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

Issue:
BALD or only BatchBALD implemented?
### Describe the issue linked to the documentation

The documentation says we have two implementations: **BALD** & **BatchBALD**. 


- [BALD Link](https://scikit-activeml.github.io/scikit-activeml-docs/generated/sphinx_gallery_examples/pool/plot-BatchBALD-Bayesian_Active_Learning_by_Disagreement_%28BALD%29.html#id1)
- [BatchBALD Link](https://scikit-activeml.github.io/scikit-activeml-docs/generated/sphinx_gallery_examples/pool/plot-BatchBALD-Batch_Bayesian_Active_Learning_by_Disagreement.html#sphx-glr-download-generated-sphinx-gallery-examples-pool-plot-batchbald-batch-bayesian-active-learning-by-disagreement-py)

It seems BALD is using BatchBALD with batch_size = 1. Is this the correct? 

I'm still understanding both implementations, however [it seems BALD and BatchBALD have different acquisition function](https://arxiv.org/pdf/1906.08158.pdf). For example. as the article, I would like to test for a certain batch_size the performance of BALD vs. BatchBALD



### Suggest a potential alternative/fix

_No response_


Discussion / hints:

Fix patch:
diff --git a/docs/examples/pool/_greedy_bald.json b/docs/examples/pool/_greedy_bald.json
new file mode 100644
index 00000000..c1985f3b
--- /dev/null
+++ b/docs/examples/pool/_greedy_bald.json
@@ -0,0 +1,22 @@
+[
+  {
+    "class" : "GreedyBALD",
+    "package" : "pool",
+    "method" : "Bayesian Active Learning by Disagreement (BALD)",
+    "category" :  "Query-by-Committee",
+    "template" : "examples/template_pool.py",
+    "tags" : ["pool",  "classification", "single-annotator"],
+    "title" : "Bayesian Active Learning by Disagreement",
+    "refs" : ["houlsby2011bayesian"],
+    "sequence" : ["title", "text_0", "plot", "refs"],
+    "text_0" : "",
+    "import_misc" : ["from skactiveml.pool import GreedyBALD"],
+    "import_clf" : ["from sklearn.gaussian_process import GaussianProcessClassifier",
+                    "from sklearn.ensemble import BaggingClassifier",
+                    "from skactiveml.classifier import SklearnClassifier"],
+    "init_clf" : ["SklearnClassifier(BaggingClassifier(\n    SklearnClassifier(GaussianProcessClassifier(), random_state=random_state), \n    random_state=random_state),\n    classes=np.unique(y_true),\n    random_state=random_state\n)"],
+    "init_qs" : "GreedyBALD(random_state=random_state)",
+    "query_params" : "X=X, y=y, ensemble=clf",
+    "plot_utility_params" : "replace_nan=None, candidates=X[unlabeled_indices(y)]"
+  }
+]
diff --git a/skactiveml/pool/__init__.py b/skactiveml/pool/__init__.py
index 300dda85..81a59144 100644
--- a/skactiveml/pool/__init__.py
+++ b/skactiveml/pool/__init__.py
@@ -5,7 +5,7 @@
 
 from . import multiannotator
 from . import utils
-from ._batch_bald import BatchBALD, batch_bald
+from ._bald import GreedyBALD, BatchBALD, batch_bald
 from ._cost_embedding_al import CostEmbeddingAL
 from ._discriminative_al import DiscriminativeAL
 from ._epistemic_uncertainty_sampling import EpistemicUncertaintySampling
@@ -59,4 +59,5 @@
     "DiscriminativeAL",
     "BatchBALD",
     "batch_bald",
+    "GreedyBALD",
 ]
diff --git a/skactiveml/pool/_batch_bald.py b/skactiveml/pool/_bald.py
similarity index 63%
rename from skactiveml/pool/_batch_bald.py
rename to skactiveml/pool/_bald.py
index 14fe6cb3..3cebb1d1 100644
--- a/skactiveml/pool/_batch_bald.py
+++ b/skactiveml/pool/_bald.py
@@ -1,6 +1,6 @@
 """
-Code is based on https://blackhc.github.io/batchbald_redux/
-distributed under the Apache-2.0 license.
+Code is based on https://blackhc.github.io/batchbald_redux/ distributed under
+the Apache-2.0 license.
 """
 import numpy as np
 from sklearn.utils import check_array
@@ -8,45 +8,57 @@
 from ..base import SkactivemlClassifier
 from ..pool._query_by_committee import _check_ensemble, QueryByCommittee
 from ..utils import (
-    rand_argmax,
     MISSING_LABEL,
+    rand_argmax,
     check_type,
     check_scalar,
     check_random_state,
+    simple_batch,
 )
 
 
-class BatchBALD(QueryByCommittee):
-    """Batch Bayesian Active Learning by Disagreement (BatchBALD)
+class _GeneralBALD(QueryByCommittee):
+    """General Bayesian Active Learning by Disagreement (_GeneralBALD)
 
-    The Bayesian-Active-Learning-by-Disagreement (BatchBALD) [1] strategy
+    The Bayesian Active Learning by Disagreement (BatchBALD) [1] strategy
     reduces the number of possible hypotheses maximally fast to minimize the
-    uncertainty about the parameters using Shannon’s entropy. It seeks the data
+    uncertainty about the parameters using Shannon's entropy. It seeks the data
     point that maximises the decrease in expected posterior entropy. For the
-    batch case, the advanced strategy BatchBALD [2] is applied.
+    batch case, by default the advanced strategy BatchBALD [2] is used.
+    If desired a greedy (top-k) selection can be applied by setting
+    `greedy_selection=True`.
 
