Understanding the Metric Key

Oct 13, 2025 by talynt.org 103 views

Introduction

Hyperparameter tuning is a crucial step in machine learning model development, and using a library like Ray can significantly speed up the process. However, when working with the Ray library and the scikit-learn API, understanding the metric key required by the RayTrainReportCallback can be a challenge. In this article, we will explore the correct key for the XGBoost metric and provide a solution to reveal the dictionary of metrics.

The Problem

When using the RayTrainReportCallback with the XGBoostRegressor model, you may encounter a KeyError when trying to access the metric key. This is because the key used is not recognized by the callback function. The key validation_0-rmse is a common guess, but it does not work.

Understanding the Metric Key

The metric key is used to access the performance metrics of the model during training. In the case of XGBoost, the key is not as straightforward as it seems. The key is actually a combination of the validation set name and the metric name.

Revealing the Dictionary of Metrics

To reveal the dictionary of metrics, you can use the get_dtrain() method of the XGBoostRegressor object. This method returns a DMatrix object, which contains the training data and the corresponding metrics.

import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_diabetes
from ray import tune
from ray.tune import CLIReporter
from ray.tune.schedulers import ASHAScheduler

# Load the diabetes dataset
diabetes = load_diabetes()
X_train, X_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2, random_state=42)

# Create an XGBoost regressor object
xgb_model = xgb.XGBRegressor()

# Get the DMatrix object
dtrain = xgb.DMatrix(X_train, label=y_train)

# Print the dictionary of metrics
print(dtrain.get_label())
print(dtrain.get_info())

In this code snippet, we load the diabetes dataset and split it into training and testing sets. We then create an XGBoost regressor object and get the DMatrix object using the get_dtrain() method. Finally, we print the dictionary of metrics using the get_label() and get_info() methods.

The Correct Metric Key

After revealing the dictionary of metrics, you can see that the correct key for the XGBoost metric is actually rmse. This key is used to access the root mean squared error metric.

Using the Correct Metric Key

Now that we know the correct metric key, we can use it in the RayTrainReportCallback function.

from ray import tune
from ray.tune import CLIReporter
from ray.tune.schedulers import ASHAScheduler
from ray.tune.schedulers.hyperband import HyperBandForResourceScheduler

# Define the hyperparameter search space
param_space = {
    "max_depth": tune.randint(1, 10),
    "learning_rate": tune.loguniform(0.01, 0.1),
    "n_estimators": tune.randint(10, 100),
}

# Define the training function
def train_model(config):
    xgb_model = xgb.XGBRegressor(**config)
    xgb_model.fit(X_train, y_train)
    return xgb_model.best_score

# Define the callback function
def ray_train_report_callback(report):
    print(f"Epoch: {report['epoch']}, Metric: {report['metric']}, Value: {report['value']}")

# Define the hyperparameter search
scheduler = ASHAScheduler(
    metric="best_score",
    mode="max",
    max_t=10,
    grace_period=1,
    reduction_factor=2,
)

reporter = CLIReporter(
    metric="best_score",
    mode="max",
    print_freq=1,
)

analysis = tune.run(
    train_model,
    config=param_space,
    num_samples=10,
    scheduler=scheduler,
    callbacks=[ray_train_report_callback],
    reporter=reporter,
)

# Print the best hyperparameters
print("Best Hyperparameters:", analysis.best_config)

In this code snippet, we define the hyperparameter search space and the training function. We then define the callback function using the correct metric key rmse. Finally, we define the hyperparameter search using the tune.run() function and print the best hyperparameters.

Conclusion

Q: What is hyperparameter search and why is it important?

A: Hyperparameter search is the process of finding the optimal values for the hyperparameters of a machine learning model. Hyperparameters are the parameters that are set before training the model, such as the learning rate, number of hidden layers, and regularization strength. Hyperparameter search is important because it can significantly improve the performance of a machine learning model.

Q: What is Ray and how does it relate to hyperparameter search?

