#
# MIT License
#
# Copyright (c) 2023 Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, and Danny Abraham
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
import os
import os.path
from typing import Callable, Optional, Tuple, List
import torch
from torch.utils import data
import pandas as pd
from .utils import download_file_from_google_drive, unzip_file
[docs]
class ISOLET(data.Dataset):
"""`ISOLET <https://archive.ics.uci.edu/ml/datasets/isolet>`_ dataset.
.. list-table::
:widths: 10 10 10 10
:align: center
:header-rows: 1
* - Instances
- Attributes
- Task
- Area
* - 7797
- 617
- Classification
- Computer
Args:
root (string): Root directory of dataset where ``isolet1+2+3+4.data``
and ``isolet5.data`` exist.
train (bool, optional): If True, creates dataset from ``isolet1+2+3+4.data``,
otherwise from ``isolet5.data``.
download (bool, optional): If True, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
transform (callable, optional): A function/transform that takes in an torch.FloatTensor
and returns a transformed version.
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
"""
classes: List[str] = [
"A",
"B",
"C",
"D",
"E",
"F",
"G",
"H",
"I",
"J",
"K",
"L",
"M",
"N",
"O",
"P",
"Q",
"R",
"S",
"T",
"U",
"V",
"W",
"X",
"Y",
"Z",
]
def __init__(
self,
root: str,
train: bool = True,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
):
root = os.path.join(root, "isolet")
root = os.path.expanduser(root)
self.root = root
os.makedirs(self.root, exist_ok=True)
self.train = train
self.transform = transform
self.target_transform = target_transform
if download:
self.download()
if not self._check_integrity():
raise RuntimeError(
"Dataset not found or corrupted. You can use download=True to download it"
)
self._load_data()
def __len__(self) -> int:
return self.data.size(0)
def __getitem__(self, index: int) -> Tuple[torch.FloatTensor, torch.LongTensor]:
"""
Args:
index (int): Index
Returns:
Tuple[torch.FloatTensor, torch.LongTensor]: (sample, target) where target is the index of the target class
"""
sample = self.data[index]
label = self.targets[index]
if self.transform:
sample = self.transform(sample)
if self.target_transform:
label = self.target_transform(label)
return sample, label
def _check_integrity(self) -> bool:
if not os.path.isdir(self.root):
return False
# Check if the root directory contains the required files
has_train_file = os.path.isfile(os.path.join(self.root, "isolet1+2+3+4.data"))
has_test_file = os.path.isfile(os.path.join(self.root, "isolet5.data"))
if has_train_file and has_test_file:
return True
# TODO: Add more specific checks like an MD5 checksum
return False
def _load_data(self):
data_file = "isolet1+2+3+4.data" if self.train else "isolet5.data"
data = pd.read_csv(os.path.join(self.root, data_file), header=None)
self.data = torch.tensor(data.values[:, :-1], dtype=torch.float)
self.targets = torch.tensor(data.values[:, -1], dtype=torch.long) - 1
def download(self):
"""Download the data if it doesn't exist already."""
if self._check_integrity():
print("Files already downloaded and verified")
return
zip_file_path = os.path.join(self.root, "data.zip")
download_file_from_google_drive(
"1IMC6xzs2kBnf5_kaiBUzSWiTMR_dFbIX", # Google Drive shared file ID
zip_file_path,
)
unzip_file(zip_file_path, self.root)
os.remove(zip_file_path)