def test_distance2():
with util_numpy.test_uses_numpy() as np:
s = np.array([[0., 0, 1, 2, 1, 0, 1.3, 0, 0],
[0., 0, 1, 2, 1, 0, 1, 0,
0], [0., 1, 2, 0, 0, 0, 0, 0, 0],
[0., 1, 2, 0, 0, 0, 0, 0,
0], [1., 2, 0, 0, 0, 0, 0, 1, 1],
[1., 2, 0, 0, 0, 0, 0, 1, 1],
[1., 2, 0, 0, 1, 0, 0, 1, 1]])
l = np.array([1, 1, 1, 1, 0, 0, 0])
if directory:
if not dtwvis.test_without_visualization():
plot_series(s, l)
savefig = str(directory / "dts.dot")
else:
savefig = None
prototypeidx = 0
ml_values, cl_values, clfs, importances = \
dtww.series_to_dt(s, l, prototypeidx, max_clfs=50, savefig=savefig)
logger.debug(f"ml_values = {dict(ml_values)}")
logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if not dtwvis.test_without_visualization():
if directory:
plot_margins(s[prototypeidx], weights, clfs)
def test_distance5():
with util_numpy.test_uses_numpy() as np:
s = np.array([
[0., 0, 0, 2, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0
[0., 0, 2, 0, -2, 0, 2, 0, -2, 0, 2, 0, -2, 0, 0], # 1
[0., 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0] # 2
])
l = np.array([1, 1, 0])
if directory:
if not dtwvis.test_without_visualization():
plot_series(s, l)
prototypeidx = 0
ml_values, cl_values, clf, importances = dtww.series_to_dt(
s, l, prototypeidx, window=4)
logger.debug(f"ml_values = {dict(ml_values)}")
logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if directory:
if not dtwvis.test_without_visualization():
plot_margins(s[prototypeidx], weights, clf)
def test_distance6():
with util_numpy.test_uses_numpy() as np:
s = np.loadtxt(Path(__file__).parent / "rsrc" / "series_0.csv",
delimiter=',')
l = np.loadtxt(Path(__file__).parent / "rsrc" / "labels_0.csv",
delimiter=',')
if directory:
if not dtwvis.test_without_visualization():
plot_series(s, l)
savefig = str(directory / "dts.dot")
else:
savefig = None
prototypeidx = 3
labels = np.zeros(l.shape)
labels[l == l[prototypeidx]] = 1
ml_values, cl_values, clf, importances = \
dtww.series_to_dt(s, labels, prototypeidx, window=0, min_ig=0.1, savefig=savefig)
logger.debug(f"ml_values = {dict(ml_values)}")
logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if directory:
if not dtwvis.test_without_visualization():
plot_margins(s[prototypeidx], weights, clf, prototypeidx)
def test_distance7():
s = np.array([[0.0, 0.3, 0.5, 0.8, 1.0, 0.1, 0.0, 0.1],
[0.0, 0.2, 0.3, 0.7, 1.1, 0.0, 0.1, 0.0],
[0.1, 0.0, 1.0, 1.0, 1.0, 0.9, 0.0, 0.0],
[0.0, 0.0, 1.1, 0.9, 1.0, 1.0, 0.0, 0.0],
[0.0, 0.1, 1.1, 1.0, 0.9, 0.9, 0.0, 0.0],
[0.0, 0.1, 1.0, 1.1, 0.9, 1.0, 0.0, 0.1],
[0.0, 0.1, 0.4, 0.3, 0.2, 0.3, 0.0, 0.0],
[0.1, 0.0, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1]])
l = np.array([1, 1, 0, 0, 0, 0, 0, 0])
prototypeidx = 0
if directory:
plot_series(s, l, prototypeidx)
savefig = str(directory / "dts.dot")
else:
savefig = None
ml_values, cl_values, clf, imp = dtww.series_to_dt(
s,
l,
prototypeidx,
window=0,
min_ig=0.01,
savefig=savefig,
warping_paths_fnc=dtww.warping_paths)