     Parameters
     ----------
-    n_MC_samples : int > 0, default=n_estimators
+    n_MC_samples : int > 0, optional (default=n_estimators)
         The number of monte carlo samples used for label estimation.
-    missing_label : scalar or string or np.nan or None, default=np.nan
+    greedy_selection : bool, optional (default=False)
+        Flag to either use BatchBALD (`greedy_selection=False`) or a greedy
+        (top-k) selection (`greedy_selection=True`) if `batch_size>1`.
+    eps : float  > 0, optional (default=1e-7)
+        Minimum probability threshold to compute log-probabilities.
+    missing_label : scalar or string or np.nan or None, optional
+        (default=np.nan)
         Value to represent a missing label.
-    random_state : int or np.random.RandomState, default=None
+    random_state : int or None or np.random.RandomState, optional
+        (default=None)
         The random state to use.
 
     References
     ----------
-    [1] Houlsby, Neil, et al. Bayesian active learning for classification and
-        preference learning. arXiv preprint arXiv:1112.5745, 2011.
-    [2] Kirsch, Andreas; Van Amersfoort, Joost; GAL, Yarin.
-        Batchbald: Efficient and diverse batch acquisition for deep bayesian
-        active learning. Advances in neural information processing systems,
-        2019, 32. Jg.
+    [1] Houlsby, Neil, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel.
+        "Bayesian Active Learning for Classification and Preference Learning."
+        arXiv preprint arXiv:1112.5745 (2011).
+    [2] Kirsch, Andreas, Joost Van Amersfoort, and Yarin Gal. "BatchBALD:
+        Efficient and Diverse Batch Acquisition for Deep Bayesian  Active
+        Learning." Advances in Neural Information Processing Systems 32 (2019).
     """
 
     def __init__(
         self,
         n_MC_samples=None,
+        greedy_selection=False,
+        eps=1e-7,
         missing_label=MISSING_LABEL,
         random_state=None,
     ):
@@ -54,6 +66,8 @@ def __init__(
             missing_label=missing_label, random_state=random_state
         )
         self.n_MC_samples = n_MC_samples
+        self.greedy_selection = greedy_selection
+        self.eps = eps
 
     def query(
         self,
@@ -128,7 +142,9 @@ def query(
         X, y, candidates, batch_size, return_utilities = self._validate_data(
             X, y, candidates, batch_size, return_utilities, reset=True
         )
-
+        check_scalar(
+            self.greedy_selection, "greedy_selection", target_type=bool
+        )
         X_cand, mapping = self._transform_candidates(candidates, X, y)
 
         # Validate classifier type.
@@ -152,31 +168,143 @@ def query(
             n_MC_samples_ = self.n_MC_samples
         check_scalar(n_MC_samples_, "n_MC_samples", int, min_val=1)
 
+        utils_batch_size = 1 if self.greedy_selection else batch_size
         batch_utilities_cand = batch_bald(
-            probas, batch_size, n_MC_samples_, self.random_state_
+            probas=probas,
+            batch_size=utils_batch_size,
+            n_MC_samples=n_MC_samples_,
+            eps=self.eps,
+            random_state=self.random_state_,
         )
+
         if mapping is None:
             batch_utilities = batch_utilities_cand
         else:
-            batch_utilities = np.full((batch_size, len(X)), np.nan)
+            batch_utilities = np.full((utils_batch_size, len(X)), np.nan)
             batch_utilities[:, mapping] = batch_utilities_cand
 
