clean up methods

Signed-off-by: kta-intel <[email protected]>

openfl/federated/data/loader_xgb.py

@@ -2,9 +2,10 @@
 
 import numpy as np
 import xgboost as xgb
+from openfl.federated.data.loader import DataLoader
 
 
-class XGBoostDataLoader:
+class XGBoostDataLoader(DataLoader):
     """A class used to represent a Data Loader for XGBoost models.
 
     Attributes:
@@ -44,37 +45,6 @@ def get_feature_shape(self):
         """
         return self.X_train[0].shape
 
-    def get_train_loader(self, batch_size=None, num_batches=None):
-        """Returns the data loader for the training data.
-
-        Args:
-            batch_size (int, optional): The batch size for the data loader
-                (default is None).
-            num_batches (int, optional): The number of batches for the data
-                loader (default is None).
-
-        Returns:
-            generator: The generator object for the training data.
-        """
-        return self._get_batch_generator(
-            X=self.X_train,
-            y=self.y_train,
-            batch_size=batch_size,
-            num_batches=num_batches,
-        )
-
-    def get_valid_loader(self, batch_size=None):
-        """Returns the data loader for the validation data.
-
-        Args:
-            batch_size (int, optional): The batch size for the data loader
-                (default is None).
-
-        Returns:
-            generator: The generator object for the validation data.
-        """
-        return self._get_batch_generator(X=self.X_valid, y=self.y_valid, batch_size=batch_size)
-
     def get_train_data_size(self):
         """Returns the total number of training samples.
 
@@ -90,55 +60,7 @@ def get_valid_data_size(self):
             int: The total number of validation samples.
         """
         return self.X_valid.shape[0]
-
-    @staticmethod
-    def _batch_generator(X, y, idxs, batch_size, num_batches):
-        """Generates batches of data.
-
-        Args:
-            X (np.array): The input data.
-            y (np.array): The label data.
-            idxs (np.array): The index of the dataset.
-            batch_size (int): The batch size for the data loader.
-            num_batches (int): The number of batches.
-
-        Yields:
-            tuple: The input data and label data for each batch.
-        """
-        for i in range(num_batches):
-            a = i * batch_size
-            b = a + batch_size
-            yield X[idxs[a:b]], y[idxs[a:b]]
-
-    def _get_batch_generator(self, X, y, batch_size, num_batches=None):
-        """Returns the dataset generator.
-
-        Args:
-            X (np.array): The input data.
-            y (np.array): The label data.
-            batch_size (int): The batch size for the data loader.
-            num_batches (int, optional): The number of batches (default is
-                None).
-
-        Returns:
-            generator: The dataset generator.
-        """
-        if batch_size is None:
-            batch_size = self.batch_size
-
-        # shuffle data indices
-        if self.random_seed is not None:
-            np.random.seed(self.random_seed)
-
-        idxs = np.random.permutation(np.arange(X.shape[0]))
-
-        # compute the number of batches
-        if num_batches is None:
-            num_batches = ceil(X.shape[0] / batch_size)
-
-        # build the generator and return it
-        return self._batch_generator(X, y, idxs, batch_size, num_batches)
-
+
     def get_dmatrix(self, X, y):
         """Returns the DMatrix for the given data.