def compute_multiple(data, ncols, alg, compress=False, n_power_iter=0, step=1):
"""
Compute separable NMF of the input matrix with k columns, for
k = 1 to ncols.
:param data: Input matrix
:type data: numpy.ndarray
:param ncols: Maximum number of columns to select
:type ncols: int
:param alg: Choice of algorithm for computing the columns.
One of 'SPA' or 'XRAY'.
:type alg: basestring
:param compress: Whether to use compression or not
:type compress: bool
:param n_power_iter: Number of power iterations for compression
:type n_power_iter: int
:param step: Step size for k
:type step: int
:return:
The selected columns, the right factors of the separable NMF
decomposition, and the relative errors.
:rtype: list of tuples of:
- list of ints
- right factor matrix
- relative error of the approximation
"""
if compress:
data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter)
else:
if isinstance(data, da.Array):
_, data_comp = csnmf.tsqr.qr(data)
elif isinstance(data, np.ndarray):
_, data_comp = np.linalg.qr(data)
else:
raise TypeError('Cannot compute QR decomposition of matrices '
'of type ' + type(data).__name__)
colnorms = _compute_colnorms(data_comp)
try:
data_comp = np.array(data_comp)
colnorms = np.array(colnorms)
except:
raise
results = []
for k in range(step, ncols, step):
cols = mrnmf.select_columns(data_comp, colnorms, alg, k)
mat_h, error = mrnmf.nnls_frob(data_comp, cols)
results.append((cols, mat_h, error))
return results
def compute(data, ncols, alg, compress=False, n_power_iter=0):
"""
Compute separable NMF of the input matrix.
:param data: Input matrix
:type data: numpy.ndarray
:param ncols: Number of columns to select
:type ncols: int
:param alg: Choice of algorithm for computing the columns.
One of 'SPA' or 'XRAY'.
:type alg: basestring
:param compress: Whether to use compression or not
:type compress: bool
:param n_power_iter: Number of power iterations for compression
:type n_power_iter: int
:return:
The selected columns, the right factor of the separable NMF
decomposition, and the relative error.
:rtype: tuple of:
- list of ints
- right factor matrix
- relative error of the approximation
"""
if compress:
#data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter, our=False)
data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter, our=True)
else:
if isinstance(data, da.Array):
_, data_comp = csnmf.tsqr.qr(data)
elif isinstance(data, np.ndarray):
_, data_comp = np.linalg.qr(data)
else:
raise TypeError('Cannot compute QR decomposition of matrices '
'of type ' + type(data).__name__)
data_comp = np.array(data_comp)
if alg == 'SPA':
colnorms = _compute_colnorms(data_comp)
colnorms = np.array(colnorms)
else:
colnorms = None
cols = mrnmf.select_columns(data_comp, alg, ncols, colnorms=colnorms)
mat_h, error = mrnmf.nnls_frob(data_comp, cols)
return cols, mat_h, error
def compute(data, ncols, alg, compress=False, n_power_iter=0):
"""
Compute separable NMF of the input matrix.
:param data: Input matrix
:type data: numpy.ndarray
:param ncols: Number of columns to select
:type ncols: int
:param alg: Choice of algorithm for computing the columns.
One of 'SPA' or 'XRAY'.
:type alg: basestring
:param compress: Whether to use compression or not
:type compress: bool
:param n_power_iter: Number of power iterations for compression
:type n_power_iter: int
:return:
The selected columns, the right factor of the separable NMF
decomposition, and the relative error.
