class LDA (object):
""" LDA - Latent Dirichlet Porcesses """
def __init__(self, setting):
self.setting = setting
self.mallet_path = setting['malletpath']
self.number_of_topics = setting['nooftopics']
self.number_of_iter = setting['noofiterations']
self.stack_importer = StackImporter(setting)
self.lda_importer = LDAImporter(setting)
self.experiments = Experiments(setting)
self.model = None
self.corpus = None
self.dictionary = None
self.answer_corpus = None
directory = self.setting['lda_folder']
file_name = 'local_lda_model' + self.setting['theme'] + '.gs'
self.path = ''.join([directory, file_name])
def __iter__(self):
for document in self.corpus:
yield self.dictionary.doc2bow(document)
def calculate_similarities(self):
# Open database connections
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
# Clean similarity table
self.lda_importer.create_clean_similarities_table()
self._learn_model()
logging.info("Loading dictionary ...")
self._load_dictionary()
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
answer_corpus = StackCorpus(self.stack_importer.connection, "answer")
# Get topics in the question
bow = self.dictionary.doc2bow(question.body)
question_topics = self.model[bow]
for answer in answer_corpus:
# Get topics in the answer
bow = self.dictionary.doc2bow(answer.body)
answer_topics = self.model[bow]
# Similarities
similarities.append((question.id, answer.id, self._compare_documents(question_topics, answer_topics)))
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.lda_importer.save_similarities(similarities)
# Close database connections
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
def _learn_model(self):
self.model = models.wrappers.LdaMallet(self.mallet_path, corpus=self, num_topics=self.number_of_topics,
id2word=self.dictionary, iterations=self.number_of_iter)
def _load_dictionary(self):
self.stack_importer.open_stack_db()
# Load dictionary
question_corpus = self.stack_importer.get_question_corpus()
answer_corpus = self.stack_importer.get_answer_corpus()
corpus = question_corpus + answer_corpus
self.dictionary = self.stack_importer.get_dictionary_from_corpora([question_corpus, answer_corpus])
self.stack_importer.close_stack_db()
def run_experiment_1_avg(self, experiment_type='1_avg', algorithm='esa'):
self.experiments.open_experiment_db()
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
total_answers = self.stack_importer.get_number_of_answers()
# Get number of answers for each question
number_of_answers = self.stack_importer.get_number_of_original_answers()
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
similar_answers = {}
original_answers = {}
for question in question_corpus:
original_answers[question.id] = self.stack_importer.get_question_original_answers(question.id)
similar_answers[question.id] = self.esa_importer.load_similarities_for_question(question.id, -1, False)
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1,total_answers+1):
logging.info("Calculating with limit %s", str(limit))
avg_precision, avg_recall = self.experiments.run_experiment_1_avg(number_of_answers,
original_answers, similar_answers, experiment_type, limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision, recall)
# Write them in a file
folder = self.setting["experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")
###############################################################################
# Create the local model
###############################################################################
def calculate_local_similarities(self):
""" Calculates similarities between local questions/answers.
Returns the list of filtered users """
# Keep filtered users
filtered_users = []
# Open database connections
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
# Clean similarity table
self.lda_importer.create_clean_similarities_table()
# For each question calculate its similarity with the all the answers given
# by the users who answered the given question
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
# Get the users that gave an answer to the question
users = self.stack_importer.get_users_from_question(question.id)
print "Users that replied: " + str(len(users))
# Calculate the similarities of question with all
# answers from the given users (related or not to question)
for user_id in users:
user_answers = self.stack_importer.get_user_answers_to_questions(user_id)
# Only consider users with more than 1 answer
if len(user_answers) > 5:
print "User " + str(user_id)
self._learn_local_model(user_id)
# Get topics in the question
bow = self.dictionary.doc2bow(question.body)
question_topics = self.model[bow]
# Get topics in the answers and calculate similarities with current question
for answer in user_answers:
bow = self.dictionary.doc2bow(answer.body)
answer_topics = self.model[bow]
# Similarities
similarities.append((question.id, answer.id, self._compare_documents(question_topics, answer_topics)))
else:
filtered_users.append(user_id)
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.lda_importer.save_similarities(similarities)
# Close database connections
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
return filtered_users
def _learn_local_model(self, user_id):
""" Learns the LDA model with local knowledge """
# Load question and answer corpus
question_corpus = self.stack_importer.get_user_question_corpus(user_id)
self.answer_corpus = self.stack_importer.get_user_answer_corpus(user_id)
self.corpus = question_corpus + self.answer_corpus
self.dictionary = self.stack_importer.get_dictionary_from_corpora([question_corpus, self.answer_corpus])
# Create model
self.model = models.wrappers.LdaMallet(self.mallet_path, corpus=self, num_topics=self.number_of_topics,
id2word=self.dictionary, iterations=self.number_of_iter)
@staticmethod
def _compare_documents(document1, document2):
""" Calculates the distance between the given documents """
doc1_topic_description = []
doc2_topic_description = []
for (topic, weight) in document1:
doc1_topic_description.append(weight)
for (topic, weight) in document2:
doc2_topic_description.append(weight)
return Metric.js_distance(doc1_topic_description, doc2_topic_description)
def run_experiment_2_avg(self, experiment_type='2_avg', algorithm='lda_local_2'):
self.experiments.open_experiment_db()
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
total_answers = self.stack_importer.get_number_of_answers()
# Get number of answers for each question
number_of_answers = self.stack_importer.get_number_of_original_answers()
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
similar_answers = {}
original_answers = {}
for question in question_corpus:
original_answers[question.id] = self.stack_importer.get_question_original_answers(question.id)
similar_answers[question.id] = self.lda_importer.load_similarities_for_question(question.id, -1, False)
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1,total_answers+1):
print "Calculating with limit " + str(limit)
avg_precision, avg_recall = self.experiments.run_experiment_1_avg(number_of_answers,
original_answers, similar_answers, experiment_type, limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision, recall)
# Write them in a file
folder = self.setting["experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")
class ESA (object):
""" ESA - Explicit Semantic Analysis """
def __init__(self, setting):
self.setting = setting
self.idf_values = None
self.wiki_corpus = None
self.wiki_dictionary = None
self.wiki_vectors = []
self.wiki_processor = WikiPreprocessor(setting)
self.wiki_importer = WikiImporter(setting, self.wiki_processor)
self.stack_corpus = None
self.answer_vectors = {}
self.question_vectors = {}
self.user_vectors = {}
self.user_content = {}
self.stack_importer = StackImporter(setting)
self.esa_importer = ESAImporter(setting)
self.inverted_index = defaultdict(list)
self.number_of_concepts = 0
self.experiments = Experiments(setting)
###############################################################################
# Clean and load data
###############################################################################
def clean_and_load_data(self):
""" Cleans the data and saves it in a database """
self.wiki_importer.import_wiki_data()
###############################################################################
# Create and manage data used by ESA algorithm
###############################################################################
def build_esa_db(self):
""" Initializes the ESA database """
logging.info("\nCreating ESA database ...")
