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Added UCI multiclass datasets; added filter for min instances per class to UCI multiclass datasets

This commit is contained in:
Lorenzo Volpi 2024-04-10 20:33:36 +02:00
parent 75af15ae4a
commit 1a7a658191
1 changed files with 158 additions and 47 deletions
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205
quapy/data/datasets.py
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@ -14,41 +14,76 @@ from quapy.util import download_file_if_not_exists, download_file, get_quapy_hom
REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb'] REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
TWITTER_SENTIMENT_DATASETS_TEST = ['gasp', 'hcr', 'omd', 'sanders', TWITTER_SENTIMENT_DATASETS_TEST = [
'semeval13', 'semeval14', 'semeval15', 'semeval16', 'gasp', 'hcr', 'omd', 'sanders',
'sst', 'wa', 'wb'] 'semeval13', 'semeval14', 'semeval15', 'semeval16',
TWITTER_SENTIMENT_DATASETS_TRAIN = ['gasp', 'hcr', 'omd', 'sanders', 'sst', 'wa', 'wb',
'semeval', 'semeval16', ]
'sst', 'wa', 'wb'] TWITTER_SENTIMENT_DATASETS_TRAIN = [
UCI_BINARY_DATASETS = ['acute.a', 'acute.b', 'gasp', 'hcr', 'omd', 'sanders',
'balance.1', 'balance.2', 'balance.3', 'semeval', 'semeval16',
'breast-cancer', 'sst', 'wa', 'wb',
'cmc.1', 'cmc.2', 'cmc.3', ]
'ctg.1', 'ctg.2', 'ctg.3', UCI_BINARY_DATASETS = [
#'diabetes', # <-- I haven't found this one... 'acute.a', 'acute.b',
'german', 'balance.1', 'balance.2', 'balance.3',
'haberman', 'breast-cancer',
'ionosphere', 'cmc.1', 'cmc.2', 'cmc.3',
'iris.1', 'iris.2', 'iris.3', 'ctg.1', 'ctg.2', 'ctg.3',
'mammographic', #'diabetes', # <-- I haven't found this one...
'pageblocks.5', 'german',
#'phoneme', # <-- I haven't found this one... 'haberman',
'semeion', 'ionosphere',
'sonar', 'iris.1', 'iris.2', 'iris.3',
'spambase', 'mammographic',
'spectf', 'pageblocks.5',
'tictactoe', #'phoneme', # <-- I haven't found this one...
'transfusion', 'semeion',
'wdbc', 'sonar',
'wine.1', 'wine.2', 'wine.3', 'spambase',
'wine-q-red', 'wine-q-white', 'spectf',
'yeast'] 'tictactoe',
'transfusion',
'wdbc',
'wine.1', 'wine.2', 'wine.3',
'wine-q-red',
'wine-q-white',
'yeast',
]
UCI_MULTICLASS_DATASETS = ['dry-bean', UCI_MULTICLASS_DATASETS = [
'wine-quality', 'dry-bean',
'academic-success', 'wine-quality',
'digits', 'academic-success',
'letter'] 'digits',
'letter',
'abalone',
'obesity',
'covertype',
'nursery',
'diabetes',
'yeast',
'hand_digits',
'satellite',
'shuttle',
'cmc',
'isolet',
'waveform.v1',
'molecular',
'poker_hand',
'connect-4',
'cardiotocography',
'mhr',
'chess2',
'page_block',
'room',
'phishing2',
'rt-iot22',
'support2',
'image_seg',
'steel_plates',
'hcv',
]
LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B'] LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B']
@ -586,7 +621,7 @@ def fetch_UCIMulticlassDataset(dataset_name, data_home=None, test_split=0.3, ver
return Dataset(*data.split_stratified(1 - test_split, random_state=0)) return Dataset(*data.split_stratified(1 - test_split, random_state=0))
def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=False) -> LabelledCollection: def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=False, min_ipc=100) -> LabelledCollection:
""" """
Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`. Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.
