tensortrade.pipeline.transformers.catboost_rfecv module

class tensortrade.pipeline.transformers.catboost_rfecv.CatBoostRFECVTransformer(num_features: int = 20, seed: int = 42, *, iterations: int = 1000, target_column: str = 'close', target_shift: int = 3, task_type: str = 'CPU', learning_rate: float | None = None, max_depth: int = 8)[source]

Bases: AbstractTransformer

Transformer for selecting top features using Recursive Feature Elimination with Cross-Validation (RFECV) with CatBoostRegressor as the underlying model.

Parameters:

  • num_features (int) – The number of top features to select. (Default = 20)

  • seed (int) – The seed used for the RFECV. (Default = 42)

  • iterations (int) – CatBoostRegressor iterations. Should be at minimum 5 to 10 times the number of features. (Default = 1000)

  • target_column (str) – The name of the target column on which the feature elimination should be performed. (Default = ‘close’)

  • target_shift (int) – The number of periods to shift the target column to create the prediction target. (Default = 3)

  • task_type (str) – The type of the CatBoostRegressor task, can be CPU or GPU. (Default = ‘CPU’)

  • learning_rate (Optional[float]) – Learning rate used for the CatBoostRegressor. If None, it is chosen dynamically by CatBoost.

  • max_depth (int) – Max depth used for the CatBoostRegressor. (Default = 8)

transform(df: DataFrame) → DataFrame[source]

Transforms the input DataFrame by selecting the top features using RFECV with CatBoostRegressor.

Parameters:

df (DataFrame) – The input DataFrame containing the features and target column.

Returns:

A DataFrame reduced to the top features based on RFECV.

Return type:

DataFrame