moabbr 0.1.1

MOABB interface to tombolo for analysis of ML benchmarks

moabbr

MOABB interface to tombolo for analysis of machine learning benchmarks.

MOABB evaluation results are a pandas DataFrame with one row per pipeline/dataset/subject/session. moabbr transforms this into the format tombolo expects using DuckDB, then calls the tombolo Docker image to run the analysis. Each dataset is treated as an independent study and each pipeline as a treatment.

Requirements

Docker must be installed and running, and the tombolo image must be pulled:

docker pull ethandavisecd/tombolo:latest

Installation

pip install moabbr

Usage

results is the pd.DataFrame returned by a MOABB evaluation, with columns dataset, pipeline, subject, and score.

from moabbr import nma, bnma

data = moabbr.nma(results)    # frequentist NMA via netmeta
data = moabbr.bnma(results)   # Bayesian NMA via gemtc

Both functions accept a greater_is_better flag (default True). Set to False for metrics where lower is better (e.g. error rate).

Plots

from moabbr.plots import (
    ranking_plot,
    league_table,
    forest_plot,
    heterogeneity_table,
    prediction_table,  # nma only
    convergence_table, # bnma only
)

ranking_plot(result)
league_table(result)
forest_plot(result, reference="my_pipeline")
heterogeneity_table(result)

Each function returns a matplotlib.figure.Figure.