def test_uneven_sampling(bs_setup):
bs = MovingBlockBootstrap(block_size=31, y=bs_setup.y_series, x=bs_setup.x_df)
for _, kw in bs.bootstrap(10):
assert kw["y"].shape == bs_setup.y_series.shape
assert kw["x"].shape == bs_setup.x_df.shape
bs = CircularBlockBootstrap(block_size=31, y=bs_setup.y_series, x=bs_setup.x_df)
for _, kw in bs.bootstrap(10):
assert kw["y"].shape == bs_setup.y_series.shape
assert kw["x"].shape == bs_setup.x_df.shape
def test_uneven_sampling(self):
bs = MovingBlockBootstrap(block_size=31, y=self.y_series, x=self.x_df)
for _, kw in bs.bootstrap(10):
assert kw['y'].shape == self.y_series.shape
assert kw['x'].shape == self.x_df.shape
bs = CircularBlockBootstrap(block_size=31, y=self.y_series, x=self.x_df)
for _, kw in bs.bootstrap(10):
assert kw['y'].shape == self.y_series.shape
assert kw['x'].shape == self.x_df.shape
def test_uneven_sampling(self):
bs = MovingBlockBootstrap(block_size=31, y=self.y_series, x=self.x_df)
for _, kw in bs.bootstrap(10):
assert kw['y'].shape == self.y_series.shape
assert kw['x'].shape == self.x_df.shape
bs = CircularBlockBootstrap(block_size=31, y=self.y_series, x=self.x_df)
for _, kw in bs.bootstrap(10):
assert kw['y'].shape == self.y_series.shape
assert kw['x'].shape == self.x_df.shape
def augmentation(X, Y, noise = False, bootstrapping = True, noiseSTD = [0.1/2, 0.1/2, 0.01/2, 0.0002/2,0.01/2,0.02/2], nr_boot =1000, bootstrap_bl_size = 488, boot_freq = 100):
if noise:
Xn = X.copy()
for i, j, k in np.ndindex(X.shape):
Xn[i, j, k] += np.random.normal(0, 1)*noiseSTD[k]
X = np.vstack([X, Xn])
Y = np.vstack([Y, Y])
if bootstrapping:
Xb = X.copy()
pt = PowerTransformer(method='yeo-johnson', standardize=True)
for i in range(Xb.shape[0]):
pt.fit(Xb[i])
lambda_param = pt.lambdas_
transformed = pt.transform(Xb[i])
result = seasonal_decompose(transformed, model='additive', freq=boot_freq)
# Moving Block Bootstrap on Residuals
bootstrapRes = MBB(bootstrap_bl_size, result.resid)
for data in bootstrapRes.bootstrap(nr_boot):
bs_x = data[0][0]
reconSeriesYC = result.trend + result.seasonal + bs_x
Xb[i] = pt.inverse_transform(reconSeriesYC)
for i,j,k in np.ndindex(X.shape):
if np.isnan(Xb[i,j,k]):
Xb[i,j,k] = X[i,j,k]
X = np.vstack([X, Xb])
Y = np.vstack([Y, Y])
return X, Y
def moving_block_bootstrap_method(X, Y, block_size=50, n_samples=50):
boot_samples = []
bs = MovingBlockBootstrap(block_size, X, y=Y)
for samp in bs.bootstrap(n_samples):
boot_samples.append((samp[0][0], samp[1]['y']))
return boot_samples
def get_MBB_reminders(num_samples, remainder):
reminders = np.zeros((num_samples, remainder.size))
bs = MBB(3, remainder)
for i, data in enumerate(bs.bootstrap(num_samples)):
reminders[i] = data[0][0]
return reminders