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"""Collection of imbalanced datasets.
This collection of datasets has been proposed in [1]_. The
characteristics of the available datasets are presented in the table
below.
ID Name Repository & Target Ratio #S #F
1 ecoli UCI, target: imU 8.6:1 336 7
2 optical_digits UCI, target: 8 9.1:1 5,620 64
3 satimage UCI, target: 4 9.3:1 6,435 36
4 pen_digits UCI, target: 5 9.4:1 10,992 16
5 abalone UCI, target: 7 9.7:1 4,177 10
6 sick_euthyroid UCI, target: sick euthyroid 9.8:1 3,163 42
7 spectrometer UCI, target: >=44 11:1 531 93
8 car_eval_34 UCI, target: good, v good 12:1 1,728 21
9 isolet UCI, target: A, B 12:1 7,797 617
10 us_crime UCI, target: >0.65 12:1 1,994 100
11 yeast_ml8 LIBSVM, target: 8 13:1 2,417 103
12 scene LIBSVM, target: >one label 13:1 2,407 294
13 libras_move UCI, target: 1 14:1 360 90
14 thyroid_sick UCI, target: sick 15:1 3,772 52
15 coil_2000 KDD, CoIL, target: minority 16:1 9,822 85
16 arrhythmia UCI, target: 06 17:1 452 278
17 solar_flare_m0 UCI, target: M->0 19:1 1,389 32
18 oil UCI, target: minority 22:1 937 49
19 car_eval_4 UCI, target: vgood 26:1 1,728 21
20 wine_quality UCI, wine, target: <=4 26:1 4,898 11
21 letter_img UCI, target: Z 26:1 20,000 16
22 yeast_me2 UCI, target: ME2 28:1 1,484 8
23 webpage LIBSVM, w7a, target: minority 33:1 34,780 300
24 ozone_level UCI, ozone, data 34:1 2,536 72
25 mammography UCI, target: minority 42:1 11,183 6
26 protein_homo KDD CUP 2004, minority 111:1 145,751 74
27 abalone_19 UCI, target: 19 130:1 4,177 10
References
----------
.. [1] Ding, Zejin, "Diversified Ensemble Classifiers for Highly
Imbalanced Data Learning and their Application in Bioinformatics."
Dissertation, Georgia State University, (2011).
"""
# Author: Guillaume Lemaitre
# License: BSD 3 clause
import tarfile
from collections import OrderedDict
from io import BytesIO
from os import makedirs
from os.path import isfile, join
from urllib.request import urlopen
import numpy as np
from sklearn.datasets import get_data_home
from sklearn.utils import Bunch, check_random_state
from ..utils._param_validation import validate_params
URL = "https://zenodo.org/record/61452/files/benchmark-imbalanced-learn.tar.gz"
PRE_FILENAME = "x"
POST_FILENAME = "data.npz"
MAP_NAME_ID_KEYS = [
"ecoli",
"optical_digits",
"satimage",
"pen_digits",
"abalone",
"sick_euthyroid",
"spectrometer",
"car_eval_34",
"isolet",
"us_crime",
"yeast_ml8",
"scene",
"libras_move",
"thyroid_sick",
"coil_2000",
"arrhythmia",
"solar_flare_m0",
"oil",
"car_eval_4",
"wine_quality",
"letter_img",
"yeast_me2",
"webpage",
"ozone_level",
"mammography",
"protein_homo",
"abalone_19",
]
MAP_NAME_ID = OrderedDict()
MAP_ID_NAME = OrderedDict()
for v, k in enumerate(MAP_NAME_ID_KEYS):
MAP_NAME_ID[k] = v + 1
MAP_ID_NAME[v + 1] = k
@validate_params(
{
"data_home": [None, str],
"filter_data": [None, tuple],
"download_if_missing": ["boolean"],
"random_state": ["random_state"],
"shuffle": ["boolean"],
"verbose": ["boolean"],
},
prefer_skip_nested_validation=True,
)
def fetch_datasets(
*,
data_home=None,
filter_data=None,
download_if_missing=True,
random_state=None,
shuffle=False,
verbose=False,
):
"""Load the benchmark datasets from Zenodo, downloading it if necessary.
.. versionadded:: 0.3
Parameters
----------
data_home : str, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
filter_data : tuple of str/int, default=None
A tuple containing the ID or the name of the datasets to be returned.
Refer to the above table to get the ID and name of the datasets.
download_if_missing : bool, default=True
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
random_state : int, RandomState instance or None, default=None
Random state for shuffling the dataset.
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
shuffle : bool, default=False
Whether to shuffle dataset.
verbose : bool, default=False
Show information regarding the fetching.
Returns
-------
datasets : OrderedDict of Bunch object,
The ordered is defined by ``filter_data``. Each Bunch object ---
referred as dataset --- have the following attributes:
dataset.data : ndarray of shape (n_samples, n_features)
dataset.target : ndarray of shape (n_samples,)
dataset.DESCR : str
Description of the each dataset.
Notes
-----
This collection of datasets have been proposed in [1]_. The
characteristics of the available datasets are presented in the table
below.
