def get_qual(args, topic_thresh, sim_bits, test_perc=0.4):
# get new values on test set based on current thresholds
args.topic_thr = topic_thresh
create_vecs(args)
if args.exp == 'vk':
g, n, C1, C2, C3, C4 = get_aux_vk(args)
learn_params(args, g, n, C1, C2, C3, C4)
# read the learned parameters
basefile = open(os.path.join(args.data_dir, args.exp, 'base_weights.txt'),
newline='')
basefile = csv.reader(basefile, delimiter=' ')
qfile = open(os.path.join(args.data_dir, args.exp, 'marg_weights.txt'),
newline='')
qfile = csv.reader(qfile, delimiter=' ')
topic_features = np.load(args.topic_features)
user_features = np.load(args.user_features)
b = dict()
q = dict()
for u, v, val in basefile:
b[int(u), int(v)] = float(val)
for u, v, val in qfile:
q[int(u), int(v)] = float(val)
# calculate tp, fp, tn, fn
tp = []
fp = []
tn = []
fn = []
logfile = open(args.log_file, newline='')
logs = list(csv.reader(logfile, delimiter=' '))
num_logs = sum(1 for row in logs)
templogs = logs[:int(num_logs * test_perc)]
testlogs = []
for log in templogs:
nplog = np.array(log, dtype=np.int)
testlogs.append(nplog)
testlogs = np.array(testlogs)
print("Test log in consideration: ", testlogs.shape)
# testlogs is now a numpy array
# to estimate auc, taking 100 points on the curve
for mu in tqdm(np.linspace(0, 1, 100)):
print(mu)
tpx = 0
fpx = 0
tnx = 0
fnx = 0
for log in reversed(testlogs):
# [v, a, t_v] = [int(x) for x in log], already converted into an integer
v, a_v, t_v = [x for x in log]
# v published a message in the test log
# set based on similarity
action_set = []
for u in g.predecessors(v):
# list of permissible actions performed by u
a_performed_by_u = get_actions_from_logs(
testlogs, u, t_v, sim_bits)
for au in a_performed_by_u:
similar = False
for a in action_set:
if check_sim(topic_features[au],
topic_features[a],
nbits=sim_bits):
similar = True
break
if not similar:
action_set.append(a)
# now they are unique actions
prob = 0.0
for a in action_set:
for u in g.predecessors(v):
# get valid predecessors for the actions
if checklog(logs, u, a, t_v, sim_bits):
prob += b[(u, v)] + q[(u, v)] * get_alpha(
user_features[v], topic_features[a])
prob = min(1, max(prob, 0))
prediction = (prob > mu)
gt = checklog(logs, v, a)
if prediction == True:
if gt == True:
tpx += 1
else:
fpx += 1
else:
if gt == True:
fnx += 1
else:
tnx += 1
tp.append(tpx)
fp.append(fpx)
tn.append(tnx)
fn.append(fnx)
tp = np.array(tp)
fp = np.array(fp)
tn = np.array(tn)
fn = np.array(fn)
tpr = tp / (tp + fn)
fpr = fp / (fp + tn)
auc = -1 * np.trapz(tpr, fpr)
print(auc)
return auc
def _get_qual_citation(args, topic_thresh, test_perc=0.4):
print("Calling inner function")
create_vecs(args)
# get new values on test set based on current thresholds
if args.exp == 'citation':
g, n, C1, C2, C3, C4 = get_aux_cit(args)
b, q = learn_params(args, g, n, C1, C2, C3, C4)
# need to reload since re-saved when creating the vectors
topic_features = np.load(args.topic_features)
user_features = np.load(args.user_features)
# calculate tp, fp, tn, fn
tp = []
fp = []
tn = []
fn = []
# to estimate auc, taking 100 points on the curve
for mu in tqdm(np.linspace(0, 1, 100)):
# print(mu)
tpx = 0
fpx = 0
tnx = 0
fnx = 0
for v in cited.keys(): # per entry of the log
for msg in cited[v]:
# get action set
for u in g.predecessors(v):
if u in published.keys():
action_set = set([p for p in published[u]])
prob = 0.0
# prob for an action
for a in action_set:
for u in g.predecessors(v):
# get valid predecessors for the actions
if u in published.keys() and a in published[u]:
prob += b[(u, v)] + q[(u, v)] * get_alpha(
user_features[v], topic_features[a])
prob = min(1, max(prob, 0))
prediction = (prob > mu)
gt = (a == msg)
if prediction == True:
if gt == True:
tpx += 1
else:
fpx += 1
else:
if gt == True:
fnx += 1
else:
tnx += 1
print("Results for mu = ", mu, "(tp, fp, tn, fn)", tpx, fpx, tnx,
fnx)
tp.append(tpx)
fp.append(fpx)
tn.append(tnx)
fn.append(fnx)
tp = np.array(tp)
fp = np.array(fp)
tn = np.array(tn)
fn = np.array(fn)
tpr = tp / (tp + fn)
fpr = fp / (fp + tn)
print("TPR: ", tpr)
print("FPR: ", fpr)
auc = -1 * np.trapz(tpr, fpr)
print('auc: ', auc)
return auc
def _get_qual(args, topic_thresh, sim_bits):
print("Inner qual function")
# get new values on test set based on current thresholds
args.topic_thr = topic_thresh
args.nbits = sim_bits
create_vecs(args)
if args.exp == 'vk':
g, n, C1, C2, C3, C4 = get_aux_vk(args)
b, q = learn_params(args, g, n, C1, C2, C3, C4)
topic_features = np.load(args.topic_features)
