link fix

README.md

@@ -68,7 +68,7 @@ class prevalence of the training set. For this reason, any quantification model
 should be tested across many samples, even ones characterized by class prevalence 
 values different or very different from those found in the training set.
 QuaPy implements sampling procedures and evaluation protocols that automate this workflow.
-See the [documentation](https://hlt-isti.github.io/QuaPy/build/html/) for detailed examples.
+See the [documentation](https://hlt-isti.github.io/QuaPy/manuals.html) for detailed examples.
 
 ## Features
 

docs/source/index.md

@@ -25,15 +25,15 @@ The following script fetches a dataset of tweets, trains, applies, and evaluates
 ```python
 import quapy as qp
 
-dataset = qp.datasets.fetch_UCIBinaryDataset("yeast")
+training, test = qp.datasets.fetch_UCIBinaryDataset("yeast").train_test
-training, test = dataset.train_test
 
 # create an "Adjusted Classify & Count" quantifier
 model = qp.method.aggregative.ACC()
-model.fit(training)
+Xtr, ytr = training.Xy
+model.fit(Xtr, ytr)
 
-estim_prevalence = model.quantify(test.X)
+estim_prevalence = model.predict(test.X)
-true_prevalence  = test.prevalence()
+true_prevalence = test.prevalence()
 
 error = qp.error.mae(true_prevalence, estim_prevalence)
 print(f'Mean Absolute Error (MAE)={error:.3f}')
@@ -59,19 +59,19 @@ API <quapy>
 
 ## Features
 
-- Implementation of many popular quantification methods (Classify-&-Count and its variants, Expectation Maximization, quantification methods based on structured output learning, HDy, QuaNet, quantification ensembles, among others).
+* Implementation of many popular quantification methods (Classify-&-Count and its variants, Expectation Maximization,
-- Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
+quantification methods based on structured output learning, HDy, QuaNet, quantification ensembles, among others).
-- Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
+* Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
-- Datasets frequently used in quantification (textual and numeric), including:
+* Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
-    - 32 UCI Machine Learning binary datasets.
+* Datasets frequently used in quantification (textual and numeric), including:
-    - 5 UCI Machine Learning multiclass datasets (new in v0.1.8!).
+    * 32 UCI Machine Learning datasets.
-    - 11 Twitter quantification-by-sentiment datasets.
+    * 11 Twitter quantification-by-sentiment datasets.
-    - 3 product reviews quantification-by-sentiment datasets.
+    * 3 product reviews quantification-by-sentiment datasets. 
-    - 4 tasks from LeQua competition (new in v0.1.7!)
+    * 4 tasks from LeQua 2022 competition and 4 tasks from LeQua 2024 competition
-    - IFCB dataset of plankton water samples (new in v0.1.8!).
+    * IFCB for Plancton quantification 
-- Native support for binary and single-label multiclass quantification scenarios.
+* Native support for binary and single-label multiclass quantification scenarios.
-- Model selection functionality that minimizes quantification-oriented loss functions.
+* Model selection functionality that minimizes quantification-oriented loss functions.
-- Visualization tools for analysing the experimental results.
+* Visualization tools for analysing the experimental results.
 
 ## Citing QuaPy
 
@@ -97,3 +97,7 @@ In case you want to contribute improvements to quapy, please generate pull reque
 :width: 250px
 :alt: SoBigData++
 ```
+
+This work has been supported by the QuaDaSh project 
+_"Finanziato dall’Unione europea---Next Generation EU, 
+Missione 4 Componente 2 CUP B53D23026250001"_.