def fit(self, x, y=None):
clf = ShapeletModel(
n_shapelets_per_size={self.input_span: self.state_num},
weight_regularizer=.01,
verbose_level=0)
clf.fit(x, y)
joblib.dump(clf, self.modelpath / 'states.m')
def cluster(timeseries_df,data_labels):
Shapelet_list = []
D = '/home/abhilash/Datasets/UCRArchive_2018/TwoLeadECG/TXT_Files/'
for i in range(1,max(data_labels)+1):
print('Class',i)
ts_df=timeseries_df[timeseries_df['0']==i]
ts_df=ts_df.reset_index(drop=True)
labels = ts_df['0']
ts_df = ts_df.drop(ts_df.columns[0], axis=1)
S='class'+str(i)+'labels.txt'
pred_label=pd.read_csv(D+S,header=None)
pred_label=np.ravel(np.array(pred_label))
shapelet_sizes = grabocka_params_to_shapelet_size_dict(n_ts=ts_df.shape[0],
ts_sz=ts_df.shape[1],
n_classes=2,
l=0.36,
r=1)
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
optimizer=Adagrad(lr=.1),
weight_regularizer=.01,
max_iter=50,
verbose=0)
shp_clf.fit(ts_df, pred_label)
shapelets=shp_clf.shapelets_;
temp_list=[]
for i in range(0, shapelets.shape[0]):
temp= shapelets[i].T
temp_list.append(temp[0])
Shapelet_list.append(temp_list)
return Shapelet_list
def cluster(shape,timeseries_df,data_labels,k):
Shapelet_list = []
for i in range(1,max(data_labels)+1):
ts_df=timeseries_df[timeseries_df['0']==i]
ts_df=ts_df.reset_index(drop=True)
# cluster_list.append(extractU_Shapelets(shape)
labels = ts_df['0']
ts_df = ts_df.drop(ts_df.columns[0], axis=1)
S=uShapeletClustering.extractU_Shapelets.extract_Shapelets(ts_df.copy(),shape,k)
S = np.array(list(S))
pred_label=uShapeletClustering.Kmeans.Kmeans(ts_df.copy(),S,k,labels)
shapelet_sizes = grabocka_params_to_shapelet_size_dict(n_ts=ts_df.shape[0],
ts_sz=ts_df.shape[1],
n_classes=2,
l=0.5,
r=1)
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
optimizer=Adagrad(lr=.1),
weight_regularizer=.01,
max_iter=50,
verbose=0)
shp_clf.fit(ts_df, pred_label)
shapelets=shp_clf.shapelets_;
temp_list=[]
for i in range(0, shapelets.shape[0]):
temp= shapelets[i].T
temp_list.append(temp[0])
Shapelet_list.append(temp_list)
return Shapelet_list
def load_model(self, series_length, labels, checkpoint):
'''
Load model from checkpoint into Shapelet classifier
'''
if self.shapelet_clf is None:
base_size = int(self.length * series_length)
self.shapelet_sizes = {}
for sz_idx in range(self.num_shapelet_lengths):
shp_sz = base_size * (sz_idx + 1)
self.shapelet_sizes[shp_sz] = int(self.num_shapelets *
series_length)
self.shapelet_clf = ShapeletModel(
n_shapelets_per_size=self.shapelet_sizes,
optimizer=self.optimizer,
weight_regularizer=self.weight_regularizer,
max_iter=self.epochs,
batch_size=self.batch_size)
# first generate new model into which to load the weights
self.encode(labels)
self.shapelet_clf.generate_model(series_length,
len(self.get_classes()))
# load weights
self.shapelet_clf.model.load_weights(checkpoint)
def fit_transfer_model(self,
X_train,
y_train,
checkpoint,
nclasses_prior=2,
source_dir=None,
val_data=None):
# encode training and validation labels
y_train = self.encode(y_train)
y_val = self.encode(val_data[1])
# scale training and validation data to between 0 and 1
X_train_scaled = self.__ScaleData(X_train)
X_val_scaled = self.__ScaleData(val_data[0])
if self.shapelet_clf is None:
base_size = int(self.length * X_train.shape[1])
self.shapelet_sizes = {}
