def test_segmentxy():
# test illegal parameter settings
with pytest.raises(ValueError):
transform.SegmentXY(width=0) # illegal width value
with pytest.raises(ValueError):
transform.SegmentXY(
overlap=None, step=None) # either overlap or step must be defined
with pytest.raises(ValueError):
transform.SegmentXY(overlap=-1, step=None) # illegal overlap value
with pytest.raises(ValueError):
transform.SegmentXY(step=0) # illegal step value
with pytest.raises(ValueError):
transform.SegmentXY(order=None) # illegal order
# test _step property working as expected
seg = transform.SegmentXY(width=10, overlap=0.5)
assert seg._step == 5
# test precedence of step over overlap
seg = transform.SegmentXY(width=10, overlap=1, step=1)
assert seg._step == 1
# illegal overlap value, but valid step value
seg = transform.SegmentXY(overlap=-1, step=1)
assert seg._step == 1
# test shape of segmented data
Nt = 100
width = 5
nvars = 5
seg = transform.SegmentXY(width=width)
# multivariate ts data without context data
X = [
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars)
]
y = [np.random.rand(Nt), np.random.rand(Nt), np.random.rand(Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width, nvars)
# univariate ts data without context data
X = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width)
# multivariate ts data with context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = TS_Data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, nvars)
assert Xsc.shape == (N, 4)
# ts data with univariate context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = TS_Data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, nvars)
assert Xsc.shape == (N, 1)
# same number as context vars and time vars
# this would cause broadcasting failure before implementation of TS_Data class
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, nvars)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = TS_Data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, nvars)
assert Xsc.shape == (N, 5)
def test_segmentxy():
Nt = 100
width = 5
nvars = 5
seg = transform.SegmentXY(width=width)
# multivariate ts data without context data
X = [
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars),
np.random.rand(Nt, nvars)
]
y = [np.random.rand(Nt), np.random.rand(Nt), np.random.rand(Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width, nvars)
# univariate ts data without context data
X = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(3 * Nt)]
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
N = len(ys)
assert Xs.shape == (N, width)
# multivariate ts data with context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, 4)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = TS_Data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, nvars)
assert Xsc.shape == (N, 4)
# ts data with univariate context data
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = TS_Data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, nvars)
assert Xsc.shape == (N, 1)
# same number as context vars and time vars
# this would cause broadcasting failure before implementation of TS_Data class
Xt = [
np.random.rand(Nt, nvars),
np.random.rand(2 * Nt, nvars),
np.random.rand(Nt, nvars)
]
Xc = np.random.rand(3, nvars)
y = [np.random.rand(Nt), np.random.rand(2 * Nt), np.random.rand(Nt)]
X = TS_Data(Xt, Xc)
seg.fit(X, y)
Xs, ys, _ = seg.transform(X, y)
Xst, Xsc = get_ts_data_parts(Xs)
N = len(ys)
assert Xst.shape == (N, width, nvars)
assert Xsc.shape == (N, 5)