def structUserData(sn, uid):
print(uid)
# norm profile
profile = ut.readJson2Dict(inputPath + sn + "/profile/", uid)
posts = ut.readJson2Dict(inputPath + sn + "/wall/", uid)
print("profile:" + interPath + sn + "/profile/" + uid)
newProfile = normProfile(sn, profile)
print("wall:" + interPath + sn + "/wall/" + uid)
newPosts = normWall(sn, posts)
ut.writeDict2Json(interPath + sn + "/profile/", uid, newProfile)
ut.writeDict2Json(interPath + sn + "/wall/", uid, newPosts)
# wall statisitcs
langDistri = ut.getDistri([post["lang"] for post in newPosts])
# sentiment sum
sentiments = [post["sentiment"]["polarity"] for post in newPosts]
sentiment_score = sum(sentiments) / len(sentiments) if len(
sentiments) > 0 else 0
# topic sum
topicDistris = [post["topic_distri"] for post in newPosts]
userTopicDistri = ut.mergeDict(topicDistris)
userTopicDistri = ut.normVector(userTopicDistri)
# tf
tfs = [post["tf"] for post in newPosts]
userTf = ut.mergeDict(tfs)
return (userTf, langDistri, sentiment_score, userTopicDistri)
def getBehaviorScore(sn1, user1, sn2, user2): posts1 = ut.readJson2Dict(interPath+sn1+"/wall/", user1) posts2 = ut.readJson2Dict(interPath+sn2+"/wall/", user2) text1 = ut.readJson2Dict(interPath+sn1+"/text/", user1) text2 = ut.readJson2Dict(interPath+sn2+"/text/", user2) scoresBehavior = calBehaviorScore(posts1, posts2, text1, text2) return scoresBehavior
def structUserData(sn, uid):
print(uid)
# norm profile
profile = ut.readJson2Dict(inputPath+sn+"/profile/", uid)
posts = ut.readJson2Dict(inputPath+sn+"/wall/", uid)
print("profile:"+interPath+sn+"/profile/"+uid)
newProfile = normProfile(sn, profile)
print("wall:"+interPath+sn+"/wall/"+uid)
newPosts = normWall(sn, posts)
ut.writeDict2Json(interPath+sn+"/profile/", uid, newProfile)
ut.writeDict2Json(interPath+sn+"/wall/", uid, newPosts)
# wall statisitcs
langDistri = ut.getDistri([post["lang"] for post in newPosts])
# sentiment sum
sentiments = [post["sentiment"]["polarity"] for post in newPosts]
sentiment_score = sum(sentiments)/len(sentiments) if len(sentiments)>0 else 0
# topic sum
topicDistris = [post["topic_distri"] for post in newPosts]
userTopicDistri = ut.mergeDict(topicDistris)
userTopicDistri = ut.normVector(userTopicDistri)
# tf
tfs = [post["tf"] for post in newPosts]
userTf = ut.mergeDict(tfs)
return (userTf, langDistri, sentiment_score, userTopicDistri)
def statNameScore():
gtsLoose = ut.readCommaLine2List(interPath, gtLooseFileName)
gtsStrict = ut.readCommaLine2List(interPath, gtStrictFileName)
gts = gtsStrict
twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName)
twitterNameId = ut.readJson2Dict(interPath, twitterNameIdFileName)
results = list()
for gt in gts:
googleId = gt[0]
twitterId = gt[1]
twitterName = twitterIdName[twitterId]
print(googleId)
print(twitterName)
googleProfile = ut.readJson2Dict(interPath + "google/profile/",
googleId)
twitterProfile = ut.readJson2Dict(interPath + "twitter/profile/",
twitterName)
nameScore = ft.calNameScore(googleProfile, twitterProfile)
displaynameScore = ft.calDisplayNameScore(googleProfile,
twitterProfile)
totalScore = nameScore + displaynameScore
results.append([
googleId, twitterId,
str(nameScore),
str(displaynameScore),
str(totalScore)
])
ut.writeList2CommaLine(interPath, "name_score", results)
def getBehaviorScore(sn1, user1, sn2, user2):
posts1 = ut.readJson2Dict(interPath + sn1 + "/wall/", user1)
posts2 = ut.readJson2Dict(interPath + sn2 + "/wall/", user2)
text1 = ut.readJson2Dict(interPath + sn1 + "/text/", user1)
text2 = ut.readJson2Dict(interPath + sn2 + "/text/", user2)
scoresBehavior = calBehaviorScore(posts1, posts2, text1, text2)
return scoresBehavior
def checkTwitterData(uname):
profile = ut.readJson2Dict(inputPath+"twitter/profile/", uname)
posts = ut.readJson2Dict(inputPath+"twitter/wall/", uname)
profile_bool = True
posts_bool = True
if len(profile)==0 or type(profile.get("errors", 0))==list:
profile_bool = False
