def warn(*args, **kwargs): pass import warnings warnings.warn = warn import os import zipfile from os.path import join import pandas as pd from quapy.data.base import Dataset, LabelledCollection from quapy.data.preprocessing import text2tfidf, reduce_columns from quapy.data.reader import * from quapy.util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb'] TWITTER_SENTIMENT_DATASETS_TEST = ['gasp', 'hcr', 'omd', 'sanders', 'semeval13', 'semeval14', 'semeval15', 'semeval16', 'sst', 'wa', 'wb'] TWITTER_SENTIMENT_DATASETS_TRAIN = ['gasp', 'hcr', 'omd', 'sanders', 'semeval', 'semeval16', 'sst', 'wa', 'wb'] UCI_DATASETS = ['acute.a', 'acute.b', 'balance.1', 'balance.2', 'balance.3', 'breast-cancer', 'cmc.1', 'cmc.2', 'cmc.3', 'ctg.1', 'ctg.2', 'ctg.3', #'diabetes', # <-- I haven't found this one... 'german', 'haberman', 'ionosphere', 'iris.1', 'iris.2', 'iris.3', 'mammographic', 'pageblocks.5', #'phoneme', # <-- I haven't found this one... 'semeion', 'sonar', 'spambase', 'spectf', 'tictactoe', 'transfusion', 'wdbc', 'wine.1', 'wine.2', 'wine.3', 'wine-q-red', 'wine-q-white', 'yeast'] LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B'] _TXA_SAMPLE_SIZE = 250 _TXB_SAMPLE_SIZE = 1000 LEQUA2022_SAMPLE_SIZE = { 'TXA': _TXA_SAMPLE_SIZE, 'TXB': _TXB_SAMPLE_SIZE, 'T1A': _TXA_SAMPLE_SIZE, 'T1B': _TXB_SAMPLE_SIZE, 'T2A': _TXA_SAMPLE_SIZE, 'T2B': _TXB_SAMPLE_SIZE, 'binary': _TXA_SAMPLE_SIZE, 'multiclass': _TXB_SAMPLE_SIZE } def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False) -> Dataset: """ Loads a Reviews dataset as a Dataset instance, as used in `Esuli, A., Moreo, A., and Sebastiani, F. "A recurrent neural network for sentiment quantification." Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018. `_. The list of valid dataset names can be accessed in `quapy.data.datasets.REVIEWS_SENTIMENT_DATASETS` :param dataset_name: the name of the dataset: valid ones are 'hp', 'kindle', 'imdb' :param tfidf: set to True to transform the raw documents into tfidf weighted matrices :param min_df: minimun number of documents that should contain a term in order for the term to be kept (ignored if tfidf==False) :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default ~/quay_data/ directory) :param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for faster subsequent invokations :return: a :class:`quapy.data.base.Dataset` instance """ assert dataset_name in REVIEWS_SENTIMENT_DATASETS, \ f'Name {dataset_name} does not match any known dataset for sentiment reviews. ' \ f'Valid ones are {REVIEWS_SENTIMENT_DATASETS}' if data_home is None: data_home = get_quapy_home() URL_TRAIN = f'https://zenodo.org/record/4117827/files/{dataset_name}_train.txt' URL_TEST = f'https://zenodo.org/record/4117827/files/{dataset_name}_test.txt' os.makedirs(join(data_home, 'reviews'), exist_ok=True) train_path = join(data_home, 'reviews', dataset_name, 'train.txt') test_path = join(data_home, 'reviews', dataset_name, 'test.txt') download_file_if_not_exists(URL_TRAIN, train_path) download_file_if_not_exists(URL_TEST, test_path) pickle_path = None if pickle: pickle_path = join(data_home, 'reviews', 'pickle', f'{dataset_name}.pkl') data = pickled_resource(pickle_path, Dataset.load, train_path, test_path, from_text) if tfidf: text2tfidf(data, inplace=True) if min_df is not None: reduce_columns(data, min_df=min_df, inplace=True) data.name = dataset_name return data def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_home=None, pickle=False) -> Dataset: """ Loads a Twitter dataset as a :class:`quapy.data.base.Dataset` instance, as used in: `Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis. Social Network Analysis and Mining6(19), 1–22 (2016) `_ Note that the datasets 'semeval13', 'semeval14', 'semeval15' share the same training set. The list of valid dataset names corresponding to training sets can be accessed in `quapy.data.