def test_ndim_kmeans():
with util_numpy.test_uses_numpy() as np:
k = 4
max_it = 10
max_dba_it = 20
# series = np.array(
# [[[0., 0], [1, 2], [1, 0], [1, 0]],
# [[0., 1], [2, 0], [0, 0], [0, 0]],
# [[1., 2], [0, 0], [0, 0], [0, 1]],
# [[0., 0], [1, 2], [1, 0], [1, 0]],
# [[0., 1], [2, 0], [0, 0], [0, 0]],
# [[1., 2], [0, 0], [0, 0], [0, 1]]])
series = [np.array([[0., 0], [1, 2], [1, 0], [1, 0]]),
np.array([[0., 1], [2, 0], [0, 0], [0, 0]]),
np.array([[1., 2], [0, 0], [0, 0], [0, 1]]),
np.array([[0., 0], [1, 2], [1, 0], [1, 0]]),
np.array([[0., 1], [2, 0], [0, 0], [0, 0]]),
np.array([[1., 2], [0, 0], [0, 0], [0, 1]])]
print(type(series))
# print(series.shape)
# window = int(series.shape[1] * 1.0)
window=None
print(f'window={window}')
# Perform k-means
tic = time.perf_counter()
model = KMeans(k=k, max_it=max_it, max_dba_it=max_dba_it, drop_stddev=2,
dists_options={"window": window},
initialize_with_kmedoids=False,
initialize_with_kmeanspp=False)
cluster_idx, performed_it = model.fit(series, use_c=False, use_parallel=False)
toc = time.perf_counter()
print(f'DBA ({performed_it} iterations: {toc - tic:0.4f} sec')
def test_nparray_kmeans():
with util_numpy.test_uses_numpy() as np:
k = 4
max_it = 10
max_dba_it = 20
series = np.array([[0., 0, 1, 2, 1, 0, 1, 0, 0],
[0., 1, 2, 0, 0, 0, 0, 0, 0],
[1., 2, 0, 0, 0, 0, 0, 1, 1],
[0., 0, 1, 2, 1, 0, 1, 0, 0],
[0., 1, 2, 0, 0, 0, 0, 0, 0],
[1., 2, 0, 0, 0, 0, 0, 1, 1]])
print(type(series))
print(series.shape)
window = int(series.shape[1] * 1.0)
# Perform k-means
tic = time.perf_counter()
model = KMeans(k=k,
max_it=max_it,
max_dba_it=max_dba_it,
dists_options={"window": window},
initialize_with_kmedoids=False,
initialize_with_kmeanspp=True)
cluster_idx, performed_it = model.fit(series,
use_c=True,
use_parallel=False)
toc = time.perf_counter()
print(f'DBA ({performed_it} iterations: {toc - tic:0.4f} sec')
if directory and not dtwvis.test_without_visualization():
try:
import matplotlib.pyplot as plt
except ImportError:
raise MatplotlibException("No matplotlib available")
fig, ax = plt.subplots(nrows=k,
ncols=2,
figsize=(10, 4),
sharex='all',
sharey='all')
fn = directory / "test_nparray_barycenter.png"
all_idx = set()
for ki in range(k):
ax[ki, 0].plot(model.means[ki])
for idx in cluster_idx[ki]:
ax[ki, 1].plot(series[idx], alpha=0.3)
if idx in all_idx:
raise Exception(f'Series in multiple clusters: {idx}')
all_idx.add(idx)
assert (len(all_idx) == len(series))
fig.savefig(str(fn))
plt.close()
def test_ndim_kmeans(use_c, use_parallel):
with util_numpy.test_uses_numpy() as np:
random.seed(3980)
np.random.seed(3980)
k = 2
max_it = 10
max_dba_it = 20
series = np.array([[[0., 0], [1, 2], [1, 0], [1, 0]],
[[0., 1], [2, 0], [0, 0], [0, 0]],
[[1., 2], [0, 0], [0, 0], [0, 1]],
[[0., 0], [1, 2], [1, 0], [1, 0]],
[[0., 1], [2, 0], [0, 0], [0, 0]],
[[1., 2], [0, 0], [0, 0], [0, 1]]])
window = None
# Perform k-means
tic = time.perf_counter()
model = KMeans(k=k,
max_it=max_it,
max_dba_it=max_dba_it,
drop_stddev=2,
dists_options={"window": window},
initialize_with_kmedoids=False,
initialize_with_kmeanspp=False)
cluster_idx, performed_it = model.fit(series,
use_c=use_c,
use_parallel=use_parallel)
toc = time.perf_counter()
# print(f'DBA ({performed_it} iterations: {toc - tic:0.4f} sec')
# for ki in range(k):
# print(f'Cluster {ki+1}: {model.cluster_idx[ki]}')
# print(model.means[ki])
assert set(model.cluster_idx[0]) == {
1, 2, 4, 5
}, f'{model.cluster_idx[0]} != {{1, 2, 4, 5}}'
assert set(
model.cluster_idx[1]) == {0,
3}, f'{model.cluster_idx[1]} != {{0, 3}}'
def test_trace_kmeans():
with util_numpy.test_uses_numpy() as np:
k = 4
max_it = 10
max_dba_it = 20
rsrc_fn = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'rsrc', 'Trace_TRAIN.txt')
data = np.loadtxt(rsrc_fn)
labels = data[:, 0]
series = data[:, 1:]
mask = np.full((len(labels), ), False, dtype=bool)
mask[:] = (labels == 1)
# c = series[0, :]
print(type(series))
print(series.shape)
window = int(series.shape[1] * 1.0)
# Z-normalize sequences
series = (series -
series.mean(axis=1)[:, None]) / series.std(axis=1)[:, None]
# Align start and/or end values
# avg_start = series[:, :20].mean(axis=1)
# avg_end = series[:, 20:].mean(axis=1)
# series = (series - avg_end[:, None])
# Perform k-means
tic = time.perf_counter()
