def test_typed_dict():
# load training data
X, y = load_gunpoint(split="train", return_X_y=True)
word_length = 6
alphabet_size = 4
p = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
levels=2,
typed_dict=True,
)
p.fit(X, y)
word_list = p.bag_to_string(p.transform(X, y)[0][0])
word_length = 6
alphabet_size = 4
p2 = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
levels=2,
typed_dict=False,
)
p2.fit(X, y)
word_list2 = p2.bag_to_string(p2.transform(X, y)[0][0])
assert word_list == word_list2
def test_transformer(binning_method):
# load training data
X, y = load_gunpoint(split="train", return_X_y=True)
word_length = 6
alphabet_size = 4
p = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
binning_method=binning_method,
)
p.fit(X, y)
assert p.breakpoints.shape == (word_length, alphabet_size)
i = sys.float_info.max if binning_method == "information-gain" else 0
assert np.equal(i, p.breakpoints[1, :-1]).all() # imag component is 0 or inf
_ = p.transform(X, y)