+--+--------------+-------------------------------+-------+---------+-----+
|ID|Name | Repository & Target | Ratio | #S | #F |
+==+==============+===============================+=======+=========+=====+
|1 |ecoli | UCI, target: imU | 8.6:1 | 336 | 7 |
+--+--------------+-------------------------------+-------+---------+-----+
|2 |optical_digits| UCI, target: 8 | 9.1:1 | 5,620 | 64 |
+--+--------------+-------------------------------+-------+---------+-----+
|3 |satimage | UCI, target: 4 | 9.3:1 | 6,435 | 36 |
+--+--------------+-------------------------------+-------+---------+-----+
|4 |pen_digits | UCI, target: 5 | 9.4:1 | 10,992 | 16 |
+--+--------------+-------------------------------+-------+---------+-----+
|5 |abalone | UCI, target: 7 | 9.7:1 | 4,177 | 10 |
+--+--------------+-------------------------------+-------+---------+-----+
|6 |sick_euthyroid| UCI, target: sick euthyroid | 9.8:1 | 3,163 | 42 |
+--+--------------+-------------------------------+-------+---------+-----+
|7 |spectrometer | UCI, target: >=44 | 11:1 | 531 | 93 |
+--+--------------+-------------------------------+-------+---------+-----+
|8 |car_eval_34 | UCI, target: good, v good | 12:1 | 1,728 | 21 |
+--+--------------+-------------------------------+-------+---------+-----+
|9 |isolet | UCI, target: A, B | 12:1 | 7,797 | 617 |
+--+--------------+-------------------------------+-------+---------+-----+
|10|us_crime | UCI, target: >0.65 | 12:1 | 1,994 | 100 |
+--+--------------+-------------------------------+-------+---------+-----+
|11|yeast_ml8 | LIBSVM, target: 8 | 13:1 | 2,417 | 103 |
+--+--------------+-------------------------------+-------+---------+-----+
|12|scene | LIBSVM, target: >one label | 13:1 | 2,407 | 294 |
+--+--------------+-------------------------------+-------+---------+-----+
|13|libras_move | UCI, target: 1 | 14:1 | 360 | 90 |
+--+--------------+-------------------------------+-------+---------+-----+
|14|thyroid_sick | UCI, target: sick | 15:1 | 3,772 | 52 |
+--+--------------+-------------------------------+-------+---------+-----+
|15|coil_2000 | KDD, CoIL, target: minority | 16:1 | 9,822 | 85 |
+--+--------------+-------------------------------+-------+---------+-----+
|16|arrhythmia | UCI, target: 06 | 17:1 | 452 | 278 |
+--+--------------+-------------------------------+-------+---------+-----+
|17|solar_flare_m0| UCI, target: M->0 | 19:1 | 1,389 | 32 |
+--+--------------+-------------------------------+-------+---------+-----+
|18|oil | UCI, target: minority | 22:1 | 937 | 49 |
+--+--------------+-------------------------------+-------+---------+-----+
|19|car_eval_4 | UCI, target: vgood | 26:1 | 1,728 | 21 |
+--+--------------+-------------------------------+-------+---------+-----+
|20|wine_quality | UCI, wine, target: <=4 | 26:1 | 4,898 | 11 |
+--+--------------+-------------------------------+-------+---------+-----+
|21|letter_img | UCI, target: Z | 26:1 | 20,000 | 16 |
+--+--------------+-------------------------------+-------+---------+-----+
|22|yeast_me2 | UCI, target: ME2 | 28:1 | 1,484 | 8 |
+--+--------------+-------------------------------+-------+---------+-----+
|23|webpage | LIBSVM, w7a, target: minority | 33:1 | 34,780 | 300 |
+--+--------------+-------------------------------+-------+---------+-----+
|24|ozone_level | UCI, ozone, data | 34:1 | 2,536 | 72 |
+--+--------------+-------------------------------+-------+---------+-----+
|25|mammography | UCI, target: minority | 42:1 | 11,183 | 6 |
+--+--------------+-------------------------------+-------+---------+-----+
|26|protein_homo | KDD CUP 2004, minority | 111:1 | 145,751 | 74 |
+--+--------------+-------------------------------+-------+---------+-----+
|27|abalone_19 | UCI, target: 19 | 130:1 | 4,177 | 10 |
+--+--------------+-------------------------------+-------+---------+-----+
References
----------
.. [1] Ding, Zejin, "Diversified Ensemble Classifiers for Highly
Imbalanced Data Learning and their Application in Bioinformatics."
Dissertation, Georgia State University, (2011).
"""
data_home = get_data_home(data_home=data_home)
zenodo_dir = join(data_home, "zenodo")
datasets = OrderedDict()
if filter_data is None:
filter_data_ = MAP_NAME_ID.keys()
else:
list_data = MAP_NAME_ID.keys()
filter_data_ = []
for it in filter_data:
if isinstance(it, str):
if it not in list_data:
raise ValueError(
f"{it} is not a dataset available. "
f"The available datasets are {list_data}"
)
else:
filter_data_.append(it)
elif isinstance(it, int):
if it < 1 or it > 27:
raise ValueError(
f"The dataset with the ID={it} is not an "
f"available dataset. The IDs are "
f"{range(1, 28)}"
)
else:
# The index start at one, then we need to remove one
# to not have issue with the indexing.
filter_data_.append(MAP_ID_NAME[it])
else:
raise ValueError(
f"The value in the tuple should be str or int."
f" Got {type(it)} instead."
)
# go through the list and check if the data are available
for it in filter_data_:
filename = PRE_FILENAME + str(MAP_NAME_ID[it]) + POST_FILENAME
filename = join(zenodo_dir, filename)
available = isfile(filename)
if download_if_missing and not available:
makedirs(zenodo_dir, exist_ok=True)
if verbose:
print("Downloading %s" % URL)
f = BytesIO(urlopen(URL).read())
tar = tarfile.open(fileobj=f)
tar.extractall(path=zenodo_dir)
elif not download_if_missing and not available:
raise IOError("Data not found and `download_if_missing` is False")
data = np.load(filename)
X, y = data["data"], data["label"]
if shuffle:
ind = np.arange(X.shape[0])
rng = check_random_state(random_state)
rng.shuffle(ind)
X = X[ind]
y = y[ind]
datasets[it] = Bunch(data=X, target=y, DESCR=it)
return datasets
|
