def __LGC(self,X,W,Y,labeledIndexes, alpha = 0.1, useEstimatedFreq = None, hook=None):
""" Init """
import scipy.sparse
if scipy.sparse.issparse(W):
W = W.todense()
Y = np.copy(Y)
if Y.ndim == 1:
Y = gutils.init_matrix(Y,labeledIndexes)
Y[np.logical_not(labeledIndexes),:] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
""" Estimate frequency of classes"""
num_labeled = Y[labeledIndexes].shape[0]
num_classes = Y.shape[1]
if not useEstimatedFreq is None:
if isinstance(useEstimatedFreq,bool):
estimatedFreq = np.sum(Y[labeledIndexes],axis=0) / num_labeled
else:
estimatedFreq = useEstimatedFreq
else:
estimatedFreq = np.repeat(1/num_classes,num_classes)
omega = estimatedFreq
""" """
mu = (1-alpha)/alpha
n = Y.shape[0]
c = Y.shape[1]
I = np.identity(Y.shape[0])
S = I - gutils.lap_matrix(W, is_normalized=True)
""" stuff that has matrix multiplication with theta """
theta = (1/mu)*np.asarray(np.linalg.inv(I - alpha*S))
F_lgc = (theta@Y)*mu
theta_1n = np.sum(theta,axis=1).flatten()
theta_1n_ratio = (theta_1n/(np.sum(theta_1n)))[:,np.newaxis] #Shape: nx1
print(theta_1n_ratio.shape)
""" Intermediate calc """
zeta = n*omega - np.sum(F_lgc,axis=0) #Shape: 1xc
zeta = np.reshape(zeta,(1,c))
ypsilon = np.ones(shape=(n,1)) - np.sum(F_lgc,axis=1)[:,np.newaxis] -\
theta_1n_ratio * (n - np.sum(F_lgc.flatten())) #Shape: nx1
F = F_lgc
F += theta_1n_ratio @ zeta
F += (1/c)*(ypsilon@ np.ones((1,c)))
log_args = [np.round(x,3) for x in [np.sum(F,axis=1)[0:10], np.sum(F,axis=0), n*omega]]
LOG.info("F sum on rows: {} (expected 1,1,...,1); F sum col: {} (expected {})".format(*log_args) )
return F
def __LGC(self, X, W, Y, labeledIndexes, alpha=0.1, hook=None):
import scipy.sparse
if scipy.sparse.issparse(W):
W = W.todense()
Y = np.copy(Y)
if Y.ndim == 1:
Y = gutils.init_matrix(Y, labeledIndexes)
Y[np.logical_not(labeledIndexes), :] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
#Get D^{-1/2}
d_sqrt = gutils.deg_matrix(W, pwr=-1 / 2)
I = np.identity(Y.shape[0])
S = I - gutils.lap_matrix(W, is_normalized=True)
return (np.matmul(np.linalg.inv(I - alpha * S), Y))
def __MR(self, X, W, Y, labeledIndexes, p, tuning_iter, hook=None):
Y = np.copy(Y)
if Y.ndim == 1:
Y[np.logical_not(labeledIndexes)] = 0
Y = gutils.init_matrix(Y, labeledIndexes)
Y[np.logical_not(labeledIndexes), :] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
l = np.reshape(np.array(np.where(labeledIndexes)), (-1))
num_lab = l.shape[0]
if not isinstance(p, int):
p = int(p * num_lab)
if p > Y.shape[0]:
p = Y.shape[0]
LOG.warn("Warning: p greater than the number of labeled indexes",
LOG.ll.FILTER)
W = scipy_to_np(W)
L = gutils.lap_matrix(W, is_normalized=False)
D = gutils.deg_matrix(W)
def check_symmetric(a, tol=1e-8):
return np.allclose(a, a.T, atol=tol)
if check_symmetric(L):
E = sp.eigh(L, D, eigvals=(1, p))[1]
else:
LOG.warn("Warning: Laplacian not symmetric", LOG.ll.FILTER)
eigenValues, eigenVectors = sp.eig(L, D)
idx = eigenValues.argsort()
eigenValues = eigenValues[idx]
assert eigenValues[0] <= eigenValues[eigenValues.shape[0] - 1]
eigenVectors = eigenVectors[:, idx]
E = eigenVectors[:, 1:(p + 1)]
e_lab = E[labeledIndexes, :]
""" TIKHONOV REGULARIZATION. Currently set to 0."""
