pharmacoml 0.1.1

ML-Enhanced Pharmacometrics Toolkit

pharmacoml

ML-Enhanced Pharmacometrics Toolkit for Python

pharmacoml brings machine learning methods to pharmacometrics workflows — usable with or without NONMEM.

Why pharmacoml?

Existing ML tools for pharmacometrics (pyDarwin, shap-cov) require a NONMEM license. pharmacoml is estimation-tool agnostic — it works with empirical Bayes estimates from NONMEM, nlmixr2, Monolix, Pumas, or manual estimation.

Quick Start

The recommended default workflow is the hybrid preselection path: explainable boosting with benchmark-approved recursive elimination for discovery, augmented adaptive LASSO for confirmation, optional stochastic-gates and symbolic-structure confirmation, and tiered output for downstream SCM/backward elimination.

from pharmacoml.covselect import HybridScreener
import pandas as pd

ebes = pd.read_csv("individual_parameters.csv")
covs = pd.read_csv("covariates.csv")

report = HybridScreener(include_scm=True).fit(ebes, covs)

report.confirmed_covariates()  # recommended daily-use answer
report.core_covariates()       # strongest ML signals
report.candidate_covariates()  # broader shortlist for exploration
report.scm_covariates()        # explicit SCM-confirmed set
report.proxy_groups()
print(report.to_nonmem_candidates())

Experimental Consensus

For broader research comparisons, the experimental namespace exposes a curated multi-model consensus workflow. It runs a scikit-first model set by default, aggregates top-k covariate frequency across model families, and lets you compare that consensus against the main hybrid workflow.

from pharmacoml.covselect.experimental import MultiModelConsensusScreener

report = MultiModelConsensusScreener(
    top_k=3,
    n_bootstrap=8,
    include_neural=False,
).fit(ebes, covs)

report.consensus_covariates()
report.selection_frequency_table()

Benchmark-Gated Development

pharmacoml now carries a release benchmark suite for default-calibration work:

• pheno (Pharmpy phenobarbital example)
• Eleveld/Wahlquist public propofol data
• ggPMX Monolix theophylline example
• Asiimwe-style correlated-covariate simulation
• Shap-Cov-style collinear simulation
• optional Kekic public synthetic scenarios when the repo is available locally

The benchmark harness compares the default hybrid workflow against optional variants such as RFE and shrinkage-awareness. The current benchmark-approved defaults are rfe_enabled=True and shrinkage_awareness=True.

PYTHONPATH=. python benchmarks/run_public_benchmarks.py --check

By default, that command now writes a reusable report bundle to benchmarks/reports/fixed_public/:

• public_benchmark_report.md
• public_benchmark_summary.csv
• public_benchmark_details.csv
• public_benchmark_report.json

Use --no-report to skip artifact generation, or --report-dir <path> to write the bundle somewhere else.

Optional Advanced Backends

pharmacoml now includes:

• a nonlinear stochastic-gates engine (STG) with an input-gated MLP body
• symbolic covariate structure search with three backends:
  • basis (default, lightweight pharmacometric basis search)
  • gplearn (optional true symbolic regression)
  • pysr (optional true symbolic regression; install manually)

Example:

report = HybridScreener(
    include_symbolic=True,
    symbolic_backend="basis",
    include_stg=True,
).fit(ebes, covs)

Install optional extras:

pip install -e ".[dev,dl,symbolic]"

Installation

git clone https://github.com/s-rani1/pharmacoml.git
cd pharmacoml
pip install -e ".[dev]"

Docs

Static docs pages live in docs/ and can be used directly on GitHub Pages:

• docs/index.html
• docs/tutorial.html
• docs/benchmarks.html

Modules

Module	Description	Status
pharmacoml.covselect	Hybrid ML-assisted covariate screening with SCM bridge	🚧 Active
pharmacoml.expose	Exposure-response with interpretable ML	📋 Planned
pharmacoml.trajpk	PK/PD trajectory clustering	📋 Planned

License

MIT