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN`, while the test sets can be accessed in `quapy.data.datasets.TWITTER_SENTIMENT_DATASETS_TEST` :param dataset_name: the name of the dataset: valid ones are 'gasp', 'hcr', 'omd', 'sanders', 'semeval13', 'semeval14', 'semeval15', 'semeval16', 'sst', 'wa', 'wb' :param for_model_selection: if True, then returns the train split as the training set and the devel split as the test set; if False, then returns the train+devel split as the training set and the test set as the test set :param min_df: minimun number of documents that should contain a term in order for the term to be kept :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default ~/quay_data/ directory) :param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for faster subsequent invokations :return: a :class:`quapy.data.base.Dataset` instance """ assert dataset_name in TWITTER_SENTIMENT_DATASETS_TRAIN + TWITTER_SENTIMENT_DATASETS_TEST, \ f'Name {dataset_name} does not match any known dataset for sentiment twitter. ' \ f'Valid ones are {TWITTER_SENTIMENT_DATASETS_TRAIN} for model selection and ' \ f'{TWITTER_SENTIMENT_DATASETS_TEST} for test (datasets "semeval14", "semeval15", "semeval16" share ' \ f'a common training set "semeval")' if data_home is None: data_home = get_quapy_home() URL = 'https://zenodo.org/record/4255764/files/tweet_sentiment_quantification_snam.zip' unzipped_path = join(data_home, 'tweet_sentiment_quantification_snam') if not os.path.exists(unzipped_path): downloaded_path = join(data_home, 'tweet_sentiment_quantification_snam.zip') download_file(URL, downloaded_path) with zipfile.ZipFile(downloaded_path) as file: file.extractall(data_home) os.remove(downloaded_path) if dataset_name in {'semeval13', 'semeval14', 'semeval15'}: trainset_name = 'semeval' testset_name = 'semeval' if for_model_selection else dataset_name print(f"the training and development sets for datasets 'semeval13', 'semeval14', 'semeval15' are common " f"(called 'semeval'); returning trainin-set='{trainset_name}' and test-set={testset_name}") else: if dataset_name == 'semeval' and for_model_selection==False: raise ValueError('dataset "semeval" can only be used for model selection. ' 'Use "semeval13", "semeval14", or "semeval15" for model evaluation.') trainset_name = testset_name = dataset_name if for_model_selection: train = join(unzipped_path, 'train', f'{trainset_name}.train.feature.txt') test = join(unzipped_path, 'test', f'{testset_name}.dev.feature.txt') else: train = join(unzipped_path, 'train', f'{trainset_name}.train+dev.feature.txt') if dataset_name == 'semeval16': # there is a different test name in the case of semeval16 only test = join(unzipped_path, 'test', f'{testset_name}.dev-test.feature.txt') else: test = join(unzipped_path, 'test', f'{testset_name}.test.feature.txt') pickle_path = None if pickle: mode = "train-dev" if for_model_selection else "train+dev-test" pickle_path = join(unzipped_path, 'pickle', f'{testset_name}.