def test_sfa_anova():
# load training data
X, Y = load_gunpoint(split="train", return_X_y=True)
word_length = 6
alphabet_size = 4
for binning in ["information-gain", "equi-depth"]:
print("SFA with ANOVA one-sided test")
window_size = 32
p = SFA(word_length=word_length, anova=True,
alphabet_size=alphabet_size, window_size=window_size,
binning_method=binning).fit(X, Y)
print(p.breakpoints)
print(p.support)
print(p.dft_length)
assert p.breakpoints.shape == (word_length, alphabet_size)
print("SFA with first feq coefficients")
p2 = SFA(word_length=word_length, anova=False,
alphabet_size=alphabet_size, window_size=window_size,
binning_method=binning).fit(X, Y)
print(p2.breakpoints)
print(p2.support)
print(p2.dft_length)
assert(p.dft_length != p2.dft_length)
assert(p.breakpoints != p2.breakpoints).any()
def test_transformer():
# 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="equi-depth").fit(X, Y)
print("Equi Depth")
print(p.breakpoints)
assert p.breakpoints.shape == (word_length, alphabet_size)
assert np.equal(0, p.breakpoints[1, :-1]).all() # imag component is 0
p = SFA(word_length=word_length, alphabet_size=alphabet_size,
binning_method="equi-width").fit(X, Y)
print("Equi Width")
print(p.breakpoints)
assert p.breakpoints.shape == (word_length, alphabet_size)
assert np.equal(0, p.breakpoints[1, :-1]).all() # imag component is 0
p = SFA(word_length=word_length, alphabet_size=alphabet_size,
binning_method="information-gain").fit(X, Y)
print("Information Gain")
print(p.breakpoints)
assert p.breakpoints.shape == (word_length, alphabet_size)
print(p.breakpoints[1, :-1])
assert np.equal(0, p.breakpoints[1, :-1]).all() # imaginary component is 0
def test_dft_mft():
# load training data
X, Y = load_gunpoint(split="train", return_X_y=True)
X_tab = tabularize(X, return_array=True)
word_length = 6
alphabet_size = 4
print("Single DFT transformation")
window_size = np.shape(X_tab)[1]
p = SFA(word_length=word_length, alphabet_size=alphabet_size,
window_size=window_size, binning_method="equi-depth").fit(X, Y)
dft = p._discrete_fourier_transform(X_tab[0])
mft = p._mft(X_tab[0])
assert ((mft-dft < 0.0001).all())
print("Windowed DFT transformation")
for norm in [True, False]:
for window_size in [140]:
p = SFA(word_length=word_length, norm=norm,
alphabet_size=alphabet_size, window_size=window_size,
binning_method="equi-depth").fit(X, Y)
mft = p._mft(X_tab[0])
for i in range(len(X_tab[0]) - window_size + 1):
dft_transformed = p._discrete_fourier_transform(
X_tab[0, i:window_size+i])
assert(mft[i] - dft_transformed < 0.001).all()
assert(len(mft) == len(X_tab[0]) - window_size + 1)
assert(len(mft[0]) == word_length)
def test_transformer():
# 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,
anova=False,
binning_method="equi-depth",
).fit(X, y)
# print("Equi Depth")
# print(p.breakpoints)
assert p.breakpoints.shape == (word_length, alphabet_size)
assert np.equal(0, p.breakpoints[1, :-1]).all() # imag component is 0
_ = p.transform(X, y)
p = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
anova=False,
binning_method="equi-width",
).fit(X, y)
# print("Equi Width")
# print(p.breakpoints)
assert p.breakpoints.shape == (word_length, alphabet_size)
assert np.equal(0, p.breakpoints[1, :-1]).all() # imag component is 0
_ = p.transform(X, y)
p = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
anova=False,
binning_method="information-gain",
).fit(X, y)
# print("Information Gain")
# print(p.breakpoints)
assert p.breakpoints.shape == (word_length, alphabet_size)
# print(p.breakpoints[1, :-1])
assert np.equal(sys.float_info.max, p.breakpoints[1, :-1]).all()
_ = p.transform(X, y)
def test_word_lengths():
# load training data
X, y = load_gunpoint(split="train", return_X_y=True)
word_lengths = [6, 7]
alphabet_size = 4
window_sizes = [5, 6]
try:
for binning in ["equi-depth", "information-gain"]:
for word_length in word_lengths:
for bigrams in [True, False]:
for norm in [True, False]:
for anova in [True, False]:
for window_size in window_sizes:
p = SFA(
word_length=word_length,
anova=anova,
alphabet_size=alphabet_size,
bigrams=bigrams,
window_size=window_size,
norm=norm,
binning_method=binning,
).fit(X, y)
# print("Norm", norm, "Anova", anova)
# print(np.shape(p.breakpoints), word_length,
# window_size)
# print("dft_length", p.dft_length,
# "word_length", p.word_length)
assert p.breakpoints is not None
_ = p.transform(X, y)
except Exception as err:
raise AssertionError("An unexpected exception {0} raised.".format(
repr(err)))
def test_word_length():
# load training data
X, Y = load_gunpoint(split="train", return_X_y=True)
word_lengths = [6, 7, 8, 11]
alphabet_size = 4
window_sizes = [4, 5, 6, 16]
for binning in ["equi-depth", "information-gain"]:
for word_length in word_lengths:
for norm in [True, False]:
for anova in [True, False]:
for window_size in window_sizes:
p = SFA(word_length=word_length, anova=anova,
alphabet_size=alphabet_size,
window_size=window_size, norm=norm,
binning_method=binning).fit(X, Y)
print("Norm", norm, "Anova", anova)
print(np.shape(p.breakpoints), word_length,
window_size)
print("dft_length", p.dft_length, "word_length",
p.word_length)