def test_cosine_similarity_same(self):
text1 = "happy birthday"
text2 = "happy birthday"
cs = CosineSimilarity.cosine_similarity_of(text1, text2)
#strings used due to floating number problem.
self.assertEqual("%.2f" % cs, "1.00")
def get_recommendations(keywords):
df = pd.read_csv('richCityData.csv')
score_dict = {}
for index, row in df.iterrows():
score_dict[index] = CosineSimilarity.cosine_similarity_of(row['description'], keywords)
#sort cities by score and index.
sorted_scores = sorted(score_dict.items(), key=operator.itemgetter(1), reverse=True)
counter = 0
#create an empty results data frame.
resultDF = pd.DataFrame(columns=('city', 'popularity', 'description', 'image'))
#get highest scored 5 cities.
for i in sorted_scores:
print(i[0], i[1])
resultDF = resultDF.append({'city': df.iloc[i[0]]['city'], 'popularity': df.iloc[i[0]]['popularity'], 'description': df.iloc[i[0]]['description'], 'image': df.iloc[i[0]]['image']}, ignore_index=True)
counter += 1
if counter>4:
break
#convert DF to json.
json_result = json.dumps(resultDF.to_dict('records'))
return json_result
def test_cosine_similarity_different(self):
text1 = "hello sir"
text2 = "good afternoon"
cs = CosineSimilarity.cosine_similarity_of(text1, text2)
#strings used due to floating number problem.
self.assertEqual("%.2f" % cs, "0.00")
def get_recommendations_include_rating_count_threshold_positive_negative_reviews(
keywords):
df = pd.read_csv('city_data_cleared.csv')
score_dict = {}
for index, row in df.iterrows():
cs_score = CosineSimilarity.cosine_similarity_of(
row['description'], keywords)
rating = row['rating']
rating_count = row['rating_count']
positive_review_count = row['positive_review']
negative_review_count = row['negative_review']
rating_contribution = RatingExtractor.get_rating_weight_with_count_and_reviews(
rating, rating_count, positive_review_count,
negative_review_count)
final_score = RecommenderEngine.calculate_final_score(
cs_score, rating_contribution)
score_dict[index] = final_score
#sort cities by score and index.
sorted_scores = sorted(score_dict.items(),
key=operator.itemgetter(1),
reverse=True)
counter = 0
#create an empty results data frame.
resultDF = pd.DataFrame(columns=('city', 'popularity', 'description',
'score'))
#get highest scored 5 cities.
for i in sorted_scores:
#print index and score of the city.
#print(i[0], i[1])
resultDF = resultDF.append(
{
'city': df.iloc[i[0]]['city'],
'popularity': df.iloc[i[0]]['popularity'],
'description': df.iloc[i[0]]['description'],
'score': i[1]
},
ignore_index=True)
counter += 1
if counter > 4:
break
#convert DF to json.
json_result = json.dumps(resultDF.to_dict('records'))
return json_result
def analyse_with_cosine():
stats = DocsStats()
rude_comments = SiteComment.rude_comments()
rude_docs = list()
for comment in rude_comments:
rude_docs.append(
Document(stats, comment.id, comment.body, comment.processed_body))
unverified_comments = SiteComment.comments_for_analysis()
unverified_docs = list()
for comment in unverified_comments:
unverified_docs.append(
Document(stats, comment.id, comment.body, comment.processed_body))
stats.calculate_idfs()
stats.vectorise_documents()
cosine = CosineSimilarity(rude_docs)
rude_cluster = cosine.biggest_cluster()
for item in rude_cluster:
print("- ", item.body, "\r\n")
def get_response(self, question, no_responses):
responses = []
action = """"""
similarity_before = 0.2
cosine_similarity = CosineSimilarity()
if not self.current_intention:
for intention in self.intentions:
for dialog in intention.get_dialogs():
for defined_question in dialog.get_questions():
similarity = cosine_similarity.compare(defined_question, question)
if similarity > similarity_before:
similarity_before = similarity
self.current_intention = intention
responses = dialog.get_responses()
action = dialog.get_action()
else:
for dialog in self.current_intention.get_dialogs():
for defined_question in dialog.get_questions():
similarity = cosine_similarity.compare(defined_question, question)
if similarity > similarity_before:
similarity_before = similarity
responses = dialog.get_responses()
action = dialog.get_action()
if action:
exec(action.replace("INPUT", "\"" + question + "\""))
if len(responses) > 0:
if type(responses) == str:
return responses
return responses[randint(0,len(responses)-1)]
else:
if not self.current_intention:
return no_responses[randint(0,len(no_responses)-1)]
else:
self.current_intention = None
return self.get_response(question)
def get_recommendations(resume, jobs_df):
score_dict = {}
for index, row in jobs_df.iterrows():
score_dict[index] = CosineSimilarity.cosine_similarity_of(
row['description_cleaned'], resume)
# Sort descriptions by score and index
sorted_scores = sorted(score_dict.items(),
key=operator.itemgetter(1),
reverse=True)
counter = 0
# Create results data frame
resultDF = pd.DataFrame(columns=[
'Job Index', 'Company', 'Title', 'Location', 'Description',
'Job Description'
]) # , 'score'])
# Get the 10 jobs with the highest similarity scores
for i in sorted_scores:
# print index & score of the job description
resultDF = resultDF.append(
{
'Description': jobs_df.iloc[i[0]]['job_description'],
'Title': jobs_df.iloc[i[0]]['title'],
'Company': jobs_df.iloc[i[0]]['company_name'],
'Location': jobs_df.iloc[i[0]]['location'],
'Job Index': jobs_df.iloc[i[0]]['Unnamed: 0']
},
ignore_index=True)
# 'score': i[1]}, ignore_index=True)
counter += 1
if counter > 10:
break
json_result = json.dumps(resultDF.to_dict('records'))
resultDF.fillna('', inplace=True)
return resultDF
def get_rating_recommendations(keywords):
df = pd.read_csv('ratingRichCityData.csv')
score_dict = {}
for index, row in df.iterrows():
cs = CosineSimilarity.cosine_similarity_of(row['description'], keywords)
rating = row['rating']
rating_count = row['rating_count']
positive_review_count = row['positive_review']
negative_review_count = row['negative_review']
rat_value = RatingExtractor.get_rating_with_count_and_reviews(rating,rating_count,positive_review_count,negative_review_count)
score = RecommenderEngine.calculate_score_from(cs,rat_value)
score_dict[index] = score
sorted_scores = sorted(score_dict.items(), key=operator.itemgetter(1), reverse=True)
counter = 0
resultDF = pd.DataFrame(columns=('city', 'popularity', 'description', 'image'))
#get highest scored 5 cities.
for i in sorted_scores:
print(i[0], i[1])
resultDF = resultDF.append({'city': df.iloc[i[0]]['city'], 'popularity': df.iloc[i[0]]['popularity'], 'description': df.iloc[i[0]]['description'], 'image': df.iloc[i[0]]['image']}, ignore_index=True)
counter += 1
if counter>4:
break
#convert DF to json.
json_result = json.dumps(resultDF.to_dict('records'))
return json_result
from flask import Flask, render_template, request
import time
from Corpus import NGramGenerator
from cosine_similarity import CosineSimilarity
app = Flask(__name__)
myGenerator = NGramGenerator()
myGenerator.generateIndex("cleaned_files")
# for k,v in myGenerator.one_gram_corpus.items():
# print k
# for ik, iv in v.docTermFreqDict.items():
# print " %d => %d"%(ik,iv)
cs = CosineSimilarity()
cs.createMatix(myGenerator.one_gram_corpus, myGenerator.bi_gram_index,
myGenerator.tri_gram_index)
@app.route("/")
def main():
return render_template('/index.html')
@app.route('/search', methods=['POST'])
def search():
resultArray = []
now = time.time()
query_word_and_tf = defaultdict(int)
from Generator import NGramGenerator
from QueryListGenerator import QueryProcessor
from cosine_similarity import CosineSimilarity
from evaluation import Effectiveness
myGenerator = NGramGenerator()
myGenerator.generate_cleaned_files(
"/Users/ashishbulchandani/PycharmProjects/final-project/cacm",
"/Users/ashishbulchandani/PycharmProjects/final-project/cleaned_files")
myGenerator.generateUnigramCorpus(
"/Users/ashishbulchandani/PycharmProjects/final-project/cleaned_files")
comparer = CosineSimilarity(myGenerator.one_gram_corpus,
myGenerator.total_docs,
"/task1_cosine_similarity_run.txt")
queryProcessor = QueryProcessor()
querie_dict = queryProcessor.get_query_list(
'/Users/ashishbulchandani/PycharmProjects/final-project/cacm.query')
# Input, parse the query and generate weight for each term
query_word_and_tf = defaultdict(int)
eval = Effectiveness()
eval.setFilePaths(
"/Users/ashishbulchandani/PycharmProjects/final-project/run_task1/evalution_cosine_similarity/Map.txt",
"/Users/ashishbulchandani/PycharmProjects/final-project/run_task1/evalution_cosine_similarity/Mrr.txt",
"/Users/ashishbulchandani/PycharmProjects/final-project/run_task1/evalution_cosine_similarity/p_at_k.txt",
