Datasets:
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import os
import datasets
from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler
import numpy as np
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
class Reuters10K(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("0.0.1")
def _info(self):
return datasets.DatasetInfo(
description="Reuters10K dataset",
version=Reuters10K.VERSION,
)
def _split_generators(self, dl_manager):
train_url = "train.npy"
test_url = "test.npy"
downloaded_files = dl_manager.download_and_extract({
"train": train_url,
"test": test_url
})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": downloaded_files["train"]
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": downloaded_files["test"]
},
)
]
def _generate_examples(self, filepath):
"""Yields examples."""
train_dataset = np.load(filepath, allow_pickle=True)
X_train = train_dataset.item()['data']
Y_train = train_dataset.item()['label']
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
# yield "key", {"text": text, "label": label}
for i, (x, y) in enumerate(zip(X_train, Y_train)):
yield i, {"features": x, "label": y}
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