def get_recommendation(matrix_name, time_stamp, coordinates, user_id, ignore, number):
"""
Main handler function for recommend.
:param time_stamp: Time from beginning of the epoch (1.1.1970)
:param coordinates: Dictionary with coordinates.
:param user_id: Identification of the current user.
:param ignore: Identification of POI user ignored from previous recommend.
:param number: Number of POIs to be recommended to end user.
:return: Dictionary representing answer for request to recommend.
"""
# Get all POIs in radius of 300 meters from user.
recommendation_matrix = read_matrix(matrix_name)
points_of_interest = fetch_poi(matrix_name, recommendation_matrix['categories'],
coordinates['lat'], coordinates['lon'], 300)
# First we have to prepare necessary variables.
length = len(points_of_interest[1])
poi_dict = dict()
# Add temporary keys and merge the two dictionaries.
poi_lst_of_dict = points_of_interest[0]
poi_lst_foursquare = dict()
for i in range(0, length):
poi_lst_foursquare['some-temporary-key-%s-%d' % (user_id, (i+1))] = points_of_interest[1][i]
chunk_lst = make_chunks(poi_lst_foursquare, int(length/15) + 1)
threads = []
for chunk in chunk_lst:
t = WritePOIToDatabase(chunk)
threads.append(t)
t.start()
poi_lst_of_dict.update(poi_lst_foursquare)
# Get all required data for reading correct element of weight matrix.
act_rest_answer = get_user_activity(user_id)
if 'error' in act_rest_answer:
activity = 'not recognised'
else:
req_act = dict()
for k, v in act_rest_answer['svm_vector'].iteritems():
req_act[k] = float(v)
activity = get_curr_activity(req_act)
# Get id of all poi and rate them based on activity, context and distance
lookup_table = get_grades(recommendation_matrix, activity, time_stamp)
for key, val in poi_lst_of_dict.iteritems():
val['fw_core']['category'] = val['fw_core']['label']['']
val['fw_core']['label'] = val['fw_core']['short_name']
val['fw_core']['short_name'] = val['fw_core']['name']
category_num = decode_category(recommendation_matrix['categories'], val['fw_core']['category'])
f_res = lookup_table[category_num]
s_res = grade_distance(coordinates['lat'], coordinates['lon'],
val['fw_core']['location']['wgs84']['latitude'],
val['fw_core']['location']['wgs84']['longitude'])
poi_dict[key] = f_res * s_res
# Slice out ignored.
if ignore != 'None' and ignore in poi_dict:
save_ignored_for_current_user(user_id, ignore)
ignored = get_ignored_for_current_user(user_id)
for ig_poi in ignored:
if ig_poi in poi_dict:
del poi_dict[ig_poi]
elif ig_poi[:18] == 'some-temporary-key':
key = Keys.objects.filter(temp=ig_poi, time__gte=timezone.now()-timedelta(minutes=30))
del poi_dict[key.real]
# Sort POIs based on grades and return first 5 elements.
sort_poi_lst = sorted(poi_dict.items(), key=operator.itemgetter(1), reverse=True)
ret_dict = {"POIS": [], "activity": activity}
if len(sort_poi_lst) > number:
n_it = number
else:
n_it = len(sort_poi_lst)
for num in range(0, n_it):
ret_dict['POIS'].append({sort_poi_lst[num][0]: poi_lst_of_dict[sort_poi_lst[num][0]]})
# Delete old keys
keys = Keys.objects.filter(time__lt=(timezone.now()-timedelta(minutes=30)))
keys.delete()
# Wait until all threads finish the job.
for thread in threads:
thread.join()
return ret_dict
def get_recommendation(time_stamp, coordinates, user_id, ignore):
"""
Main handler function for recommend.
:param time_stamp: Time from beginning of the epoch (1.1.1970)
:param coordinates: Dictionary with coordinates.
:param user_id: Identification of the current user.
:param ignore: Identification of POI user ignored from previous recommend.
:return: Dictionary representing answer for request to recommend.
"""
''' Get all POIs in radius of 300 meters from user. '''
points_of_interest = fetch_poi(coordinates['lat'], coordinates['lon'], 300)
# First we have to assign writing jobs to threads.
length = len(points_of_interest[1])
poi_dict = dict()
# Add temporary keys and merge the two dictionaries.
poi_lst_of_dict = points_of_interest[0]
poi_lst_foursquare = dict()
for i in range(0, length):
poi_lst_foursquare['some-temporary-key-%s %d' % (user_id, (i+1))] = points_of_interest[1][i]
chunk_lst = make_chunks(poi_lst_foursquare, int(length/15) + 1)
threads = []
for chunk in chunk_lst:
t = WritePOIToDatabase(chunk)
threads.append(t)
t.start()
poi_lst_of_dict.update(poi_lst_foursquare)
''' Get all required data. '''
part_of_day = get_time_section(time_stamp)
act_rest_answer = get_user_activity(user_id)
if 'error' in act_rest_answer:
activity = 3 # If activity recognition provider encountered some error.
else:
req_act = dict()
for k, v in act_rest_answer['svm_vector'].iteritems():
req_act[k] = float(v)
activity = get_curr_activity(req_act)
''' Get id of all poi and rate them based on activity, context and distance. '''
for key, val in poi_lst_of_dict.iteritems():
f_res = lookup_table[part_of_day][activity][decode_category(val['fw_core']['category'])]
s_res = grade_distance(coordinates['lat'], coordinates['lon'], val['fw_core']['location']['wgs84'][
'latitude'], val['fw_core']['location']['wgs84']['longitude'])
poi_dict[key] = f_res * s_res
''' Slice out ignored. '''
if ignore != 'None' and ignore in poi_dict:
save_ignored_for_current_user(user_id, ignore)
ignored = get_ignored_for_current_user(user_id)
for ig_poi in ignored:
if ig_poi in poi_dict:
del poi_dict[ig_poi]
elif ig_poi[:18] == 'some-temporary-key':
key = Keys.objects.filter(temp=ig_poi, time__gte=datetime.now()-timedelta(minutes=3))
del poi_dict[key.real]
''' Sort pois based on grades and return first 15 elements. '''
sort_poi_lst = sorted(poi_dict.items(), key=operator.itemgetter(1), reverse=True)
ret_dict = {"POIS": [], "activity": decode_activity(activity)}
if len(sort_poi_lst) > 5:
n_it = 5
else:
n_it = len(sort_poi_lst)
for num in range(0, n_it):
ret_dict['POIS'].append({sort_poi_lst[num][0]: poi_lst_of_dict[sort_poi_lst[num][0]]})
# Delete old keys
keys = Keys.objects.filter(time__lt=(datetime.now()-timedelta(minutes=3)))
keys.delete()
# Wait until all threads finish the job.
for thread in threads:
thread.join()
return ret_dict
