def Feedback(self, searchList):
queryVector = self.buildQueryVector(searchList)
'''
self.vectorIDF = [float(2048.0/x) for x in self.vectorIDF]
self.vectorIDF = [float(math.log10(x)) for x in self.vectorIDF]
self.tfidf = [map(lambda (a,b):a*b,zip(self.vectorIDF, documentVector)) for documentVector in self.documentVectors]
'''
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.tfidf
]
maxone = 0
targetone = 0
for i in range(len(ratings)):
if (ratings[i] > maxone):
maxone = ratings[i]
targetone = i
newqueryVector = []
for i in range(len(queryVector)):
newqueryVector.append(queryVector[i] +
((0.5) * self.documentVectors[targetone][i]))
#print(queryVector)
#print(newqueryVector)
ratings2 = [
util.cosine(newqueryVector, documentVector)
for documentVector in self.tfidf
]
#print(ratings)
return ratings2
def feedbacksearch(self, searchList, wordString, flag):
queryVector = self.buildQueryVector(searchList, flag)
feedback = self.makeVector(wordString, flag)
for index in range( 0, len(queryVector)):
queryVector[index] = float( queryVector[index] + feedback[index] * (1/2) )
ratings = [util.cosine(queryVector, documentVector) for documentVector in self.documentVectors]
return ratings
def baseline_average_cos(article_num, num_times, print_out=True, equal_prob=True):
'''
Generates two random actual articles of source article article_num
Computes the cosine similarity
Does this num_times and outputs the lowest cos, highest, and average
'''
lowest = float("inf")
highest = float("-inf")
total = 0.0
for _ in xrange(num_times):
if equal_prob:
a1 = source_articles.spin_articles(article_num)[0]
a2 = source_articles.spin_articles(article_num)[0]
else:
a1, a2 = source_articles.spin_articles(article_num, 2)
cos_sim = cosine(a1, a2)
lowest = min(lowest, cos_sim)
highest = max(highest, cos_sim)
total += cos_sim
average = total / num_times
if print_out:
print 'Ran {0} times for article {1}'.format(num_times, article_num)
if equal_prob: print 'Generated with equal probability'
else: print 'Generated using heuristic to produce low cosine'
print 'Lowest : {0}\nHighest : {1}\nAverage : {2}\n'.format(lowest, highest, average)
def search(self,searchList):
""" search for documents that match based on a list of terms """
queryVector = self.buildQueryVector(searchList)
ratings = [util.cosine(queryVector, documentVector) for documentVector in self.documentVectors]
#ratings.sort(reverse=True)
return ratings
def related(self, documentId):
""" find documents that are related to the document indexed by passed Id within the document Vectors"""
rating_dic = {}
for key, value in self.documentVectors.items():
ratings = util.cosine(self.documentVectors[documentId], value)
rating_dic[key] = ratings
return rating_dic
def related(self, documentId):
""" find documents that are related to the document indexed by passed Id within the document Vectors"""
ratings = [
util.cosine(self.documentVectors[documentId], documentVector)
for documentVector in self.documentVectors
]
return ratings
def search(self,searchList): """ search for documents that match based on a list of terms """ queryVector = self.buildQueryVector(searchList) ratings = [util.cosine(queryVector, documentVector) for documentVector in self.documentVectors] #ratings.sort(reverse=True) return ratings
def search(self, searchList, compare, flag):
queryVector = self.buildQueryVector(searchList, flag)
if compare == "cos":
ratings = [util.cosine(queryVector, documentVector) for documentVector in self.documentVectors]
elif compare == "dis":
