Skip to content

docs: Update LightGBM API examples to use modern callbacks #2100

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
ayushozha wants to merge 1 commit into
base: main
Choose a base branch
from
Open

docs: Update LightGBM API examples to use modern callbacks #2100

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 15 additions & 5 deletions docs/start/integration.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#

.. code-block:: Python

def fit(self, dataset: DatasetH, num_boost_round = 1000, **kwargs):
def fit(self, dataset: DatasetH, num_boost_round=1000, early_stopping_rounds=50, verbose_eval=20, **kwargs):

# prepare dataset for lgb training and evaluation
df_train, df_valid = dataset.prepare(
Expand All @@ -57,16 +57,22 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
dtrain = lgb.Dataset(x_train.values, label=y_train)
dvalid = lgb.Dataset(x_valid.values, label=y_valid)

# Set up callbacks (required for LightGBM >= 4.0)
evals_result = {}
callbacks = [
lgb.early_stopping(stopping_rounds=early_stopping_rounds),
lgb.log_evaluation(period=verbose_eval),
lgb.record_evaluation(evals_result),
]

# fit the model
self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
valid_sets=[dtrain, dvalid],
valid_names=["train", "valid"],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
callbacks=callbacks,
**kwargs
)

Expand Down Expand Up @@ -94,14 +100,18 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
# Based on existing model and finetune by train more rounds
dtrain, _ = self._prepare_data(dataset)

# Use callback for logging (required for LightGBM >= 4.0)
callbacks = [lgb.log_evaluation(period=verbose_eval)]

self.model = lgb.train(
self.params,
dtrain,
num_boost_round=num_boost_round,
init_model=self.model,
valid_sets=[dtrain],
valid_names=["train"],
verbose_eval=verbose_eval,
callbacks=callbacks,
)

Configuration File
Expand Down