Configure XGBoost "rank:map" Objective

XGBoost’s “rank:map” objective is a powerful tool for tackling learning to rank problems, where the goal is to optimize the Mean Average Precision (MAP) metric.

This objective is particularly useful in applications such as search engines, recommendation systems, and ad ranking, where the order of the results is crucial.

To demonstrate how to configure XGBoost with the “rank:map” objective, let’s walk through an example using a synthetic dataset:

import xgboost as xgb
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Generate a synthetic dataset for ranking
X, y = make_classification(n_samples=1000, n_features=20, n_informative=5, n_clusters_per_class=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Group data points to form queries in a ranking context
# Ensuring the sum of groups equals the number of training data points (80% of 1000)
group_sizes = [200, 200, 200, 200]

# Convert data into DMatrix, specifying group information for ranking
dtrain = xgb.DMatrix(X_train, label=y_train)
dtrain.set_group(group_sizes)

# Define parameters for the model using the 'rank:map' objective
params = {
    'objective': 'rank:map',
    'learning_rate': 0.1,
    'gamma': 0.1,
    'min_child_weight': 0.1,
    'max_depth': 6
}

# Train the model
bst = xgb.train(params, dtrain, num_boost_round=100)

# Make predictions on the test set
predictions = bst.predict(xgb.DMatrix(X_test))
print("Predicted rankings:", predictions)

When using the “rank:map” objective, it’s essential to consider the following:

1. Defining Groups/Queries: Ensure that your data points are correctly grouped to represent the queries or user sessions for which you want to optimize the ranking. The model will learn to rank the items within each group.

2. Hyperparameter Tuning: The gamma, min_child_weight, and max_depth hyperparameters significantly impact the model’s complexity and its ability to learn the ranking function. Tune these parameters to find the optimal balance between model complexity and ranking performance.

3. Evaluation Metrics: In addition to MAP, consider using other ranking metrics such as Normalized Discounted Cumulative Gain (NDCG) or Mean Reciprocal Rank (MRR) to evaluate your model’s performance. These metrics provide insights into how well your model is ranking the items within each group.

By configuring XGBoost with the “rank:map” objective and following these tips, you can effectively train models for learning to rank tasks and optimize the order of results in various applications.