self.esa_importer.open_esa_db()
# Initialize database
self.esa_importer.create_esa_db()
# Save the dictionary and corpus of the Wikipedia data
self.wiki_dictionary = self.wiki_importer.build_wiki_kb()
# Save the inverse document frequencies in the ESA database
number_of_documents = self.wiki_dictionary.num_docs #self.wiki_importer.get_number_of_concepts()
self.esa_importer.save_wiki_inverse_document_frequencies(number_of_documents)
self.esa_importer.close_esa_db()
def load_esa_index(self):
""" Gets the inverted index from the database """
self.esa_importer.open_esa_db()
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
logging.info("\nDone")
self.esa_importer.close_esa_db()
###############################################################################
# Build TF-IDF Vectors
###############################################################################
def create_tf_idf_vectors(self):
""" Creates them if not already in database """
self.esa_importer.open_esa_db()
# Calculate tfidf vectors for the Wikipedia articles
self.create_tf_idf_wiki_vectors()
# Save terms and vectors to ESA db
#self.esa_importer.save_inverted_index(self.wiki_vectors)
logging.info("\nDone")
self.esa_importer.close_esa_db()
def create_tf_idf_wiki_vectors(self):
""" Keeping only non-zero entries of the vectors """
wiki_corpus, self.wiki_dictionary = self.esa_importer.get_wiki_corpus_dictionary()
logging.info("Retrieving idf values ...")
inv_doc_freq = {}
self.esa_importer.get_wiki_inverse_document_frequencies(inv_doc_freq)
logging.info("Building the tfidf vectors and the inverse index ...")
tfidf_model = TfidfModel(self.wiki_dictionary, inv_doc_freq)
inverted_index = defaultdict(list)
for document in wiki_corpus:
vector = tfidf_model[document]
for term_id, value in vector:
inverted_index[term_id].append( (document.document_id, value) )
#print "Added " + str(document.document_id)
logging.info("\n\tDone.")
self.esa_importer.save_inverted_index(inverted_index)
self.save_index_to_file(inverted_index)
def _create_tf_idf_stack_vectors(self, only_questions=False):
""" Create the tfidf vectors for the Stackexchange data. """
# Load question and answer corpus
logging.info("Loading stack corpus and dictionary ...")
question_corpus = self.stack_importer.get_question_corpus()
answer_corpus = self.stack_importer.get_answer_corpus()
corpus = question_corpus + answer_corpus
dictionary = self.stack_importer.get_dictionary_from_corpora([question_corpus, answer_corpus])
dict_size = len(dictionary)
# Save stack dictionary
stack_dict = {}
for word_id, word in enumerate(dictionary.token2id):
stack_dict[unicode(word)] = word_id
self.idf_values = zeros(dict_size)
logging.info("Determining question vectors ...")
questions = StackCorpus(self.stack_importer.connection, "question")
for question in questions:
question_vector = zeros(dict_size)
for word in question.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
question_vector[word_id] = self.tf_idf(word, word_id, question.body, corpus)
self.question_vectors[question.id] = question_vector
logging.info("\n\tDone.")
if only_questions: # Skip the answers
return stack_dict
logging.info("Determining answer vectors ...")
answers = StackCorpus(self.stack_importer.connection, "answer")
for answer in answers:
answer_vector = zeros(dict_size)
for word in answer.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
tf_idf = self.tf_idf(word, word_id, answer.body, corpus)
answer_vector[word_id] = tf_idf
self.answer_vectors[answer.id] = answer_vector
logging.info("\n\tDone.")
return stack_dict
def _create_local_tf_idf_stack_vectors(self, user_id):
""" Create the tfidf vectors for the local Stackexchange data of the given user """
# Load question and answer corpus
#logging.info("Loading stack corpus and dictionary ...")
question_corpus = self.stack_importer.get_user_question_corpus(user_id)
answer_corpus = self.stack_importer.get_user_answer_corpus(user_id)
corpus = question_corpus + answer_corpus
dictionary = self.stack_importer.get_dictionary_from_corpora([question_corpus, answer_corpus])
dict_size = len(dictionary)
# Save stack dictionary
stack_dict = {}
for word_id, word in enumerate(dictionary.token2id):
stack_dict[unicode(word)] = word_id
self.idf_values = zeros(dict_size)
#logging.info("Determining question vectors ...")
questions = self.stack_importer.get_user_local_questions(user_id)
for question in questions:
question_vector = zeros(dict_size)
for word in question.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
question_vector[word_id] = self.tf_idf(word, word_id, question.body, corpus)
self.question_vectors[question.id] = question_vector
#logging.info("\n\tDone.")
#logging.info("Determining answer vectors ...")
answers = self.stack_importer.get_user_local_answers(user_id)
for answer in answers:
answer_vector = zeros(dict_size)
for word in answer.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
tf_idf = self.tf_idf(word, word_id, answer.body, corpus)
answer_vector[word_id] = tf_idf
self.answer_vectors[answer.id] = answer_vector
#logging.info("\n\tDone.")
return stack_dict
def _create_user_tf_idf_stack_vector(self, user_id, stack_dict):
""" Create the tfidf vector representation of a user, based on her answers"""
aux = self.user_content.get(user_id, None)
if aux is not None:
return aux
user_corpus = []
user_words = []
answers = self.stack_importer.get_user_answers_to_questions(user_id)
for answer in answers:
user_corpus.append(answer.body)
for word in answer.body:
user_words.append(word)
self.user_content[user_id] = user_words
dict_size = len(stack_dict)
user_vector = zeros(dict_size)
for word in set(user_words):
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
tf_idf = self.tf_idf(word, word_id, user_words, user_corpus)
user_vector[word_id] = tf_idf
self.user_vectors[user_id] = user_vector
return user_words
@staticmethod
def tf(word, document):
""" Returns the normalized frequency of the word in the given document """
word_count = document.count(unicode(word))
return float(word_count) / len(document)
@staticmethod
def df(word, corpus):
""" Returns the number of documents in the collection that contain the given word """
return sum(1 for document in corpus if unicode(word) in document)
#@staticmethod
def idf(self, word, corpus):
""" Returns the inverse document frequency of the word in the documents collection """
return math.log(len(corpus)) / self.df(word, corpus)
def tf_idf(self, word, word_index, document, corpus):
""" Returns the TF-IDF value for the given
word in the document of the corpus """
# Calculate the term frequency value (tf)
tf = self.tf(word, document)
if tf == 0.0:
return 0.0
# Calculate the inverse document frequency value (idf)
if self.idf_values[word_index] == 0.0:
self.idf_values[word_index] = self.idf(word, corpus)
return float(tf * self.idf_values[word_index])
###############################################################################
# Associations and Similarities of Stackexchange questions/answers using
# Wikipedia's articles as concepts.
###############################################################################
def calculate_similarities(self):
""" Applies the ESA algorithm to the global stack data """
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
self.esa_importer.create_clean_concept_doc_relation()
self.esa_importer.create_clean_similarities_table()
logging.info("Loading the inverted index ...")
