https://github.com/ibab/python-mle

A Python package for performing Maximum Likelihood Estimates
https://github.com/ibab/python-mle

Last synced: 2 days ago
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A Python package for performing Maximum Likelihood Estimates

• Host: GitHub
• URL: https://github.com/ibab/python-mle
• Owner: ibab
• License: mit
• Created: 2015-01-26T22:50:59.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2020-05-03T18:27:20.000Z (over 4 years ago)
• Last Synced: 2024-12-24T17:58:34.875Z (5 days ago)
• Language: Python
• Homepage:
• Size: 99.6 KB
• Stars: 126
• Watchers: 13
• Forks: 31
• Open Issues: 24
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# python-mle

[![Build Status](https://travis-ci.org/ibab/python-mle.svg?branch=master)](https://travis-ci.org/ibab/python-mle)

A Python package for performing Maximum Likelihood Estimates.

Inspired by [RooFit](https://root.cern.ch/drupal/content/roofit) and [pymc](https://github.com/pymc-devs/pymc).

mle is a Python framework for constructing probability models and estimating their parameters from data using the [Maximum Likelihood](http://en.wikipedia.org/wiki/Maximum_likelihood) approach.

While being less flexible than a full Bayesian probabilistic modeling framework, it can handle larger datasets (> 10^6 entries) and more complex statistical models.

To achieve maximum performance, this package (like pymc) uses [Theano](http://deeplearning.net/software/theano/tutorial/) to optimize and compile statistical models.

This also means that models can automatically be evaluated using multiple CPU cores or GPUs.

Derivatives used for the likelihood optimization are calculated using automatic differentiation.

Currently, the package is only a basic prototype and will change heavily in the future.

## Example

```python

import numpy as np

from mle import *

# Define model

x = var('x', observed=True, vector=True)

y = var('y', observed=True, vector=True)

a = var('a')

b = var('b')

sigma = var('sigma')

model = Normal(y, a * x + b, sigma)

# Generate data

xs = np.linspace(0, 2, 20)

ys = 0.5 * xs + 0.3 + np.random.normal(0, 0.1, 20)

# Fit model to data

result = model.fit({'x': xs, 'y': ys}, {'a': 1, 'b': 1, 'sigma': 1})

print(result)

```

```

Optimization terminated successfully.

         Current function value: -21.632165

         Iterations: 25

         Function evaluations: 38

         Gradient evaluations: 38

   status: 0

  success: True

     njev: 38

     nfev: 38

 hess_inv: array([[  1.55949709e-04,  -2.06891597e-06,   4.52439923e-06],

       [ -2.06891597e-06,   8.94222021e-04,  -8.85856496e-04],

       [  4.52439923e-06,  -8.85856496e-04,   1.21017793e-03]])

      fun: -21.632165325132977

        x: {'a': 0.44739489680783401, 'b': 0.31133017710324606, 'sigma': 0.082040126713057424}

  message: 'Optimization terminated successfully.'

      jac: array([ -8.72776888e-07,   5.92010624e-08,   8.06620475e-08])

```