You can run
springtime as a command-line tool in a terminal or use it as a
python library e.g. in a Jupyter notebook. Below, we explain both CLI and API.
CLI to run recipes
The main component of
springtime command-line is the recipe (scientific
workflow). A recipe is a file with
yaml extension that includes a set of
instructions to reproduce a certain result. Recipes are written in a nice and
datasets: npn_obs: dataset: RNPN species_ids: functional_type: "Deciduous broadleaf" # multiple species phenophase_ids: name: breaking leaf buds years: [2015, 2020] area: name: Washington bbox: [ -124.08406940413612, 45.50277198520317, -117.39620059586387, 49.99938001479683, ] daymet: dataset: daymet_multiple_points points: source: npn_obs years: [2015, 2020] variables: - tmin - tmax resample: frequency: month operator: median dropna: True experiment: experiment_type: regression # --> pycaret.regression.RegressionExperiment setup: ... # setup of the experiment init_kwargs: ... # intial arguments for models compare_models: include: - 'lr' # linear regression - 'rf' # random forest regressor - 'sklearn.svm.SVR' - 'interpret.glassbox.ExplainableBoostingRegressor' cross_validation: true n_select: 2 plots: - error - residuals
Such a recipe can then be executed with
springtime command in a terminal:
We provide several "recipes" for running experiments.
Springtime is written in Python (with parts in R) and can also be used in an interactive (IPython/Jupyter) session. For example:
from springtime.datasets.PEP725Phenor import PEP725Phenor dataset = PEP725Phenor(species='Syringa vulgaris') dataset.download() df = dataset.load()
We provide several notebooks for downloading data from various sources. See "Datasets" documentation.