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
#print "Has beer " + str(self.inverted_index.get(unicode("beer"), None))
logging.info("Calculating stack tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors()
# For each question calculate similarity with each answer
logging.info("\nCalculating questions-answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
for question in question_corpus:
q_vector = self.get_esa_vector(question.id, question.body, self.question_vectors[question.id], stack_dictionary, 1)
q_vector_norm = norm(q_vector)
similarities = []
answer_corpus = StackCorpus(self.stack_importer.connection, "answer")
for answer in answer_corpus:
a_vector = self.get_esa_vector(answer.id, answer.body, self.answer_vectors[answer.id], stack_dictionary, 2)
sim = self.similarity(q_vector, q_vector_norm, a_vector)
similarities.append( (question.id, answer.id, sim) )
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
def calculate_tf_idf_similarities(self):
"""Applies the TF-IDF algorithm to the global stack data"""
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
self.esa_importer.create_clean_similarities_table()
logging.info("Calculating stack tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors()
# For each question calculate similarity with each answer
question_corpus = StackCorpus(self.stack_importer.connection, "question")
logging.info("\nCalculating questions-answers similarities ...")
for question in question_corpus:
q_vector = self.question_vectors[question.id]
q_vector_norm = norm(q_vector)
similarities = []
answer_corpus = StackCorpus(self.stack_importer.connection, "answer")
for answer in answer_corpus:
a_vector = self.answer_vectors[answer.id]
sim = self.similarity(q_vector, q_vector_norm, a_vector)
similarities.append( (question.id, answer.id, sim) )
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
def calculate_local_tfidf_similarities(self):
""" Applies TF-IDF to the local stack data, in order
to calculate questions/answers similarities. The local
data is measured per user.
Returns the list of users that were filtered. """
# Keep filtered users
filtered_users = []
# Open database connections
self.esa_importer.open_esa_db()
self.stack_importer.open_stack_db()
# Clean similarity table
self.esa_importer.create_clean_similarities_table()
# For each question calculate its similarity with the all the answers given
# by the users who answered the given question
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
# Get the users that gave an answer to the question
users = self.stack_importer.get_users_from_question(question.id)
print "Users that replied: " + str(len(users))
# Calculate the similarities of question with all
# answers from the given users (related or not to question)
for user_id in users:
user_answers = self.stack_importer.get_user_answers_to_questions(user_id)
# Only consider users with more than 1 answer
if len(user_answers) > 5:
print "User " + str(user_id)
a = []
for answer in user_answers:
a.append(answer.id)
print a
# Calculate tf_idf vectors for the given user
self.question_vectors.clear()
self.answer_vectors.clear()
stack_dictionary = self._create_local_tf_idf_stack_vectors(user_id)
q_vector = self.question_vectors[question.id]
q_vector_norm = norm(q_vector)
for answer in user_answers:
a_vector = self.answer_vectors[answer.id]
sim = self.similarity(q_vector, q_vector_norm, a_vector)
similarities.append( (question.id, answer.id, sim) )
else:
filtered_users.append(user_id)
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
# Close database connections
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
return filtered_users
def calculate_local_esa_similarities(self):
""" Applies the ESA algorithm to the local stack data.
This local data is measured per user. Returns the list
of filtered users """
# Keep filtered users
filtered_users = []
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
#self.esa_importer.create_clean_concept_doc_relation()
self.esa_importer.create_clean_similarities_table()
logging.info("Loading the inverted index ...")
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
# For each question calculate its similarity with all the answers given
# by the users who answered the given question
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
# Get the users that gave an answer to the question
users = self.stack_importer.get_users_from_question(question.id)
print "Users that replied: " + str(len(users))
# Calculate the similarities of question with all
# answers from the given users (related or not to question)
for user_id in users:
user_answers = self.stack_importer.get_user_answers_to_questions(user_id)
# Only consider users with more than 5 answers
if len(user_answers) > 5:
print "User " + str(user_id)
# Calculate tf_idf vectors for the given user
self.question_vectors.clear()
self.answer_vectors.clear()
stack_dictionary = self._create_local_tf_idf_stack_vectors(user_id)
q_vector = self.get_esa_vector(question.id, question.body, self.question_vectors[question.id], stack_dictionary, 1)
q_vector_norm = norm(q_vector)
for answer in user_answers:
a_vector = self.get_esa_vector(answer.id, answer.body, self.answer_vectors[answer.id], stack_dictionary, 2)
sim = self.similarity(q_vector, q_vector_norm, a_vector)
similarities.append( (question.id, answer.id, sim) )
else:
filtered_users.append(user_id)
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
return filtered_users
def get_esa_vector(self, id, document, tfidf_vector, dictionary, type):
""" Creates the interpretation vector of the given document.
- The document should be a set of tokens, already preprocessed
- The vector represents the relatedness of the document
with all the Wikipedia articles
- Type indicates the type of document: question (1) or answer (2) """
# Interpretation vector with dimensions = Wikipedia articles
interpretation = zeros(2080905)
for token in set(document):
documents = self.inverted_index.get(unicode(token), None)
word_id = dictionary.get(unicode(token), -1)
if documents is not None and word_id != -1:
#print str(len(documents))
for document_id, value in documents:
interpretation[document_id] += (value * tfidf_vector[word_id])
return interpretation
def similarity(self, vector1, norm_vector1, vector2):
""" Calculates the cosine similarity between the given vectors """
# Cosine similartity
sim = float(dot(vector1, vector2) / (norm_vector1 * norm(vector2)))
return sim
def save_relatedness_to_file(self, file_name):
self.esa_importer.open_esa_db()
self.esa_importer.write_relatedness_to_file(file_name)
self.esa_importer.close_esa_db()
### EXTRA ###
def save_index_to_file(self, index=None, file_name='../data/ESA/index.txt'):
index = defaultdict(list)
# Extract it from DB
self.esa_importer.open_esa_db()
self.esa_importer.get_pruned_inverted_index(index)
self.esa_importer.close_esa_db()
# Copy to file
logging.info("Saving them in a file ...")