-        best_indices = rand_argmax(
-            batch_utilities, axis=1, random_state=self.random_state_
+        if self.greedy_selection:
+            return simple_batch(
+                batch_utilities[0],
+                self.random_state_,
+                batch_size=batch_size,
+                return_utilities=return_utilities,
+            )
+        else:
+            best_indices = rand_argmax(
+                batch_utilities, axis=1, random_state=self.random_state_
+            )
+            if return_utilities:
+                return best_indices, batch_utilities
+            else:
+                return best_indices
+
+
+class BatchBALD(_GeneralBALD):
+    """Batch Bayesian Active Learning by Disagreement (BatchBALD)
+
+    The Bayesian Active Learning by Disagreement (BatchBALD) [1] strategy
+    reduces the number of possible hypotheses maximally fast to minimize the
+    uncertainty about the parameters using Shannon's entropy. It seeks the data
+    point that maximises the decrease in expected posterior entropy. For the
+    batch case, the advanced strategy BatchBALD [2] is used.
+
+    Parameters
+    ----------
+    n_MC_samples : int > 0, optional (default=n_estimators)
+        The number of monte carlo samples used for label estimation.
+    eps : float  > 0, optional (default=1e-7)
+        Minimum probability threshold to compute log-probabilities.
+    missing_label : scalar or string or np.nan or None, optional
+        (default=np.nan)
+        Value to represent a missing label.
+    random_state : int or None or np.random.RandomState, optional
+        (default=None)
+        The random state to use.
+
+    References
+    ----------
+    [1] Houlsby, Neil, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel.
+        "Bayesian Active Learning for Classification and Preference Learning."
+        arXiv preprint arXiv:1112.5745 (2011).
+    [2] Kirsch, Andreas, Joost Van Amersfoort, and Yarin Gal. "BatchBALD:
+        Efficient and Diverse Batch Acquisition for Deep Bayesian  Active
+        Learning." Advances in Neural Information Processing Systems 32 (2019).
+    """
+
+    def __init__(
+        self,
+        n_MC_samples=None,
+        eps=1e-7,
+        missing_label=MISSING_LABEL,
+        random_state=None,
+    ):
+        super().__init__(
+            n_MC_samples=n_MC_samples,
+            greedy_selection=False,
+            eps=eps,
+            missing_label=missing_label,
+            random_state=random_state,
         )
 
-        if return_utilities:
-            return best_indices, batch_utilities
-        else:
-            return best_indices
 
+class GreedyBALD(_GeneralBALD):
+    """Greedy Bayesian Active Learning by Disagreement (GreedyBALD)
+
+    The Bayesian Active Learning by Disagreement (BALD) [1] strategy
+    reduces the number of possible hypotheses maximally fast to minimize the
+    uncertainty about the parameters using Shannon's entropy. It seeks the data
+    point that maximises the decrease in expected posterior entropy. For the
+    batch case, a greedy (top-k) selection is applied
+
+    Parameters
+    ----------
+    n_MC_samples : int > 0, optional (default=n_estimators)
+        The number of monte carlo samples used for label estimation.
+    eps : float  > 0, optional (default=1e-7)
+        Minimum probability threshold to compute log-probabilities.
+    missing_label : scalar or string or np.nan or None, optional
+        (default=np.nan)
+        Value to represent a missing label.
+    random_state : int or None or np.random.RandomState, optional
+        (default=None)
+        The random state to use.
 
-def batch_bald(probas, batch_size, n_MC_samples=None, random_state=None):
-    """BatchBALD: Efficient and Diverse Batch Acquisition
-        for Deep Bayesian Active Learning
+    References
+    ----------
+    [1] Houlsby, Neil, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel.
+        "Bayesian Active Learning for Classification and Preference Learning."
+        arXiv preprint arXiv:1112.5745 (2011).
+    """
 
-    BatchBALD [1] is an extension of BALD (Bayesian Active Learning by
-    Disagreement) [2] whereby points are jointly scored by estimating the
+    def __init__(
+        self,
+        n_MC_samples=None,
+        eps=1e-7,
+        missing_label=MISSING_LABEL,
+        random_state=None,
+    ):
+        super().__init__(
+            n_MC_samples=n_MC_samples,
+            greedy_selection=True,
+            eps=eps,
+            missing_label=missing_label,
+            random_state=random_state,
+        )
+
+
+def batch_bald(
+    probas,
+    batch_size,
+    n_MC_samples=None,
+    random_state=None,
+    eps=1e-7,
+):
+    """BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian
+    Active Learning
+
+    BatchBALD [2] is an extension of BALD  [1] (Bayesian Active Learning by
+    Disagreement) whereby points are jointly scored by estimating the
     mutual information between a joint of multiple data points and the model
     parameters.
 
@@ -184,25 +312,28 @@ def batch_bald(probas, batch_size, n_MC_samples=None, random_state=None):
     ----------
     probas : array-like of shape (n_estimators, n_samples, n_classes)
         The probability estimates of all estimators, samples, and classes.
-    batch_size : int, default=1
+    batch_size : int, optional (default=1)
         The number of samples to be selected in one AL cycle.
-    n_MC_samples : int > 0, default=n_estimators
+    n_MC_samples : int > 0, optional (default=n_estimators)
         The number of monte carlo samples used for label estimation.
+    eps : float  > 0, optional (default=1e-7)
+        Minimum probability threshold to compute log-probabilities.
     random_state : int or np.random.RandomState, default=None
         The random state to use.
 
     Returns
     -------
-    scores: np.ndarray of shape (n_samples)
-        The BatchBALD-scores.
+    utilities: numpy.ndarray of shape (batch_size, n_samples)
+        Sample utilities computed according to BatchBALD [2].
 
