maxlen=training_size)\n",
+    "    X_train.extend(X_init)\n",
+    "    y_train = deque(maxlen=training_size)\n",
+    "    y_train.extend(y_init)\n",
+    "    # train the model with the initially available data\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    # initialize the list that stores the result of the classifier's prediction\n",
+    "    correct_classifications = []\n",
+    "    count = 0\n",
+    "    for t, (x_t, y_t) in enumerate(zip(X_stream, y_stream)):\n",
+    "        X_cand = x_t.reshape([1, -1])\n",
+    "        y_cand = y_t\n",
+    "        correct_classifications.append(clf.predict(X_cand)[0] == y_cand)\n",
+    "        # check whether to sample the instance or not\n",
+    "        # call_func is used since a classifier is not needed for RandomSampler and PeriodicSampler\n",
+    "        sampled_indices, utilities = call_func(query_strategy.query, X_cand=X_cand, clf=clf, return_utilities=True)\n",
+    "        # create budget_manager_param_dict for BIQF used by PALS\n",
+    "        budget_manager_param_dict = {\"utilities\": utilities}\n",
+    "        # update the query strategy and budget_manager to calculate the right budget\n",
+    "        query_strategy.update(X_cand, sampled_indices, budget_manager_param_dict)\n",
+    "        if len(sampled_indices):\n",
+    "            count += 1\n",
+    "        X_train.append(x_t)\n",
+    "        y_train.append(y_cand if len(sampled_indices) > 0 else clf.missing_label)\n",
+    "        # train the classifier\n",
+    "        clf.fit(X_train, y_train)\n",
+    "    # calculate and show the average accuracy \n",
+    "    print(\"Query Strategy: \", query_strategy_name, \", Avg Accuracy: \", np.mean(correct_classifications), \", Acquisation count:\", count)\n",
+    "    plt.plot(gaussian_filter1d(np.array(correct_classifications, dtype=float), 50), label=query_strategy_name)\n",
+    "plt.legend()"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "1292eb3755adf66b9290dde067207a19fd3b0f909d1a320c3e592979ed6b674f"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.11 64-bit ('al_forgetting_cd': conda)",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}


Test patch:
diff --git a/skactiveml/pool/multi/tests/test_wrapper.py b/skactiveml/pool/multi/tests/test_wrapper.py
index 21d45577..c5e5c379 100644
--- a/skactiveml/pool/multi/tests/test_wrapper.py
+++ b/skactiveml/pool/multi/tests/test_wrapper.py
@@ -155,7 +155,7 @@ def test_query_one_annotator_per_sample_batch_size_one(self):
         def y_aggregate(y_t):
             w = np.repeat(np.arange(y.shape[1]).reshape(1, -1), y.shape[0],
                           axis=0)
-            return majority_vote(y_t, w=w)
+            return majority_vote(y_t, w=w, random_state=0)
 
         wrapper = MultiAnnotWrapper(uncertainty, y_aggregate=y_aggregate,
                                     random_state=self.random_state)
diff --git a/skactiveml/utils/tests/test_aggregation.py b/skactiveml/utils/tests/test_aggregation.py
index 5eb3257f..0433cb62 100644
--- a/skactiveml/utils/tests/test_aggregation.py
+++ b/skactiveml/utils/tests/test_aggregation.py
@@ -39,6 +39,14 @@ def test_majority_vote(self):
 
         np.testing.assert_array_equal(y_aggregated_exp, y_aggregated_rec)
 
+    def test_majority_vote_no_label(self):
+        y = np.full(shape=(3, 2), fill_value=np.nan)
+
+        y_aggregate_exp = np.full(shape=(3,), fill_value=np.nan)
+        y_aggregate_rec = majority_vote(y=y)
+
+        np.testing.assert_array_equal(y_aggregate_exp, y_aggregate_rec)
+
     def test_weighted_majority_vote_encoding(self):
         y = [['tokyo', 'paris', 'tokyo'],
              ['paris', 'paris', 'nan'],
diff --git a/skactiveml/utils/tests/test_validation.py b/skactiveml/utils/tests/test_validation.py
index e1f5703f..60140ba7 100644
--- a/skactiveml/utils/tests/test_validation.py
+++ b/skactiveml/utils/tests/test_validation.py
@@ -4,7 +4,7 @@
 import numpy as np
 
 from skactiveml.utils import check_cost_matrix, check_classes, \
-    check_missing_label, check_scalar, check_X_y
+    check_missing_label, check_scalar, check_X_y, check_type, check_bound
 from skactiveml.utils import check_random_state, check_class_prior
 
 
@@ -119,3 +119,33 @@ def test_check_random_state(self):
         ra = check_random_state(np.random.RandomState(None))
         rb = check_random_state(np.random.RandomState(None))