TIK = np.zeros(shape=e_lab.shape)
try:
A = np.linalg.inv(e_lab.T @ e_lab + TIK.T @ TIK) @ e_lab.T
except:
A = np.linalg.pinv(e_lab.T @ e_lab + TIK.T @ TIK) @ e_lab.T
F = np.zeros(shape=Y.shape)
y_m = np.argmax(Y, axis=1)[labeledIndexes]
for i in range(Y.shape[1]):
c = np.ones(num_lab)
c[y_m != i] = -1
a = A @ np.transpose(c)
LOG.debug(a, LOG.ll.FILTER)
for j in np.arange(F.shape[0]):
F[j, i] = np.dot(a, E[j, :])
ERmat = -1 * np.ones((Y.shape[0], ))
Y_amax = np.argmax(Y, axis=1)
for i in np.where(labeledIndexes):
ERmat[i] = np.square(Y[i, Y_amax[i]] - F[i, Y_amax[i]])
removed_Lids = np.argsort(ERmat)
removed_Lids = removed_Lids[::-1]
labeledIndexes = np.array(labeledIndexes)
Y = np.copy(Y)
for i in range(tuning_iter):
labeledIndexes[removed_Lids[i]] = False
if not hook is None:
hook._step(step=i,
X=X,
W=W,
Y=Y,
labeledIndexes=labeledIndexes)
return Y, labeledIndexes
def __GTAM(self,
X,
W,
Y,
labeledIndexes,
mu=99.0,
useEstimatedFreq=True,
num_iter=None,
constant_prop=False,
hook=None):
'''BEGIN initialization'''
Y = np.copy(Y)
labeledIndexes = np.array(labeledIndexes)
if Y.ndim == 1:
Y = gutils.init_matrix(Y, labeledIndexes)
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
num_labeled = Y[labeledIndexes].shape[0]
num_unlabeled = Y.shape[0] - num_labeled
num_classes = Y.shape[1]
""" Estimate frequency of classes"""
if not useEstimatedFreq is None:
if isinstance(useEstimatedFreq, bool):
estimatedFreq = np.sum(Y[labeledIndexes], axis=0) / num_labeled
else:
estimatedFreq = useEstimatedFreq
else:
estimatedFreq = np.repeat(1 / num_classes, num_classes)
LOG.debug("Estimated frequency: {}".format(estimatedFreq),
LOG.ll.CLASSIFIER)
""" IMPORTANT! ERASES LABELS """
Y[np.logical_not(labeledIndexes), :] = 0
D = gutils.deg_matrix(W, flat=True)
#Identity matrix
I = np.identity(W.shape[0])
#Get graph laplacian
L = gutils.lap_matrix(W, is_normalized=True)
#Propagation matrix
P = np.linalg.inv(I + L / mu)
P_t = P.transpose()
#Matrix A
A = ((P_t @ L) @ P) + mu * ((P_t - I) @ (P - I))
A = np.asarray(A)
#A = A + A.transpose()
W = scipy.sparse.coo_matrix(W)
Z = []
Q = None
def divide_row_by_sum(e):
e = gutils.scipy_to_np(e)
e = e / np.sum(e + 1e-100, axis=1, keepdims=True)
return e
#Determine nontuning iter
if num_iter is None:
num_iter = num_unlabeled
else:
num_iter = min(num_iter, num_unlabeled)
id_min_line, id_min_col = -1, -1
'''END initialization'''
#######################################################################################
'''BEGIN iterations'''
for i in np.arange(num_iter):
'''Z matrix - The binary values of current Y are replaced with their corresponding D entries.
