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9e88f22167
* fix a bug when using ray & update ray on aml When using with_parameters(), the config argument must be the first argument in the trainable function. * make training function runnable standalone
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
from ray_on_aml.core import Ray_On_AML
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import lightgbm as lgb
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import numpy as np
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from sklearn.datasets import load_breast_cancer
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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from flaml import tune
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from flaml.model import LGBMEstimator
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def train_breast_cancer(config):
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params = LGBMEstimator(**config).params
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X_train = ray.get(X_train_ref)
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train_set = lgb.Dataset(X_train, label=y_train)
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gbm = lgb.train(params, train_set)
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preds = gbm.predict(X_test)
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pred_labels = np.rint(preds)
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tune.report(mean_accuracy=accuracy_score(y_test, pred_labels), done=True)
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if __name__ == "__main__":
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ray_on_aml = Ray_On_AML()
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ray = ray_on_aml.getRay()
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if ray:
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X, y = load_breast_cancer(return_X_y=True)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
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X_train_ref = ray.put(X_train)
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flaml_lgbm_search_space = LGBMEstimator.search_space(X_train.shape)
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config_search_space = {
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hp: space["domain"] for hp, space in flaml_lgbm_search_space.items()
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}
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low_cost_partial_config = {
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hp: space["low_cost_init_value"]
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for hp, space in flaml_lgbm_search_space.items()
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if "low_cost_init_value" in space
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}
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analysis = tune.run(
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train_breast_cancer,
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metric="mean_accuracy",
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mode="max",
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config=config_search_space,
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num_samples=-1,
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time_budget_s=60,
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use_ray=True,
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)
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# print("Best hyperparameters found were: ", analysis.best_config)
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print("The best trial's result: ", analysis.best_trial.last_result)
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