TensorProb is a probabalistic graphical modeling framework based on TensorFlow.
It's a Python library that allows you to construct complex multi-dimensional probability distributions from basic building blocks and to infer their parameters from data.
This is an example for fitting a normal distribution to data:
import numpy as np
from tensorprob import Model, Parameter, Normal
# Define the model
with Model() as model:
mu = Parameter()
sigma = Parameter(lower=0)
X = Normal(mu, sigma)
# Declare variables for which we have data
model.observed(X)
# Set the initial values
model.assign({
mu: 10,
sigma: 10,
})
# Create dataset with Numpy
data = np.random.normal(0, 1, 1000)
# Perform the fit
model.fit(data)The fitted distribution can be visualized with model.pdf
import matplotlib.pyplot as plt
xs = np.linspace(-5, 5, 200)
plt.hist(data, bins=20, histtype='step', color='k', normed=True)
plt.plot(xs, model.pdf(xs), 'b-')
The posterior distribution (or likelihood function) are constructed and evaluated using TensorFlow, which means you can make use of multiple CPU cores and GPUs simultaneously. This also makes it easy to add new custom probability distributions, or to debug your model if it's not doing what you expect.
TensorProb can be used for a variety of inference tasks. For example, we can use it to fit a line assuming that the data points are distributed according to a normal distribution in the vertical direction. This is equivalent to a least squares fit.
The model can be expressed as
from tensorprob import Model, Parameter, Normal
with Model() as model:
a = Parameter()
b = Parameter()
sigma = Parameter(lower=0)
X = Parameter()
y = Normal(a * X + b, sigma)
model.observed(X, y)
model.assign({
a: 2,
b: 2,
sigma: 10,
})
results = model.fit(xs, ys)
print(results)
import numpy as np
xs = np.linspace(0, 1, 100)
ys = 1 * xs + 0 + np.random.normal(0, .1, len(xs))
import matplotlib.pyplot as plt
plt.plot(xs, ys, 'ro')
x_ = np.linspace(0, 1, 200)
plt.plot(x_, model.state[a] * x_ + model.state[b], 'b-')
We happily accept contributions to the project!
Please have a look at CONTRIBUTING.md for instructions and guidelines for contributing.