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expansion.py
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expansion.py
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import collections
import nltk
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import pairwise_distances_argmin_min
from numpy import dot
from numpy.linalg import norm
from sklearn import metrics
def read_df_rel(based_dir, file_input_name):
file_input = based_dir + file_input_name
ff = open(file_input)
delim=","
df = pd.read_csv(file_input,delimiter=delim,header=0)
return df
def is_any_entities_present(sent, entity_list):
for ent in entity_list:
if ent.lower() in nltk.word_tokenize(sent.lower()):
return True
return False
def getheadWord(s):
res=str(s).split('{')
if len(res)==1:
return res[0].split('}')[0]
else:
return res[1].split('}')[0]
def findRelevantSentences(char):
res=[]
tokens=nltk.word_tokenize(char)
for i,row in df.iterrows():
if is_any_entities_present(row['text'], tokens):
res.append(i)
return res
def findAllrels(s,d,g_ext,refs):
res=set()
s_m=refs[s]
d_m=refs[d]
if not s_m or not d_m:
return []
for s_0 in s_m:
for d_0 in d_m:
res_0=g_ext.get_edge_data(s_0,d_0)
if res_0:
for a in res_0:
res.add(res_0[a]['label'])
return res
def is_any_entities_present(sent, entity_list):
for ent in entity_list:
if ent.lower() in nltk.word_tokenize(sent.lower()):
return ent
return None
def elbow_plot(data, maxK=10, seed_centroids=None,ShoulPlot=True):
if len(data)<3:
return 0
sse = {}
maxK=min(maxK,len(data)-1)
for k in range(1, maxK):
if ShoulPlot:
pass
# print("k: ", k)
if seed_centroids is not None:
seeds = seed_centroids.head(k)
kmeans = KMeans(n_clusters=k, max_iter=500, n_init=100, random_state=0, init=np.reshape(seeds, (k,1))).fit(data)
else:
kmeans = KMeans(n_clusters=k, max_iter=300, n_init=100, random_state=0).fit(data)
sse[k] = kmeans.inertia_
if ShoulPlot:
plt.figure()
plt.plot(list(sse.keys()), list(sse.values()))
plt.show()
y=list(sse.values())
x1 = range(1, len(y)+1)
from kneed import KneeLocator
if len(y)<3:
return 0
kn = KneeLocator(x1,y , curve='convex', direction='decreasing')
if not kn.knee:
return 0
return int(kn.knee)
def findBests(edges,grTruth):
if not edges:
return
rel_truth_embed=bc.encode([grTruth])[0]
X=bc.encode(edges)
ind=0
if len(X)>0:
scores=[]
for i in range(len(X)):
a=X[i]
b=rel_truth_embed
cos_sim = dot(a, b)/(norm(a)*norm(b))
scores.append(cos_sim)
ind=scores.index(max(scores))
num_class=elbow_plot(X, maxK=10, seed_centroids=None,ShoulPlot=False)
if num_class==0:
return edges,edges[ind]
km = KMeans(n_clusters=min(num_class,len(edges)-1)).fit(X)
closest, _ = pairwise_distances_argmin_min(km.cluster_centers_, X)
res=[]
for i in closest:
res.append(edges[i])
return res,edges[ind]
def findBests2(edges,grTruth):
if not edges:
return
rel_truth_embed=bc.encode([grTruth])[0]
X=bc.encode(edges)
num_class=elbow_plot(X, maxK=10, seed_centroids=None,ShoulPlot=False)
km = KMeans(n_clusters=min(num_class,len(edges)-1)).fit(X)
closest, _ = pairwise_distances_argmin_min(km.cluster_centers_, X)
res=[]
for i in closest:
res.append(edges[i])
return res
import pickle
def save_obj(obj, name ):
with open(name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(name ):
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)