         self.assertTrue(ra.rand() != rb.rand())
+
+    def test_check_type(self):
+        self.assertRaises(TypeError, check_type, 10, 'a', str)
+        self.assertRaises(TypeError, check_type, 10, 'a', str, bool)
+        self.assertRaises(TypeError, check_type, 10, 'a', str, bool, map, list)
+        check_type(10, 'a', int)
+
+    def test_check_bound(self):
+        self.assertRaises(ValueError, check_bound, X=7)
+        self.assertRaises(ValueError, check_bound, bound=7)
+
+        X = np.array([[3, 4], [2, 7], [-1, 5]])
+        wrong_X = np.array([[3, 4, 2]])
+        correct_bound = np.array([[-1, 4], [3, 7]])
+        small_bound = np.array([[1, 4], [3, 7]])
+        wrong_bound = np.array([[1, 4]])
+
+        re_correct_bound = check_bound(bound=correct_bound, X=X)
+        re_no_bound = check_bound(X=X)
+        re_no_X = check_bound(bound=correct_bound)
+        np.testing.assert_array_equal(correct_bound, re_correct_bound)
+        np.testing.assert_array_equal(correct_bound, re_no_bound)
+        np.testing.assert_array_equal(correct_bound, re_no_X)
+        with self.assertWarns(Warning):
+            check_bound(small_bound, re_correct_bound)
+        self.assertRaises(ValueError, check_bound, X=wrong_X)
+        self.assertRaises(ValueError, check_bound, bound=wrong_bound)
+        self.assertRaises(ValueError, check_bound)
+        self.assertRaises(ValueError, check_bound, X=X,
+                          bound_must_be_given=True)
diff --git a/skactiveml/visualization/multi/tests/__init__.py b/skactiveml/visualization/multi/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/skactiveml/visualization/multi/tests/images/data_set_expected_result.pdf b/skactiveml/visualization/multi/tests/images/data_set_expected_result.pdf
new file mode 100644
index 00000000..ce171d46
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diff --git a/skactiveml/visualization/multi/tests/images/data_set_mf_expected_result.pdf b/skactiveml/visualization/multi/tests/images/data_set_mf_expected_result.pdf
new file mode 100644
index 00000000..ebe3a10f
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diff --git a/skactiveml/visualization/multi/tests/images/ma_plot_current_state_expected_result.pdf b/skactiveml/visualization/multi/tests/images/ma_plot_current_state_expected_result.pdf
new file mode 100644
index 00000000..09d2b22a
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diff --git a/skactiveml/visualization/multi/tests/images/plot_decision_boundary_expected_result.pdf b/skactiveml/visualization/multi/tests/images/plot_decision_boundary_expected_result.pdf
new file mode 100644
index 00000000..9b4f9f8f
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diff --git a/skactiveml/visualization/multi/tests/images/plot_utility_X_expected_result.pdf b/skactiveml/visualization/multi/tests/images/plot_utility_X_expected_result.pdf
new file mode 100644
index 00000000..c9ff29d5
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diff --git a/skactiveml/visualization/multi/tests/images/plot_utility_expected_result.pdf b/skactiveml/visualization/multi/tests/images/plot_utility_expected_result.pdf
new file mode 100644
index 00000000..c5f682ed
Binary files /dev/null and b/skactiveml/visualization/multi/tests/images/plot_utility_expected_result.pdf differ
diff --git a/skactiveml/visualization/multi/tests/test_multi_annot_feature_space.py b/skactiveml/visualization/multi/tests/test_multi_annot_feature_space.py
new file mode 100644
index 00000000..4fad6308
--- /dev/null
+++ b/skactiveml/visualization/multi/tests/test_multi_annot_feature_space.py
@@ -0,0 +1,185 @@
+import os
+import unittest
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import testing
+from matplotlib.testing.compare import compare_images
+from sklearn.datasets import make_classification
+
+from skactiveml import visualization
+from skactiveml.classifier import PWC
+from skactiveml.pool.multi import IEThresh
+from skactiveml.utils import check_bound, majority_vote
+from skactiveml.visualization.multi import plot_ma_data_set, plot_ma_utility, plot_ma_decision_boundary, \
+    plot_ma_current_state
+
+
+class TestFeatureSpace(unittest.TestCase):
+
+    def setUp(self):
+        self.path_prefix = os.path.dirname(visualization.__file__) + \
+                           '/multi/tests/images/'
+
+        self.X, self.y_true = make_classification(n_features=2, n_redundant=0,
