diff --git a/quapy/data/datasets.py b/quapy/data/datasets.py
index 499b997..181d737 100644
--- a/quapy/data/datasets.py
+++ b/quapy/data/datasets.py
@@ -7,7 +7,8 @@ import zipfile
from os.path import join
import pandas as pd
import scipy
-
+import pickle
+from ucimlrepo import fetch_ucirepo
from quapy.data.base import Dataset, LabelledCollection
from quapy.data.preprocessing import text2tfidf, reduce_columns
from quapy.data.reader import *
@@ -45,6 +46,12 @@ UCI_DATASETS = ['acute.a', 'acute.b',
'wine-q-red', 'wine-q-white',
'yeast']
+UCI_MULTICLASS_DATASETS = ['dry-bean',
+ 'wine-quality',
+ 'academic-success',
+ 'digits',
+ 'letter']
+
LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B']
_TXA_SAMPLE_SIZE = 250
@@ -548,11 +555,108 @@ def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) ->
data.stats()
return data
-
def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
df[col] = df[col].apply(lambda x:repl[x]).astype(astype, copy=False)
+def fetch_UCIMulticlassDataset(dataset_name, data_home=None, test_split=0.3, verbose=False) -> Dataset:
+ """
+ Loads a UCI multiclass dataset as an instance of :class:`quapy.data.base.Dataset`, as used in
+ `Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
+ Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
+ Information Fusion, 34, 87-100. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_
+ and
+ `Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
+ Dynamic ensemble selection for quantification tasks.
+ Information Fusion, 45, 1-15. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
+ The datasets do not come with a predefined train-test split (see :meth:`fetch_UCILabelledCollection` for further
+ information on how to use these collections), and so a train-test split is generated at desired proportion.
+ The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`
+
+ :param dataset_name: a dataset name
+ :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
+ ~/quay_data/ directory)
+ :param test_split: proportion of documents to be included in the test set. The rest conforms the training set
+ :param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets
+ :return: a :class:`quapy.data.base.Dataset` instance
+ """
+ data = fetch_UCIMulticlassLabelledCollection(dataset_name, data_home, verbose)
+ return Dataset(*data.split_stratified(1 - test_split, random_state=0))
+
+
+def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=False) -> LabelledCollection:
+ """
+ Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`, as used in
+ `Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
+ Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
+ Information Fusion, 34, 87-100. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_
+ and
+ `Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
+ Dynamic ensemble selection for quantification tasks.
+ Information Fusion, 45, 1-15. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
+ The datasets do not come with a predefined train-test split, and so Pérez-Gállego et al. adopted a 5FCVx2 evaluation
+ protocol, meaning that each collection was used to generate two rounds (hence the x2) of 5 fold cross validation.
+ This can be reproduced by using :meth:`quapy.data.base.Dataset.kFCV`, e.g.:
+
+ >>> import quapy as qp
+ >>> collection = qp.datasets.fetch_UCILabelledCollection("dry-bean")
+ >>> for data in qp.domains.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
+ >>> ...
+
+ The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_MULTICLASS_DATASETS`
+
+ :param dataset_name: a dataset name
+ :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
+ ~/quay_data/ directory)
+ :param test_split: proportion of documents to be included in the test set. The rest conforms the training set
+ :param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets
+ :return: a :class:`quapy.data.base.LabelledCollection` instance
+ """
+ assert dataset_name in UCI_MULTICLASS_DATASETS, \
+ f'Name {dataset_name} does not match any known dataset from the UCI Machine Learning datasets repository (multiclass). ' \
+ f'Valid ones are {UCI_MULTICLASS_DATASETS}'
+
+ if data_home is None:
+ data_home = get_quapy_home()
+
+ identifiers = {"dry-bean": 602,
+ "wine-quality": 186,
+ "academic-success": 697,
+ "digits": 80,
+ "letter": 59}
+
+ full_names = {"dry-bean": "Dry Bean Dataset",
+ "wine-quality": "Wine Quality",
+ "academic-success": "Predict students' dropout and academic success",
+ "digits": "Optical Recognition of Handwritten Digits",
+ "letter": "Letter Recognition"
+ }
+
+ identifier = identifiers[dataset_name]
+ fullname = full_names[dataset_name]
+
+ print(f'Loading UCI Muticlass {dataset_name} ({fullname})')
+
+ file = join(data_home, 'uci_multiclass', dataset_name + '.pkl')
+ if os.path.exists(file):
+ with open(file, 'rb') as file:
+ data = pickle.load(file)
+ else:
+ data = fetch_ucirepo(id=identifier)
+ X, y = data['data']['features'].to_numpy(), data['data']['targets'].to_numpy().squeeze()
+ data = LabelledCollection(X, y)
+ os.makedirs(os.path.dirname(file), exist_ok=True)
+ with open(file, 'wb') as file:
+ pickle.dump(data, file)
+
+ data.stats()
+ return data
+
+
+def _df_replace(df, col, repl={'yes': 1, 'no': 0}, astype=float):
+ df[col] = df[col].apply(lambda x: repl[x]).astype(astype, copy=False)
+
+
def fetch_lequa2022(task, data_home=None):
"""
Loads the official datasets provided for the `LeQua <https://lequa2022.github.io/index>`_ competition.