autonml 0.1.6

AutonML : CMU's AutoML System

CMU TA2 (Built using DARPA D3M ecosystem)

Autoⁿ ML is an automated machine learning system developed by CMU Auton Lab to power data scientists with efficient model discovery and advanced data analytics. Auton ML also powers the D3M Subject Matter Expert (SME) User Interfaces such as Two Ravens http://2ra.vn/.

Taking your machine learning capacity to the nth power.

Installation

AutonML can be installed as: pip install autonml

Keep in mind, additional D3M primitives must be installed for proper functioning of the AutonML pipeline. To do this, we provide an installation script install.sh available here. This single-use command

Recommended steps to install autonml:

pip install autonml
pip install pmdarima==1.8.1 d3m-common-primitives d3m-sklearn-wrap sri-d3m rpi-d3m-primitives dsbox-primitives dsbox-corex distil-primitives d3m-esrnn d3m-nbeats --no-binary pmdarima 

This installation may take time to complete, owing to the fact that pip's dependecy resolvers may take time resolving potential package conflicts. To make installation faster, you can add pip's legacy resolver as --use-deprecated=legacy-resolver. Caution: using old resolvers may present unresolved package conflicts.

AutonML uses additional primitives to expand its time-series forecasting capabilities. It is highly recommended these be installed as below until these additional primitives are made available on pip.

git clone -b dev —single-branch https://gitlab.com/sray/cmu-ta2.git
cd cmu-ta2
chmod 777 install.sh
./install.sh

D3M dataset

• Any dataset to be used should be in D3M dataset format (directory structure with TRAIN, TEST folders and underlying .json files).
• Example available of a single dataset here
• More datasets available here
• Any non-D3M data can be converted to D3M dataset. (See section below on "Convert raw dataset to D3M dataset").

Run the AutonML pipeline

We can run the AutonML pipeline in two ways. It can be run as a standalone CLI command, accessed via the autonml_main command. This command takes five arguments, listed below:

• Path to the data directory (must be in D3M format)
• Output directory where results are to be stored. This directory will be dynamically created if it does not exist.
• Timeout (measured in minutes)
• Number of CPUs to be used
• Path to problemDoc.json (see example below)

INPUT_DIR=/home/<user>/d3m/datasets/185_baseball_MIN_METADATA
OUTPUT_DIR=/output
TIMEOUT=2
NUMCPUS=8
PROBLEMPATH=${INPUT_DIR}/TRAIN/problem_TRAIN/problemDoc.json

autonml_main ${INPUT_DIR} ${OUTPUT_DIR} ${TIMEOUT} ${NUMCPUS} ${PROBLEMPATH} 

The above script will do the following-

1. Run search for best pipelines for the specified dataset using TRAIN data.
2. JSON pipelines (with ranks) will be output in JSON format at /output/<search_dir>/pipelines_ranked/
3. CSV prediction files of the pipelines trained on TRAIN data and predicted on TEST data will be available at /output/<search_dir>/predictions/
4. Training data predictions (cross-validated mostly) are produced in the current directory as /output/<search_dir>/training_predictions/<pipeline_id>_train_predictions.csv.
5. Python code equivalent of executing a JSON pipeline on a dataset produced at /output/<search_dir>/executables/

An example -

python ./output/6b92f2f7-74d2-4e86-958d-4e62bbd89c51/executables/131542c6-ea71-4403-9c2d-d899e990e7bd.json.code.py 185_baseball predictions.csv