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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Monster-Monash custom downloader"""
import numpy as np
import os
import datasets
_DATASET = "LenDB"
_SHAPE = (3, 540)
#_DESCRIPTION = ""
#_CITATION = ""
#_HOMEPAGE = ""
#_LICENSE = ""
_URLS = {
'data': f"{_DATASET}_X.npy",
'labels': f"{_DATASET}_y.npy",
'fold_0': "test_indices_fold_0.txt",
'fold_1': "test_indices_fold_1.txt",
'fold_2': "test_indices_fold_2.txt",
'fold_3': "test_indices_fold_3.txt",
'fold_4': "test_indices_fold_4.txt",
}
class Monster(datasets.GeneratorBasedBuilder):
"""Generic Monster class for all downloader."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="full", version=VERSION, description="All data"),
datasets.BuilderConfig(name="fold_0", version=VERSION, description="Cross-validation fold 0"),
datasets.BuilderConfig(name="fold_1", version=VERSION, description="Cross-validation fold 1"),
datasets.BuilderConfig(name="fold_2", version=VERSION, description="Cross-validation fold 2"),
datasets.BuilderConfig(name="fold_3", version=VERSION, description="Cross-validation fold 3"),
datasets.BuilderConfig(name="fold_4", version=VERSION, description="Cross-validation fold 4"),
]
DEFAULT_CONFIG_NAME = "full" # By default all data is returned in a single split.
def _info(self):
features = datasets.Features(
{
"X": datasets.Array2D(_SHAPE, "float32"),
"y": datasets.Value("int64")
}
)
return datasets.DatasetInfo(
# description=_DESCRIPTION,
features=features,
supervised_keys=("X", "y"),
# homepage=_HOMEPAGE,
# license=_LICENSE,
# citation=_CITATION,
)
def _split_generators(self, dl_manager):
data = dl_manager.download_and_extract(_URLS['data'])
labels = dl_manager.download_and_extract(_URLS['labels'])
if self.config.name == "full":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data": data,
"labels": labels,
"fold": None,
"split": "all",
},
),
]
else:
fold = dl_manager.download_and_extract(_URLS[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data": data,
"labels": labels,
"fold": fold,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data": data,
"labels": labels,
"fold": fold,
"split": "test"
},
),
]
def _generate_examples(self, data, labels, fold, split):
X = np.load(data)
y = np.load(labels)
if self.config.name == "full":
for row in range(y.shape[0]):
yield(row, {"X": X[row], "y": y[row]})
else:
test_indices = np.loadtxt(fold, dtype='int')
if split == "test":
for row in test_indices:
yield(int(row), {"X": X[row], "y": y[row]})
elif split == "train":
train_indices = np.delete(np.arange(y.shape[0]), test_indices)
for row in train_indices:
yield(int(row), {"X": X[row], "y": y[row]})
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