if len(posts)==0:
posts_bool = False
return (profile_bool, posts_bool, profile)
def checkGoogleData(uid):
profile = ut.readJson2Dict(inputPath+"google/profile/", uid)
posts = ut.readJson2Dict(inputPath+"google/wall/", uid)
profile_bool = True
posts_bool = True
if profile.get("status", 0) == "error" or len(profile)==0:
profile_bool = False
if type(posts) == dict or len(posts)==0:
posts_bool = False
return (profile_bool, posts_bool)
def checkTwitterData(uname):
profile = ut.readJson2Dict(inputPath + "twitter/profile/", uname)
posts = ut.readJson2Dict(inputPath + "twitter/wall/", uname)
profile_bool = True
posts_bool = True
if len(profile) == 0 or type(profile.get("errors", 0)) == list:
profile_bool = False
if len(posts) == 0:
posts_bool = False
return (profile_bool, posts_bool, profile)
def checkGoogleData(uid):
profile = ut.readJson2Dict(inputPath + "google/profile/", uid)
posts = ut.readJson2Dict(inputPath + "google/wall/", uid)
profile_bool = True
posts_bool = True
if profile.get("status", 0) == "error" or len(profile) == 0:
profile_bool = False
if type(posts) == dict or len(posts) == 0:
posts_bool = False
return (profile_bool, posts_bool)
def stat_others(input_path="../prediction/input/"):
cates = ["information management","marketing", "transportation", "om&or"]
data = ut.readJson2Dict(input_path, "data_labeled.json")
topics = ["Others but relevant to IM", "Others but relevant to Marketing", "Others but relevant to OM", "Others but relevant to Transportation", "Not relevant", "Other Methods", "Relevant to IM", "Relevant to Transportation", "Relevant to Marketing", "Not Relevant to All Fields"]
paper_cate = {"information management": dict(),"marketing": dict(), "transportation": dict(), "om&or": dict()}
for paper in data:
label = paper["fields"]["label_final"].strip()
tmp = label.lower()
if "relevant" in tmp or "relevent" in tmp:
# if label in topics:
cate = paper["fields"]["category"]
tmp = paper_cate[cate].get(label, list())
tmp.append(paper)
paper_cate[cate][label] = tmp
for cate in cates:
fo = open("../problem/"+cate+".csv", "w")
fields = ["title","author","journal","volume","number","pages","year","month","keyword","keyword-plus","abstract"]
fo.write(","+",".join(fields)+"\n")
cate_topics = paper_cate[cate]
for cate_topic, papers in cate_topics.items():
fo.write(cate_topic)
for paper in papers:
for field in fields:
fo.write(",\""+paper["fields"].get(field,"")+"\"")
fo.write("\n")
def stat_top_label(input_path="../result/"):
fo = codecs.open("../output/stat_top_label.csv", "w", encoding="big5")
w = csv.writer(fo)
cates = ["information management","marketing", "transportation", "om&or"]
data = ut.readJson2Dict(input_path, "data_final.json")
data_labeled = {"information management": dict(), "marketing": dict(), "transportation": dict(), "om&or": dict()}
for paper in data:
year = paper["fields"]["year"]
cate = paper["fields"]["category"]
if paper["fields"]["phased3"]==3:
s = set(paper["fields"]["label3"].split(";")).union(set(paper["fields"]["label4"].split(";")))
elif paper["fields"]["phased3"]==2:
s = set(paper["fields"]["label4"].split(";"))
else:
s =set(paper["fields"]["label3"].split(";"))
combinations = get_combination(s)
# s = ";".join(sorted(list(s)))
if data_labeled[cate].get(year, 0)==0:
data_labeled[cate][year] = dict()
for c in combinations:
data_labeled[cate][year][c] = data_labeled[cate][year].get(c,0) + 1
for cate in cates:
w.writerow([cate.upper()])
for year, sets in sorted(data_labeled[cate].items()):
topn = sorted([(count, s) for s, count in sets.items()], reverse=True)[:10]
# fo.write(str(year)+","+",".join([s for count, s in topn])+"\n")
w.writerow([year]+[s for count, s in topn])
fo.close()
def stat_journal(input_path="../result/", fname="data_final.json"):
fo = open("../output/stat_journal.csv", "w")
papers = ut.readJson2Dict(input_path, fname)
print(len(papers))
paper_cate_journal = dict()