def findEntites(arg,ent_groups,ent_id):
arg=arg.replace('}',' ').replace('{',' ')
res=[]
for ent in ent_id:
if ent in nltk.word_tokenize(arg.lower()):
res.append(ent_id[ent])
elif " "+ent in arg.lower():
res.append(ent_id[ent])
return list(set(res))
def findEdges(i,j,d):
edges=[]
edges_ids=[]
for res in d:
if i in res['arg1_id'] and j in res['arg2_id']:
edges.append(res['rel'].replace('}','').replace('{',''))
return edges
def findEdgesID(i,j,d):
edges_ids=[]
for res in d:
if i in res['arg1_id'] and j in res['arg2_id']:
edges_ids.append(res['row_number'])
return edges_ids
def getheadWord(s):
res=str(s).split('{')
if len(res)==1:
return res[0].split('}')[0]
else:
return res[1].split('}')[0]
#********** INPUT **********
edges_path="/Users/user/Documents/StoryMiner/goodreads/groundtruth/Tim_gold-standard_summaries-MiceMen_edges.csv"
nodes_path="/Users/user/Documents/StoryMiner/goodreads/groundtruth/Tim_gold-standard_summaries-MiceMen_nodes.csv"
delim = "\n"
p="/Users/user/Downloads/Data_raw_goodreads/mice.txt"
rel_path= "/Users/user/Downloads/Results_for_resubmission-2/mice_and_men/mice_relatios_1.csv"
df_ent = pd.read_csv("/Users/user/Downloads/Results_for_resubmission-2/mice_and_men/df_ent_final_ranking.csv")
res_final=load_obj("micemen4")['res_final'] #output of previous paper
df_rels=pd.read_csv(rel_path)
df_edges=pd.read_csv(edges_path)
df_nodes=pd.read_csv(nodes_path)
df = pd.read_csv(p,delimiter=delim,header=0,error_bad_lines=False)
entity_versions=entity_versions_micemen
from nltk import SnowballStemmer
from collections import defaultdict
ent_id={}
ent_groups=collections.defaultdict(set)
k=0
for ent in entity_versions:
ent_id[ent]=k
ent_groups[k]=set()
ent_groups[k].add(ent)
for ent_2 in entity_versions[ent]:
ent_id[ent_2]=k
ent_groups[k].add(ent_2)
k+=1
for i,row in df_nodes.iterrows():
name=str(row['character']).lower()
if name not in ent_id:
ent_id[name]=k
ent_groups[k]=set()
ent_groups[k].add(name)
k+=1
stemmer = SnowballStemmer('english', ignore_stopwords=False)
import nltk
from nltk.corpus import stopwords
stps=set(stopwords.words('english'))
cnds=defaultdict(int)
versions=defaultdict(set)
for ind,row in df_ent.iterrows():
c=nltk.word_tokenize(str(row['entity']))
for cc in c:
cnds[stemmer.stem(cc)]+=row['frequency_score_sum_NER_arg']
versions[stemmer.stem(cc)].add(cc)
keys=set()
dups=set()
for ind,row in df_ent.iterrows():
if len(str(row['entity']))>2:
if row['frequency_score_sum_NER_arg']>10 and row['type'] in {'PERSON', 'OTHER(ARG)'}:
c=nltk.word_tokenize(str(row['entity']))
if len(c)>1:
for cc in c:
if len(cc)>2:
if cc in keys:
keys.remove(cc)
dups.add(cc)
elif cc not in keys and cc not in keys:
keys.add(cc)
ents=set()
for ent in entity_versions:
ents.add(ent)
for ent_2 in entity_versions[ent]:
ents.add(ent_2)
for w in versions:
for ww in versions[w]:
if ww in ents:
for ww1 in versions[w]:
ents.add(ww1)
# print(ww1)
ents.add(w)
for m in list(df_nodes['character']):
for mm in m.split(' '):
ents.add(mm.lower())
tmp=list(cnds.values())
tmp.sort()
tmp=tmp[::-1]
seen=set()
candidates=[]
for i in range(len(tmp)):
for w, score in cnds.items():
if score == tmp[i] and w not in seen:
seen.add(w)
if score>200 and len(w)>2 and w not in ents:
# print(w,versions[w],score,i)
candidates.append(w)
