estimator refactored, missing evaluation

quacc/data.py

@@ -13,4 +13,3 @@ class ExtendedCollection(LabelledCollection):
         classes: Optional[List] = None,
     ):
         super().__init__(instances, labels, classes=classes)
-

quacc/estimator.py

@@ -5,7 +5,6 @@ from quapy.method.base import BaseQuantifier
 from sklearn.base import BaseEstimator
 from sklearn.model_selection import cross_val_predict
 
-import quacc as qc
 from .data import ExtendedCollection
 
 
@@ -15,9 +14,11 @@ def _check_prevalence_classes(true_classes, estim_classes, estim_prev):
             estim_prev = np.insert(estim_prev, _cls, [0.0], axis=0)
     return estim_prev
 
+
 def _get_ex_class(classes, true_class, pred_class):
     return true_class * classes + pred_class
 
+
 def _extend_instances(instances, pred_proba):
     if isinstance(instances, sp.csr_matrix):
         _pred_proba = sp.csr_matrix(pred_proba)
@@ -29,6 +30,7 @@ def _extend_instances(instances, pred_proba):
 
     return n_x
 
+
 def _extend_collection(base: LabelledCollection, pred_proba) -> ExtendedCollection:
     n_classes = base.n_classes
 

quacc/evaluation.py

@@ -1,13 +1,14 @@
-from quapy.method.base import BaseQuantifier
-from quapy.protocol import OnLabelledCollectionProtocol, AbstractStochasticSeededProtocol
+from quapy.protocol import (
+    OnLabelledCollectionProtocol,
+    AbstractStochasticSeededProtocol,
+)
 
-from .estimator import AccuracyEstimator, _extend_collection
+from .estimator import AccuracyEstimator
 
 
 def estimate(estimator: AccuracyEstimator, protocol: AbstractStochasticSeededProtocol):
-
     # ensure that the protocol returns a LabelledCollection for each iteration
-    protocol.collator = OnLabelledCollectionProtocol.get_collator('labelled_collection')
+    protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
 
     base_prevs, true_prevs, estim_prevs = [], [], []
     for sample in protocol():