Prediction

There are a number of prediction functions in XGBoost with various parameters. This document attempts to clarify some of confusions around prediction with a focus on the Python binding, R package is similar when strict_shape is specified (see below).

Prediction Options

There are a number of different prediction options for the xgboost.Booster.predict() method, ranging from pred_contribs to pred_leaf. The output shape depends on types of prediction. Also for multi-class classification problem, XGBoost builds one tree for each class and the trees for each class are called a “group” of trees, so output dimension may change due to used model. After 1.4 release, we added a new parameter called strict_shape, one can set it to True to indicate a more restricted output is desired. Assuming you are using xgboost.Booster, here is a list of possible returns:

When using normal prediction with strict_shape set to True:

Output is a 2-dim array with first dimension as rows and second as groups. For regression/survival/ranking/binary classification this is equivalent to a column vector with shape[1] == 1. But for multi-class with multi:softprob the number of columns equals to number of classes. If strict_shape is set to False then XGBoost might output 1 or 2 dim array.
When using output_margin to avoid transformation and strict_shape is set to True:

Similar to the previous case, output is a 2-dim array, except for that multi:softmax has equivalent output shape of multi:softprob due to dropped transformation. If strict shape is set to False then output can have 1 or 2 dim depending on used model.
When using pred_contribs with strict_shape set to True:

Output is a 3-dim array, with (rows, groups, columns + 1) as shape. Whether approx_contribs is used does not change the output shape. If the strict shape parameter is not set, it can be a 2 or 3 dimension array depending on whether multi-class model is being used.
When using pred_interactions with strict_shape set to True:

Output is a 4-dim array, with (rows, groups, columns + 1, columns + 1) as shape. Like the predict contribution case, whether approx_contribs is used does not change the output shape. If strict shape is set to False, it can have 3 or 4 dims depending on the underlying model.
When using pred_leaf with strict_shape set to True:

Output is a 4-dim array with (n_samples, n_iterations, n_classes, n_trees_in_forest) as shape. n_trees_in_forest is specified by the numb_parallel_tree during training. When strict shape is set to False, output is a 2-dim array with last 3 dims concatenated into 1. Also the last dimension is dropped if it equals to 1. When using apply method in scikit learn interface, this is set to False by default.

For R package, when strict_shape is specified, an array is returned, with the same value as Python except R array is column-major while Python numpy array is row-major, so all the dimensions are reversed. For example, for a Python predict_leaf output obtained by having strict_shape=True has 4 dimensions: (n_samples, n_iterations, n_classes, n_trees_in_forest), while R with strict_shape=TRUE outputs (n_trees_in_forest, n_classes, n_iterations, n_samples).

Other than these prediction types, there’s also a parameter called iteration_range, which is similar to model slicing. But instead of actually splitting up the model into multiple stacks, it simply returns the prediction formed by the trees within range. Number of trees created in each iteration equals to \(trees_i = num\_class \times num\_parallel\_tree\). So if you are training a boosted random forest with size of 4, on the 3-class classification dataset, and want to use the first 2 iterations of trees for prediction, you need to provide iteration_range=(0, 2). Then the first \(2 \times 3 \times 4\) trees will be used in this prediction.

Early Stopping

When a model is trained with early stopping, there is an inconsistent behavior between native Python interface and sklearn/R interfaces. By default on R and sklearn interfaces, the best_iteration is automatically used so prediction comes from the best model. But with the native Python interface xgboost.Booster.predict() and xgboost.Booster.inplace_predict() uses the full model. Users can use best_iteration attribute with iteration_range parameter to achieve the same behavior. Also the save_best parameter from xgboost.callback.EarlyStopping might be useful.

Base Margin

There’s a training parameter in XGBoost called base_score, and a meta data for DMatrix called base_margin (which can be set in fit method if you are using scikit-learn interface). They specifies the global bias for boosted model. If the latter is supplied then former is ignored. base_margin can be used to train XGBoost model based on other models. See demos on boosting from predictions.

Staged Prediction

Using the native interface with DMatrix, prediction can be staged (or cached). For example, one can first predict on the first 4 trees then run prediction on 8 trees. After running the first prediction, result from first 4 trees are cached so when you run the prediction with 8 trees XGBoost can reuse the result from previous prediction. The cache expires automatically upon next prediction, train or evaluation if the cached DMatrix object is expired (like going out of scope and being collected by garbage collector in your language environment).

In-place Prediction

Traditionally XGBoost accepts only DMatrix for prediction, with wrappers like scikit-learn interface the construction happens internally. We added support for in-place predict to bypass the construction of DMatrix, which is slow and memory consuming. The new predict function has limited features but is often sufficient for simple inference tasks. It accepts some commonly found data types in Python like numpy.ndarray, scipy.sparse.csr_matrix and cudf.DataFrame instead of xgboost.DMatrix. You can call xgboost.Booster.inplace_predict() to use it. Be aware that the output of in-place prediction depends on input data type, when input is on GPU data output is cupy.ndarray, otherwise a numpy.ndarray is returned.

Thread Safety

After 1.4 release, all prediction functions including normal predict with various parameters like shap value computation and inplace_predict are thread safe when underlying booster is gbtree or dart, which means as long as tree model is used, prediction itself should thread safe. But the safety is only guaranteed with prediction. If one tries to train a model in one thread and provide prediction at the other using the same model the behaviour is undefined. This happens easier than one might expect, for instance we might accidentally call clf.set_params() inside a predict function:

def predict_fn(clf: xgb.XGBClassifier, X):
    X = preprocess(X)
    clf.set_params(n_jobs=1)  # NOT safe!
    return clf.predict_proba(X, iteration_range=(0, 10))

with ThreadPoolExecutor(max_workers=10) as e:
    e.submit(predict_fn, ...)

Privacy-Preserving Prediction

Concrete ML is a third-party open-source library developed by Zama that proposes gradient boosting classes similar to ours, but predicting directly over encrypted data, thanks to Fully Homomorphic Encryption. A simple example would be as follows:

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from concrete.ml.sklearn import XGBClassifier

x, y = make_classification(n_samples=100, class_sep=2, n_features=30, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(
    x, y, test_size=10, random_state=42
)

# Train in the clear and quantize the weights
model = XGBClassifier()
model.fit(X_train, y_train)

# Simulate the predictions in the clear
y_pred_clear = model.predict(X_test)

# Compile in FHE
model.compile(X_train)

# Generate keys
model.fhe_circuit.keygen()

# Run the inference on encrypted inputs!
y_pred_fhe = model.predict(X_test, fhe="execute")

print("In clear  :", y_pred_clear)
print("In FHE    :", y_pred_fhe)
print(f"Similarity: {int((y_pred_fhe == y_pred_clear).mean()*100)}%")

More information and examples are given in the Concrete ML documentation.