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[0.14229249011857703, 0.4425000000000002],
[0.19565217391304346, 0.5900000000000001]
])
targets = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
if directory:
import matplotlib.pyplot as plt
plt.figure(figsize=(3, 3))
plt.scatter(features[:20, 0], features[:20, 1], marker="+")
plt.scatter(features[20:, 0], features[20:, 1], marker=".")
plt.xlim([-0.1, 1.1])
plt.ylim([-0.1, 1.1])
plt.savefig(str(directory / "features.png"))
plt.close()
clf = dtww.DecisionTreeClassifier()
clf.fit(features, targets, use_feature_once=False)
if directory:
from sklearn.tree import export_graphviz
export_graphviz(clf, out_file=str(directory / "hierarchy.dot"))
[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)
l = np.loadtxt(Path(__file__).parent / "rsrc" / "labels_0.csv", delimiter=',')
if directory:
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:
plot_margins(s[prototypeidx], weights, clf, prototypeidx)
[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)
[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)