GithubHelp home page GithubHelp logo

quillan86 / bootstrapped Goto Github PK

View Code? Open in Web Editor NEW

This project forked from facebookarchive/bootstrapped

0.0 1.0 0.0 644 KB

Generate bootstrapped confidence intervals for A/B testing in Python.

License: Other

Python 99.87% Shell 0.13%

bootstrapped's Introduction

bootstrapped - confidence intervals made easy

bootstrapped is a Python library that allows you to build confidence intervals from data. This is useful in a variety of contexts - including during ad-hoc a/b test analysis.

Motivating Example - A/B Test

Imagine we own a website and think changing the color of a 'subscribe' button will improve signups. One method to measure the improvement is to conduct an A/B test where we show 50% of people the old version and 50% of the people the new version. We can use the bootstrap to understand how much the button color improves responses and give us the error bars associated with the test - this will give us lower and upper bounds on how good we should expect the change to be!

The Gist - Mean of a Sample

Given a sample of data - we can generate a bunch of new samples by 're-sampling' from what we have gathered. We calculate the mean for each generated sample. We can use the means from the generated samples to understand the variation in the larger population and can construct error bars for the true mean.

bootstrapped - Benefits

  • Efficient computation of confidence intervals
  • Functions to handle single populations and a/b tests
  • Functions to understand statistical power
  • Multithreaded support to speed-up bootstrap computations
  • Dense and sparse array support

Example Usage

import numpy as np
import bootstrapped.bootstrap as bs
import bootstrapped.stats_functions as bs_stats

mean = 100
stdev = 10

population = np.random.normal(loc=mean, scale=stdev, size=50000)

# take 1k 'samples' from the larger population
samples = population[:1000]

print(bs.bootstrap(samples, stat_func=bs_stats.mean))
>> 100.08  (99.46, 100.69)

print(bs.bootstrap(samples, stat_func=bs_stats.std))
>> 9.49  (9.92, 10.36)

Extended Examples

Requirements

bootstrapped requires numpy. The power analysis functions require matplotlib and pandas.

Installation

pip install bootstrapped

How bootstrapped works

bootstrapped provides pivotal (aka empirical) based confidence intervals based on bootstrap re-sampling with replacement. The percentile method is also available.

For more information please see:

  1. Bootstrap confidence intervals (good intro)
  2. An introduction to Bootstrap Methods
  3. The Bootstrap, Advanced Data Analysis
  4. When the bootstrap dosen't work
  5. (book) An Introduction to the Bootstrap
  6. (book) Bootstrap Methods and their Application

See the CONTRIBUTING file for how to help out.

Contributors

Spencer Beecher, Don van der Drift, David Martin, Lindsay Vass, Sergey Goder, Benedict Lim, and Matt Langner.

Special thanks to Eytan Bakshy.

License

bootstrapped is BSD-licensed. We also provide an additional patent grant.

bootstrapped's People

Contributors

andytwoods avatar graingert avatar mdhbh avatar spencebeecher avatar wukaiyuan avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.