+                                                  random_state=0)
+
+        self.n_samples = self.X.shape[0]
+        self.n_annotators = 5
+
+        rng = np.random.default_rng(seed=0)
+
+        noise = rng.binomial(n=1, p=.2, size=(self.n_samples,
+                                              self.n_annotators))
+
+        self.y = (self.y_true.reshape(-1, 1) + noise) % 2
+
+        self.clf = PWC(random_state=0)
+        self.ma_qs = IEThresh(random_state=0, n_annotators=self.n_annotators)
+
+        testing.set_font_settings_for_testing()
+        testing.set_reproducibility_for_testing()
+        testing.setup()
+
+    def test_ma_plot_data_set_X(self):
+        self.assertRaises(ValueError, plot_ma_data_set, self.X.T, self.y,
+                          self.y_true)
+        self.assertRaises(ValueError, plot_ma_data_set, self.X, self.y,
+                          self.y_true.reshape(-1, 1))
+        self.assertRaises(TypeError, plot_ma_data_set, self.X, self.y,
+                          self.y_true, fig=4)
+
+    def test_ma_plot_data_set(self):
+        y = np.array(self.y, dtype=float)
+        y[np.arange(5), np.arange(5)] = np.nan
+        fig = plot_ma_data_set(self.X, y, self.y_true,
+                               fig_size=(12, 3),
+                               legend_dict={'loc': 'lower center',
+                                            'bbox_to_anchor': (0.5, 0.1),
+                                            'ncol': 3},
+                               tick_dict={'labelbottom': True,
+                                          'labelleft': True})
+
+        fig.tight_layout()
+        fig.savefig(self.path_prefix + 'data_set_returned_result.pdf')
+        comparison = compare_images(self.path_prefix +
+                                    'data_set_expected_result.pdf',
+                                    self.path_prefix +
+                                    'data_set_returned_result.pdf',
+                                    tol=0)
+        self.assertIsNone(comparison)
+
+    def test_ma_plot_data_set_mc(self):
+        X_prime, y_true_prime = make_classification(n_features=2, n_redundant=0,
+                                                    n_clusters_per_class=1,
+                                                    n_classes=4, random_state=0)
+        rng = np.random.default_rng(seed=0)
+
+        noise = np.sum(rng.multinomial(n=1, pvals=[.7, .1, .1, .1],
+                                       size=(self.n_samples,
+                                             self.n_annotators)) \
+                       * np.arange(4).reshape(1, 1, 4), axis=2)
+
+        y_prime = (self.y_true.reshape(-1, 1) + noise) % 4
+
+        fig = plot_ma_data_set(X_prime, y_prime, y_true_prime)
+        fig.tight_layout()
+        fig.savefig(self.path_prefix + 'data_set_mf_returned_result.pdf')
+        comparison = compare_images(self.path_prefix +
+                                    'data_set_mf_expected_result.pdf',
+                                    self.path_prefix +
+                                    'data_set_mf_returned_result.pdf',
+                                    tol=0)
+        self.assertIsNone(comparison)
+
+    def test_ma_plot_utility_args(self):
+        y = np.array(self.y, dtype=float)
+        y[np.arange(5), np.arange(5)] = np.nan
+        maqs_arg_dict = {'clf': self.clf, 'X': self.X, 'y': self.y,
+                         'X_cand': self.X}
+        bound = check_bound(X=self.X)
+        self.assertRaises(ValueError, plot_ma_utility, self.ma_qs,
+                          maqs_arg_dict, feature_bound=bound)
+        maqs_arg_dict = {'clf': self.clf, 'X': self.X, 'y': self.y,
+                         'A_cand': np.ones((self.n_samples, self.n_annotators))}
+        bound = check_bound(X=self.X)
+        self.assertRaises(ValueError, plot_ma_utility, self.ma_qs,
+                          maqs_arg_dict, feature_bound=bound)
+        maqs_arg_dict = {'clf': self.clf, 'X': self.X, 'y': self.y}
+        self.ma_qs.n_annotators = None
+        self.assertRaises(ValueError, plot_ma_utility, self.ma_qs,
+                          maqs_arg_dict, feature_bound=bound)
+        fig, _ = plt.subplots(ncols=7)
+        self.assertRaises(ValueError, plot_ma_utility, self.ma_qs,
+                          maqs_arg_dict, A_cand=np.ones((100, 5)), fig=fig,
