def generateTestFeatures(client_socket, infile, featurefile):
# ------------------------------------------------
doc = Document(infile)
# ------------------------------------------------
# Load pickle for label
picklefile = DIR["DATA"] + "test-labels-pickle"
global test_labels
with open(picklefile, "rb") as pfile:
test_labels = pickle.load(pfile)
# ------------------------------------------------
# For display and analysis
dir, filename = os.path.split(infile)
fcode = re.match(r"(.+)-parscit-section\.xml", filename).group(1)
# ------------------------------------------------
test_sents, sent_indices = getRankedSent(doc, fcode)
# -----------------------------------------
# Sectional Ranker
sections = []
for sec, block in doc.document.items():
sentences = ""
for key in sorted(block.keys()):
sentences += str(block[key])
sections.append(sentences)
sec_ranker = Ranker(sections)
sec_indices = sent2Section(doc, sent_indices)
# -----------------------------------------
for sentence, sent_idx, sec_idx in zip(test_sents, sent_indices, sec_indices):
key = fcode + "-" + str(sent_idx)
feature_string = test_data[key]["reallbl"]
tree = parseTrees(getDepParse(client_socket, sentence))
feature_string += processTree(tree, sec_ranker, sec_idx)
test_data[key]["depparse"] = getTree(tree)
test_data[key]["features"] = feature_string
writeToFile(featurefile, feature_string + "\n", "a")
def generateTestFeatures(client_socket, infile, featurefile):
#------------------------------------------------
doc = Document(infile)
#------------------------------------------------
# Load pickle for label
picklefile = DIR['DATA'] + 'test-labels-pickle'
global test_labels
with open(picklefile, 'rb') as pfile:
test_labels = pickle.load(pfile)
#------------------------------------------------
# For display and analysis
dir, filename = os.path.split(infile)
fcode = re.match(r'(.+)-parscit-section\.xml', filename).group(1)
#------------------------------------------------
test_sents, sent_indices = getRankedSent(doc, fcode)
#-----------------------------------------
# Sectional Ranker
sections = []
for sec, block in doc.document.items():
sentences = ''
for key in sorted(block.keys()):
sentences += (str(block[key]))
sections.append(sentences)
sec_ranker = Ranker(sections)
sec_indices = sent2Section(doc, sent_indices)
#-----------------------------------------
for sentence, sent_idx, sec_idx in zip(test_sents, sent_indices,
sec_indices):
key = fcode + '-' + str(sent_idx)
feature_string = test_data[key]['reallbl']
tree = parseTrees(getDepParse(client_socket, sentence))
feature_string += processTree(tree, sec_ranker, sec_idx)
test_data[key]['depparse'] = getTree(tree)
test_data[key]['features'] = feature_string
writeToFile(featurefile, feature_string + '\n', 'a')
def generateTrainFeatures(client_socket, infile, featurefile):
#------------------------------------------------
doc = Document(infile)
all_sentences, all_offset = doc.all_sentences()
#------------------------------------------------
# Positive sentences
pos_sents, offset = doc.section_sentences('abstract')
sent_indices = range(offset, offset + len(pos_sents))
#-----------------------------------------
# Sectional Ranker
sections = []
for sec, block in doc.document.items():
sentences = ''
for key in sorted(block.keys()):
sentences += (str(block[key]))
sections.append(sentences)
sec_ranker = Ranker(sections)
sec_indices = sent2Section(doc, sent_indices)
#-----------------------------------------
# Count ranker
#count_ranker = Ranker(all_sentences, tfidf=False)
#-----------------------------------------
for sentence, sent_idx, sec_idx in zip(pos_sents, sent_indices,
sec_indices):
feature_string = '+1'
tree = parseTrees(getDepParse(client_socket, sentence))
feature_string += processTree(tree, sec_ranker, sec_idx)
writeToFile(featurefile, feature_string + '\n', 'a')
#------------------------------------------------
# Negative sentences
neg_ranker = TextRank(all_sentences)
neg_ranker.rank()
num = 5
x = -1
neg_sents = []
sent_indices = []
while num > 0:
idx = neg_ranker.scores[x][0] + all_offset
x -= 1
if not validSentence(doc[idx]):
continue
else:
sent_indices.append(idx)
neg_sents.append(doc[idx].sentence.encode('utf-8'))
num -= 1
sec_indices = sent2Section(doc, sent_indices)
#------------------------------------------------
for sentence, sent_idx, sec_idx in zip(neg_sents, sent_indices,
sec_indices):
feature_string = '-1'
tree = parseTrees(getDepParse(client_socket, sentence))
feature_string += processTree(tree, sec_ranker, sec_idx)
writeToFile(featurefile, feature_string + '\n', 'a')
#------------------------------------------------
print "File processed to create feature vectors for training."