# logger.debug(f"ml_values = {dict(ml_values)}")
# logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if directory:
plot_margins(s[prototypeidx], weights, clf, imp)
def test_distance4():
s = np.array([
[0., 0, 1, 2, 1, 0, 1.3, 0, 0], # 0
[0., 1, 2, 0, 0, 0, 0, 0, 0], # 1
[1., 2, 0, 0, 0, 0, 0, 1, 1], # 2
[0., 0, 1, 2, 1, 0, 1, 0, 0], # 3
[0., 1, 2, 0, 0, 0, 0, 0, 0], # 4
[1., 2, 0, 0, 0, 0, 0, 1, 1], # 5
[1., 2, 0, 0, 1, 0, 0, 1, 1], # 6
[1., 2, 0.05, 0.01, 0.9, 0, 0, 1, 1]
]) # 7
l = np.array([1, 0, 0, 1, 0, 0, 0, 0])
if directory:
plot_series(s, l)
savefig = str(directory / "dts.dot")
else:
savefig = None
prototypeidx = 0
ml_values, cl_values, clf, importances = \
dtww.series_to_dt(s, l, prototypeidx, window=2, min_ig=0.1, savefig=savefig)
logger.debug(f"ml_values = {dict(ml_values)}")
logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if directory:
plot_margins(s[prototypeidx], weights, clf)
def test_distance2(directory=None):
directory = prepare_directory(directory)
s = np.array([[0., 0, 1, 2, 1, 0, 1.3, 0, 0], [0., 0, 1, 2, 1, 0, 1, 0, 0],
[0., 1, 2, 0, 0, 0, 0, 0, 0], [0., 1, 2, 0, 0, 0, 0, 0, 0],
[1., 2, 0, 0, 0, 0, 0, 1, 1], [1., 2, 0, 0, 0, 0, 0, 1, 1],
[1., 2, 0, 0, 1, 0, 0, 1, 1]])
l = np.array([1, 1, 1, 1, 0, 0, 0])
if directory:
plot_series(s, l)
prototypeidx = 0
ml_values, cl_values, clfs = dtww.series_to_dt(s,
l,
prototypeidx,
max_clfs=50,
savefig=str(directory /
"dts.dot"))
logger.debug(f"ml_values = {dict(ml_values)}")
logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if directory:
plot_margins(s[prototypeidx], weights, clfs)
def test_distance6(directory=None):
directory = prepare_directory(directory)
s = np.loadtxt(Path(__file__).parent / "rsrc" / "series_0.csv",
delimiter=',')
l = np.loadtxt(Path(__file__).parent / "rsrc" / "labels_0.csv",
delimiter=',')
if directory:
plot_series(s, l)
prototypeidx = 3
labels = np.zeros(l.shape)
labels[l == l[prototypeidx]] = 1
ml_values, cl_values, clf = dtww.series_to_dt(s,
labels,
prototypeidx,
window=0,
min_ig=0.1,
savefig=str(directory /
"dts.dot"))
logger.debug(f"ml_values = {dict(ml_values)}")
logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if directory:
plot_margins(s[prototypeidx], weights, clf, prototypeidx)
def test_distance5(directory=None):
directory = prepare_directory(directory)
s = np.array([
[0., 0, 0, 2, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0
[0., 0, 2, 0, -2, 0, 2, 0, -2, 0, 2, 0, -2, 0, 0], # 1
[0., 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0] # 2
])
l = np.array([1, 1, 0])
if directory:
plot_series(s, l)
prototypeidx = 0
ml_values, cl_values, clf = dtww.series_to_dt(s, l, prototypeidx, window=4)
logger.debug(f"ml_values = {dict(ml_values)}")
logger.debug(f"cl_values = {dict(cl_values)}")
weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx],
ml_values,
cl_values,
only_max=False,
strict_cl=True)
if directory:
plot_margins(s[prototypeidx], weights, clf)