Then, we normalize each row so that row sums to its estimated influence
'''
ul = np.logical_not(labeledIndexes)
Z = gutils.calc_Z(Y,
labeledIndexes,
D,
estimatedFreq,
weigh_by_degree=self.weigh_by_degree)
if Q is None:
#Compute graph gradient
Q = np.matmul(A, Z)
if not hook is None:
Q_pure = np.copy(Q)
Q[labeledIndexes, :] = np.inf
else:
Q[id_min_line, :] = np.inf
new_el_pct = Z[id_min_line, id_min_col] / np.sum(Z[:,
id_min_col])
Q[ul,id_min_col] =\
(1 - new_el_pct) * Q[ul,id_min_col] + Z[id_min_line,id_min_col] * A[ul,id_min_line]
#Find minimum unlabeled index
if constant_prop:
expectedNumLabels = estimatedFreq * sum(labeledIndexes)
actualNumLabels = np.sum(Y[labeledIndexes], axis=0)
class_to_label = np.argmax(expectedNumLabels - actualNumLabels)
id_min_col = class_to_label
id_min_line = np.argmin(Q[:, class_to_label])
else:
id_min = np.argmin(Q)
id_min_line = id_min // num_classes
id_min_col = id_min % num_classes
#Update Y and labeledIndexes
labeledIndexes[id_min_line] = True
Y[id_min_line, id_min_col] = 1
#Maybe plot current iteration
if not hook is None:
hook._step(step=i,
Y=Y,
labeledIndexes=labeledIndexes,
P=P,
Z=Z,
Q=Q_pure,
id_min_line=id_min_line,
id_min_col=id_min_col)
'''END iterations'''
######################################################################################################
return np.asarray(P @ Z)
def __MR(self,X,W,Y,labeledIndexes,p,hook=None):
Y = np.copy(Y)
if Y.ndim == 1:
Y = gutils.init_matrix(Y,labeledIndexes)
Y[np.logical_not(labeledIndexes),:] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
l = np.reshape(np.array(np.where(labeledIndexes)),(-1))
num_lab = l.shape[0]
if not isinstance(p, int):
p = int(p * num_lab)
if p > Y.shape[0]:
p = Y.shape[0]
LOG.warn("Warning: p greater than the number of labeled indexes",LOG.ll.CLASSIFIER)
W = gutils.scipy_to_np(W)
W = 0.5* (W + W.T)
L = gutils.lap_matrix(W, is_normalized=False)
D = gutils.deg_matrix(W)
def check_symmetric(a, tol=1e-8):
return np.allclose(a, a.T, atol=tol)
def is_pos_sdef(x):
return np.all(np.linalg.eigvals(x) >= -1e-06)
if check_symmetric(L):
eigenVectors, E = sp.eigh(L,D,eigvals=(1,p))
else:
LOG.warn("Warning: Laplacian not symmetric",LOG.ll.CLASSIFIER)
eigenValues, eigenVectors = sp.eig(L,D)
idx = eigenValues.argsort()
eigenValues = eigenValues[idx]
assert eigenValues[0] <= eigenValues[eigenValues.shape[0]-1]
eigenVectors = eigenVectors[:,idx]
E = eigenVectors[:,1:(p+1)]
e_lab = E[labeledIndexes,:]
#TIK = np.ones(shape=e_lab.shape)
TIK = np.zeros(shape=e_lab.shape)
try:
A = np.linalg.inv(e_lab.T @ e_lab + TIK.T@TIK) @ e_lab.T
except:
A = np.linalg.pinv(e_lab.T @ e_lab + TIK.T@TIK) @ e_lab.T
F = np.zeros(shape=Y.shape)
y_m = np.argmax(Y, axis=1)[labeledIndexes]
for i in range(p):
if not hook is None:
hook._step(step=i,X=X,W=W,Y=E[:,i])
for i in range(Y.shape[1]):
c = np.ones(num_lab)