for p in papers:
cate = p["fields"]["category"].upper()
journal = p["fields"]["journal"]
vol = p["fields"]["volume"]
no = p["fields"]["number"].replace(",","")
if paper_cate_journal.get(cate, 0) == 0:
paper_cate_journal[cate] = dict()
if paper_cate_journal[cate].get(journal, 0) == 0:
paper_cate_journal[cate][journal] = dict()
if paper_cate_journal[cate][journal].get(vol, 0) == 0:
paper_cate_journal[cate][journal][vol] = dict()
if paper_cate_journal[cate][journal][vol].get(no, 0) == 0:
paper_cate_journal[cate][journal][vol][no] = 0
paper_cate_journal[cate][journal][vol][no]+=1
len(paper_cate_journal)
for cate, journals in paper_cate_journal.items():
for j, vols in sorted(journals.items()):
for v, nos in sorted(vols.items()):
for no, count in sorted(nos.items()):
fo.write(cate+","+j+","+v+","+no+","+str(count)+"\n")
fo.close()
def readData(users_google, users_twitter, twitterIdName):
profileGoogle = dict()
profileTwitter = dict()
wallGoogle = dict()
wallTwitter = dict()
textGoogle = dict()
textTwitter = dict()
for user in users_google:
profileGoogle[user] = ut.readJson2Dict(interPath+"google/profile/", user)
# wallGoogle[user] = ut.readJson2Dict(interPath+"google/wall/", user)
# textGoogle[user] = ut.readJson2Dict(interPath+"google/text/", user)
for user in users_twitter:
twitterName = twitterIdName[user]
profileTwitter[user] = ut.readJson2Dict(interPath+"twitter/profile/", twitterName)
# wallTwitter[user] = ut.readJson2Dict(interPath+"twitter/wall/", twitterName)
# textTwitter[user] = ut.readJson2Dict(interPath+"twitter/text/", twitterName)
return profileGoogle, profileTwitter, wallGoogle, wallTwitter, textGoogle, textTwitter
print("load file over")
def stat_user2(input_path="../result/"):
cates = ["information management","marketing", "transportation", "om&or"]
data = ut.readJson2Dict(input_path, "data_final.json")
users = ut.readJson2Dict("../website/public/", "data_user1.json")
data_labeled = {"information management": list(), "marketing": list(), "transportation": list(), "om&or": list()}
results = dict()
for paper in data:
data_labeled[paper["fields"]["category"]].append(paper)
with codecs.open("../output/stat_user2.txt", "w", encoding="big5") as fo:
for cate in cates:
fo.write(cate.upper()+"\n")
papers = data_labeled[cate]
print(len(papers))
time3s = [paper["fields"]["time3"] for paper in papers if paper["fields"]["time3"]<1200 and paper["fields"]["time3"]>0]
time4s = [paper["fields"]["time4"] for paper in papers if paper["fields"]["time4"]<1200 and paper["fields"]["time4"]>0]
avg_time3 = sum(time3s)/len(time3s)
avg_time4 = sum(time4s)/len(time4s)
users_cate = [user["fields"]["name"] for user in users if user["fields"]["category"]==cate]
fo.write(" ".join([users_cate[0], str(avg_time3), users_cate[1], str(avg_time4)])+"\n")
def output_edit_im():
data = ut.readJson2Dict("../output/", "parsed_edit_im.json")
fields = ["title","author","journal","volume","number","pages","year","month","keyword","keyword-plus","abstract"]
fo = open(output_path+"papers_im_editorial"+".csv", "w")
fo.write("index,"+",".join(fields)+"\n")
for index, paper in enumerate(data):
fo.write(str(index+1))
for field in fields:
fo.write(",\""+paper.get(field,"")+"\"")
fo.write("\n")
fo.close()
def output_data(input_path ="../result/", output_path="../result/"):
data = ut.readJson2Dict(input_path, "data_final.json")
cates = ["information management","marketing", "transportation", "om&or"]
paper_cate = {"information management": list(), "marketing": list(), "transportation": list(), "om&or": list()}
output_topics()
for paper in data:
paper_cate[paper["fields"]["category"]].append(paper)
for cate in cates:
papers = paper_cate[cate]
# output_article(papers, output_path, cate)
output_label(papers, output_path, cate)
def readData(users_google, users_twitter, twitterIdName):
profileGoogle = dict()
profileTwitter = dict()
wallGoogle = dict()
wallTwitter = dict()
