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autobazaar 0.2.1

An open source project from Data to AI Lab at MIT.

AutoBazaar

License: MIT
Development Status: Pre-Alpha
Documentation: https://HDI-Project.github.io/AutoBazaar/
Homepage: https://github.com/HDI-Project/AutoBazaar
Paper: https://arxiv.org/pdf/1905.08942.pdf

Overview
AutoBazaar is an AutoML system created using The Machine Learning Bazaar,
a research project and framework for building ML and AutoML systems by the Data To AI Lab at MIT.
See below for more references.
It comes in the form of a python library which can be used directly inside any other python
project, as well as a CLI which allows searching for pipelines to solve a problem directly
from the command line.
Install
Requirements
AutoBazaar has been developed and tested on Python 3.5, 3.6 and 3.7
Also, although it is not strictly required, the usage of a
virtualenv is highly recommended in order to avoid
interfering with other software installed in the system where AutoBazaar is run.
Install with pip
The easiest and recommended way to install AutoBazaar is using
pip:
pip install autobazaar

This will pull and install the latest stable release from PyPI.
If you want to install from source or contribute to the project please read the
Contributing Guide.
Data Format
AutoBazaar works with datasets in the D3M Schema Format
as input.
This dataset schema, developed by MIT Lincoln Labs Laboratory for DARPA's Data-Driven Discovery
of Models (D3M) Program, requires the data to be in plainly readable formats such as CSV files or
JPG images, and to be set within a folder hierarchy alongside some metadata specifications
in JSON format, which include information about all the data contained, as well as the problem
that we are trying to solve.
For more details about the schema and about how to format your data to be compliant with it,
refer to the Schema Documentation
As an example, you can browse some datasets which have been included in this repository for
demonstration purposes:

185_baseball: Single Table Regression
196_autoMpg: Single Table Classification

Quickstart
In this short tutorial we will guide you through a series of steps that will help you getting
started with AutoBazaar using its CLI command abz.
For more details about its usage and the available options, please execute abz --help
on your command line.
1. Prepare your Data
Make sure to have your data prepared in the Data Format explained above inside
and uncompressed folder in a filesystem directly accessible by AutoBazaar.
In order to check, whether your dataset is available and ready to use, you can execute
the abz command in your command line with its list subcommand.
If your dataset is in a different place than inside a folder called data within your
current working directory, do not forget to add the -i argument to your command indicating
the path to the folder that contains your dataset.
Assuming that the data is inside a folder called input within your current folder,
you can run:
$ abz list -i /path/to/your/datasets/folder

The output should be a table which includes the details of all the datasets found inside
the indicated directory:
data_modality task_type task_subtype metric size_human train_samples
dataset
185_baseball single_table classification multi_class f1Macro 148K 1073
196_autoMpg single_table regression univariate meanSquaredError 32K 298
30_personae text classification binary f1 1,4M 116
32_wikiqa multi_table classification binary f1 4,9M 23406
60_jester single_table collaborative_filtering meanAbsoluteError 44M 880719

Note: If you see an error saying that No matching datasets found, please review your
dataset format and make sure to have indicated the right path.
For the rest of this quickstart, we will be using the 185_baseball dataset that you can
find inside the input folder
contained in this repository.
2. Start the search process
Once your data is ready, you can start the AutoBazaar search process using the abz search
command.
To do this, you will need to provide again the path to where your datasets are contained, as
well as the name of the datasets that you want to process.
For example if you want to search for the best
$ abz search -i /path/to/your/datasets/folder name_of_your_dataset

This will evaluate the default pipeline without performing additional tuning iteration on it.
In order to start an actual tuning process, you will need to provide at least one of the
following additional options:

-b, --budget: Maximum number of tuning iterations to perform.
-t, --timeout: Maximum time that the system needs to run, in seconds.
-c, --checkpoints: Comma separated string containing the different checkpoints where
the best pipeline so far must be stored and evaluated against the test dataset. There must be
no spaces between the checkpoint times. For example, to store the best pipeline every 10 minutes
until 30 minutes have passed, you would use the option -c 600,1200,1800.

For example, to search process the 185_baseball dataset during 30 seconds evaluating the
best pipeline so far every 10 seconds but with a maximum of 10 tuning iterations, we would
use the following command:
abz search 185_baseball -c10,20,30 -b10

For further details about the available options, please execute abz search --help in your
terminal.
3. Explore the results
Once the AutoBazaar has finished searching for the best pipeline, a table will be printed
in stdout with a summary of the best pipeline found for each dataset.
If multiple checkpoints were provided, details about the best pipeline in each checkpoint
will also be included.
The output will be a table similar to this one:
pipeline score rank cv_score metric data_modality task_type task_subtype elapsed iterations load_time trivial_time fit_time cv_time error step
dataset
185_baseball fce28425-e45c-4620-9d3c-d329b8684bea 0.316961 0.682957 0.317043 f1Macro single_table classification multi_class 10.024457 0.0 0.011041 0.026212 NaN NaN None None
185_baseball f7428924-79ee-439d-bc32-998a9efea619 0.675132 0.390927 0.609073 f1Macro single_table classification multi_class 21.412262 1.0 0.011041 0.026212 9.99484 NaN None None
185_baseball 397780a5-6bf6-48c9-9a85-06b0d08c5a9d 0.675132 0.357361 0.642639 f1Macro single_table classification multi_class 31.712946 2.0 0.011041 0.026212 9.99484 12.618179 None None

Alternatively, a -r option can be passed with the name of a CSV file, and the results will
be stored there:
abz search 185_baseball -c10,20,30 -b10 -r results.csv

What's next?
For more details about AutoBazaar and all its possibilities and features, please check the
project documentation site!
Credits
AutoBazaar is an open-source project from the Data to AI Lab at MIT built by the following team:

Micah Smith micahs@mit.edu
Carles Sala csala@mit.edu
Max Kanter max.kanter@featurelabs.com
Kalyan Veeramachaneni kalyanv@mit.edu

Citing AutoBazaar
If you use AutoBazaar for your research, please consider citing the following paper (https://arxiv.org/pdf/1905.08942.pdf):
@article{smith2019mlbazaar,
author = {Smith, Micah J. and Sala, Carles and Kanter, James Max and Veeramachaneni, Kalyan},
title = {The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development},
journal = {arXiv e-prints},
year = {2019},
eid = {arXiv:1905.08942},
pages = {arxiv:1904.09535},
archivePrefix = {arXiv},
eprint = {1905.08942},
}

History
0.2.1 - 2020-06-22
This release curates some of the dependencies and updates the example datasets
and documentation.
0.2.0 - 2019-11-26
Second Release:

Improved CLI interface
Improved Dataset support
New Docker image
Newer dependencies

This is the version used to generate the results explained in the third version of
The Machine Learning Bazaar Paper
0.1.0 - 2019-06-24
First Release.
This is a slightly cleaned up version of the software used to generate the results
explained in the first version of The Machine Learning Bazaar Paper