sampy

sampy is a python package built to be a simple API for statistical distributions. The goal is to provide analysts with a simple tool to model data and sample known or empirical distributions.

Installation

sampy is distributed on PyPi and can be installed with pip.

pip install sampy

Generic API

Every distribution method in sampy initializes with the only the most immediate parameters for the distribution and a seed. Take the example of the normal distribution below.

# standard normal distribution
normal = sampy.Normal(center=0, scale=1)

# Poisson process
poisson = sampy.Poisson(rate=1)

# coin toss distribution
coin = sampy.Binomial(n_trials=1, bias=0.5)

# rolling dice distribution
die = sampy.DiscreteUniform(low=1, high=6, high_inclusive=True)

Setting Seeds

sampy sets multiple methods for intializing the random number generator state. The RNG state is built from the RandomState object from numpy and as such can take integer values to explicitly set the seed. The random state is set from within distribution classes, however we'll illustrate this with the underlying utility function.

# seed set by system clock
sampy.utils.set_random_state(None)

# seed set by integer
sampy.utils.set_random_state(42)

One feature added is the ability to safely hash strings, so that seeds can be set as helpful comments or descriptions.

dist = sampy.Binomial(1, 0.5, seed='docs example of sampy seeds')
dist.seed
>>> 'docs example of sampy seeds'

Fitting Distribution Models

sampy provides a scikit-learn api style that allows users to fit data as well as incrementally update parameters via the partial_fit method.

# sample from true distribution
true = sampy.Normal(0, 1, seed='sampy docs examples for model fit')
X = true.sample(10)

# train model
model = sampy.Normal().fit(X)
model
>>> Normal(center=-0.19680447372083085, scale=0.8069214766303398)

This shows that the method of moments implementation decently in limited data, however 10 data points is very limited and in real engineering systems data can be streamed. A model should be able to update as new data is introduced.

# continue fitting data
for x in true.sample(100, 10):
    model.partial_fit(x)
model
>>> Normal(center=-0.0588523276136086, scale=0.9130479685560422)

To simplify the API further, we have also added the class method from_data to quickly intialize a new model distribution without the extra call initialization.

model = sampy.Normal.from_data(X, seed='Example of from_data class method')

Note: Both from_data and fit are identical in implementation that they clear the distribution of any pre-defined parameters. The from_data method can be beneficial in cases when parameters have no prior estimate.