ATM - Auto Tune Models
- Free software: MIT license
- Documentation: http://atm.readthedocs.io/en/latest/
ATM is an open source software library under the Human Data Interaction project (HDI) at MIT. It is a distributed, scalable AutoML system designed with ease of use in mind.
Summary
For a given classification problem, ATM's goal is to find
- a classification method, such as decision tree, support vector machine, or random forest, and
- a set of hyperparameters for that method
which generate the best classifier possible.
ATM takes in a dataset with pre-extracted feature vectors and labels as a CSV file. It then begins training and testing classifiers (machine learning models) in parallel. As time goes on, ATM will use the results of previous classifiers to intelligently select which methods and hyperparameters to try next. Along the way, ATM saves data about each classifier it trains, including the hyperparameters used to train it, extensive performance metrics, and a serialized version of the model itself.
ATM has the following features:
- It allows users to run the system for multiple datasets and multiple problem configurations in parallel.
- It can be run locally, on AWS*, or on a custom compute cluster*
- It can be configured to use a variety of AutoML approaches for hyperparameter tuning and selection, available in the accompanying library btb
- It stores models, metrics and cross validated accuracy information about each classifier it has trained.
*work in progress! See issue #40
Current status
ATM and the accompanying library BTB are under active development. We have made the transition and improvements.
Setup/Installation
This section describes the quickest way to get started with ATM on a machine running Ubuntu Linux. We hope to have more in-depth guides in the future, but for now, you should be able to substitute commands for the package manager of your choice to get ATM up and running on most modern Unix-based systems.
ATM is compatible with and has been tested on Python 2.7, 3.5, and 3.6.
-
Clone the project
git clone https://github.com/hdi-project/ATM.git /path/to/atm cd /path/to/atm
-
Install a database
You will need to install the libmysqlclient-dev package (for sqlalchemy)
sudo apt install libmysqlclient-dev
and at least one of the following databases.
- for SQLite (simpler):
sudo apt install sqlite3
- for MySQL:
sudo apt install mysql-server mysql-client
-
Install python dependencies.
This will also install btb, the core AutoML library in development under the HDI project, as an egg which will track changes to the git repository.
Here, usage of
virtualenv
is shown, but you can substituteconda
or your preferred environment manager as well.virtualenv venv . venv/bin/activate python setup.py install
Quick Usage
Below we will give a quick tutorial of how to run ATM on your desktop.
We will use a featurized dataset, already saved in data/test/pollution_1.csv
.
This is one of the datasets available on openml.org.
More details can be found here.
In this problem the goal is predict mortality
using the metrics associated with the air
pollution. Below we show a snapshot of the csv
file.
The data has 15 features and the last column is the class
label.
PREC | JANT | JULT | OVR65 | POPN | EDUC | HOUS | DENS | NONW | WWDRK | POOR | HC | NOX | SO@ | HUMID | class |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
35 | 23 | 72 | 11.1 | 3.14 | 11 | 78.8 | 4281 | 3.5 | 50.7 | 14.4 | 8 | 10 | 39 | 57 | 1 |
44 | 29 | 74 | 10.4 | 3.21 | 9.8 | 81.6 | 4260 | 0.8 | 39.4 | 12.4 | 6 | 6 | 33 | 54 | 1 |
47 | 45 | 79 | 6.5 | 3.41 | 11.1 | 77.5 | 3125 | 27.1 | 50.2 | 20.6 | 18 | 8 | 24 | 56 | 1 |
43 | 35 | 77 | 7.6 | 3.44 | 9.6 | 84.6 | 6441 | 24.4 | 43.7 | 14.3 | 43 | 38 | 206 | 55 | 1 |
53 | 45 | 80 | 7.7 | 3.45 | 10.2 | 66.8 | 3325 | 38.5 | 43.1 | 25.5 | 30 | 32 | 72 | 54 | 1 |
43 | 30 | 74 | 10.9 | 3.23 | 12.1 | 83.9 | 4679 | 3.5 | 49.2 | 11.3 | 21 | 32 | 62 | 56 | 0 |
45 | 30 | 73 | 9.3 | 3.29 | 10.6 | 86 | 2140 | 5.3 | 40.4 | 10.5 | 6 | 4 | 4 | 56 | 0 |
.. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
.. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
.. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
37 | 31 | 75 | 8 | 3.26 | 11.9 | 78.4 | 4259 | 13.1 | 49.6 | 13.9 | 23 | 9 | 15 | 58 | 1 |
35 | 46 | 85 | 7.1 | 3.22 | 11.8 | 79.9 | 1441 | 14.8 | 51.2 | 16.1 | 1 | 1 | 1 | 54 | 0 |
-
Create a datarun
python scripts/enter_data.py
This command will create a
datarun
. In ATM, a "datarun" is a single logical machine learning task. If you run the above command without any arguments, it will use the default settings found inatm/config.py
to create a new SQLite3 database at./atm.db
, create a newdataset
instance which refers to the data above, and create adatarun
instance which points to that dataset. More about what is stored in this database and what is it used for can be found here.The command should produce a lot of output, the end of which looks something like this:
========== Summary ========== Training data: data/test/pollution_1.csv Test data: <None> Dataset ID: 1 Frozen set selection strategy: uniform Parameter tuning strategy: gp_ei Budget: 100 (classifier) Datarun ID: 1
The most important piece of information is the datarun ID.
