def GTest_CI(X, Y, Z):
g = 0
sig_level_indep = 0.05
hm_x = len(np.unique(X))
hm_y = len(np.unique(Y))
hm_z = len(np.unique(Z))
hm_samples = X.size
if Z.size == 0:
return GTest_I(X, Y)
else:
# if (len(Z.shape)>1 and Z.shape[1]>1):
# Z = joint(Z)
# states = np.unique(Z)
# for i in states:
# pattern = i
# sub_cond_idx = np.where(Z == pattern)
# temp_mi = mi(X[sub_cond_idx], Y[sub_cond_idx],0)
# g = g + sub_cond_idx.length*temp_mi
g = 2 * hm_samples * cmi(X, Y, Z)
p_val = 1 - chi2.cdf(g, (hm_x - 1) * (hm_y - 1) * hm_z)
if p_val < sig_level_indep:
Independency = 0 # reject the Null-hypothesis
else:
Independency = 1
return Independency
def Large_Scale_IPCMB(data, targets, threshold):
#data is training data without label column
numfeat = data.shape[1]
subsize = 100
count = 0
Feat = []
while count * subsize <= numfeat:
if (count + 1) * subsize <= numfeat:
sub_D = data[:, count * subsize:subsize + count * subsize]
results = tian_IPCMB(sub_D, targets, threshold)
index = results[0] + count * subsize
Feat = set(Feat).union(set(index))
else:
sub_D = data[:, count * subsize:]
results = tian_IPCMB(sub_D, targets, threshold)
index = results[0] + count * subsize
Feat = set(Feat).union(set(index))
count = count + 1
Feat = list(Feat) #convert set object to list
cmbVector = joint(data[:, Feat])
for i in np.setdiff1d(np.arange(numfeat), Feat):
temp = cmi(data[:, i], targets, cmbVector)
if temp > threshold:
Feat.append(i)
MB = Feat
return np.array(MB)
def sort_by_cmi(feat_indices, targets, cond_indices, data):
"""
Returns the indices found in 'feat_indices' in order of I(X;Y|Z), where Z
is the joint distribution described by data[cond_indices], X is the joint
distribution of data[feat_indices[i]], and Y is the joint distribution of
data[targets]. If Z is empty, then the result is I(X;Y)
"""
feats_to_cmi = dict()
if (cond_indices.size == 0):
for feature in feat_indices:
feats_to_cmi[feature] = mi(data[:, feature], targets)
else:
for feature in feat_indices:
feats_to_cmi[feature] = cmi(data[:, feature], targets,
joint(data[:, cond_indices]))
sorted_features = np.array(
sorted(feat_indices, key=lambda f: -feats_to_cmi[f]))
return sorted_features
def RecognizePC(targets, ADJt, data, THRESHOLD, NumTest):
MIs = []
NonPC = []
cutSetSize = 0
data_check = 1
#targets = data[:, T]
Sepset = [[]] * data.shape[1]
seperators = [[]] * data.shape[1]
#% Search
datasizeFlag = 0
while ADJt.size > cutSetSize:
for xind in range(0, ADJt.size): # for each x in ADJt
X = ADJt[xind]
if cutSetSize == 0:
NumTest = NumTest + 1
TEMP = mi(data[:, X], targets, 0)
MIs.append([TEMP]) #compute mutual information
#print("Vertex MI ",X,TEMP)
if TEMP <= THRESHOLD:
NonPC.append(X)
elif cutSetSize == 1:
Diffx = np.setdiff1d(ADJt, X)
C = list(combinations(Diffx, cutSetSize))
for sind in range(0, len(C)): # for each S in ADJT\x, size
S = np.array(list(C[sind]))
cmbVector = joint(data[:, S])
if data_check:
datasizeFlag = checkDataSize(data[:, X], targets,
cmbVector)
if datasizeFlag != 1:
NumTest = NumTest + 1
TEMP = cmi(data[:, X], targets, cmbVector, 0)
MIs.append([TEMP])
if TEMP <= THRESHOLD:
NonPC = set(NonPC).union(set([X]))
Sepset[X] = set(Sepset[X]).union(set(S))
break
else:
break
else: # set size > 1
Diffx = np.setdiff1d(ADJt, X)
C = list(combinations(Diffx, cutSetSize - 1))
midBreakflag = 0
for sind in range(0, len(C)): # for each S in ADJT\x, size
S = np.array(list(C[sind]))
RestSet = np.setdiff1d(Diffx, S)
for addind in range(0, RestSet.size):
col = set(S).union(set([RestSet[addind]]))
cmbVector = joint(data[:, np.array(list(col))])
if data_check:
datasizeFlag = checkDataSize(
data[:, X], targets, cmbVector)
if datasizeFlag != 1:
NumTest = NumTest + 1
TEMP = cmi(data[:, X], targets, cmbVector, 0)
