mirror of
https://github.com/microsoft/autogen.git
synced 2025-07-31 21:02:33 +00:00
8a44dd4318
* data in csv * support ray ObjectRef #365 * use object store to store data when using ray * make lgbm tuning example a test * homepage title
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
from flaml import tune
|
|
from flaml.model import LGBMEstimator
|
|
import lightgbm
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.datasets import fetch_california_housing
|
|
from sklearn.metrics import mean_squared_error
|
|
import ray
|
|
|
|
data = fetch_california_housing(return_X_y=False, as_frame=True)
|
|
X, y = data.data, data.target
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
X, y, test_size=0.33, random_state=42
|
|
)
|
|
X_train_ref = ray.put(X_train)
|
|
print(isinstance(X_train_ref, ray.ObjectRef))
|
|
|
|
|
|
def train_lgbm(config: dict) -> dict:
|
|
# convert config dict to lgbm params
|
|
params = LGBMEstimator(**config).params
|
|
# train the model
|
|
# train_set = lightgbm.Dataset(X_train, y_train)
|
|
X_train = ray.get(X_train_ref)
|
|
train_set = lightgbm.Dataset(X_train, y_train)
|
|
model = lightgbm.train(params, train_set)
|
|
# evaluate the model
|
|
pred = model.predict(X_test)
|
|
mse = mean_squared_error(y_test, pred)
|
|
# return eval results as a dictionary
|
|
return {"mse": mse}
|
|
|
|
|
|
# load a built-in search space from flaml
|
|
flaml_lgbm_search_space = LGBMEstimator.search_space(X_train.shape)
|
|
# specify the search space as a dict from hp name to domain; you can define your own search space same way
|
|
config_search_space = {
|
|
hp: space["domain"] for hp, space in flaml_lgbm_search_space.items()
|
|
}
|
|
# give guidance about hp values corresponding to low training cost, i.e., {"n_estimators": 4, "num_leaves": 4}
|
|
low_cost_partial_config = {
|
|
hp: space["low_cost_init_value"]
|
|
for hp, space in flaml_lgbm_search_space.items()
|
|
if "low_cost_init_value" in space
|
|
}
|
|
# initial points to evaluate
|
|
points_to_evaluate = [
|
|
{
|
|
hp: space["init_value"]
|
|
for hp, space in flaml_lgbm_search_space.items()
|
|
if "init_value" in space
|
|
}
|
|
]
|
|
# run the tuning, minimizing mse, with total time budget 3 seconds
|
|
analysis = tune.run(
|
|
train_lgbm,
|
|
metric="mse",
|
|
mode="min",
|
|
config=config_search_space,
|
|
low_cost_partial_config=low_cost_partial_config,
|
|
points_to_evaluate=points_to_evaluate,
|
|
time_budget_s=3,
|
|
num_samples=-1,
|
|
)
|
|
print(analysis.best_result)
|