precommit check fails without none type check in embeddings assignment (

#220)

src/htwgnlp/embeddings.py

@@ -35,6 +35,9 @@ def __init__(self) -> None:
     def embedding_values(self) -> np.ndarray:
         """Returns the embedding values.
 
+        Raises:
+            ValueError: if the embeddings have not been loaded yet
+
         Returns:
             np.ndarray: the embedding values as a numpy array of shape (n, d), where n is the vocabulary size and d is the number of dimensions
         """
@@ -77,6 +80,9 @@ def get_embeddings(self, word: str) -> np.ndarray | None:
         Args:
             word (str): the word to get the embedding vector for
 
+        Raises:
+            ValueError: if the embeddings have not been loaded yet
+
         Returns:
             np.ndarray | None: the embedding vector for the given word in the form of a numpy array of shape (d,), where d is the number of dimensions, or None if the word is not in the vocabulary
         """
@@ -125,6 +131,7 @@ def get_most_similar_words(
             metric (Literal["euclidean", "cosine"], optional): the metric to use for computing the similarity. Defaults to "euclidean".
 
         Raises:
+            ValueError: if the embeddings have not been loaded yet
             ValueError: if the metric is not "euclidean" or "cosine"
             AssertionError: if the word is not in the vocabulary
 
@@ -146,6 +153,7 @@ def find_closest_word(
             metric (Literal["euclidean", "cosine"], optional): the metric to use for computing the similarity. Defaults to "euclidean".
 
         Raises:
+            ValueError: if the embeddings have not been loaded yet
             ValueError: if the metric is not "euclidean" or "cosine"
 
         Returns:

tests/htwgnlp/test_embeddings.py

@@ -15,6 +15,11 @@ def embeddings():
     return WordEmbeddings()
 
 
+@pytest.fixture
+def non_loaded_embeddings():
+    return WordEmbeddings()
+
+
 @pytest.fixture
 def loaded_embeddings(embeddings):
     embeddings._load_raw_embeddings("notebooks/data/embeddings.pkl")
@@ -47,12 +52,16 @@ def test_load_embeddings_to_dataframe(loaded_embeddings):
     assert loaded_embeddings._embeddings_df.shape == (243, 300)
 
 
-def test_embedding_values(loaded_embeddings):
+def test_embedding_values(loaded_embeddings, non_loaded_embeddings):
+    with pytest.raises(ValueError):
+        non_loaded_embeddings.embedding_values
     assert isinstance(loaded_embeddings.embedding_values, np.ndarray)
     assert loaded_embeddings.embedding_values.shape == (243, 300)
 
 
-def test_get_embeddings(loaded_embeddings):
+def test_get_embeddings(loaded_embeddings, non_loaded_embeddings):
+    with pytest.raises(ValueError):
+        non_loaded_embeddings.embedding_values
     assert isinstance(loaded_embeddings.get_embeddings("happy"), np.ndarray)
     assert loaded_embeddings.get_embeddings("happy").shape == (300,)
     assert loaded_embeddings.get_embeddings("non_existent_word") is None
@@ -104,13 +113,17 @@ def test_cosine_similarity(loaded_embeddings, test_vector):
     )
 
 
-def test_find_closest_word(loaded_embeddings, test_vector):
+def test_find_closest_word(loaded_embeddings, test_vector, non_loaded_embeddings):
+    with pytest.raises(ValueError):
+        non_loaded_embeddings.embedding_values
     for metric in ["euclidean", "cosine"]:
         assert isinstance(loaded_embeddings.find_closest_word(test_vector, metric), str)
         assert loaded_embeddings.find_closest_word(test_vector, metric) == "Bahamas"
 
 
-def test_get_most_similar_words(loaded_embeddings):
+def test_get_most_similar_words(loaded_embeddings, non_loaded_embeddings):
+    with pytest.raises(ValueError):
+        non_loaded_embeddings.embedding_values
     assert isinstance(loaded_embeddings.get_most_similar_words("Germany"), list)
     assert loaded_embeddings.get_most_similar_words("Germany") == [
         "Austria",