class_docstr: A predictor for XGBoost models.
  
  Args:
      model: The XGBoost booster to use for predictions.
      preprocessor: A preprocessor used to transform data batches prior
          to prediction.
docstr: Run inference on data batch.
  
  The data is converted into an XGBoost DMatrix before being inputted to
  the model.
  
  Args:
      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:
  
  .. testcode::
  
      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])
  
  .. testcode::
  
      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.
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code: def predict(
      self,
      data: DataBatchType,
      feature_columns: Optional[Union[List[str], List[int]]] = None,
      dmatrix_kwargs: Optional[Dict[str, Any]] = None,
      **predict_kwargs,
  ) -> DataBatchType:
      """Run inference on data batch.
  
      The data is converted into an XGBoost DMatrix before being inputted to
      the model.
  
      Args:
          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:
  
      .. testcode::
  
          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])
  
      .. testcode::
  
          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.
  
      """
      return Predictor.predict(
          self,
          data,
          feature_columns=feature_columns,
          dmatrix_kwargs=dmatrix_kwargs,
          **predict_kwargs,
      )
  
file_path: ray/python/ray/train/xgboost/xgboost_predictor.py
imports: from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  import pandas
  import xgboost
  from ray.air.constants import TENSOR_COLUMN_NAME
  from ray.air.data_batch_type import DataBatchType
  from ray.air.util.data_batch_conversion import _unwrap_ndarray_object_type_if_needed
  from ray.train.predictor import Predictor
  from ray.train.xgboost import XGBoostCheckpoint
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 4 for calling the XGBoostPredictor obj's underlying member method code with example usage: obj.predict(data, feature_columns, dmatrix_kwargs) and returns: Predictor