for sz_idx in range(self.num_shapelet_lengths):
shp_sz = base_size * (sz_idx + 1)
self.shapelet_sizes[shp_sz] = int(self.num_shapelets *
X_train.shape[1])
self.shapelet_clf = ShapeletModel(
n_shapelets_per_size=self.shapelet_sizes,
optimizer=self.optimizer,
weight_regularizer=self.weight_regularizer,
max_iter=self.epochs,
batch_size=self.batch_size)
# fit shapelet classifier
self.shapelet_clf.fit_transfer_model(X_train_scaled, y_train,
nclasses_prior, checkpoint,
source_dir, (X_val_scaled, y_val))
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))
def learningShapeletClassifier(X_train, Y_train):
shapelet_sizes = grabocka_params_to_shapelet_size_dict(
n_ts=X_train.shape[0],
ts_sz=X_train.shape[1],
n_classes=len(set(Y_train)),
l=0.1,
r=2)
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
optimizer=Adagrad(lr=.1),
weight_regularizer=.01,
max_iter=200,
verbose_level=0)
shp_clf.fit(X_train, Y_train)
return shp_clf
def test_serialize_shapelets():
def get_model_weights(model):
return model.model_.get_weights()
n, sz, d = 15, 10, 3
rng = numpy.random.RandomState(0)
X = rng.randn(n, sz, d)
for y in [rng.randint(low=0, high=3, size=n),
rng.choice(["one", "two", "three"], size=n)]:
shp = ShapeletModel(max_iter=1)
_check_not_fitted(shp)
shp.fit(X, y)
_check_params_predict(shp, X, ['predict'],
check_params_fun=get_model_weights,
formats=["json", "pickle"])
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():
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]))
from sklearn.model_selection import cross_validate
cross_validate(clf, time_series, y, cv=2)
def fit_lts(X_train, y_train, X_test, y_test, shap_dict, 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
clf = ShapeletModel(n_shapelets_per_size=shap_dict,
max_iter=max_it, verbose_level=0, 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
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_series_lengths():
pytest.importorskip('tensorflow')
from tslearn.shapelets import ShapeletModel
# Test long shapelets
y = [0, 1]
time_series = to_time_series_dataset([[1, 2, 3, 4, 5], [3, 2, 1]])
clf = ShapeletModel(n_shapelets_per_size={8: 1},
max_iter=1,
verbose=0,
random_state=0)
np.testing.assert_raises(ValueError, clf.fit, time_series, y)
# Test small max_size
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=4,
random_state=0)
np.testing.assert_raises(ValueError, clf.fit, time_series, y)
class ShapletStateRecognition(BaseMLModelTemplate):
def build_model(self, **kwargs):
self.his_len = kwargs['his_len']
self.segment_dim = kwargs['segment_dim']
self.model_obj = ShapeletModel(n_shapelets_per_size={self.segment_dim: self.param.n_state}, weight_regularizer=.01, verbose_level=0)
def fit(self, x, y=None):
self.model_obj.fit(x, y)
self.store(self.param.model_save_path)
def predict(self, x):
self.restore(self.param.model_save_path)
shaplets = []
for s in self.model_obj.shapelets_:
shaplets.append(s)
shaplets = np.reshape(shaplets, [self.param.n_state, self.segment_dim])
print('shaplets:', shaplets.shape)
state_pattern = shaplets
tmpdata = np.reshape(x, [-1, self.his_len, self.segment_dim])
state_proba = np.zeros([x.shape[0], self.his_len, self.param.n_state], dtype=np.float)
for i in range(x.shape[0]):
for j in range(self.his_len):
