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Simplify lgbm example (#358)

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* simplify lgbm examples

* provide link to lgbm example script.

* simply lgbm example in the example script.

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
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Rui Zhuang 2021-12-23 23:05:14 -08:00 committed by GitHub
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commit c6c0c29769
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3 changed files with 4 additions and 6 deletions
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3
test/ray/distribute_tune.py
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@ -13,9 +13,8 @@ train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25
def train_breast_cancer(config):
params = LGBMEstimator(**config).params
num_boost_round = params.pop("n_estimators")
train_set = lgb.Dataset(train_x, label=train_y)
gbm = lgb.train(params, train_set, num_boost_round)
gbm = lgb.train(params, train_set)
preds = gbm.predict(test_x)
pred_labels = np.rint(preds)
tune.report(

3
test/tune.py
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@ -14,10 +14,9 @@ X_train, X_test, y_train, y_test = train_test_split(
def train_lgbm(config: dict) -> dict:
# convert config dict to lgbm params
params = LGBMEstimator(**config).params
num_boost_round = params.pop("n_estimators")
# train the model
train_set = lightgbm.Dataset(X_train, y_train)
model = lightgbm.train(params, train_set, num_boost_round)
model = lightgbm.train(params, train_set)
# evaluate the model
pred = model.predict(X_test)
mse = mean_squared_error(y_test, pred)

4
website/docs/Getting-Started.md
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@ -49,10 +49,9 @@ from flaml.model import LGBMEstimator
def train_lgbm(config: dict) -> dict:
# convert config dict to lgbm params
params = LGBMEstimator(**config).params
num_boost_round = params.pop("n_estimators")
# train the model
train_set = lightgbm.Dataset(X_train, y_train)
model = lightgbm.train(params, train_set, num_boost_round)
model = lightgbm.train(params, train_set)
# evaluate the model
pred = model.predict(X_test)
mse = mean_squared_error(y_test, pred)
@ -75,6 +74,7 @@ analysis = tune.run(
low_cost_partial_config=low_cost_partial_config, time_budget_s=3, num_samples=-1,
)
```
Please see this [script](https://github.com/microsoft/FLAML/blob/main/test/tune.py) for the complete version of the above example.
### Where to Go Next?