:rtype: tuple of:
- list of ints
- right factor matrix
- relative error of the approximation
"""
if compress:
data_comp, _ = csnmf.compression.compress(data, ncols, n_power_iter)
else:
if isinstance(data, da.Array):
_, data_comp = csnmf.tsqr.qr(data)
elif isinstance(data, np.ndarray):
_, data_comp = np.linalg.qr(data)
else:
raise TypeError('Cannot compute QR decomposition of matrices '
'of type ' + type(data).__name__)
data_comp = np.array(data_comp)
if alg == 'SPA':
colnorms = _compute_colnorms(data_comp)
colnorms = np.array(colnorms)
else:
colnorms = None
cols = mrnmf.select_columns(data_comp, alg, ncols, colnorms=colnorms)
mat_h, error = mrnmf.nnls_frob(data_comp, cols)
return cols, mat_h, error
def test_movie(hdf_filename, base_output_name, ncols=None, interval=None,
max_blockshape=(1e5, 100)):
f = h5py.File(hdf_filename, 'r')
img_shape = np.array(f['img_shape'], dtype=np.int)
m = min(max_blockshape[0], reduce(mul, img_shape))
if interval is not None:
n = min(max_blockshape[1], -reduce(sub, interval))
else:
n = max_blockshape[1]
m = int(m)
n = int(n)
data = da.from_array(f['data'], chunks=(m,n))
if interval is not None:
data = data[:, interval[0]:interval[1]]
data = np.array(data.compute())
if ncols is None:
ncols = data.shape[1] / 120
print(data.shape, ncols, m, n)
t = timeit.default_timer()
cols, mat_h, error = csnmf.snmf.compute(data, ncols, 'SPA', compress=True)
t = timeit.default_timer() - t
print(error)
data = np.array(data)
error = mrnmf.nnls_frob(data, cols)[1]
def argsort(seq):
return sorted(range(len(seq)), key=seq.__getitem__)
cols_order = argsort(cols)
cols = sorted(cols)
mat_h = mat_h[cols_order, :]
res_dict = {'cols': cols, 'error': error, 'time': t}
base_str = 'error {error:.4f}; time {time:.2f}; cols {cols}'
print(base_str.format(**res_dict))
if interval is not None and ncols <= 10:
colors = ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99',
'#e31a1c', '#fdbf6f', '#ff7f00', '#cab2d6', '#6a3d9a']
cmap = ListedColormap(colors)
fourcc = cv2.cv.CV_FOURCC(*'mp4v')
out = cv2.VideoWriter(base_output_name + '.avi',
fourcc, 8.0, (img_shape[1], img_shape[0]), True)
max_val = np.argmax(mat_h, axis=0)
for i in range(data.shape[1]):
img = np.reshape(data[:, i], img_shape) * 255
img = img.astype(np.uint8)
norm_idx = float(max_val[i]) / ncols
c = map(lambda x: int(x*255), cmap(norm_idx))[::-1]
cv2.rectangle(img, (img_shape[1]-50, img_shape[0]-50),
(img_shape[1], img_shape[0]), c, cv2.cv.CV_FILLED)
out.write(img)
out.release()
border_width = 40
arrangement = int(math.ceil(math.sqrt(ncols)))
plt.figure()
for i, c in enumerate(cols):
img = np.reshape(data[:, c], img_shape)
norm_idx = float(i) / ncols
ax = plt.subplot(arrangement, arrangement, i+1,
axisbg=cmap(norm_idx))
ax.imshow(img, aspect='equal', origin='lower',
extent=(border_width, img_shape[1] - border_width,
border_width, img_shape[0] - border_width))
ax.imshow(img, alpha=0)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.tight_layout()
plt.savefig(base_output_name + '_representatives.pdf', dpi=300)
mat_h_norm = mat_h / np.sum(mat_h, axis=0)
plt.figure()
ax = plt.axes()
for i in range(ncols):
bottom = np.sum(mat_h_norm[:i, :], axis=0)
norm_idx = float(i) / ncols
ax.bar(range(data.shape[1]), mat_h_norm[i, :], 1,
color=cmap(norm_idx),
linewidth=0, bottom=bottom)
