"""
Utility functions for the UEA multivariate time series classification
archive.
"""
# Author: Johann Faouzi <johann.faouzi@gmail.com>
# License: BSD-3-Clause
import numpy as np
import os
import pickle
from scipy.io.arff import loadarff
from sklearn.utils import Bunch
from urllib.request import urlretrieve
import zipfile
def _correct_uea_name_download(dataset):
if dataset == 'Ering':
return 'ERing'
else:
return dataset
[docs]def uea_dataset_list():
"""List of available UEA datasets.
Returns
-------
datasets : list
List of available datasets from the UEA Time Series
Classification Archive.
References
----------
.. [1] `List of datasets on the UEA & UCR archive
<http://www.timeseriesclassification.com/dataset.php>`_
Examples
--------
>>> from pyts.datasets import uea_dataset_list
>>> uea_dataset_list()[:3]
['ArticularyWordRecognition', 'AtrialFibrillation', 'BasicMotions']
"""
module_path = os.path.dirname(__file__)
finfo = os.path.join(module_path, 'info', 'uea.pickle')
dictionary = pickle.load(open(finfo, 'rb'))
datasets = sorted(dictionary.keys())
return datasets
[docs]def uea_dataset_info(dataset=None):
"""Information about the UEA datasets.
Parameters
----------
dataset : str, list of str or None (default = None)
The data sets for which the information will be returned.
If None, the information for all the datasets is returned.
Returns
-------
dictionary : dict
Dictionary with the information for each dataset.
References
----------
.. [1] `List of datasets on the UEA & UCR archive
<http://www.timeseriesclassification.com/dataset.php>`_
Examples
--------
>>> from pyts.datasets import uea_dataset_info
>>> uea_dataset_info('AtrialFibrillation')['n_timestamps']
640
"""
module_path = os.path.dirname(__file__)
finfo = os.path.join(module_path, 'info', 'uea.pickle')
dictionary = pickle.load(open(finfo, 'rb'))
datasets = list(dictionary.keys())
if dataset is None:
return dictionary
elif isinstance(dataset, str):
if dataset not in datasets:
raise ValueError(
"{0} is not a valid name. The list of available names "
"can be obtained by calling the "
"'pyts.datasets.uea_dataset_list' function."
.format(dataset)
)
else:
return dictionary[dataset]
elif isinstance(dataset, (list, tuple, np.ndarray)):
dataset = np.asarray(dataset)
invalid_datasets = np.setdiff1d(dataset, datasets)
if invalid_datasets.size > 0:
raise ValueError(
"The following names are not valid: {0}. The list of "
"available names can be obtained by calling the "
"'pyts.datasets.uea_dataset_list' function."
.format(invalid_datasets)
)
else:
info = {}
for data in dataset:
info[data] = dictionary[data]
return info
[docs]def fetch_uea_dataset(dataset, use_cache=True, data_home=None,
return_X_y=False): # noqa 207
"""Fetch dataset from UEA TSC Archive by name.
Fetched data sets are saved by default in the
``pyts/datasets/cached_datasets/UEA/`` folder. To avoid
downloading the same data set several times, it is
highly recommended not to change the default values
of ``use_cache`` and ``path``.
Parameters
----------
dataset : str
Name of the dataset.
use_cache : bool (default = True)
If True, look if the data set has already been fetched
and load the fetched version if it is the case. If False,
download the data set from the UCR Time Series Classification
Archive.
data_home : None or str (default = None)
The path of the folder containing the cached data set.
If None, the ``pyts.datasets.cached_datasets/UEA/`` folder is
used. If the data set is not found, it is downloaded and cached
in this path.
return_X_y : bool (default = False)
If True, returns ``(data_train, data_test, target_train, target_test)``
instead of a Bunch object. See below for more information about the
`data` and `target` object.
Returns
-------
data : Bunch
Dictionary-like object, with attributes:
data_train : array of floats
The time series in the training set.
data_test : array of floats
The time series in the test set.
target_train : array of integers
The classification labels in the training set.
target_test : array of integers
The classification labels in the test set.