textGoogle = dict()
textTwitter = dict()
for user in users_google:
profileGoogle[user] = ut.readJson2Dict(interPath + "google/profile/",
user)
# wallGoogle[user] = ut.readJson2Dict(interPath+"google/wall/", user)
# textGoogle[user] = ut.readJson2Dict(interPath+"google/text/", user)
for user in users_twitter:
twitterName = twitterIdName[user]
profileTwitter[user] = ut.readJson2Dict(interPath + "twitter/profile/",
twitterName)
# wallTwitter[user] = ut.readJson2Dict(interPath+"twitter/wall/", twitterName)
# textTwitter[user] = ut.readJson2Dict(interPath+"twitter/text/", twitterName)
return profileGoogle, profileTwitter, wallGoogle, wallTwitter, textGoogle, textTwitter
print("load file over")
def statNameScore(): gtsLoose = ut.readCommaLine2List(interPath, gtLooseFileName) gtsStrict = ut.readCommaLine2List(interPath, gtStrictFileName) gts = gtsStrict twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName) twitterNameId = ut.readJson2Dict(interPath, twitterNameIdFileName) results = list() for gt in gts: googleId = gt[0] twitterId = gt[1] twitterName = twitterIdName[twitterId] print(googleId) print(twitterName) googleProfile = ut.readJson2Dict(interPath+"google/profile/", googleId) twitterProfile = ut.readJson2Dict(interPath+"twitter/profile/", twitterName) nameScore = ft.calNameScore(googleProfile, twitterProfile) displaynameScore = ft.calDisplayNameScore(googleProfile, twitterProfile) totalScore = nameScore + displaynameScore results.append([googleId, twitterId, str(nameScore), str(displaynameScore), str(totalScore)]) ut.writeList2CommaLine(interPath, "name_score", results)
def structData():
# init
s = time.time()
usersTf1 = dict()
usersTf2 = dict()
usersLangDistri1 = dict()
usersLangDistri2 = dict()
usersSentimentScore1 = dict()
usersSentimentScore2 = dict()
usersTopicDistir1 = dict()
usersTopicDistri2 = dict()
twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName)
gts_loose = ut.readCommaLine2List(interPath, gtLooseFileName)
gts_strict = ut.readCommaLine2List(interPath, gtStrictFileName)
gts = gts_strict
if not os.path.isdir(interPath + sn1):
os.makedirs(interPath + sn1 + "/profile")
os.makedirs(interPath + sn1 + "/wall")
os.makedirs(interPath + sn1 + "/text")
os.makedirs(interPath + sn2 + "/profile")
os.makedirs(interPath + sn2 + "/wall")
os.makedirs(interPath + sn2 + "/text")
# norm profile and wall
for gt in gts:
uid1 = gt[0]
uid2 = gt[1]
try:
if sn1 == "twitter":
uid1 = twitterIdName[uid1]
if sn2 == "twitter":
uid2 = twitterIdName[uid2]
except:
continue
# if not os.path.exists(interPath+sn1+"/profile/"+uid1):
# norm profile and posts: google and twitter
(userTf1, langDistri1, userSentimentScore1,
userTopicDistri1) = structUserData(sn1, uid1)
(userTf2, langDistri2, userSentimentScore2,
userTopicDistri2) = structUserData(sn2, uid2)
usersTf1[uid1] = userTf1
usersTf2[uid2] = userTf2
usersLangDistri1[uid1] = langDistri1
usersLangDistri2[uid2] = langDistri2
usersSentimentScore1[uid1] = userSentimentScore1
usersSentimentScore2[uid2] = userSentimentScore2
usersTopicDistir1[uid1] = userTopicDistri1
usersTopicDistri2[uid2] = userTopicDistri2
# build dictionary and idf
writeStatWalls(usersTf1, usersTf2, usersLangDistri1, usersLangDistri2,
usersSentimentScore1, usersSentimentScore2,
usersTopicDistir1, usersTopicDistri2)
e = time.time()
print(e - s)
def getScores(sn1, user1, sn2, user2, g1, g2, g0): # read the twitter name id mapping file twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName) if sn2 == "twitter": user2_name = twitterIdName[user2] scores = getProfileScore(sn1, user1, sn2, user2_name)+getSocialScore(sn1, user1, sn2, user2, g1, g2, g0)+getBehaviorScore(sn1, user1, sn2, user2_name) elif sn1 == "twitter": user1_name = twitterIdName[user1] scores = getProfileScore(sn1, user1_name, sn2, user2)+getSocialScore(sn1, user1, sn2, user2, g1, g2, g0)+getBehaviorScore(sn1, user1_name, sn2, user2) else: scores = getProfileScore(sn1, user1, sn2, user2)+getSocialScore(sn1, user1, sn2, user2, g1, g2, g0)+getBehaviorScore(sn1, user1, sn2, user2) return scores