with open(file_name, 'a') as f:
for word, doc_list in index.iteritems():
#print word
f.write(word + '\n')
f.write(' '.join([str(x) for x in doc_list]))
f.write('\n')
def testing_beer_concept(self):
tfidf_norm_values = []
tfidf_values = []
append_values = tfidf_values.append
append_norm_values = tfidf_norm_values.append
self.esa_importer.open_esa_db()
wiki_corpus, self.wiki_dictionary = self.esa_importer.get_wiki_corpus_dictionary()
# IDF is fixed
idf = 4.8225774331876625
df = 0
for document in wiki_corpus:
content = document.content.split(' ')
if unicode("beer") in content:
doc_tf = defaultdict(float)
size = 0 # length of the document
df += 1
# Faster than Counter
for word in content:
doc_tf[word] += 1.0
size += 1
# Calculate tfidf value for word "beer" in Wiki data
norm_value = (doc_tf[unicode("beer")] / size) * idf
value = doc_tf[unicode("beer")] * idf
append_values( (document.document_id, value) )
append_norm_values( (document.document_id, norm_value) )
print "DF : " + str(df)
# Sort each list
sorted_norm_values = sorted(tfidf_norm_values, key=itemgetter(1))
sorted_norm_values = sorted_norm_values[::-1]
sorted_values = sorted(tfidf_values, key=itemgetter(1))
sorted_values = sorted_values[::-1]
# Print top 10 in each list
print "Normalized : "
print ' , '.join([str(id) + " " + str(value) for id,value in sorted_norm_values])
print "\nNot normalized"
print ' , '.join([str(id) + " " + str(value) for id,value in sorted_values])
self.esa_importer.close_esa_db()
def prun_inverted_index(self):
""" Prun the inverted index """
self.esa_importer.open_esa_db()
index = EsaIndex(self.esa_importer.connection)
result = []
append = result.append
for term, vector in index:
append( (term, vector) )
self.esa_importer.save_pruned_index(result)
self.esa_importer.close_esa_db()
###############################################################################
# Find the right person
# Then, following a naive strong tie strategy, we could check for each question
# which other users would have been asked following two strategies: (a) based
# on the social network ties (the ones with strongest ties) and (b) based on
# the content similarity (which answer is most similar to the question using
# TF-IDF or ESA, whatever you like best). Finally, we can compare both results
# with the ground truth (which users got actually asked in the dataset).
###############################################################################
def calculate_esa_similarities_to_users(self):
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
self.esa_importer.create_clean_similarities_table()
logging.info("Loading the inverted index ...")
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
logging.info("Calculating questions tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors(only_questions=True)
# For each question determine which other users would have been asked
logging.info("Calculating questions/users similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
users = self.stack_importer.get_active_users()
for question in question_corpus:
print "Question " + str(question.id)
q_vector = self.get_esa_vector(question.id, question.body, self.question_vectors[question.id], stack_dictionary, 1)
q_vector_norm = norm(q_vector)
similarities = []
for user_id in users:
user_body = self._create_user_tf_idf_stack_vector(user_id, stack_dictionary)
u_vector = self.get_esa_vector(user_id, user_body, self.user_vectors[user_id], stack_dictionary, 2)
sim = self.similarity(q_vector, q_vector_norm, u_vector)
similarities.append( (question.id, user_id, sim) )
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
def calculate_tfidf_similarities_to_users(self):
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
#self.esa_importer.create_clean_concept_doc_relation()
self.esa_importer.create_clean_similarities_table()
logging.info("Calculating questions tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors(only_questions=True)
# For each question determine which other users would have been asked
logging.info("Calculating questions/users similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
users = self.stack_importer.get_active_users()
for question in question_corpus:
print "Question " + str(question.id)
q_vector = self.question_vectors[question.id]
q_vector_norm = norm(q_vector)
similarities = []
for user_id in users:
user_body = self._create_user_tf_idf_stack_vector(user_id, stack_dictionary)
u_vector = self.user_vectors[user_id]
sim = self.similarity(q_vector, q_vector_norm, u_vector)
similarities.append( (question.id, user_id, sim) )
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
###############################################################################
# Experiments - Calculate statistics on the data
###############################################################################
def initialize_experiments(self):
self.experiments.open_experiment_db()
self.experiments.create_experiments_db()
self.experiments.close_experiment_db()
def run_experiment_1(self):
self.experiments.open_experiment_db()
self.experiments.run_experiment_1(True)
self.experiments.close_experiment_db()
def run_experiment_1_avg(self, experiment_type='1_avg', algorithm='esa'):
self.experiments.open_experiment_db()
self.esa_importer.open_esa_db()
self.stack_importer.open_stack_db()
total_answers = self.stack_importer.get_number_of_answers()
# Get number of answers for each question
number_of_answers = self.stack_importer.get_number_of_original_answers()
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
similar_answers = {}
original_answers = {}
for question in question_corpus:
original_answers[question.id] = self.stack_importer.get_question_original_answers(question.id)
similar_answers[question.id] = self.esa_importer.load_similarities_for_question(question.id, -1, False)
self.stack_importer.close_stack_db()
self.esa_importer.close_esa_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1,total_answers+1):
logging.info("Calculating with limit %s", str(limit))
avg_precision, avg_recall = self.experiments.run_experiment_1_avg(number_of_answers,
original_answers, similar_answers, experiment_type, limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision, recall)
# Write them in a file
folder = self.setting["experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")
def run_experiment_2_avg(self, algorithm='esa'):
""" Same as run_experiment_1_avg but similarities were
calculated with local data per user """
self.run_experiment_1_avg('2_avg', algorithm)
def run_experiment_3_avg(self, algorithm='esa', experiment_type='3_avg'):
""" Similar to experiment_1, but checking users instead of answers """
self.experiments.open_experiment_db()
self.esa_importer.open_esa_db()
self.stack_importer.open_stack_db()
# Get the number of active users
active_users = len(self.stack_importer.get_active_users())
# Get the users that gave an answer to each question
asked_users = self.stack_importer.get_original_users()
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection, "question")
similar_users = {}
original_users = {}
for question in question_corpus:
aux = asked_users.get(question.id, None)
if aux is not None:
original_users[question.id] = aux
similar_users[question.id] = self.esa_importer.load_similarities_for_question(question.id, -1, False)
self.stack_importer.close_stack_db()
self.esa_importer.close_esa_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1,active_users+1):
#print "Calculating with limit " + str(limit)
logging.info("Calculating with limit %s", str(limit))
avg_precision, avg_recall = self.experiments.run_experiment_3_avg(asked_users,
original_users, similar_users, experiment_type, limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision, recall)
# Write them in a file
folder = self.setting["experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")
class ESA(object):
""" ESA - Explicit Semantic Analysis """
def __init__(self, setting):
self.setting = setting
self.idf_values = None
self.wiki_corpus = None
self.wiki_dictionary = None
self.wiki_vectors = []
self.wiki_processor = WikiPreprocessor(setting)
self.wiki_importer = WikiImporter(setting, self.wiki_processor)
self.stack_corpus = None
self.answer_vectors = {}
self.question_vectors = {}
self.user_vectors = {}
self.user_content = {}
self.stack_importer = StackImporter(setting)
self.esa_importer = ESAImporter(setting)
self.inverted_index = defaultdict(list)
self.number_of_concepts = 0
self.experiments = Experiments(setting)
###############################################################################
# Clean and load data
###############################################################################
def clean_and_load_data(self):
""" Cleans the data and saves it in a database """
self.wiki_importer.import_wiki_data()
###############################################################################
# Create and manage data used by ESA algorithm
###############################################################################
def build_esa_db(self):
""" Initializes the ESA database """
logging.info("\nCreating ESA database ...")