     References
     ----------
-    [1] Kirsch, Andreas, Joost Van Amersfoort, and Yarin Gal. "Batchbald:
-        Efficient and diverse batch acquisition for deep bayesian active
-        learning." Advances in neural information processing systems 32 (2019).
-    [2] Houlsby, Neil, et al. "Bayesian active learning for classification and
-        preference learning." arXiv preprint arXiv:1112.5745 (2011).
+    [1] Houlsby, Neil, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel.
+        "Bayesian Active Learning for Classification and Preference Learning."
+        arXiv preprint arXiv:1112.5745 (2011).
+    [2] Kirsch, Andreas, Joost Van Amersfoort, and Yarin Gal. "BatchBALD:
+        Efficient and Diverse Batch Acquisition for Deep Bayesian  Active
+        Learning." Advances in Neural Information Processing Systems 32 (2019).
     """
     # Validate input parameters.
     if probas.ndim != 3:
@@ -211,12 +342,21 @@ def batch_bald(probas, batch_size, n_MC_samples=None, random_state=None):
         )
     probs_K_N_C = check_array(probas, ensure_2d=False, allow_nd=True)
     check_scalar(batch_size, "batch_size", int, min_val=1)
+    check_scalar(
+        eps,
+        "eps",
+        min_val=0,
+        max_val=0.1,
+        target_type=(float, int),
+        min_inclusive=False,
+    )
     if n_MC_samples is None:
         n_MC_samples = len(probas)
     check_scalar(n_MC_samples, "n_MC_samples", int, min_val=1)
     random_state = check_random_state(random_state)
 
     probs_N_K_C = probs_K_N_C.swapaxes(0, 1)
+    np.clip(probs_N_K_C, a_min=eps, a_max=1, out=probs_N_K_C)
     log_probs_N_K_C = np.log(probs_N_K_C)
     N, K, C = log_probs_N_K_C.shape
 
@@ -314,7 +454,9 @@ def _batch_multi_choices(probs_b_C, M, random_state):
 
     # samples: Ni... x draw_per_xx
     choices = [
-        random_state.choice(C, size=M, p=probs_B_C[b], replace=True)
+        random_state.choice(
+            C, size=M, p=probs_B_C[b] / np.sum(probs_B_C[b]), replace=True
+        )
         for b in range(B)
     ]
     choices = np.array(choices, dtype=int)
@@ -338,11 +480,15 @@ def _gather_expand(data, axis, index):
 
 
 class _SampledJointEntropy:
-    """Random variables (all with the same # of categories $C$) can be added via `_SampledJointEntropy.add_variables`.
+    """
+    Random variables (all with the same # of categories $C$) can be added
+    via `_SampledJointEntropy.add_variables`.
 
     `_SampledJointEntropy.compute` computes the joint entropy.
 
-    `_SampledJointEntropy.compute_batch` computes the joint entropy of the added variables with each of the variables in the provided batch probabilities in turn.
+    `_SampledJointEntropy.compute_batch` computes the joint entropy of the
+    added variables with each of the variables in the provided batch
+    probabilities in turn.
     """
 
     def __init__(self, sampled_joint_probs_M_K, random_state):
@@ -432,7 +578,10 @@ def add_variables(self, log_probs_N_K_C):
         return self
 
     def compute_batch(self, log_probs_B_K_C, output_entropies_B=None):
-        """Computes the joint entropy of the added variables together with the batch (one by one)."""
+        """
+        Computes the joint entropy of the added variables together with the
+        batch (one by one).
+        """
         return self.inner.compute_batch(log_probs_B_K_C, output_entropies_B)
 
 
diff --git a/skactiveml/pool/_query_by_committee.py b/skactiveml/pool/_query_by_committee.py
index 6278e19d..2f48839e 100644
--- a/skactiveml/pool/_query_by_committee.py
+++ b/skactiveml/pool/_query_by_committee.py
@@ -21,6 +21,7 @@
     compute_vote_vectors,
     MISSING_LABEL,
     check_equal_missing_label,
+    check_scalar,
 )
 
 
@@ -36,6 +37,9 @@ class QueryByCommittee(SingleAnnotatorPoolQueryStrategy):
         The method to calculate the disagreement in the case of classification.
         KL_divergence or vote_entropy are possible. In the case of regression
         the empirical variance is used.
+    eps : float  > 0, optional (default=1e-7)
+        Minimum probability threshold to compute log-probabilities (only
+        relevant for `method='KL_divergence'`).
     missing_label : scalar or string or np.nan or None, default=np.nan
         Value to represent a missing label.
     random_state : int or np.random.RandomState, default=None
@@ -58,6 +62,7 @@ class QueryByCommittee(SingleAnnotatorPoolQueryStrategy):
     def __init__(
         self,
         method="KL_divergence",
+        eps=1e-7,
         missing_label=MISSING_LABEL,
         random_state=None,
     ):
@@ -65,6 +70,7 @@ def __init__(
             missing_label=missing_label, random_state=random_state
         )
         self.method = method
+        self.eps = eps
 
     def query(
         self,
@@ -169,7 +175,7 @@ def query(
                 probas = np.array(
                     [est.predict_proba(X_cand) for est in est_arr]
                 )
-                utilities_cand = average_kl_divergence(probas)
+                utilities_cand = average_kl_divergence(probas, self.eps)
             else:  # self.method == "vote_entropy":
                 votes = np.array([est.predict(X_cand) for est in est_arr]).T
                 utilities_cand = vote_entropy(votes, classes)
@@ -194,7 +200,7 @@ def query(
         )
 
 
-def average_kl_divergence(probas):
+def average_kl_divergence(probas, eps=1e-7):
     """Calculates the average Kullback-Leibler (KL) divergence for measuring
     the level of disagreement in QueryByCommittee.
 
