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Alejandro Moreo Fernandez 2025-10-03 17:29:19 +02:00
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15
README.md
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@ -13,7 +13,7 @@ for facilitating the analysis and interpretation of the experimental results.
### Last updates:
* Version 0.1.9 is released! major changes can be consulted [here](CHANGE_LOG.txt).
* Version 0.2.0 is released! major changes can be consulted [here](CHANGE_LOG.txt).
* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
### Installation
@ -46,12 +46,12 @@ of the test set.
```python
import quapy as qp
dataset = qp.datasets.fetch_UCIBinaryDataset("yeast")
training, test = dataset.train_test
training, test = qp.datasets.fetch_UCIBinaryDataset("yeast").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.predict(test.X)
true_prevalence = test.prevalence()
@ -79,7 +79,8 @@ quantification methods based on structured output learning, HDy, QuaNet, quantif
* 32 UCI Machine Learning datasets.
* 11 Twitter 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 for Plancton quantification
* Native support for binary and single-label multiclass quantification scenarios.
* Model selection functionality that minimizes quantification-oriented loss functions.
* Visualization tools for analysing the experimental results.
@ -116,3 +117,7 @@ are provided:
## Acknowledgments:
<img src="docs/source/SoBigData.png" alt="SoBigData++" width="250"/>
This work has been supported by the QuaDaSh project
_"Finanziato dall’Unione europea---Next Generation EU,
Missione 4 Componente 2 CUP B53D23026250001"_.

3
quapy/data/datasets.py
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@ -114,7 +114,8 @@ def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle
"""
Loads a Reviews dataset as a Dataset instance, as used in
`Esuli, A., Moreo, A., and Sebastiani, F. "A recurrent neural network for sentiment quantification."
Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018. <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_.
Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.
<https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_.
The list of valid dataset names can be accessed in `quapy.data.datasets.REVIEWS_SENTIMENT_DATASETS`
:param dataset_name: the name of the dataset: valid ones are 'hp', 'kindle', 'imdb'