self.esa_importer.open_esa_db()
# Initialize database
self.esa_importer.create_esa_db()
# Save the dictionary and corpus of the Wikipedia data
self.wiki_dictionary = self.wiki_importer.build_wiki_kb()
# Save the inverse document frequencies in the ESA database
number_of_documents = self.wiki_dictionary.num_docs #self.wiki_importer.get_number_of_concepts()
self.esa_importer.save_wiki_inverse_document_frequencies(
number_of_documents)
self.esa_importer.close_esa_db()
def load_esa_index(self):
""" Gets the inverted index from the database """
self.esa_importer.open_esa_db()
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
logging.info("\nDone")
self.esa_importer.close_esa_db()
###############################################################################
# Build TF-IDF Vectors
###############################################################################
def create_tf_idf_vectors(self):
""" Creates them if not already in database """
self.esa_importer.open_esa_db()
# Calculate tfidf vectors for the Wikipedia articles
self.create_tf_idf_wiki_vectors()
# Save terms and vectors to ESA db
#self.esa_importer.save_inverted_index(self.wiki_vectors)
logging.info("\nDone")
self.esa_importer.close_esa_db()
def create_tf_idf_wiki_vectors(self):
""" Keeping only non-zero entries of the vectors """
wiki_corpus, self.wiki_dictionary = self.esa_importer.get_wiki_corpus_dictionary(
)
logging.info("Retrieving idf values ...")
inv_doc_freq = {}
self.esa_importer.get_wiki_inverse_document_frequencies(inv_doc_freq)
logging.info("Building the tfidf vectors and the inverse index ...")
tfidf_model = TfidfModel(self.wiki_dictionary, inv_doc_freq)
inverted_index = defaultdict(list)
for document in wiki_corpus:
vector = tfidf_model[document]
for term_id, value in vector:
inverted_index[term_id].append((document.document_id, value))
#print "Added " + str(document.document_id)
logging.info("\n\tDone.")
self.esa_importer.save_inverted_index(inverted_index)
self.save_index_to_file(inverted_index)
def _create_tf_idf_stack_vectors(self, only_questions=False):
""" Create the tfidf vectors for the Stackexchange data. """
# Load question and answer corpus
logging.info("Loading stack corpus and dictionary ...")
question_corpus = self.stack_importer.get_question_corpus()
answer_corpus = self.stack_importer.get_answer_corpus()
corpus = question_corpus + answer_corpus
dictionary = self.stack_importer.get_dictionary_from_corpora(
[question_corpus, answer_corpus])
dict_size = len(dictionary)
# Save stack dictionary
stack_dict = {}
for word_id, word in enumerate(dictionary.token2id):
stack_dict[unicode(word)] = word_id
self.idf_values = zeros(dict_size)
logging.info("Determining question vectors ...")
questions = StackCorpus(self.stack_importer.connection, "question")
for question in questions:
question_vector = zeros(dict_size)
for word in question.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
question_vector[word_id] = self.tf_idf(
word, word_id, question.body, corpus)
self.question_vectors[question.id] = question_vector
logging.info("\n\tDone.")
if only_questions: # Skip the answers
return stack_dict
logging.info("Determining answer vectors ...")
answers = StackCorpus(self.stack_importer.connection, "answer")
for answer in answers:
answer_vector = zeros(dict_size)
for word in answer.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
tf_idf = self.tf_idf(word, word_id, answer.body, corpus)
answer_vector[word_id] = tf_idf
self.answer_vectors[answer.id] = answer_vector
logging.info("\n\tDone.")
return stack_dict
def _create_local_tf_idf_stack_vectors(self, user_id):
""" Create the tfidf vectors for the local Stackexchange data of the given user """
# Load question and answer corpus
#logging.info("Loading stack corpus and dictionary ...")
question_corpus = self.stack_importer.get_user_question_corpus(user_id)
answer_corpus = self.stack_importer.get_user_answer_corpus(user_id)
corpus = question_corpus + answer_corpus
dictionary = self.stack_importer.get_dictionary_from_corpora(
[question_corpus, answer_corpus])
dict_size = len(dictionary)
# Save stack dictionary
stack_dict = {}
for word_id, word in enumerate(dictionary.token2id):
stack_dict[unicode(word)] = word_id
self.idf_values = zeros(dict_size)
#logging.info("Determining question vectors ...")
questions = self.stack_importer.get_user_local_questions(user_id)
for question in questions:
question_vector = zeros(dict_size)
for word in question.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
question_vector[word_id] = self.tf_idf(
word, word_id, question.body, corpus)
self.question_vectors[question.id] = question_vector
#logging.info("\n\tDone.")
#logging.info("Determining answer vectors ...")
answers = self.stack_importer.get_user_local_answers(user_id)
for answer in answers:
answer_vector = zeros(dict_size)
for word in answer.body:
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
tf_idf = self.tf_idf(word, word_id, answer.body, corpus)
answer_vector[word_id] = tf_idf
self.answer_vectors[answer.id] = answer_vector
#logging.info("\n\tDone.")
return stack_dict
def _create_user_tf_idf_stack_vector(self, user_id, stack_dict):
""" Create the tfidf vector representation of a user, based on her answers"""
aux = self.user_content.get(user_id, None)
if aux is not None:
return aux
user_corpus = []
user_words = []
answers = self.stack_importer.get_user_answers_to_questions(user_id)
for answer in answers:
user_corpus.append(answer.body)
for word in answer.body:
user_words.append(word)
self.user_content[user_id] = user_words
dict_size = len(stack_dict)
user_vector = zeros(dict_size)
for word in set(user_words):
word_id = stack_dict.get(unicode(word), -1)
if word_id != -1:
tf_idf = self.tf_idf(word, word_id, user_words, user_corpus)
user_vector[word_id] = tf_idf
self.user_vectors[user_id] = user_vector
return user_words
@staticmethod
def tf(word, document):
""" Returns the normalized frequency of the word in the given document """
word_count = document.count(unicode(word))
return float(word_count) / len(document)
@staticmethod
def df(word, corpus):
""" Returns the number of documents in the collection that contain the given word """
return sum(1 for document in corpus if unicode(word) in document)
#@staticmethod
def idf(self, word, corpus):
""" Returns the inverse document frequency of the word in the documents collection """
return math.log(len(corpus)) / self.df(word, corpus)
def tf_idf(self, word, word_index, document, corpus):
""" Returns the TF-IDF value for the given
word in the document of the corpus """
# Calculate the term frequency value (tf)
tf = self.tf(word, document)
if tf == 0.0:
return 0.0
# Calculate the inverse document frequency value (idf)
if self.idf_values[word_index] == 0.0:
self.idf_values[word_index] = self.idf(word, corpus)
return float(tf * self.idf_values[word_index])
###############################################################################
# Associations and Similarities of Stackexchange questions/answers using
# Wikipedia's articles as concepts.
###############################################################################
def calculate_similarities(self):
""" Applies the ESA algorithm to the global stack data """
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
self.esa_importer.create_clean_concept_doc_relation()
self.esa_importer.create_clean_similarities_table()
logging.info("Loading the inverted index ...")