@ -610,6 +645,8 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=
~/quay_data/ directory) ~/quay_data/ directory)
:param test_split: proportion of documents to be included in the test set. The rest conforms the training set :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 :param verbose: set to True (default is False) to get information (stats) about the dataset
:param min_ipc: minimum number of istances per class. Classes with less instances than min_ipc are discarded
(deafult is 100)
:return: a :class:`quapy.data.base.LabelledCollection` instance :return: a :class:`quapy.data.base.LabelledCollection` instance
""" """
assert dataset_name in UCI_MULTICLASS_DATASETS, \ assert dataset_name in UCI_MULTICLASS_DATASETS, \
@ -621,19 +658,71 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=
data_home = get_quapy_home() data_home = get_quapy_home()
identifiers = { identifiers = {
"dry-bean": 602, 'dry-bean': 602,
"wine-quality": 186, 'wine-quality': 186,
"academic-success": 697, 'academic-success': 697,
"digits": 80, 'digits': 80,
"letter": 59 'letter': 59,
'abalone': 1,
'obesity': 544,
'covertype': 31,
'nursery': 76,
'diabetes': 296,
'yeast': 110,
'hand_digits': 81,
'satellite': 146,
'shuttle': 148,
'cmc': 30,
'isolet': 54,
'waveform.v1': 107,
'molecular': 69,
'poker_hand': 158,
'connect-4': 26,
'cardiotocography': 193,
'mhr': 863,
'chess2': 23,
'page_block': 78,
'room': 864,
'phishing2': 379,
'rt-iot22': 942,
'support2': 880,
'image_seg': 147,
'steel_plates': 198,
'hcv': 503,
} }
full_names = { full_names = {
"dry-bean": "Dry Bean Dataset", 'dry-bean': 'Dry Bean Dataset',
"wine-quality": "Wine Quality", 'wine-quality': 'Wine Quality',
"academic-success": "Predict students' dropout and academic success", 'academic-success': 'Predict students\' dropout and academic success',
"digits": "Optical Recognition of Handwritten Digits", 'digits': 'Optical Recognition of Handwritten Digits',
"letter": "Letter Recognition" 'letter': 'Letter Recognition',
'abalone': 'Abalone',
'obesity': 'Estimation of Obesity Levels Based On Eating Habits and Physical Condition',
'covertype': 'Covertype',
'nursery': 'Nursery',
'diabetes': 'Diabetes 130-US Hospitals for Years 1999-2008',
'yeast': 'Yeast',
'hand_digits': 'Pen-Based Recognition of Handwritten Digits',
'satellite': 'Statlog Landsat Satellite',
'shuttle': 'Statlog Shuttle',
'cmc': 'Contraceptive Method Choice',
'isolet': 'ISOLET',
'waveform.v1': 'Waveform Database Generator (Version 1)',
'molecular': 'Molecular Biology (Splice-junction Gene Sequences)',
'poker_hand': 'Poker Hand',
'connect-4': 'Connect-4',
'cardiotocography': 'Cardiotocography',
'mhr': 'Maternal Health Risk',
'chess2': 'Chess (King-Rook vs. King)',
'page_block': 'Page Blocks Classification',
'room': 'Room Occupancy Estimation',
'phishing2': 'Website Phishing',
'rt-iot22': 'RT-IoT2022',
'support2': 'SUPPORT2',
'image_seg': 'Statlog (Image Segmentation)',
'steel_plates': 'Steel Plates Faults',
'hcv': 'Hepatitis C Virus (HCV) for Egyptian patients',
} }
identifier = identifiers[dataset_name] identifier = identifiers[dataset_name]
@ -644,14 +733,36 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=
file = join(data_home, 'uci_multiclass', dataset_name+'.pkl') file = join(data_home, 'uci_multiclass', dataset_name+'.pkl')
def download(id): def download(id, name):
data = fetch_ucirepo(id=id) data = fetch_ucirepo(id=id)
X, y = data['data']['features'].to_numpy(), data['data']['targets'].to_numpy().squeeze() X, y = data['data']['features'].to_numpy(), data['data']['targets'].to_numpy().squeeze()
# classes represented as arrays are transformed to tuples to treat them as signle objects
if name == 'support2':
y[:, 2] = np.fromiter((str(elm) for elm in y[:, 2]), dtype='object')
if y.ndim > 1:
y = np.fromiter((tuple(elm) for elm in y), dtype='object')
classes = np.sort(np.unique(y)) classes = np.sort(np.unique(y))
y = np.searchsorted(classes, y) y = np.searchsorted(classes, y)
return LabelledCollection(X, y) return LabelledCollection(X, y)
data = pickled_resource(file, download, identifier) def filter_classes(data: LabelledCollection, min_ipc):
classes = data.classes_
# restrict classes to only those with at least min_ipc instances
classes = classes[data.counts() >= min_ipc]
# filter X and y keeping only datapoints belonging to valid classes
filter_idx = np.in1d(data.y, classes)
X, y = data.X[filter_idx], data.y[filter_idx]
# map classes to range(len(classes))
y = np.searchsorted(classes, y)
return LabelledCollection(X, y)
data = pickled_resource(file, download, identifier, dataset_name)
data = filter_classes(data, min_ipc)
if data.n_classes <= 2:
raise ValueError(
f'Dataset {dataset_name} has too few valid classes to be multiclass with {min_ipc=}. '
'Try a lower value for min_ipc.'
)
if verbose: if verbose:
data.stats() data.stats()