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+#!/bin/env python3.5
+#variant_37.py
+#
+#usage:
+#$ ./variant_37.py
+#
+#author: <Tatjana Chernenko, Utaemon Toyota>
+
+"""
+<VARIANT 37>
+
+------------------- DESCRIPTION -------------------
+
+The pipeline system performs the 37th variant of the system for WSI (word sense induction) task (the Task 11 at SemEval 2013).
+
+The system creates semantic related clusters from the given snippets (the text fragments we get back from the search engine) for each pre-defined ambigue topic.
+
+------------------- METHODS -------------------
+
+For the WSI purposes it uses the following methods:
+- For pre-rpocessing: tokenization + remove punctuation
+- Language model: sent2vec, plaintext_unigramm - trained Model (Wikipedia 2017)
+- Compositional semantics: vector mixture model (BOW (bag-of-words) representation with summarization for each snippet)
+- Clustering: Mean Shift clustering with sklearn.cluster (http://scikit-learn.org/stable/modules/generated/sklearn.cluster.MeanShift.html#sklearn.cluster.MeanShift) with default parameters.
+
+------------------- EVALUATION -------------------
+
+=========== Final average value of F1: =====================
+average F1 = 0.6636355295257614
+
+=========== Final average value of Rand Index: =============
+average Rand Index = 0.6465151515151515
+
+=========== Final average value of Adjusted Rand Index: ====
+average Adj Rand Index = 0.03561869550794858
+
+=========== Final average value of Jaccard Index: ==========
+average Jaccard Index = 0.6278794821842425
+
+================ Statistics: ====================================
+============ average number of created clusters: 7.5
+============ average cluster size: 15.050505050505052
+
+"""
+import sys
+sys.path.append("/home/students/toyota")
+import sent2vec
+from collections import defaultdict, deque
+import re
+import math
+import numpy as np
+from sklearn.cluster import MeanShift
+
+# Read Data; get the number of topics:
+def read_data(topics_file, results_file):
+ with open(topics_file, "r") as f:
+ topics_data = f.readlines()
+ with open(results_file, "r") as f:
+ results_data = f.readlines()
+ number_topics = 0
+ for line in topics_data:
+ if not line.startswith("ID"):
+ number_topics += 1
+ return topics_data, results_data, number_topics
+
+# Create a vocabulary with topics as keys and lists with lists of snippets with IDs as values:
+def devide_text_into_topics(results_data, number_topics):
+ text = defaultdict(list)
+ for line in results_data:
+ if not line.startswith("ID"):
+ if line.split()[0].split(".")[0] not in text.keys():
+ text[line.split()[0].split(".")[0]] = []
+ help_structure = []
+ help_structure.append(line.split(" ")[0])
+ help_structure.append(line.split(" ")[2:])
+ text[line.split()[0].split(".")[0]].append(help_structure)
+ else:
+ help_structure = []
+ help_structure.append(line.split(" ")[0])
+ help_structure.append(line.split(" ")[2:])
+ text[line.split()[0].split(".")[0]].append(help_structure)
+ # Clean sentences from "\n":
+ for values in text.values():
+ for paragr in values:
+ for sent in paragr:
+ if sent == "\n":
+ paragr.remove(sent)
+ #print(text["45"]) # example of the output for the topic "45"
+ return text
+
+# Preprocess Data (tokenize every sentence in every topic):
+def preprocess_data(text):
+ # Tokenize:
+ for value in text.values():
+ for paragr in value:
+ for i in range(1,len(paragr)):
+ tokens = re.findall(r"\w+", str(paragr[i])) # remove punctuation
+ words = []
+ for word in tokens:
+ if word == "n":
+ words.append(" ")
+ else:
+ words.append(word.strip()) # delete first empty placeholder
+ paragr[i] = words
+
+ prepr_data = text
+ #print(prepr_data["45"]) # example of the output for the topic "45"
+ return prepr_data
+
+# For every word in a sentence make a vector representation with sense2vec; make a compositional vector for every sentence as sum of BOW vectors:
+def compos_sent2vec(prepr_data,len_vector):
+ model = sent2vec.Sent2vecModel()
+ model.load_model("/proj/toyota/plaintext_unigramm.bin")
+ for value in prepr_data.values():
+ for paragr in value: #one snippet
+ par_list = [] # list with a snippet
+ vector_paragr = np.zeros(len_vector) #vector for one snippet for a sum
+ for sent in paragr[1:]: #sent in a snippet
+ vector_sent = [] #list for all sentences in a snippet
+ for word in sent: #word
+ try:
+ query_vector = model.embed_sentence(word)
+ vector_sent.append(query_vector) #add a word-vector to a list for sentences in a snippet - BOW for all words in a snippet - now for a sentence - for every sentence
+ except:
+ continue
+ summe = np.zeros(len_vector) # vector for a summ
+ for vector in vector_sent: # for one word in all sentences
+ summe+=vector # summ all words in a snippet - vector for a snippet
+ par_list.append(summe)#?#add a summ(vector for a snippet) to a list with a snippet
+ for sentence in par_list: # for all snippet-vectors
+ vector_paragr+=sentence # sum all snippets
+ paragr.append(vector_paragr) #add to a snippet a summ of all snippets
+ compos_vectors = prepr_data
+ #print(compos_vectors["45"]) # example of the output for the topic "45"
+ return compos_vectors
+
+# Cluster sentences in every topic with Mean Shift (http://scikit-learn.org/stable/modules/generated/sklearn.cluster.MeanShift.html#sklearn.cluster.MeanShift) and create an output file:
+def cluster(compos_vectors, output, number_clusters):
+ f = open(output, "a")
+ f.write("subTopicID"+" "+"resultID\n")
+ lines = []
+ for value in compos_vectors.values():
+ #print("\n\nTOPIC: ", value[0][0].split(".")[0], "\n")
+ z = []
+ for sent in value:
+ sent_id = sent[0]
+ vector = sent[-1]
+ z.append(vector)
+ all_for_topic = np.array(z)
+ meanshift = MeanShift().fit(all_for_topic)
+ for i in range(len(meanshift.labels_)):
+ one_line = str(value[0][0].split(".")[0])+"."+str(meanshift.labels_[i]) + " " + (str(value[i][0])+"\n")
+ lines.append(one_line)
+ sort = sorted(lines)
+ for el in sort:
+ f.write(el)
+ f.close()
+ return meanshift
+
+if __name__ == "__main__":
+ path = "/proj/toyota/Pool"
+ trial_path = "/semeval-2013_task11_trial"
+ trial_topics = "/topics.txt"
+ trial_subtopics = "/subTopics.txt"
+ trial_results = "/results.txt"
+
+ topics_data, results_data, number_topics = read_data(path+trial_path+trial_topics,path+trial_path+trial_results)
+ text = devide_text_into_topics(results_data, number_topics)
+ prepr_data = preprocess_data(text)
+ compos_vectors = compos_sent2vec(prepr_data, 700)
+ clusters = cluster(compos_vectors, path+"/output_37.txt", 7)
+ print("Done.")
+