Use AutoXGBoost to auto-tune XGBoost parameters

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Run in Google Colab  View source on GitHub

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In this guide we will describe how to use Orca AutoXGBoost for automated xgboost tuning

Orca AutoXGBoost enables distributed automated hyper-parameter tuning for XGBoost, which includes AutoXGBRegressor and AutoXGBClassifier for sklearnXGBRegressor and XGBClassifier respectively. See more about xgboost scikit-learn API.

Step 0: Prepare Environment

Conda is needed to prepare the Python environment for running this example. Please refer to the install guide for more details.

Step 1: Init Orca Context

from bigdl.orca import init_orca_context, stop_orca_context

if cluster_mode == "local":
    init_orca_context(cores=6, memory="2g", init_ray_on_spark=True) # run in local mode
elif cluster_mode == "k8s":
    init_orca_context(cluster_mode="k8s", num_nodes=2, cores=4, init_ray_on_spark=True) # run on K8s cluster
elif cluster_mode == "yarn":
    init_orca_context(
      cluster_mode="yarn-client", cores=4, num_nodes=2, memory="2g", init_ray_on_spark=True, 
      driver_memory="10g", driver_cores=1) # run on Hadoop YARN cluster

This is the only place where you need to specify local or distributed mode. View Orca Context for more details.

Note: You should export HADOOP_CONF_DIR=/path/to/hadoop/conf/dir when running on Hadoop YARN cluster. View Hadoop User Guide for more details.

Step 2: Define Search space

You should define a dictionary as your hyper-parameter search space.

The keys are hyper-parameter names you want to search for XGBRegressor, and you can specify how you want to sample each hyper-parameter in the values of the search space. See automl.hp for more details.

from bigdl.orca.automl import hp

search_space = {
    "n_estimators": hp.grid_search([50, 100, 200]),
    "max_depth": hp.choice([2, 4, 6]),
}

Step 3: Automatically fit and search with Orca AutoXGBoost

First create an AutoXGBRegressor.

from bigdl.orca.automl.xgboost import AutoXGBRegressor

auto_xgb_reg = AutoXGBRegressor(cpus_per_trial=2, 
                                name="auto_xgb_classifier",
                                min_child_weight=3,
                                random_state=2)

Next, use the AutoXGBRegressor to fit and search for the best hyper-parameter set.

auto_xgb_reg.fit(data=(X_train, y_train),
                 validation_data=(X_test, y_test),
                 search_space=search_space,
                 n_sampling=2,
                 metric="rmse")

Step 4: Get best model and hyper parameters

You can get the best learned model and the best hyper-parameter set for further deployment. The best model is an sklearn XGBRegressor instance.

best_model = auto_xgb_reg.get_best_model()
best_config = auto_xgb_reg.get_best_config()

Note: You should call stop_orca_context() when your application finishes.