API reference: XGBoost integration#

You can use the Neptune integration with XGBoost to capture model training metadata with NeptuneCallback.

`NeptuneCallback`#

Neptune callback for logging metadata during XGBoost model training.

Prerequisites

This callback requires xgboost>=1.3.0.

The callback logs the following:

Metrics
All parameters
Learning rate
The pickled model
Visualizations (feature importances and trees)
If early stopping is activated, best_score and best_iteration are also logged.

The callback works with the xgboost.train() and xgboost.cv() functions, and with model.fit() from the scikit-learn API.

Metrics are logged for every dataset in the evals list and for every metric specified.

Example: With evals = [(dtrain, "train"), (dval, "valid")] and "eval_metric": ["mae", "rmse"], four metrics are created:

Parameters

Name	Type	Default	Description
`run`	`Run` or `Handler`	-	An existing run reference, as returned by `neptune.init_run()`, or a namespace handler.
`base_namespace`	`str`, optional	`"training"`	Namespace under which all metadata logged by the Neptune callback will be stored.
`log_model`	`bool`	`True`	Whether to log the model as a pickled file at the end of training.
`log_importance`	`bool`	`True`	Whether to log feature importance charts at the end of training.
`max_num_features`	`int`	`10`	Max number of top features to log on the importance charts. Works when `log_importances` is set to `True`. If `None` or `<1`, all features will be displayed. For details, see `xgboost.plot_importance()` .
`log_tree`	`list` of `int`	`None`	Indexes of target trees to log as charts. Requires the Graphviz library to be installed. For details, see `xgboost.to_graphviz()` .
`tree_figsize`	`int`	`30`	Controls the size of the visualized tree image. Increase this in case you work with large trees. Works when `log_trees` is not `None`.

Examples

Create a Neptune run:

import neptune

run = neptune.init_run()

If Neptune can't find your project name or API token

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 takes api_token and project as arguments:

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

In the bottom-left corner, expand the user menu and select Get my API token.
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 a Neptune callback and pass it to xgb.train():

from neptune.integrations.xgboost import NeptuneCallback

neptune_callback = NeptuneCallback(run=run)

xgb.train( ..., callbacks=[neptune_callback])

When creating the callback, you can specify what you want to log and where:

neptune_callback = NeptuneCallback(
    run=run,
    base_namespace="experiment",
    log_model=False,
    log_tree=[0, 1, 2, 3],
)