def evaluate_one_model(pickle_path,
pk_sys_set,
system_path,
system_id,
modeltype='mymodel',
topksent=10,
on_mance=False,
on_sysce=False):
print "model type =", modeltype, "top k =", topksent
for i, (file_name, system_name) in enumerate(pk_sys_set):
print i, file_name
pickle_file = os.path.join(pickle_path, file_name)
if modeltype == 'mymodel': # MyModel.py
model = MyModel(pickle_file)
if modeltype == 'tfidf': # tf_idf.py
model = TfIdf(pickle_file)
if modeltype == 'graphb': # graph_base.py
model = GraphBased(pickle_file)
if modeltype == 'graphw': # word_graph.py
model = WordGraph(pickle_file)
if modeltype == 'context1': # sxpContextModel.py SubPara Model
model = conTextModel(pickle_file)
if modeltype == 'mysecmodel': # MySecModel.py
model = MySecModel(pickle_file)
if modeltype == 'myseccontextmodel': # MySecContextModel.py SecSub Model
model = SecConTextModel(pickle_file)
if modeltype == 'hybrid': # sxpHybridGraph.py
model = HybridGraph(pickle_file)
if modeltype == 'sectitle': # MySecTitleNetwork.py
model = MySecTitleModel(pickle_file)
# -- save top k sentences to text --
topksent_path = os.path.join(system_path,
system_name + "." + system_id + ".txt")
tops = model.OutPutTopKSent(topksent, 1, -1)
st = ProduceSystem(tops, file_name, 1)
WriteStrFile(topksent_path, st, 'utf-8')
if on_mance:
topk_mance_sent_path = os.path.join(
system_path, system_name + "." + system_id + ".mance.txt")
topk_mance_sent = OutPutTopKmanCESent(model, topksent, 1, -1)
mance_st = ProduceSystem(topk_mance_sent, file_name, 1)
WriteStrFile(topk_mance_sent_path, mance_st, 'utf-8')
if on_sysce:
topk_sysce_sent_path = os.path.join(
system_path, system_name + "." + system_id + ".sysce.txt")
topk_sysce_sent = OutPutTopKsysCESent(model, topksent, 1, -1)
sysce_st = ProduceSystem(topk_sysce_sent, file_name, 1)
WriteStrFile(topk_sysce_sent_path, sysce_st, 'utf-8')
# -- save all ranked sentences to text --
allsent = model.OutputAllRankSentence()
pkfname = topksent_path + 'allsent.pk'
StoreSxptext(allsent, pkfname)
i += 1
print "ranking complete!"
def evaluate_all_duc(fp, papers_path, pk_path, modeltype='mymodel', inc_abscon=True, useabstr=1, maxtxt=-1, topk=10):
print "model type =", modeltype
# -- if modeltype is not in our model list, not execute the following process --
if modeltype not in idname.keys():
return
# -- get algorithms generated summaries directory --
if not inc_abscon:
system_dir = os.path.join(fp, 'systemFliter_html1')
else:
system_dir = os.path.join(fp, 'systemFliter_html2')
if not os.path.exists(system_dir):
os.makedirs(system_dir)
# -- get the original filename list in the path variable --
file_list = sxpGetDirFileList(papers_path)[0]
# -- for each file, get its sxpText and run modeltype ranking model on it --
for i, file_name in enumerate(file_list):
# ---- get file name ----
fset = file_name.split('.')
if fset[-1] != 'xhtml': # if current file is not a txt file, it is not one of the original papers
continue
print "processing", i, "th paper named as", file_name
# -- get single pickle file directory --
if not inc_abscon:
pickle_path = os.path.join(pk_path, file_name +'_1.pickle')
else:
pickle_path = os.path.join(pk_path, file_name +'_2.pickle')
# -- run ranking model on sxpText object at pickle_path --
if modeltype == 'mymodel':
model = MyModel(pickle_path)
if modeltype == 'tfidf':
model = TfIdf(pickle_path)
if modeltype == 'graphb':
model = GraphBased(pickle_path)
if modeltype == 'graphw':
model = WordGraph(pickle_path)
if modeltype == 'context1':
model = conTextModel(pickle_path)
if modeltype == 'mysecmodel':
model = MySecModel(pickle_path)
if modeltype == 'myseccontextmodel':
model = SecConTextModel(pickle_path)
if modeltype == 'hybrid': # sxpHybridGraph.py
model = HybridGraph(pickle_path)
if modeltype == 'sectitle': # MySecTitleNetwork.py
model = MySecTitleModel(pickle_path)
# -- get the .html file's suffix NO. --
idstr = idname[modeltype]
# -- save original model ranked topk sentences to text --
topksent_path = os.path.join(system_dir, file_name+'.html.'+idstr)
tops = model.OutPutTopKSent(topk, useabstr, maxtxt)
st = ProduceSystem(tops, file_name, 1)
WriteStrFile(topksent_path, st, 'utf-8')
# -- save pattern matched cause-effect links ranked topk sentences to text --
sysce_topk = topk if topk <= len(model.text.sysce_sent_id_dict) else len(model.text.sysce_sent_id_dict)
if sysce_topk > 0:
topk_sysce_sent_path = os.path.join(system_dir, file_name+'.html.'+str(30 + int(idstr)))
topk_sysce_sent = OutPutTopKsysCESent(model, sysce_topk, useabstr, maxtxt)
sysce_st = ProduceSystem(topk_sysce_sent, file_name, 1)
WriteStrFile(topk_sysce_sent_path, sysce_st, 'utf-8')
print "ranking complete"
def evaluate_all_kg_sec(system_path,
pk_path,
modeltype='mymodel',
ce_opt='mance',
bias=0.65,
fname_topk_dict={},
fname_maxstr_dict={},
useabstr=0,
maxstr=2500,
topk=25):
print "model type =", modeltype
# -- get the original filename list in the path variable --
file_list = sxpGetDirFileList(pk_path)[0]
# -- for each file, get its sxpText and run modeltype ranking model on it --
for i, file_name in enumerate(file_list):
# ---- get file name ----
fset = file_name.split('.')
