ray.train.lightgbm.LightGBMPredictor.predict#

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

Run inference on data batch.

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.

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

Examples

>>> import numpy as np
>>> import lightgbm as lgbm
>>> from ray.train.lightgbm import LightGBMPredictor
>>>
>>> train_X = np.array([[1, 2], [3, 4]])
>>> train_y = np.array([0, 1])
>>>
>>> model = lgbm.LGBMClassifier().fit(train_X, train_y)
>>> predictor = LightGBMPredictor(model=model.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 lightgbm as lgbm
>>> from ray.train.lightgbm import LightGBMPredictor
>>>
>>> train_X = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
>>> train_y = pd.Series([0, 1])
>>>
>>> model = lgbm.LGBMClassifier().fit(train_X, train_y)
>>> predictor = LightGBMPredictor(model=model.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.