loadmydata 0.0.7rc1

Collections of utility functions to download open-source data sets.

loadmydata

Utility functions for loading time series data sets (Python 3.7++).

The list of available data sets currently includes:

• the UEA/UCR repository.

Install

This package relies on requests, tqdm, yarl (for the download), and numpy.

Use pip to install.

pip install loadmydata

Alternatively, you can use conda:

conda config --add channels conda-forge
conda install loadmydata

Data format

Consider a data set of N time series y⁽¹⁾, y⁽²⁾,..., y^(N). Each y⁽ⁿ⁾ has T⁽ⁿ⁾ samples and d dimensions. Note that time series can have variable lengths, i.e. different T⁽ⁿ⁾ but they share the same dimensionality d.

Such a data set is contained in a numpy array of shape (N, T, d) where T:=max_n T⁽ⁿ⁾. Time series with less than T samples are padded at the end with numpy.nan. In addition, the extra padding is masked using numpy's MaskedArray.

from loadmydata.padding import get_signal_shape

# Assume that X contains a time series data set of shape (N, T, d)
for signal in X:
    # signal is a masked array of shape (T, d).
    # The true number of samples of the signal (without extra padding)
    # can be accessed with `get_signal_shape`.
    n_samples, n_dims = get_signal_shape(signal)
    # To get the signal without the extra padding, do
    signal_without_padding = signal[:n_samples]
    # do something with signal_without_padding
    ...

UEA/UCR time series classification repository

The UEA/UCR repository focuses on time series classification. As a result, each signal is associated with a label to predict.

The list of available data sets from the UEA/UCR repository is available here.

Usage example

from loadmydata.load_uea_ucr import load_uea_ucr_data

dataset_name = "ArrowHead"  # "AbnormalHeartbeat", "ACSF1", etc.
data = load_uea_ucr_data(dataset_name)

print(data.description)
print(data.X_train.shape)
print(data.X_test.shape)
print(data.y_train.shape)
print(data.y_test.shape)