def test_shapelet_lengths():
pytest.importorskip('tensorflow')
from tslearn.shapelets import ShapeletModel
# Test variable-length
y = [0, 1]
time_series = to_time_series_dataset([[1, 2, 3, 4, 5], [3, 2, 1]])
clf = ShapeletModel(n_shapelets_per_size={3: 1},
max_iter=1,
verbose=0,
random_state=0)
clf.fit(time_series, y)
weights_shapelet = [np.array([[1, 2, 3]])]
clf.set_weights(weights_shapelet, layer_name="shapelets_0_0")
tr = clf.transform(time_series)
np.testing.assert_allclose(tr,
np.array([[0.], [8. / 3]]))
# Test max_size to predict longer series than those passed at fit time
y = [0, 1]
time_series = to_time_series_dataset([[1, 2, 3, 4, 5], [3, 2, 1]])
clf = ShapeletModel(n_shapelets_per_size={3: 1},
max_iter=1,
verbose=0,
max_size=6,
random_state=0)
clf.fit(time_series[:, :-1], y) # Fit with size 4
weights_shapelet = [np.array([[1, 2, 3]])]
clf.set_weights(weights_shapelet, layer_name="shapelets_0_0")
tr = clf.transform(time_series)
np.testing.assert_allclose(tr,
np.array([[0.], [8. / 3]]))
def test_shapelets():
pytest.importorskip('keras')
from tslearn.shapelets import ShapeletModel
n, sz, d = 15, 10, 2
rng = np.random.RandomState(0)
time_series = rng.randn(n, sz, d)
y = rng.randint(2, size=n)
clf = ShapeletModel(n_shapelets_per_size={2: 5},
max_iter=1,
verbose=0,
optimizer="sgd",
random_state=0)
clf.fit(time_series, y)
np.testing.assert_allclose(clf.shapelets_[0],
np.array([[0.56373, 0.494684],
[1.235707, 1.119235]]),
atol=1e-2)
np.testing.assert_allclose(clf.predict(time_series),
np.array([0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,
1, 0]))
cross_validate(clf, time_series, y, cv=2)
model = ShapeletModel(n_shapelets_per_size={3: 2, 4: 1},
max_iter = 1)
model.fit(time_series, y)
for shp, shp_bis in zip(model.shapelets_,
model.shapelets_as_time_series_):
np.testing.assert_allclose(shp,
to_time_series(shp_bis, remove_nans=True))
# Test set_weights / get_weights
clf = ShapeletModel(n_shapelets_per_size={2: 5},
max_iter=1,
verbose=0,
random_state=0)
clf.fit(time_series, y)
preds_before = clf.predict_proba(time_series)
weights = clf.get_weights()
# Change number of iterations, then refit, then set weights
clf.max_iter *= 2
clf.fit(time_series, y)
clf.set_weights(weights)
np.testing.assert_allclose(preds_before,
clf.predict_proba(time_series))
def test_shapelets():
pytest.importorskip('tensorflow')
from tslearn.shapelets import ShapeletModel
import tensorflow as tf
n, sz, d = 15, 10, 2
rng = np.random.RandomState(0)
time_series = rng.randn(n, sz, d)
y = rng.randint(2, size=n)
clf = ShapeletModel(n_shapelets_per_size={2: 5},
max_iter=1,
verbose=0,
optimizer="sgd",
random_state=0)
cross_validate(clf, time_series, y, cv=2)
clf = ShapeletModel(n_shapelets_per_size={2: 5},
max_iter=1,
verbose=0,
optimizer=tf.optimizers.Adam(.1),
random_state=0)
cross_validate(clf, time_series, y, cv=2)
model = ShapeletModel(n_shapelets_per_size={3: 2, 4: 1}, max_iter=1)
model.fit(time_series, y)
for shp, shp_bis in zip(model.shapelets_,
model.shapelets_as_time_series_):
np.testing.assert_allclose(shp,
to_time_series(shp_bis, remove_nans=True))
# Test set_weights / get_weights
clf = ShapeletModel(n_shapelets_per_size={2: 5},
max_iter=1,
verbose=0,
random_state=0)
clf.fit(time_series, y)
preds_before = clf.predict_proba(time_series)
weights = clf.get_weights()
# Change number of iterations, then refit, then set weights
clf.max_iter *= 2
clf.fit(time_series, y)
clf.set_weights(weights)
np.testing.assert_allclose(preds_before,
clf.predict_proba(time_series))
