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https://github.com/bambinos/bambi

BAyesian Model-Building Interface (Bambi) in Python.
https://github.com/bambinos/bambi

bayesian-inference bayesian-statistics python regression-models statistical-analysis statistical-modeling

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BAyesian Model-Building Interface (Bambi) in Python.

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BAyesian Model-Building Interface in Python



## Overview



Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the [PyMC](https://github.com/pymc-devs/pymc) probabilistic programming framework, and is designed to make it extremely easy to fit mixed-effects models common in social sciences settings using a Bayesian approach.



## Installation



Bambi requires a working Python interpreter (3.10+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms.



Assuming a standard Python environment is installed on your machine (including pip), Bambi itself can be installed in one line using pip:



    pip install bambi



Alternatively, if you want the bleeding edge version of the package you can install from GitHub:



    pip install git+https://github.com/bambinos/bambi.git



### Dependencies



Bambi requires working versions of ArviZ, formulae, NumPy, pandas and PyMC. Dependencies are listed in `pyproject.toml` and should all be installed by the Bambi installer; no further action should be required.



## Examples



In the following two examples we assume the following basic setup



```python

import arviz as az

import bambi as bmb

import numpy as np

import pandas as pd

```



### Linear regression



A simple fixed effects model is shown in the example below.



```python

# Read in a dataset from the package content

data = bmb.load_data("sleepstudy")



# See first rows

data.head()

 

# Initialize the fixed effects only model

model = bmb.Model('Reaction ~ Days', data)



# Get model description

print(model)



# Fit the model using 1000 on each chain

results = model.fit(draws=1000)



# Key summary and diagnostic info on the model parameters

az.summary(results)



# Use ArviZ to plot the results

az.plot_trace(results)

```

``` 

   Reaction  Days  Subject

0  249.5600     0      308

1  258.7047     1      308

2  250.8006     2      308

3  321.4398     3      308

4  356.8519     4      308

```

```

       Formula: Reaction ~ Days

        Family: gaussian

          Link: mu = identity

  Observations: 180

        Priors:

    target = mu

        Common-level effects

            Intercept ~ Normal(mu: 298.5079, sigma: 261.0092)

            Days ~ Normal(mu: 0.0, sigma: 48.8915)



        Auxiliary parameters

            sigma ~ HalfStudentT(nu: 4.0, sigma: 56.1721)

```

```

                   mean     sd   hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  r_hat

Intercept       251.552  6.658  238.513  263.417      0.083    0.059    6491.0    2933.0    1.0

Days             10.437  1.243    8.179   12.793      0.015    0.011    6674.0    3242.0    1.0

Reaction_sigma   47.949  2.550   43.363   52.704      0.035    0.025    5614.0    2974.0    1.0

```



First, we create and build a Bambi `Model`. Then, the method `model.fit()` tells the sampler to start

running and it returns an `InferenceData` object, which can be passed to several ArviZ functions

such as `az.summary()` to get a summary of the parameters distribution and sample diagnostics or

`az.plot_trace()` to visualize them.



### Logistic regression



In this example we will use a simulated dataset created as shown below.



```python

data = pd.DataFrame({

    "g": np.random.choice(["Yes", "No"], size=50),

    "x1": np.random.normal(size=50),

    "x2": np.random.normal(size=50)

})

```



Here we just add the `family` argument set to `"bernoulli"` to tell Bambi we are modelling a binary

response. By default, it uses a logit link. We can also use some syntax sugar to specify which event

we want to model. We just say `g['Yes']` and Bambi will understand we want to model the probability

of a `"Yes"` response. But this notation is not mandatory. If we use `"g ~ x1 + x2"`, Bambi will

pick one of the events to model and will inform us which one it picked.



```python

model = bmb.Model("g['Yes'] ~ x1 + x2", data, family="bernoulli")

fitted = model.fit()

```



After this, we can evaluate the model as before. 



### More



For a more in-depth introduction to Bambi see our [Quickstart](https://github.com/bambinos/bambi#quickstart) and check the notebooks in the [Examples](https://bambinos.github.io/bambi/notebooks/) webpage.



## Documentation



The Bambi documentation can be found in the [official docs](https://bambinos.github.io/bambi/index.html)



## Citation



If you use Bambi and want to cite it please use



```bibtex

@article{Capretto2022,

 title={Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python},

 volume={103},

 url={https://www.jstatsoft.org/index.php/jss/article/view/v103i15},

 doi={10.18637/jss.v103.i15},

 number={15},

 journal={Journal of Statistical Software},

 author={Capretto, Tomás and Piho, Camen and Kumar, Ravin and Westfall, Jacob and Yarkoni, Tal and Martin, Osvaldo A},

 year={2022},

 pages={1–29}

}

```



## Contributions



Bambi is a community project and welcomes contributions. Additional information can be found in the [Contributing](https://github.com/bambinos/bambi/blob/main/CONTRIBUTING.md) Readme.



For a list of contributors see the [GitHub contributor](https://github.com/bambinos/bambi/graphs/contributors) page



## Donations



If you want to support Bambi financially, you can [make a donation](https://numfocus.org/donate-to-pymc) to our sister project PyMC.



## Code of Conduct



Bambi wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/bambinos/bambi/blob/main/CODE_OF_CONDUCT.md)



## License



[MIT License](https://github.com/bambinos/bambi/blob/main/LICENSE)