model = KMeans(k=k,
max_it=max_it,
max_dba_it=max_dba_it,
drop_stddev=2,
dists_options={"window": window},
initialize_with_kmedoids=False,
initialize_with_kmeanspp=True)
try:
cluster_idx, performed_it = model.fit(series,
use_c=True,
use_parallel=False)
except PyClusteringException:
return
toc = time.perf_counter()
print(f'DBA ({performed_it} iterations: {toc - tic:0.4f} sec')
if directory and not dtwvis.test_without_visualization():
try:
import matplotlib.pyplot as plt
except ImportError:
raise MatplotlibException("No matplotlib available")
fig, ax = plt.subplots(nrows=k,
ncols=2,
figsize=(10, 4),
sharex='all',
sharey='all')
fn = directory / "test_trace_barycenter.png"
all_idx = set()
for ki in range(k):
ax[ki, 0].plot(model.means[ki])
for idx in cluster_idx[ki]:
ax[ki, 1].plot(series[idx], alpha=0.3)
if idx in all_idx:
raise Exception(f'Series in multiple clusters: {idx}')
all_idx.add(idx)
assert (len(all_idx) == len(series))
fig.savefig(str(fn))
plt.close()
fig, ax = plt.subplots(nrows=k,
ncols=1,
figsize=(5, 4),
sharex='all',
sharey='all')
fn = directory / "test_trace_barycenter_solution.png"
for i in range(len(labels)):
ax[int(labels[i]) - 1].plot(series[i], alpha=0.3)
fig.savefig(str(fn))
plt.close()
def test_trace_kmeans_differencing():
with util_numpy.test_uses_numpy() as np, util_numpy.test_uses_scipy() as scipy:
k = 4
max_it = 10
max_dba_it = 20
nb_prob_samples = 0
use_c = True
rsrc_fn = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'rsrc', 'Trace_TRAIN.txt')
data = np.loadtxt(rsrc_fn)
labels = data[:, 0]
series = data[:, 1:]
mask = np.full((len(labels),), False, dtype=bool)
mask[:] = (labels == 1)
# c = series[0, :]
print(type(series))
print(series.shape)
window = int(series.shape[1] * 0.5)
# Differencing
# The baseline differences are not relevant thus we cluster based
# on the result of differencing.
# Also the high-freq noise dominates the local differences, thus
# we apply a low-pass filter first.
signal = scipy.import_signal()
series_orig = series.copy()
series = np.diff(series, n=1, axis=1)
fs = 100 # sample rate, Hz
cutoff = 10 # cut off frequency, Hz
nyq = 0.5 * fs # Nyquist frequency
b, a = signal.butter(2, cutoff / nyq, btype='low', analog=False, output='ba')
series = signal.filtfilt(b, a, series, axis=1)
# Perform k-means
tic = time.perf_counter()
model = KMeans(k=k, max_it=max_it, max_dba_it=max_dba_it, drop_stddev=1,
nb_prob_samples=nb_prob_samples,
dists_options={"window": window},
initialize_with_kmedoids=False,
initialize_with_kmeanspp=True)
try:
cluster_idx, performed_it = model.fit(series, use_c=use_c, use_parallel=False)
except PyClusteringException:
return
toc = time.perf_counter()
print(f'DBA ({performed_it} iterations: {toc - tic:0.4f} sec')
if directory and not dtwvis.test_without_visualization():
try:
import matplotlib.pyplot as plt
except ImportError:
raise MatplotlibException("No matplotlib available")
fig, ax = plt.subplots(nrows=k, ncols=3, figsize=(10,4),
sharex='all', sharey='all')
fn = directory / "test_trace_barycenter.png"
all_idx = set()
mask = np.full((k, len(series_orig)), False, dtype=bool)
for ki in range(k):
ax[ki, 0].plot(model.means[ki])
for idx in cluster_idx[ki]:
ax[ki, 2].plot(series_orig[idx], alpha=0.3)
mask[ki, idx] = True
if idx in all_idx:
raise Exception(f'Series in multiple clusters: {idx}')
all_idx.add(idx)
series_orig = (series_orig - series_orig.mean(axis=1)[:, None]) / series_orig.std(axis=1)[:, None]
for ki, mean in enumerate(model.means):
# dba = dba_loop(series_orig, c=None, mask=mask[ki, :],
# max_it=max_it, thr=None, use_c=use_c,
# nb_prob_samples=nb_prob_samples)
print(mean.shape)
dba = np.r_[0, mean].cumsum()
ax[ki, 1].plot(dba)
assert(len(all_idx) == len(series))
ax[0, 0].set_title("DBA Differencing + LP")
ax[0, 1].set_title("DBA Original series")
ax[0, 2].set_title("Clustered series")
fig.savefig(str(fn))
plt.close()
fig, ax = plt.subplots(nrows=k, ncols=1, figsize=(5, 4),
sharex='all', sharey='all')
fn = directory / "test_trace_barycenter_solution.png"
for i in range(len(labels)):
ax[int(labels[i]) - 1].plot(series_orig[i], alpha=0.3)
fig.savefig(str(fn))
plt.close()