MIs.append([TEMP])
if TEMP <= THRESHOLD:
NonPC = set(NonPC).union(set([X]))
# Line has an error
Sepset[X] = set(Sepset[X]).union(
set(S), set([RestSet[addind]]))
midBreakflag = 1
break
else:
break
if midBreakflag == 1:
break
if len(NonPC) > 0:
ADJt = np.setdiff1d(ADJt, np.array(list(NonPC)))
cutSetSize = cutSetSize + 1
# print("NonPC")
# print(NonPC)
# print(len(NonPC))
NonPC = []
elif datasizeFlag == 1:
break
else:
cutSetSize = cutSetSize + 1
ADJ = ADJt
result = []
result.append(ADJ)
result.append(Sepset)
result.append(NumTest)
result.append(cutSetSize)
result.append(MIs)
return result
def CMI_adaptive_pure_soft(X, Y, cond_set, hm_HypoTest):
cond_mi = 0
if (len(cond_set.shape) == 1):
cond_set = cond_set.reshape((cond_set.size,1))
if (cond_set.size == 0):
results = MI_adaptive_soft(X, Y, hm_HypoTest)
cond_mi = results[0]
hm_HypoTest = results[1]
results = []
results.append(cond_mi)
results.append(hm_HypoTest)
return results
naive_cmi = cmi(X, Y, cond_set)
if naive_cmi == 0:
results = []
results.append(cond_mi)
results.append(hm_HypoTest)
return results
Cx, X = np.unique(X, return_inverse = True)
Cy, Y = np.unique(Y, return_inverse = True)
m = len(Cx)
n = len(Cy)
hm_sample, hm_condvar = cond_set.shape
entire_uniform = 1
if hm_condvar == 1:
combo_set = np.unique(cond_set)
j = []
for i in range(combo_set.shape[0]):
pattern = combo_set[i]
sub_cond_idx = np.argwhere(cond_set==pattern).T[0]
p_cond = len(sub_cond_idx) / hm_sample
sub_cond_idx = np.array(sub_cond_idx)
results = MI_adaptive_soft(X[sub_cond_idx], Y[sub_cond_idx], hm_HypoTest)
temp_mi = results[0]
hm_HypoTest = results[2]
if temp_mi == np.inf:
temp_mi = 0
else:
entire_uniform = 0
cond_mi = cond_mi + p_cond*temp_mi
else:
var_1 = cond_set[:,1]
var_2 = joint(cond_set[:, 2:])
C1,var_1 = np.unique(X, return_inverse = True)
C2,var_2 = np.unique(Y, return_inverse = True)
#C1 = np.unique(X, return_inverse = True)
#C2 = np.unique(Y, return_inverse = True)
p = len(C1)
q = len(C2)
joint_set, hm_HypoTest, isUniform = jointPDFAdapPartition(var_1, var_2, p, q, hm_HypoTest)
for j in range(p):
for k in range(q):
get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if y == x]
index = get_indexes(C1[j], var_1)
index = np.array(index)
sub_cond_idx = get_indexes(C2[k], var_2[index])
sub_cond_idx = np.array(sub_cond_idx)
sub_cond_idx = sub_cond_idx.astype(int)
p_cond = len(sub_cond_idx) / hm_sample
if len(sub_cond_idx) == 0:
temp_mi = 0
else:
results = MI_adaptive_soft(X[sub_cond_idx], Y[sub_cond_idx], hm_HypoTest)
temp_mi = results[0]
hm_HypoTest = results[2]
if temp_mi == np.inf:
temp_mi = 0
else:
entire_uniform = 0
cond_mi = cond_mi + p_cond*temp_mi
if entire_uniform:
cond_mi = np.inf
return cond_mi, hm_HypoTest
def tian_STMB_new(train_data, targets, threshold = 0.02):
NumTest = 0
numf = train_data.shape[1] # feature number
#targets = data[:, targetindex] # selected index data
# %% Recognize Target PC
CanMB = np.arange(numf) # candidates
Results = RecognizePC(targets, CanMB, train_data, threshold, NumTest)
PCD = Results[0]
Sepset_t = Results[1]
NumTest = Results[2]
cutSetSize = Results[3]
spouse = [[]]*numf
#print("===========PC Result==========")
#print(PCD)
# print(Sepset_t)
# print(cutSetSize)
#scores = []
Des = [[]]*PCD.size
datasizeFlag = 0
#%% Find Markov blanket
for yind in range(PCD.size):
flag = 0
y = PCD[yind]
searchset = np.setdiff1d(CanMB, PCD)
for xind in range(searchset.size):
x = searchset[xind]
col = set(Sepset_t[x]).union(set([y]))
cmbVector = joint(train_data[:, np.array(list(col))])
datasizeFlag = checkDataSize(train_data[:, x], targets, cmbVector)
#print("datasizeFlag",x,datasizeFlag)
if datasizeFlag != 1:
NumTest = NumTest + 1
T = cmi(train_data[:, x], targets, cmbVector, 0)
