samplefit

samplefit is a Python library to assess sample fit, as opposed to model fit, via the Sample Fit Reliability algorithm as developed by Okasa & Younge (2022). samplefit is linked to the statsmodels library (Seabold & Perktold, 2010) and follows the same command workflow.

Copyright (c) 2022 Gabriel Okasa & Kenneth A. Younge.

AUTHOR:  Gabriel Okasa & Kenneth A. Younge
SOURCE:  https://github.com/okasag/samplefit
LICENSE: Access to this code is provided under an MIT License.

Repo maintainer: Gabriel Okasa ([email protected])

Introduction

samplefit is a Python library for the assessment of sample fit in econometric models. In particular, samplefit implements the Sample Fit Reliability (SFR) algorithm, a re-sampling procedure to estimate the reliability of the data, check the sensitivity of the results, and improve the robustness of the analysis. To that end, SFR is a computational approach with three aspects: Scoring, to estimate a reliability score for every observation in a sample based on the expected estimation loss over sub-samples; Annealing, to test the sensitivity of results to the sequential removal of unreliable data; and Fitting, to estimate a weighted regression that adjusts for the reliability of the sample.

Detailed documentation of the samplefit library is available here.

Paper describing the SFR algorithm is available here.

Installation

To install the samplefit library from PyPi run:

pip install samplefit

or alternatively, to clone the repo run:

git clone https://github.com/okasag/samplefit.git

The required modules can be installed by navigating to the root of the cloned project and executing the following command: pip install -r requirements.txt.

Example

The example below demonstrates the workflow of using the samplefit library in conjunction with the well-known statsmodels library.

Import libraries:

import samplefit as sf
import statsmodels.api as sm

Get data:

boston = sm.datasets.get_rdataset("Boston", "MASS")
Y = boston.data['crim']
X = boston.data['lstat']
X = sm.add_constant(X)

Assess model fit:

model = sm.OLS(endog=Y, exog=X)
model_fit = model.fit()
model_fit.summary()

Assess sample fit:

sample = sf.SFR(linear_model=model)
sample_fit = sample.fit()
sample_fit.summary()

Assess sample reliability:

sample_scores = sample.score()
sample_scores.plot()

Assess sample sensitivity:

sample_annealing = sample.anneal()
sample_annealing.plot()

References

• Okasa, Gabriel, and Kenneth A. Younge. “Sample Fit Reliability.” arXiv preprint arXiv:2209.06631. https://arxiv.org/abs/2209.06631. 2022.
• Seabold, Skipper, and Josef Perktold. “statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference. 2010.