def test_XY_dataset_sparse_y():
X = ["One and two", "One only", "Two nothing else", "Two and three"]
Y = np.array([[1, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 0]])
Y_sparse = csr_matrix(Y)
vec = KerasVectorizer()
X_vec = vec.fit_transform(X)
data = tf.data.Dataset.from_tensor_slices((X_vec, Y))
data = data.shuffle(100, seed=42)
clf = CNNClassifier(batch_size=2, sparse_y=True, multilabel=True)
clf.fit(data)
assert clf.score(data, Y_sparse) > 0.3
def test_XY_dataset():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
vec = KerasVectorizer()
X_vec = vec.fit_transform(X)
data = tf.data.Dataset.from_tensor_slices((X_vec, Y))
data = data.shuffle(100, seed=42)
clf = CNNClassifier(batch_size=2)
clf.fit(data)
assert clf.score(data, Y) > 0.3
def test_save_load():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
vec = KerasVectorizer()
X_vec = vec.fit_transform(X)
model = CNNClassifier()
model.fit(X_vec, Y)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save(tmp_dir)
loaded_model = CNNClassifier()
loaded_model.load(tmp_dir)
assert hasattr(loaded_model, 'model')
assert loaded_model.score(X_vec, Y) > 0.6