c[y_m != i] = -1
a = A @ np.transpose(c)
LOG.debug(a,LOG.ll.CLASSIFIER)
for j in np.arange(F.shape[0]):
F[j,i] = np.dot(a,E[j,:])
F[j,i] = max(F[j,i],0)
return (F)
def LGCLVO(self,
X,
W,
Y,
labeledIndexes,
mu=99.0,
useEstimatedFreq=True,
tuning_iter=0,
hook=None,
constant_prop=False,
useLGCMat=False,
useZ=False):
'''BEGIN initialization'''
Y = np.copy(Y)
#We make a deep copy of labeledindexes
labeledIndexes = np.array(labeledIndexes)
if Y.ndim == 1:
Y = gutils.init_matrix(Y, labeledIndexes)
Y[np.logical_not(labeledIndexes), :] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
num_labeled = Y[labeledIndexes].shape[0]
num_unlabeled = Y.shape[0] - num_labeled
num_classes = Y.shape[1]
D = np.sum(W, axis=0)
if useEstimatedFreq:
estimatedFreq = np.sum(Y[labeledIndexes], axis=0) / num_labeled
else:
estimatedFreq = np.repeat(1 / num_classes, num_classes)
if useLGCMat:
W = self.get_prop_W(W, Y, mu)
W = 0.5 * (W + W.transpose())
#Identity matrix
I = np.identity(W.shape[0])
#Get graph laplacian
L = gutils.lap_matrix(W, is_normalized=True)
#Propagation matrix
P = np.linalg.inv(I + 0.5 * (L + L.transpose()) / mu)
P_t = P.transpose()
#Matrix A
A = ((P_t @ L) @ P) + mu * ((P_t - I) @ (P - I))
A = A + A.transpose()
Z = []
#######################################################################################
'''BEGIN iterations'''
for i in np.arange(tuning_iter):
'''Z matrix - The binary values of current Y are replaced with their corresponding D entries.
Then, we normalize each row so that row sums to its estimated influence
'''
if useZ:
Z = gutils.calc_Z(Y,
labeledIndexes,
D,
estimatedFreq,
reciprocal=False)
Q = np.matmul(A, Z)
else:
Q = np.matmul(A, Y)
#During label tuning, we'll also 'unlabel' the argmax
unlabeledIndexes = np.logical_not(labeledIndexes)
temp = Q[unlabeledIndexes, :]
Q[unlabeledIndexes, :] = -np.inf
id_max = np.argmax(Q)
id_max_line = id_max // num_classes
id_max_col = id_max % num_classes
Q[unlabeledIndexes, :] = temp
Q[labeledIndexes, :] = np.inf
#Find minimum unlabeled index
if constant_prop:
id_min_line = np.argmin(Q[:, id_max_col])
id_min_col = id_max_col
else:
id_min = np.argmin(Q)
id_min_line = id_min // num_classes
id_min_col = id_min % num_classes
#Label OP
labeledIndexes[id_min_line] = True
Y[id_min_line, id_min_col] = 1
#Unlabel OP
labeledIndexes[id_max_line] = False
Y[id_max_line, id_max_col] = 0
if not hook is None:
hook._step(step=i,
X=X,
W=W,
Y=Y,
labeledIndexes=labeledIndexes,
l_i=id_max_line,
l_j=id_max_col,
ul_i=id_min_line,
ul_j=id_min_col)
'''END iterations'''
return Y, labeledIndexes
def __SIIS(self,
X,
W,
Y,
labeledIndexes,
m,
alpha,
beta,
rho,
max_iter,
hook=None):
Y = np.copy(Y)
if Y.ndim == 1:
Y = gutils.init_matrix(Y, labeledIndexes)
Y[np.logical_not(labeledIndexes), :] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
if m is None:
m = W.shape[0]
