hopsy 0.1.1

A python interface for hops, the highly optimized polytope sampling toolbox.

HOPSY - Python bindings for HOPS

A python interface for HOPS - the Highly Optimized toolbox for Polytope Sampling. Built using pybind11

hopsy is the attempt to offer some of the key functionatlity of hops through a Python interface. hops is a highly template-based C++-library for Markov chain Monte Carlo sampling on convex polytopes

P = {x : Ax ≤ b},

which often arises in metabolic flux analysis.

Installation

Install using pip with

pip install hopsy

or clone this repository and pip install. Note the --recursive option which is needed for hops, eigen and pybind11 submodules:

git clone --recursive [url-to-this-repo]
cd hopsy
sudo python3 -m pip install -e .

Alternatively, the project can be built using basic CMake commands:

git clone --recursive [url-to-this-repo]
cd hopsy
mkdir build/ && cd build/
cmake ..
make 

In this case, the compiled shared library will be located in build/ and can be used within the directory.

Prerequisites for compiling from source

On Unix (Linux, OS X)

• A compiler with C++11 support
• CMake >= 3.4 or Pip 10+
• Ninja or Pip 10+
• Docker (optional, for building wheels)

License

Examples

A basic usage example is presented below. More examples can be found in tests/ directory.

import hopsy
import numpy as np

# the polytope is defined as 
#          P := {x : Ax <= b}
# thus we need to define A and b. these constraints form the simple box [0,1]^2.
A = np.array([[1, 0], [0, 1], [-1, 0], [0, -1]])
b = np.array([[1], [1], [0], [0]]);

# next we define our target distribution as an isotropic Gaussian with mean 0 and 
# identity covariance.
mu = np.zeros((2,1))
cov = np.identity(2)

model = hopsy.MultivariateGaussianModel(mu, cov)

# the complete problem is defined by the target distribution and the constrained domain, 
# defined by the above mentioned inequality
problem = hopsy.Problem(A, b, model)

# the run object contains and constructs the markov chains. in the default case, the
# Run object will have a single chain using the Hit-and-Run proposal algorithm and is
# set to produce 10,000 samples.
run = hopsy.Run(problem)

# we finally sample
run.sample()

# from the run, we can now extract the produced data
data = run.data

# the states is a list of lists of numpy.ndarrays, which can be casted to a numpy.ndarray
# which then has the shape (m,n,d), where m is the number of chains, n the number of samples
# and d the dimenion
states  = data.states