user_features = np.load(args.user_features)
# calculate tp, fp, tn, fn
tp = []
fp = []
tn = []
fn = []
# testlogs is now a numpy array
# to estimate auc, taking 100 points on the curve
for mu in tqdm(np.linspace(0, 1, 10)):
tpx = 0
fpx = 0
tnx = 0
fnx = 0
for log in reversed(testlogs):
# [v, a, t_v] = [int(x) for x in log], already converted into an integer
v, a_v, t_v = [int(x) for x in log]
# v published a message in the test log
# set based on similarity
action_list = []
action_set = []
for u in g.predecessors(v):
# list of permissible actions performed by u
for au in actions_of[u]:
if action_table[(u, au)] > t_v:
continue
action_list.append(au)
for a1 in action_list:
similar = False
for a2 in action_set:
if check_sim(topic_features[a1],
topic_features[a2],
nbits=sim_bits):
similar = True
break
if not similar:
action_set.append(a1)
print("Formed action set for node: ", v)
# now they are unique actions
prob = 0.0
for a in action_set:
for u in g.predecessors(v):
# get valid predecessors for the actions
for au in actions_of[u]:
if action_table[(u, au)] > t_v:
continue
if check_sim(topic_features[au],
topic_features[a],
nbits=sim_bits):
prob += b[(u, v)] + q[(u, v)] * get_alpha(
user_features[v], topic_features[a])
# summed up prob for a by all users and through all their actions
prob = min(1, max(prob, 0))
prediction = (prob > mu)
# gt -> this log entry is similar to the action
# gt = checklog(logs, v, a)
gt = check_sim(topic_features[a_v],
topic_features[a],
nbits=sim_bits)
if prediction == True:
if gt == True:
tpx += 1
else:
fpx += 1
else:
if gt == True:
fnx += 1
else:
tnx += 1
print("(tp, fp, tn, fn): ", tpx, fpx, tnx, fnx)
tp.append(tpx)
fp.append(fpx)
tn.append(tnx)
fn.append(fnx)
tp = np.array(tp)
fp = np.array(fp)
tn = np.array(tn)
fn = np.array(fn)
tpr = tp / (tp + fn)
fpr = fp / (fp + tn)
print("TPR: ", tpr)
print("FPR: ", fpr)
auc = -1 * np.trapz(tpr, fpr)
print("auc: ", auc)
return auc
def get_qual_citation(args, topic_thresh, test_perc=0.4):
args.topic_thr = topic_thresh
create_vecs(args)
# get new values on test set based on current thresholds
if args.exp == 'citation':
g, n, C1, C2, C3, C4 = get_aux_cit(args)
learn_params(args, g, n, C1, C2, C3, C4)
# read the learned parameters
basefile = open(os.path.join(args.data_dir, args.exp, 'base_weights.txt'),
newline='')
basefile = csv.reader(basefile, delimiter=' ')
qfile = open(os.path.join(args.data_dir, args.exp, 'marg_weights.txt'),
newline='')
qfile = csv.reader(qfile, delimiter=' ')
topic_features = np.load(args.topic_features)
user_features = np.load(args.user_features)
b = dict()
q = dict()
for u, v, val in basefile:
b[int(u), int(v)] = float(val)
for u, v, val in qfile:
q[int(u), int(v)] = float(val)
# calculate tp, fp, tn, fn
tp = []
fp = []
tn = []
fn = []
logfile = open(args.log_file, newline='')
logs = list(csv.reader(logfile, delimiter=' '))
num_logs = sum(1 for row in logs)
templogs = logs[:int(num_logs * test_perc)]
testlogs = []
for log in templogs:
nplog = np.array(log, dtype=np.int)
testlogs.append(nplog)
testlogs = np.array(testlogs)
print("Size of Test log in consideration: ", testlogs.shape)
# testlogs is now a numpy array
published = dict()
cited = dict()
for log in testlogs:
[u, v, c, p] = [int(x) for x in log]
# if v == 1344:
# print(c)
if u in published.keys():
published[u].add(c)
else:
published[u] = set()
published[u].add(c)
if v in published.keys():
published[v].add(p)
else:
published[v] = set()
published[v].add(p)
if v in cited.keys():
cited[v].add(c)
else:
cited[v] = set()
cited[v].add(c)
print("Preprocessed test log.")
# to estimate auc, taking 100 points on the curve
for mu in tqdm(np.linspace(0, 1, 100)):
tpx = 0
fpx = 0
tnx = 0
fnx = 0
for v in cited.keys(): # per entry of the log
for msg in cited[v]:
# get action set
for u in g.predecessors(v):
if u in published.keys():
action_set = set([p for p in published[u]])
prob = 0.0
# prob for an action
for a in action_set:
for u in g.predecessors(v):
# get valid predecessors for the actions
if u in published.keys() and a in published[u]:
prob += b[(u, v)] + q[(u, v)] * get_alpha(
user_features[v], topic_features[a])
prob = min(1, max(prob, 0))
prediction = (prob > mu)
gt = (a == msg)
if prediction == True:
if gt == True:
tpx += 1
else:
fpx += 1
else:
if gt == True:
fnx += 1
else:
tnx += 1
tp.append(tpx)
fp.append(fpx)
tn.append(tnx)
fn.append(fnx)
tp = np.array(tp)
fp = np.array(fp)
tn = np.array(tn)
fn = np.array(fn)
tpr = tp / (tp + fn)
fpr = fp / (fp + tn)
auc = -1 * np.trapz(tpr, fpr)
print('auc: ', auc)
return auc