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https://github.com/hammerlab/stancache

Filecache for stan models
https://github.com/hammerlab/stancache

filecache mcmc reproducible-research stan-model

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Filecache for stan models

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stancache

===============================



author: Jacqueline Buros Novik



Overview

--------



Filecache for stan models



Installation

--------------------



You can install this package from pypi using pip:



    $ pip install stancache



Or clone the repo & run setup.py:



    $ git clone https://github.com/hammerlab/stancache.git

    $ python setup.py install



Introduction

------------



This is a filecache for [pystan](https://pystan.readthedocs.io/en/latest/) models fit to data. Each pystan model fit to data is comprised of two parts - the compiled model code & the result of MCMC sampling of that model given data. Both model compilation & model sampling can be time-consuming operations, so both are cached as separate [pickled](https://docs.python.org/3/library/pickle.html) objects on the filesystem. 



This separation allows one to (for example) compile a model once & execute the model several times - caching the result each time. You might be testing the model on different samples of data, or using different initializations or passing in different parameters.



Loading pickled pystan.fit objects into memory is also safer using `cached_stan_fit()` since this will ensure that the compiled model is first unpickled before the fit model.



Getting started

---------------



### Configuration



The configuration uses python's [configparser](https://docs.python.org/2/library/configparser.html) module, allowing the user to either load a `config.ini` file from disk or set the configuration in code.



`stancache` looks for a default config file to be located in `'~/.stancache.ini'`. You can modify this using `stancache.config.load_config('/another/config/file.ini')`. 



Currently, the config settings include



* `CACHE_DIR` (defaults to `.cached_models`)

* `SEED` (seed value passed to `pystan.stan` for reproducible research)

* `SET_SEED` (boolean, whether to set the random.seed systemwide in addition to stan_seed)



You can use `config.set_value(NAME=value)` to modify a setting.



For example, you might want to set up a shared-nfs-mount containing fitted models among your collaborators:



```python

from stancache import config

config.set_value(CACHE_DIR='/mnt/trial-analyses/cohort1/stancache')

```



An updated list of configuration defaults is available in [defaults.py](https://github.com/hammerlab/stancache/blob/master/stancache/defaults.py)



### Fitting cached models



Once you have configured your settings, you would then use `stancache.cached_stan_fit` to fit your model, like so:



```python

from stancache import stancache

fit1 = stancache.cached_stan_fit(file = '/path/to/model.stan', data=dict(), chains=4, iter=100)

```



The options to `cached_stan_fit` are the same as those to `pystan.stan` (see [pystan.stan documentation](https://pystan.readthedocs.io/en/latest/api.html#pystan.stan)).



Also see `?stancache.cached_stan_fit` for more details.



### Caching other items



The caching is very sensitive to certain things which would change the returned object, such as the sort order of your data elements within the dictionary. But is not sensitive to other things, such as whether you use a file-based stan code or string-based version of same code. 



In practice, we find that it can be helpful to cache data-preparation steps, especially when simulating data. There is thus a `stancache.cached()` wrapper function for this purpose,. This will save or cache all objects _other_ than `pystan.stan` objects to disk using the same file-cache settings as are used for stancache.



### Avoiding re-executing a model



There are a number of scenarios where you might want to use a cache of fitted models in read-only mode. You can avoid accidentally re-fitting the model by setting `cache_only=True`.



For example, you may have fit a set of models which you want to read into a jupyter notebook for model exploration. Or, you may be reviewing a colleague's fitted model objects. Note that this is foolproof so please back up your work. 



Contributing

------------



TBD



Examples

--------



For example (borrowing from [pystan's docs](https://pystan.readthedocs.io/en/latest/getting_started.html)):



```python

import stancache



schools_code = """

data {

    int J; // number of schools

    real y[J]; // estimated treatment effects

    real sigma[J]; // s.e. of effect estimates

}

parameters {

    real mu;

    real tau;

    real eta[J];

}

transformed parameters {

    real theta[J];

    for (j in 1:J)

    theta[j] <- mu + tau * eta[j];

}

model {

    eta ~ normal(0, 1);

    y ~ normal(theta, sigma);

}

"""



schools_dat = {'J': 8,

               'y': [28,  8, -3,  7, -1,  1, 18, 12],

               'sigma': [15, 10, 16, 11,  9, 11, 10, 18]}



# fit model to data

fit = stancache.cached_stan_fit(model_code=schools_code, data=schools_dat,

                                iter=1000, chains=4)



# load fit model from cache

fit2 = stancache.cached_stan_fit(model_code=schools_code, data=schools_dat,

                                 iter=1000, chains=4)

```



In addition, there are a number of publicly-accessible ipynbs using [stancache](http://github.com/hammerlab/stancache). 



These include:



* [survivalstan-examples](http://github.com/jburos/survivalstan-examples)

* [immune-infiltrate-explorations](http://github.com/hammerlab/immune-infiltrate-explorations)

    - e.g. [model-single-origin-samples/0.830 model3 by cell_type (n=500).ipynb](http://nbviewer.jupyter.org/github/hammerlab/immune-infiltrate-explorations/blob/master/model-single-origin-samples/0.830%20model3%20by%20cell_type%20%28n%3D500%29.ipynb)

    

If you know of other examples, please let us know and we will add them to this list.