for k in range(self.param.n_state):
state_proba[i, j, k] = np.sqrt(np.sum(tmpdata[i, j] - shaplets[k]) ** 2)
state_proba[i, j] = (state_proba[i, j] - min(state_proba[i, j])) / (max(state_proba[i, j]) - min(state_proba[i, j]))
return np.reshape(state_proba, [-1, self.his_len, self.param.n_state]).astype(np.float32), np.array(state_pattern,dtype=np.float32)
def store(self, path, **kwargs):
save_model_name = "shaplet_{}_{}.state_model".format(self.param.data_name, self.param.n_state)
joblib.dump(self.model_obj, os.path.join(path, save_model_name))
def restore(self, path, **kwargs):
save_model_name = "shaplet_{}_{}.state_model".format(self.param.data_name, self.param.n_state)
self.model_obj = joblib.load(os.path.join(path, save_model_name))
# Get statistics of the dataset
n_ts, ts_sz = X_train.shape[:2]
n_classes = len(set(y_train))
# Set the number of shapelets per size as done in the original paper
shapelet_sizes = grabocka_params_to_shapelet_size_dict(n_ts=n_ts,
ts_sz=ts_sz,
n_classes=n_classes,
l=0.125,
r=1)
# Define the model and fit it using the training data
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
weight_regularizer=.01,
max_iter=100,
verbose=0,
random_state=42)
shp_clf.fit(X_train, y_train)
# Get the number of extracted shapelets, the (minimal) distances from
# each of the timeseries to each of the shapelets, and the corresponding
# locations (index) where the minimal distance was found
n_shapelets = sum(shapelet_sizes.values())
distances = shp_clf.transform(X_train)
predicted_locations = shp_clf.locate(X_train)
plt.figure()
plt.title("Example locations of shapelet matches "
"({} shapelets extracted)".format(n_shapelets))
def classify_with_shapelets():
from keras.optimizers import Adagrad
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMinMax
from tslearn.shapelets import ShapeletModel, grabocka_params_to_shapelet_size_dict
feat_path = sys.argv[1]
label_type = sys.argv[2]
results_base_path = sys.argv[3]
data_path = sys.argv[4]
pitch_path = os.path.join(data_path, 'pitch.txt')
energy_path = os.path.join(data_path, 'energy.txt')
#Raw pitch and energy
raw_pitch, raw_energy = read_pitch_energy(pitch_path, energy_path)
# Tunable Parameters = shapelet length, threshold, shapelet redundancy value
# sweep shapelet length
pitch_shapelet = {}
energy_shapelet = {}
for shapelet_len in [10, 25, 50]:
for spkr in raw_pitch:
# Compute shapelets from raw frames (i.e. no segmented info like where phone/word is)
pitch_shapelet[spkr] = compute_shapelet_frame(
raw_pitch[spkr], shapelet_len, True)
# energy_shapelet[spkr] = compute_shapelet_frame(raw_energy[spkr], shapelet_len)
# pitch_shapelet[spkr] = np.array(raw_pitch[spkr])
# print(len(raw_pitch[spkr]))
# exit()
acc = []
for sim in range(10):
y_true = []
y_pred = []
for spkr in tqdm(late_balanced.keys()):
test_spkr = [spkr]
train_spkrs = late_balanced.keys()
train_spkrs.remove(test_spkr[0])
X_train = np.array([
np.array(shapelet).reshape(shapelet_len, 1)
for x in train_spkrs for shapelet in pitch_shapelet[x]
])
y_train = np.array([
late_balanced[x] for x in train_spkrs
for shapelet in pitch_shapelet[x]
])
X_test = np.array([
np.array(shapelet).reshape(shapelet_len, 1)
for x in test_spkr for shapelet in pitch_shapelet[x]
])
y_test = np.array([
late_balanced[x] for x in test_spkr
