def __init__(self):
print 'Initializing Recommender..'
directory_name = rm.CACHE
self.data_retriever = DataRetriever(directory_name)
self.project_data = self.data_retriever.parseProjectData()
self.user_data, self.user_follower_map = self.data_retriever.parseUserFollowers()
self.language_proj = defaultdict()
class Recommender():
"""Initialize the recommender"""
def __init__(self):
print 'Initializing Recommender..'
directory_name = rm.CACHE
self.data_retriever = DataRetriever(directory_name)
self.project_data = self.data_retriever.parseProjectData()
self.user_data, self.user_follower_map = self.data_retriever.parseUserFollowers()
self.language_proj = defaultdict()
def get_languages(self):
lang_dict = {}
for lang in self.language_proj.keys():
_lang = lang.replace(' ','$')
lang_dict[_lang] = lang
return lang_dict
def get_aoi(self):
return self.categories
"""Get different scores for each project"""
def build_project_features(self):
try:
with open(rm.NB_PROB, 'rb') as f:
print "Reading probabilities from:", rm.NB_PROB
self.project_vector = pickle.load(f)
self.categories = pickle.load(f)
print 'done.'
print '#Projects:', len(self.project_vector)
print '#Categories:', len(self.categories)
except:
print "Generating a new Naive Base classifier"
self.project_vector_builder = ProjectVectorBuilder(self.project_data)
self.project_vector = self.project_vector_builder.build_projects_vector()
self.categories = list(self.project_vector_builder.nb.clf.classes_)
with open(rm.NB_PROB, 'wb') as f:
pickle.dump(self.project_vector, f)
with open(rm.NB_PROB, 'ab') as f:
pickle.dump(self.categories, f)
self.user_ranking = pagerank(self.user_data)
with open(os.path.join(rm.CACHE, 'lang_to_projects.p'), 'rb') as f:
self.language_proj = pickle.load(f)
with open(os.path.join(rm.CACHE, 'new_LOC.p'),'rb') as f:
self.difficulty_score = pickle.load(f)
def recommend_projects(self, languages, area_interest, difficulty):
print "Calling recommender"
projects = set()
#Filter based on languages
for language in languages:
projects = projects.union(self.language_proj[language])
similar_projects = []
for project in projects:
if project not in self.project_vector: continue
if self.project_vector[project]['category'] in area_interest:
project_desc = self.project_vector[project]
project_desc['html_url'] = self.project_data[project]['html_url']
project_desc['full_name'] = self.project_data[project]['full_name']
similar_projects.append(project_desc)
sorted_similar_projects = sorted(similar_projects, key=lambda k: k['prob'], reverse=True)
#pp.pprint(sorted_similar_projects)
zipped = map(list, zip(*self.user_ranking))
userLists = zipped[0]
PRs = zipped[1]
sortedProjsLength = len(sorted_similar_projects)
for i in range(0,len(sorted_similar_projects)):
proj = sorted_similar_projects[i]
project = self.project_data[proj[u'full_name']]
owner = project[u'owner']
if owner[u'login'] in userLists:
userIndex = userLists.index(owner[u'login'])
sorted_similar_projects[i]['page_rank_of_owner'] = PRs[userIndex]
sorted_similar_projects[i]['owner'] = owner[u'login']
#sorted_similar_projects[i]['contributors'] = self.project_data[proj['full_name']]['contributors'][0]['login']
if len(self.project_data[proj['full_name']]['contributors']) >=1:
sorted_similar_projects[i]['contributors'] = self.project_data[proj['full_name']]['contributors'][0]['login']
sorted_similar_projects[i]['contributors_url'] = self.project_data[proj['full_name']]['contributors'][0]['html_url']
else:
sorted_similar_projects[i]['contributors'] = ''
sorted_similar_projects[i]['contributors_url'] =''
else:
sorted_similar_projects[i]['page_rank_of_owner'] = 0
sorted_similar_projects[i]['owner'] = owner[u'login']
if len(self.project_data[proj['full_name']]['contributors']) >=1:
sorted_similar_projects[i]['contributors'] = self.project_data[proj['full_name']]['contributors'][0]['login']
sorted_similar_projects[i]['contributors_url'] = self.project_data[proj['full_name']]['contributors'][0]['html_url']
else:
sorted_similar_projects[i]['contributors'] = ''
sorted_similar_projects[i]['contributors_url'] = ''
# sort the sorted_similar_projects based on the key 'page_rank_of_owner' value
# have the contributors tag with the first contributor for the server side handling
#"""
if len(sorted_similar_projects) > 10:
firstListToSort = sorted_similar_projects[0:sortedProjsLength/2]
secListToSort = sorted_similar_projects[sortedProjsLength/2 + 1 : sortedProjsLength*4/5 ]
thirListToSort = sorted_similar_projects[sortedProjsLength*4/5 + 1 : ]
#"""
sorted1 = sorted(firstListToSort, key=lambda k: k['page_rank_of_owner'], reverse=True)
sorted2 = sorted(secListToSort, key=lambda k: k['page_rank_of_owner'], reverse=True)
sorted3 = sorted(thirListToSort, key=lambda k: k['page_rank_of_owner'], reverse=True)
#print len(sorted1)
#print len(sorted2)
#print len(sorted3)
sorted1.extend(sorted2)
sorted3.extend(sorted1)
#pp.pprint(sorted3)
#print 'lenght after merging all: ',len(sorted3)
return sorted3
return sorted_similar_projects