ratings = [util.Euclidean(queryVector, documentVector) for documentVector in self.documentVectors]
return ratings
def related(self,documentId):
""" find documents that are related to the document indexed by passed Id within the document Vectors"""
ratings = {}
for key, value in documentVectors.items():
rating = util.cosine(self.documentVectors[documentId], value)
#ratings.sort(reverse=True)
ratings[key] = rating
return ratings
def TF_Cosine(self, query):
queryTFVector = [self.makeTfVector(query)]
tf_cos = []
for documentTFVector in self.documentTFVectors:
tf_cos.append(util.cosine(queryTFVector, documentTFVector))
return tf_cos
def searchTFIDFWithCosine(self, searchList):
""" search for documents that match based on a list of terms """
queryVector = self.buildTFIDFQueryVector(searchList)
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.TFIDFVectors
]
return ratings
def relevence_search(self, searchVector, formula="cosine", weighting='tf'):
ratings = {}
for key, value in self.documentVectors.items():
if formula == "cosine":
rating = util.cosine(searchVector, value)
elif formula == "euclidean":
rating = util.euclidean(searchVector, value)
ratings[key] = rating
ratings = {k: v for k, v in sorted(ratings.items(), key=lambda item: item[1], reverse=True)}
return ratings
def get_cosines(src_articles):
positive_cosines = []
negative_cosines = []
while len(positive_cosines) < NUM_POSITIVES:
num = random.randint(0, src_articles.count - 1)
a1, a2 = src_articles.spin_dissimilar_articles(num, 2)
positive_cosines.append(cosine(" ".join(a1), " ".join(a2)))
while len(negative_cosines) < NUM_NEGATIVES:
num1 = random.randint(0, src_articles.count - 1)
similar_articles = list(src_articles.get_very_similar_articles(num1))
if not similar_articles:
continue
num2 = random.choice(similar_articles)
a1 = " ".join(src_articles.spin_articles(num1)[0])
a2 = " ".join(src_articles.spin_articles(num2)[0])
negative_cosines.append(cosine(a1, a2))
return positive_cosines, negative_cosines
def searchTf(self, query):
""" search for documents that match based on a list of terms """
queryVector = self.makeTfVector(query)
tf_cos = [
util.cosine(queryVector, documentVector)
for documentVector in self.tfVectors
]
tf_dist = [
util.euclidean(queryVector, documentVector)
for documentVector in self.tfVectors
]
return [tf_cos, tf_dist]
def search(self,searchList, formula="cosine", weighting="tf"):
""" search for documents that match based on a list of terms """
ratings = {}
queryVector = self.buildQueryVector(searchList, weighting)
for key, value in self.documentVectors.items():
if formula == "cosine":
rating = util.cosine(queryVector, value)
elif formula == "euclidean":
rating = util.euclidean(queryVector, value)
ratings[key] = rating
ratings = {k: v for k, v in sorted(ratings.items(), key=lambda item: item[1], reverse=True)}
return ratings
def search_nltk(self, searchList, method="1"):
rating_dic = {}
for key, value in self.documentVectors.items():
rating_dic[key] = util.cosine(searchList, value)
result = {
k: v
for k, v in sorted(
rating_dic.items(), key=lambda item: item[1], reverse=True)
}
return list(result.items())[:10]
def tf_idf_search(self, searchList):
queryVector = self.buildQueryVector(searchList)
# print (queryVector)
self.tfidVectors = util.tf_idf(self.documentVectors)
# print self.tfidVectors
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.tfidVectors
]
# ratings = [util.cosine(queryVector, util.tf_idf(documentVectors)) for documentVector in self.documentVectors]
# print(ratings)
return ratings
def f_search(self,searchList,doc,way):