for shapelet in pitch_shapelet[x]
])
# print('train data', X_train.shape)
# #print('train data first', X_train[0])
# print('train label', y_train.shape)
# exit()
shapelet_sizes = grabocka_params_to_shapelet_size_dict(
n_ts=X_train.shape[0],
ts_sz=X_train.shape[1],
n_classes=len(set(y_train)),
l=0.1,
r=2)
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
optimizer=Adagrad(lr=.1),
weight_regularizer=.01,
max_iter=50,
verbose_level=0)
shp_clf.fit(X_train, y_train)
predicted_locations = shp_clf.locate(X_test)
print('predicted_locations.shape', predicted_locations.shape)
# test_ts_id = 0
# plt.figure()
# plt.title("Example locations of shapelet matches (%d shapelets extracted)" % sum(shapelet_sizes.values()))
# plt.plot(X_test[test_ts_id].ravel())
# for idx_shp, shp in enumerate(shp_clf.shapelets_):
# t0 = predicted_locations[test_ts_id, idx_shp]
# plt.plot(np.arange(t0, t0 + len(shp)), shp, linewidth=2)
# plt.tight_layout()
# plt.savefig(test_ts_id+'_test.png', format='png')
# exit()
prediction = shp_clf.predict(X_test)
prediction_prob = shp_clf.predict_proba(X_test)
y_pred += prediction.tolist()
y_true += y_test.tolist()
###After LOO
# test_ts_id = 0
# plt.figure()
# plt.title("Example locations of shapelet matches (%d shapelets extracted)" % sum(shapelet_sizes.values()))
# plt.plot(X_test[test_ts_id].ravel())
# for idx_shp, shp in enumerate(shp_clf.shapelets_):
# t0 = predicted_locations[test_ts_id, idx_shp]
# plt.plot(np.arange(t0, t0 + len(shp)), shp, linewidth=2)
# plt.tight_layout()
# plt.savefig('test.png', format='png')
local_acc = balanced_accuracy_score(y_true, y_pred)
acc.append(local_acc)
# print('acc', acc)
# print('final acc', np.mean(acc))
# print('final acc std', np.std(acc))
if not os.path.exists(os.path.join(results_base_path, 'regression')):
os.makedirs(os.path.join(results_base_path, 'regression'))
results_file = os.path.join(results_base_path, 'regression',
'shapelet_' + str(len_feats) + '.txt')
with open(results_file, 'w') as w:
# w.write("Confusion Matrix\n")
# # w.write(confusion_matrix(y_true, y_pred).tolist())
# w.write('{}\n\n'.format(confusion_matrix(y_true, y_pred)))
w.write('regression: {} ({})\n'.format(np.mean(acc_list),
np.std(acc_list)))
w.write('baseline: {} ({})'.format(np.mean(acc_baseline),
np.std(acc_baseline)))
w.write("\nFeature Importance\n")
for i in tot_importance:
tot_importance[i] = np.mean(tot_importance[i])
for i in sorted(tot_importance.items(),
key=operator.itemgetter(1),
reverse=True):
w.write("{} = {}\n".format(i[0], i[1]))
# * genetic algorithm
# * learning time series shapelets
ge = SingleGeneticExtractor(verbose=True,
population_size=100,
iterations=1000,
wait=100)
ge.fit(X, y)
gen_shapelet = ge.shapelets[0]
bfe = BruteForceExtractor()
bf_shapelet = bfe.extract(X, y)[0]
clf = ShapeletModel(n_shapelets_per_size={len(ts1): 1},
max_iter=5000,
verbose_level=0,
batch_size=1,
optimizer='sgd',
weight_regularizer=0)
clf.fit(np.reshape(X, (X.shape[0], X.shape[1], 1)), y)
lts_shapelet = clf.shapelets_[0]
# Plot the shapelets and orderline
fig, ax = plt.subplots(3, 3, sharey=True)
ax[0][0].axis('off')
ax[0][0].annotate('Brute Force', (0, 0.5), fontsize=24, va='center', ha='left')
ax[0][1].axis('off')
ax[0][1].plot(range(len(bf_shapelet)), bf_shapelet, c=cmap(0.))