c = Y.shape[1]
W = scipy.sparse.csr_matrix(W) / np.mean(W.data)
D = gutils.deg_matrix(W, pwr=1.0)
L = gutils.lap_matrix(W, is_normalized=True)
U, SIGMA = gutils.extract_lap_eigvec(L, m, remove_first_eig=True)
U = scipy.sparse.csr_matrix(U)
SIGMA = _to_np(SIGMA)
J = gutils.labels_indicator(labeledIndexes)
""" !!! """
P = SIISClassifier.edge_mat(W)
""" Initialize params """
LAMB_1 = np.ones((P.shape[0], c))
LAMB_2 = np.ones((Y.shape[0], c))
mu = 1.0
mu_max = 10000000.0
eps = 1 / (10000)
""" Reusable matrices """
JU = _to_np(J @ U)
PU = _to_np(P @ U)
PU_T = PU.transpose()
JU_T = JU.transpose()
A = np.zeros((m, c))
Q = None
B = None
improvement = 1
iter = 0
""" TODO: Tensorflow version
import tensorflow as tf
with tf.Session() as sess:
A = tf.Variable(1e-06*tf.ones((m,c),dtype=tf.float64))
sess.run(tf.global_variables_initializer())
C = tf.reduce_sum(tf.linalg.norm(tf.matmul(PU,A),axis=1)) +\
alpha*tf.reduce_sum(tf.linalg.norm(tf.matmul(_to_np(U)[labeledIndexes,:],A)-Y[labeledIndexes,:],axis=1)) +\
beta* tf.trace(tf.matmul(tf.matmul(tf.transpose(A),SIGMA),A))
opt = tf.train.AdamOptimizer(learning_rate=0.5*1e-02)
opt_min = opt.minimize(C)
sess.run(tf.global_variables_initializer())
for i in range(2000):
sess.run(opt_min)
LOG.debug(sess.run(C),LOG.ll.CLASSIFIER)
LOG.debug(sess.run(C),LOG.ll.CLASSIFIER)
F = _to_np(U)@sess.run(A)
LOG.debug(F.shape,LOG.ll.CLASSIFIER)
"""
A = np.zeros((m, c))
while iter <= max_iter and improvement > eps:
""" Update Q """
N = PU @ A - (1 / mu) * LAMB_1
N_norm = np.linalg.norm(N, axis=1)
to_zero = N_norm <= (1 / mu)
mult = ((N_norm - (1 / mu)) / N_norm)
N = N * mult[:, np.newaxis]
N[to_zero, :] = 0.0
Q = N
""" Update B """
M = JU @ A - Y - (1 / mu) * LAMB_2
M_norm = np.linalg.norm(M, axis=1)
to_zero = M_norm <= (alpha / mu)
mult = ((M_norm - (alpha / mu)) / M_norm)
M = M * mult[:, np.newaxis]
M[to_zero, :] = 0.0
B = M
old_A = A
""" Update A """
A_inv_term = 2 * beta * SIGMA + mu * PU_T @ PU + mu * JU_T @ JU
A_inv_term = np.linalg.inv(A_inv_term)
A = A_inv_term @ \
(PU_T@ LAMB_1 + JU_T@LAMB_2 +\
mu * PU_T@Q + mu* JU_T @ (B + Y) )
""" Update Lagrangian coeffs """
LAMB_1 = LAMB_1 + mu * (Q - PU @ A)
LAMB_2 = LAMB_2 + mu * (B - JU @ A + Y)
""" Update penalty coeffficients """
mu = min(rho * mu, mu_max)
if not old_A is None:
improvement = (np.max(np.abs(A - old_A))) / np.max(
np.abs(old_A))
LOG.debug("Iter {}".format(iter), LOG.ll.CLASSIFIER)
iter += 1
C = np.sum(np.linalg.norm(PU@A,axis=1)) + alpha*np.sum(np.linalg.norm(JU@A - Y,axis=1)) +\
beta*np.trace(A.T@SIGMA@A)
LOG.debug("Iter {} - Cost {}".format(iter, C), LOG.ll.CLASSIFIER)
F = U @ A
for i in range(F.shape[0]):
mx = np.argmax(F[i, :])
F[i, :] = 0.0
F[i, mx] = 1.0
return F
def LGCLVO(self,
X,
W,
Y,
labeledIndexes,
mu=99.0,
useEstimatedFreq=True,
tuning_iter=0,
hook=None,
constant_prop=False,
useZ=True,