""" search for documents that match based on a list of terms """
queryVector = self.buildQueryVector(searchList)
fVector = self.makeTagVector(doc)
for i in range(0,len(queryVector)):
queryVector[i] += fVector[i]
if way == "cosine":
ratings = [util.cosine(queryVector, documentVector) for documentVector in self.documentVectors]
elif way == "euclid":
ratings = [util.euclid(queryVector, documentVector) for documentVector in self.documentVectors]
#ratings.sort(reverse=True)
return ratings
def baseline_cos_different_articles(a1_num, a2_num, num_times):
lowest = float("inf")
highest = float("-inf")
total = 0.0
for _ in xrange(num_times):
a1 = source_articles.spin_articles(a1_num)[0]
a2 = source_articles.spin_articles(a2_num)[0]
cos_sim = cosine(a1, a2)
lowest = min(lowest, cos_sim)
highest = max(highest, cos_sim)
total += cos_sim
average = total / num_times
return lowest, highest, average
def search1and2(self, searchList, compare, flag):
""" search for documents that match based on a list of terms """
queryVector = self.buildQueryVector(searchList)
if compare == "cos":
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.documentVectors
]
elif compare == "el":
ratings = [
util.Euclidean(queryVector, documentVector)
for documentVector in self.documentVectors
]
return self.Sort(ratings, flag)
def search(self, searchList, method="0"):
""" search for documents that match based on a list of terms """
rating_dic = {}
queryVector = self.buildQueryVector(searchList, method)
for key, value in self.documentVectors.items():
rating_dic[key] = util.cosine(queryVector, value)
result = {
k: v
for k, v in sorted(
rating_dic.items(), key=lambda item: item[1], reverse=True)
}
return list(result.items())[:10]
def search(self, relevanceType):
""" search for documents that match based on a list of terms """
self.queryVector = self.buildQueryVector(self.queryList)
if relevanceType == 'cs':
ratings = [
util.cosine(self.queryVector, documentVector)
for documentVector in self.documentVectors
]
elif relevanceType == 'eu':
ratings = [
util.euclidean(self.queryVector, documentVector)
for documentVector in self.documentVectors
]
return ratings
def tfidf(self, queryVector, flag):
for i in range(len(queryVector)):
if self.idf[i] > 0:
queryVector[i] = queryVector[i] * math.log10(
float(7034 / self.idf[i]))
if flag == "cos":
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.documentVectors
]
elif flag == "el":
ratings = [
util.Euclidean(queryVector, documentVector)
for documentVector in self.documentVectors
]
return ratings
def searchRelevantFeedback(self, QueryFeedback):
# first element is query, second element is relevant feedback
query = self.buildTFIDFQueryVector(QueryFeedback[0])
feedback = self.buildTFIDFQueryVector(QueryFeedback[1])
NewQuery = [0] * len(query)
for i in range(len(query)):
NewQuery[i] = 1 * query[i] + 0.5 * feedback[i]
ratings = [
util.cosine(NewQuery, documentVector)
for documentVector in self.TFIDFVectors
]
return ratings
def search(self, searchList, way, idf=False):
""" search for documents that match based on a list of terms """
queryVector = self.buildQueryVector(searchList)
if idf == True:
queryVector = self.tfidf(queryVector)
# print(queryVector)
if way == 'cos':
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.documentVectors
]
else:
ratings = [
util.euc_distance(queryVector, documentVector)
for documentVector in self.documentVectors
]
#ratings.sort(reverse=True)
return ratings
def feedback(self, first_doc):
''' get first result from #1-3, then get noun and verb to make new query to get relevance feedback '''