#print("CMI",y,x,T)
if T > threshold: # v structure
for s in np.setdiff1d(np.union1d(PCD,[x]), np.array([y])):
T = cmi(train_data[:, y], targets, train_data[:, s], 0)
#print("Vertex CMI",s,y,x,T)
if T < threshold:
temp = set(Des[yind]).union(set([y]))
Des[yind] = np.array(list(temp))
flag = 1
break
else:
temp = set(spouse[y]).union(set([x]))
spouse[y]= np.array(list(temp))
if flag == 1:
break
des = [item for sublist in Des for item in sublist]
PCD = np.setdiff1d(PCD, des)
#print(PCD)
#assert(1==2)
#%% Shrink spouse
NonS = []
S = []
for i in np.setdiff1d(np.arange(numf), PCD):
spouse[i] = [] # empty
for y in np.arange(len(spouse)):
if spouse[y] != []:
S.append( y) # Y has spouses
# shrink
spousecan = spouse[y]
for sind in np.arange(spousecan.size):
s = spousecan[sind]
col = set([y]).union(set(spousecan),set(PCD))
cmbVector = joint(train_data[:, np.setdiff1d(np.array(list(col)), s)])
datasizeFlag = checkDataSize(train_data[:, s], targets, cmbVector)
if datasizeFlag != 1:
NumTest = NumTest + 1
T = cmi(train_data[:, s], targets, cmbVector, 0)
if T < threshold:
NonS = set(NonS).union(set([s]))
spouse[y] = np.setdiff1d(spousecan, np.array(list(NonS)))
NonS = []
b = []
for i in range(len(spouse)):
if len(spouse[i]) > 0:
b = set(b).union(set(spouse[i]))
# remove false spouse from PC
M = PCD # setdiff(PCD,S); % M has no spouses in PCD set
PCsize = M.size
testSet = set(S).union(set(b))
#testSet = np.array(list(temp))
C = np.zeros(shape = (PCsize, 1))
for x in M:
col = set(PCD).union(set(testSet))
cmbVector = joint(train_data[:, np.setdiff1d(np.array(list(col)), x)])
datasizeFlag = checkDataSize(train_data[:, x], targets, cmbVector)
if datasizeFlag != 1:
NumTest = NumTest + 1
T = cmi(train_data[:, x], targets, cmbVector, 0)
if T < threshold:
PCD = np.setdiff1d(PCD, x)
PCsize2 =np.mean(C)
MB = set(PCD).union(set(b))
result = []
result.append(np.array(list(MB)))
result.append(PCD)
result.append(spouse)
result.append(NumTest)
result.append(Sepset_t)
result.append(cutSetSize)
result.append(PCsize)
result.append(PCsize2)
return result
def tian_IPCMB(
train_data, target, threshold
): #train_data is not including targets, targets is the label vector
NumTest = 0
numSample = train_data.shape[0]
numf = train_data.shape[1] # do not include the target
CanMB = np.arange(numf)
#target = target.reshape([numSample,1])
Results = RecognizePC(target, CanMB, train_data, threshold, NumTest)
PC = Results[0]
Sepset_t = Results[1]
NumTest = Results[2]
#cutSetSize = Results[3]
MB = PC
#association = []
#Recognize a true positive, and its PC as spouse candidate
children = []
targetindex = 0
for xind in np.arange(len(PC)):
X = PC[xind]
CanADJX = np.arange(numf)
rest_idx = np.setdiff1d(np.arange(numf), X) #numf-1
temp_trainD = np.hstack((target, train_data[:, rest_idx]))
Results = RecognizePC(train_data[:, X], CanADJX, temp_trainD,
threshold, NumTest)
temp_CanSP = Results[0]
NumTest = Results[2]
if ~np.in1d(targetindex, temp_CanSP):
MB = np.setdiff1d(MB, X)
continue
temp_idx = np.where(temp_CanSP != 0)
CanSP = temp_CanSP[temp_idx]
temp_idx = np.where(CanSP <= X)
CanSP[temp_idx] = CanSP[temp_idx] - 1
# recognize true positives
DiffY = np.setdiff1d(CanSP, MB) # in CanSP but not in MB
DiffY = np.setdiff1d(DiffY, X) # X should not in Sepset
for yind in np.arange(len(DiffY)):
Y = DiffY[yind]
SepsetTY = Sepset_t[Y]
cmbVector = joint(train_data[:,
list(set(SepsetTY).union(set([X])))])
NumTest = NumTest + 1
if cmi(train_data[:, Y], target, cmbVector, 0) > threshold:
children = set(children).union(set([X]))
children = list(children)
MB = set(MB).union(set([Y]))
MB = list(MB)
result = []
result.append(np.array(MB))
result.append(PC)
result.append(NumTest)
result.append(children)
return result
