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https://github.com/herilalaina/mosaic_ml
AutoML with MCTS
https://github.com/herilalaina/mosaic_ml
automl hyperparameter-optimization pipeline-configuration
Last synced: 3 months ago
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AutoML with MCTS
- Host: GitHub
- URL: https://github.com/herilalaina/mosaic_ml
- Owner: herilalaina
- License: other
- Created: 2018-05-16T01:48:41.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-05-04T08:37:06.000Z (almost 3 years ago)
- Last Synced: 2024-08-03T18:12:50.798Z (6 months ago)
- Topics: automl, hyperparameter-optimization, pipeline-configuration
- Language: Python
- Homepage:
- Size: 314 KB
- Stars: 14
- Watchers: 4
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: Readme.md
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README
# Automated Machine Learning with MCTS
[](https://travis-ci.org/herilalaina/mosaic_ml)
Mosaic ML is a Python library for machine learning pipeline configuration
using Monte Carlo Tree Search.
The original paper can be found here: [https://www.ijcai.org/Proceedings/2019/457](https://www.ijcai.org/Proceedings/2019/457)
Authors: Herilalaina Rakotoarison, Marc Schoenauer and Michèle Sebag
### Installation
**Requirements**:
* Python (3.5 or higher)
* Numy
* Cython
* scipy
* Mosaic (https://github.com/herilalaina/mosaic)
**Installation**:
```bash
pip install cython numpy scipy pytest
sudo apt-get install build-essential swig
pip install git+https://github.com/herilalaina/[email protected]
pip install git+https://github.com/herilalaina/mosaic_ml
```
### Usage
The entry script is ``python examples/run_mosaic_ml.py -h``.
```
--openml-task-id OPENML_TASK_ID
OpenML Task ID (default 252)
--overall-time-budget OVERALL_TIME_BUDGET
Overall time budget in seconds (default 360)
--eval-time-budget EVAL_TIME_BUDGET
Time budget for each machine learning evaluation
(default 100)
--memory-limit MEMORY_LIMIT
RAM Memory limit (default 3034)
--seed SEED Seed for reproducibility (default 42)
--nb-init-metalearning NB_INIT_METALEARNING
Number of initial configurations from Auto-Sklearn
(default 25)
--ensemble-size ENSEMBLE_SIZE
Size of ensemble set (default 50)
```
**Mosaic ML** has three different components:
* *vanilla*: MCTS for algorithm selection and Bayesian Optimization for hyperparameter tuning
```bash
python examples/run_mosaic_ml.py --nb-init-metalearning 0 --ensemble-size 1
```
* *metalearning*: initialize with a set of configurations fetched from [Auto-Sklearn](https://automl.github.io/auto-sklearn/master/index.html) then apply *vanilla setting*
```bash
python examples/run_mosaic_ml.py --nb-init-metalearning 25 --ensemble-size 1
```
* *ensemble (with metalearning)*: add an ensemble selection method ([Caruana et al, 04](https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf)) in the top of the *metalearning* setting
```bash
python examples/run_mosaic_ml.py --nb-init-metalearning 25 --ensemble-size 50
```