diff --git a/docs/build/html/Datasets.html b/docs/build/html/Datasets.html
index 1636fa0..775690d 100644
--- a/docs/build/html/Datasets.html
+++ b/docs/build/html/Datasets.html
@@ -80,13 +80,13 @@ Take a look at the following code:
-One can easily produce new samples at desired class prevalences:
+One can easily produce new samples at desired class prevalence values:
sample_size = 10
prev = [0.4, 0.1, 0.5]
sample = data.sampling(sample_size, *prev)
print('instances:', sample.instances)
-print('labels:', sample.classes)
+print('labels:', sample.labels)
print('prevalence:', F.strprev(sample.prevalence(), prec=2))
QuaPy also implements the artificial sampling protocol that produces (via a
-Python’s generator) a series of LabelledCollection objects with equidistant
-prevalences ranging across the entire prevalence spectrum in the simplex space, e.g.:
-for sample in data.artificial_sampling_generator(sample_size=100, n_prevalences=5):
- print(F.strprev(sample.prevalence(), prec=2))
-
-
produces one sampling for each (valid) combination of prevalences originating from
-splitting the range [0,1] into n_prevalences=5 points (i.e., [0, 0.25, 0.5, 0.75, 1]),
-that is:
-[0.00, 0.00, 1.00]
-[0.00, 0.25, 0.75]
-[0.00, 0.50, 0.50]
-[0.00, 0.75, 0.25]
-[0.00, 1.00, 0.00]
-[0.25, 0.00, 0.75]
-...
-[1.00, 0.00, 0.00]
-
-
See the Evaluation wiki for
-further details on how to use the artificial sampling protocol to properly
-evaluate a quantification method.
+However, generating samples for evaluation purposes is tackled in QuaPy
+by means of the evaluation protocols (see the dedicated entries in the Wiki
+for evaluation and
+protocols).
Reviews Datasets
Three datasets of reviews about Kindle devices, Harry Potter’s series, and
@@ -636,6 +617,78 @@ time the dataset is invoked.
+
+LeQua Datasets
+QuaPy also provides the datasets used for the LeQua competition.
+In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification
+problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide
+raw documents instead.
+Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B
+are multiclass quantification problems consisting of estimating the class prevalence
+values of 28 different merchandise products.
+Every task consists of a training set, a set of validation samples (for model selection)
+and a set of test samples (for evaluation). QuaPy returns this data as a LabelledCollection
+(training) and two generation protocols (for validation and test samples), as follows:
+training, val_generator, test_generator = fetch_lequa2022(task=task)
+
+
See the lequa2022_experiments.py in the examples folder for further details on how to
+carry out experiments using these datasets.
+The datasets are downloaded only once, and stored for fast reuse.
+Some statistics are summarized below:
+
+
+Dataset |
+classes |
+train size |
+validation samples |
+test samples |
+docs by sample |
+type |
+
|---|
+
+
+T1A |
+2 |
+5000 |
+1000 |
+5000 |
+250 |
+vector |
+
+T1B |
+28 |
+20000 |
+1000 |
+5000 |
+1000 |
+vector |
+
+T2A |
+2 |
+5000 |
+1000 |
+5000 |
+250 |
+text |
+
+T2B |
+28 |
+20000 |
+1000 |
+5000 |
+1000 |
+text |
+
+
+
+For further details on the datasets, we refer to the original
+paper:
+Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022).
+A Detailed Overview of LeQua@ CLEF 2022: Learning to Quantify.
+
+
Adding Custom Datasets
QuaPy provides data loaders for simple formats dealing with
@@ -667,12 +720,15 @@ all classes to be present in the collection).
paths, in order to create a training and test pair of LabelledCollection,
e.g.:
import quapy as qp
+
train_path = '../my_data/train.dat'
test_path = '../my_data/test.dat'
+
def my_custom_loader(path):
with open(path, 'rb') as fin:
...
return instances, labels
+
data = qp.data.Dataset.load(train_path, test_path, my_custom_loader)