ax.set_ylim(0, 1)
plt.savefig(base_output_name + '_activation.pdf', dpi=300)
for i, c in enumerate(cols):
img = np.reshape(data[:, c], img_shape)
plt.figure()
ax = plt.axes()
ax.imshow(img)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.savefig(base_output_name + '_representative_{0}.png'.format(i))
plt.close()
plt.close('all')
def test_movie(hdf_filename, base_output_name, ncols=None, interval=None,
max_blockshape=(1e5, 100)):
f = h5py.File(hdf_filename, 'r')
img_shape = np.array(f['img_shape'], dtype=np.int)
f.close()
m = min(max_blockshape[0], reduce(mul, img_shape))
if interval is not None:
n = min(max_blockshape[1], -reduce(sub, interval))
else:
n = max_blockshape[1]
m = int(m)
n = int(n)
data = into(Array, hdf_filename + '::/data', blockshape=(m, n))
if interval is not None:
data = data[:, interval[0]:interval[1]]
data = np.array(data)
if ncols is None:
ncols = data.shape[1] / 120
print(data.shape, ncols, m, n)
t = timeit.default_timer()
cols, mat_h, error = csnmf.snmf.compute(data, ncols, 'SPA', compress=True)
t = timeit.default_timer() - t
print(error)
data = np.array(data)
error = mrnmf.nnls_frob(data, cols)[1]
def argsort(seq):
return sorted(range(len(seq)), key=seq.__getitem__)
cols_order = argsort(cols)
cols = sorted(cols)
mat_h = mat_h[cols_order, :]
res_dict = {'cols': cols, 'error': error, 'time': t}
base_str = 'error {error:.4f}; time {time:.2f}; cols {cols}'
print(base_str.format(**res_dict))
if interval is not None and ncols <= 10:
colors = ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99',
'#e31a1c', '#fdbf6f', '#ff7f00', '#cab2d6', '#6a3d9a']
cmap = ListedColormap(colors)
fourcc = cv2.cv.CV_FOURCC(*'mp4v')
out = cv2.VideoWriter(base_output_name + '.avi',
fourcc, 8.0, (img_shape[1], img_shape[0]), True)
max_val = np.argmax(mat_h, axis=0)
for i in range(data.shape[1]):
img = np.reshape(data[:, i], img_shape) * 255
img = img.astype(np.uint8)
norm_idx = float(max_val[i]) / ncols
c = map(lambda x: int(x*255), cmap(norm_idx))[::-1]
cv2.rectangle(img, (img_shape[1]-50, img_shape[0]-50),
(img_shape[1], img_shape[0]), c, cv2.cv.CV_FILLED)
out.write(img)
out.release()
border_width = 40
arrangement = int(math.ceil(math.sqrt(ncols)))
plt.figure()
for i, c in enumerate(cols):
img = np.reshape(data[:, c], img_shape)
norm_idx = float(i) / ncols
ax = plt.subplot(arrangement, arrangement, i+1,
axisbg=cmap(norm_idx))
ax.imshow(img, aspect='equal', origin='lower',
extent=(border_width, img_shape[1] - border_width,
border_width, img_shape[0] - border_width))
ax.imshow(img, alpha=0)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.tight_layout()
plt.savefig(base_output_name + '_representatives.pdf', dpi=300)
mat_h_norm = mat_h / np.sum(mat_h, axis=0)
plt.figure()
ax = plt.axes()
for i in range(ncols):
bottom = np.sum(mat_h_norm[:i, :], axis=0)
norm_idx = float(i) / ncols
ax.bar(range(data.shape[1]), mat_h_norm[i, :], 1,
color=cmap(norm_idx),
linewidth=0, bottom=bottom)
ax.set_ylim(0, 1)
plt.savefig(base_output_name + '_activation.pdf', dpi=300)
for i, c in enumerate(cols):
img = np.reshape(data[:, c], img_shape)
plt.figure()
ax = plt.axes()
ax.imshow(img)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.savefig(base_output_name + '_representative_{0}.png'.format(i))
plt.close()
plt.close('all')
