feat(tests): customize tests to the new flow

tests/test_algo.py

@@ -1,18 +1,32 @@
+from typing import Optional
 from unittest.mock import patch
-
 from gbfs.algorithms.base import FeatureSelectorBase
+import pandas as pd
+
+
+class ConcreteFeatureSelector(FeatureSelectorBase):
+    def select_features(self) -> Optional[list]:
+        return []
 
 
 @patch('gbfs.models.dim_reducer.DimReducerProtocol')
-def test_initialization(mock_dim_reducer_protocol):
+@patch('pandas.read_csv')
+def test_initialization(mock_read_csv, mock_dim_reducer_protocol):
     """
     Test initialization of FeatureSelectorBase with mocked dependencies.
     """
+    # Mock read_csv to return a DataFrame
+    mock_read_csv.return_value = pd.DataFrame({
+        'feature1': [1, 2, 3],
+        'feature2': [4, 5, 6],
+        'class': [0, 1, 0]
+    })
+
     dataset_path = 'tests/dataset.csv'
     separability_metric = 'jm'
     label_column = 'class'
 
-    fs_base = FeatureSelectorBase(
+    fs_base = ConcreteFeatureSelector(
         dataset_path,
         separability_metric,
         mock_dim_reducer_protocol,

tests/test_data_processor.py

@@ -59,10 +59,11 @@ def test_compute_data_properties():
             }
         ),
     )
+    feature_costs = {'feature1': 1.0, 'feature2': 1.0}
 
     data_processor = DataProcessor('tests/dataset.csv')
 
-    data_props = data_processor._compute_data_properties(mock_data_collection)
+    data_props = data_processor._compute_data_properties(mock_data_collection, feature_costs)
 
     assert isinstance(data_props, DataProps), 'Should return a DataProps instance.'
     assert data_props.n_features == 2, 'There should be two features.'