+                          feature_bound=bound)
+        self.ma_qs.n_annotators = 5
+        self.assertRaises(ValueError, plot_ma_utility, self.ma_qs,
+                          maqs_arg_dict, fig=fig, feature_bound=bound)
+
+    def test_ma_plot_utility(self):
+        y = np.array(self.y, dtype=float)
+        y[np.arange(5), np.arange(5)] = np.nan
+        maqs_arg_dict = {'clf': self.clf, 'X': self.X, 'y': self.y}
+        bound = check_bound(X=self.X)
+        fig = plot_ma_utility(self.ma_qs, maqs_arg_dict, feature_bound=bound,
+                              title='utility', fig_size=(20, 5))
+        fig.tight_layout()
+        fig.savefig(self.path_prefix + 'plot_utility_returned_result.pdf')
+        comparison = compare_images(self.path_prefix +
+                                    'plot_utility_expected_result.pdf',
+                                    self.path_prefix +
+                                    'plot_utility_returned_result.pdf',
+                                    tol=0)
+        self.assertIsNone(comparison)
+
+    def test_ma_plot_utility_with_X(self):
+        maqs_arg_dict = {'clf': self.clf, 'X': self.X, 'y': self.y}
+        A_cand = np.ones((self.n_samples, self.n_annotators))
+        fig = plot_ma_utility(self.ma_qs, maqs_arg_dict, X_cand=self.X,
+                              A_cand=A_cand)
+        fig.tight_layout()
+        fig.savefig(self.path_prefix + 'plot_utility_X_returned_result.pdf')
+        comparison = compare_images(self.path_prefix +
+                                    'plot_utility_X_expected_result.pdf',
+                                    self.path_prefix +
+                                    'plot_utility_X_returned_result.pdf',
+                                    tol=0)
+        self.assertIsNone(comparison)
+
+    def test_ma_plot_decision_boundary_args(self):
+        bound = check_bound(X=self.X)
+        self.assertRaises(ValueError, plot_ma_decision_boundary, self.clf,
+                          bound)
+
+    def test_ma_plot_decision_boundary(self):
+        bound = check_bound(X=self.X)
+        self.clf.fit(self.X, majority_vote(self.y, random_state=0))
+        fig = plot_ma_decision_boundary(self.clf, bound,
+                                        n_annotators=self.n_annotators)
+        fig.tight_layout()
+        fig.savefig(self.path_prefix +
+                    'plot_decision_boundary_returned_result.pdf')
+        comparison = compare_images(self.path_prefix +
+                                    'plot_decision_boundary'
+                                    '_expected_result.pdf',
+                                    self.path_prefix +
+                                    'plot_decision_boundary'
+                                    '_returned_result.pdf',
+                                    tol=0)
+        self.assertIsNone(comparison)
+
+    def test_ma_plot_current_state(self):
+        maqs_arg_dict = {'clf': self.clf, 'X': self.X, 'y': self.y}
+        self.clf.fit(self.X, majority_vote(self.y, random_state=0))
+        fig = plot_ma_current_state(self.X, self.y, self.y_true, self.ma_qs,
+                                    self.clf, maqs_arg_dict)
+        fig.tight_layout()
+        fig.savefig(self.path_prefix +
+                    'ma_plot_current_state_returned_result.pdf')
+        comparison = compare_images(self.path_prefix +
+                                    'ma_plot_current_state_expected_result.pdf',
+                                    self.path_prefix +
+                                    'ma_plot_current_state_returned_result.pdf',
+                                    tol=0)
+        self.assertIsNone(comparison)
diff --git a/skactiveml/visualization/tests/test_auxiliary_functions.py b/skactiveml/visualization/tests/test_auxiliary_functions.py
new file mode 100644
index 00000000..2ce10be6
--- /dev/null
+++ b/skactiveml/visualization/tests/test_auxiliary_functions.py
@@ -0,0 +1,17 @@
+import unittest
+
+import numpy as np
+
+from skactiveml.visualization import mesh
+
+
+class TestAuxiliaryFunctions(unittest.TestCase):
+
+    def test_check_mesh(self):
+        bound = np.array([[0, 0], [1, 1]])
+        res = 10
+        X_mesh, Y_mesh, mesh_instances = mesh(bound, res)
+
+        self.assertEqual(X_mesh.shape, (10, 10))
+        self.assertEqual(Y_mesh.shape, (10, 10))
+        self.assertEqual(mesh_instances.shape, (100, 2))