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.
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.
Python 2.6 and Python 2.7 are currently supported by these bindings.
The only mandatory third-party dependency is the requests library. This library is 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.
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
To import the module:
import bigml.api
Alternatively you can just import the BigML class:
from bigml.api import BigML
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)
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
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.
For details on the underlying API, see the BigML API documentation.