views-runs 1.14.2

Tools for doing model runs with views

views-runs

This package is meant to help views researchers with training models, by providing a common interface for data partitioning and stepshift model training. It also functions as a central hub package for other classes and functions used by views researchers, including stepshift (StepshiftedModels) and views_partitioning (DataPartitioner).

Installation

To install views-runs, use pip:

pip install views-runs

This also installs the vendored libraries stepshift and views_partitioning.

Usage

The library offers a class imported at views_runs.ViewsRun, that wraps the to central components of a ViEWS 3 run: A partitioning scheme expressed via a views_partitioning.DataPartitioner instance, and a stepshifted modelling process expressed via a stepshift.views.StepshiftedModels instance. For documentation on the data partitioner, see views_partitioning. For documentation on stepshifted modelling, see views.StepshiftedModels.

The wrapper takes care of applying these two classes to your data, in order to produce predictions in a familiar and predictable format, as well as ensuring that there is no overlap between training and testing partitions. Instantiating a run requires instances of both of these classes, like so:

run = ViewsRun(
   DataPartitioner({"A":{"train":(1,100),"test":(101,200)}}),
   StepshiftedModels(LogisticRegression,[1,2,3,4,5,6],"my_dependent_variable"),
)

This instance can then be applied to a time-unit indexed dataframe to train the models, and produce predictions for the timespans defined in the data partitioner:

run.fit("A","train",dataframe)
predictions = run.predict("A","test",dataframe)

Examples

There are notebooks that show various workflows with views_runs and the vendored libraries:

• BasicExample.ipynb

Funding

The contents of this repository is the outcome of projects that have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 694640, ViEWS) and Horizon Europe (Grant agreement No. 101055176, ANTICIPATE; and No. 101069312, ViEWS (ERC-2022-POC1)), Riksbankens Jubileumsfond (Grant agreement No. M21-0002, Societies at Risk), Uppsala University, Peace Research Institute Oslo, the United Nations Economic and Social Commission for Western Asia (ViEWS-ESCWA), the United Kingdom Foreign, Commonwealth & Development Office (GSRA – Forecasting Fatalities in Armed Conflict), the Swedish Research Council (DEMSCORE), the Swedish Foundation for Strategic Environmental Research (MISTRA Geopolitics), the Norwegian MFA (Conflict Trends QZA-18/0227), and the United Nations High Commissioner for Refugees (the Sahel Predictive Analytics project).