def reviseTwitterRelationship():
names = list()
twitterNameId = ut.readJson2Dict(interPath, "twitterNameId")
with open(interPath + "twitter/relationship_file_revise", "w") as fo:
with open(interPath + "twitter/relationship_file", "r") as fi:
for line in fi:
ids = line.split(" ")
user = ids[0]
friends = ids[1]
if user not in names and twitterNameId.get(user, 0) != 0:
uid = twitterNameId[user]
fo.write(uid + " " + friends)
else:
print(user)
def reviseTwitterRelationship():
names = list()
twitterNameId = ut.readJson2Dict(interPath, "twitterNameId")
with open(interPath+"twitter/relationship_file_revise", "w") as fo:
with open(interPath+"twitter/relationship_file", "r") as fi:
for line in fi:
ids = line.split(" ")
user = ids[0]
friends = ids[1]
if user not in names and twitterNameId.get(user, 0) != 0:
uid = twitterNameId[user]
fo.write(uid+" "+friends)
else:
print(user)
def structData(): # init s = time.time() usersTf1 = dict() usersTf2 = dict() usersLangDistri1 = dict() usersLangDistri2 = dict() usersSentimentScore1 = dict() usersSentimentScore2 = dict() usersTopicDistir1 = dict() usersTopicDistri2 = dict() twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName) gts_loose = ut.readCommaLine2List(interPath, gtLooseFileName) gts_strict = ut.readCommaLine2List(interPath, gtStrictFileName) gts = gts_strict if not os.path.isdir(interPath+sn1): os.makedirs(interPath+sn1+"/profile") os.makedirs(interPath+sn1+"/wall") os.makedirs(interPath+sn1+"/text") os.makedirs(interPath+sn2+"/profile") os.makedirs(interPath+sn2+"/wall") os.makedirs(interPath+sn2+"/text") # norm profile and wall for gt in gts: uid1 = gt[0] uid2 = gt[1] try: if sn1 == "twitter": uid1 = twitterIdName[uid1] if sn2 =="twitter": uid2 = twitterIdName[uid2] except: continue # if not os.path.exists(interPath+sn1+"/profile/"+uid1): # norm profile and posts: google and twitter (userTf1, langDistri1, userSentimentScore1, userTopicDistri1) = structUserData(sn1, uid1) (userTf2, langDistri2, userSentimentScore2, userTopicDistri2) = structUserData(sn2, uid2) usersTf1[uid1] = userTf1 usersTf2[uid2] = userTf2 usersLangDistri1[uid1] = langDistri1 usersLangDistri2[uid2] = langDistri2 usersSentimentScore1[uid1] = userSentimentScore1 usersSentimentScore2[uid2] = userSentimentScore2 usersTopicDistir1[uid1] = userTopicDistri1 usersTopicDistri2[uid2] = userTopicDistri2 # build dictionary and idf writeStatWalls(usersTf1, usersTf2, usersLangDistri1, usersLangDistri2, usersSentimentScore1, usersSentimentScore2, usersTopicDistir1, usersTopicDistri2) e = time.time() print(e-s)
def stat_user(input_path="../prediction/input/"):
cates = ["information management","marketing", "transportation", "om&or"]
data = ut.readJson2Dict(input_path, "data_labeled.json")
users = ut.readJson2Dict("../website/public/", "data_user1.json")
data_labeled = {"information management": {"1":list(), "2": list()}, "marketing": {"1":list(), "2": list()}, "transportation": {"1":list(), "2": list()}, "om&or": {"1":list(), "2": list()}}
results = dict()
# four cate, two step
for paper in data:
if paper["fields"]["is_phased1"]:
data_labeled[paper["fields"]["category"]]["1"].append(paper)
elif paper["fields"]["is_phased2"]:
data_labeled[paper["fields"]["category"]]["2"].append(paper)
else:
pass
with codecs.open("../output/stat_user.txt", "w", encoding="big5") as fo:
for cate in cates:
fo.write(cate.upper()+"\n")
# print(cate.upper())
for i in range(1,3):
papers = data_labeled[cate][str(i)]
time1 = sum([paper["fields"]["time1"] for paper in papers if paper["fields"]["time1"]<1200])/len(papers)
time2 = sum([paper["fields"]["time2"] for paper in papers if paper["fields"]["time2"]<1200])/len(papers)
users_cate = [user["fields"]["name"] for user in users if user["fields"]["category"]==cate]
