LightGBM integration API reference

API reference for the NeptuneCallback class of the Neptune-LightGBM integration.

You can useNeptuneCallbackto capture model training metadata and log model summary after training.

NeptuneCallback

Neptune callback for logging metadata during LightGBM model training.

The callback logs parameters, evaluation results, and info about thetrain_set:

• feature names
• number of data points ( num_rows )
• number of features ( num_features )

Evaluation results are logged separately for everyvalid_sets. For example, with"metric": "logloss"andvalid_names=["train","valid"], two logs are created:train/loglossandvalid/logloss.

The callback works with thelgbm.train()andlgbm.cv()functions, and with the scikit-learn APImodel.fit().

Parameters

Example

Create a Neptune run:

import neptune

run = neptune.init_run()

Instantiate the callback and pass it to training function:

from neptune.integrations.lightgbm import NeptuneCallback

neptune_callback = NeptuneCallback(run=run)
gbm = lgb.train(params, ..., callbacks=[neptune_callback])

As a best practice, you should save your Neptune API token and project name as environment variables:

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8"

export NEPTUNE_PROJECT="ml-team/classification"

Alternatively, you can pass the information when using a function that takesapi_tokenandprojectas arguments:

run = neptune.init_run(
 api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8", # (1)!
 project="ml-team/classification", # (2)!
)

1. In the bottom-left corner, expand the user menu and select Get my API token .
2. You can copy the path from the project details ( → Details & privacy ).

If you haven't registered, you can log anonymously to a public project:

api_token=neptune.ANONYMOUS_API_TOKEN
project="common/quickstarts"

Make sure not to publish sensitive data through your code!

create_booster_summary()

Create a model summary after training that can be assigned to the run namespace.

Tip

To have all the information in a single run, you can log the summary to the same run that you used for logging model training.

Parameters

Seelightgbm.plot_importancefor details.

Returns

dictwith all metadata, which you can assign to the Neptune run:

run["booster_summary"] = create_booster_summary(...)

Examples

Initialize a Neptune run:

import neptune

run = neptune.init_run(project="workspace-name/project-name") # (1)!

1. The full project name. For example,"ml-team/classification".
2. You can copy the name from the project details ( → Details & privacy )
3. You can also find a pre-filled project string in Experiments → Create a new run .

The full project name. For example,"ml-team/classification".

Train LightGBM model and log booster summary to Neptune:

from neptune.integrations.lightgbm import create_booster_summary

gbm = lgb.train(params, ...)
run["lgbm_summary"] = create_booster_summary(booster=gbm)

You can customize what to log:

run["lgbm_summary"] = create_booster_summary(
 booster=gbm,
 log_trees=True,
 list_trees=[0, 1, 2, 3, 4],
 log_confusion_matrix=True,
 y_pred=y_pred,
 y_true=y_test,
)

In order to log a confusion matrix, the predicted labels and ground truth are required:

y_pred = np.argmax(gbm.predict(X_test), axis=1)
run["lgbm_summary"] = create_booster_summary(
 booster=gbm,
 log_confusion_matrix=True,
 y_pred=y_pred,
 y_true=y_test,
)

See also

neptune-lightgbm repo onGitHub

Related Documentation

• Neptune Package
• Run API

This page is originally sourced from the legacy docs.