251 lines
11 KiB
Python
251 lines
11 KiB
Python
import zipfile
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from util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
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import os
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from os.path import join
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from data.base import Dataset, LabelledCollection
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from data.reader import *
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from data.preprocessing import text2tfidf, reduce_columns
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import pandas as pd
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REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
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TWITTER_SENTIMENT_DATASETS = ['gasp', 'hcr', 'omd', 'sanders',
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'semeval13', 'semeval14', 'semeval15', 'semeval16',
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'sst', 'wa', 'wb']
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def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False):
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"""
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Load a Reviews dataset as a Dataset instance, as used in:
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Esuli, A., Moreo, A., and Sebastiani, F. "A recurrent neural network for sentiment quantification."
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Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.
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:param dataset_name: the name of the dataset: valid ones are 'hp', 'kindle', 'imdb'
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:param tfidf: set to True to transform the raw documents into tfidf weighted matrices
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:param min_df: minimun number of documents that should contain a term in order for the term to be
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kept (ignored if tfidf==False)
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:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
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~/quay_data/ directory)
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:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
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faster subsequent invokations
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:return: a Dataset instance
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"""
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assert dataset_name in REVIEWS_SENTIMENT_DATASETS, \
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f'Name {dataset_name} does not match any known dataset for sentiment reviews. ' \
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f'Valid ones are {REVIEWS_SENTIMENT_DATASETS}'
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if data_home is None:
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data_home = get_quapy_home()
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URL_TRAIN = f'https://zenodo.org/record/4117827/files/{dataset_name}_train.txt'
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URL_TEST = f'https://zenodo.org/record/4117827/files/{dataset_name}_test.txt'
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os.makedirs(join(data_home, 'reviews'), exist_ok=True)
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train_path = join(data_home, 'reviews', dataset_name, 'train.txt')
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test_path = join(data_home, 'reviews', dataset_name, 'test.txt')
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download_file_if_not_exists(URL_TRAIN, train_path)
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download_file_if_not_exists(URL_TEST, test_path)
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pickle_path = None
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if pickle:
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pickle_path = join(data_home, 'reviews', 'pickle', f'{dataset_name}.pkl')
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data = pickled_resource(pickle_path, Dataset.load, train_path, test_path, from_text)
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if tfidf:
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text2tfidf(data, inplace=True)
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if min_df is not None:
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reduce_columns(data, min_df=min_df, inplace=True)
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data.name = dataset_name
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return data
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def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_home=None, pickle=False):
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"""
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Load a Twitter dataset as a Dataset instance, as used in:
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Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
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Social Network Analysis and Mining6(19), 1–22 (2016)
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:param dataset_name: the name of the dataset: valid ones are 'gasp', 'hcr', 'omd', 'sanders', 'semeval13',
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'semeval14', 'semeval15', 'semeval16', 'sst', 'wa', 'wb'
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:param for_model_selection: if True, then returns the train split as the training set and the devel split
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as the test set; if False, then returns the train+devel split as the training set and the test set as the
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test set
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:param min_df: minimun number of documents that should contain a term in order for the term to be kept
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:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
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~/quay_data/ directory)
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:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
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faster subsequent invokations
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:return: a Dataset instance
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"""
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assert dataset_name in TWITTER_SENTIMENT_DATASETS, \
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f'Name {dataset_name} does not match any known dataset for sentiment twitter. ' \
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f'Valid ones are {TWITTER_SENTIMENT_DATASETS}'
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if data_home is None:
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data_home = get_quapy_home()
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URL = 'https://zenodo.org/record/4255764/files/tweet_sentiment_quantification_snam.zip'
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unzipped_path = join(data_home, 'tweet_sentiment_quantification_snam')
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if not os.path.exists(unzipped_path):
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downloaded_path = join(data_home, 'tweet_sentiment_quantification_snam.zip')
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download_file(URL, downloaded_path)
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with zipfile.ZipFile(downloaded_path) as file:
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file.extractall(data_home)
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os.remove(downloaded_path)
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if dataset_name in {'semeval13', 'semeval14', 'semeval15'}:
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trainset_name = 'semeval'
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testset_name = 'semeval' if for_model_selection else dataset_name
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print(f"the training and development sets for datasets 'semeval13', 'semeval14', 'semeval15' are common "
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f"(called 'semeval'); returning trainin-set='{trainset_name}' and test-set={testset_name}")
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else:
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trainset_name = testset_name = dataset_name
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if for_model_selection:
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train = join(unzipped_path, 'train', f'{trainset_name}.train.feature.txt')
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test = join(unzipped_path, 'test', f'{testset_name}.dev.feature.txt')
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else:
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train = join(unzipped_path, 'train', f'{trainset_name}.train+dev.feature.txt')
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if dataset_name == 'semeval16': # there is a different test name in the case of semeval16 only
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test = join(unzipped_path, 'test', f'{testset_name}.dev-test.feature.txt')
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else:
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test = join(unzipped_path, 'test', f'{testset_name}.test.feature.txt')
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pickle_path = None
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if pickle:
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mode = "train-dev" if for_model_selection else "train+dev-test"
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pickle_path = join(unzipped_path, 'pickle', f'{testset_name}.{mode}.pkl')
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data = pickled_resource(pickle_path, Dataset.load, train, test, from_sparse)
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if min_df is not None:
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reduce_columns(data, min_df=min_df, inplace=True)
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data.name = dataset_name
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return data
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UCI_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'] # ongoing...
