example update (#359)

update some examples for consistencies with others.

test/tune_example.py

@@ -0,0 +1,57 @@
+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
+
+X, y = fetch_california_housing(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.33, random_state=42
+)
+
+
+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)
+    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,
+)