@@ -202,6 +208,8 @@ def average_kl_divergence(probas):
     ----------
     probas : array-like, shape (n_estimators, n_samples, n_classes)
         The probability estimates of all estimators, samples, and classes.
+    eps : float  > 0, optional (default=1e-7)
+        Minimum probability threshold to compute log-probabilities.
 
     Returns
     -------
@@ -214,7 +222,15 @@ def average_kl_divergence(probas):
         for text classification. In Proceedings of the International Conference
         on Machine Learning (ICML), pages 359-367. Morgan Kaufmann, 1998.
     """
-    # Check probabilities.
+    # Check parameters.
+    check_scalar(
+        eps,
+        "eps",
+        min_val=0,
+        max_val=0.1,
+        target_type=(float, int),
+        min_inclusive=False,
+    )
     probas = check_array(probas, allow_nd=True)
     if probas.ndim != 3:
         raise ValueError(
@@ -222,6 +238,9 @@ def average_kl_divergence(probas):
         )
     n_estimators = probas.shape[0]
 
+    np.clip(probas, a_min=eps, a_max=1, out=probas)
+    probas /= probas.sum(axis=2, keepdims=True)
+
     # Calculate the average KL divergence.
     probas_mean = np.mean(probas, axis=0)
     with np.errstate(divide="ignore", invalid="ignore"):
diff --git a/tutorials/01_deep_pool_al_with_skorch.ipynb b/tutorials/01_deep_pool_al_with_skorch.ipynb
index 454754a2..55171af8 100644
--- a/tutorials/01_deep_pool_al_with_skorch.ipynb
+++ b/tutorials/01_deep_pool_al_with_skorch.ipynb
@@ -17,11 +17,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
+    "import os.path\n",
+    "\n",
     "import matplotlib as mlp\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -30,14 +30,17 @@
     "\n",
     "from copy import deepcopy\n",
     "from skactiveml.classifier import SklearnClassifier\n",
-    "from skactiveml.pool import UncertaintySampling, QueryByCommittee, RandomSampling\n",
+    "from skactiveml.pool import UncertaintySampling, QueryByCommittee, RandomSampling, BatchBALD\n",
     "from skactiveml.utils import call_func\n",
-    "from sklearn.datasets import load_digits\n",
+    "from sklearn.datasets import fetch_openml\n",
     "from sklearn.ensemble import VotingClassifier\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from skorch import NeuralNetClassifier\n",
     "from torch import nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "from torchvision.transforms import Compose\n",
     "\n",
     "mlp.rcParams[\"figure.facecolor\"] = \"white\"\n",
     "\n",
@@ -47,47 +50,46 @@
     "\n",
     "torch.manual_seed(RANDOM_STATE)\n",
     "torch.cuda.manual_seed(RANDOM_STATE)\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "## Loading Digit Data Set\n",
-    "For simplicity, we load the digit data set available through the `sklearn` package. The 8x8 images show handwritten digits from 0 to 9 and the task is to recognize the digits in the images."
+    "For simplicity, we use the `sklearn` function `fetch_openml` to load the MNIST dataset. The dataset contains 28px x 28px images of handwritten digits from 0 to 9 and the task is to recognize the digits in the images."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 1080x360 with 10 Axes>",
-      "image/png": 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\n"
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-     "metadata": {
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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 10 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
+    "import torchvision\n",
+    "\n",
     "# Load digit data set.\n",
-    "X, y_true = load_digits(return_X_y=True)\n",
+    "X, y_true = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n",
     "\n",
     "# Visualize first 10 images.\n",
     "fig, axes = plt.subplots(figsize=(15, 5), nrows=1, ncols=10)\n",
     "for i in range(10):\n",
     "    axes[i].set_title(f'Digit: {y_true[i]}', fontsize=FONTSIZE)\n",
-    "    axes[i].imshow(X[i].reshape((8, 8)))\n",
+    "    axes[i].imshow(X[i].reshape(28,28))\n",
     "plt.show()\n",
     "\n",
     "# Standardize data.\n",
@@ -95,7 +97,7 @@
     "\n",
     "# Reshape samples to n_samples x n_channels x width x height to fit skorch\n",
     "# requirements.\n",
-    "X = X.reshape((len(X), 1, 8, 8))\n",
+    "X = X.reshape((len(X), 1, 28, 28))\n",
     "\n",
     "# Set data types according to skorch requirements.\n",
     "X, y_true = X.astype(np.float32), y_true.astype(np.int64)\n",
@@ -111,9 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "## Convolutional Neural Network Ensemble\n",
     "In a next step, we define a convolutional neural network (CNN) ensemble consisting of multiple base CNNs with different learning rates and initializations."
@@ -122,40 +122,42 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Define base module.\n",
     "class ClassifierModule(nn.Module):\n",
-    "    def __init__(self):\n",
+    "    def __init__(self, dropout=0.5):\n",
     "        super(ClassifierModule, self).__init__()\n",
-    "        self.conv_layer = nn.Sequential(\n",
-    "            nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=0),\n",
-    "            nn.ReLU(),\n",
-    "            nn.MaxPool2d(kernel_size=2, stride=2)\n",
-    "        )\n",
-    "        self.dense_layer = nn.Linear(288, len(classes))\n",
-    "        self.outpout = nn.Softmax(dim=-1)\n",
-    "\n",
-    "    def forward(self, X):\n",
-    "        X = self.conv_layer(X)\n",
-    "        X = X.reshape(X.size(0), -1)\n",
-    "        X= self.dense_layer(X)\n",
-    "        X = self.outpout(X)\n",
-    "        return X\n",
+    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)\n",
+    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)\n",
+    "        self.conv2_drop = nn.Dropout2d(p=dropout)\n",
+    "        self.fc1 = nn.Linear(1600, 100) # 1600 = number channels * width * height\n",
+    "        self.fc2 = nn.Linear(100, 10)\n",
+    "        self.fc1_drop = nn.Dropout(p=dropout)\n",
     "\n",
+    "    def forward(self, x):\n",
+    "        x = torch.relu(F.max_pool2d(self.conv1(x), 2))\n",
+    "        x = torch.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
+    "        \n",
+    "        # flatten over channel, height and width = 1600\n",
+    "        x = x.view(-1, x.size(1) * x.size(2) * x.size(3))\n",
