Fixed codestyle

src/samplers.py

@@ -1,19 +1,21 @@
 """Samplers for training, validation, and testing."""
 
-import numpy as np
-import pandas as pd
 import abc
 
+import numpy as np
+
+
 class Sampler(abc.ABC):
     """Abstract base class for data samplers."""
+
     def __init__(self):
         """Blank constructor."""
         pass
 
     @abc.abstractmethod
     def create_samplers(self):
         """Create training, test, and validation samplers.
-        
+
         This should return a dictionary with "train", "val", "test" as keys and
         indices of datapoints as values.
         """
@@ -24,8 +26,10 @@ def name():
         """Name of the sampling method."""
         pass
 
+
 class RandomSampler(Sampler):
     """Perform uniform random sampling on datapoints."""
+
     def __init__(self, seed, dataset_size):
         """Initialize sampler.
 
@@ -66,21 +70,21 @@ def create_samplers(self, sample_config):
         train_size = int(np.ceil(sample_config["train"] * self.dataset_size))
         train_idx = idx_array[:train_size]
         val_size = int(np.ceil(sample_config["val"] * self.dataset_size))
-        val_idx = idx_array[train_size: train_size + val_size]
-        test_idx = idx_array[train_size + val_size:]
+        val_idx = idx_array[train_size : train_size + val_size]
+        test_idx = idx_array[train_size + val_size :]
 
         # Create samples
         samples = {"train": train_idx, "val": val_idx, "test": test_idx}
-        
+
         return samples
-    
+
     @staticmethod
     def name():
+        """Name of the sampling method."""
         return "random"
 
+
 if __name__ == "__main__":
     rs = RandomSampler(0, 100)
     samples = rs.create_samplers({"train": 0.6, "val": 0.2, "test": 0.2})
     print(samples)
-
-