BigMLer makes BigML even easier.

BigMLer wraps BigML's API Python bindings to offer a high-level command-line script to easily create and publish datasets and models, create ensembles, make local predictions from multiple models, and simplify many other machine learning tasks. For additional information, see the full documentation for BigMLer on Read the Docs.

BigMLer is open sourced 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 2.7 and 3 are currently supported by BigMLer.

BigMLer requires bigml 4.3.1 or higher. Using proportional missing strategy will additionally request the use of the numpy and scipy libraries. They are not automatically installed as a dependency, as they are quite heavy and exclusively required in this case. Therefore, they have been left for the user to install them if required.

Note that using proportional missing strategy for local predictions can also require numpy and scipy libraries. They are not installed by default. Check the bindings documentation for more info.

To install the latest stable release with pip

$ pip install bigmler

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

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

For a detailed description of install instructions on Windows see the BigMLer on Windows section.

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

BigML 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

Otherwise, you can initialize directly when running the BigMLer script as follows

bigmler --train data/iris.csv --username myusername --api_key ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

For a detailed description of authentication instructions on Windows see the BigMLer on Windows section.

To install BigMLer on Windows environments, you'll need Python for Windows (v.2.7.x) installed.

In addition to that, you'll need the pip tool to install BigMLer. To install pip, first you need to open your command line window (write cmd in the input field that appears when you click on Start and hit enter), download this python file and execute it

c:\Python27\python.exe distribute_setup.py

After that, you'll be able to install pip by typing the following command

c:\Python27\Scripts\easy_install.exe pip

And finally, to install BigMLer, just type

c:\Python27\Scripts\pip.exe install bigmler

and BigMLer should be installed in your computer. Then issuing

bigmler --version

should show BigMLer version information.

Finally, to start using BigMLer to handle your BigML resources, you need to set your credentials in BigML for authentication. If you want them to be permanently stored in your system, use

setx BIGML_USERNAME myusername
setx BIGML_API_KEY ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

Also, you can instruct BigMLer to work in BigML's Sandbox environment by using the parameter ---dev

bigmler --train data/iris.csv --dev

Using the development flag you can run tasks under 1 MB without spending any of your BigML credits.

To run BigMLer you can use the console script directly. The --help option will describe all the available options

bigmler --help

Alternatively you can just call bigmler as follows

python bigmler.py --help

This will display the full list of optional arguments. You can read a brief explanation for each option below.

Let's see some basic usage examples. Check the installation and authentication sections in BigMLer on Read the Docs if you are not familiar with BigML.

You can create a new model just with

bigmler --train data/iris.csv

If you check your dashboard at BigML, you will see a new source, dataset, and model. Isn't it magic?

You can generate predictions for a test set using

bigmler --train data/iris.csv --test data/test_iris.csv

You can also specify a file name to save the newly created predictions

bigmler --train data/iris.csv --test data/test_iris.csv --output predictions

If you do not specify the path to an output file, BigMLer will auto-generate one for you under a new directory named after the current date and time (e.g., MonNov1212_174715/predictions.csv). With --prediction-info flag set to brief only the prediction result will be stored (default is normal and includes confidence information).

A different objective field (the field that you want to predict) can be selected using

bigmler --train data/iris.csv --test data/test_iris.csv  --objective 'sepal length'

If you do not explicitly specify an objective field, BigML will default to the last column in your dataset.

Also, if your test file uses a particular field separator for its data, you can tell BigMLer using --test-separator. For example, if your test file uses the tab character as field separator the call should be like

bigmler --train data/iris.csv --test data/test_iris.tsv \
        --test-separator '\t'

If you don't provide a file name for your training source, BigMLer will try to read it from the standard input

cat data/iris.csv | bigmler --train

BigMLer will try to use the locale of the model both to create a new source (if --train flag is used) and to interpret test data. In case it fails, it will try en_US.UTF-8 or English_United States.1252 and a warning message will be printed. If you want to change this behaviour you can specify your preferred locale

bigmler --train data/iris.csv --test data/test_iris.csv \
--locale "English_United States.1252"

If you check your working directory you will see that BigMLer creates a file with the model ids that have been generated (e.g., FriNov0912_223645/models). This file is handy if then you want to use those model ids to generate local predictions. BigMLer also creates a file with the dataset id that has been generated (e.g., TueNov1312_003451/dataset) and another one summarizing the steps taken in the session progress: bigmler_sessions. You can also store a copy of every created or retrieved resource in your output directory (e.g., TueNov1312_003451/model_50c23e5e035d07305a00004f) by setting the flag --store.

BigMLer will accept flags written with underscore as word separator like --clear_logs for compatibility with prior versions. Also --field-names is accepted, although the more complete --field-attributes flag is preferred. --stat_pruning and --no_stat_pruning are discontinued and their effects can be achived by setting the actual --pruning flag to statistical or no-pruning values respectively.

To run the tests you will need to install nose that is installed on setup, and set up your authentication via environment variables, as explained above. With that in place, you can run the test suite simply by issuing

$ python setup.py nosetests

For additional information, see the full documentation for BigMLer on Read the Docs.