def generateTrainFeatures(client_socket, infile, featurefile):
# ------------------------------------------------
doc = Document(infile)
all_sentences, all_offset = doc.all_sentences()
# ------------------------------------------------
# Positive sentences
pos_sents, offset = doc.section_sentences("abstract")
sent_indices = range(offset, offset + len(pos_sents))
# -----------------------------------------
# Sectional Ranker
sections = []
for sec, block in doc.document.items():
sentences = ""
for key in sorted(block.keys()):
sentences += str(block[key])
sections.append(sentences)
sec_ranker = Ranker(sections)
sec_indices = sent2Section(doc, sent_indices)
# -----------------------------------------
# Count ranker
# count_ranker = Ranker(all_sentences, tfidf=False)
# -----------------------------------------
for sentence, sent_idx, sec_idx in zip(pos_sents, sent_indices, sec_indices):
feature_string = "+1"
tree = parseTrees(getDepParse(client_socket, sentence))
feature_string += processTree(tree, sec_ranker, sec_idx)
writeToFile(featurefile, feature_string + "\n", "a")
# ------------------------------------------------
# Negative sentences
neg_ranker = TextRank(all_sentences)
neg_ranker.rank()
num = 5
x = -1
neg_sents = []
sent_indices = []
while num > 0:
idx = neg_ranker.scores[x][0] + all_offset
x -= 1
if not validSentence(doc[idx]):
continue
else:
sent_indices.append(idx)
neg_sents.append(doc[idx].sentence.encode("utf-8"))
num -= 1
sec_indices = sent2Section(doc, sent_indices)
# ------------------------------------------------
for sentence, sent_idx, sec_idx in zip(neg_sents, sent_indices, sec_indices):
feature_string = "-1"
tree = parseTrees(getDepParse(client_socket, sentence))
feature_string += processTree(tree, sec_ranker, sec_idx)
writeToFile(featurefile, feature_string + "\n", "a")
# ------------------------------------------------
print "File processed to create feature vectors for training."
for i in range(07):
set = []
for k in range(11):
curr = choice(bucket)
set.append(curr)
bucket.remove(curr)
all_sets.append(set)
for i in range(07):
test_set = all_sets[i]
train_set = []
for set in [all_sets[z] for z in range(07) if z != i]:
train_set.extend(set)
for key in train_set:
writeToFile(featurefile, data[key]['features'] + '\n', 'a')
trainSvm(featurefile, model, gamma=1)
predictSvm(featurefile, model, outfile)
outstring = "Training Fold : " + str(i)
print "************* " + outstring + " *************"
analyze(featurefile, outfile, resfile, outstring)
deleteFiles([featurefile, outfile])
for key in test_set:
writeToFile(featurefile, data[key]['features'] + '\n', 'a')
predictSvm(featurefile, model, outfile)
outstring = "Testing Fold : " + str(i)
pre, rec = analyze(featurefile, outfile, resfile, outstring)
precision.append(pre)
recall.append(rec)
for i in range(07):
set = []
for k in range(11):
curr = choice(bucket)
set.append(curr)
bucket.remove(curr)
all_sets.append(set)
for i in range(07):
test_set = all_sets[i]
train_set = []
for set in [all_sets[z] for z in range(07) if z != i]:
train_set.extend(set)
for key in train_set:
writeToFile(featurefile, data[key]['features'] + '\n', 'a')
trainSvm(featurefile, model, gamma=1)
predictSvm(featurefile, model, outfile)
outstring = "Training Fold : " + str(i)
print "************* " + outstring + " *************"
analyze(featurefile, outfile, resfile, outstring)
deleteFiles([featurefile, outfile])
for key in test_set:
writeToFile(featurefile, data[key]['features'] + '\n', 'a')
predictSvm(featurefile, model, outfile)
outstring = "Testing Fold : " + str(i)
pre, rec = analyze(featurefile, outfile, resfile, outstring)
precision.append(pre)
recall.append(rec)