+    "        \n",
+    "        x = torch.relu(self.fc1_drop(self.fc1(x)))\n",
+    "        x = torch.softmax(self.fc2(x), dim=-1)\n",
+    "        return x    \n",
+    "    \n",
     "# Create list of three base CNNs.\n",
-    "learning_rates = [1.e-3, 1.e-2, 1.e-1]\n",
     "estimators = []\n",
-    "for i, learning_rate in enumerate(learning_rates):\n",
+    "for i in range(3):\n",
     "    net = NeuralNetClassifier(\n",
     "        ClassifierModule,\n",
-    "        max_epochs=100,\n",
-    "        lr=learning_rate,\n",
-    "        verbose=0,\n",
+    "        max_epochs=10,\n",
+    "        lr=0.002,\n",
+    "        verbose=False,\n",
+    "        optimizer=torch.optim.Adam,\n",
     "        train_split=False,\n",
+    "        device=device,\n",
     "    )\n",
     "    net.initialize()\n",
     "    estimators.append((f'clf {i}',\n",
@@ -174,9 +176,80 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
+   "source": [
+    "## Data Augmentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from torchvision.transforms import transforms\n",
+    "\n",
+    "transform = transforms.Compose(\n",
+    "    [\n",
+    "        transforms.RandomAffine(degrees=(-10, 10), translate=(0.05, 0.05), scale=(0.9, 1.2)),\n",
+    "        transforms.RandomPerspective(distortion_scale=0.4, p=1.0),\n",
+    "        transforms.GaussianBlur(kernel_size=(3, 3), sigma=(0.01, 0.1)),\n",
+    "        #transforms.ElasticTransform(alpha=250.0, sigma=5.0)\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "def augmentate(X, y, n):\n",
+    "    y_augmented = np.tile(y, n)\n",
+    "    X_augmented = np.empty((0, *(X.shape[1:])), dtype=np.float32)\n",
+    "    for x in np.tile(X, (n, *[1]*(X.ndim-1))):\n",
+    "        X_augmented = np.append(X_augmented, transform(torch.from_numpy(x)).reshape(1, *(X.shape[1:])), axis=0)\n",
+    "    return np.array(X_augmented), y_augmented\n",
+    "\n",
+    "\n",
+    "# Visualize 30 augmented images.\n",
+    "for i in range(3):\n",
+    "    X_aug, y_aug = augmentate(X_train[i].reshape(1,1,28,28), y_train[i], 10)\n",
+    "    fig, axes = plt.subplots(figsize=(15, 5), nrows=1, ncols=10)\n",
+    "    for j in range(10):\n",
+    "        axes[j].set_title(f'Digit: {y_train[i]}', fontsize=FONTSIZE)\n",
+    "        axes[j].imshow(X_aug[j].reshape(28,28))\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Active Classification\n",
     "For our ensemble, we evaluate three different query strategies, i.e., random sampling, uncertainty sampling, and query-by-committee, regarding their sample selection. For this purpose, we start with zero labels and make 25 iterations of an active learning cycle with a batch size of 20."
@@ -184,29 +257,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Execute active learning using random sampling.\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n",
+      "20\n",
+      "21\n",
+      "22\n",
+      "23\n",
+      "24\n",
       "Execute active learning using uncertainty sampling.\n",
-      "Execute active learning using query-by-committee.\n"
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n",
+      "20\n",
+      "21\n",
+      "22\n",
+      "23\n",
+      "24\n",
+      "Execute active learning using query-by-committee.\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n",
+      "20\n",
+      "21\n",
+      "22\n",
+      "23\n",
+      "24\n",
+      "Execute active learning using batch BALD.\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n",
+      "20\n",
+      "21\n",
+      "22\n",
+      "23\n",
+      "24\n"
      ]
     }
    ],
    "source": [
     "# Define setup.\n",
     "n_cycles = 25\n",
+    "n_augmentations = 10\n",
     "batch_size = 20\n",
     "qs_dict = {\n",
     "    'random sampling': RandomSampling(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n",
     "    'uncertainty sampling': UncertaintySampling(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n",
     "    'query-by-committee': QueryByCommittee(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n",
+    "    'batch BALD': BatchBALD(random_state=RANDOM_STATE, missing_label=MISSING_LABEL),\n",
     "}\n",
     "acc_dict = {key: np.zeros(n_cycles + 1) for key in qs_dict}\n",
     "\n",
@@ -222,30 +396,34 @@
     "\n",
     "    # Create array of missing labels as initial labels.\n",
     "    y = np.full_like(y_train, fill_value=MISSING_LABEL, dtype=np.int64)\n",
+    "    X = X_train\n",
+    "    for i in range(10):\n",
+    "        y[np.argwhere(y_train==i)[0]] = i\n",
     "\n",
     "    # Execute active learning cycle.\n",
     "    for c in range(n_cycles):\n",
+    "        print(c)\n",
     "        # Fit and evaluate ensemble.\n",
-    "        acc = ensemble.fit(X_train, y).score(X_test, y_test)\n",
+    "        acc = ensemble.fit(X, y).score(X_test, y_test)\n",
     "        acc_dict[qs_name][c] = acc\n",
     "\n",
     "        # Select and update training data.\n",
     "        query_idx = call_func(\n",
-    "             qs.query, X=X_train, y=y, clf=ensemble, fit_clf=False, ensemble=ensemble,\n",
+    "             qs.query, X=X, y=y, clf=ensemble, fit_clf=False, ensemble=ensemble,\n",
     "             fit_ensemble=False, batch_size=batch_size\n",
     "        )\n",
     "        y[query_idx] = y_train[query_idx]\n",
-    "\n",
+    "        X_augmented, y_augmented = augmentate(X[query_idx], y[query_idx], n_augmentations)\n",
+    "        y = np.append(y, y_augmented, axis=0)\n",
+    "        X = np.append(X, X_augmented, axis=0)\n",
     "    # Fit and evaluate ensemble.\n",
-    "    ensemble.fit(X_train, y)\n",
+    "    ensemble.fit(X, y)\n",
     "    acc_dict[qs_name][n_cycles] = ensemble.score(X_test, y_test)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "## Visualize Results\n",
     "In the following, we plot the obtained learning curves including the area under learning curve (AULC) scores per query strategy."
@@ -253,19 +431,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 1152x648 with 1 Axes>",
-      "image/png": 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\n"
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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -285,23 +461,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
+ "nbformat_minor": 1
+}
\ No newline at end of file


