bigml 0.4.1

An open source binding to BigML.io, the public BigML API

BigML makes machine learning easy by taking care of the details required to add data-driven decisions and predictive power to your company. Unlike other machine learning services, BigML creates beautiful predictive models that can be easily understood and interacted with.

These BigML Python bindings allow you to interact with BigML.io, the API for BigML. You can use it to easily create, retrieve, list, update, and delete BigML resources (i.e., sources, datasets, models and, predictions).

This module is licensed under the Apache License, Version 2.0.

Support

Please report problems and bugs to our BigML.io issue tracker.

Discussions about the different bindings take place in the general BigML mailing list. Or join us in our Campfire chatroom.

Requirements

Python 2.6 and Python 2.7 are currently supported by these bindings.

The only mandatory third-party dependencies are the requests, poster and unidecode libraries. These libraries are automatically installed during the setup.

The bindings will also use simplejson if you happen to have it installed, but that is optional: we fall back to Python’s built-in JSON libraries is simplejson is not found.

Installation

To install the latest stable release with pip:

$ pip install bigml

You can also install the development version of the bindings directly from the Git repository:

$ pip install -e git://github.com/bigmlcom/python.git#egg=bigml_python

Importing the module

To import the module:

import bigml.api

Alternatively you can just import the BigML class:

from bigml.api import BigML

Authentication

All the requests to BigML.io must be authenticated using your username and API key and are always transmitted over HTTPS.

This module will look for your username and API key in the environment variables BIGML_USERNAME and BIGML_API_KEY respectively. You can add the following lines to your .bashrc or .bash_profile to set those variables automatically when you log in:

export BIGML_USERNAME=myusername
export BIGML_API_KEY=ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

With that environment set up, connecting to BigML is a breeze:

from bigml.api import BigML
api = BigML()

Otherwise, you can initialize directly when instantiating the BigML class as follows:

api = BigML('myusername', 'ae579e7e53fb9abd646a6ff8aa99d4afe83ac291')

Also, you can initialize the library to work in the Sandbox environment by passing the parameter dev_mode:

api = BigML(dev_mode=True)

Quick Start

Imagine that you want to use this csv file containing the Iris flower dataset to predict the species of a flower whose sepal length is 5 and whose sepal width is 2.5. A preview of the dataset is shown below. It has 4 numeric fields: sepal length, sepal width, petal length, petal width and a categorical field: species. By default, BigML considers the last field in the dataset as the objective field (i.e., the field that you want to generate predictions for).

sepal length,sepal width,petal length,petal width,species
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
...
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
...
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica

You can easily generate a prediction following these steps:

from bigml.api import BigML

api = BigML()

source = api.create_source('./data/iris.csv')
dataset = api.create_dataset(source)
model = api.create_model(dataset)
prediction = api.create_prediction(model, {'sepal length': 5, 'sepal width': 2.5})

You can then print the prediction using the pprint method:

>>> api.pprint(prediction)
species for {"sepal width": 2.5, "sepal length": 5} is Iris-virginica

Additional Information

We’ve just barely scratched the surface. For additional information, see the full documentation for the Python bindings on Read the Docs. Alternatively, the same documentation can be built from a local checkout of the source by installing Sphinx ($ pip install sphinx) and then running:

$ cd docs
$ make html

Then launch docs/_build/html/index.html in your browser.

How to Contribute

Please follow the next steps:

1. Fork the project on github.com.

2. Create a new branch.

3. Commit changes to the new branch.

4. Send a pull request.

For details on the underlying API, see the BigML API documentation.

History

0.4.1 (2012-09-17)

• Fix missing distribution field in new models

• Add new Field class to deal with BigML auto-generated ids

• Add by_name flag to predict methods to avoid reverse name lookups

• Add summarize method in models to generate class grouped printed output

0.4.0 (2012-08-20)

• Development Mode

• Remote Sources

• Bigger files streamed with Poster

• Asynchronous Uploading

• Local Models

• Local Predictions

• Rule Generation

• Python Generation

• Overall clean up

0.3.1 (2012-07-05)

• Initial release for the “andromeda” version of BigML.io.