ray.train.xgboost.XGBoostPredictor#

class ray.train.xgboost.XGBoostPredictor(model: xgboost.core.Booster, preprocessor: Optional[Preprocessor] = None)[source]#

Bases: ray.train.predictor.Predictor

A predictor for XGBoost models.

Parameters

  • model – The XGBoost booster to use for predictions.

  • preprocessor – A preprocessor used to transform data batches prior to prediction.

PublicAPI (beta): This API is in beta and may change before becoming stable.

classmethod from_checkpoint(checkpoint: ray.air.checkpoint.Checkpoint) → ray.train.xgboost.xgboost_predictor.XGBoostPredictor[source]#

Instantiate the predictor from a Checkpoint.

The checkpoint is expected to be a result of XGBoostTrainer.

Parameters

checkpoint – The checkpoint to load the model and preprocessor from. It is expected to be from the result of a XGBoostTrainer run.

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]][source]#

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]])
>>> predictor.predict(data, feature_columns=[0, 1])
array([0.5, 0.5], dtype=float32)

>>> 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"])
>>> predictor.predict(data, feature_columns=["A", "B"])
   predictions
0          0.5
1          0.5
Returns

Prediction result.