text = nltk.word_tokenize(first_doc)
pos_tagged = nltk.pos_tag(text)
feedbackQueryList = [
e[0] for e in filter(
lambda x: x[1][:2] == 'NN' or x[1][:2] == 'VB', pos_tagged)
] #
feedbackQueryVector = self.buildQueryVector(feedbackQueryList)
if len(self.queryVector) == 0:
self.queryVector = self.buildQueryVector(self.queryList)
queryVector = [
e + 0.5 * a for e, a in zip(self.queryVector, feedbackQueryVector)
]
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.documentVectors
]
return ratings
def get_comparison_stats(self, article1, article2, classified_article1, classified_article2, do_print=True):
'''
returns cosine of all possible things
of two articles and their comparisons
'''
a1_a2 = cosine(" ".join(article1), " ".join(article2))
ca1_ca2 = cosine(" ".join(classified_article1), " ".join(classified_article2))
a1_ca1 = cosine(" ".join(classified_article1), " ".join(article1))
a2_ca2 = cosine(" ".join(classified_article2), " ".join(article2))
a1_ca2 = cosine(" ".join(classified_article2), " ".join(article1))
a2_ca1 = cosine(" ".join(classified_article1), " ".join(article2))
if do_print:
print "COSINE OF ARTICLES: {0}".format(a1_a2)
print "COSINE OF CLASSIFIED ARTICLES: {0}".format(ca1_ca2)
print "cosine of A1 and classified A1: {0}".format(a1_ca1)
print "cosine of A2 and classified A2: {0}".format(a2_ca2)
print "cosine of A1 and classified A2: {0}".format(a1_ca2)
print "cosine of A2 and classified A1: {0}".format(a2_ca1)
print "ratio: {0}".format(((a2_ca1 / a1_ca1) + (a1_ca2 / a2_ca2)) / 2)
return (a1_a2, ca1_ca2, a1_ca1, a2_ca2, a1_ca2, a2_ca1)
print('TF Weighting + Cosine Similarity:')
print_top5(top5_tf_cos)
print('TF Weighting + Euclidean Distance:')
print_top5(top5_tf_dist)
print('TF-IDF Weighting + Cosine Similarity:')
print_top5(top5_tfidf_cos)
print('TF-IDF Weighting + Euclidean Distance:')
print_top5(top5_tfidf_dist)
# for Q2 Relevance feedback
newSearchIndex = indexList.index(top5_tfidf_cos[0][0])
documents = doc[newSearchIndex]
feedBackVector = vectorSpace.makeVecRelevance(documents)
ansVec = searchVector + feedBackVector
finalScore = [
util.cosine(ansVec, docVec) for docVec in vectorSpace.tfidfVec
]
Q2 = sorted(list(zip(indexList, finalScore)),
reverse=True,
key=itemgetter(1))[:5]
print('Relevance Feedback + TF-IDF Weighting + Cosine Similarity:')
print_top5(Q2)
time_end = datetime.datetime.now()
time_cost = time_end - time_start
print("cost time :", time_cost)
def agreement(self, index1, index2):
return util.cosine(self.allIdeas[index1], self.allIdeas[index2])
def searchtfidfcosine(self,searchList):
queryVector = self.buildQueryVector(searchList)
queryVector = self.computeidf(queryVector)
tempVectors = [self.computeidf(documentVector) for documentVector in self.documentVectors]
ratings = [util.cosine(queryVector , documentVector) for documentVector in tempVectors]
return ratings
def main(query):
#create vector space model instance
vectorSpace = VectorSpace(documents)
#caculate different conmbinations
tf_cos = vectorSpace.TF_Cosine(query)
tf_euclidean = vectorSpace.TF_Euclidean(query)
tfidf_cos = vectorSpace.TFIDF_Cosine(query)
tfidf_euclidean = vectorSpace.TFIDF_Euclidean(query)
#sort with top five score
top5_tf_cos = sorted(list(zip(indexList, tf_cos)),
reverse=True,
key=lambda x: x[1])[:5]
top5_tf_euclidean = sorted(list(zip(indexList, tf_euclidean)),
reverse=False,
key=lambda x: x[1])[:5]
top5_tfidf_cos = sorted(list(zip(indexList, tfidf_cos)),
reverse=True,
key=lambda x: x[1])[:5]
top5_tfidf_euclidean = sorted(list(zip(indexList, tfidf_euclidean)),
