QuaPy/quapy/data/datasets.py

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import zipfile
from util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
import os
from os.path import join
from data.base import Dataset, LabelledCollection
from data.reader import *
from data.preprocessing import text2tfidf, reduce_columns
import pandas as pd
REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
TWITTER_SENTIMENT_DATASETS = ['gasp', 'hcr', 'omd', 'sanders',
'semeval13', 'semeval14', 'semeval15', 'semeval16',
'sst', 'wa', 'wb']
def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False):
"""
Load 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.
: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 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)
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data.name = dataset_name
return data
def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_home=None, pickle=False):
"""
Load a Twitter dataset as a Dataset instance, as used in:
Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
Social Network Analysis and Mining6(19), 122 (2016)
: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 Dataset instance
"""
assert dataset_name in TWITTER_SENTIMENT_DATASETS, \
f'Name {dataset_name} does not match any known dataset for sentiment twitter. ' \
f'Valid ones are {TWITTER_SENTIMENT_DATASETS}'
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:
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)
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data.name = dataset_name
return data
UCI_DATASETS = ['acute.a', 'acute.b',
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'balance.1', 'balance.2', 'balance.3',
'breast-cancer',
'cmc.1', 'cmc.2', 'cmc.3',
'ctg.1', 'ctg.2', 'ctg.3'] # ongoing...
def fetch_UCIDataset(dataset_name, data_home=None, verbose=False):
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()
identifier_map = {
'acute.a': 'acute',
'acute.b': 'acute',
'balance.1': 'balance-scale',
'balance.2': 'balance-scale',
'balance.3': 'balance-scale',
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'breast-cancer': 'breast-cancer-wisconsin',
'cmc.1': 'cmc',
'cmc.2': 'cmc',
'cmc.3': 'cmc',
'ctg.1': 'ctg',
'ctg.2': 'ctg',
'ctg.3': 'ctg',
}
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)',
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'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)',
}
data_folder = {
'acute': 'diagnosis',
'balance-scale': 'balance-scale',
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'breast-cancer-wisconsin': 'breast-cancer-wisconsin',
'cmc': 'cmc'
}
identifier = identifier_map[dataset_name]
URL = f'http://archive.ics.uci.edu/ml/machine-learning-databases/{identifier}'
data_path = join(data_home, 'uci_datasets', identifier)
download_file_if_not_exists(f'{URL}/{data_folder[identifier]}.data', f'{data_path}/{identifier}.data')
download_file_if_not_exists(f'{URL}/{data_folder[identifier]}.names', f'{data_path}/{identifier}.names')
if verbose:
print(open(f'{data_path}/{identifier}.names', 'rt').read())
print(f'Loading {dataset_name} ({dataset_fullname[dataset_name]})')
if identifier == 'acute':
df = pd.read_csv(f'{data_path}/{identifier}.data', header=None, encoding='utf-16', sep='\t')
if dataset_name == 'acute.a':
y = binarize(df[6], pos_class='yes')
elif dataset_name == 'acute.b':
y = binarize(df[7], pos_class='yes')
mintemp, maxtemp = 35, 42
df[0] = df[0].apply(lambda x:(float(x.replace(',','.'))-mintemp)/(maxtemp-mintemp)).astype(float, copy=False)
[df_replace(df, col) for col in range(1, 6)]
X = df.loc[:, 0:5].values
if identifier == 'balance-scale':
df = pd.read_csv(f'{data_path}/{identifier}.data', 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
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if identifier == 'breast-cancer-wisconsin':
df = pd.read_csv(f'{data_path}/{identifier}.data', 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=4)
if identifier == 'cmc':
df = pd.read_csv(f'{data_path}/{identifier}.data', 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)
data = LabelledCollection(X, y)
data.stats()
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raise NotImplementedError()
#print(df)
#print(df.loc[:, 0:5].values)
#print(y)
# X = __read_csv(f'{data_path}/{identifier}.data', separator='\t')
# print(X)
#X, y = from_csv(f'{data_path}/{dataset_name}.data')
#y, classnames = reindex_labels(y)
#def __read_csv(path, separator=','):
# x = []
# for instance in tqdm(open(path, 'rt', encoding='utf-16').readlines(), desc=f'reading {path}'):
# x.append(instance.strip().split(separator))
# return x
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)