# TODO: if dist_tsx too close to other dist_tsy, then change y-coordinate so that points do not overlap
i = 0
while i < 5:
try:
# Sample random hyper-parameters for LTS
K = np.random.choice([0.05, 0.15, 0.3])
L = np.random.choice([0.025, 0.075, 0.125, 0.175, 0.2])
R = np.random.choice([1, 2, 3])
_lambda = np.random.choice([0.01, 0.1, 1])
n_iterations = np.random.choice([2000, 5000, 10000])
shapelet_dict = grabocka_params_to_shapelet_size_dict(
X_train.shape[0], X_train.shape[1], int(K * X_train.shape[1]), L,
R)
clf = ShapeletModel(n_shapelets_per_size=shapelet_dict,
max_iter=n_iterations,
verbose_level=0,
batch_size=1,
optimizer='sgd',
weight_regularizer=_lambda)
clf.fit(np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)),
y_train)
X_distances_train = clf.transform(X_train)
X_distances_test = clf.transform(X_test)
lr = GridSearchCV(LogisticRegression(), {
'penalty': ['l1', 'l2'],
'C': [0.001, 0.01, 0.1, 1.0, 10.0]
})
lr.fit(X_distances_train, y_train)
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 build_model(self, **kwargs):
self.his_len = kwargs['his_len']
self.segment_dim = kwargs['segment_dim']
self.model_obj = ShapeletModel(n_shapelets_per_size={self.segment_dim: self.param.n_state}, weight_regularizer=.01, verbose_level=0)
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))
n_ts, ts_sz = X_train.shape[:2]
n_classes = len(set(y_train))
# Set the number of shapelets per size as done in the original paper
shapelet_sizes = grabocka_params_to_shapelet_size_dict(n_ts=n_ts,
ts_sz=ts_sz,
n_classes=n_classes,
l=0.1,
r=1)
# Define the model using parameters provided by the authors (except that we
# use fewer iterations here)
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
optimizer=tf.optimizers.Adam(.01),
batch_size=16,
weight_regularizer=.01,
max_iter=200,
random_state=42,
verbose=0)
shp_clf.fit(X_train, y_train)
# Make predictions and calculate accuracy score
pred_labels = shp_clf.predict(X_test)
print("Correct classification rate:", accuracy_score(y_test, pred_labels))
# Plot the different discovered shapelets
plt.figure()
for i, sz in enumerate(shapelet_sizes.keys()):
plt.subplot(len(shapelet_sizes), 1, i + 1)
plt.title("%d shapelets of size %d" % (shapelet_sizes[sz], sz))
for shp in shp_clf.shapelets_:
n_ts, ts_sz = X_train.shape[:2]
n_classes = len(set(y_train))
# Set the number of shapelets per size as done in the original paper
shapelet_sizes = grabocka_params_to_shapelet_size_dict(n_ts=n_ts,
ts_sz=ts_sz,
n_classes=n_classes,
l=0.1,
r=2)
# Define the model using parameters provided by the authors (except that we use
# fewer iterations here)
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
optimizer=Adagrad(lr=.1),
weight_regularizer=.01,
max_iter=200,
verbose=0)
shp_clf.fit(X_train, y_train)
predicted_labels = shp_clf.predict(X_test)
print("Correct classification rate:", accuracy_score(y_test, predicted_labels))
plt.figure()
for i, sz in enumerate(shapelet_sizes.keys()):
plt.subplot(len(shapelet_sizes), 1, i + 1)
plt.title("%d shapelets of size %d" % (shapelet_sizes[sz], sz))
for shp in shp_clf.shapelets_:
if ts_size(shp) == sz:
plt.plot(shp.ravel())
plt.xlim([0, max(shapelet_sizes.keys()) - 1])
import matplotlib.pyplot as plt
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMinMax
from tslearn.shapelets import ShapeletModel
numpy.random.seed(0)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
X_train = TimeSeriesScalerMinMax().fit_transform(X_train)
X_test = TimeSeriesScalerMinMax().fit_transform(X_test)
shapelet_sizes = {50: 3}
shp_clf = ShapeletModel(n_shapelets_per_size=shapelet_sizes,
optimizer=Adagrad(lr=.1),
weight_regularizer=.01,
max_iter=300,
verbose_level=0)
shp_clf.fit(X_train, y_train)
predicted_locations = shp_clf.locate(X_test)
test_ts_id = 0
plt.figure()
plt.title("Example locations of shapelet matches (%d shapelets extracted)" %
sum(shapelet_sizes.values()))
plt.plot(X_test[test_ts_id].ravel())
for idx_shp, shp in enumerate(shp_clf.shapelets_):
t0 = predicted_locations[test_ts_id, idx_shp]
plt.plot(numpy.arange(t0, t0 + len(shp)), shp, linewidth=2)
plt.tight_layout()
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
Parameters
----------
X : {array-like}
Training data. Shape [n_samples, n_features].