fname = '.'.join(fset[0:2])
secname = fset[2]
if fset[-1] != 'pk': # if current file is not a pickle file, it is not one of the original papers
continue
# ---- Get system summaries directory ----
system_dir = os.path.join(system_path, fname, secname)
if not os.path.exists(system_dir):
os.makedirs(system_dir)
# ---- get the upper bound of a system summary's str number ----
if fname_maxstr_dict.has_key(fname + '.' + secname):
maxstr = fname_maxstr_dict[fname + '.' + secname]
if fname_topk_dict.has_key(fname + '.' + secname):
topk = fname_topk_dict[fname + '.' + secname]
print "processing", i, "th paper named as", file_name
# -- get single pickle file directory --
pickle_path = os.path.join(pk_path, file_name)
# ----------- Original Sentence Ranking Models --------------
if modeltype == 'mymodel':
model = MyModel(pickle_path)
if modeltype == 'tfidf':
model = TfIdf(pickle_path)
if modeltype == 'graphb':
model = GraphBased(pickle_path)
if modeltype == 'graphw':
model = WordGraph(pickle_path)
if modeltype == 'context1':
model = conTextModel(pickle_path)
if modeltype == 'mysecmodel':
model = MySecModel(pickle_path)
if modeltype == 'myseccontextmodel':
model = SecConTextModel(pickle_path)
if modeltype == 'hybrid': # sxpHybridGraph.py
model = HybridGraph(pickle_path)
if modeltype == 'sectitle': # MySecTitleNetwork.py
model = MySecTitleModel(pickle_path)
# --------------- CEBias Ranking Models -------------------
if modeltype == 'mymodel2':
model = MyModel2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'tfidf2':
model = TfIdf2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'graphb2':
model = GraphBased2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'graphw2':
model = WordGraph2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'context2':
model = conTextModel2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'mysecmodel2':
model = MySecModel2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'myseccontextmodel2':
model = SecConTextModel2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'hybrid2': # sxpHybridGraph.py
model = HybridGraph2(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'sectitle2': # MySecTitleNetwork.py
model = MySecTitleModel2(pickle_path, opt=ce_opt, bias=bias)
# --------------- CEIter Ranking Models -------------------
if modeltype == 'mymodel3':
model = MyModel3(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'context3':
model = conTextModel3(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'mysecmodel3':
model = MySecModel3(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'myseccontextmodel3':
model = SecConTextModel3(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'hybrid3': # sxpHybridGraph.py
model = HybridGraph3(pickle_path, opt=ce_opt, bias=bias)
if modeltype == 'sectitle3': # MySecTitleNetwork.py
model = MySecTitleModel3(pickle_path, opt=ce_opt, bias=bias)
# -- get the .html file's suffix NO. --
if modeltype in idname.keys():
idstr = idname[modeltype]
# -- save original model ranked topk sentences to text --
topksent_path = os.path.join(system_dir,
fname + '.' + secname + '.html.' + idstr)
topksent_path_2 = os.path.join(
system_dir, fname + '.' + secname + '.html.sent.' + idstr)
tops = model.OutPutTopKSent(topk, useabstr, maxstr)
st = ProduceSystem(tops, fname + '.' + secname, 1)
if useabstr < 0:
WriteStrFile(topksent_path_2, st, 'utf-8')
else:
WriteStrFile(topksent_path, st, 'utf-8')
print "ranking complete"
def evaluate_all_duc(fp,
papers_path,
pk_path,
modeltype='mymodel',
inc_abscon=True,
useabstr=1,
maxtxt=-1,
topk=10):
print "model type =", modeltype
# -- if modeltype is not in our model list, not execute the following process --
if modeltype not in idname.keys():
return
# -- get algorithms generated summaries directory --
if not inc_abscon:
system_dir = os.path.join(fp, 'systemPure_html1')
rankedsentdir = os.path.join(fp, 'RankedSentPure_1')
else:
system_dir = os.path.join(fp, 'systemPure_html2')
rankedsentdir = os.path.join(fp, 'RankedSentPure_2')
if not os.path.exists(system_dir):
os.makedirs(system_dir)
if not os.path.exists(rankedsentdir):
os.makedirs(rankedsentdir)
# -- get the original filename list in the path variable --
file_list = sxpGetDirFileList(papers_path)[0]
