import lightgbm as lgb
import numpy as np
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split
from ray import tune
def LightGBMCallback(env):
"""Assumes that `valid_0` is the target validation score."""
_, metric, score, _ = env.evaluation_result_list[0]
tune.report(**{metric: score})
def train_breast_cancer(config):
data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
train_x, test_x, train_y, test_y = train_test_split(
data, target, test_size=0.25)
train_set = lgb.Dataset(train_x, label=train_y)
test_set = lgb.Dataset(test_x, label=test_y)
gbm = lgb.train(
config,
train_set,
valid_sets=[test_set],
verbose_eval=False,
callbacks=[LightGBMCallback])
preds = gbm.predict(test_x)
pred_labels = np.rint(preds)
tune.report(
mean_accuracy=sklearn.metrics.accuracy_score(test_y, pred_labels),
done=True)
if __name__ == "__main__":
config = {
"objective": "binary",
"metric": "binary_error",
"verbose": -1,
"boosting_type": tune.grid_search(["gbdt", "dart"]),
"num_leaves": tune.randint(10, 1000),
"learning_rate": tune.loguniform(1e-8, 1e-1)
}
from ray.tune.schedulers import ASHAScheduler
tune.run(
train_breast_cancer,
config=config,
num_samples=2,
scheduler=ASHAScheduler(metric="binary_error", mode="min"))