Skip to main content

Decorator for reusable models in PyMC3

Project description

Build Status Coverage Status

sampled

Decorator for reusable models in PyMC3

Provides syntactic sugar for reusable models with PyMC3. This lets you separate creating a generative model from using the model.

Here is an example of creating a model:

import numpy as np
import pymc3 as pm
from sampled import sampled

@sampled
def linear_model(X, y):
    shape = X.shape
    X = pm.Normal('X', mu=np.mean(X, axis=0), sd=np.std(X, axis=0), shape=shape)
    coefs = pm.Normal('coefs', mu=np.zeros(shape[1]), sd=np.ones(shape[1]), shape=shape[1])
    pm.Normal('y', mu=np.dot(X, coefs), sd=np.ones(shape[0]), shape=shape[0])

Now here is how to use the model:

X = np.random.normal(size=(1000, 10))
w = np.random.normal(size=10)
y = X.dot(w) + np.random.normal(scale=0.1, size=1000)

with linear_model(X=X, y=y):
    sampled_coefs = pm.sample(draws=1000, tune=500)

np.allclose(sampled_coefs.get_values('coefs').mean(axis=0), w, atol=0.1) # True

You can also use this to build graphical networks – here is a continuous version of the STUDENT example from Koller and Friedman’s “Probabilistic Graphical Models”, chapter 3:

@sampled
def student():
    difficulty = pm.Beta('difficulty', alpha=5, beta=5)
    intelligence = pm.Beta('intelligence', alpha=5, beta=5)
    SAT = pm.Beta('SAT', alpha=20 * intelligence, beta=20 * (1 - intelligence))
    grade_avg = 0.5 + 0.5 * tt.sqrt((1 - difficulty) * intelligence)
    grade = pm.Beta('grade', alpha=20 * grade_avg, beta=20 * (1 - grade_avg))
    recommendation = pm.Binomial('recommendation', n=1, p=0.7 * grade)

Observations may be passed into any node, and we can observe how that changes posterior expectations:

# no prior knowledge
with student():
    prior = pm.sample(draws=1000, tune=500)

prior.get_values('recommendation').mean()  # 0.502

# 99th percentile SAT score --> higher chance of a recommendation
with student(SAT=0.99):
    good_sats = pm.sample(draws=1000, tune=500)

good_sats.get_values('recommendation').mean()  # 0.543

# A good grade in a hard class --> very high chance of recommendation
with student(difficulty=0.99, grade=0.99):
    hard_class_good_grade = pm.sample(draws=1000, tune=500)

hard_class_good_grade.get_values('recommendation').mean()  # 0.705

References

  • Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sampled-0.1.1.tar.gz (3.4 kB view hashes)

Uploaded source

Built Distribution

sampled-0.1.1-py2.py3-none-any.whl (4.9 kB view hashes)

Uploaded 3 6

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page