"""Functions to load datasets."""
# Author: Johann Faouzi <johann.faouzi@gmail.com>
# License: BSD-3-Clause
import os
from .uea import _load_uea_dataset
from .ucr import _load_ucr_dataset
def _load_dataset(name, archive, return_X_y):
r"""Load and return dataset.
Parameters
----------
name : str
Name of the dataset.
archive : 'UCR' or 'UEA'
Archive the dataset belongs to.
return_X_y : bool
If True, return
``(data_train, data_test, target_train, target_test)`` instead of a
Bunch 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
""" # noqa: E501
module_path = os.path.dirname(__file__)
folder = os.path.join(module_path, 'cached_datasets', archive, '')
if archive == 'UCR':
bunch = _load_ucr_dataset(name, folder)
else:
bunch = _load_uea_dataset(name, folder)
if return_X_y:
return (bunch.data_train, bunch.data_test,
bunch.target_train, bunch.target_test)
return bunch
[docs]def load_basic_motions(return_X_y=False):
r"""Load and return the Basic Motions dataset.
The data was generated as part of a student project where four students
performed four activities whilst wearing a smart watch. The watch collects
3D accelerometer and a 3D gyroscope It consists of four classes, which are
walking, resting, running and badminton. Participants were required to
record motion a total of five times, and the data is sampled once every
tenth of a second, for a ten second period.
================ ==============
Training samples 40
Test samples 40
Dimensionality 6
Timestamps 100
Classes 4
================ ==============
Parameters
----------
return_X_y : bool (default = False)
If True, return
``(data_train, data_test, target_train, target_test)`` instead of a
Bunch 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
References
----------
.. [1] `UCR archive entry for the PigCVP dataset
<http://www.timeseriesclassification.com/description.php?Dataset=BasicMotions>`_
Examples
--------
>>> from pyts.datasets import load_basic_motions
>>> bunch = load_basic_motions()
>>> bunch.data_train.shape
(40, 6, 100)
>>> X_train, X_test, y_train, y_test = load_basic_motions(return_X_y=True)
>>> X_train.shape
(40, 6, 100)
""" # noqa: E501
return _load_dataset('BasicMotions', 'UEA', return_X_y)
[docs]def load_coffee(return_X_y=False):
r"""Load and return the Coffee dataset.
Food spectrographs are used in chemometrics to classify food types, a task
that has obvious applications in food safety and quality assurance. The
coffee data set is a two class problem to distinguish between Robusta and
Aribica coffee beans.
================ ==============
Training samples 28
Test samples 28
Timestamps 286
Classes 2
================ ==============
Parameters
----------
return_X_y : bool (default = False)
If True, return
``(data_train, data_test, target_train, target_test)`` instead of a
Bunch 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
References
----------
.. [1] R. Briandet, E.K. Kemsley, and R.H. Wilson, "Discrimination of
Arabica and Robusta in Instant Coffee by Fourier Transform Infrared
Spectroscopy and Chemometrics". Journal of Agricultural and Food
Chemistry (1996).
.. [2] A. Bagnall, L. Davis, J. Hills and J. Lines, "Transformation Based
Ensembles for Time Series Classification". SDM (2012).
.. [3] `UCR archive entry for the PigCVP dataset
<http://www.timeseriesclassification.com/description.php?Dataset=Coffee>`_
Examples
--------
>>> from pyts.datasets import load_coffee
>>> bunch = load_coffee()
>>> bunch.data_train.shape
(28, 286)
>>> X_train, X_test, y_train, y_test = load_coffee(return_X_y=True)
>>> X_train.shape
(28, 286)
""" # noqa: E501
return _load_dataset('Coffee', 'UCR', return_X_y)
[docs]def load_gunpoint(return_X_y=False):
r"""Load and return the GunPoint dataset.
This dataset involves one female actor and one male actor making a motion
with their hand. The two classes are: Gun-Draw and Point: For Gun-Draw the
actors have their hands by their sides. They draw a replicate gun from a
hip-mounted holster, point it at a target for approximately one second,
then return the gun to the holster, and their hands to their sides. For
Point the actors have their gun by their sides. They point with their index
fingers to a target for approximately one second, and then return their
hands to their sides. For both classes, we tracked the centroid of the
actor's right hands in both X- and Y-axes, which appear to be highly
correlated. The data in the archive is just the X-axis.
================ ==============
Training samples 50
Test samples 150
Timestamps 150
Classes 2
================ ==============
Parameters
----------
return_X_y : bool (default = False)
If True, return
``(data_train, data_test, target_train, target_test)`` instead of a
Bunch 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
References
----------
.. [1] `UCR archive entry for the PigCVP dataset
<http://www.timeseriesclassification.com/description.php?Dataset=GunPoint>`_
Examples
--------
>>> from pyts.datasets import load_gunpoint
>>> bunch = load_gunpoint()
>>> bunch.data_train.shape
(50, 150)
>>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
>>> X_train.shape
(50, 150)
""" # noqa: E501
return _load_dataset('GunPoint', 'UCR', return_X_y)
[docs]def load_pig_central_venous_pressure(return_X_y=False):
r"""Load and return the PigCVP dataset.
In the test set, a class is represented by four examples, the second and
third 2000 data points of the before time series and the second and third
2000 data points of the after time series. Data created by Mathieu
Guillame-Bert et al. Data edited by Shaghayegh Gharghabi and Eamonn Keogh.
================ ==============
Training samples 104
Test samples 208
Timestamps 2000
Classes 52
================ ==============
Parameters
----------
return_X_y : bool (default = False)
If True, return
``(data_train, data_test, target_train, target_test)`` instead of a
Bunch 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
References
----------
.. [1] M. Guillame-Bert and A. Dubrawski, "Classification of Time Sequences
using Graphs of Temporal Constraints". Journal of Machine Learning
Research, 2017.
.. [2] `UCR archive entry for the PigCVP dataset
<http://www.timeseriesclassification.com/description.php?Dataset=PigCVP>`_
Examples
--------
>>> from pyts.datasets import load_pig_central_venous_pressure
>>> bunch = load_pig_central_venous_pressure()
>>> bunch.data_train.shape
(104, 2000)
>>> X_train, X_test, y_train, y_test = load_pig_central_venous_pressure(
... return_X_y=True)
>>> X_train.shape
(104, 2000)
""" # noqa: E501
return _load_dataset('PigCVP', 'UCR', return_X_y)