normalize_rows=True):
Y = np.copy(Y)
#We make a deep copy of labeledindexes
labeledIndexes = np.array(labeledIndexes)
lids = np.where(labeledIndexes)[0]
if Y.ndim == 1:
Y = gutils.init_matrix(Y, labeledIndexes)
Y[np.logical_not(labeledIndexes), :] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
W = 0.5 * (W + W.transpose())
num_labeled = Y[labeledIndexes].shape[0]
num_unlabeled = Y.shape[0] - num_labeled
num_classes = Y.shape[1]
D = gutils.deg_matrix(W, flat=True)
if not useEstimatedFreq is None:
if isinstance(useEstimatedFreq, bool):
estimatedFreq = np.sum(Y[labeledIndexes], axis=0) / num_labeled
else:
estimatedFreq = useEstimatedFreq
else:
estimatedFreq = np.repeat(1 / num_classes, num_classes)
if scipy.sparse.issparse(W):
l = np.sum(labeledIndexes)
itertool_prod = [[i, j] for i in range(l) for j in range(l)]
row = np.asarray([lids[i] for i in range(l)])
col = np.asarray([i for i in range(l)])
data = np.asarray([1.0] * l)
temp_Y = _to_np(
scipy.sparse.coo_matrix((data, (row, col)),
shape=(W.shape[0], l)))
PL = LGC_iter_TF(X,
W,
Y=temp_Y,
labeledIndexes=labeledIndexes,
alpha=1 / (1 + mu),
num_iter=10000)
PL = PL[labeledIndexes, :]
PL[range(PL.shape[0]), range(PL.shape[0])] = 0 #Set diagonal to 0
PL = PL
del temp_Y
row = np.asarray(
[lids[x[0]] for x in itertool_prod if x[0] != x[1]])
col = np.asarray(
[lids[x[1]] for x in itertool_prod if x[0] != x[1]])
data = [PL[x[0], x[1]] for x in itertool_prod if x[0] != x[1]]
P = scipy.sparse.coo_matrix((data, (row, col)),
shape=W.shape).tocsr()
P = P
else:
#Identity matrix
I = np.identity(W.shape[0])
#Get graph laplacian
L = gutils.lap_matrix(W, is_normalized=True)
#Propagation matrix
P = np.zeros(W.shape)
P[np.ix_(labeledIndexes,
labeledIndexes)] = np.linalg.inv(I + 0.5 *
(L + L.transpose()) /
mu)[np.ix_(
labeledIndexes,
labeledIndexes)]
P[labeledIndexes, labeledIndexes] = 0
P[np.ix_(labeledIndexes, labeledIndexes)] = P[np.ix_(
labeledIndexes, labeledIndexes)] / np.sum(P[np.ix_(
labeledIndexes, labeledIndexes)],
axis=0,
keepdims=False)
W = scipy.sparse.csr_matrix(W)
Z = []
detected_noisylabels = []
suggested_labels = []
where_noisylabels = []
Q_values = []
Y_flat = np.argmax(Y, axis=1)
def divide_row_by_sum(e):
e = _to_np(e)
if normalize_rows:
e = e / np.sum(e + 1e-100, axis=1, keepdims=True)
return e
else:
return e
def find_argmin(Q, class_to_unlabel):
id_min_line = np.argmin(Q[:, class_to_unlabel])
id_min_col = class_to_unlabel
return id_min_line, id_min_col, Q[id_min_line, id_min_col]
#######################################################################################
'''BEGIN iterations'''
Q = None
cleanIndexes = np.copy(labeledIndexes)
for i_iter in range(tuning_iter):
found_noisy = True
if np.sum(labeledIndexes) > 0 and found_noisy:
'''Z matrix - The binary values of current Y are replaced with their corresponding D entries.
