lilio 0.2.1

python package for generating calendars for machine learning timeseries analysis.

lilio: Calendar generator for machine learning with timeseries data

A python package for generating calendars to resample timeseries into training and target data for machine learning.

Installation

To install the in-development version from the GitHub repository, do:

python3 -m pip install git+https://github.com/AI4S2S/lilio.git

Configure the package for development and testing

The testing framework used here is pytest. Before running the test, we get a local copy of the source code and install lilio via the command:

git clone https://github.com/AI4S2S/lilio.git
cd lilio
python3 -m pip install -e .

Then, run tests:

python3 -m pytest

How the lilio calendars work

In a typical ML-based timeseries analysis, the first step is always data processing. A calendar-based datetime module time is implemented for time operations. For instance, a user is looking for predictors for winter climate at seasonal timescales (~180 days). First, a calendar object is created using AdventCalendar:

>>> calendar = s2spy.time.AdventCalendar(anchor="11-30", freq='180d')
>>> calendar = calendar.map_years(2020, 2021)
>>> calendar.show()
i_interval                         -1                         1
anchor_year
2021         [2021-06-03, 2021-11-30)  [2021-11-30, 2022-05-29)
2020         [2020-06-03, 2020-11-30)  [2020-11-30, 2021-05-29)

Now, the user can load the data input_data (e.g. pandas DataFrame) and resample it to the desired timescales configured in the calendar:

>>> calendar = calendar.map_to_data(input_data)
>>> bins = s2spy.time.resample(calendar, input_data)
>>> bins
  anchor_year  i_interval                  interval  mean_data  target
0        2020          -1  [2020-06-03, 2020-11-30)      275.5    True
1        2020           1  [2020-11-30, 2021-05-29)       95.5   False
2        2021          -1  [2021-06-03, 2021-11-30)      640.5    True
3        2021           1  [2021-11-30, 2022-05-29)      460.5   False

Depending on data preparations, we can choose different types of calendars e.g. MonthlyCalendar and WeeklyCalendar.

Contributing

If you want to contribute to the development of lilio, have a look at the contribution guidelines.

Credits

This package was created with Cookiecutter and the NLeSC/python-template.