Metadata-Version: 2.1
Name: fife
Version: 1.0.0
Summary: Finite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and finite time series forecasting
Home-page: https://github.com/IDA-HumanCapital/fife
Author: Institute for Defense Analyses
Author-email: humancapital@ida.org
License: AGPLv3+
Project-URL: Bug Tracker, https://github.com/IDA-HumanCapital/fife/issues
Project-URL: Source Code, https://github.com/IDA-HumanCapital/fife
Project-URL: Documentation, https://fife.readthedocs.io/en/latest
Description: The Finite-Interval Forecasting Engine (FIFE) provides machine learning and other models for discrete-time survival analysis and finite time series forecasting.
        
        Suppose you have a dataset that looks like this:
        
        | ID | period | feature_1 | feature_2 | feature_3 | ... |
        |----|--------|-----------|-----------|-----------|-----|
        | 0  | 2016   | 7.2       | A         | 2AX       | ... |
        | 0  | 2017   | 6.4       | A         | 2AX       | ... |
        | 0  | 2018   | 6.6       | A         | 1FX       | ... |
        | 0  | 2019   | 7.1       | A         | 1FX       | ... |
        | 1  | 2016   | 5.3       | B         | 1RM       | ... |
        | 1  | 2017   | 5.4       | B         | 1RM       | ... |
        | 2  | 2017   | 6.7       | A         | 1FX       | ... |
        | 2  | 2018   | 6.9       | A         | 1RM       | ... |
        | 2  | 2019   | 6.9       | A         | 1FX       | ... |
        | 3  | 2017   | 4.3       | B         | 2AX       | ... |
        | 3  | 2018   | 4.1       | B         | 2AX       | ... |
        | 4  | 2019   | 7.4       | B         | 1RM       | ... |
        | ...| ...    | ...       | ...       |...        | ... |
        
        The entities with IDs 0, 2, and 4 are observed in the dataset in 2019.
        
        * What are each of their probabilities of being observed in 2020? 2021? 2022?
        * How reliable can we expect those probabilities to be?
        * How do the values of the features guide our predictions?
        
        FIFE answers these and other questions for any "unbalanced panel dataset" - a dataset where entities are observed periodically, but may depart the dataset after varying numbers of periods.
        
        FIFE supports feedforward neural networks (using Keras) and gradient-boosted tree models (using LightGBM).
        
        Read the documentation for FIFE at: https://fife.readthedocs.io/en/latest.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Description-Content-Type: text/markdown
