def test_distance7(directory=None):
directory = prepare_directory(directory)
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)
ml_values, cl_values, clf, imp = dtww.series_to_dt(
s,
l,
prototypeidx,
window=0,
min_ig=0.01,
savefig=str(directory / "dts.dot"),
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_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_distance4(directory=None):
directory = prepare_directory(directory)
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)
prototypeidx = 0
ml_values, cl_values, clf = dtww.series_to_dt(s,
l,
prototypeidx,
window=2,
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)
def plot_margins(serie, weights, clfs, importances=None):
global directory
if directory is None:
directory = prepare_directory()
try:
from sklearn import tree
except ImportError:
return
feature_names = ["f{} ({}, {})".format(i // 2, i, '-' if (i % 2) == 0 else '+') for i in range(2 * len(serie))]
out_str = io.StringIO()
for clf in clfs:
tree.export_graphviz(clf, out_file=out_str, feature_names=feature_names)
print("\n\n", file=out_str)
with open(str(directory / "tree.dot"), "w") as ofile:
print(out_str.getvalue(), file=ofile)
dtww.plot_margins(serie, weights, filename=str(directory / "margins.png"), importances=importances)
def test_distance1():
directory = prepare_directory()
s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 2, 1, 0, 0])
s2 = np.array([0., 1, 2, 3, 1, 10, 1, 0, 2, 1, 0, 0, 0])
d, paths = dtw.warping_paths(s1, s2)
# print(d, "\n", paths)
dtwvis.plot_warpingpaths(s1, s2, paths, filename=directory / "temp1.png")
weights = np.full((len(s1), 8), np.inf)
weights[:, 2:4] = 0.0
weights[4:7, 2:4] = 10.0
weights[:, 4:6] = 0.0
weights[4:7, 4:6] = 10.0
d, paths = dtww.warping_paths(s1, s2, weights)
# print(d, "\n", paths)
dtwvis.plot_warpingpaths(s1, s2, paths, filename=directory / "temp2.png")
def plot_series(s, l, idx=None):
global directory
if directory is None:
directory = prepare_directory()
import matplotlib.pyplot as plt
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
fig1, ax1 = plt.subplots(nrows=len(s), ncols=1)
fig2, ax2 = plt.subplots(nrows=1, ncols=1)
for i, si in enumerate(s):
if i == idx:
color = colors[0]
else:
color = colors[int(1 + l[i])]
ax1[i].plot(si, color=color)
ax2.plot(si, color=color)
fig1.savefig(str(directory / "series1.png"))
fig2.savefig(str(directory / "series2.png"))
def test_distance3(directory=None):
directory = prepare_directory(directory)
s = np.array([[0., 0, 1, 2, 1, 0, 1.3, 0, 0], [0., 1, 2, 0, 0, 0, 0, 0,
0]])
w = np.array([[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 1.1, 1., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 1.1, 1., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 2., 2.2, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 1., 1.1, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 1.3, 1.43, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf]])
d, paths = dtww.warping_paths(s[0], s[1], w, window=0)
path = dtw.best_path(paths)
if directory:
wp_fn = directory / "warping_paths.png"
dtwvis.plot_warpingpaths(s[0], s[1], paths, path, filename=wp_fn)
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)