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
#print "Has beer " + str(self.inverted_index.get(unicode("beer"), None))
logging.info("Calculating stack tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors()
# For each question calculate similarity with each answer
logging.info("\nCalculating questions-answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
for question in question_corpus:
q_vector = self.get_esa_vector(question.id, question.body,
self.question_vectors[question.id],
stack_dictionary, 1)
q_vector_norm = norm(q_vector)
similarities = []
answer_corpus = StackCorpus(self.stack_importer.connection,
"answer")
for answer in answer_corpus:
a_vector = self.get_esa_vector(answer.id, answer.body,
self.answer_vectors[answer.id],
stack_dictionary, 2)
sim = self.similarity(q_vector, q_vector_norm, a_vector)
similarities.append((question.id, answer.id, sim))
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
def calculate_tf_idf_similarities(self):
"""Applies the TF-IDF algorithm to the global stack data"""
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
self.esa_importer.create_clean_similarities_table()
logging.info("Calculating stack tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors()
# For each question calculate similarity with each answer
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
logging.info("\nCalculating questions-answers similarities ...")
for question in question_corpus:
q_vector = self.question_vectors[question.id]
q_vector_norm = norm(q_vector)
similarities = []
answer_corpus = StackCorpus(self.stack_importer.connection,
"answer")
for answer in answer_corpus:
a_vector = self.answer_vectors[answer.id]
sim = self.similarity(q_vector, q_vector_norm, a_vector)
similarities.append((question.id, answer.id, sim))
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
def calculate_local_tfidf_similarities(self):
""" Applies TF-IDF to the local stack data, in order
to calculate questions/answers similarities. The local
data is measured per user.
Returns the list of users that were filtered. """
# Keep filtered users
filtered_users = []
# Open database connections
self.esa_importer.open_esa_db()
self.stack_importer.open_stack_db()
# Clean similarity table
self.esa_importer.create_clean_similarities_table()
# For each question calculate its similarity with the all the answers given
# by the users who answered the given question
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
# Get the users that gave an answer to the question
users = self.stack_importer.get_users_from_question(question.id)
print "Users that replied: " + str(len(users))
# Calculate the similarities of question with all
# answers from the given users (related or not to question)
for user_id in users:
user_answers = self.stack_importer.get_user_answers_to_questions(
user_id)
# Only consider users with more than 1 answer
if len(user_answers) > 5:
print "User " + str(user_id)
a = []
for answer in user_answers:
a.append(answer.id)
print a
# Calculate tf_idf vectors for the given user
self.question_vectors.clear()
self.answer_vectors.clear()
stack_dictionary = self._create_local_tf_idf_stack_vectors(
user_id)
q_vector = self.question_vectors[question.id]
q_vector_norm = norm(q_vector)
for answer in user_answers:
a_vector = self.answer_vectors[answer.id]
sim = self.similarity(q_vector, q_vector_norm,
a_vector)
similarities.append((question.id, answer.id, sim))
else:
filtered_users.append(user_id)
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
# Close database connections
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
return filtered_users
def calculate_local_esa_similarities(self):
""" Applies the ESA algorithm to the local stack data.
This local data is measured per user. Returns the list
of filtered users """
# Keep filtered users
filtered_users = []
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
#self.esa_importer.create_clean_concept_doc_relation()
self.esa_importer.create_clean_similarities_table()
logging.info("Loading the inverted index ...")
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
# For each question calculate its similarity with all the answers given
# by the users who answered the given question
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
# Get the users that gave an answer to the question
users = self.stack_importer.get_users_from_question(question.id)
print "Users that replied: " + str(len(users))
# Calculate the similarities of question with all
# answers from the given users (related or not to question)
for user_id in users:
user_answers = self.stack_importer.get_user_answers_to_questions(
user_id)
# Only consider users with more than 5 answers
if len(user_answers) > 5:
print "User " + str(user_id)
# Calculate tf_idf vectors for the given user
self.question_vectors.clear()
self.answer_vectors.clear()
stack_dictionary = self._create_local_tf_idf_stack_vectors(
user_id)
q_vector = self.get_esa_vector(
question.id, question.body,
self.question_vectors[question.id], stack_dictionary,
1)
q_vector_norm = norm(q_vector)
for answer in user_answers:
a_vector = self.get_esa_vector(
answer.id, answer.body,
self.answer_vectors[answer.id], stack_dictionary,
2)
sim = self.similarity(q_vector, q_vector_norm,
a_vector)
similarities.append((question.id, answer.id, sim))
else:
filtered_users.append(user_id)
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
return filtered_users
def get_esa_vector(self, id, document, tfidf_vector, dictionary, type):
""" Creates the interpretation vector of the given document.
- The document should be a set of tokens, already preprocessed
- The vector represents the relatedness of the document
with all the Wikipedia articles
- Type indicates the type of document: question (1) or answer (2) """
# Interpretation vector with dimensions = Wikipedia articles
interpretation = zeros(2080905)
for token in set(document):
documents = self.inverted_index.get(unicode(token), None)
word_id = dictionary.get(unicode(token), -1)
if documents is not None and word_id != -1:
#print str(len(documents))
for document_id, value in documents:
interpretation[document_id] += (value *
tfidf_vector[word_id])
return interpretation
def similarity(self, vector1, norm_vector1, vector2):
""" Calculates the cosine similarity between the given vectors """
# Cosine similartity
sim = float(dot(vector1, vector2) / (norm_vector1 * norm(vector2)))
return sim
def save_relatedness_to_file(self, file_name):
self.esa_importer.open_esa_db()
self.esa_importer.write_relatedness_to_file(file_name)
self.esa_importer.close_esa_db()
### EXTRA ###
def save_index_to_file(self,
index=None,
file_name='../data/ESA/index.txt'):
index = defaultdict(list)
# Extract it from DB
self.esa_importer.open_esa_db()
self.esa_importer.get_pruned_inverted_index(index)
self.esa_importer.close_esa_db()
# Copy to file
logging.info("Saving them in a file ...")
