simplfiying the minimal working exaple in the README

CHANGE_LOG.txt

@@ -1,9 +1,5 @@
 Change Log 0.1.9
 ----------------
-- [TODO] add LeQua2024 and normalized match distance to qp.error
-- [TODO] add CDE-iteration and Bayes-CDE methods
-- [TODO] add Friedman's method and DeBias
-- [TODO] check ignore warning stuff (check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings)
 
 - Added LeQua 2024 datasets and normalized match distance to qp.error
 

README.md

@@ -45,19 +45,18 @@ of the test set.
 
 ```python
 import quapy as qp
-from sklearn.linear_model import LogisticRegression
 
-dataset = qp.datasets.fetch_twitter('semeval16')
+dataset = qp.datasets.fetch_UCIBinaryDataset("yeast")
+training, test = dataset.train_test
 
 # create an "Adjusted Classify & Count" quantifier
-model = qp.method.aggregative.ACC(LogisticRegression())
-model.fit(dataset.training)
+model = qp.method.aggregative.ACC()
+model.fit(training)
 
-estim_prevalence = model.quantify(dataset.test.instances)
-true_prevalence  = dataset.test.prevalence()
+estim_prevalence = model.quantify(test.X)
+true_prevalence  = test.prevalence()
 
 error = qp.error.mae(true_prevalence, estim_prevalence)
-
 print(f'Mean Absolute Error (MAE)={error:.3f}')
 ```
 

TODO.txt

@@ -0,0 +1,6 @@
+- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
+- [TODO] add HistNetQ
+- [TODO] add CDE-iteration and Bayes-CDE methods
+- [TODO] add Friedman's method and DeBias
+- [TODO] check ignore warning stuff
+    check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings

examples/4b.lequa2024_experiments.py

@@ -33,9 +33,9 @@ quantifier = KDEyML(classifier=LogisticRegression())
 
 # model selection
 param_grid = {
-    'classifier__C': np.logspace(-3, 3, 7),          # classifier-dependent: inverse of regularization strength
-    'classifier__class_weight': ['balanced', None],  # classifier-dependent: weights of each class
-    'bandwidth': np.linspace(0.01, 0.2, 20)          # quantifier-dependent: bandwidth of the kernel
+    'classifier__C': np.logspace(-3, 3, 7),       # classifier-dependent: inverse of regularization strength
+    'classifier__class_weight': ['balanced', None],         # classifier-dependent: weights of each class
+    'bandwidth': np.linspace(0.01, 0.2, 20)  # quantifier-dependent: bandwidth of the kernel
 }
 model_selection = GridSearchQ(quantifier, param_grid, protocol=val_generator, error='mrae', refit=False, verbose=True)
 quantifier = model_selection.fit(training)