fo.write(" ".join(["Phase"+str(i), users_cate[0], str(time1), users_cate[1], str(time2)])+"\n")
def output_err_data():
data = ut.readJson2Dict(result_path, "data_final.json")
papers_428 = list()
# 2. excel row 428
for p in data:
if p["fields"]["journal"] == "JOURNAL OF MANAGEMENT INFORMATION SYSTEMS" and p["fields"]["volume"]=="31" and p["fields"]["number"]=="4":
# if p["fields"]["journal"] == "JOURNAL OF MANAGEMENT INFORMATION SYSTEMS":
print(p["fields"]["title"])
papers_428.append(p)
output_paper("../output/stat_error_428.csv", papers_428)
# 3. excel row 657
papers_657 = list()
for p in data:
if p["fields"]["journal"] == "TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES" and p["fields"]["volume"]=="47":
# if p["fields"]["journal"] == "JOURNAL OF MANAGEMENT INFORMATION SYSTEMS":
print(p["fields"]["title"])
papers_657.append(p)
output_paper("../output/stat_error_657.csv", papers_657)
def getScores(sn1, user1, sn2, user2, g1, g2, g0):
# read the twitter name id mapping file
twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName)
if sn2 == "twitter":
user2_name = twitterIdName[user2]
scores = getProfileScore(sn1, user1, sn2, user2_name) + getSocialScore(
sn1, user1, sn2, user2, g1, g2, g0) + getBehaviorScore(
sn1, user1, sn2, user2_name)
elif sn1 == "twitter":
user1_name = twitterIdName[user1]
scores = getProfileScore(sn1, user1_name, sn2, user2) + getSocialScore(
sn1, user1, sn2, user2, g1, g2, g0) + getBehaviorScore(
sn1, user1_name, sn2, user2)
else:
scores = getProfileScore(sn1, user1, sn2, user2) + getSocialScore(
sn1, user1, sn2, user2, g1, g2, g0) + getBehaviorScore(
sn1, user1, sn2, user2)
return scores
def stat_label2(input_path="../result/"):
cates = ["information management","marketing", "transportation", "om&or"]
data = ut.readJson2Dict(input_path, "data_final.json")
data_labeled = {"information management": list(), "marketing": list(), "transportation": list(), "om&or": list()}
for paper in data:
if paper["fields"]["phased3"]==3:
data_labeled[paper["fields"]["category"]].append(paper)
for cate in cates:
print(cate.upper())
papers = data_labeled[cate]
jaccards = list()
aligns = list()
for paper in papers:
s1 = set(paper["fields"]["label3"].split(";"))
s2 = set(paper["fields"]["label4"].split(";"))
jaccards.append(jaccard(s1,s2))
aligns.append(align_ratio(s1,s2,3))
print("Jaccards:", sum(jaccards)/len(jaccards))
print("Alignment Ratio:", sum(aligns)/len(aligns))
def stat_other_label(input_path="../result/"):
mapping = {'information management': 'Others but relevant to IM','marketing':'Others but relevant to Marketing', 'transportation':'Others but relevant to Transportation', 'om&or':'Others but relevant to OM&OR'}
data = ut.readJson2Dict(input_path, "data_final.json")
data_labeled = {"information management": list(), "marketing": list(), "transportation": list(), "om&or": list()}
# read data
for paper in data:
cate = paper["fields"]["category"]
if paper["fields"]["phased3"]==3:
s = set(paper["fields"]["label3"].split(";")).union(set(paper["fields"]["label4"].split(";")))
elif paper["fields"]["phased3"]==2:
s = set(paper["fields"]["label4"].split(";"))
else:
s =set(paper["fields"]["label3"].split(";"))
if mapping[cate] in s:
data_labeled[cate].append(paper)
# output data
cols = ['isi', 'title', 'author', 'journal', 'year', 'month', 'volume', 'number', 'pages', 'keyword', 'keywords_plus','web_of_science_categories', 'abstract']
for cate, papers in data_labeled.items():
output_paper("../output/stat_label_others_"+cate+".csv", papers)
def stat_label_distri(input_path="../result/"):
cates = ["information management","marketing", "transportation", "om&or"]
data = ut.readJson2Dict(input_path, "data_final.json")
data_labeled = {"information management": list(), "marketing": list(), "transportation": list(), "om&or": list()}
for paper in data:
if paper["fields"]["phased3"]==3:
s = set(paper["fields"]["label3"].split(";")).union(set(paper["fields"]["label4"].split(";")))
