epi

EPI is a python package for inverse parameter inference

https://github.com/systems-theory-in-systems-biology/epi

Science Score: 44.0%

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Low similarity (17.4%) to scientific vocabulary

Keywords

data-science inverse-problems simulation

Scientific Fields

Artificial Intelligence and Machine Learning Computer Science - 83% confidence

Last synced: 6 months ago · JSON representation ·

Repository

EPI is a python package for inverse parameter inference

Basic Info

Host: GitHub
Owner: Systems-Theory-in-Systems-Biology
License: other
Language: Python
Default Branch: main
Homepage: https://systems-theory-in-systems-biology.github.io/EPI/
Size: 11.7 MB

Statistics

Stars: 13
Watchers: 0
Forks: 1
Open Issues: 14
Releases: 0

Topics

data-science inverse-problems simulation

Created over 3 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Citation

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:

bash pip install eulerpi

bash pip install eulerpi[sbml]

for the support of sbml models.

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

bash 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 BaseModel class and implement the abstract functions. Here's an example code snippet:

```python

my_model.py

import jax.numpy as jnp

from eulerpi.core.models import BaseModel

class MyModel(BaseModel):

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:

```python from eulerpi.sampling import inference

from my_model import MyModel

This line is needed for multiprocessing in python

if name == "main": centralparam = np.array([0.5, -1.5, ...]) paramlimits = 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:

text 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.

Owner

Name: Systems-Theory-in-Systems-Biology
Login: Systems-Theory-in-Systems-Biology
Kind: organization

Repositories: 3
Profile: https://github.com/Systems-Theory-in-Systems-Biology

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Wagner"
  given-names: "Vincent"
  orcid: "https://orcid.org/0000-0003-2848-7810"
- family-names: "Castellaz"
  given-names: "Benjamin"
  orcid: "https://orcid.org/0009-0006-9526-8422"
- family-names: "Kaiser"
  given-names: "Lars"
- family-names: "Höpfl"
  given-names: "Sebastian"
  orcid: "https://orcid.org/0000-0002-5300-0915"
- family-names: "Radde"
  given-names: "Nicole E."
  orcid: "https://orcid.org/0000-0002-5145-0058"

title: "Eulerian Parameter Inference: A Probabilistic Change of Variables for Model-Based Inference with High-Variability Data Sets"

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Top Authors

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TheVincentWagner (22)
castellinilinguini (14)
kaiserls (13)
shoepfl (2)

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kaiserls (51)
dependabot[bot] (30)
castellinilinguini (16)
TheVincentWagner (6)
tjwsch (1)
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Packages

Total packages: 1
Total downloads:
- pypi 21 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 16
Total maintainers: 1

pypi.org: eulerpi

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.

Homepage: https://github.com/Systems-Theory-in-Systems-Biology/EPI
Documentation: https://Systems-Theory-in-Systems-Biology.github.io/EPI/
License: MIT License
Latest release: 0.11.0
published 7 months ago

Versions: 16
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 21 Last month

Rankings

Dependent packages count: 7.0%

Stargazers count: 21.8%

Average: 22.5%

Dependent repos count: 30.4%

Forks count: 30.6%

Maintainers (1)

TheVincentWagner