autogen/flaml/default

• FLAML-Zero: Zero-shot AutoML
• • Zero-shot AutoML
  • Perform Meta Learning
  • • Prepare a collection of training tasks
    • Prepare the candidate configurations
    • Evaluate each candidate configuration on each task
    • Learn data-dependent defaults
  • Reference

FLAML-Zero: Zero-shot AutoML

Zero-shot AutoML

There are several ways to use zero-shot AutoML, i.e., train a model with the data-dependent default configuration.

0. Use estimators in flaml.default.estimator.

from flaml.default import LGBMRegressor

estimator = LGBMRegressor()
estimator.fit(X_train, y_train)
estimator.predict(X_test, y_test)

1. Use AutoML.fit(). set starting_points="data" and max_iter=0.

X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
automl = AutoML()
automl_settings = {
    "time_budget": 2,
    "task": "classification",
    "log_file_name": "test/iris.log",
    "starting_points": "data",
    "max_iter": 0,
}
automl.fit(X_train, y_train, **automl_settings)

2. Use flaml.default.preprocess_and_suggest_hyperparams.

from flaml.default import preprocess_and_suggest_hyperparams

X, y = load_iris(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
hyperparams, estimator_class, X_transformed, y_transformed, feature_transformer, label_transformer = preprocess_and_suggest_hyperparams(
    "classification", X_train, y_train, "lgbm"
)
model = estimator_class(**hyperparams)  # estimator_class is LGBMClassifier
model.fit(X_transformed, y_train)  # LGBMClassifier can handle raw labels
X_test = feature_transformer.transform(X_test)  # preprocess test data
y_pred = model.predict(X_test)

If you want to use your own meta-learned defaults, specify the path containing the meta-learned defaults. For example,

X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
automl = AutoML()
automl_settings = {
    "time_budget": 2,
    "task": "classification",
    "log_file_name": "test/iris.log",
    "starting_points": "data:test/default",
    "estimator_list": ["lgbm", "xgb_limitdepth", "rf"]
    "max_iter": 0,
}
automl.fit(X_train, y_train, **automl_settings)

Since this is a multiclass task, it will look for the following files under test/default/:

• all/multiclass.json.
• {learner_name}/multiclass.json for every learner_name in the estimator_list.

Read the next subsection to understand how to generate these files if you would like to meta-learn the defaults yourself.

To perform hyperparameter search starting with the data-dependent defaults, remove max_iter=0.

Perform Meta Learning

FLAML provides a package flaml.default to learn defaults customized for your own tasks/learners/metrics.

Prepare a collection of training tasks

Collect a diverse set of training tasks. For each task, extract its meta feature and save in a .csv file. For example, test/default/all/metafeatures.csv:

Dataset,NumberOfInstances,NumberOfFeatures,NumberOfClasses,PercentageOfNumericFeatures
2dplanes,36691,10,0,1.0
adult,43957,14,2,0.42857142857142855
Airlines,485444,7,2,0.42857142857142855
Albert,382716,78,2,0.3333333333333333
Amazon_employee_access,29492,9,2,0.0
bng_breastTumor,104976,9,0,0.1111111111111111
bng_pbc,900000,18,0,0.5555555555555556
car,1555,6,4,0.0
connect-4,60801,42,3,0.0
dilbert,9000,2000,5,1.0
Dionis,374569,60,355,1.0
poker,922509,10,0,1.0

The first column is the dataset name, and the latter four are meta features.

Prepare the candidate configurations

You can extract the best configurations for each task in your collection of training tasks by running flaml on each of them with a long enough budget. Save the best configuration in a .json file under {location_for_defaults}/{learner_name}/{task_name}.json. For example,

X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
automl.fit(X_train, y_train, estimator_list=["lgbm"], **settings)
automl.save_best_config("test/default/lgbm/iris.json")

