def test_infer_from_data():
X = ["One", "Two words", "Three words here"]
keras_vectorizer = KerasVectorizer()
keras_vectorizer.fit(X)
assert keras_vectorizer.sequence_length == 3
def test_build_embedding_matrix_word_vectors():
X = ["One", "Two", "Three"]
vocab_size = 1
keras_vectorizer = KerasVectorizer(vocab_size=vocab_size)
keras_vectorizer.fit(X)
embedding_matrix = keras_vectorizer.build_embedding_matrix(
embeddings_name_or_path="glove-twitter-25")
assert embedding_matrix.shape == (5, 25)
def test_build_embedding_matrix():
X = ["One", "Two", "Three"]
vocab_size = 1
keras_vectorizer = KerasVectorizer(vocab_size=vocab_size)
keras_vectorizer.fit(X)
with tempfile.TemporaryDirectory() as tmp_dir:
embeddings_path = os.path.join(tmp_dir, "embeddings.csv")
embeddings = [
"one 0 1 0 0 0",
"two 0 0 1 0 0",
"three 0 0 0 1 0",
"four 0 0 0 0 1",
]
with open(embeddings_path, "w") as embeddings_path_tmp:
for line in embeddings:
embeddings_path_tmp.write(line)
embeddings_path_tmp.write("\n")
embedding_matrix = keras_vectorizer.build_embedding_matrix(
embeddings_name_or_path=embeddings_path)
assert embedding_matrix.shape == (5, 5)