ray.train.sklearn.SklearnPredictor#

class ray.train.sklearn.SklearnPredictor(estimator: sklearn.base.BaseEstimator, preprocessor: Optional[Preprocessor] = None)[source]#

Bases: ray.train.predictor.Predictor

A predictor for scikit-learn compatible estimators.

Parameters

  • estimator – The fitted scikit-learn compatible estimator to use for predictions.

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

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

classmethod from_checkpoint(checkpoint: ray.air.checkpoint.Checkpoint) → ray.train.sklearn.sklearn_predictor.SklearnPredictor[source]#

Instantiate the predictor from a Checkpoint.

The checkpoint is expected to be a result of SklearnTrainer.

Parameters

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

predict(data: Union[numpy.ndarray, pandas.DataFrame, Dict[str, numpy.ndarray]], feature_columns: Optional[Union[List[str], List[int]]] = None, num_estimator_cpus: Optional[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. Either a pandas DataFrame or numpy array.

  • 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.

  • num_estimator_cpus – If set to a value other than None, will set the values of all n_jobs and thread_count parameters in the estimator (including in nested objects) to the given value.

  • **predict_kwargs – Keyword arguments passed to estimator.predict.

Examples

>>> import numpy as np
>>> from sklearn.ensemble import RandomForestClassifier
>>> from ray.train.sklearn import SklearnPredictor
>>>
>>> train_X = np.array([[1, 2], [3, 4]])
>>> train_y = np.array([0, 1])
>>>
>>> model = RandomForestClassifier().fit(train_X, train_y)
>>> predictor = SklearnPredictor(estimator=model)
>>>
>>> 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
>>> from sklearn.ensemble import RandomForestClassifier
>>> from ray.train.sklearn import SklearnPredictor
>>>
>>> train_X = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
>>> train_y = pd.Series([0, 1])
>>>
>>> model = RandomForestClassifier().fit(train_X, train_y)
>>> predictor = SklearnPredictor(estimator=model)
>>>
>>> # 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.