diff --git a/quapy/data/datasets.py b/quapy/data/datasets.py index 5a0dde1..9d34222 100644 --- a/quapy/data/datasets.py +++ b/quapy/data/datasets.py @@ -6,7 +6,8 @@ import os import zipfile from os.path import join import pandas as pd -import scipy + +from ucimlrepo import fetch_ucirepo from quapy.data.base import Dataset, LabelledCollection from quapy.data.preprocessing import text2tfidf, reduce_columns @@ -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 @@ -549,6 +556,109 @@ def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) -> return data +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`. + + The list of available datasets is taken from https://archive.ics.uci.edu/, following these criteria: + - It has more than 1000 instances + - It is suited for classification + - It has more than two classes + - It is available for Python import (requires ucimlrepo package) + + >>> import quapy as qp + >>> dataset = qp.datasets.fetch_UCIMulticlassDataset("dry-bean") + >>> train, test = dataset.train_test + >>> ... + + 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 + ~/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 (stats) about the dataset + :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`. + + The list of available datasets is taken from https://archive.ics.uci.edu/, following these criteria: + - It has more than 1000 instances + - It is suited for classification + - It has more than two classes + - It is available for Python import (requires ucimlrepo package) + + >>> import quapy as qp + >>> collection = qp.datasets.fetch_UCIMulticlassLabelledCollection("dry-bean") + >>> X, y = collection.Xy + >>> ... + + 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 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 (stats) about the dataset + :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 ' \ + f'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] + + if verbose: + print(f'Loading UCI Muticlass {dataset_name} ({fullname})') + + file = join(data_home, 'uci_multiclass', dataset_name+'.pkl') + + def download(id): + data = fetch_ucirepo(id=id) + X, y = data['data']['features'].to_numpy(), data['data']['targets'].to_numpy().squeeze() + classes = np.sort(np.unique(y)) + y = np.searchsorted(classes, y) + return LabelledCollection(X, y) + + data = pickled_resource(file, download, identifier) + + if verbose: + 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)