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776aa55189
* v0.2.2 separate the HPO part into the module flaml.tune enhanced implementation of FLOW^2, CFO and BlendSearch support parallel tuning using ray tune add support for sample_weight and generic fit arguments enable mlflow logging Co-authored-by: Chi Wang (MSR) <chiw@microsoft.com> Co-authored-by: qingyun-wu <qw2ky@virginia.edu>
70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
import unittest
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from sklearn.datasets import fetch_openml
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from sklearn.model_selection import train_test_split
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import numpy as np
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from flaml.automl import AutoML
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from flaml.model import XGBoostSklearnEstimator
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from flaml import tune
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# dataset = "blood-transfusion-service-center"
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# dataset = "Australian"
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dataset = "credit-g"
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# dataset = "phoneme"
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# dataset = "kc1"
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class XGBoost2D(XGBoostSklearnEstimator):
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@classmethod
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def search_space(cls, data_size, task):
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upper = min(32768,int(data_size))
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return {
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'n_estimators': {
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'domain': tune.qloguniform(lower=4, upper=upper, q=1),
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'init_value': 4,
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},
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'max_leaves': {
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'domain': tune.qloguniform(lower=4, upper=upper, q=1),
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'init_value': 4,
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},
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}
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def test_simple(method=None):
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automl = AutoML()
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automl.add_learner(learner_name = 'XGBoost2D',
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learner_class = XGBoost2D)
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automl_settings = {
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"estimator_list": ['XGBoost2D'],
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# "metric": 'accuracy',
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"task": 'classification',
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"log_file_name": f"test/xgboost2d_{dataset}_{method}.log",
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# "model_history": True,
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# "log_training_metric": True,
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# "split_type": split_type,
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"n_jobs": 1,
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"hpo_method": method,
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"log_type": "all",
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"time_budget": 3#6000,
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}
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X, y = fetch_openml(name=dataset, return_X_y=True)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,
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random_state=42)
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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def _test_optuna():
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test_simple(method="optuna")
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def test_grid():
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test_simple(method="grid")
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if __name__ == "__main__":
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unittest.main()
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