posteriordb 0.2.0

Python versions

Currently only python 3.6+ is supported. Python 3.5+ support can be added if needed. We don't plan to support python 2.

Installation

Installation from PyPI is recommended.

pip install posteriordb

Installing from the local clone.

git clone https://github.com/MansMeg/posteriordb
cd posteriordb
pip install python/

Using the posterior database from python

The included database contains convenience functions to access data, model code and information for individual posteriors.

First we create the posterior database to use, here the cloned posterior database.

>>> from posteriordb import PosteriorDatabase
>>> import os
>>> pdb_path = os.path.join(os.getcwd(), "posterior_database")
>>> my_pdb = PosteriorDatabase(pdb_path)

The above code requires that your working directory is in the main folder of your copy of this project. Alternatively, you can specify the path to the folder directly.

Online database can be used with the PosteriorDatabaseGithub class. Remember to create and set GITHUB_PAT environmental variable. It's recommended that users create a read-only (no extra permissions) GitHub Personal Access Token (PAT) for posteriordb use. It's also recommended that the GITHUB_PAT environmental variable is added to user environmental variables and it is not shown in Python script as in the example below.

If not explicitly defined, PosteriorDatabase and PosteriorDatabaseGithub will create a new (or use old database) located at POSTERIOR_DB_PATH if it's defined. PosteriorDatabaseGithub will finally use $HOME/.posteriordb/posterior_database as a fallback location if no environmental variables have been set. Each model and data is only downloaded and cached when needed.

>>> from posteriordb import PosteriorDatabaseGithub
>>> import os
>>> # It is recommended that GITHUB_PAT is added to the user environmental variables
>>> # outside python and not in a python script as shown in this example code
>>> os.environ["GITHUB_PAT"] = "token-string-here"
>>> my_pdb = PosteriorDatabaseGithub()

To list the posteriors available, use posterior_names.

>>> pos = my_pdb.posterior_names()
>>> pos[:5]

['roaches-roaches_negbin',
 'syn_gmK2D1n200-gmm_diagonal_nonordered',
 'radon_mn-radon_variable_intercept_centered',
 'syn_gmK3D2n300-gmm_nonordered',
 'radon-radon_hierarchical_intercept_centered']

In the same fashion, we can list data and models included in the database as

>>> mn = my_pdb.model_names()
>>> mn[:5]

['gmm_diagonal_nonordered',
 'radon_pool',
 'radon_partial_pool_noncentered',
 'blr',
 'radon_hierarchical_intercept_noncentered']


>>> dn = my_pdb.dataset_names()
>>> dn[:5]

['radon_mn',
 'wells_centered',
 'radon',
 'wells_centered_educ4_interact',
 'wells_centered_educ4']

The posterior's name is made up of the data and model fitted to the data. Together, these two uniquely define a posterior distribution. To access a posterior object we can use the posterior name.

>>> posterior = my_pdb.posterior("eight_schools-eight_schools_centered")

From the posterior we can access the dataset and the model

>>> model = posterior.model
>>> data = posterior.data

We can also access the names of posteriors, models and datasets.

>>> posterior.name
"eight_schools-eight_schools_centered"

>>> model.name
"eight_schools_centered"

>>> data.name
"eight_schools"

We can access the same model and dataset also directly from the posterior database

>>> model = my_pdb.model("eight_schools_centered")
>>> data = my_pdb.data("eight_schools")

From the model we can access model code and information about the model

>>> model.code("stan")
data {
  int <lower=0> J; // number of schools
  real y[J]; // estimated treatment
  real<lower=0> sigma[J]; // std of estimated effect
}
parameters {
  real theta[J]; // treatment effect in school j
  real mu; // hyper-parameter of mean
  real<lower=0> tau; // hyper-parameter of sdv
}
model {
  tau ~ cauchy(0, 5); // a non-informative prior
  theta ~ normal(mu, tau);
  y ~ normal(theta , sigma);
  mu ~ normal(0, 5);
}

>>> model.code_file_path("stan")
'/home/eero/posterior_database/content/models/stan/eight_schools_centered.stan'

>>> model.information
{'keywords': ['bda3_example', 'hiearchical'],
 'description': 'A centered hiearchical model for the 8 schools example of Rubin (1981)',
 'urls': ['http://www.stat.columbia.edu/~gelman/arm/examples/schools'],
 'title': 'A centered hiearchical model for 8 schools',
 'references': ['rubin1981estimation', 'gelman2013bayesian'],
 'added_by': 'Mans Magnusson',
 'added_date': '2019-08-12'}

Note that the references are referencing to BibTeX items that can be found in content/references/references.bib.

From the dataset we can access the data values and information about it

>>> data.values()
{'J': 8,
 'y': [28, 8, -3, 7, -1, 1, 18, 12],
 'sigma': [15, 10, 16, 11, 9, 11, 10, 18]}

>>> data.file_path()
'/tmp/tmpx16edu0w'

>>> data.information
{'keywords': ['bda3_example'],
 'description': 'A study for the Educational Testing Service to analyze the effects of\nspecial coaching programs on test scores. See Gelman et. al. (2014), Section 5.5 for details.',
 'urls': ['http://www.stat.columbia.edu/~gelman/arm/examples/schools'],
 'title': 'The 8 schools dataset of Rubin (1981)',
 'references': ['rubin1981estimation', 'gelman2013bayesian'],
 'added_by': 'Mans Magnusson',
 'added_date': '2019-08-12'}

To access gold standard posterior draws we can use reference_draws as follows.

>>> posterior.reference_draws_info()
{'name': 'eight_schools-eight_schools_noncentered',
 'inference': {'method': 'stan_sampling',
  'method_arguments': {'chains': 10,
   'iter': 20000,
   'warmup': 10000,
   'thin': 10,
   'seed': 4711,
   'control': {'adapt_delta': 0.95}}},
 'diagnostics': {'diagnostic_information': {'names': ['mu',
    'tau',
    'theta[1]',
    ...

>>> gs = posterior.reference_draws()
>>> import pandas as pd
>>> pd.DataFrame(gs)

	theta[1]	                                        theta[2]
0	[10.6802773011458, 6.45383910854259, -2.241629...	[9.71770681295263, 4.41030824418493, 0.7617047...
1	[5.70891361633589, 10.3012059848039, 4.2439533...	[-2.32310565394337, 14.8121789773659, 6.517256...
2	[7.23747096507585, -0.427831558524343, 9.14782...	[7.35425759420389, 8.69579738064637, 8.9058764...
3	[4.44915522912766, 2.34711393762556, 17.680378...	[2.4368039319606, 5.89809320808632, 8.63031558...
...