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fixing requests

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pglez82 2023-10-18 14:12:40 +02:00
parent a9f10f77f4
commit ffab2131a8
1 changed files with 27 additions and 37 deletions
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64
quapy/data/datasets.py
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@ -7,9 +7,10 @@ import zipfile
from os.path import join
import pandas as pd
import scipy
import pickle
from ucimlrepo import fetch_ucirepo
from quapy.util import pickled_resource
from quapy.data.base import Dataset, LabelledCollection
from quapy.data.preprocessing import text2tfidf, reduce_columns
from quapy.data.reader import *
@ -558,23 +559,20 @@ def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=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_UCIMulticlassLabelledCollection` for further
information on how to use these collections), and so a train-test split is generated at desired proportion.
Loads a UCI multiclass dataset as an instance of :class:`quapy.data.base.Dataset`.
The list of available datasets is taken from https://archive.ics.uci.edu/, following these criteria:
- The dataset has more than 1000 instances
- The dataset is suited for classification
- the dataset has more than two classes
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
:param verbose: set to True (default is False) to get information (stats) about the dataset
:return: a :class:`quapy.data.base.Dataset` instance
"""
data = fetch_UCIMulticlassLabelledCollection(dataset_name, data_home, verbose)
@ -582,30 +580,23 @@ def fetch_UCIMulticlassDataset(dataset_name, data_home=None, test_split=0.3, ver
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.:
Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.
It needs the library `ucimlrepo` for downloading the datasets from https://archive.ics.uci.edu/.
>>> import quapy as qp
>>> collection = qp.datasets.fetch_UCIMulticlassLabelledCollection("dry-bean")
>>> for data in qp.domains.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
>>> dataset = qp.datasets.fetch_UCIMulticlassLabelledCollection("dry-bean")
>>> ...
The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_MULTICLASS_DATASETS`
The datasets are downloaded only once and pickled into disk, saving time for consecutive calls.
: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
:param data_home: specify the quapy home directory where the dataset 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
:param verbose: set to True (default is False) to get information (stats) about the dataset
:return: a :class:`quapy.data.base.LabelledCollection` instance
"""
assert dataset_name in UCI_MULTICLASS_DATASETS, \
@ -634,19 +625,18 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=
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)
def download(id):
data = fetch_ucirepo(id=id)
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)
classes = np.sort(np.unique(y))
y = np.searchsorted(classes, y)
return LabelledCollection(X,y)
data = pickled_resource(file, download, identifier)
data.stats()
if verbose:
data.stats()
return data