{mode}.pkl') data = pickled_resource(pickle_path, Dataset.load, train, test, from_sparse) if min_df is not None: reduce_columns(data, min_df=min_df, inplace=True) data.name = dataset_name return data def fetch_UCIDataset(dataset_name, data_home=None, test_split=0.3, verbose=False) -> Dataset: """ Loads a UCI 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. `_ 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. `_. The datasets do not come with a predefined train-test split (see :meth:`fetch_UCILabelledCollection` for further information on how to use these collections), and so a train-test split is generated at desired proportion. The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_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 :return: a :class:`quapy.data.base.Dataset` instance """ data = fetch_UCILabelledCollection(dataset_name, data_home, verbose) return Dataset(*data.split_stratified(1 - test_split, random_state=0)) def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) -> Dataset: """ Loads a UCI 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. `_ 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. `_. 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.: >>> import quapy as qp >>> collection = qp.datasets.fetch_UCILabelledCollection("yeast") >>> for data in qp.data.Dataset.kFCV(collection, nfolds=5, nrepeats=2): >>> ... The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_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 :return: a :class:`quapy.data.base.Dataset` instance """ assert dataset_name in UCI_DATASETS, \ f'Name {dataset_name} does not match any known dataset from the UCI Machine Learning datasets repository. ' \ f'Valid ones are {UCI_DATASETS}' if data_home is None: data_home = get_quapy_home() dataset_fullname = { 'acute.a': 'Acute Inflammations (urinary bladder)', 'acute.b': 'Acute Inflammations (renal pelvis)', 'balance.1': 'Balance Scale Weight & Distance Database (left)', 'balance.2': 'Balance Scale Weight & Distance Database (balanced)', 'balance.3': 'Balance Scale Weight & Distance Database (right)', 'breast-cancer': 'Breast Cancer Wisconsin (Original)', 'cmc.1': 'Contraceptive Method Choice (no use)', 'cmc.2': 'Contraceptive Method Choice (long term)', 'cmc.3': 'Contraceptive Method Choice (short term)', 'ctg.1': 'Cardiotocography Data Set (normal)', 'ctg.2': 'Cardiotocography Data Set (suspect)', 'ctg.3': 'Cardiotocography Data Set (pathologic)', 'german': 'Statlog German Credit Data', 'haberman': "Haberman's Survival Data", 'ionosphere': 'Johns Hopkins University Ionosphere DB', 'iris.1': 'Iris Plants Database(x)', 'iris.2': 'Iris Plants Database(versicolour)', 'iris.3': 'Iris Plants Database(virginica)', 'mammographic': 'Mammographic Mass', 'pageblocks.5': 'Page Blocks Classification (5)', 'semeion': 'Semeion Handwritten Digit (8)', 'sonar': 'Sonar, Mines vs. Rocks', 'spambase': 'Spambase Data Set', 'spectf': 'SPECTF Heart Data', 'tictactoe': 'Tic-Tac-Toe Endgame Database', 'transfusion': 'Blood Transfusion Service Center Data Set', 'wdbc': 'Wisconsin Diagnostic Breast Cancer', 'wine.1': 'Wine Recognition Data (1)', 'wine.2': 'Wine Recognition Data (2)', 'wine.3': 'Wine Recognition Data (3)', 'wine-q-red': 'Wine Quality Red (6-10)', 'wine-q-white': 'Wine Quality White (6-10)', 'yeast': 'Yeast', } # the identifier is an alias for the dataset group, it's part of the url data-folder, and is the name we use # to download the raw dataset identifier_map = { 'acute.a': 'acute', 'acute.b': 'acute', 'balance.1': 'balance-scale', 'balance.2': 'balance-scale', 'balance.3': 'balance-scale', 'breast-cancer': 'breast-cancer-wisconsin', 'cmc.1': 'cmc', 'cmc.2': 'cmc', 