print 'Started working on user ' + str(user_id)
# Compute the event from the DB
neura_events = connect_db.get_neura_events(user_id, dateInt_startTime, dateInt_endTime)
timeline_events = connect_db.get_timeline_events(user_id, dateInt_startTime, dateInt_endTime)
# Count users without neura_event
if len(neura_events) > 0 :
users_with_neura_events += 1
if len(timeline_events) > 0 :
users_with_timeline_events += 1
# Add errors
if (len(timeline_events)>0) & (len(neura_events) > 0):
result_per_user = detection.get_detection_rate(neura_events,timeline_events,time_threshold,dict())
result[user_id] = result_per_user["total"]
result[user_id]['id'] = user_id
else :
#result[user_id] = dict()
#result[user_id]['true_positive'] = float('NaN')
#result[user_id]['false_positive'] = float('NaN')
#result[user_id]['accuracy'] = float('NaN')
#result[user_id]['id'] = user_id
print "NO DATA"
# Show users distribution
print 'Number of users with neura events is ' + str(users_with_neura_events)
print 'Number of users with timeline events is ' + str(users_with_timeline_events)
# Translate result from dict to list
print 'Started working on user ' + str(user_id) # Compute the event from the DB neura_events = connect_db.get_neura_events(user_id, dateInt_startTime, dateInt_endTime) timeline_events = connect_db.get_timeline_events(user_id, dateInt_startTime, dateInt_endTime) # Count users without neura_event if len(neura_events) > 0 : users_with_neura_events += 1 if len(timeline_events) > 0 : users_with_timeline_events += 1 # Add errors if (len(timeline_events)>0) & (len(neura_events) > 0): result = detection.get_detection_rate(neura_events,timeline_events,time_threshold,result) else : print "NO DATA" # Show users distribution print 'Number of users with neura events is ' + str(users_with_neura_events) print 'Number of users with timeline events is ' + str(users_with_timeline_events) # Translate result from dict to list result_list = [] for key in result: result_list.append(result[key]) # write file to csv import csv