"/Users/ashishbulchandani/PycharmProjects/final-project/run_task1/evalution_cosine_similarity/table_precision_recal.txt",
"/Users/ashishbulchandani/PycharmProjects/final-project/cacm.rel.txt")
# print(data[i], x, label[i])
if x != label[i]:
errCount += 1
print(data[i], x, label[i])
return (errCount / len(bagOfWords)) * 100
def classification(test_data, test_bagOfWords, original_data, original_labels, original_bagOfWords, k=3):
"""
kNN Model Based Classifier for test data (actual data)
"""
for i in range(len(test_bagOfWords)):
x = classify(np.array(test_bagOfWords[i]), np.array(original_bagOfWords), original_labels, k)
print(test_data[i], x)
if __name__ == '__main__':
c = CosineSimilarity()
t = TextProcessing()
f = FileProcessor('experience_classification', 'train')
data, label = f.cleanFile()
feature_in_category = t.get_feature_in_category(data, label)
local_neighbour = t.get_local_neighbours(feature_in_category)
global_neighbour = t.get_global_neighbours(feature_in_category)
global_words = [global_neighbour[i][0] for i in global_neighbour]
sorted_local_neighbours = t.get_local_neighbours_sorted(feature_in_category)
# for i in sorted_local_neighbours:
# print(sorted_local_neighbours[i])
## Training Data: 4-6%
revised_data = model_construction(data, global_words, sorted_local_neighbours)
# for i in revised_data:
# print(i)
def test_recommender():
"""
"""
pass
def recommend():
"""
"""
pass
if __name__ == '__main__':
t = TextProcessing()
c = CosineSimilarity()
byte_file_path = '/Users/Rahul/Desktop/Main/Side_projects/project_2/lifeline/Scripts/link_new/files/linkedin_people_description'
byte_file = FileProcessor(byte_file_path, 'train')
items = byte_file.readByteFile()
items = [item for item in items if item != '\n' and item != None]
# big_list = [] # education + experience list
# for i in items:
# print(i)
# edu = get_education(i)
# exp = get_experience(i)
# big_list.append(edu)
# big_list.append(exp)
#
# vocabSet = c.vocabSet(big_list)
# wordVectors = [c.bag_of_words(vocabSet, i) for i in big_list]
def test_cosine_similarity_some(self):
text1 = "apple banana orange"
text2 = "orange berry ananas"
cs = CosineSimilarity.cosine_similarity_of(text1, text2)
self.assertEqual("%.2f" % cs, "0.33")
names, count = [], []
for val, key in data_count.items():
if key > 1:
names.append(val)
count.append(key)
fig1, ax1 = plt.subplots()
ax1.pie(count, labels=names, autopct='%1.1f%%', shadow=True, startangle=90)
ax1.axis(
'equal') # Equal aspect ratio ensures that pie is drawn as a circle.
# plt.tight_layout()
plt.show()
if __name__ == '__main__':
original_data = read_byteFile(
'/Users/Rahul/Desktop/Main/Side_projects/project_2/lifeline/Scripts/link_new/files/linkedin_people_description'
)
original_data = [i for i in original_data if i != '\n' and i != None]
edu, degree = education_data(original_data)
edu_dict = count(edu)
pie_graph(edu_dict)
c = CosineSimilarity()
# experience, company = experience_data(original_data, 'experience_unclassified')
# classification(experience, 'experience_classification', 'experience_unclassified')
######################################################
# TODO: transfer all of this file shit to linkedin file....
######################################################
# write_to_file('experience_unclassified', experience)
def __init__(self, measure, measure_const=MeasureConstants()):
self.measure = None
if measure == "cosine":
self.measure = CosineSimilarity()
if measure == "bag_of_words":
self.measure = BOWSimilarity()
myGenerator = NGramGenerator()
myGenerator.generate_stopped_cleaned_files(
"/Users/ashishbulchandani/PycharmProjects/SE_Web/wiki_webpages",
"/Users/ashishbulchandani/PycharmProjects/SE_Web/cleaned_files",
"/Users/ashishbulchandani/PycharmProjects/SE_Web/common_words.txt")
myGenerator.generateIndex(
"/Users/ashishbulchandani/PycharmProjects/SE_Web/cleaned_files")
# for k,v in myGenerator.one_gram_corpus.items():
# print k
# for ik, iv in v.docTermFreqDict.items():
# print " %d => %d"%(ik,iv)
cs = CosineSimilarity()
cs.createMatix(myGenerator.one_gram_corpus, myGenerator.bi_gram_index,
myGenerator.tri_gram_index)
# Input, parse the query and generate weight for each term
query_word_and_tf = defaultdict(int)
query_number = 1
var = ""
def getFormatedDockey(docKey):
space = " "
for i in range(60 - len(docKey)):
space += " "
docKey += space
return docKey