image-match/python/py/Lib/site-packages/ray/train/lightgbm/lightgbm_predictor.py

from typing import TYPE_CHECKING, List, Optional, Union

import lightgbm
import pandas as pd
from pandas.api.types import is_object_dtype

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.lightgbm import LightGBMCheckpoint
from ray.train.predictor import Predictor
from ray.util.annotations import Deprecated

if TYPE_CHECKING:
    from ray.data.preprocessor import Preprocessor


@Deprecated
class LightGBMPredictor(Predictor):
    """A predictor for LightGBM models.

    Args:
        model: The LightGBM booster to use for predictions.
        preprocessor: A preprocessor used to transform data batches prior
            to prediction.
    """

    def __init__(
        self, model: lightgbm.Booster, preprocessor: Optional["Preprocessor"] = None
    ):
        self.model = model
        super().__init__(preprocessor)

    def __repr__(self):
        return (
            f"{self.__class__.__name__}(model={self.model!r}, "
            f"preprocessor={self._preprocessor!r})"
        )

    @classmethod
    def from_checkpoint(cls, checkpoint: LightGBMCheckpoint) -> "LightGBMPredictor":
        """Instantiate the predictor from a LightGBMCheckpoint.

        Args:
            checkpoint: The checkpoint to load the model and preprocessor from.

        """
        model = checkpoint.get_model()
        preprocessor = checkpoint.get_preprocessor()
        return cls(model=model, preprocessor=preprocessor)

    def predict(
        self,
        data: DataBatchType,
        feature_columns: Optional[Union[List[str], List[int]]] = None,
        **predict_kwargs,
    ) -> DataBatchType:
        """Run inference on data batch.

        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``.
            **predict_kwargs: Keyword arguments passed to
                ``lightgbm.Booster.predict``.

        Examples:
            .. testcode::

                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.

        """
        return Predictor.predict(
            self, data, feature_columns=feature_columns, **predict_kwargs
        )

    def _predict_pandas(
        self,
        data: "pd.DataFrame",
        feature_columns: Optional[Union[List[str], List[int]]] = None,
        **predict_kwargs,
    ) -> pd.DataFrame:
        feature_names = None
        if TENSOR_COLUMN_NAME in data:
            data = data[TENSOR_COLUMN_NAME].to_numpy()
            data = _unwrap_ndarray_object_type_if_needed(data)
            if feature_columns:
                # In this case feature_columns is a list of integers
                data = data[:, feature_columns]
            # Turn into dataframe to make dtype resolution easy
            data = pd.DataFrame(data, columns=feature_names)
            data = data.infer_objects()

            # Pandas does not detect categorical dtypes. Any remaining object
            # dtypes are probably categories, so convert them.
            # This will fail if we have a category composed entirely of
            # integers, but this is the best we can do here.
            update_dtypes = {}
            for column in data.columns:
                dtype = data.dtypes[column]
                if is_object_dtype(dtype):
                    update_dtypes[column] = pd.CategoricalDtype()

            if update_dtypes:
                data = data.astype(update_dtypes, copy=False)
        elif feature_columns:
            # feature_columns is a list of integers or strings
            data = data[feature_columns]

        df = pd.DataFrame(self.model.predict(data, **predict_kwargs))
        df.columns = (
            ["predictions"]
            if len(df.columns) == 1
            else [f"predictions_{i}" for i in range(len(df.columns))]
        )
        return df