'cmc.3': 'cmc', 'ctg.1': '00193', 'ctg.2': '00193', 'ctg.3': '00193', 'german': 'statlog/german', 'haberman': 'haberman', 'ionosphere': 'ionosphere', 'iris.1': 'iris', 'iris.2': 'iris', 'iris.3': 'iris', 'mammographic': 'mammographic-masses', 'pageblocks.5': 'page-blocks', 'semeion': 'semeion', 'sonar': 'undocumented/connectionist-bench/sonar', 'spambase': 'spambase', 'spectf': 'spect', 'tictactoe': 'tic-tac-toe', 'transfusion': 'blood-transfusion', 'wdbc': 'breast-cancer-wisconsin', 'wine-q-red': 'wine-quality', 'wine-q-white': 'wine-quality', 'wine.1': 'wine', 'wine.2': 'wine', 'wine.3': 'wine', 'yeast': 'yeast', } # the filename is the name of the file within the data_folder indexed by the identifier file_name = { 'acute': 'diagnosis.data', '00193': 'CTG.xls', 'statlog/german': 'german.data-numeric', 'mammographic-masses': 'mammographic_masses.data', 'page-blocks': 'page-blocks.data.Z', 'undocumented/connectionist-bench/sonar': 'sonar.all-data', 'spect': ['SPECTF.train', 'SPECTF.test'], 'blood-transfusion': 'transfusion.data', 'wine-quality': ['winequality-red.csv', 'winequality-white.csv'], 'breast-cancer-wisconsin': 'breast-cancer-wisconsin.data' if dataset_name=='breast-cancer' else 'wdbc.data' } # the filename containing the dataset description (if any) desc_name = { 'acute': 'diagnosis.names', '00193': None, 'statlog/german': 'german.doc', 'mammographic-masses': 'mammographic_masses.names', 'undocumented/connectionist-bench/sonar': 'sonar.names', 'spect': 'SPECTF.names', 'blood-transfusion': 'transfusion.names', 'wine-quality': 'winequality.names', 'breast-cancer-wisconsin': 'breast-cancer-wisconsin.names' if dataset_name == 'breast-cancer' else 'wdbc.names' } identifier = identifier_map[dataset_name] filename = file_name.get(identifier, f'{identifier}.data') descfile = desc_name.get(identifier, f'{identifier}.names') fullname = dataset_fullname[dataset_name] URL = f'http://archive.ics.uci.edu/ml/machine-learning-databases/{identifier}' data_dir = join(data_home, 'uci_datasets', identifier) if isinstance(filename, str): # filename could be a list of files, in which case it will be processed later data_path = join(data_dir, filename) download_file_if_not_exists(f'{URL}/{filename}', data_path) if descfile: try: download_file_if_not_exists(f'{URL}/{descfile}', f'{data_dir}/{descfile}') if verbose: print(open(f'{data_dir}/{descfile}', 'rt').read()) except Exception: print('could not read the description file') elif verbose: print('no file description available') print(f'Loading {dataset_name} ({fullname})') if identifier == 'acute': df = pd.read_csv(data_path, header=None, encoding='utf-16', sep='\t') df[0] = df[0].apply(lambda x: float(x.replace(',', '.'))).astype(float, copy=False) [_df_replace(df, col) for col in range(1, 6)] X = df.loc[:, 0:5].values if dataset_name == 'acute.a': y = binarize(df[6], pos_class='yes') elif dataset_name == 'acute.b': y = binarize(df[7], pos_class='yes') if identifier == 'balance-scale': df = pd.read_csv(data_path, header=None, sep=',') if dataset_name == 'balance.1': y = binarize(df[0], pos_class='L') elif dataset_name == 'balance.2': y = binarize(df[0], pos_class='B') elif dataset_name == 'balance.3': y = binarize(df[0], pos_class='R') X = df.loc[:, 1:].astype(float).values if identifier == 'breast-cancer-wisconsin' and dataset_name=='breast-cancer': df = pd.read_csv(data_path, header=None, sep=',') Xy = df.loc[:, 1:10] Xy[Xy=='?']