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def fetch_UCIDataset(dataset_name, data_home=None, verbose=False):
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assert dataset_name in UCI_DATASETS, \
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f'Name {dataset_name} does not match any known dataset from the UCI Machine Learning datasets repository. ' \
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f'Valid ones are {UCI_DATASETS}'
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if data_home is None:
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data_home = get_quapy_home()
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identifier_map = {
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'acute.a': 'acute',
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'acute.b': 'acute',
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'balance.1': 'balance-scale',
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'balance.2': 'balance-scale',
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'balance.3': 'balance-scale',
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'breast-cancer': 'breast-cancer-wisconsin',
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'cmc.1': 'cmc',
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'cmc.2': 'cmc',
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'cmc.3': 'cmc',
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'ctg.1': 'ctg',
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'ctg.2': 'ctg',
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'ctg.3': 'ctg',
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}
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dataset_fullname = {
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'acute.a': 'Acute Inflammations (urinary bladder)',
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'acute.b': 'Acute Inflammations (renal pelvis)',
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'balance.1': 'Balance Scale Weight & Distance Database (left)',
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'balance.2': 'Balance Scale Weight & Distance Database (balanced)',
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'balance.3': 'Balance Scale Weight & Distance Database (right)',
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'breast-cancer': 'Breast Cancer Wisconsin (Original)',
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'cmc.1': 'Contraceptive Method Choice (no use)',
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'cmc.2': 'Contraceptive Method Choice (long term)',
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'cmc.3': 'Contraceptive Method Choice (short term)',
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'ctg.1': 'Cardiotocography Data Set (normal)',
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'ctg.2': 'Cardiotocography Data Set (suspect)',
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'ctg.3': 'Cardiotocography Data Set (pathologic)',
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}
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data_folder = {
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'acute': 'diagnosis',
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'balance-scale': 'balance-scale',
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'breast-cancer-wisconsin': 'breast-cancer-wisconsin',
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'cmc': 'cmc'
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}
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identifier = identifier_map[dataset_name]
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URL = f'http://archive.ics.uci.edu/ml/machine-learning-databases/{identifier}'
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data_path = join(data_home, 'uci_datasets', identifier)
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download_file_if_not_exists(f'{URL}/{data_folder[identifier]}.data', f'{data_path}/{identifier}.data')
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download_file_if_not_exists(f'{URL}/{data_folder[identifier]}.names', f'{data_path}/{identifier}.names')
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if verbose:
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print(open(f'{data_path}/{identifier}.names', 'rt').read())
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print(f'Loading {dataset_name} ({dataset_fullname[dataset_name]})')
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if identifier == 'acute':
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df = pd.read_csv(f'{data_path}/{identifier}.data', header=None, encoding='utf-16', sep='\t')
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if dataset_name == 'acute.a':
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y = binarize(df[6], pos_class='yes')
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elif dataset_name == 'acute.b':
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y = binarize(df[7], pos_class='yes')
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mintemp, maxtemp = 35, 42
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df[0] = df[0].apply(lambda x:(float(x.replace(',','.'))-mintemp)/(maxtemp-mintemp)).astype(float, copy=False)
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[df_replace(df, col) for col in range(1, 6)]
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X = df.loc[:, 0:5].values
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if identifier == 'balance-scale':
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df = pd.read_csv(f'{data_path}/{identifier}.data', header=None, sep=',')
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if dataset_name == 'balance.1':
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y = binarize(df[0], pos_class='L')
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elif dataset_name == 'balance.2':
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y = binarize(df[0], pos_class='B')
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elif dataset_name == 'balance.3':
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y = binarize(df[0], pos_class='R')
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X = df.loc[:, 1:].astype(float).values
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if identifier == 'breast-cancer-wisconsin':
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df = pd.read_csv(f'{data_path}/{identifier}.data', header=None, sep=',')
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Xy = df.loc[:, 1:10]
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Xy[Xy=='?']=np.nan
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Xy = Xy.dropna(axis=0)
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X = Xy.loc[:, 1:9]
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X = X.astype(float).values
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y = binarize(Xy[10], pos_class=4)
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if identifier == 'cmc':
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df = pd.read_csv(f'{data_path}/{identifier}.data', header=None, sep=',')
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X = df.loc[:, 0:8].astype(float).values
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y = df[9].astype(int).values
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if dataset_name == 'cmc.1':
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y = binarize(y, pos_class=1)
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elif dataset_name == 'cmc.2':
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y = binarize(y, pos_class=2)
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elif dataset_name == 'cmc.3':
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y = binarize(y, pos_class=3)
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data = LabelledCollection(X, y)
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data.stats()
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raise NotImplementedError()
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#print(df)
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#print(df.loc[:, 0:5].values)
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#print(y)
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# X = __read_csv(f'{data_path}/{identifier}.data', separator='\t')
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# print(X)
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#X, y = from_csv(f'{data_path}/{dataset_name}.data')
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#y, classnames = reindex_labels(y)
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#def __read_csv(path, separator=','):
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# x = []
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# for instance in tqdm(open(path, 'rt', encoding='utf-16').readlines(), desc=f'reading {path}'):
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# x.append(instance.strip().split(separator))
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# return x
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def df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
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df[col] = df[col].apply(lambda x:repl[x]).astype(astype, copy=False) |