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bigmler 0.3.6

A Higher Level API to, the public BigML API

Latest Version: 1.7.1

BigMLer - A Higher-Level API to BigML's API

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.

BigMLer is open sourced under the `Apache License, Version
2.0 <>`_.


Please report problems and bugs to our ` 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 is currently supported by BigMLer.

BigMLer requires `bigml 0.7.4 <>`_  or higher.

BigMLer Installation

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://

For a detailed description of install instructions on Windows see the
`BigMLer on Windows <#bigmler-on-windows>`_ section.

BigML Authentication

All the requests to 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 <#bigmler-on-windows>`_ section.

BigMLer on Windows

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::


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

    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

BigML Development Mode

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.

Using BigMLer

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 --help

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

Quick Start

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

Prior Versions Compatibility Issues

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.

Additional Information

For additional information, see
the `full documentation for BigMLer on Read the Docs <>`_.

.. :changelog:


0.3.6 (2013-08-21)

- Adding --test-separator flag

0.3.5 (2013-08-16)

- Bug fixing: resume crash when remote predictions were not completed
- Bug fixing: Fields object for input data dict building lacked fields
- Bug fixing: test data was repeated in remote prediction function
- Bug fixing: Adding replacement=True as default for ensembles' creation

0.3.4 (2013-08-09)

- Adding --max-parallel-evaluations flag
- Bug fixing: matching seeds in models and evaluations for cross validation

0.3.3 (2013-08-09)
- Changing --model-fields and --dataset-fields flag to allow adding/removing
  fields with +/- prefix
- Refactoring local and remote prediction functions
- Adding 'full data' option to the --prediction-info flag to join test input
  data with prediction results in predictions file
- Fixing errors in documentation and adding install for windows info

0.3.2 (2013-07-04)
- Adding new flag to control predictions file information
- Bug fixing: using default sample-rate in ensemble evaluations
- Adding standard deviation to evaluation measures in cross-validation
- Bug fixing: using only-model argument to download fields in models

0.3.1 (2013-05-14)

- Adding delete for ensembles
- Creating ensembles when the number of models is greater than one
- Remote predictions using ensembles

0.3.0 (2013-04-30)

- Adding cross-validation feature
- Using user locale to create new resources in BigML
- Adding --ensemble flag to use ensembles in predictions and evaluations

0.2.1 (2013-03-03)

- Deep refactoring of main resources management
- Fixing bug in batch_predict for no headers test sets
- Fixing bug for wide dataset's models than need query-string to retrieve all fields
- Fixing bug in test asserts to catch subprocess raise
- Adding default missing tokens to models
- Adding stdin input for --train flag
- Fixing bug when reading descriptions in --field-attributes
- Refactoring to get status from api function
- Adding confidence to combined predictions

0.2.0 (2012-01-21)
- Evaluations management
- console monitoring of process advance
- resume option
- user defaults
- Refactoring to improve readability

0.1.4 (2012-12-21)

- Improved locale management.
- Adds progressive handling for large numbers of models.
- More options in field attributes update feature.
- New flag to combine local existing predictions.
- More methods in local predictions: plurality, confidence weighted.

0.1.3 (2012-12-06)

- New flag for locale settings configuration.
- Filtering only finished resources.

0.1.2 (2012-12-06)

- Fix to ensure windows compatibility.

0.1.1 (2012-11-07)

- Initial release.
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