Package: autobazaar
An open source project from Data to AI Lab at MIT.AutoBazaarLicense:MITDevelopment Status:Pre-AlphaDocumentation:https://HDI-Project.github.io/AutoBazaar/Homepage:https://github.com/HDI-Project/AutoBazaarPaper:https://arxiv.org/pdf/1905.08942.pdfOverviewAutoBazaar is an AutoML system created usingThe Machine Learning Bazaar,
a research project and framework for building ML and AutoML systems by the Data To AI Lab at MIT.
Seebelowfor 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.InstallRequirementsAutoBazaarhas been developed and tested onPython 3.5, 3.6 and 3.7Also, although it is not strictly required, the usage of avirtualenvis highly recommended in order to avoid
interfering with other software installed in the system whereAutoBazaaris run.Install with pipThe easiest and recommended way to installAutoBazaaris usingpip:pip install autobazaarThis will pull and install the latest stable release fromPyPI.If you want to install from source or contribute to the project please read theContributing Guide.Data FormatAutoBazaar works with datasets in theD3M Schema Formatas 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 theSchema DocumentationAs an example, you can browse some datasets which have been included in this repository for
demonstration purposes:185_baseball: Single Table Regression196_autoMpg: Single Table ClassificationQuickstartIn this short tutorial we will guide you through a series of steps that will help you getting
started withAutoBazaarusing its CLI commandabz.For more details about its usage and the available options, please executeabz --helpon your command line.1. Prepare your DataMake sure to have your data prepared in theData Formatexplained above inside
and uncompressed folder in a filesystem directly accessible byAutoBazaar.In order to check, whether your dataset is available and ready to use, you can execute
theabzcommand in your command line with itslistsubcommand.
If your dataset is in a different place than inside a folder calleddatawithin your
current working directory, do not forget to add the-iargument to your command indicating
the path to the folder that contains your dataset.Assuming that the data is inside a folder calledinputwithin your current folder,
you can run:$ abz list -i /path/to/your/datasets/folderThe 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         880719Note:If you see an error saying thatNo 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 the185_baseballdataset that you can
find inside theinput foldercontained in this repository.2. Start the search processOnce your data is ready, you can start the AutoBazaar search process using theabz searchcommand.
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_datasetThis 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 the185_baseballdataset 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:abzsearch185_baseball-c10,20,30-b10For further details about the available options, please executeabz search --helpin your
terminal.3. Explore the resultsOnce theAutoBazaarhas 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  NoneAlternatively, a-roption can be passed with the name of a CSV file, and the results will
be stored there:abzsearch185_baseball-c10,20,30-b10-rresults.csvWhat's next?For more details aboutAutoBazaarand all its possibilities and features, please check theproject documentation site!CreditsAutoBazaar is an open-source project from the Data to AI Lab at MIT built by the following team:Micah Smithmicahs@mit.eduCarles Salacsala@mit.eduMax Kantermax.kanter@featurelabs.comKalyan Veeramachanenikalyanv@mit.eduCiting AutoBazaarIf 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},
}History0.2.1 - 2020-06-22This release curates some of the dependencies and updates the example datasets
and documentation.0.2.0 - 2019-11-26Second Release:Improved CLI interfaceImproved Dataset supportNew Docker imageNewer dependenciesThis is the version used to generate the results explained in the third version ofThe Machine Learning Bazaar Paper0.1.0 - 2019-06-24First Release.This is a slightly cleaned up version of the software used to generate the results
explained in the first version ofThe Machine Learning Bazaar Paper