def callback_recovery(loc):
d = loc['dict_obj']
d.wc.append(emd(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
d.wfs.append(emd(loc['dictionary'], d.generating_dict,
'fubinistudy', scale=True))
d.hc.append(hausdorff(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
d.hfs.append(hausdorff(loc['dictionary'], d.generating_dict,
'fubinistudy', scale=True))
d.bd.append(beta_dist(d.generating_dict, loc['dictionary']))
d.dr99.append(detection_rate(loc['dictionary'],
d.generating_dict, 0.99))
d.dr97.append(detection_rate(loc['dictionary'],
d.generating_dict, 0.97))
def callback_recovery(loc):
d = loc["dict_obj"]
d.wc.append(
emd(loc["dictionary"], d.generating_dict, "chordal", scale=True))
d.wfs.append(
emd(loc["dictionary"], d.generating_dict, "fubinistudy", scale=True))
d.hc.append(
hausdorff(loc["dictionary"], d.generating_dict, "chordal", scale=True))
d.hfs.append(
hausdorff(loc["dictionary"],
d.generating_dict,
"fubinistudy",
scale=True))
d.bd.append(beta_dist(d.generating_dict, loc["dictionary"]))
d.dr99.append(detection_rate(loc["dictionary"], d.generating_dict, 0.99))
d.dr97.append(detection_rate(loc["dictionary"], d.generating_dict, 0.97))
def test_inhomogeneous_dims():
idx = arange(n_dims)
for g in ['chordal_principal_angles', 'binetcauchy', 'geodesic']:
for i in range(n_dims, 0, -1):
assert_almost_equal(
0., emd(dm, [a[:, idx[:i]] for a in dm], g, scale=True))
assert_almost_equal(
0., hausdorff(dm, [a[:, idx[:i]] for a in dm], g, scale=True))
for g in ["chordal", "fubinistudy", "frobenius"]:
assert_raises(ValueError, emd, dm, [a[:, :-1] for a in dm], g)
assert_raises(ValueError, hausdorff, dm, [a[:, :-1] for a in dm], g)
def callback_distance(loc):
ii, iter_offset = loc['ii'], loc['iter_offset']
n_batches = loc['n_batches']
if np.mod((ii-iter_offset)/int(n_batches), n_iter) == 0:
# Compute distance only every 5 iterations, as in previous case
d = loc['dict_obj']
d.wasserstein.append(emd(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
d.detect_rate.append(detection_rate(loc['dictionary'],
d.generating_dict, 0.99))
d.objective_error.append(loc['current_cost'])
def callback_distance(loc):
ii, iter_offset = loc['ii'], loc['iter_offset']
n_batches = loc['n_batches']
if np.mod((ii-iter_offset)/int(n_batches), n_iter) == 0:
# Compute distance only every 5 iterations, as in previous case
d = loc['dict_obj']
d.wasserstein.append(emd(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
d.detection_rate.append(detectionRate(loc['dictionary'],
d.generating_dict, 0.99))
d.objective_error.append(loc['current_cost'])
def callback_distance(loc):
ii, iter_offset = loc["ii"], loc["iter_offset"]
n_batches = loc["n_batches"]
if np.mod((ii - iter_offset) / int(n_batches), n_iter) == 0:
# Compute distance only every 5 iterations, as in previous case
d = loc["dict_obj"]
d.wasserstein.append(
emd(loc["dictionary"], d.generating_dict, "chordal", scale=True))
d.detect_rate.append(
detection_rate(loc["dictionary"], d.generating_dict, 0.99))
d.objective_error.append(loc["current_cost"])
def callback_recovery(loc):
d = loc['dict_obj']
d.wc.append(emd(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
d.wfs.append(emd(loc['dictionary'], d.generating_dict,
'fubinistudy', scale=True))
d.wcpa.append(emd(loc['dictionary'], d.generating_dict,
'chordal_principal_angles', scale=True))
d.wbc.append(emd(loc['dictionary'], d.generating_dict,
'binetcauchy', scale=True))
d.wg.append(emd(loc['dictionary'], d.generating_dict,
'geodesic', scale=True))
d.wfb.append(emd(loc['dictionary'], d.generating_dict,
'frobenius', scale=True))
d.hc.append(hausdorff(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
d.hfs.append(hausdorff(loc['dictionary'], d.generating_dict,
'fubinistudy', scale=True))
d.hcpa.append(hausdorff(loc['dictionary'], d.generating_dict,
'chordal_principal_angles', scale=True))
d.hbc.append(hausdorff(loc['dictionary'], d.generating_dict,
'binetcauchy', scale=True))
d.hg.append(hausdorff(loc['dictionary'], d.generating_dict,
'geodesic', scale=True))
d.hfb.append(hausdorff(loc['dictionary'], d.generating_dict,
'frobenius', scale=True))
d.dr99.append(detectionRate(loc['dictionary'],
d.generating_dict, 0.99))
d.dr97.append(detectionRate(loc['dictionary'],
d.generating_dict, 0.97))
def callback_recovery(loc):
d = loc["dict_obj"]
d.wc.append(emd(loc["dictionary"], d.generating_dict, "chordal", scale=True))
d.wfs.append(emd(loc["dictionary"], d.generating_dict, "fubinistudy", scale=True))
d.wcpa.append(
emd(loc["dictionary"], d.generating_dict, "chordal_principal_angles", scale=True)
)
d.wbc.append(emd(loc["dictionary"], d.generating_dict, "binetcauchy", scale=True))