=np.nan Xy = Xy.dropna(axis=0) X = Xy.loc[:, 1:9] X = X.astype(float).values y = binarize(Xy[10], pos_class=2) if identifier == 'breast-cancer-wisconsin' and dataset_name=='wdbc': df = pd.read_csv(data_path, header=None, sep=',') X = df.loc[:, 2:32].astype(float).values y = df[1].values y = binarize(y, pos_class='M') if identifier == 'cmc': df = pd.read_csv(data_path, header=None, sep=',') X = df.loc[:, 0:8].astype(float).values y = df[9].astype(int).values if dataset_name == 'cmc.1': y = binarize(y, pos_class=1) elif dataset_name == 'cmc.2': y = binarize(y, pos_class=2) elif dataset_name == 'cmc.3': y = binarize(y, pos_class=3) if identifier == '00193': df = pd.read_excel(data_path, sheet_name='Data', skipfooter=3) df = df[list(range(1,24))] # select columns numbered (number 23 is the target label) # replaces the header with the first row new_header = df.iloc[0] # grab the first row for the header df = df[1:] # take the data less the header row df.columns = new_header # set the header row as the df header X = df.iloc[:, 0:22].astype(float).values y = df['NSP'].astype(int).values if dataset_name == 'ctg.1': y = binarize(y, pos_class=1) # 1==Normal elif dataset_name == 'ctg.2': y = binarize(y, pos_class=2) # 2==Suspect elif dataset_name == 'ctg.3': y = binarize(y, pos_class=3) # 3==Pathologic if identifier == 'statlog/german': df = pd.read_csv(data_path, header=None, delim_whitespace=True) X = df.iloc[:, 0:24].astype(float).values y = df[24].astype(int).values y = binarize(y, pos_class=1) if identifier == 'haberman': df = pd.read_csv(data_path, header=None) X = df.iloc[:, 0:3].astype(float).values y = df[3].astype(int).values y = binarize(y, pos_class=2) if identifier == 'ionosphere': df = pd.read_csv(data_path, header=None) X = df.iloc[:, 0:34].astype(float).values y = df[34].values y = binarize(y, pos_class='b') if identifier == 'iris': df = pd.read_csv(data_path, header=None) X = df.iloc[:, 0:4].astype(float).values y = df[4].values if dataset_name == 'iris.1': y = binarize(y, pos_class='Iris-setosa') # 1==Setosa elif dataset_name == 'iris.2': y = binarize(y, pos_class='Iris-versicolor') # 2==Versicolor elif dataset_name == 'iris.3': y = binarize(y, pos_class='Iris-virginica') # 3==Virginica if identifier == 'mammographic-masses': df = pd.read_csv(data_path, header=None, sep=',') df[df == '?'] = np.nan Xy = df.dropna(axis=0) X = Xy.iloc[:, 0:5] X = X.astype(float).values y = binarize(Xy.iloc[:,5], pos_class=1) if identifier == 'page-blocks': data_path_ = data_path.replace('.Z', '') if not os.path.exists(data_path_): raise FileNotFoundError(f'Warning: file {data_path_} does not exist. If this is the first time you ' f'attempt to load this dataset, then you have to manually unzip the {data_path} ' f'and name the extracted file {data_path_} (unfortunately, neither zipfile, nor ' f'gzip can handle unix compressed files automatically -- there is a repo in GitHub ' f'https://github.com/umeat/unlzw where the problem seems to be solved anyway).') df = pd.read_csv(data_path_, header=None, delim_whitespace=True) X = df.iloc[:, 0:10].astype(float).values y = df[10].values y = binarize(y, pos_class=5) # 5==block "graphic" if identifier == 'semeion': df = pd.read_csv(data_path, header=None, delim_whitespace=True ) X = df.iloc[:, 0:256].astype(float).values y = df[263].values # 263 stands for digit 8 (labels are one-hot vectors from col 256-266) y = binarize(y, pos_class=1) if identifier == 'undocumented/connectionist-bench/sonar': df = pd.read_csv(data_path, header=None, sep=',') X = df.iloc[:, 0:60].astype(float).values y = df[60].values y = binarize(y, pos_class='R') if identifier == 'spambase': df = pd.read_csv(data_path, header=None, sep=',') X = df.iloc[:, 0:57].astype(float).values y = df[57].values y = binarize(y, pos_class=1) if identifier == 'spect': dfs = [] for file in filename: data_path = join(data_dir, file) download_file_if_not_exists(f'{URL}/{file}', data_path) dfs.append(pd.read_csv(data_path, header=None, sep=',')) df = pd.concat(dfs) X = df.iloc[:, 1:45].astype(float).values y = df[0].values y = binarize(y, pos_class=0) if identifier == 'tic-tac-toe': df = pd.read_csv(data_path, header=None, sep=',') X = df.iloc[:, 0:9].replace('o',0).replace('b',1).replace('x',2).values y = df[9].values y = binarize(y, pos_class='negative') if identifier == 'blood-transfusion': df = pd.read_csv(data_path, sep=',') X = df.iloc[:, 0:4].astype(float).values y = df.iloc[:, 4].values y = binarize(y, pos_class=1) if identifier == 'wine': df = pd.read_csv(data_path, header=None, sep=',') X = df.iloc[:, 1:14].astype(float).values y = df[0].values if dataset_name == 'wine.1': y = binarize(y, pos_class=1) elif dataset_name == 'wine.2': y = binarize(y, pos_class=2) elif dataset_name == 'wine.3': y = binarize(y, pos_class=3) if identifier == 'wine-quality': filename = filename[0] if dataset_name=='wine-q-red' else filename[1] data_path = join(data_dir, filename) download_file_if_not_exists(f'{URL}/{filename}', data_path) df = pd.read_csv(data_path, sep=';') X = df.iloc[:, 0:11].astype(float).values y = df.iloc[:, 11].values > 5 if identifier == 'yeast': df = pd.read_csv(data_path, header=None, delim_whitespace=True) X = df.iloc[:, 1:9].astype(float).values y = df.iloc[:, 9].values y = binarize(y, pos_class='NUC') data = LabelledCollection(X, y) 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) def fetch_lequa2022(task, data_home=None): """ """ from quapy.data._lequa2022 import load_raw_documents, load_vector_documents, SamplesFromDir assert task in LEQUA2022_TASKS, \ f'Unknown task {task}. Valid ones are {LEQUA2022_TASKS}' if data_home is None: data_home = get_quapy_home() URL_TRAINDEV=f'https://zenodo.org/record/6546188/files/{task}.train_dev.zip' URL_TEST=f'https://zenodo.org/record/6546188/files/{task}.test.zip' URL_TEST_PREV=f'https://zenodo.org/record/6546188/files/{task}.test_prevalences.zip' lequa_dir = join(data_home, 'lequa2022') os.makedirs(lequa_dir, exist_ok=True) def download_unzip_and_remove(unzipped_path, url): tmp_path = join(lequa_dir, task + '_tmp.zip') download_file_if_not_exists(url, tmp_path) with zipfile.ZipFile(tmp_path) as file: file.extractall(unzipped_path) os.remove(tmp_path) if not os.path.exists(join(lequa_dir, task)): download_unzip_and_remove(lequa_dir, URL_TRAINDEV) download_unzip_and_remove(lequa_dir, URL_TEST) download_unzip_and_remove(lequa_dir, URL_TEST_PREV) if task in ['T1A', 'T1B']: load_fn = load_vector_documents elif task in ['T2A', 'T2B']: load_fn = load_raw_documents tr_path = join(lequa_dir, task, 'public', 'training_data.txt') train = LabelledCollection.load(tr_path, loader_func=load_fn) val_samples_path = join(lequa_dir, task, 'public', 'dev_samples') val_true_prev_path = join(lequa_dir, task, 'public', 'dev_prevalences.txt') val_gen = SamplesFromDir(val_samples_path, val_true_prev_path, load_fn=load_fn) test_samples_path = join(lequa_dir, task, 'public', 'test_samples') test_true_prev_path = join(lequa_dir, task, 'public', 'test_prevalences.txt') test_gen = SamplesFromDir(test_samples_path, test_true_prev_path, load_fn=load_fn) return train, val_gen, test_gen