fix: B904

Author: mkumar73

mambular/preprocessing/prepro_utils.py

@@ -57,10 +57,7 @@ def fit(self, X, y=None):
             self: Returns the instance itself.
         """
         # Fit should determine the mapping from original categories to sequential integers starting from 0
-        self.mapping_ = [
-            {category: i + 1 for i, category in enumerate(np.unique(col))}
-            for col in X.T
-        ]
+        self.mapping_ = [{category: i + 1 for i, category in enumerate(np.unique(col))} for col in X.T]
         for mapping in self.mapping_:
             mapping[None] = 0  # Assign 0 to unknown values
         return self
@@ -75,12 +72,7 @@ def transform(self, X):
             X_transformed (ndarray of shape (n_samples, n_features)): The transformed data with integer values.
         """
         # Transform the categories to their mapped integer values
-        X_transformed = np.array(
-            [
-                [self.mapping_[col].get(value, 0) for col, value in enumerate(row)]
-                for row in X
-            ]
-        )
+        X_transformed = np.array([[self.mapping_[col].get(value, 0) for col, value in enumerate(row)] for row in X])
         return X_transformed
 
     def get_feature_names_out(self, input_features=None):
@@ -122,9 +114,7 @@ def fit(self, X, y=None):
         Returns:
             self: Returns the instance itself.
         """
-        self.max_bins_ = (
-            np.max(X, axis=0).astype(int) + 1
-        )  # Find the maximum bin index for each feature
+        self.max_bins_ = np.max(X, axis=0).astype(int) + 1  # Find the maximum bin index for each feature
         return self
 
     def transform(self, X):
@@ -207,9 +197,7 @@ def get_feature_names_out(self, input_features=None):
             feature_names (array of shape (n_features,)): The original feature names.
         """
         if input_features is None:
-            raise ValueError(
-                "input_features must be provided to generate feature names."
-            )
+            raise ValueError("input_features must be provided to generate feature names.")
         return np.array(input_features)
 
 
@@ -243,10 +231,10 @@ def __init__(self, model_name="paraphrase-MiniLM-L3-v2", model=None):
                 from sentence_transformers import SentenceTransformer
 
                 self.model = SentenceTransformer(model_name)
-            except ImportError:
+            except ImportError as e:
                 raise ImportError(
                     "sentence-transformers is not installed. Install it via `pip install sentence-transformers` or provide a preloaded model."
-                )
+                ) from e
 
     def fit(self, X, y=None):
         """Fit method (not required for a transformer but included for compatibility)."""
@@ -264,13 +252,11 @@ def transform(self, X):
         - A 2D numpy array with embeddings for each text input.
         """
         if isinstance(X, np.ndarray):
-            X = (
-                X.flatten().astype(str).tolist()
-            )  # Convert to a list of strings if passed as an array
+            X = X.flatten().astype(str).tolist()  # Convert to a list of strings if passed as an array
         elif isinstance(X, list):
             X = [str(x) for x in X]  # Ensure everything is a string
 
-        embeddings = self.model.encode(
-            X, convert_to_numpy=True
-        )  # Get sentence embeddings
+        if self.model is None:
+            raise ValueError("Model is not initialized. Ensure that the model is properly loaded.")
+        embeddings = self.model.encode(X, convert_to_numpy=True)  # Get sentence embeddings
         return embeddings