reverse=False,
key=lambda x: x[1])[:5]
#print out the output
print('Term Frequency Weighting + Cosine Similarity:')
print_top(top5_tf_cos)
print('Term Frequency Weighting + Euclidean Distance:')
print_top(top5_tf_euclidean)
print('TF-IDF Weighting + Cosine Similarity:')
print_top(top5_tfidf_cos)
print('TF-IDF Weighting + Euclidean Distance:')
print_top(top5_tfidf_euclidean)
#Relevance Feedback
#get the document of the first score of the tfidf + cosine similarity by given query
indx_fb = indexList.index(top5_tfidf_cos[0][0])
fb = documents[indx_fb]
#the new query term weighting scheme is [1 * original query + 0.5 * feedback query]
feedback_vector = vectorSpace.makeFeedbackVector(fb)
query_vector = np.array(vectorSpace.makeTfIdfVector(query))
rf_vector = query_vector + feedback_vector
# evaluate the relevance vector with each document by tfidf + cosine similarity
rf_tfidf_cos = []
for documentTFIDFVector in vectorSpace.documentTFIDFVectors:
rf_tfidf_cos.append(util.cosine(rf_vector, documentTFIDFVector))
top5_rf_tfidf_cos = sorted(list(zip(indexList, rf_tfidf_cos)),
reverse=True,
key=lambda x: x[1])[:5]
#print out the output
print('Relevance Feedback + TF-IDF Weighting + Cosine Similarity:')
print_top(top5_rf_tfidf_cos)
for documentVector in self.documentVectors
]
return ratings
def IDFCOS(self, searchList):
queryVector = self.buildQueryVector(searchList)
self.vectorIDF = [float(2048.0 / x) for x in self.vectorIDF]
self.vectorIDF = [float(math.log10(x)) for x in self.vectorIDF]
self.tfidf = [
map(lambda (a, b): a * b, zip(self.vectorIDF, documentVector))
for documentVector in self.documentVectors
]
#print(tfidf)
ratings = [
util.cosine(queryVector, documentVector)
for documentVector in self.tfidf
]
#print(ratings)
return ratings
def IDFED(self, searchList):
queryVector = self.buildQueryVector(searchList)
#print(self.vectorIDF)
#tfidf = [map(lambda (a,b):a*b,zip(self.vectorIDF, documentVector)) for documentVector in self.documentVectors]
#print(tfidf)
ratings = [
util.Euclidean_Distance(queryVector, documentVector)
for documentVector in self.tfidf
]
def cal_fq_tfidf_cs(vectorSpace, files, query):
# Feedback Query + TF-IDF Weighting + Cosine Similarity
#
# step 1
# step 2
# step 3
# step 4
sorted_indices, _ = cal_tfidf_cs(vectorSpace, files, query)
# The new query term weighting = [1 * original query + 0.5 * feedback query]
'''
For instance, suppose the index vector is
["network" ,"computer" , "share", "ask", "soccer", "song"],
the query is "network", and the content of the feedback document is:
Jimmy shares songs via the computer network.
Then we will get a new query vector like this:
1 * [1, 0, 0, 0, 0, 0] + 0.5 * [1, 1, 1, 0, 0, 1] = [1.5, 0.5, 0.5, 0, 0, 0.5]
'''
# get ranked first vector
first_vector = vectorSpace.documentVectors[sorted_indices[0]]
my_dict = vectorSpace.vectorKeywordIndex
def get_key(val):
for key, value in my_dict.items():
if val == value:
return key
return "key doesn't exist"
# map the vector' item into words
words = []
for i in range(len(first_vector)):
if first_vector[i] > 0:
for j in range(first_vector[i]):
words.append(get_key(i))
# do the pos tagging to words
tagged = nltk.pos_tag(words)
feedback = [0] * len(vectorSpace.vectorKeywordIndex)
# find the verb and noun words and transform to feedback vector
pos = ['NN', 'VB', 'VBP', 'VBD', 'VBG']
for tup in tagged:
if tup[1] in pos:
feedback[my_dict[tup[0]]] += 1
feedback = np.array(feedback)
queryVector = vectorSpace.buildQueryVector([query])
new_query = queryVector + 0.5 * feedback
new_query = list(new_query)
