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frameworks.yaml
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frameworks.yaml
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---
#for doc purpose using <placeholder:default_value> syntax when it applies.
# FORMAT:
__dummy_framework_with_defaults:
version: ''
module: # defaults to `frameworks.framework_name`
setup_args: ''
params: {}
project: http://url/to/project/repo
image: # will result in built image `author/image:tag`
author: automlbenchmark
image: # defaults to `framework name to lowercase`
tag: # defaults to `framework version`
### Non AutoML reference frameworks
constantpredictor:
version: 'latest'
project: https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html
constantpredictor_enc:
extends: constantpredictor
params:
encode: true
DecisionTree:
version: '0.22.2'
project: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
RandomForest:
version: '0.22.2'
project: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
params:
n_estimators: 2000
# _n_jobs: 1 # faster, fitter, happier (running OoM on some datasets when using multiprocessing)
# verbose: true
TunedRandomForest:
version: '0.22.2'
project: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
params:
n_estimators: 2000
# _n_jobs: 1 # cf. RandomForest
# _tuning:
# n_estimators: 500
### AutoML frameworks
AutoGluon:
version: "0.0.12"
project: https://autogluon.mxnet.io
# params:
# _save_artifacts: ['leaderboard', 'models', 'info']
AutoGluon_best:
extends: AutoGluon
description: "provides the most accurate overall predictor"
params:
presets: best_quality
autosklearn:
version: '0.8.0'
project: https://automl.github.io/auto-sklearn/
# params:
# _save_artifacts: ['models']
# _n_jobs: 1
AutoWEKA:
version: '2.6'
project: https://www.cs.ubc.ca/labs/beta/Projects/autoweka/
MLPlan:
module: frameworks.MLPlan
version: '0.2.3'
project: http://mlplan.org
MLPlanWEKA:
extends: MLPlan
params:
_backend: weka
MLPlanSKLearn:
extends: MLPlan
params:
_backend: sklearn
autoxgboost:
version: 'latest'
project: https://github.com/ja-thomas/autoxgboost
GAMA:
version: '20.1.0'
project: https://github.com/PGijsbers/gama
H2OAutoML:
version: '3.30.0.4'
setup_args: 'zahradnik'
project: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
# params:
# _save_artifacts: ['leaderboard', 'logs', 'models', 'models_predictions', 'mojos']
hyperoptsklearn:
version: 'latest'
project: http://hyperopt.github.io/hyperopt-sklearn/
# params:
# max_evals: 1000
# algo: hyperopt.tpe.suggest
# verbose: true
oboe:
version: 'latest'
project: https://github.com/udellgroup/oboe
# params:
# build_ensemble: false
# selection_method: random
# verbose: true
ranger:
version: 'latest'
project: https://github.com/imbs-hl/ranger
TPOT:
version: '0.11.5'
project: https://github.com/EpistasisLab/tpot
# params:
# _save_artifacts: ['models']
# max_eval_time_mins: 2
# population_size: 25
# verbosity: 2
mljarsupervised:
version: '0.6.0'
project: https://github.com/mljar/mljar-supervised
params:
mode: "Compete" # set mode for Compete, default mode is Explain
# algorithms: ["Baseline"]
# _save_artifacts: True