def test_transform(self):
xform = TransformChain([SignalGenerator(), SegmentSignal()])
x, y = xform.transform('sin')
self.assertEqual(list(numpy.round(x[0][:3], 4)), [0, 0.0063, 0.0126])
self.assertEqual(len(x[0]), xform.transforms[1].chunksize)
self.assertEqual(len(x[1]), xform.transforms[1].chunksize)
return
def test_already_flat(self):
gen = TransformChain(VariableSignalGenerator(fs=10000), Reduce())
X, Y = gen.fit_transform([{'sin': 100}, {'cos': 150}], [0, 1])
self.assertEqual(len(X), 2)
self.assertEqual(len(X[0]), 10000)
self.assertEqual(list(Y), [0, 1])
return
def test_inverse_fit_transform(self):
# non-flattening transform
ogen = TransformChain(VariableSignalGenerator(fs=10000),
WhiteNoise(sigma=0.1, clones=2),
SegmentSignal(chunksize=200))
# flattening transform
fgen = ogen.clone()
fgen.add(Reduce())
# transform/inverse-transform
fX, fY = fgen.fit_transform([{'sin': 100}, {'cos': 150}], [0, 1])
tX, tY = fgen.inverse_fit_transform(fX, fY)
# expected inversion
fX, fY = ogen.fit_transform([{'sin': 100}, {'cos': 150}], [0, 1])
oX, oY = ogen.inverse_fit_transform(fX, fY)
# check dimensionality
self.assertEqual(len(oX), len(tX))
self.assertEqual(len(oX[0]), len(tX[0]))
self.assertEqual(len(oY), len(tY))
self.assertEqual(list(oX[3]), list(tX[3]))
self.assertEqual(list(oY), list(tY))
return
def test_fit(self):
gen = SignalGenerator()
gen.fit(['sin', 'cos'])
xform = TransformChain([
WhiteNoise(mu=0, sigma=2, clones=10),
FilterNoisiest(),
])
xform.fit(gen.X, gen.Y)
self.assertEqual(len(xform.X), 20)
self.assertEqual(len(xform.transforms[0].X), 22)
self.assertEqual(len(xform.transforms[1].X), 20)
return
def test_fit_simulate(self):
xform = TransformChain(
[SignalGenerator(),
WhiteNoise(clones=2),
SegmentSignal()])
xform.fit(['sin', 'cos'])
self.assertEqual(list(numpy.round(xform.X[0][0][:3], 4)),
[0, 0.0063, 0.0126])
self.assertNotEqual(list(numpy.round(xform.X[1][0][:3], 4)),
[1.0, 1.0, 0.9999])
self.assertEqual(len(xform.X[0][0]), xform.transforms[2].chunksize)
self.assertEqual(len(xform.X[1][0]), xform.transforms[2].chunksize)
return
def test_chained(self):
xform = TransformChain(
VariableSignalGenerator(fs=1000),
SegmentSignal(chunksize=200),
Reduce(),
FeatureTransform(
NormalizedPower(),
DominantFrequency(fs=1000)
)
)
xform.fit(self.data)
self.assertEqual([round(x, 2) for x in xform._X[0]], [0.64, 5])
self.assertEqual([round(x, 2) for x in xform._X[-1]], [0.64, 15])
self.assertEqual(len(xform._X), len(self.data) * 5)
return
def test_simple(self):
xform = TransformChain(
VariableSignalGenerator(fs=1000),
FeatureTransform(
NormalizedPower(),
DominantFrequency(fs=1000)
)
)
xform.fit(self.data[:10])
self.assertEqual(
list(map(list, numpy.round(xform._X, 2))),
[[0.64, 5], [0.63, 6], [0.63, 6], [0.63, 7], [0.63, 7], [0.64, 8], [0.64, 8], [0.64, 9], [0.64, 9], [0.64, 10]]
)
self.assertEqual(len(xform._X), 10)
return
def test_fit(self):
xform = TransformChain([SignalGenerator(), SegmentSignal()])
xform.fit(['sin', 'cos'])
self.assertEqual(list(numpy.round(xform.X[0][0][:3], 4)),
[0, 0.0063, 0.0126])
self.assertEqual(list(numpy.round(xform.X[0][1][:3], 4)),
[0.5878, 0.5929, 0.5979])
self.assertEqual(len(xform.X[0][0]), xform.transforms[1].chunksize)
self.assertEqual(len(xform.X[0][1]), xform.transforms[1].chunksize)
self.assertEqual(list(numpy.round(xform.X[1][0][:3], 4)),
[1.0, 1.0, 0.9999])
self.assertEqual(list(numpy.round(xform.X[1][1][:3], 4)),
[0.809, 0.8053, 0.8016])
self.assertEqual(len(xform.X[1][0]), xform.transforms[1].chunksize)
self.assertEqual(len(xform.X[1][1]), xform.transforms[1].chunksize)
return
def test_fit_transform(self):
# normal multi-layer transform
gen = TransformChain(VariableSignalGenerator(fs=10000),
WhiteNoise(sigma=0.1, clones=2),
SegmentSignal(chunksize=200))
X, Y = gen.fit_transform([{'sin': 100}, {'cos': 150}], [0, 1])
self.assertEqual(len(X), len(Y))
self.assertEqual(len(X), 6)
self.assertEqual(len(X[0]), len(Y[0]))
self.assertEqual(len(X[0]), 50)
self.assertEqual(len(X[0][0]), 200)
# add flattening layer
gen.add(Reduce())
X, Y = gen.fit_transform([{'sin': 100}, {'cos': 150}], [0, 1])
self.assertEqual(len(X), len(Y))
self.assertEqual(len(X), 300)
self.assertEqual(len(X[0]), 200)
return