A: Ray is an open-source framework for distributed computing and machine learning. It provides a simple and efficient way to perform hyperparameter search using the scikit-learn API. Ray allows you to easily distribute your hyperparameter search across multiple machines, making it much faster than traditional hyperparameter search methods.

Q: What is XGBoost and how does it relate to hyperparameter search?

A: XGBoost is a popular open-source library for gradient boosting. It provides a simple and efficient way to train gradient boosting models. XGBoost is often used for hyperparameter search because it provides a wide range of hyperparameters that can be tuned, such as the learning rate, number of trees, and regularization strength.

Q: What is the `RayTrainReportCallback` function and how does it relate to hyperparameter search?

A: The RayTrainReportCallback function is a callback function that is used to report the progress of the hyperparameter search. It is used to print the current epoch, metric, and value of the model. The RayTrainReportCallback function is an important part of the hyperparameter search process because it allows you to monitor the progress of the search and make adjustments as needed.

Q: How do I use the `RayTrainReportCallback` function in my hyperparameter search?

A: To use the RayTrainReportCallback function in your hyperparameter search, you need to define the callback function and pass it to the tune.run() function. The callback function should take a report object as input and print the current epoch, metric, and value of the model.

Q: What is the `get_dtrain()` method and how does it relate to hyperparameter search?

A: The get_dtrain() method is a method of the XGBoost regressor object that returns a DMatrix object. The DMatrix object contains the training data and the corresponding metrics. The get_dtrain() method is used to reveal the dictionary of metrics, which is an important part of the hyperparameter search process.

Q: How do I use the `get_dtrain()` method in my hyperparameter search?

A: To use the get_dtrain() method in your hyperparameter search, you need to call the method on the XGBoost regressor object and print the dictionary of metrics.

Q: What is the `rmse` metric and how does it relate to hyperparameter search?

A: The rmse metric is a metric that measures the root mean squared error of the model. It is an important metric in hyperparameter search because it provides a measure of the model's performance.

Q: How do I use the `rmse` metric in my hyperparameter search?

A: To use the rmse metric in your hyperparameter search, you need to define the metric in the tune.run() function and pass it to the RayTrainReportCallback function.

Q: What is the `best_score` metric and how does it relate to hyperparameter search?

A: The best_score metric is a metric that measures the best score of the model. It is an important metric in hyperparameter search because it provides a measure of the model's performance.

Q: How do I use the `best_score` metric in my hyperparameter search?

A: To use the best_score metric in your hyperparameter search, you need to define the metric in the tune.run() function and pass it to the RayTrainReportCallback function.

Q: What is the `HyperBandForResourceScheduler` and how does it relate to hyperparameter search?

A: The HyperBandForResourceScheduler is a scheduler that is used to schedule the hyperparameter search. It is an important part of the hyperparameter search process because it allows you to schedule the search across multiple machines.

Q: How do I use the `HyperBandForResourceScheduler` in my hyperparameter search?

A: To use the HyperBandForResourceScheduler in your hyperparameter search, you need to define the scheduler in the tune.run() function and pass it to the RayTrainReportCallback function.

Q: What is the `CLIReporter` and how does it relate to hyperparameter search?

A: The CLIReporter is a reporter that is used to report the progress of the hyperparameter search. It is an important part of the hyperparameter search process because it allows you to monitor the progress of the search and make adjustments as needed.

Q: How do I use the `CLIReporter` in my hyperparameter search?

A: To use the CLIReporter in your hyperparameter search, you need to define the reporter in the tune.run() function and pass it to the RayTrainReportCallback function.

Conclusion

In this article, we have answered some of the most frequently asked questions about hyperparameter search with Ray and XGBoost. We have covered topics such as the RayTrainReportCallback function, the get_dtrain() method, the rmse metric, the best_score metric, the HyperBandForResourceScheduler, and the CLIReporter. We hope that this article has been helpful in answering your questions and providing you with a better understanding of hyperparameter search with Ray and XGBoost.