with open(file_name, 'a') as f:
for word, doc_list in index.iteritems():
#print word
f.write(word + '\n')
f.write(' '.join([str(x) for x in doc_list]))
f.write('\n')
def testing_beer_concept(self):
tfidf_norm_values = []
tfidf_values = []
append_values = tfidf_values.append
append_norm_values = tfidf_norm_values.append
self.esa_importer.open_esa_db()
wiki_corpus, self.wiki_dictionary = self.esa_importer.get_wiki_corpus_dictionary(
)
# IDF is fixed
idf = 4.8225774331876625
df = 0
for document in wiki_corpus:
content = document.content.split(' ')
if unicode("beer") in content:
doc_tf = defaultdict(float)
size = 0 # length of the document
df += 1
# Faster than Counter
for word in content:
doc_tf[word] += 1.0
size += 1
# Calculate tfidf value for word "beer" in Wiki data
norm_value = (doc_tf[unicode("beer")] / size) * idf
value = doc_tf[unicode("beer")] * idf
append_values((document.document_id, value))
append_norm_values((document.document_id, norm_value))
print "DF : " + str(df)
# Sort each list
sorted_norm_values = sorted(tfidf_norm_values, key=itemgetter(1))
sorted_norm_values = sorted_norm_values[::-1]
sorted_values = sorted(tfidf_values, key=itemgetter(1))
sorted_values = sorted_values[::-1]
# Print top 10 in each list
print "Normalized : "
print ' , '.join(
[str(id) + " " + str(value) for id, value in sorted_norm_values])
print "\nNot normalized"
print ' , '.join(
[str(id) + " " + str(value) for id, value in sorted_values])
self.esa_importer.close_esa_db()
def prun_inverted_index(self):
""" Prun the inverted index """
self.esa_importer.open_esa_db()
index = EsaIndex(self.esa_importer.connection)
result = []
append = result.append
for term, vector in index:
append((term, vector))
self.esa_importer.save_pruned_index(result)
self.esa_importer.close_esa_db()
###############################################################################
# Find the right person
# Then, following a naive strong tie strategy, we could check for each question
# which other users would have been asked following two strategies: (a) based
# on the social network ties (the ones with strongest ties) and (b) based on
# the content similarity (which answer is most similar to the question using
# TF-IDF or ESA, whatever you like best). Finally, we can compare both results
# with the ground truth (which users got actually asked in the dataset).
###############################################################################
def calculate_esa_similarities_to_users(self):
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
self.esa_importer.create_clean_similarities_table()
logging.info("Loading the inverted index ...")
self.esa_importer.get_pruned_inverted_index(self.inverted_index)
logging.info("Calculating questions tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors(
only_questions=True)
# For each question determine which other users would have been asked
logging.info("Calculating questions/users similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
users = self.stack_importer.get_active_users()
for question in question_corpus:
print "Question " + str(question.id)
q_vector = self.get_esa_vector(question.id, question.body,
self.question_vectors[question.id],
stack_dictionary, 1)
q_vector_norm = norm(q_vector)
similarities = []
for user_id in users:
user_body = self._create_user_tf_idf_stack_vector(
user_id, stack_dictionary)
u_vector = self.get_esa_vector(user_id, user_body,
self.user_vectors[user_id],
stack_dictionary, 2)
sim = self.similarity(q_vector, q_vector_norm, u_vector)
similarities.append((question.id, user_id, sim))
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
def calculate_tfidf_similarities_to_users(self):
# Open database connections
self.stack_importer.open_stack_db()
self.esa_importer.open_esa_db()
# Clean tables
logging.info("Cleaning similarity tables ...")
#self.esa_importer.create_clean_concept_doc_relation()
self.esa_importer.create_clean_similarities_table()
logging.info("Calculating questions tfidf vectors ...")
stack_dictionary = self._create_tf_idf_stack_vectors(
only_questions=True)
# For each question determine which other users would have been asked
logging.info("Calculating questions/users similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
users = self.stack_importer.get_active_users()
for question in question_corpus:
print "Question " + str(question.id)
q_vector = self.question_vectors[question.id]
q_vector_norm = norm(q_vector)
similarities = []
for user_id in users:
user_body = self._create_user_tf_idf_stack_vector(
user_id, stack_dictionary)
u_vector = self.user_vectors[user_id]
sim = self.similarity(q_vector, q_vector_norm, u_vector)
similarities.append((question.id, user_id, sim))
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.esa_importer.save_similarities(similarities)
self.esa_importer.close_esa_db()
self.stack_importer.close_stack_db()
logging.info("\nDone")
###############################################################################
# Experiments - Calculate statistics on the data
###############################################################################
def initialize_experiments(self):
self.experiments.open_experiment_db()
self.experiments.create_experiments_db()
self.experiments.close_experiment_db()
def run_experiment_1(self):
self.experiments.open_experiment_db()
self.experiments.run_experiment_1(True)
self.experiments.close_experiment_db()
def run_experiment_1_avg(self, experiment_type='1_avg', algorithm='esa'):
self.experiments.open_experiment_db()
self.esa_importer.open_esa_db()
self.stack_importer.open_stack_db()
total_answers = self.stack_importer.get_number_of_answers()
# Get number of answers for each question
number_of_answers = self.stack_importer.get_number_of_original_answers(
)
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
similar_answers = {}
original_answers = {}
for question in question_corpus:
original_answers[
question.
id] = self.stack_importer.get_question_original_answers(
question.id)
similar_answers[
question.
id] = self.esa_importer.load_similarities_for_question(
question.id, -1, False)
self.stack_importer.close_stack_db()
self.esa_importer.close_esa_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1, total_answers + 1):
logging.info("Calculating with limit %s", str(limit))
avg_precision, avg_recall = self.experiments.run_experiment_1_avg(
number_of_answers, original_answers, similar_answers,
experiment_type, limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision,
recall)
# Write them in a file
folder = self.setting[
"experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")
def run_experiment_2_avg(self, algorithm='esa'):
""" Same as run_experiment_1_avg but similarities were
calculated with local data per user """
self.run_experiment_1_avg('2_avg', algorithm)
def run_experiment_3_avg(self, algorithm='esa', experiment_type='3_avg'):
""" Similar to experiment_1, but checking users instead of answers """
self.experiments.open_experiment_db()
self.esa_importer.open_esa_db()
self.stack_importer.open_stack_db()
# Get the number of active users
active_users = len(self.stack_importer.get_active_users())
# Get the users that gave an answer to each question
asked_users = self.stack_importer.get_original_users()
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
similar_users = {}
original_users = {}
for question in question_corpus:
aux = asked_users.get(question.id, None)
if aux is not None:
original_users[question.id] = aux
similar_users[
question.