# -- for each file, get its sxpText and run modeltype ranking model on it --
for i, file_name in enumerate(file_list):
# ---- get file name ----
if not re.match(ur'AP\d{6}-\d{4}|FBIS\d-\d+', file_name
): # if current file is not one of the original papers
continue
print "processing", i, "th paper named as", file_name
# -- get single pickle file directory --
if not inc_abscon:
pickle_path = os.path.join(pk_path, file_name)
else:
pickle_path = os.path.join(pk_path, file_name)
# -- run ranking model on sxpText object at pickle_path --
if modeltype == 'mymodel':
model = MyModel(pickle_path)
if modeltype == 'tfidf':
model = TfIdf(pickle_path)
if modeltype == 'graphb':
model = GraphBased(pickle_path)
if modeltype == 'graphw':
model = WordGraph(pickle_path)
if modeltype == 'context1':
model = conTextModel(pickle_path)
if modeltype == 'mysecmodel':
model = MySecModel(pickle_path)
if modeltype == 'myseccontextmodel':
model = SecConTextModel(pickle_path)
if modeltype == 'hybrid': # sxpHybridGraph.py
model = HybridGraph(pickle_path)
if modeltype == 'sectitle': # MySecTitleNetwork.py
model = MySecTitleModel(pickle_path)
# -- get the .html file's suffix NO. --
idstr = idname[modeltype]
# -- save ranked sentence to a pickle file --
ranked_sent_fp = os.path.join(
rankedsentdir, file_name + ".html." + idstr + ".allsent.pk")
sentidlst = sorted(model.text.ce_sys.iteritems(),
key=lambda asd: asd[1],
reverse=True)
ranked_sentences = [
model.text.sentenceset[sentid[0]].sentence_text
for sentid in sentidlst
]
StoreSxptext(ranked_sentences, ranked_sent_fp)
# -- save original model ranked topk sentences to text --
topksent_path = os.path.join(system_dir, file_name + '.html.' + idstr)
tops = model.OutPutTopKSent(topk, useabstr, maxtxt)
st = ProduceSystem(tops, file_name, 1)
WriteStrFile(topksent_path, st, 'utf-8')
def evaluate_all_kg(fp,
papers_path,
pk_path,
modeltype='mymodel',
inc_abscon=True,
useabstr=1,
maxtxt=-1,
fname_topk_dict={}):
print "model type =", modeltype
# systemdir should be managed accoding to the inc_abscon parameter
if not inc_abscon:
system_dir = os.path.join(fp, 'systemPure_1')
system_mandir = os.path.join(fp, 'systemPure_man1')
else:
system_dir = os.path.join(fp, 'systemPure_2')
system_mandir = os.path.join(fp, 'systemPure_man2')
if not os.path.exists(system_dir):
os.makedirs(system_dir)
if not os.path.exists(system_mandir):
os.makedirs(system_mandir)
# -- get the original filename list in the path variable --
file_list = sxpGetDirFileList(papers_path)[0]
# -- for each file, get its sxpText and run modeltype ranking model on it --
for i, file_name in enumerate(file_list):
# ---- get topk sentence ----
if not fname_topk_dict.has_key(file_name):
continue
else:
topk = fname_topk_dict[file_name]
# ---- get file name ----
fset = file_name.split('.')
if fset[-1] != 'txt': # if current file is not a txt file, it is not one of the original papers
continue
print "processing", i, "th paper named as", file_name
# -- get single pickle file directory --
if not inc_abscon:
pickle_path = os.path.join(pk_path, file_name + '_1.pk')
else:
pickle_path = os.path.join(pk_path, file_name + '_2.pk')
# -- run ranking model on sxpText object at pickle_path --
if modeltype == 'mymodel':
model = MyModel(pickle_path)
if modeltype == 'tfidf':
model = TfIdf(pickle_path)
if modeltype == 'graphb':
model = GraphBased(pickle_path)
if modeltype == 'graphw':
model = WordGraph(pickle_path)
if modeltype == 'context1':
model = conTextModel(pickle_path)
if modeltype == 'mysecmodel':
model = MySecModel(pickle_path)
if modeltype == 'myseccontextmodel':
model = SecConTextModel(pickle_path)
if modeltype == 'hybrid': # sxpHybridGraph.py
model = HybridGraph(pickle_path)
if modeltype == 'sectitle': # MySecTitleNetwork.py
model = MySecTitleModel(pickle_path)
# -- get the .html file's suffix NO. --
if modeltype in idname.keys():
idstr = idname[modeltype]
# -- save original model ranked topk sentences to text --
tops = model.OutPutTopKSent(topk, useabstr, maxtxt)
st = ProduceSystem(tops, file_name, 1)
topksent_path = os.path.join(system_dir, file_name + '.html.' + idstr)
WriteStrFile(topksent_path, st, 'utf-8')
topksent_path = os.path.join(system_mandir,
file_name + '.html.' + idstr)
WriteStrFile(topksent_path, st, 'utf-8')
print "ranking complete"