mirror of
https://github.com/microsoft/autogen.git
synced 2025-09-18 12:44:20 +00:00
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
import lightgbm as lgb
|
|
import numpy as np
|
|
from sklearn.datasets import load_breast_cancer
|
|
from sklearn.metrics import accuracy_score
|
|
from sklearn.model_selection import train_test_split
|
|
from flaml import tune
|
|
from flaml.automl.model import LGBMEstimator
|
|
from flaml.tune.spark.utils import check_spark
|
|
import os
|
|
import pytest
|
|
|
|
spark_available, _ = check_spark()
|
|
skip_spark = not spark_available
|
|
|
|
pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
|
|
|
|
os.environ["FLAML_MAX_CONCURRENT"] = "2"
|
|
X, y = load_breast_cancer(return_X_y=True)
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
|
|
|
|
|
|
def train_breast_cancer(config):
|
|
params = LGBMEstimator(**config).params
|
|
train_set = lgb.Dataset(X_train, label=y_train)
|
|
gbm = lgb.train(params, train_set)
|
|
preds = gbm.predict(X_test)
|
|
pred_labels = np.rint(preds)
|
|
result = {
|
|
"mean_accuracy": accuracy_score(y_test, pred_labels),
|
|
}
|
|
return result
|
|
|
|
|
|
def test_tune_spark():
|
|
flaml_lgbm_search_space = LGBMEstimator.search_space(X_train.shape)
|
|
config_search_space = {hp: space["domain"] for hp, space in flaml_lgbm_search_space.items()}
|
|
|
|
analysis = tune.run(
|
|
train_breast_cancer,
|
|
metric="mean_accuracy",
|
|
mode="max",
|
|
config=config_search_space,
|
|
num_samples=-1,
|
|
time_budget_s=5,
|
|
use_spark=True,
|
|
verbose=3,
|
|
n_concurrent_trials=4,
|
|
)
|
|
|
|
# print("Best hyperparameters found were: ", analysis.best_config)
|
|
print("The best trial's result: ", analysis.best_trial.last_result)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_tune_spark()
|