def test_util():
df = load_watch()
data = util.make_ts_data(df['X'], df['side'])
util.get_ts_data_parts(data)
util.check_ts_data(data, df['y'])
util.check_ts_data(df['X'], df['y'])
util.ts_stats(df['X'], df['y'], fs=1., class_labels=df['y_labels'])
def test_util():
df = load_watch()
data = TS_Data(df['X'], df['side'])
Xt, Xc = util.get_ts_data_parts(data)
assert np.array_equal(Xc, df['side'])
assert np.all([np.array_equal(Xt[i], df['X'][i]) for i in range(len(df['X']))])
util.check_ts_data(data, df['y'])
util.check_ts_data(df['X'], df['y'])
util.ts_stats(df['X'], df['y'], fs=1., class_labels=df['y_labels'])
def test_watch():
df = load_watch()
data = TS_Data(df['X'], df['side'])
kernel_size=conv_kernel_size,
padding='valid',
activation='relu',
input_shape=input_shape))
model.add(LSTM(units=lstm_units, dropout=0.1, recurrent_dropout=0.1))
model.add(Dense(n_classes, activation="softmax"))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
# load the data
data = load_watch()
X = data['X']
y = data['y']
# temporal splitting of data
splitter = TemporalKFold(n_splits=3)
Xs, ys, cv = splitter.split(X, y)
# create a segment learning pipeline
width = 100
pipe = Pype([('seg', SegmentX(order='C')),
('crnn',
KerasClassifier(build_fn=crnn_model,
epochs=1,
batch_size=256,
verbose=0))])
def test_watch():
df = load_watch()
data = TS_Data(df['X'], df['side'])
assert isinstance(data, TS_Data)