y : {array-like, sparse matrix}
Target values of shape = [n_samples] or [n_samples, n_outputs]
"""
self.X = X
self.y = y
n_shapelets_per_size = self.shapelet_model_params.get(
"n_shapelets_per_size", "heuristic")
if n_shapelets_per_size == "heuristic":
n_ts, ts_sz = X.shape[:2]
n_classes = len(set(y))
n_shapelets_per_size = grabocka_params_to_shapelet_size_dict(
n_ts=n_ts,
ts_sz=ts_sz,
n_classes=n_classes,
l=self.shapelet_model_params.get("l", 0.1),
r=self.shapelet_model_params.get("r", 2))
shp_clf = ShapeletModel(
n_shapelets_per_size=n_shapelets_per_size,
optimizer=self.shapelet_model_params.get("optimizer", "sgd"),
weight_regularizer=self.shapelet_model_params.get(
"weight_regularizer", .01),
max_iter=self.shapelet_model_params.get("max_iter", 100),
random_state=self.random_state,
verbose=self.shapelet_model_params.get("verbose", 0))
shp_clf.fit(X, y)
X_transformed = shp_clf.transform(X)
self.X_transformed = X_transformed
if self.tau is not None:
self.X_thresholded = 1 * (self.X_transformed < self.tau)
clf = DecisionTreeClassifier()
param_grid = self.decision_tree_grid_search_params
grid = GridSearchCV(clf,
param_grid=param_grid,
scoring='accuracy',
n_jobs=-1,
verbose=0)
grid.fit(self.X_thresholded, y)
else:
grids = []
grids_scores = []
for quantile in self.tau_quantiles:
_X_thresholded = 1 * (self.X_transformed < (np.quantile(
self.X_transformed, quantile)))
clf = DecisionTreeClassifier()
param_grid = self.decision_tree_grid_search_params
grid = GridSearchCV(clf,
param_grid=param_grid,
scoring='accuracy',
n_jobs=-1,
verbose=0)
grid.fit(_X_thresholded, y)
grids.append(grid)
grids_scores.append(grid.best_score_)
grid = grids[np.argmax(np.array(grids_scores))]
best_quantile = self.tau_quantiles[np.argmax(
np.array(grids_scores))]
self.tau = np.quantile(self.X_transformed, best_quantile)
self.X_thresholded = 1 * (self.X_transformed < self.tau)
clf = DecisionTreeClassifier(**grid.best_params_)
clf.fit(self.X_thresholded, y)
if self.prune_duplicate_tree_leaves:
prune_duplicate_leaves(
clf) # FIXME: does it influence the .tree properties?
self.decision_tree = clf
self.decision_tree_explorable = NewTree(clf)
self.decision_tree_explorable.build_tree()
self._shapelet_model = shp_clf
self._build_tree_graph()
return self
class Shapelets():
def __init__(self,
epochs,
length,
num_shapelet_lengths,
num_shapelets,
learning_rate,
weight_regularizer,
batch_size=256,
optimizer=Adam):
'''
initialize shapelet hyperparameters
hyperparameters:
epochs : number of training epochs
length : base shapelet length, expressed as fraction of length of time series
num_shapelet_lengths : number of different shapelet lengths
num_shapelets : number of unique shapelets to learn at each shapelet length,
expressed as fraction of length of time series
learning rate : learning rate of Keras optimizer
weight regularizer : weight regularization used when fitting model
'''
self.epochs = epochs
self.length = length
self.num_shapelet_lengths = num_shapelet_lengths
self.num_shapelets = num_shapelets
self.weight_regularizer = weight_regularizer
self.batch_size = batch_size
self.optimizer = optimizer(lr=learning_rate)
self.shapelet_sizes = None
self.shapelet_clf = None
self.encoder = LabelEncoder()
def clear_session(self):
try:
assert (self.shapelet_clf is not None)
except:
raise ValueError(
"Cannot clear session that has not been initialized")
self.shapelet_clf.clear_session()
return
def load_model(self, series_length, labels, checkpoint):
'''
Load model from checkpoint into Shapelet classifier
'''
if self.shapelet_clf is None:
base_size = int(self.length * series_length)
self.shapelet_sizes = {}
