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QuaPy/quapy/data/datasets.py

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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']
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. <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_.
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) <https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf>`_
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. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_
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. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
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. <https://www.sciencedirect.com/science/article/pii/S1566253516300628>`_
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. <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
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', 'dev_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
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