Typing fixed, gitignore updated

.gitignore

@@ -2,4 +2,5 @@
 quavenv/*
 *.pdf
 quacc/__pycache__/*
-tests/__pycache__/*
+tests/__pycache__/*
+.coverage

quacc/data.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import List, Optional, Self
 
 import numpy as np
 import math
@@ -44,7 +44,7 @@ class ExtendedCollection(LabelledCollection):
     ):
         super().__init__(instances, labels, classes=classes)
 
-    def split_by_pred(self):
+    def split_by_pred(self) -> List[Self]:
         _ncl = int(math.sqrt(self.n_classes))
         _indexes = ExtendedCollection._split_index_by_pred(_ncl, self.instances)
         if isinstance(self.instances, np.ndarray):
@@ -128,7 +128,9 @@ class ExtendedCollection(LabelledCollection):
         return n_x
 
     @classmethod
-    def extend_collection(cls, base: LabelledCollection, pred_proba: np.ndarray) -> Any:
+    def extend_collection(
+        cls, base: LabelledCollection, pred_proba: np.ndarray
+    ) -> Self:
         n_classes = base.n_classes
 
         # n_X = [ X | predicted probs. ]

quacc/estimator.py

@@ -8,17 +8,17 @@ from sklearn.base import BaseEstimator
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import cross_val_predict
 
-from quacc.data import ExtendedCollection as EC
+from quacc.data import ExtendedCollection
 
 
 class AccuracyEstimator:
-    def extend(self, base: LabelledCollection, pred_proba=None) -> EC:
+    def extend(self, base: LabelledCollection, pred_proba=None) -> ExtendedCollection:
         if not pred_proba:
             pred_proba = self.c_model.predict_proba(base.X)
-        return EC.extend_collection(base, pred_proba)
+        return ExtendedCollection.extend_collection(base, pred_proba)
 
     @abstractmethod
-    def fit(self, train: LabelledCollection | EC):
+    def fit(self, train: LabelledCollection | ExtendedCollection):
         ...
 
     @abstractmethod
@@ -32,7 +32,7 @@ class MulticlassAccuracyEstimator(AccuracyEstimator):
         self.q_model = SLD(LogisticRegression())
         self.e_train = None
 
-    def fit(self, train: LabelledCollection | EC):
+    def fit(self, train: LabelledCollection | ExtendedCollection):
         # check if model is fit
         # self.model.fit(*train.Xy)
         if isinstance(train, LabelledCollection):
@@ -40,7 +40,7 @@ class MulticlassAccuracyEstimator(AccuracyEstimator):
                 self.c_model, *train.Xy, method="predict_proba"
             )
 
-            self.e_train = EC.extend_collection(train, pred_prob_train)
+            self.e_train = ExtendedCollection.extend_collection(train, pred_prob_train)
         else:
             self.e_train = train
 
@@ -49,7 +49,7 @@ class MulticlassAccuracyEstimator(AccuracyEstimator):
     def estimate(self, instances, ext=False):
         if not ext:
             pred_prob = self.c_model.predict_proba(instances)
-            e_inst = EC.extend_instances(instances, pred_prob)
+            e_inst = ExtendedCollection.extend_instances(instances, pred_prob)
         else:
             e_inst = instances
 
@@ -71,9 +71,9 @@ class BinaryQuantifierAccuracyEstimator(AccuracyEstimator):
         self.c_model = c_model
         self.q_model_0 = SLD(LogisticRegression())
         self.q_model_1 = SLD(LogisticRegression())
-        self.e_train: EC = None
+        self.e_train = None
 
-    def fit(self, train: LabelledCollection | EC):
+    def fit(self, train: LabelledCollection | ExtendedCollection):
         # check if model is fit
         # self.model.fit(*train.Xy)
         if isinstance(train, LabelledCollection):
@@ -81,9 +81,9 @@ class BinaryQuantifierAccuracyEstimator(AccuracyEstimator):
                 self.c_model, *train.Xy, method="predict_proba"
             )
 
-            self.e_train = EC.extend_collection(train, pred_prob_train)
-        else:
-            self.e_train = train
+            self.e_train = ExtendedCollection.extend_collection(train, pred_prob_train)
+        elif isinstance(train, ExtendedCollection):
+            self.e_train = train 
 
         self.n_classes = self.e_train.n_classes
         [e_train_0, e_train_1] = self.e_train.split_by_pred()
@@ -95,12 +95,12 @@ class BinaryQuantifierAccuracyEstimator(AccuracyEstimator):
         # TODO: test
         if not ext:
             pred_prob = self.c_model.predict_proba(instances)
-            e_inst = EC.extend_instances(instances, pred_prob)
+            e_inst = ExtendedCollection.extend_instances(instances, pred_prob)
         else:
             e_inst = instances
 
         _ncl = int(math.sqrt(self.n_classes))
-        s_inst, norms = EC.split_inst_by_pred(_ncl, e_inst)
+        s_inst, norms = ExtendedCollection.split_inst_by_pred(_ncl, e_inst)
         [estim_prev_0, estim_prev_1] = [
             self._quantify_helper(inst, norm, q_model)
             for (inst, norm, q_model) in zip(