ray.train.xgboost.XGBoostPredictor.predict

XGBoostPredictor.predict(data: Union[numpy.ndarray, pandas.DataFrame, Dict[str, numpy.ndarray]], feature_columns: Optional[Union[List[str], List[int]]] = None, dmatrix_kwargs: Optional[Dict[str, Any]] = None, **predict_kwargs) → Union[numpy.ndarray, pandas.DataFrame, Dict[str, numpy.ndarray]]

Run inference on data batch.

The data is converted into an XGBoost DMatrix before being inputted to the model.

Parameters

• data – A batch of input data.

• feature_columns – The names or indices of the columns in the data to use as features to predict on. If None, then use all columns in data.

• dmatrix_kwargs – Dict of keyword arguments passed to xgboost.DMatrix.

• **predict_kwargs – Keyword arguments passed to xgboost.Booster.predict.

Examples:

import numpy as np
import xgboost as xgb
from ray.train.xgboost import XGBoostPredictor
train_X = np.array([[1, 2], [3, 4]])
train_y = np.array([0, 1])
model = xgb.XGBClassifier().fit(train_X, train_y)
predictor = XGBoostPredictor(model=model.get_booster())
data = np.array([[1, 2], [3, 4]])
predictions = predictor.predict(data)
# Only use first and second column as the feature
data = np.array([[1, 2, 8], [3, 4, 9]])
predictions = predictor.predict(data, feature_columns=[0, 1])

import pandas as pd
import xgboost as xgb
from ray.train.xgboost import XGBoostPredictor
train_X = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
train_y = pd.Series([0, 1])
model = xgb.XGBClassifier().fit(train_X, train_y)
predictor = XGBoostPredictor(model=model.get_booster())
# Pandas dataframe.
data = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
predictions = predictor.predict(data)
# Only use first and second column as the feature
data = pd.DataFrame([[1, 2, 8], [3, 4, 9]], columns=["A", "B", "C"])
predictions = predictor.predict(data, feature_columns=["A", "B"])

Returns

Prediction result.