for sz_idx in range(self.num_shapelet_lengths):
shp_sz = base_size * (sz_idx + 1)
self.shapelet_sizes[shp_sz] = int(self.num_shapelets *
series_length)
self.shapelet_clf = ShapeletModel(
n_shapelets_per_size=self.shapelet_sizes,
optimizer=self.optimizer,
weight_regularizer=self.weight_regularizer,
max_iter=self.epochs,
batch_size=self.batch_size)
# first generate new model into which to load the weights
self.encode(labels)
self.shapelet_clf.generate_model(series_length,
len(self.get_classes()))
# load weights
self.shapelet_clf.model.load_weights(checkpoint)
def fit_transfer_model(self,
X_train,
y_train,
checkpoint,
nclasses_prior=2,
source_dir=None,
val_data=None):
# encode training and validation labels
y_train = self.encode(y_train)
y_val = self.encode(val_data[1])
# scale training and validation data to between 0 and 1
X_train_scaled = self.__ScaleData(X_train)
X_val_scaled = self.__ScaleData(val_data[0])
if self.shapelet_clf is None:
base_size = int(self.length * X_train.shape[1])
self.shapelet_sizes = {}
for sz_idx in range(self.num_shapelet_lengths):
shp_sz = base_size * (sz_idx + 1)
self.shapelet_sizes[shp_sz] = int(self.num_shapelets *
X_train.shape[1])
self.shapelet_clf = ShapeletModel(
n_shapelets_per_size=self.shapelet_sizes,
optimizer=self.optimizer,
weight_regularizer=self.weight_regularizer,
max_iter=self.epochs,
batch_size=self.batch_size)
# fit shapelet classifier
self.shapelet_clf.fit_transfer_model(X_train_scaled, y_train,
nclasses_prior, checkpoint,
source_dir, (X_val_scaled, y_val))
def fit(self, X_train, y_train, source_dir=None, val_data=None):
'''
fit shapelet classifier on training data
parameters:
X_train : training time series
y_train : training labels
'''
if self.shapelet_clf is None:
base_size = int(self.length * X_train.shape[1])
self.shapelet_sizes = {}
for sz_idx in range(self.num_shapelet_lengths):
shp_sz = base_size * (sz_idx + 1)
self.shapelet_sizes[shp_sz] = int(self.num_shapelets *
X_train.shape[1])
self.shapelet_clf = ShapeletModel(
n_shapelets_per_size=self.shapelet_sizes,
optimizer=self.optimizer,
weight_regularizer=self.weight_regularizer,
max_iter=self.epochs,
batch_size=self.batch_size)
# encode training and validation labels
y_train = self.encode(y_train)
y_val = self.encode(val_data[1])
# scale training and validation data to between 0 and 1
X_train_scaled = self.__ScaleData(X_train)
X_val_scaled = self.__ScaleData(val_data[0])
# fit classifier
self.shapelet_clf.fit(X_train_scaled, y_train, source_dir,
(X_val_scaled, y_val))
def __ScaleData(self, input_data):
'''
scale input data to range [0,1]
parameters:
input_data : input data to rescale
'''
return TimeSeriesScalerMinMax().fit_transform(input_data)
def predict(self, X_test):
'''
classifications for time series in test data set
parameters:
X_test: test time series on which to predict classes
returns: classifications for test data set
'''
X_test_scaled = self.__ScaleData(X_test)
return self.shapelet_clf.predict(X_test_scaled)
def predict_proba(self, X_test):
'''
class probabilities for time series in test data set
parameters:
X_test: test time series on which to predict classes
returns: classifications for test data set
'''
X_test_scaled = self.__ScaleData(X_test)
return self.shapelet_clf.predict_proba(X_test_scaled)
def encode(self, categories):
'''
fit label encoder on input categories. returns transformed categories
'''
self.encoder.fit(categories)
return self.encoder.transform(categories)
def decode(self, y_probs, p_threshold):
'''
decode prediction probabilities y_probs into prediction / confidence give p_threshold
'''
prob_max = np.amax(y_probs, axis=1)
prediction_indices = prob_max > p_threshold