Then, we normalize each row so that row sums to its estimated influence
'''
if (not self.use_baseline) or Q is None:
if useZ:
Z = gutils.calc_Z(Y,
labeledIndexes,
D,
estimatedFreq,
weigh_by_degree=False)
F = P @ Z
if scipy.sparse.issparse(F):
F = np.asarray(F.toarray())
#Compute graph gradient
Q = (divide_row_by_sum(F) - divide_row_by_sum(Z))
else:
F = P @ Y
if scipy.sparse.issparse(F):
F = np.asarray(F.toarray())
Q = (divide_row_by_sum(F) - divide_row_by_sum(Y))
#import scipy.stats
#During label tuning, we'll also 'unlabel' the argmax
unlabeledIndexes = np.logical_not(cleanIndexes)
if self.early_stop:
Q[np.sum(F, axis=1) == 0.0, :] = 9999
Q[unlabeledIndexes, :] = np.inf
#Find minimum unlabeled index
if constant_prop:
expectedNumLabels = estimatedFreq * np.sum(labeledIndexes)
actualNumLabels = np.sum(Y[labeledIndexes, :], axis=0)
temp = expectedNumLabels - actualNumLabels
class_priority = np.argsort(temp)
found_noisy = False
for class_to_unlabel in class_priority:
id_min_line, id_min_col, val = find_argmin(
Q, class_to_unlabel)
if val < 0:
#This means that the class would have a different label under the modified label prop
found_noisy = True
break
else:
id_min = np.argmin(Q)
id_min_line = id_min // num_classes
id_min_col = id_min % num_classes #The class previously assigned to instance X_{id_min_line}
found_noisy = Q[id_min_line, id_min_col] < 0
if found_noisy:
id_max_col = np.argmax(
Q[id_min_line, :]) #The new, suggested class
detected_noisylabels.append(id_min_col)
where_noisylabels.append(id_min_line)
suggested_labels.append(id_max_col)
Q_values.append(Q[id_min_line, id_min_col])
#Unlabel OP
if labeledIndexes[id_min_line] == False:
raise Exception(
"Error: unlabeled instance was selected")
if not Y[id_min_line, id_min_col] == 1:
raise Exception("Error: picked wrong class to unlabel")
labeledIndexes[id_min_line] = False
cleanIndexes[id_min_line] = False
if not Y[id_min_line, id_min_col] == 1:
raise Exception(
"Tried to remove label from unlabeled instance")
Y[id_min_line, id_min_col] = 0
if self.relabel:
labeledIndexes[id_min_line] = True
Y[id_min_line, :] = 0
Y[id_min_line, id_max_col] = 1
if not hook is None:
hook._step(step=(i_iter + 1),
X=X,
W=W,
Y=Y,
labeledIndexes=labeledIndexes)
'''
MATPLOTLIB stuff
'''
"""
import cv2 as cv
#ret2,th2 = cv.threshold(255*np.asarray(Q_values).astype(np.uint8),0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
from skimage.filters import threshold_multiotsu
Q_values = np.asarray(Q_values)
th = threshold_multiotsu(Q_values)
th = np.where(Q_values < th[0])[0]
for i in range(th.shape[0]):
th2 = max(0,i - 1)
if not th[i] == i:
break
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(5,2))
ax = fig.add_subplot()
ax.plot(np.arange(len(Q_values)),Q_values)
ax.axvline(10,color='red')
#plt.axvline(th2,color='purple')
#plt.axhline(-0.5,color='green')
print(th2)
plt.show()
"""
'''END iterations'''
LOG.info(
"NUMBER OF DETECTED NOISY INSTANCES:{}".format(
len(detected_noisylabels)), LOG.ll.FILTER)
return Y, labeledIndexes
def LDST(self,
X,
W,
Y,