d.wg.append(emd(loc["dictionary"], d.generating_dict, "geodesic", scale=True))
d.wfb.append(emd(loc["dictionary"], d.generating_dict, "frobenius", scale=True))
d.hc.append(hausdorff(loc["dictionary"], d.generating_dict, "chordal", scale=True))
d.hfs.append(
hausdorff(loc["dictionary"], d.generating_dict, "fubinistudy", scale=True)
)
d.hcpa.append(
hausdorff(
loc["dictionary"], d.generating_dict, "chordal_principal_angles", scale=True
)
)
d.hbc.append(
hausdorff(loc["dictionary"], d.generating_dict, "binetcauchy", scale=True)
)
d.hg.append(hausdorff(loc["dictionary"], d.generating_dict, "geodesic", scale=True))
d.hfb.append(hausdorff(loc["dictionary"], d.generating_dict, "frobenius", scale=True))
d.dr99.append(detection_rate(loc["dictionary"], d.generating_dict, 0.99))
d.dr97.append(detection_rate(loc["dictionary"], d.generating_dict, 0.97))
def test_inhomogeneous_dims():
idx = arange(n_dims)
for g in ['chordal_principal_angles', 'binetcauchy', 'geodesic']:
for i in range(n_dims, 0, -1):
assert_almost_equal (0., emd(dm, [a[:,idx[:i]] for a in dm],
g, scale=True))
assert_almost_equal(0., hausdorff(dm, [a[:,idx[:i]] for a in dm],
g, scale=True))
for g in ["chordal", "fubinistudy", "frobenius"]:
assert_raises(ValueError, emd, dm,
[a[:,:-1] for a in dm], g)
assert_raises(ValueError, hausdorff, dm,
[a[:,:-1] for a in dm], g)
learned_dict = MiniBatchMultivariateDictLearning(n_kernels=n_kernels,
batch_size=batch_size, n_iter=n_iter,
n_nonzero_coefs=n_nonzero_coefs,
n_jobs=n_jobs, learning_rate=learning_rate,
kernel_init_len=kernel_init_len, verbose=1,
dict_init=dict_init, random_state=rng_global)
# Update learned dictionary at each iteration and compute a distance
# with the generating dictionary
for i in range(max_iter):
learned_dict = learned_dict.partial_fit(X)
# Compute the detection rate
detect_rate.append(detection_rate(learned_dict.kernels_,
generating_dict, 0.99))
# Compute the Wasserstein distance
wasserstein.append(emd(learned_dict.kernels_, generating_dict,
'chordal', scale=True))
# Get the objective error
objective_error.append(learned_dict.error_.sum())
plot_univariate(array(objective_error), array(detect_rate),
array(wasserstein), n_iter, 'univariate-case')
# Another possibility is to rely on a callback function such as
def callback_distance(loc):
ii, iter_offset = loc['ii'], loc['iter_offset']
n_batches = loc['n_batches']
if np.mod((ii-iter_offset)/int(n_batches), n_iter) == 0:
# Compute distance only every 5 iterations, as in previous case
d = loc['dict_obj']
d.wasserstein.append(emd(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
learned_dict = MiniBatchMultivariateDictLearning(n_kernels=n_kernels,
batch_size=batch_size, n_iter=n_iter,
n_nonzero_coefs=n_nonzero_coefs,
n_jobs=n_jobs, learning_rate=learning_rate,
kernel_init_len=kernel_init_len, verbose=1,
dict_init=dict_init, random_state=rng_global)
# Update learned dictionary at each iteration and compute a distance
# with the generating dictionary
for i in range(max_iter):
learned_dict = learned_dict.partial_fit(X)
# Compute the detection rate
detection_rate.append(detectionRate(learned_dict.kernels_,
generating_dict, 0.99))
# Compute the Wasserstein distance
wasserstein.append(emd(learned_dict.kernels_, generating_dict,
'chordal', scale=True))
# Get the objective error
objective_error.append(learned_dict.error_.sum())
plot_multivariate(array(objective_error), array(detection_rate),
100.-array(wasserstein), n_iter, 'multivariate-case')
# Another possibility is to rely on a callback function such as
def callback_distance(loc):
ii, iter_offset = loc['ii'], loc['iter_offset']
n_batches = loc['n_batches']
if np.mod((ii-iter_offset)/int(n_batches), n_iter) == 0:
# Compute distance only every 5 iterations, as in previous case
d = loc['dict_obj']
d.wasserstein.append(emd(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
kernel_init_len=kernel_init_len,
verbose=1,
dict_init=dict_init,
random_state=rng_global,
)
# Update learned dictionary at each iteration and compute a distance
# with the generating dictionary
for _ in range(max_iter):
learned_dict = learned_dict.partial_fit(X)
# Compute the detection rate
detect_rate.append(
detection_rate(learned_dict.kernels_, generating_dict, 0.99))
# Compute the Wasserstein distance
wasserstein.append(
emd(learned_dict.kernels_, generating_dict, "chordal", scale=True))
# Get the objective error
objective_error.append(learned_dict.error_.sum())
plot_univariate(
array(objective_error),
array(detect_rate),
array(wasserstein),
n_iter,
"univariate-case",
)
# Another possibility is to rely on a callback function such as
def callback_distance(loc):
ii, iter_offset = loc["ii"], loc["iter_offset"]