# feedback rating
print("Feedback Queries + TF-IDF Weighting + Cosine Similarity feedback")
scores = [
util.cosine(new_query, documentVector)
for documentVector in vectorSpace.documentVectors
]
# Indices of N largest elements in list
indices = np.argpartition(scores, -5)[-5:]
# save as (index, value)
d = {}
for i in indices:
d[i] = scores[i]
# sort dict by value instead of key
sd = sorted(d.items(), key=lambda item: item[1], reverse=True)
sorted_indices = [s[0] for s in sd]
sorted_scores = [s[1] for s in sd]
# find docid
docid = []
for index in sorted_indices:
x = files[index]
docid.append(os.path.splitext(x)[0])
round_score = [round(score, 6) for score in sorted_scores]
d = {'docID': docid, 'Score': round_score}
return pd.DataFrame(data=d)
def searchTFidfCos(self, searchList):
searchVec = self.makeVectorforTFidf(searchList)
vector = [util.cosine(searchVec, docVec) for docVec in self.tfidfVec]
return vector
def related(self,documentId): """ find documents that are related to the document indexed by passed Id within the document Vectors""" ratings = [util.cosine(self.documentVectors[documentId], documentVector) for documentVector in self.documentVectors] ratings.sort(reverse=True) return ratings
# print(my_dict[tup[0]])
feedback[my_dict[tup[0]]] += 1
# print(feedback)
feedback = np.array(feedback)
queryVector = vectorSpace.buildQueryVector(["drill wood sharp"])
new_query = queryVector + 0.5 * feedback
new_query = list(new_query)
print(new_query)
# feedback rating
print("TF-IDF Weighting + Cosine Similarity feedback")
scores = [
util.cosine(new_query, documentVector)
for documentVector in vectorSpace.documentVectors
]
# Indices of N largest elements in list
indices = np.argpartition(scores, -5)[-5:]
# save as (index, value)
d = {}
for i in indices:
d[i] = scores[i]
# sort dict by value instead of key
sd = sorted(d.items(), key=lambda item: item[1], reverse=True)
# print(sd)
top5_tf_cos = sorted(list(zip(indexes, tf_cos)), reverse=True, key=sortByRatings)[:5]
top5_tf_dist = sorted(list(zip(indexes, tf_dist)), reverse=False, key=sortByRatings)[:5]
top5_tfidf_cos = sorted(list(zip(indexes, tfidf_cos)), reverse=True, key=sortByRatings)[:5]
top5_tfidf_dist = sorted(list(zip(indexes, tfidf_dist)), reverse=False, key=sortByRatings)[:5]
print('Term Frequency Weighting + Cosine Similarity:')
printResult(top5_tf_cos)
print('Term Frequency Weighting + Euclidean Distance:')
printResult(top5_tf_dist)
print('TF-IDF Weighting + Cosine Similarity:')
printResult(top5_tfidf_cos)
print('TF-IDF Weighting + Euclidean Distance:')
printResult(top5_tfidf_dist)
#create feedback-relevance vector
newQueryIndex = indexes.index(top5_tfidf_cos[0][0])
doc = contents[newQueryIndex]
feedbackVector = vectorspace.getRelevanceFeedbackVector(doc)
qfVector = queryVector+feedbackVector
# compute the scores and re-rank
scores = [util.cosine(qfVector, documentVector) for documentVector in vectorspace.tfidfVectors]
relevanceFeedback = sorted(list(zip(indexes, scores)), reverse=True, key=sortByRatings)[:5]
print('Feedback Queries + TF-IDF Weighting + Cosine Similarity:')
printResult(relevanceFeedback)
from VectorSpace import *
import util
f = open('data/data.txt','r')
descs = []
descs.append(f.read())
f.close()
f = open('data/id3v23.txt','r')
descs.append(f.read())
f.close()
vs = VectorSpace(descs)
print util.cosine(vs.documentVectors[0],vs.documentVectors[1])
print util.cosine(vs.documentVectors[1],vs.documentVectors[0])
def s3(t1, terms):
return set([t2 for t2 in terms if t1 != t2 and len(t1)>3
and cosine(t1,t2) > 0.8])