labeledIndexes,
mu=99.0,
useEstimatedFreq=True,
tuning_iter=0,
hook=None,
constant_prop=False,
useZ=True):
'''BEGIN initialization'''
Y = np.copy(Y)
#We make a deep copy of labeledindexes
labeledIndexes = np.array(labeledIndexes)
if Y.ndim == 1:
Y = gutils.init_matrix(Y, labeledIndexes)
Y[np.logical_not(labeledIndexes), :] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
W = 0.5 * (W + W.transpose())
num_labeled = Y[labeledIndexes].shape[0]
num_unlabeled = Y.shape[0] - num_labeled
num_classes = Y.shape[1]
D = gutils.deg_matrix(W, flat=True)
""" Estimate frequency of classes"""
if not useEstimatedFreq is None:
if isinstance(useEstimatedFreq, bool):
estimatedFreq = np.sum(Y[labeledIndexes], axis=0) / num_labeled
else:
estimatedFreq = useEstimatedFreq
else:
estimatedFreq = np.repeat(1 / num_classes, num_classes)
#Identity matrix
I = np.identity(W.shape[0])
#Get graph laplacian
L = gutils.lap_matrix(W, is_normalized=True)
#Propagation matrix
""" !!!!!! """
P = np.linalg.inv(I + 0.5 * (L + L.transpose()) / mu)
#P = np.zeros(W.shape)
#P[np.ix_(labeledIndexes,labeledIndexes)] = np.linalg.inv( I + 0.5*(L + L.transpose())/mu )[np.ix_(labeledIndexes,labeledIndexes)]
P_t = P.transpose()
#Matrix A
A = ((P_t @ L) @ P) + mu * ((P_t - I) @ (P - I))
Z = []
#######################################################################################
'''BEGIN iterations'''
for i_iter in np.arange(tuning_iter):
if np.sum(labeledIndexes) > 0:
'''Z matrix - The binary values of current Y are replaced with their corresponding D entries.
Then, we normalize each row so that row sums to its estimated influence
'''
if useZ:
Z = gutils.calc_Z(Y,
labeledIndexes,
D,
estimatedFreq,
weigh_by_degree=self.weigh_by_degree)
#Compute graph gradient
Q = np.matmul(A, Z)
else:
Q = np.matmul(A, Y)
for i_labeled in np.where(labeledIndexes)[0]:
assigned_class = np.argmax(Y[i_labeled, :])
other_classes = list(range(Y.shape[1]))
other_classes.remove(assigned_class)
best_other = min([Q[i_labeled, j] for j in other_classes])
for j in range(Y.shape[1]):
if self.gradient_fix:
Q[i_labeled, assigned_class] = -best_other
Q[i_labeled, other_classes] = -np.inf
#During label tuning, we'll also 'unlabel' the argmax
unlabeledIndexes = np.logical_not(labeledIndexes)
Q[unlabeledIndexes, :] = -np.inf
#Find minimum unlabeled index
if constant_prop:
raise ""
"""expectedNumLabels = estimatedFreq * sum(labeledIndexes)
actualNumLabels = np.sum(Y[labeledIndexes],axis=0)
class_to_unlabel = np.argmax(actualNumLabels - expectedNumLabels)
id_max_line = np.argmax(Q[:,class_to_unlabel])
id_max_col = class_to_unlabel
"""
else:
id_max = np.argmax(Q)
id_max_line = id_max // num_classes
id_max_col = id_max % num_classes
if not Y[id_max_line, id_max_col] == 1:
print(Y[id_max_line, :])
raise Exception(
"Tried to remove label from unlabeled instance")
#Unlabel OP
labeledIndexes[id_max_line] = False
Y[id_max_line, id_max_col] = 0
if not hook is None:
hook._step(step=i_iter + 1,
X=X,
W=W,
Y=Y,
labeledIndexes=labeledIndexes)
'''END iterations'''
return Y, labeledIndexes