def test_openDataset():
from tslearn.datasets import UCR_UEA_datasets
X_train, y_train, X_test, y_test = UCR_UEA_datasets().load_dataset(
"GunPoint")
print("Train shape", X_train.shape)
print("Test shape", X_test.shape)
print(set(y_train))
dl = getDataLoader(data=X_train, label=y_train)
print("nb data", len(dl.dataset))
plt.plot(range(X_train.shape[1]), X_train[0, :] / X_train.max())
plt.plot(range(X_train.shape[1]), X_train[25, :] / X_train.max())
plt.show()
best_scores_train = {k : 0. for k in _kernels}
# lead-lag only if number of channels is <= 5
x_train, _, _, _ = UCR_UEA_datasets(use_cache=True).load_dataset(name)
if x_train.shape[1] <= 200 and x_train.shape[2] <= 8:
transforms = tqdm([(True,True), (False,True), (True,False), (False,False)], position=1, leave=False)
else: # do not try lead-lag as dimension is already high
transforms = tqdm([(True,False), (False,False)], position=1, leave=False)
# grid-search for path-transforms (add-time, lead-lag)
for (at,ll) in transforms:
transforms.set_description(f"add-time: {at}, lead-lag: {ll}")
# load train data
x_train, y_train, _, _ = UCR_UEA_datasets(use_cache=True).load_dataset(name)
x_train /= x_train.max()
# encode outputs as labels
y_train = LabelEncoder().fit_transform(y_train)
# path-transform
x_train = sigkernel.transform(x_train, at=at, ll=ll, scale=.1)
# subsample every time steps if certain length is exceeded
subsample = max(int(np.floor(x_train.shape[1]/149)),1)
x_train = x_train[:,::subsample,:]
datasets.set_description(f"dataset: {name} --- shape: {x_train.shape}")
#==================================================================================
# Linear, RBF and GAK kernels
#==================================================================================
