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