LSA word embedding examples

+c1:	Human machine interface for ABC computer applications
+c2:	A survey of user opinion of computer system response time
+c3:	The EPS user interface management system
+c4:	System and human system engineering testing of EPS
+c5:	Relation of user perceived response time to error measurement
+m1:	The generation of random, binary, ordered trees
+m2:	The intersection graph of paths in trees
+m3:	Graph minors IV: Widths of trees and well-quasi-ordering
+m4:	Graph minors: A survey

+"""Pirated example from Gensim library (a NLP specialized tool):
+https://radimrehurek.com/gensim/tut2.html
+https://radimrehurek.com/gensim/wiki.html#latent-semantic-analysis
+
+Ignacio Arroyo
+"""
+
+import gensim
+import logging
+from six import iteritems
+from gensim import corpora
+import argparse
+
+from pdb import set_trace as st # Debug the program step by step calling st()
+                                # anywhere.
+class corpus_streamer(object):
+    """ This Object streams the input raw text file row by row.
+    """
+    def __init__(self, file_name, dictionary=None, strings=None):
+        self.file_name=file_name
+        self.dictionary=dictionary
+        self.strings=strings
+
+    def __iter__(self):
+        for line in open(self.file_name):
+        # assume there's one document per line, tokens separated by whitespace
+            if self.dictionary and not self.strings:
+                yield self.dictionary.doc2bow(line.lower().split())
+            elif not self.dictionary and self.strings:
+                yield line.strip().lower()
+# Logging all our program
+logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
+                    level=logging.INFO)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--n_topics", help="Number of eigenvectors picked up.",
+                    default=2, type=int)
+parser.add_argument("--input", help="Input file to perform LSA.",
+                    required=True)
+
+args = parser.parse_args()
+
+n_topics=args.n_topics
+n_docs=0
+input_file=args.input
+#input_file='/medargsia/iarroyof/Volumen de 384 GB/data/GUs_textform_noPeriods.txt'
+#input_file='lsa_example.csv'
+#input_file='wiki_sample/wiki_75_AA.txt.cln'
+#input_file='wiki_sample/wiki_77_AA.txt'
+
+# A little stopwords list
+stoplist = set('for a of the and to in _ [ ]'.split())
+# Do not load the text corpus into memory, but stream it!
+fille=corpus_streamer(input_file, strings=True)
+dictionary=corpora.Dictionary(line.lower().split() for line in fille)#open(input_file))
+# remove stop words and words that appear only once
+stop_ids=[dictionary.token2id[stopword] for stopword in stoplist
+                                             if stopword in dictionary.token2id]
+once_ids=[tokenid for tokenid, docfreq in iteritems(dictionary.dfs)
+                                                            if docfreq == 1]
+dictionary.filter_tokens(stop_ids + once_ids)
+# remove gaps in id sequence after words that were removed
+dictionary.compactify()
+# Store the dictionary
+dictionary.save('lsa_mini.dict')
+# Reading sentences from file into a list of strings.
+# Use instead streaming objects:
+# Load stored word-id map (dictionary)
+stream_it = corpus_streamer(input_file, dictionary=dictionary)
+#for vector in stream_it:  # load one vector into memory at a time
+#    print vector
+# Convert to sparse matrix
+sparse_corpus = [text for text in stream_it]
+# Store to disk, for later use collect statistics about all tokens
+corpora.MmCorpus.serialize('lsa_mini.mm',
+                            sparse_corpus)
+## LSA zone
+# load the dictionary saved before
+id2word = dictionary.load('lsa_mini.dict')
+# Now load the sparse matrix corpus from file into a (memory friendly) streaming
+# object.
+corpus=corpora.MmCorpus('lsa_mini.mm')
+
+## IF TfidfModel
+tfidf = gensim.models.TfidfModel(corpus) # step 1 -- initialize a model
+corpus = tfidf[corpus]
+## FI TfidfModel
+# Compute the LSA vectors
+lsa=gensim.models.lsimodel.LsiModel(corpus, id2word=dictionary,
+                                                     num_topics=n_topics)
+# Print the n topics in our corpus:
+#lsa.print_topics(n_topics)
+f=open("topics_file.txt","wb")
+f.write("-------------------------------------------------\n")
+for t in lsa.show_topics():
+    f.write("%s\n" % str(t))
+    
+f.write("-------------------------------------------------\n")
+f.close()
+# create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi
+corpus_lsa = lsa[corpus]
+# Stream sentences from file into a list of strings called "sentences"
+sentences=corpus_streamer(input_file, strings=True)
+n=0
+for pertenence, sentence in zip(corpus_lsa, sentences):
+    if n_docs <= 0:
+    	#print "%s\t\t%s" % (pertenence, sentence.split("\t")[0])
+    	p=[dict(pertenence)[x] if x in dict(pertenence) else 0.0 
+                                            for x in xrange(n_topics)]
+    	print "%s %s" % ("".join(sentence.split("\t")[0].split()), 
+                            "".join(str(p)[1:].strip("]").split(",")) )
+    else:
+        if n<n_docs:
+            pertenence=[dict(pertenence)[x] if x in dict(pertenence) else 0.0 
+                                                    for x in xrange(n_topics)]
+            print "%s\t\t%s" % (pertenence, sentence)
+            n+=1
+        else:
+            break
+
+
+
+# ============================== Homework ======================================
+# Modify the program for doing this for a sample of the English Wikipedia.
+# Compute LSA for 20 topics and print the fist 10 topics.
+# Take care of avoiding loading and printing documents of a large corpus, so
+# change the number of documents to print or sample the entire set randomly and
+# print a subset.
+# ==============================================================================