A Python toolkit for (likelihood-free) inference and the mechanization of the scientific method.
Installation
From source
git clone https://github.com/montefiore-ai/hypothesis
cd hypothesis
pip install -e .Inference
AALR-MCMC
TODO
Adversarial Variational Optimization
TODO
Amortized ratio estimation
TODO
Approximate Bayesian Computation
TODO
Approximate Bayesian Computation - Sequential Monte Carlo
TODO
Likelihood-free Inference by Ratio Estimation
TODO
Metropolis-Hastings
TODO
Benchmark problems
M/G/1
from hypothesis.benchmark.mg1 import Simulator
from hypothesis.benchmark.mg1 import Prior
simulator = Simulator()
prior = Prior()
inputs = prior.sample((10,)) # Draw 10 samples from the prior.
outputs = simulator(inputs)Biomolecular docking
✔️ Supports experimental design
from hypothesis.benchmark.biomoleculardocking import Simulator
from hypothesis.benchmark.biomoleculardocking import Prior
simulator = Simulator()
prior = Prior()
inputs = prior.sample((10,)) # Draw 10 samples from the prior.
outputs = simulator(inputs)
from hypothesis.benchmark.biomoleculardocking import PriorExperiment # Experimental design space
prior_experiment = PriorExperiment()
experimental_designs = prior_experiment.sample((10,))
outputs = simulator(inputs, experimental_designs)Stochastic Death model
✔️ Supports experimental design
from hypothesis.benchmark.death import Simulator
from hypothesis.benchmark.death import Prior
simulator = Simulator()
prior = Prior()
inputs = prior.sample((10,)) # Draw 10 samples from the prior.
outputs = simulator(inputs)
from hypothesis.benchmark.death import PriorExperiment # Experimental design space
prior_experiment = PriorExperiment()
experimental_designs = prior_experiment.sample((10,))
outputs = simulator(inputs, experimental_designs)Stochastic SIR (Susceptible-Infected-Recovered) model
✔️ Supports experimental design
from hypothesis.benchmark.sir import Simulator
from hypothesis.benchmark.sir import Prior
simulator = Simulator()
prior = Prior()
inputs = prior.sample((10,)) # Draw 10 samples from the prior.
outputs = simulator(inputs)
from hypothesis.benchmark.sir import PriorExperiment # Experimental design space
prior_experiment = PriorExperiment()
experimental_designs = prior_experiment.sample((10,))
outputs = simulator(inputs, experimental_designs)Stochastic Spatial SIR (Susceptible-Infected-Recovered) model
✔️ Supports experimental design
from hypothesis.benchmark.spatialsir import Simulator
from hypothesis.benchmark.spatialsir import Prior
simulator = Simulator()
prior = Prior()
inputs = prior.sample((10,)) # Draw 10 samples from the prior.
outputs = simulator(inputs)
from hypothesis.benchmark.spatialsir import PriorExperiment # Experimental design space
prior_experiment = PriorExperiment()
experimental_designs = prior_experiment.sample((10,))
outputs = simulator(inputs, experimental_designs)Tractable
from hypothesis.benchmark.tractable import Simulator
from hypothesis.benchmark.tractable import Prior
simulator = Simulator()
prior = Prior()
inputs = prior.sample((10,)) # Draw 10 samples from the prior.
outputs = simulator(inputs)Weinberg
✔️ Supports experimental design
from hypothesis.benchmark.weinberg import Simulator
from hypothesis.benchmark.weinberg import Prior
simulator = Simulator()
prior = Prior()
inputs = prior.sample((10,)) # Draw 10 samples from the prior.
outputs = simulator(inputs)
from hypothesis.benchmark.weinberg import PriorExperiment # Experimental design space
prior_experiment = PriorExperiment()
experimental_designs = prior_experiment.sample((10,))
outputs = simulator(inputs, experimental_designs)License
Hypothesis is BSD-style licensed, as found in the LICENSE file.