id] = self.esa_importer.load_similarities_for_question(
question.id, -1, False)
self.stack_importer.close_stack_db()
self.esa_importer.close_esa_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1, active_users + 1):
#print "Calculating with limit " + str(limit)
logging.info("Calculating with limit %s", str(limit))
avg_precision, avg_recall = self.experiments.run_experiment_3_avg(
asked_users, original_users, similar_users, experiment_type,
limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision,
recall)
# Write them in a file
folder = self.setting[
"experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")
class LDA(object):
""" LDA - Latent Dirichlet Porcesses """
def __init__(self, setting):
self.setting = setting
self.mallet_path = setting['malletpath']
self.number_of_topics = setting['nooftopics']
self.number_of_iter = setting['noofiterations']
self.stack_importer = StackImporter(setting)
self.lda_importer = LDAImporter(setting)
self.experiments = Experiments(setting)
self.model = None
self.corpus = None
self.dictionary = None
self.answer_corpus = None
directory = self.setting['lda_folder']
file_name = 'local_lda_model' + self.setting['theme'] + '.gs'
self.path = ''.join([directory, file_name])
def __iter__(self):
for document in self.corpus:
yield self.dictionary.doc2bow(document)
def calculate_similarities(self):
# Open database connections
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
# Clean similarity table
self.lda_importer.create_clean_similarities_table()
self._learn_model()
logging.info("Loading dictionary ...")
self._load_dictionary()
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
answer_corpus = StackCorpus(self.stack_importer.connection,
"answer")
# Get topics in the question
bow = self.dictionary.doc2bow(question.body)
question_topics = self.model[bow]
for answer in answer_corpus:
# Get topics in the answer
bow = self.dictionary.doc2bow(answer.body)
answer_topics = self.model[bow]
# Similarities
similarities.append(
(question.id, answer.id,
self._compare_documents(question_topics, answer_topics)))
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.lda_importer.save_similarities(similarities)
# Close database connections
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
def _learn_model(self):
self.model = models.wrappers.LdaMallet(
self.mallet_path,
corpus=self,
num_topics=self.number_of_topics,
id2word=self.dictionary,
iterations=self.number_of_iter)
def _load_dictionary(self):
self.stack_importer.open_stack_db()
# Load dictionary
question_corpus = self.stack_importer.get_question_corpus()
answer_corpus = self.stack_importer.get_answer_corpus()
corpus = question_corpus + answer_corpus
self.dictionary = self.stack_importer.get_dictionary_from_corpora(
[question_corpus, answer_corpus])
self.stack_importer.close_stack_db()
def run_experiment_1_avg(self, experiment_type='1_avg', algorithm='esa'):
self.experiments.open_experiment_db()
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
total_answers = self.stack_importer.get_number_of_answers()
# Get number of answers for each question
number_of_answers = self.stack_importer.get_number_of_original_answers(
)
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
similar_answers = {}
original_answers = {}
for question in question_corpus:
original_answers[
question.
id] = self.stack_importer.get_question_original_answers(
question.id)
similar_answers[
question.
id] = self.esa_importer.load_similarities_for_question(
question.id, -1, False)
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1, total_answers + 1):
logging.info("Calculating with limit %s", str(limit))
avg_precision, avg_recall = self.experiments.run_experiment_1_avg(
number_of_answers, original_answers, similar_answers,
experiment_type, limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision,
recall)
# Write them in a file
folder = self.setting[
"experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")
###############################################################################
# Create the local model
###############################################################################
def calculate_local_similarities(self):
""" Calculates similarities between local questions/answers.
Returns the list of filtered users """
# Keep filtered users
filtered_users = []
# Open database connections
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
# Clean similarity table
self.lda_importer.create_clean_similarities_table()
# For each question calculate its similarity with the all the answers given
# by the users who answered the given question
logging.info("Calculating questions/answers similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
for question in question_corpus:
print "Question " + str(question.id)
similarities = []
# Get the users that gave an answer to the question
users = self.stack_importer.get_users_from_question(question.id)
print "Users that replied: " + str(len(users))
# Calculate the similarities of question with all
# answers from the given users (related or not to question)
for user_id in users:
user_answers = self.stack_importer.get_user_answers_to_questions(
user_id)
# Only consider users with more than 1 answer
if len(user_answers) > 5:
print "User " + str(user_id)
self._learn_local_model(user_id)
# Get topics in the question
bow = self.dictionary.doc2bow(question.body)
question_topics = self.model[bow]
# Get topics in the answers and calculate similarities with current question
for answer in user_answers:
bow = self.dictionary.doc2bow(answer.body)
answer_topics = self.model[bow]
# Similarities
similarities.append(
(question.id, answer.id,
self._compare_documents(question_topics,
answer_topics)))
else:
filtered_users.append(user_id)
# Save similarities to databse
logging.info("\nSaving similarities to database ...")
self.lda_importer.save_similarities(similarities)
# Close database connections
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
return filtered_users
def _learn_local_model(self, user_id):
""" Learns the LDA model with local knowledge """
# Load question and answer corpus
question_corpus = self.stack_importer.get_user_question_corpus(user_id)
self.answer_corpus = self.stack_importer.get_user_answer_corpus(
user_id)
self.corpus = question_corpus + self.answer_corpus
self.dictionary = self.stack_importer.get_dictionary_from_corpora(
[question_corpus, self.answer_corpus])
# Create model
self.model = models.wrappers.LdaMallet(
self.mallet_path,
corpus=self,
num_topics=self.number_of_topics,
id2word=self.dictionary,
iterations=self.number_of_iter)
@staticmethod
def _compare_documents(document1, document2):
""" Calculates the distance between the given documents """
doc1_topic_description = []
doc2_topic_description = []
for (topic, weight) in document1:
doc1_topic_description.append(weight)
for (topic, weight) in document2:
doc2_topic_description.append(weight)
return Metric.js_distance(doc1_topic_description,
doc2_topic_description)
def run_experiment_2_avg(self,
experiment_type='2_avg',
algorithm='lda_local_2'):
self.experiments.open_experiment_db()
self.lda_importer.open_lda_db()
self.stack_importer.open_stack_db()
total_answers = self.stack_importer.get_number_of_answers()
# Get number of answers for each question
number_of_answers = self.stack_importer.get_number_of_original_answers(
)
# Load similarities for each question
logging.info("Loading similarities ...")
question_corpus = StackCorpus(self.stack_importer.connection,
"question")
similar_answers = {}
original_answers = {}
for question in question_corpus:
original_answers[
question.
id] = self.stack_importer.get_question_original_answers(
question.id)
similar_answers[
question.
id] = self.lda_importer.load_similarities_for_question(
question.id, -1, False)
self.stack_importer.close_stack_db()
self.lda_importer.close_lda_db()
# Calculate avg precision and recall for each case
precision = {}
recall = {}
for limit in xrange(1, total_answers + 1):
print "Calculating with limit " + str(limit)
avg_precision, avg_recall = self.experiments.run_experiment_1_avg(
number_of_answers, original_answers, similar_answers,
experiment_type, limit)
precision[limit] = avg_precision
recall[limit] = avg_recall
# Save into the database
self.experiments.save_experiment_results(experiment_type, precision,
recall)
# Write them in a file
folder = self.setting[
"experiments_folder"] + experiment_type + '_' + algorithm + '.dat'
self.experiments.write_pr_curve(experiment_type, folder)
self.experiments.close_experiment_db()
logging.info("\nDone")