Evaluate each candidate configuration on each task

Save the evaluation results in a .csv file. For example, save the evaluation results for lgbm under test/default/lgbm/results.csv:

task,fold,type,result,params
2dplanes,0,regression,0.946366,{'_modeljson': 'lgbm/2dplanes.json'}
2dplanes,0,regression,0.907774,{'_modeljson': 'lgbm/adult.json'}
2dplanes,0,regression,0.901643,{'_modeljson': 'lgbm/Airlines.json'}
2dplanes,0,regression,0.915098,{'_modeljson': 'lgbm/Albert.json'}
2dplanes,0,regression,0.302328,{'_modeljson': 'lgbm/Amazon_employee_access.json'}
2dplanes,0,regression,0.94523,{'_modeljson': 'lgbm/bng_breastTumor.json'}
2dplanes,0,regression,0.945698,{'_modeljson': 'lgbm/bng_pbc.json'}
2dplanes,0,regression,0.946194,{'_modeljson': 'lgbm/car.json'}
2dplanes,0,regression,0.945549,{'_modeljson': 'lgbm/connect-4.json'}
2dplanes,0,regression,0.946232,{'_modeljson': 'lgbm/default.json'}
2dplanes,0,regression,0.945594,{'_modeljson': 'lgbm/dilbert.json'}
2dplanes,0,regression,0.836996,{'_modeljson': 'lgbm/Dionis.json'}
2dplanes,0,regression,0.917152,{'_modeljson': 'lgbm/poker.json'}
adult,0,binary,0.927203,{'_modeljson': 'lgbm/2dplanes.json'}
adult,0,binary,0.932072,{'_modeljson': 'lgbm/adult.json'}
adult,0,binary,0.926563,{'_modeljson': 'lgbm/Airlines.json'}
adult,0,binary,0.928604,{'_modeljson': 'lgbm/Albert.json'}
adult,0,binary,0.911171,{'_modeljson': 'lgbm/Amazon_employee_access.json'}
adult,0,binary,0.930645,{'_modeljson': 'lgbm/bng_breastTumor.json'}
adult,0,binary,0.928603,{'_modeljson': 'lgbm/bng_pbc.json'}
adult,0,binary,0.915825,{'_modeljson': 'lgbm/car.json'}
adult,0,binary,0.919499,{'_modeljson': 'lgbm/connect-4.json'}
adult,0,binary,0.930109,{'_modeljson': 'lgbm/default.json'}
adult,0,binary,0.932453,{'_modeljson': 'lgbm/dilbert.json'}
adult,0,binary,0.921959,{'_modeljson': 'lgbm/Dionis.json'}
adult,0,binary,0.910763,{'_modeljson': 'lgbm/poker.json'}
...

The type column indicates the type of the task, such as regression, binary or multiclass.
The result column stores the evaluation result, assuming the large the better. The params column indicates which json config is used. For example 'lgbm/2dplanes.json' indicates that the best lgbm configuration extracted from 2dplanes is used.

Learn data-dependent defaults

To recap, the inputs required for meta-learning are:

1. Metafeatures: e.g., {location}/all/metafeatures.csv.
2. Configurations: {location}/{learner_name}/{task_name}.json.
3. Evaluation results: {location}/{learner_name}/results.csv.

For example, if the input location is "test/default", learners are lgbm, xgb_limitdepth and rf, the following command learns data-dependent defaults for binary classification tasks.

python portfolio.py --output test/default --input test/default --metafeatures test/default/all/metafeatures.csv --task binary --estimator lgbm xgb_limitdepth rf

It will produce the following files as output:

• test/default/lgbm/binary.json: the learned defaults for lgbm.
• test/default/xgb_limitdepth/binary.json: the learned defaults for xgb_limitdepth.
• test/default/rf/binary.json: the learned defaults for rf.
• test/default/all/binary.json: the learned defaults for lgbm, xgb_limitdepth and rf together.

Change "binary" into "multiclass" or "regression" for the other tasks.

Reference

For more technical details, please check our research paper.

• Mining Robust Default Configurations for Resource-constrained AutoML. Moe Kayali, Chi Wang. arXiv preprint arXiv:2202.09927 (2022).

@article{Kayali2022default,
    title={Mining Robust Default Configurations for Resource-constrained AutoML},
    author={Moe Kayali and Chi Wang},
    year={2022},
    journal={arXiv preprint arXiv:2202.09927},
}