y_pred = np.zeros(y_probs.shape[0])
# reintepret confidence in binary case
if y_probs.shape[1] == 1:
y_pred[prediction_indices] = 1
confidence = (prob_max - p_threshold) / (y_pred - p_threshold)
confidence = 0.5 + confidence / 2
else:
y_pred[prediction_indices] = np.argmax(y_probs,
axis=1)[prediction_indices]
confidence = prob_max
y_pred = y_pred.astype(int)
y_preds = self.encoder.inverse_transform(y_pred)
return y_preds, confidence
def get_classes(self):
'''
get original classes from encoder
'''
try:
assert (self.encoder is not None)
except:
raise ValueError("Encoder has not been initialized")
return self.encoder.classes_
def VisualizeShapelets(self):
'''
visualize all of shapelets learned by shapelet classifier
'''
plt.figure()
for i, sz in enumerate(self.shapelet_sizes.keys()):
plt.subplot(len(self.shapelet_sizes), 1, i + 1)
plt.title("%d shapelets of size %d" %
(self.shapelet_sizes[sz], sz))
for shapelet in self.shapelet_clf.shapelets_:
if ts_size(shapelet) == sz:
plt.plot(shapelet.ravel())
plt.xlim([0, max(self.shapelet_sizes.keys())])
plt.tight_layout()
plt.show()
def VisualizeShapeletLocations(self,
series_values,
series_id,
save_dir='visualizations',
name='shp_1'):
'''
visualize shapelets superimposed on one of the test series
parameters:
series_values: raw values on which to visualize shapelets
series_id: id of time series to visualize
save_dir directory in which to save visualizations
name name under which to save viz (bc unique every time)
n_shapelets:
'''
plt.style.use("seaborn-whitegrid")
# NK brand colors
COLORS = [
"#FA5655", "#F79690", "#B9BC2D", "#86B6B2", "#955B99", "#252B7A"
]
# others? "#8D6B2C",
# "#D0A826",
# "#FEDB03",
# "#000000",
# "#454545",
# "#FFFFFF",
# "#F8F6F1"]
n_rows, n_cols, _ = series_values.shape
test_series = series_values[series_id].reshape(-1, )
closest_inds = self.shapelet_clf.locate(test_series.reshape(1, -1,
1))[0]
closest_dists = []
for ind, shp in zip(closest_inds, self.shapelet_clf.shapelets_):
closest_dists.append(
np.linalg.norm(test_series[ind:ind + shp.shape[0]] - shp))
closest_dists = np.array(closest_dists)
# convert distance to weight where dist=0 -> wt=1 and dist=inf -> wt=0
sl_weights = 1 / (1 + closest_dists)
# plot the signal with matching shapelets color overlayed
plt.clf()
plt.plot(range(n_cols), test_series, color="k")
for ind, sl, wt, color in zip(closest_inds,
self.shapelet_clf.shapelets_, sl_weights,
COLORS):
# find closest match
t = range(ind, ind + sl.shape[0])
match = test_series[ind:ind + sl.shape[0]]
# plot shapelet on top of signal width width and alpha set by shapelet weight
plt.plot(t, match, alpha=7 * wt, linewidth=35 * wt, color=color)
plt.ylabel('Email Density')
plt.xlabel('Minute of the Hour')
plt.show()
#plt.savefig(save_dir + "/{}_signal_size_{}_id_{}.png".format(name, n_cols, series_id))
# plot shapelets
plt.clf()
# to plot the shapelets, switch to dark background
plt.style.use("seaborn-darkgrid")
# ax = plt.axes() # used below for sharex, sharey (if needed?)
# arange shapletes in grid - find greatest factor of n_shapelets
gf = 0
shp_t = self.shapelet_clf.shapelets_as_time_series_
shp = self.shapelet_clf.shapelets_
for i in range(1, shp.shape[0]):
if shp.shape[0] % i == 0:
gf = i
of = int(shp.shape[0] / gf)
n_cols = 2
for i in range(shp_t.shape[0]):
ax_i = plt.subplot(gf, of, i + 1)
# we could force them to share the same axes
# ax_i = plt.subplot(n_rows, n_cols, i + 1, sharex=ax, sharey=ax)
#ax_i.set_xticklabels([])
ax_i.set_yticklabels([])
plt.plot(range(shp_t.shape[1]),
shp[i].reshape(-1),
color=COLORS[i % len(COLORS)],
linewidth=3)
plt.xlabel('Shapelet Length')
plt.show()