to_csv_id={}
for m in list(df_nodes['character']):
for mm in m.split(' '):
if mm.lower() in ent_id:
to_csv_id[ent_id[mm.lower()]]=m
elif m.lower() in ent_id:
to_csv_id[ent_id[m.lower()]]=m
d_s_candids={}
for w in candidates:
d2=[]
for i,row in df_rels.iterrows():
if is_any_entities_present(row['sentence'].lower(),versions[w]) and len(str(row['arg1']))<50 and len(str(row['arg2']))<50:
tmp1=findEntites(str(row['arg1']), ent_groups,ent_id)
tmp2=findEntites(str(row['arg2']), ent_groups,ent_id)
if tmp1 or tmp2:
if tmp1!=tmp2:
res_tmp={}
res_tmp['row_number']=i
res_tmp['arg1']=str(row['arg1'])
res_tmp['arg2']=str(row['arg2'])
res_tmp['rel']=row['rel']
res_tmp['arg1_id']=tmp1
res_tmp['arg2_id']=tmp2
d2.append(res_tmp)
d_s_candids[w]=d2
nss={}
with open('story.txt', 'a') as the_file:
the_file.write("s\tt\te1\te2")
the_file.write('\n')
for i in range(len(res_final)):
row=res_final[i]
edge_c=4
th=4
if 'all_edges_1' not in row :
row['all_edges_1']=[]
if 'all_edges_2' not in row :
row['all_edges_2']=[]
th=4
if len(row['all_edges_1'])>0:
the_file.write(df_nodes['character'][row['source']-1]+"\t"+df_nodes['character'][row['target']-1]+"\t"+str(1)+"\t"+row['closest_source_to_target'])
nss[df_nodes['character'][row['source']-1]]=1
nss[df_nodes['character'][row['target']-1]]=1
the_file.write('\n')
elif len(row['all_edges_2'])>0:
the_file.write(df_nodes['character'][row['target']-1]+"\t"+df_nodes['character'][row['source']-1]+"\t"+str(1)+"\t"+row['closest_target_to_source'])
the_file.write('\n')
nss[df_nodes['character'][row['source']-1]]=1
nss[df_nodes['character'][row['target']-1]]=1
for w in candidates:
okaay=True
for ww in versions[w]:
if ww in stps:
okaay=False
if okaay:
d=d_s_candids[w]
all_edges_from2=defaultdict(list)
all_edges_to2=defaultdict(list)
c1=0
c2=0
for row in d:
if len(row['arg1'])<20 and len(row['arg2'])<20 and 'hobbit' not in row['arg1'].lower() and 'hobbit' not in row['arg2'].lower():
if not row['arg1_id'] and is_any_entities_present(row['arg1'],versions[w]) and not is_any_entities_present(row['arg2'],versions[w]):
all_edges_from2[row['arg2_id'][0]].append(getheadWord(row['rel']))
if not row['arg2_id'] and is_any_entities_present(row['arg2'],versions[w]) and not is_any_entities_present(row['arg1'],versions[w]):
all_edges_to2[row['arg1_id'][0]].append(getheadWord(row['rel']))
for i in all_edges_from2:
if len(all_edges_from2[i])>5:
c1+=1
# the_file.write(list(versions[w])[0]+"\t"+to_csv_id[i]+"\t"+str(2))
# the_file.write('\n')
for j in all_edges_to2:
if len(all_edges_to2[j])>5:
c1+=1
# the_file.write(to_csv_id[j]+"\t"+list(versions[w])[0]+"\t"+str(2))
# the_file.write('\n')
for i in all_edges_from2:
if len(all_edges_from2[i])>5 and c1>2:
c1+=1
nss[list(versions[w])[0]]=2
the_file.write(list(versions[w])[0]+"\t"+to_csv_id[i]+"\t"+str(2)+"\t"+','.join(sorted(all_edges_from2[i])[-3:]))
the_file.write('\n')
for j in all_edges_to2:
if len(all_edges_to2[j])>5 and c1>2:
c2+=1
nss[list(versions[w])[0]]=2
the_file.write(to_csv_id[j]+"\t"+list(versions[w])[0]+"\t"+str(2)+"\t"+','.join(sorted(all_edges_to2[j])[-3:]))
the_file.write('\n')
with open('names.txt', 'a') as the_file:
the_file.write("n\tc")
the_file.write('\n')
for n in nss:
the_file.write(n+"\t"+str(nss[n]))
the_file.write('\n')
the_file.close()