import fix
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@ -9,7 +9,7 @@ from os.path import join
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import pandas as pd
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import pandas as pd
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from ucimlrepo import fetch_ucirepo
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from ucimlrepo import fetch_ucirepo
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from quapy.data.base import Dataset, LabelledCollection
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from quapy.data.base import Dataset, LabelledCollection
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from quapy.data.preprocessing import text2tfidf, reduce_columns
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from quapy.data.preprocessing import text2tfidf, reduce_columns, standardize
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from quapy.data.reader import *
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from quapy.data.reader import *
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from quapy.util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
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from quapy.util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
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from sklearn.preprocessing import StandardScaler
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from sklearn.preprocessing import StandardScaler
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@ -260,7 +260,7 @@ def fetch_UCIBinaryDataset(dataset_name, data_home=None, test_split=0.3, standar
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data = fetch_UCIBinaryLabelledCollection(dataset_name, data_home, verbose)
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data = fetch_UCIBinaryLabelledCollection(dataset_name, data_home, verbose)
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dataset = Dataset(*data.split_stratified(1 - test_split, random_state=0), name=dataset_name)
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dataset = Dataset(*data.split_stratified(1 - test_split, random_state=0), name=dataset_name)
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if standardize:
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if standardize:
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dataset = qp.data.preprocessing.standardize(dataset)
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dataset = standardize(dataset)
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return dataset
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return dataset
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@ -643,7 +643,7 @@ def fetch_UCIMulticlassDataset(
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data = Dataset(*data.split_stratified(train_prop, random_state=0))
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data = Dataset(*data.split_stratified(train_prop, random_state=0))
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if standardize:
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if standardize:
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data = qp.data.preprocessing.standardize(data)
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data = standardize(data)
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return data
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return data
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