Test patch:
diff --git a/skactiveml/pool/tests/test_batch_bald.py b/skactiveml/pool/tests/test_bald.py
similarity index 81%
rename from skactiveml/pool/tests/test_batch_bald.py
rename to skactiveml/pool/tests/test_bald.py
index a85bd1c0..08086385 100644
--- a/skactiveml/pool/tests/test_batch_bald.py
+++ b/skactiveml/pool/tests/test_bald.py
@@ -16,7 +16,12 @@
 
 from skactiveml.classifier import SklearnClassifier, ParzenWindowClassifier
 
-from skactiveml.pool._batch_bald import BatchBALD, batch_bald
+from skactiveml.pool._bald import (
+    _GeneralBALD,
+    batch_bald,
+    BatchBALD,
+    GreedyBALD,
+)
 from skactiveml.regressor import NICKernelRegressor
 from skactiveml.tests.template_query_strategy import (
     TemplateSingleAnnotatorPoolQueryStrategy,
@@ -24,7 +29,7 @@
 from skactiveml.utils import MISSING_LABEL
 
 
-class TestBatchBALD(
+class TestGeneralBALD(
     TemplateSingleAnnotatorPoolQueryStrategy, unittest.TestCase
 ):
     def setUp(self):
@@ -41,7 +46,7 @@ def setUp(self):
             "fit_ensemble": True,
         }
         super().setUp(
-            qs_class=BatchBALD,
+            qs_class=_GeneralBALD,
             init_default_params={},
             query_default_params_clf=query_default_params_clf,
         )
@@ -55,6 +60,33 @@ def test_init_param_n_MC_samples(self):
         ]
         self._test_param("init", "n_MC_samples", test_cases)
 
