uci multiclass datasets
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pglez82
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@ -7,6 +7,8 @@ import zipfile
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from os.path import join
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import pandas as pd
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import scipy
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import pickle
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from ucimlrepo import fetch_ucirepo
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from quapy.data.base import Dataset, LabelledCollection
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from quapy.data.preprocessing import text2tfidf, reduce_columns
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@ -45,6 +47,14 @@ UCI_DATASETS = ['acute.a', 'acute.b',
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'wine-q-red', 'wine-q-white',
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'yeast']
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UCI_MULTICLASS_DATASETS = ['dry-bean',
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'wine-quality',
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'academic-success',
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'digits',
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'letter']
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KAGGLE_MULTICLASS_DATASETS = ['human-activity']
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LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B']
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_TXA_SAMPLE_SIZE = 250
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@ -548,6 +558,99 @@ def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) ->
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data.stats()
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return data
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def fetch_UCIMulticlassDataset(dataset_name, data_home=None, test_split=0.3, verbose=False) -> Dataset:
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"""
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Loads a UCI multiclass dataset as an instance of :class:`quapy.data.base.Dataset`, as used in
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`Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
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Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
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Information Fusion, 34, 87-100. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_
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and
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`Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
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Dynamic ensemble selection for quantification tasks.
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Information Fusion, 45, 1-15. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
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The datasets do not come with a predefined train-test split (see :meth:`fetch_UCILabelledCollection` for further
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information on how to use these collections), and so a train-test split is generated at desired proportion.
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The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`
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:param dataset_name: a dataset name
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:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
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~/quay_data/ directory)
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:param test_split: proportion of documents to be included in the test set. The rest conforms the training set
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:param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets
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:return: a :class:`quapy.data.base.Dataset` instance
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"""
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data = fetch_UCIMulticlassLabelledCollection(dataset_name, data_home, verbose)
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return Dataset(*data.split_stratified(1 - test_split, random_state=0))
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def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=False) -> LabelledCollection:
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"""
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Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`, as used in
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`Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
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Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
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Information Fusion, 34, 87-100. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_
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and
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`Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
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Dynamic ensemble selection for quantification tasks.
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Information Fusion, 45, 1-15. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
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The datasets do not come with a predefined train-test split, and so Pérez-Gállego et al. adopted a 5FCVx2 evaluation
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protocol, meaning that each collection was used to generate two rounds (hence the x2) of 5 fold cross validation.
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This can be reproduced by using :meth:`quapy.data.base.Dataset.kFCV`, e.g.:
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>>> import quapy as qp
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>>> collection = qp.datasets.fetch_UCILabelledCollection("dry-bean")
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>>> for data in qp.domains.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
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>>> ...
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The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_MULTICLASS_DATASETS`
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:param dataset_name: a dataset name
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:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
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~/quay_data/ directory)
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:param test_split: proportion of documents to be included in the test set. The rest conforms the training set
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:param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets
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:return: a :class:`quapy.data.base.LabelledCollection` instance
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"""
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assert dataset_name in UCI_MULTICLASS_DATASETS, \
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f'Name {dataset_name} does not match any known dataset from the UCI Machine Learning datasets repository (multiclass). ' \
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f'Valid ones are {UCI_MULTICLASS_DATASETS}'
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if data_home is None:
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data_home = get_quapy_home()
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identifiers = {"dry-bean": 602,
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"wine-quality":186,
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"academic-success":697,
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"digits":80,
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"letter":59}
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full_names = {"dry-bean": "Dry Bean Dataset",
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"wine-quality":"Wine Quality",
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"academic-success":"Predict students' dropout and academic success",
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"digits":"Optical Recognition of Handwritten Digits",
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"letter":"Letter Recognition"
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}
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identifier = identifiers[dataset_name]
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fullname = full_names[dataset_name]
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print(f'Loading UCI Muticlass {dataset_name} ({fullname})')
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file = join(data_home,'uci_multiclass',dataset_name+'.pkl')
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if os.path.exists(file):
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with open(file, 'rb') as file:
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data = pickle.load(file)
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else:
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data = fetch_ucirepo(id=identifier)
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X, y = data['data']['features'].to_numpy(), data['data']['targets'].to_numpy().squeeze()
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data = LabelledCollection(X, y)
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os.makedirs(os.path.dirname(file), exist_ok=True)
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with open(file, 'wb') as file:
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pickle.dump(data, file)
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data.stats()
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return data
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def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
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df[col] = df[col].apply(lambda x:repl[x]).astype(astype, copy=False)
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