Skip to main content

The eulerian parameter inference (eulerpi) returns a parameter distribution, which is consistent with the observed data by solving the inverse problem directly. In the case of a one-to-one mapping, this is the true underlying distribution.

Project description

EPI

pages-build-deployment Build & Publish Documentation Publish to PyPI pytest

flake8 black License: MIT Python PyPI

Eulerian Parameter Inference (EPI) is a powerful and novel method for inverse model parameter inference. The eulerpi package provides an implementation of the EPI algorithm, which takes observed data and a model as input and returns a parameter distribution consistent with the observed data by solving the inverse problem directly. In case the model describes a one-to-one mapping between parameters and simulation results, the inferred parameter distribution is the true underlying distribution.

Documentation

The full documentation of this software, including a detailed tutorial on how to use EPI and the api documentation, can be found under Documentation.

Features

EPI supports

  • SBML ode models
  • User provided models
  • Models with automatic differentiation using jax

Installation

The package is available on pypi and can be installed with:

pip install eulerpi

or

pip install eulerpi[sbml]

for the support of sbml models.

Make sure that you have the following C++ libraries installed

sudo apt install -y swig libblas-dev libatlas-base-dev libhdf5-dev

You can also build the library from the latest source code by following the Development Quickstart Guide.

Using the library

To use EPI, derive your model from the Model class and implement the abstract functions. Here's an example code snippet:

# my_model.py

import jax.numpy as jnp

from eulerpi.core.model import Model

class MyModel(Model):

    param_dim = N # The dimension of a parameter point
    data_dim = M # The dimension of a data point

    def forward(self, param):
        return jnp.array(...)

    def jacobian(self, param):
        return jnp.array(...)

To evaluate the model and infer the parameter distribution, call:

from eulerpi.sampling import inference

from my_model import MyModel

# This line is needed for multiprocessing in python
if __name__ == "__main__":
    central_param = np.array([0.5, -1.5, ...])
    param_limits = np.array([[0.0, 1.0], [-3.0, 0.0], ...])

    model = MyModel(central_param, param_limits)
    inference(model=model, data="my_data.csv")

The data argument can be a numpy-2d-array or a PathLike object that points to a CSV file. In the example shown above, the CSV file my_data.csv should contain the data in the following format:

datapoint_dim1, datapoint_dim2, datapoint_dim3, ..., datapoint_dimN
datapoint_dim1, datapoint_dim2, datapoint_dim3, ..., datapoint_dimN
datapoint_dim1, datapoint_dim2, datapoint_dim3, ..., datapoint_dimN
...
datapoint_dim1, datapoint_dim2, datapoint_dim3, ..., datapoint_dimN

This corresponds to a matrix with the shape nSamples x data_dim. For more available options and parameters for the inference method, please refer to the api documentation. Note that the inference can be done with grid-based methods (dense grids, sparse grids) or sampling methods (mcmc).

The results are stored in the following location:

  • ./Applications/<ModelName>/.../OverallParams.csv
  • ./Applications/<ModelName>/.../OverallSimResults.csv
  • ./Applications/<ModelName>/.../OverallDensityEvals.csv

These files contain the sampled parameters, the corresponding data points obtained from the model forward pass, and the corresponding density evaluation.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

eulerpi-0.9.1.tar.gz (57.4 kB view details)

Uploaded Source

Built Distribution

eulerpi-0.9.1-py3-none-any.whl (69.2 kB view details)

Uploaded Python 3

File details

Details for the file eulerpi-0.9.1.tar.gz.

File metadata

  • Download URL: eulerpi-0.9.1.tar.gz
  • Upload date:
  • Size: 57.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.10.14 Linux/6.5.0-1025-azure

File hashes

Hashes for eulerpi-0.9.1.tar.gz
Algorithm Hash digest
SHA256 d1d60241cb07c66af493121e39034fb75b4910b457a0956f6f21d7be9779233c
MD5 168a74bfb97088adf5cf86a84b549e67
BLAKE2b-256 86c7207cc91c67d67f8969a1f68e62d255e65d1c81be2d0741e454b44af7430a

See more details on using hashes here.

Provenance

File details

Details for the file eulerpi-0.9.1-py3-none-any.whl.

File metadata

  • Download URL: eulerpi-0.9.1-py3-none-any.whl
  • Upload date:
  • Size: 69.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.10.14 Linux/6.5.0-1025-azure

File hashes

Hashes for eulerpi-0.9.1-py3-none-any.whl
Algorithm Hash digest
SHA256 854127d49150abc71eb4e2e16a5f39c099b0eb7c8473c61a657221ad02501fd9
MD5 5d05be22ad43fe6f5a206d82dbbfe89d
BLAKE2b-256 78ed6851c42d10aca64ab4039634637715b99a13dbeed8fc57bed51be523aff6

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page