def lts_discovery(X_train, y_train, X_test, y_test, nr_shap, l, r, reg, max_it,
shap_out_path, pred_out_path, timing_out_path):
# Fit LTS model, print metrics on test-set, write away predictions and shapelets
shapelet_dict = grabocka_params_to_shapelet_size_dict(
X_train.shape[0], X_train.shape[1], int(nr_shap * X_train.shape[1]), l,
r)
clf = ShapeletModel(n_shapelets_per_size=shapelet_dict,
max_iter=max_it,
verbose_level=1,
batch_size=1,
optimizer='sgd',
weight_regularizer=reg)
start = time.time()
clf.fit(np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)),
y_train)
learning_time = time.time() - start
print([len(x) for x in clf.shapelets_])
print(clf.get_weights())
print('Learning shapelets took {}s'.format(learning_time))
with open(shap_out_path, 'w+') as ofp:
for shap in clf.shapelets_:
ofp.write(str(np.reshape(shap, (-1))) + '\n')
with open(timing_out_path, 'w+') as ofp:
ofp.write(str(learning_time))
X_distances_train = clf.transform(X_train)
X_distances_test = clf.transform(X_test)
fit_lr(X_distances_train, y_train, X_distances_test, y_test, pred_out_path)
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))