DESCR : str
The full description of the dataset.
url : str
The url of the dataset.
(data_train, data_test, target_train, target_test) : tuple if \
``return_X_y`` is True
Notes
-----
Missing values are represented as NaN's.
References
----------
.. [1] A. Bagnall et al, "The UEA multivariate time series
classification archive, 2018". arXiv:1811.00075 [cs, stat],
2018.
.. [2] A. Bagnall et al, "The UEA & UCR Time Series Classification
Repository", www.timeseriesclassification.com.
"""
if dataset not in uea_dataset_list():
raise ValueError(
"{0} is not a valid name. The list of available names "
"can be obtained with ``pyts.datasets.uea_dataset_list()``"
.format(dataset)
)
if data_home is None:
import pyts
home = '/'.join(pyts.__file__.split('/')[:-2]) + '/'
relative_path = 'pyts/datasets/cached_datasets/UEA/'
path = home + relative_path
else:
path = data_home
if not os.path.exists(path):
os.makedirs(path)
correct_dataset = _correct_uea_name_download(dataset)
if use_cache and os.path.exists(path + correct_dataset):
bunch = _load_uea_dataset(correct_dataset, path)
else:
url = ("http://www.timeseriesclassification.com/Downloads/{0}.zip"
.format(correct_dataset))
filename = 'temp_{}'.format(correct_dataset)
_ = urlretrieve(url, path + filename)
zipfile.ZipFile(path + filename).extractall(path + correct_dataset)
os.remove(path + filename)
bunch = _load_uea_dataset(correct_dataset, path)
if return_X_y:
return (bunch.data_train, bunch.data_test,
bunch.target_train, bunch.target_test)
return bunch
def _load_uea_dataset(dataset, path):
"""Load a UEA data set from a local folder.
Parameters
----------
dataset : str
Name of the dataset.
path : str
The path of the folder containing the cached data set.
Returns
-------
data : Bunch
Dictionary-like object, with attributes:
data_train : array of floats
The time series in the training set.
data_test : array of floats
The time series in the test set.
target_train : array
The classification labels in the training set.
target_test : array
The classification labels in the test set.
DESCR : str
The full description of the dataset.
url : str
The url of the dataset.
Notes
-----
Missing values are represented as NaN's.
"""
new_path = path + dataset + '/'
try:
description_file = [
file for file in os.listdir(new_path)
if ('Description.txt' in file
or dataset + '.txt' in file)
][0]
except IndexError:
description_file = None
if description_file is not None:
try:
with(open(new_path + description_file, encoding='utf-8')) as f:
description = f.read()
except UnicodeDecodeError:
with(open(new_path + description_file,
encoding='ISO-8859-1')) as f:
description = f.read()
else:
description = None
data_train = loadarff(new_path + dataset + '_TRAIN.arff')
X_train, y_train = _parse_relational_arff(data_train)
data_test = loadarff(new_path + dataset + '_TEST.arff')
X_test, y_test = _parse_relational_arff(data_test)
bunch = Bunch(
data_train=X_train, target_train=y_train,
data_test=X_test, target_test=y_test,
DESCR=description,
url=("http://www.timeseriesclassification.com/"
"description.php?Dataset={}".format(dataset))
)
return bunch
def _parse_relational_arff(data):
X_data = np.asarray(data[0])
n_samples = len(X_data)
X, y = [], []
if X_data[0][0].dtype.names is None:
for i in range(n_samples):
X_sample = np.asarray(
[X_data[i][name] for name in X_data[i].dtype.names]
)
X.append(X_sample.T)
y.append(X_data[i][1])
else:
for i in range(n_samples):
X_sample = np.asarray(
[X_data[i][0][name] for name in X_data[i][0].dtype.names]
)
X.append(X_sample.T)
y.append(X_data[i][1])
X = np.asarray(X).astype('float64')
y = np.asarray(y)
try:
y = y.astype('float64').astype('int64')
except ValueError:
pass
return X, y