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eulerpi-0.3.1
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
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.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | eulerpi-0.3.1 |
| نام | eulerpi |
| نسخه کتابخانه | 0.3.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lars Kaiser |
| ایمیل نویسنده | lars.g.kaiser@gmx.de |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/eulerpi/ |
| مجوز | - |
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Euler Parameter Inference (EPI) is a Python package for inverse parameter inference. It 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 the case of a one-to-one mapping, this is the true underlying distribution.
## Documentation
The full documentation to this software, including a detailed tutorial on how to use EPI and the api documentation, can be found under [Documentation](https://Systems-Theory-in-Systems-Biology.github.io/EPI/).
## Features
EPI supports
- SBML ode models
- User provided models
- Models with automatic differentation using jax
## Installation
The package is available on pypi and can be installed with:
```bash
pip install eulerpi
```
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](./DEVELOPMENT.md#quickstart).
## Using the library
To use EPI, derive your model from the `Model` class and implement the abstract functions. Here's an example code snippet:
```python
# 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:
```python
from eulerpi.sampling import inference
from my_model import MyModel
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:
```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](https://systems-theory-in-systems-biology.github.io/EPI/eulerpi.core.html#module-eulerpi.core.inference). 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.
نیازمندی
| مقدار | نام |
|---|---|
| >=0.4.8,<0.5.0 | jax |
| >=1.24.2,<2.0.0 | numpy |
| >=3.7.1,<4.0.0 | matplotlib |
| >=0.3.1,<0.4.0 | diffrax |
| >=3.1.4,<4.0.0 | emcee |
| >=0.4.7,<0.5.0 | jaxlib |
| >=4.65.0,<5.0.0 | tqdm |
| >=0.4.2,<0.5.0 | seedir |
| >=0.2.14,<0.3.0 | yfinance |
| >=0.16.1,<0.17.0 | amici |
| >=1.2.2,<2.0.0 | scikit-learn |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8.1,<4.0.0 | Python |
نحوه نصب
نصب پکیج whl eulerpi-0.3.1:
pip install eulerpi-0.3.1.whl
نصب پکیج tar.gz eulerpi-0.3.1:
pip install eulerpi-0.3.1.tar.gz