def mds_plot(dataset_name, bits, data_path, mat_path):
raw_data = sio.loadmat(data_path)
data = raw_data['data']
window_size = int(raw_data['subLen'][0][0])
mat_file = sio.loadmat(mat_path)
compressible = mat_file['compressible']
hypothesis = mat_file['hypothesis']
idx_bitsave = mat_file['idx_bitsave']
try:
cut_off = np.where(np.diff(idx_bitsave[:, 1]) > 0)[0][0]
except IndexError as error:
print('{} - {}'.format(dataset_name, bits))
return
valid_idx = idx_bitsave[:, 0][:cut_off]
valid_h = np.intersect1d(valid_idx, hypothesis)
valid_c = np.intersect1d(valid_idx, compressible)
assert (valid_h.shape[0] + valid_c.shape[0] == valid_idx.shape[0])
Y, _ = pattern_mds(valid_idx.astype(int), window_size, data)
fig, ax = plt.subplots()
h, c = [], []
for i in range(valid_idx.shape[0]):
if idx_bitsave[i, 2] == 0:
h.append([Y[i, 0], Y[i, 1]])
else:
c.append([Y[i, 0], Y[i, 1]])
h = np.array(h)
c = np.array(c)
ax.scatter(h[:, 0], h[:, 1], marker='x', color='C0', label='hypothesis')
ax.scatter(c[:, 0], c[:, 1], marker='o', color='C1', label='compressible')
ax.legend(loc=2)
ax.grid(True)
plt.title('MDS Visual of {}'.format(dataset_name))
plt.savefig('./result/gaussian/{}-{}bits-mds.png'.format(
dataset_name, bits))
plt.clf()
d[:-4], bit_dict[d[:-4]]))
idx_bitsave = result['idx_bitsave'].reshape(-1, 3)
cut_off = np.where(np.diff(idx_bitsave[:, 1]) > 0)[0]
cut_off = cut_off[0] if cut_off.shape[0] != 0 else idx_bitsave.shape[0]
idx = idx_bitsave[:, 0][:cut_off].astype(np.int)
bad_label = []
for i in idx:
if np.any(np.abs(i - tp) < 0.2 * window_size):
bad_label.append(1)
else:
bad_label.append(0)
mds2 = pattern_mds(idx, window_size, data)[0]
if mds2.shape[1] < 2:
f = plt.figure()
plt.title('{} - Gaussian'.format(d[:-4]))
plt.axis('off')
f.savefig('./mds/{}/{}-Gaussian.png'.format(d[:-4], d[:-4]),
bbox_inches='tight')
plt.show()
continue
hit = []
not_hit = []
for i in range(mds2.shape[0]):
if bad_label[i] == 1:
hit.append([mds2[i, 0], mds2[i, 1]])
def mds_plot(dataset_name, bits, data_path, mat_path):
os.makedirs('./result/gaussian/{}'.format(dataset_name))
raw_data = sio.loadmat(data_path)
data = raw_data['data']
tp = raw_data['labIdx'] - 1
p = 0.2
window_size = int(raw_data['subLen'][0][0])
mat_file = sio.loadmat(mat_path)
compressible = mat_file['compressible']
hypothesis = mat_file['hypothesis']
idx_bitsave = mat_file['idx_bitsave']
compress_table = ast.literal_eval(mat_file['compress_table'][0])
try:
cut_off = np.where(np.diff(idx_bitsave[:, 1]) > 0)[0][0]
except IndexError as error:
print('{} - {}'.format(dataset_name, bits))
return
valid_idx = idx_bitsave[:, 0][:cut_off]
valid_h = np.intersect1d(valid_idx, hypothesis)
valid_c = np.intersect1d(valid_idx, compressible)
assert (valid_h.shape[0] + valid_c.shape[0] == valid_idx.shape[0])
Y, _ = pattern_mds(valid_idx.astype(int), window_size, data)
fig, ax = plt.subplots()
h, c, fp_h, fp_c = [], [], [], []
fp_h_idx, fp_c_idx = [], []
for i in range(valid_idx.shape[0]):
if idx_bitsave[i, 2] == 0:
if validation(valid_idx[i], tp, p, window_size):
h.append([Y[i, 0], Y[i, 1]])
else:
fp_h.append([Y[i, 0], Y[i, 1]])
fp_h_idx.append(valid_idx[i])
else:
if validation(valid_idx[i], tp, p, window_size):
c.append([Y[i, 0], Y[i, 1]])
else:
fp_c.append([Y[i, 0], Y[i, 1]])
fp_c_idx.append(valid_idx[i])
h = np.array(h)
c = np.array(c)
fp_h = np.array(fp_h)
fp_c = np.array(fp_c)
for h in fp_h_idx:
coms = compress_table[h]
for c in coms:
if c not in fp_c_idx and c in valid_idx:
print('fp_h -> tp_c')
print('{}, {}'.format(h, c))
else:
print('fp_h -> fp_c')
print(fp_h.shape)
print(fp_c.shape)
if __name__ == '__main__':
data_sets = os.listdir('../eval_data/all')
for d in data_sets:
if not os.path.exists('./mds/{}'.format(d[:-4])):
os.makedirs('./mds/{}'.format(d[:-4]))
# if d not in ['Wine.mat', 'TwoLeadECG.mat', 'FaceFour.mat']:
# continue
file = sio.loadmat('../eval_data/all/{}'.format(d))
window_size = file['subLen'][0][0]
data = file['data']
tp = file['labIdx'].squeeze() - 1
mds = pattern_mds(tp, window_size, data)[0]
f = plt.figure()
plt.scatter(mds[:, 0], mds[:, 1], s=0.5 * plt.rcParams['lines.markersize'] ** 2)
plt.title(d[:-4])
plt.axis('off')
f.savefig('./mds/{}/{}.png'.format(d[:-4], d[:-4]), bbox_inches='tight')
plt.show()
randidx = np.random.permutation(data.shape[0] - window_size + 1)
randidx = randidx[:tp.shape[0]]
bad_label = []
for i in randidx:
if np.any(np.abs(i - tp) < 0.2 * window_size):
bad_label.append(1)