elif paper["fields"]["phased3"]==2:
s = set(paper["fields"]["label4"].split(";"))
else:
s =set(paper["fields"]["label3"].split(";"))
data_labeled[paper["fields"]["category"]].append(s)
for cate in cates:
print(cate.upper())
label_len_distri = dict()
for s in data_labeled[cate]:
label_len_distri[len(s)] = label_len_distri.get(len(s),0) +1
for k, v in sorted(label_len_distri.items()):
print(k, v/len(data_labeled[cate]))
def output_im_word_data():
data = ut.readJson2Dict(result_path, "data_final.json")
words = ["Neuro IS", "NeuroIS", "Neuro-IS", "Virtual World", "Online game", "Ethics", "Open Source"]
papers = list()
for paper in data:
cate = paper["fields"]["category"]
if cate != "information management":
continue
if paper["fields"]["phased3"]==3:
s = set(paper["fields"]["label3"].split(";")).union(set(paper["fields"]["label4"].split(";")))
elif paper["fields"]["phased3"]==2:
s = set(paper["fields"]["label4"].split(";"))
else:
s =set(paper["fields"]["label3"].split(";"))
if "Others but relevant to IM" in s:
continue
for w in words:
if w in paper["fields"]["abstract"]:
papers.append(paper)
break
output_paper("../output/stat_im_new_topic.csv", papers)
def stat_label(input_path="../prediction/input/"):
cates = ["information management","marketing", "transportation", "om&or"]
data = ut.readJson2Dict(input_path, "data_labeled.json")
data_labeled = {"information management": {"1":list(), "2": list()}, "marketing": {"1":list(), "2": list()}, "transportation": {"1":list(), "2": list()}, "om&or": {"1":list(), "2": list()}}
# four cate, two step
for paper in data:
if paper["fields"]["is_phased1"]:
data_labeled[paper["fields"]["category"]]["1"].append(paper)
elif paper["fields"]["is_phased2"]:
data_labeled[paper["fields"]["category"]]["2"].append(paper)
else:
pass
print(",label1 vs label2, label1 vs label_final, label_final vs label2")
for cate in cates:
# phase 1
print(cate.upper())
for i in range(1,3):
papers = data_labeled[cate][str(i)]
answers1 = [paper["fields"]["label1"] for paper in papers]
answers2 = [paper["fields"]["label2"] for paper in papers]
answers_final = [paper["fields"]["label_final"] for paper in papers]
print("Phase"+str(i),kappa(answers1, answers2), kappa(answers1, answers_final), kappa(answers2, answers_final))
def getUsersFeatures(procNum=10):
# init user pair by mapping
gts = ut.readCommaLine2List(interPath, gtStrictFileName)
sn1 = "google"
sn2 = "twitter"
users_sn1 = list()
users_sn2 = list()
# scoresMatrix = lil_matrix((len(gts), len(gts)))
scoresMatrix = dict()
for gt in gts:
users_sn1.append(gt[0])
users_sn2.append(gt[1])
# build graph
print("build graph")
s = time.time()
g1, g2, g0 = buildGraphs(users_sn1, users_sn2)
e = time.time()
print("build graph over cost: " + str(e - s))
# for profile using
print("popular count")
s = time.time()
writeMostPopularCount(g1, sn1, users_sn1, g2, sn2, users_sn2)
e = time.time()
print("popular count over cost: " + str(e - s))
print("calculate features start")
# calculate features
s = time.time()
pairs = [(a, b) for a in range(len(gts)) for b in range(len(gts))
if b >= a]
twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName)
twitterNameId = ut.readJson2Dict(interPath, twitterNameIdFileName)
profileGoogle, profileTwitter, wallGoogle, wallTwitter, textGoogle, textTwitter = readData(
users_sn1, users_sn2, twitterIdName)
#
# for pair in pairs:
# print(pair)
# scores = getScores(sn1, users_sn1[pair[0]], sn2, users_sn2[pair[1]], g1, g2, g0)
# scoresMatrix[(pair[0], pair[1])] = scores
# parallel
batchNum = round(len(pairs) / procNum)
procs = list()
q = mp.Queue()
for i in range(procNum):
batchPairs = list()
if i == procNum - 1:
batchPairs = pairs[i * batchNum:]
else:
batchPairs = pairs[i * batchNum:(i + 1) * batchNum]
# p = td.Thread(target=getScoresWorker, args=(batchPairs, sn1, users_sn1, sn2, users_sn2, g1, g2, g0, q))
p = td.Thread(target=getScoresWorker,