-
Start a worker
python scripts/worker.py
This will start a process that builds classifiers, tests them, and saves them to the
./models/
directory. The output should show which hyperparameters are being tested and the performance of each classifier (the "judgment metric"), plus the best overall performance so far.Classifier type: classify_logreg Params chosen: C = 8904.06127554 _scale = True fit_intercept = False penalty = l2 tol = 4.60893080631 dual = True class_weight = auto Judgment metric (f1): 0.536 +- 0.067 Best so far (classifier 21): 0.716 +- 0.035
Occasionally, a worker will encounter an error in the process of building and testing a classifier. When this happens, the worker will print error data to the terminal, log the error in the database, and move on to the next classifier.
And that's it! You can break out of the worker with Ctrl+c and restart it with the same command; it will pick up right where it left off. You can also run the command simultaneously in different terminals to parallelize the work -- all workers will refer to the same ModelHub database. When all 100 classifiers in your budget have been built, all workers will exit gracefully.
Customizing ATM's configuration and using your own data
ATM's default configuration is fully controlled by atm/config.py
. Our documentation will
cover the configuration in more detail, but this section provides a brief overview of how
to specify the most important values.
Running ATM on your own data
If you want to use the system for your own dataset, convert your data to a CSV file similar to the example shown above. The format is:
- Each column is a feature (or the label)
- Each row is a training example
- The first row is the header row, which contains names for each column of data
- A single column (the target or label) is named
class
Next, you'll need to use enter_data.py
to create a dataset
and datarun
for your task.
The script will look for values for each configuration variable in the following places, in order:
- Command line arguments
- Configuration files
- Defaults specified in
atm/config.py
That means there are two ways to pass configuration to the command.
-
Using YAML configuration files
Saving configuration as YAML files is an easy way to save complicated setups or share them with team members.
You should start with the templates provided in
atm/config/templates
and modify them to suit your own needs.mkdir config cp atm/config/templates/*.yaml config/ vim config/*.yaml
run.yaml
contains all the settings for a single dataset and datarun. Specify thetrain_path
to point to your own dataset.sql.yaml
contains the settings for the ModelHub SQL database. The default configuration will connect to (and create if necessary) a SQLite database at./atm.db
relative to the directory from whichenter_data.py
is run. If you are using a MySQL database, you will need to change the file to something like this:dialect: mysql database: atm username: username password: password host: localhost port: 3306 query:
aws.yaml
should contain the settings for running ATM in the cloud. This is not necessary for local operation.Once your YAML files have been updated, run the datarun creation script and pass it the paths to your new config files:
python scripts/enter_data.py --sql-config config/sql.yaml \ --aws-config config/aws.yaml \ --run-config config/run.yaml
-
Using command line arguments
You can also specify each argument individually on the command line. The names of the variables are the same as those in the YAML files. SQL configuration variables must be prepended by
sql-
, and AWS config variables must be prepended byaws-
.Using command line arguments is convenient for quick experiments, or for cases where you need to change just a couple of values from the default configuration. For example:
python scripts/enter_data.py --train-path ./data/my-custom-data.csv --selector bestkvel
You can also use a mixture of config files and command line arguments; any command line arguments you specify will override the values found in config files.
Once you've created your custom datarun, start a worker, specifying your config files and the datarun(s) you'd like to compute on.
python scripts/worker.py --sql-config config/sql.yaml \
--aws-config config/aws.yaml \
--dataruns 1
It's important that the SQL configuration used by the worker matches the configuration you
passed to enter_data.py
-- otherwise, the worker will be looking in the wrong ModelHub
database for its datarun!
Citing ATM
If you use ATM, please consider citing the following paper:
Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Arun Ross, Kalyan Veeramachaneni. ATM: A distributed, collaborative, scalable system for automated machine learning. IEEE BigData 2017, 151-162
BibTeX entry:
@inproceedings{DBLP:conf/bigdataconf/SwearingenDCCRV17,
author = {Thomas Swearingen and
Will Drevo and
Bennett Cyphers and
Alfredo Cuesta{-}Infante and
Arun Ross and
Kalyan Veeramachaneni},
title = {{ATM:} {A} distributed, collaborative, scalable system for automated
machine learning},
booktitle = {2017 {IEEE} International Conference on Big Data, BigData 2017, Boston,
MA, USA, December 11-14, 2017},
pages = {151--162},
year = {2017},
crossref = {DBLP:conf/bigdataconf/2017},
url = {https://doi.org/10.1109/BigData.2017.8257923},
doi = {10.1109/BigData.2017.8257923},
timestamp = {Tue, 23 Jan 2018 12:40:42 +0100},
biburl = {https://dblp.org/rec/bib/conf/bigdataconf/SwearingenDCCRV17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Related Projects
BTB
BTB, for Bayesian Tuning and Bandits, is the core AutoML library in development under the HDI project. BTB exposes several methods for hyperparameter selection and tuning through a common API. It allows domain experts to extend existing methods and add new ones easily. BTB is a central part of ATM, and the two projects were developed in tandem, but it is designed to be implementation-agnostic and should be useful for a wide range of hyperparameter selection tasks.
Featuretools
Featuretools is a python library for automated feature engineering. It can be used to prepare raw transactional and relational datasets for ATM. It is created and maintained by Feature Labs and is also a part of the Human Data Interaction Project.