+    def test_init_param_greedy_selection(self):
+        test_cases = [
+            (0, TypeError),
+            (1.2, TypeError),
+            (1, TypeError),
+            ("1", TypeError),
+            (False, None),
+            (True, None),
+        ]
+        self._test_param("init", "greedy_selection", test_cases)
+        self.assertTrue(GreedyBALD().greedy_selection)
+        self.assertFalse(BatchBALD().greedy_selection)
+
+    def test_init_param_eps(self):
+        test_cases = [
+            (0, ValueError),
+            (1e-3, None),
+            (0.1, None),
+            ("1", TypeError),
+            (1, ValueError),
+        ]
+        self._test_param(
+            "init",
+            "eps",
+            test_cases,
+        )
+
     def test_query_param_ensemble(self, test_cases=None):
         test_cases = [] if test_cases is None else test_cases
         test_cases += [
@@ -147,13 +179,19 @@ def test_query(self):
                 query_params["batch_size"] = batch_size
                 query_params["ensemble"] = ensemble
                 query_params["return_utilities"] = True
-                qs = self.qs_class(random_state=42)
-                np.testing.assert_equal(
-                    qs.query(**query_params)[1], qs.query(**query_params)[1]
-                )
-                idx, u = qs.query(**query_params)
-                self.assertEqual(len(idx), batch_size)
-                self.assertEqual(len(u), batch_size)
+                for greedy_selection in [False, True]:
+                    for candidates in [None, [2, 3]]:
+                        # query_params["candidates"] = candidates
+                        qs = self.qs_class(
+                            greedy_selection=greedy_selection, random_state=42
+                        )
+                        np.testing.assert_equal(
+                            qs.query(**query_params)[1],
+                            qs.query(**query_params)[1],
+                        )
+                        idx, u = qs.query(**query_params)
+                        self.assertEqual(len(idx), batch_size)
+                        self.assertEqual(len(u), batch_size)
 
 
 class Testbatch_bald(unittest.TestCase):
@@ -178,7 +216,7 @@ def test_param_batch_size(self):
         test_cases = [(0, ValueError), (1.2, TypeError), (1, None)]
         self._test_param(batch_bald, "batch_size", test_cases)
 
-    def test_init_param_n_MC_samples(self):
+    def test_param_n_MC_samples(self):
         test_cases = [
             (0, ValueError),
             (1.2, TypeError),
@@ -187,12 +225,24 @@ def test_init_param_n_MC_samples(self):
         ]
         self._test_param(batch_bald, "n_MC_samples", test_cases)
 
+    def test_param_eps(self):
+        test_cases = [
+            ("0", TypeError),
+            (1, ValueError),
+            (-1, ValueError),
+            (-0.1, ValueError),
+            (0.001, None),
+            (0, ValueError),
+            (0.1, None),
+        ]
+        self._test_param(batch_bald, "eps", test_cases)
+
     def test_param_random_state(self):
         test_cases = [(np.nan, ValueError), ("state", ValueError), (1, None)]
         self._test_param(batch_bald, "random_state", test_cases)
 
     def test_batch_bald(self):
-        # test _BALD and _BatchBald
+        # test BALD and BatchBALD
         probas = np.random.rand(10, 100, 5)
         np.testing.assert_equal(_bald(probas), _bald(probas))
         np.testing.assert_allclose(
@@ -271,8 +321,8 @@ def _test_param(
 
 
 def _bald(probas):
-    """Bayesian Active Learning by Disagreement (BatchBALD)
-    Computes the Bayesian Active Learning by Disagreement (BatchBALD) score for
+    """
+    Computes the Bayesian Active Learning by Disagreement (BALD) score for
     each sample.
 
     Parameters
@@ -283,7 +333,7 @@ def _bald(probas):
     Returns
     -------
     scores: np.ndarray, shape (n_samples)
-        The BatchBALD-scores.
+        The BALD-scores.
 
     References
     ----------
diff --git a/skactiveml/pool/tests/test_query_by_committee.py b/skactiveml/pool/tests/test_query_by_committee.py
index 3cdab458..9f65f035 100644
--- a/skactiveml/pool/tests/test_query_by_committee.py
+++ b/skactiveml/pool/tests/test_query_by_committee.py
@@ -65,6 +65,16 @@ def test_init_param_method(self, test_cases=None):
         test_cases += [(1, TypeError), ("string", TypeError)]
         self._test_param("init", "method", test_cases)
 
+    def test_init_param_eps(self):
+        test_cases = [
+            (0, ValueError),
+            (1e-3, None),
+            (0.1, None),
+            ("1", TypeError),
+            (1, ValueError),
+        ]
+        self._test_param("init", "eps", test_cases, exclude_reg=True)
+
     def test_query_param_ensemble(self, test_cases=None):
         estimators = [
             ("pwc1", ParzenWindowClassifier()),