args=(batchPairs, sn1, users_sn1, sn2, users_sn2, g1, g2,
g0, q, profileGoogle, profileTwitter, wallGoogle,
wallTwitter, textGoogle, textTwitter))
p.start()
procs.append(p)
print("update start")
for i in range(len(pairs)):
print(i)
result = q.get()
# scoresMatrix.update(result)
scoresMatrix[result["key"]] = result["value"]
print("update over")
print(len(scoresMatrix))
for proc in procs:
proc.join()
# output feature
with open(outputPath + featureFileName, "w") as fo:
for i in range(len(gts)):
for j in range(len(gts)):
if i == j:
rank = 1
else:
rank = 0
if i > j:
scores = scoresMatrix[(j, i)]
else:
scores = scoresMatrix[(i, j)]
outputStr = getFeatureStr(rank, users_sn1[i], users_sn2[j],
scores)
fo.write(outputStr)
# with open(outputPath+featureFileName, "w") as fo:
# for i in range(len(gts)):
# print(users_sn1[i])
# print(i)
# for j in range(len(gts)):
# print(j)
# if i == j:
# rank = 1
# else:
# rank = 0
# scores = getScores(sn1, users_sn1[i], sn2, users_sn2[j], g1, g2, g0)
# outputStr = getFeatureStr(rank, users_sn1[i], users_sn2[j], scores)
# fo.write(outputStr)
e = time.time()
print("write feature costs:" + str(e - s))
def getUsersFeatures(procNum = 10):
# init user pair by mapping
gts = ut.readCommaLine2List(interPath, gtStrictFileName)
sn1 = "google"
sn2 = "twitter"
users_sn1 = list()
users_sn2 = list()
# scoresMatrix = lil_matrix((len(gts), len(gts)))
scoresMatrix = dict()
for gt in gts:
users_sn1.append(gt[0])
users_sn2.append(gt[1])
# build graph
print("build graph")
s = time.time()
g1, g2, g0 = buildGraphs(users_sn1, users_sn2)
e = time.time()
print("build graph over cost: "+str(e-s))
# for profile using
print("popular count")
s = time.time()
writeMostPopularCount(g1, sn1, users_sn1, g2, sn2, users_sn2)
e = time.time()
print("popular count over cost: "+str(e-s))
print("calculate features start")
# calculate features
s = time.time()
pairs = [(a,b) for a in range(len(gts)) for b in range(len(gts)) if b>=a]
twitterIdName = ut.readJson2Dict(interPath, twitterIdNameFileName)
twitterNameId = ut.readJson2Dict(interPath, twitterNameIdFileName)
profileGoogle, profileTwitter, wallGoogle, wallTwitter, textGoogle, textTwitter = readData(users_sn1, users_sn2, twitterIdName)
#
# for pair in pairs:
# print(pair)
# scores = getScores(sn1, users_sn1[pair[0]], sn2, users_sn2[pair[1]], g1, g2, g0)
# scoresMatrix[(pair[0], pair[1])] = scores
# parallel
batchNum = round(len(pairs)/procNum)
procs = list()
q = mp.Queue()
for i in range(procNum):
batchPairs = list()
if i == procNum-1:
batchPairs = pairs[i*batchNum:]
else:
batchPairs = pairs[i*batchNum:(i+1)*batchNum]
# p = td.Thread(target=getScoresWorker, args=(batchPairs, sn1, users_sn1, sn2, users_sn2, g1, g2, g0, q))
p = td.Thread(target=getScoresWorker, args=(batchPairs, sn1, users_sn1, sn2, users_sn2, g1, g2, g0, q, profileGoogle, profileTwitter, wallGoogle, wallTwitter, textGoogle, textTwitter))
p.start()
procs.append(p)
print("update start")
for i in range(len(pairs)):
print(i)
result = q.get()
# scoresMatrix.update(result)
scoresMatrix[result["key"]] = result["value"]
print("update over")
print(len(scoresMatrix))
for proc in procs:
proc.join()
# output feature
with open(outputPath+featureFileName, "w") as fo:
for i in range(len(gts)):
for j in range(len(gts)):
if i == j:
rank = 1
else:
rank = 0
if i > j:
scores = scoresMatrix[(j, i)]
else:
scores = scoresMatrix[(i, j)]
outputStr = getFeatureStr(rank, users_sn1[i], users_sn2[j], scores)
fo.write(outputStr)
# with open(outputPath+featureFileName, "w") as fo:
# for i in range(len(gts)):
# print(users_sn1[i])
# print(i)
# for j in range(len(gts)):
# print(j)
# if i == j:
# rank = 1
# else:
# rank = 0
# scores = getScores(sn1, users_sn1[i], sn2, users_sn2[j], g1, g2, g0)
# outputStr = getFeatureStr(rank, users_sn1[i], users_sn2[j], scores)
# fo.write(outputStr)
e = time.time()
print("write feature costs:" + str(e-s))
