InferPy is a high-level API for probabilistic modeling written in Python and capable of running on top of Edward and Tensorflow. InferPy's API is strongly inspired by Keras and it has a focus on enabling flexible data processing, easy-to-code probablistic modeling, scalable inference and robust model validation.

Use InferPy is you need a probabilistic programming language that:

• Allows easy and fast prototyping of hierarchical probabilistic models with a simple and user friendly API inspired by Keras.
• Defines probabilistic models with complex probabilistic constructs containing deep neural networks.
• Automatically creates computational efficient batched models without the need to deal with complex tensor operations.
• Run seamlessly on CPU and GPU by relying on Tensorflow.

InferPy's aim is to be to Edward what Keras is to Tensorflow. Edward is a general purpose probabilistic programing language, like Tensorflow is a general computational engine. But this generality comes a at price. Edward's API is verbose and is based on distributions over Tensor objects, which are n-dimensional arrays with complex semantics operations. Probability distributions over Tensors are powerful abstractions but it is not easy to operate with them. InferPy's API is no so general like Edward's API but still covers a wide range of powerful and widely used probabilistic models, which can contain complex probability constructs containing deep neural networks.

Install InferPy from PyPI:

$ python -m pip install inferpy

The core data structures of InferPy is a probabilistic model, defined as a set of random variables with a conditional dependency structure. A random varible is an object parameterized by a set of Numpy's arrays.

Let's look at a simple (Bayesian) probabilistic component analysis model. Graphically the model can be defined as follows,

We start defining the prior of the global parameters,

import inferpy as inf
from inferpy.models import Normal

# K defines the number of components.
K=10

# d defines the number of dimensions
d=20

#Prior for the principal components
with inf.replicate(size = K):
    w = Normal(loc = 0, scale = 1, dim = d)  # x.shape = [K,d]

# Number of observations (data)
N = 1000

# define the generative model
with inf.replicate(size=N):
    z = Normal(0, 1, dim=K)  # z.shape = [N,K]
    x = Normal(inf.matmul(z,w), 1.0, observed=True, dim=d)  # x.shape = [N,d]

InferPy supports the definition of plateau notation by using the construct with inf.replicate(size = K), which replicates K times the random variables enclosed within this anotator. Every replicated variable is assumed to be independent. This with inf.replicate(size = N) construct is also useful when defining the model for the data.

As commented above, the variables are surrounded by a with statement to inidicate that the defined random variables will be reapeatedly used in each data sample. In this case, every replicated variable is conditionally idependent given the variable w defined above.

Once the random variables of the model are defined, the probablitic model itself can be created and compiled. The probabilistic model defines a joint probability distribuiton over all these random variables. Finally, you can fit your model with a given data set:

# compile and fit the model with training data
pca.compile()
pca.fit(data)

#extract the hidden representation from a set of observations
hidden_encoding = pca.posterior(z)

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