bayesian cc now inherits from the new abstract class WithConfidenceAB…

…C, just like AggregativeBootstrap

TODO.txt

@@ -1,6 +1,5 @@
-- [TODO] adapt BayesianCC to WithConfidence interface
-- [TODO] document confidence
-- [TODO] Test the return_type="index" in protocols and finish the "distributin_samples.py" example
+- [TODO] document confidence in manuals
+- [TODO] Test the return_type="index" in protocols and finish the "distributing_samples.py" example
 - [TODO] Add EDy (an implementation is available at quantificationlib)
 - [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
 - [TODO] add HistNetQ

examples/13.bayesian_quantification.py

@@ -29,7 +29,8 @@
 
 from sklearn.ensemble import RandomForestClassifier
 
-from quapy.method.aggregative import BayesianCC, ACC, PACC
+from quapy.method.aggregative import ACC, PACC
+from method.confidence import BayesianCC
 from quapy.data import LabelledCollection, Dataset
 
 

examples/15.confidence_regions.py

@@ -1,3 +1,4 @@
+from quapy.method.confidence import BayesianCC
 from quapy.method.confidence import AggregativeBootstrap
 from quapy.method.aggregative import PACC
 import quapy.functional as F
@@ -23,7 +24,8 @@
 
 # by simply wrapping an aggregative quantifier within the AggregativeBootstrap class, we can obtain confidence
 # intervals around the point estimate, in this case, at 95% of confidence
-pacc = AggregativeBootstrap(PACC(), confidence_level=0.95)
+pacc = AggregativeBootstrap(PACC(), n_test_samples=500, confidence_level=0.95)
+
 
 with qp.util.temp_seed(0):
     # we train the quantifier the usual way
@@ -73,6 +75,8 @@
 - confidence ellipse in the Centered-Log Ratio (CLR) space: creates an ellipse in the CLR space (this should be 
     convenient for taking into account the inner structure of the probability simplex)
     use: AggregativeBootstrap(PACC(), confidence_level=0.95, method='ellipse-clr')
+    
+Other methods that return confidence regions in QuaPy include the BayesianCC method.
 """
 
 

quapy/method/__init__.py

@@ -1,3 +1,4 @@
+import method.confidence
 from . import base
 from . import aggregative
 from . import non_aggregative
@@ -22,7 +23,7 @@
     aggregative.KDEyML,
     aggregative.KDEyCS,
     aggregative.KDEyHD,
-    aggregative.BayesianCC
+    method.confidence.BayesianCC
 }
 
 BINARY_METHODS = {
@@ -45,7 +46,7 @@
     aggregative.KDEyML,
     aggregative.KDEyCS,
     aggregative.KDEyHD,
-    aggregative.BayesianCC
+    method.confidence.BayesianCC
 }
 
 NON_AGGREGATIVE_METHODS = {

quapy/method/aggregative.py

@@ -14,8 +14,6 @@
 from quapy.classification.svmperf import SVMperf
 from quapy.data import LabelledCollection
 from quapy.method.base import BaseQuantifier, BinaryQuantifier, OneVsAllGeneric
-from quapy.method import _bayesian
-
 
 
 # Abstract classes
@@ -808,99 +806,6 @@ def EM(cls, tr_prev, posterior_probabilities, epsilon=EPSILON):
         return qs, ps
 
 
-class BayesianCC(AggregativeCrispQuantifier):
-    """
-    `Bayesian quantification <https://arxiv.org/abs/2302.09159>`_ method,
-    which is a variant of :class:`ACC` that calculates the posterior probability distribution
-    over the prevalence vectors, rather than providing a point estimate obtained
-    by matrix inversion.
-
-    Can be used to diagnose degeneracy in the predictions visible when the confusion
-    matrix has high condition number or to quantify uncertainty around the point estimate.
-
-    This method relies on extra dependencies, which have to be installed via:
-    `$ pip install quapy[bayes]`
-
-    :param classifier: a sklearn's Estimator that generates a classifier
-    :param val_split: a float in (0, 1) indicating the proportion of the training data to be used,
-        as a stratified held-out validation set, for generating classifier predictions.
-    :param num_warmup: number of warmup iterations for the MCMC sampler (default 500)
-    :param num_samples: number of samples to draw from the posterior (default 1000)
-    :param mcmc_seed: random seed for the MCMC sampler (default 0)
-    """
-    def __init__(self,
-                 classifier: BaseEstimator=None,
-                 val_split: float = 0.75,
-                 num_warmup: int = 500,
-                 num_samples: int = 1_000,
-                 mcmc_seed: int = 0):
-
-        if num_warmup <= 0:
-            raise ValueError(f'parameter {num_warmup=} must be a positive integer')
-        if num_samples <= 0:
-            raise ValueError(f'parameter {num_samples=} must be a positive integer')
-
-        if (not isinstance(val_split, float)) or val_split <= 0 or val_split >= 1:
-            raise ValueError(f'val_split must be a float in (0, 1), got {val_split}')
-
-        if _bayesian.DEPENDENCIES_INSTALLED is False:
-            raise ImportError("Auxiliary dependencies are required. Run `$ pip install quapy[bayes]` to install them.")
-
-        self.classifier = qp._get_classifier(classifier)
-        self.val_split = val_split
-        self.num_warmup = num_warmup
-        self.num_samples = num_samples
-        self.mcmc_seed = mcmc_seed
-
-        # Array of shape (n_classes, n_predicted_classes,) where entry (y, c) is the number of instances
-        # labeled as class y and predicted as class c.
-        # By default, this array is set to None and later defined as part of the `aggregation_fit` phase
-        self._n_and_c_labeled = None
-
-        # Dictionary with posterior samples, set when `aggregate` is provided.
-        self._samples = None
-
-    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
-        """
-        Estimates the misclassification rates.
-
-        :param classif_predictions: a :class:`quapy.data.base.LabelledCollection` containing,
-            as instances, the label predictions issued by the classifier and, as labels, the true labels
-        :param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
-        """
-        pred_labels, true_labels = classif_predictions.Xy
-        self._n_and_c_labeled = confusion_matrix(y_true=true_labels, y_pred=pred_labels, labels=self.classifier.classes_).astype(float)
-
-    def sample_from_posterior(self, classif_predictions):
-        if self._n_and_c_labeled is None:
-            raise ValueError("aggregation_fit must be called before sample_from_posterior")
-
-        n_c_unlabeled = F.counts_from_labels(classif_predictions, self.classifier.classes_).astype(float)
-
-        self._samples = _bayesian.sample_posterior(
-            n_c_unlabeled=n_c_unlabeled,
-            n_y_and_c_labeled=self._n_and_c_labeled,
-            num_warmup=self.num_warmup,
-            num_samples=self.num_samples,
-            seed=self.mcmc_seed,
-        )
-        return self._samples
-
-    def get_prevalence_samples(self):
-        if self._samples is None:
-            raise ValueError("sample_from_posterior must be called before get_prevalence_samples")
-        return self._samples[_bayesian.P_TEST_Y]
-
-    def get_conditional_probability_samples(self):
-        if self._samples is None:
-            raise ValueError("sample_from_posterior must be called before get_conditional_probability_samples")
-        return self._samples[_bayesian.P_C_COND_Y]
-
-    def aggregate(self, classif_predictions):
-        samples = self.sample_from_posterior(classif_predictions)[_bayesian.P_TEST_Y]
-        return np.asarray(samples.mean(axis=0), dtype=float)
-
-
 class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
     """
     `Hellinger Distance y <https://www.sciencedirect.com/science/article/pii/S0020025512004069>`_ (HDy).