README.md

metadata

language:
  - en
license: apache-2.0
library_name: generative-pk
datasets:
  - simulated
metrics:
  - rmse
  - npde
tags:
  - generative
  - predictive

Hierarchical Neural Process for Pharmacokinetic Data

Overview

An Amortized Context Neural Process Generative model for Pharmacokinetic Modelling

Model details:

• Authors: César Ojeda (@cesarali)
• License: Apache 2.0

Intended use

Sample Drug Concentration Behavior and Sample and Prediction of New Points or new Individual

Runtime Bundle

This repository is the consumer-facing runtime bundle for this PK model.

• Runtime repo: cesarali/AICME-runtime
• Native training/artifact repo: cesarali/AICMEPK_cluster
• Supported tasks: generate, predict
• Default task: generate
• Load path: AutoModel.from_pretrained(..., trust_remote_code=True)

Installation

You do not need to install sim_priors_pk to use this runtime bundle.

transformers is the public loading entrypoint, but transformers alone is not sufficient because this is a PyTorch model with custom runtime code. A reliable consumer environment is:

pip install torch transformers huggingface_hub lightning datasets pandas torchtyping gpytorch pot torchdiffeq torchsde ruamel.yaml pyyaml

Python Usage

from transformers import AutoModel

model = AutoModel.from_pretrained("cesarali/AICME-runtime", trust_remote_code=True)

studies = [
    {
        "context": [
            {
                "name_id": "ctx_0",
                "observations": [0.2, 0.5, 0.3],
                "observation_times": [0.5, 1.0, 2.0],
                "dosing": [1.0],
                "dosing_type": ["oral"],
                "dosing_times": [0.0],
                "dosing_name": ["oral"],
            }
        ],
        "target": [],
        "meta_data": {"study_name": "demo", "substance_name": "drug_x"},
    }
]

outputs = model.run_task(
    task="generate",
    studies=studies,
    num_samples=4,
)
print(outputs["results"][0]["samples"])

Predictive Sampling

from transformers import AutoModel

model = AutoModel.from_pretrained("cesarali/AICME-runtime", trust_remote_code=True)

predict_studies = [
    {
        "context": [
            {
                "name_id": "ctx_0",
                "observations": [0.2, 0.5, 0.3],
                "observation_times": [0.5, 1.0, 2.0],
                "dosing": [1.0],
                "dosing_type": ["oral"],
                "dosing_times": [0.0],
                "dosing_name": ["oral"],
            }
        ],
        "target": [
            {
                "name_id": "tgt_0",
                "observations": [0.25, 0.31],
                "observation_times": [0.5, 1.0],
                "remaining": [0.0, 0.0, 0.0],
                "remaining_times": [2.0, 4.0, 8.0],
                "dosing": [1.0],
                "dosing_type": ["oral"],
                "dosing_times": [0.0],
                "dosing_name": ["oral"],
            }
        ],
        "meta_data": {"study_name": "demo", "substance_name": "drug_x"},
    }
]

outputs = model.run_task(
    task="predict",
    studies=predict_studies,
    num_samples=4,
)
print(outputs["results"][0]["samples"][0]["target"][0]["prediction_samples"])

Notes

• trust_remote_code=True is required because this model uses custom Hugging Face Hub runtime code.
• The consumer API is transformers + run_task(...); the consumer does not need a local clone of this repository.
• This runtime bundle is intentionally separate from the native training export so you can evaluate both distribution paths in parallel.