lib/analysis/channel.py
import re
import numpy
from networkx.algorithms.components.connected import connected_components
import lib.util as util
import lib.config as config
def conv_len_conv_refr_time(log_dict, nicks, nick_same_list, rt_cutoff_time, cutoff_percentile):
""" Calculates the conversation length (CL) that is the length of time for which two users communicate
i.e. if a message is not replied to within Response Time(RT),
then it is considered as a part of another conversation.
This function also calculates the conversation refresh time(CRT)
For a pair of users, this is the time when one conversation ends and another one starts.
Args:
log_dict (str): Dictionary of logs data created using reader.py
nicks(List) : list of nickname created using nickTracker.py
nick_same_list :List of same_nick names created using nickTracker.py
rt_cutoff_time (int) : Response Time (RT) cutoff to be used for CL and CRT calculations
Returns:
row_cl(zip List): Conversation Length
row_crt(zip List) :Conversation Refresh time
"""
conv = []
conv_diff = []
G = util.to_graph(nick_same_list)
conn_comp_list = list(connected_components(G))
util.create_connected_nick_list(conn_comp_list)
# We use connected components algorithm to group all those nick clusters that have atleast one nick common in their clusters.
# So e.g.
# Cluster 1- nick1,nick2,nick3,nick4(some nicks of a user)
# Cluster 2 -nick5,nick6,nick2,nick7.
# Then we would get - nick1,nick2,nick3,nick4,nick5,nick6,nick7 and we can safely assume they belong to the same user.
conversations=[[] for i in range(config.MAX_CONVERSATIONS)] # This might need to be incremented from 10000 if we have more users. Same logic as the above 7000 one. Applies to all the other codes too.
## I would advice on using a different data structure which does not have an upper bound like we do in arrays.
def build_conversation(rec_list, nick, send_time, nick_to_search,
nick_receiver, nick_sender, dateadd,
conversations, conn_comp_list, line):
for names in rec_list:
conversations, nick_receiver, send_time = conv_helper(names, nick, send_time, nick_to_search,
nick_receiver, nick_sender, dateadd,
conversations, conn_comp_list, line)
return conversations, nick_receiver, send_time
def conv_helper(rec, nick, send_time, nick_to_search, nick_receiver,
nick_sender, dateadd, conversations, conn_comp_list, line):
if(rec == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
nick_receiver = util.get_nick_sen_rec(len(nicks), nick, conn_comp_list, nick_receiver)
for i in range(config.MAX_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations = conv_append(conversations, i, dateadd, line)
break
if(len(conversations[i]) == 0):
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations = conv_append(conversations, i, dateadd, line)
break
return conversations, nick_receiver, send_time
def conv_mat_diff(i,j,conversations):
"""
i(int): matrix index for row
j(int): matrix index for column
"""
return (conversations[i][j]-conversations[i][j-1])
def conv_append(conversations, index, dateadd, line):
conversations[index].append(config.HOURS_PER_DAY*config.MINS_PER_HOUR*dateadd + int(line[1:6][0:2])*config.MINS_PER_HOUR + int(line[1:6][3:5]))
return conversations
def parse_log_lines_for_conv(log_dict, nicks, conn_comp_list, conversations):
dateadd = -1 # Variable used for response time calculation. Varies from 0-365.
for day_content_all_channels in log_dict.values():
for day_content in day_content_all_channels:
day_log = day_content["log_data"]
dateadd = dateadd + 1
send_time = [] # list of all the times a user sends a message to another user
# code for making relation map between clients
for line in day_log:
flag_comma = 0
if(util.check_if_msg_line (line)):
nick_sender = ""
nick_receiver = ""
m = re.search(r"\<(.*?)\>", line)
nick_to_search = util.correctLastCharCR(m.group(0)[1:-1])
nick_sender = util.get_nick_sen_rec(len(nicks), nick_to_search, conn_comp_list, nick_sender)
for nick in nicks:
rec_list = [e.strip() for e in line.split(':')]
util.rec_list_splice(rec_list)
if not rec_list[1]:
break
rec_list = util.correct_last_char_list(rec_list)
conversations, nick_receiver, send_time = build_conversation(rec_list, nick, send_time,
nick_to_search, nick_receiver, nick_sender,
dateadd, conversations, conn_comp_list, line)
if "," in rec_list[1]:
flag_comma = 1
rec_list_2 = [e.strip() for e in rec_list[1].split(',')]
rec_list_2 = util.correct_last_char_list(rec_list_2)
conversations, nick_receiver, send_time = build_conversation(rec_list_2, nick, send_time,
nick_to_search, nick_receiver,
nick_sender, dateadd, conversations,
conn_comp_list, line)
if(flag_comma == 0):
rec = util.splice_find(line, ">", ", ", 1)
conversations, nick_receiver, send_time = conv_helper(rec, nick, send_time, nick_to_search,
nick_receiver, nick_sender, dateadd,
conversations, conn_comp_list, line)
return conversations, nick_receiver, send_time
conversations, nick_receiver, send_time = parse_log_lines_for_conv(log_dict, nicks, conn_comp_list, conversations)
# Consider all cases in which messages are addressed as - (nick1:nick2 or nick1,nick2
# or nick1,nick2:) and stores their response times in conversations.
# conversations[i] contains all the response times between userA and userB
# throughout a chosen time period.
for i in range(len(conversations)):
# remove the first two elements from every conversations[i]
# as they are the UIDS of sender and receiver respectively(and not RTs)
if(len(conversations[i]) != 0):
del conversations[i][0:2]
for i in range(len(conversations)):
if(len(conversations[i]) != 0):
first = conversations[i][0]
# response times are calculated starting from index 2.
# So now we have all the response times in conversations.
for j in range(1, len(conversations[i])):
# We are recording the conversation length in conv and CRT in conv_diff.
if(conv_mat_diff(i, j, conversations) > rt_cutoff_time):
conv.append(conversations[i][j-1] - first)
conv_diff.append(conv_mat_diff(i, j, conversations))
first = conversations[i][j]
if(j == (len(conversations[i]) - 1)):
conv.append(conversations[i][j] - first)
break
# To plot CDF we store the CL and CRT values and their number of occurences
row_cl = build_stat_dist(conv)
row_crt = build_stat_dist(conv_diff)
truncated_cl, cl_cutoff_time = truncate_table(row_cl, cutoff_percentile)
truncated_crt, crt_cutoff_time = truncate_table(row_crt, cutoff_percentile)
return truncated_cl, truncated_crt
def response_time(log_dict, nicks, nick_same_list, cutoff_percentile):
""" finds the response time of a message
i.e. the best guess for the time at which one can expect a reply for his/her message.
Args:
log_dict (str): Dictionary of logs data created using reader.py
nicks(List) : List of nickname created using nickTracker.py
nick_same_list :List of same_nick names created using nickTracker.py
cutoff_percentile (int): Cutoff percentile indicating statistical significance
Returns:
rows_RT(zip List): Response Time (This refers to the response
time of a message i.e. the best guess for the time at
which one can expect a reply for his/her message)
"""
G = util.to_graph(nick_same_list)
conn_comp_list = list(connected_components(G))
util.create_connected_nick_list(conn_comp_list)
graph_cumulative = []
graph_x_axis = []
graph_y_axis = []
def build_mean_list(conversations, index, mean_list):
for j in range(2, len(conversations[index])):
mean_list.append(conversations[index][j])
return mean_list
def resp_helper(rec, nick, send_time, nick_to_search, nick_receiver, nick_sender, conversations, conn_comp_list):
if(rec == nick):
send_time.append(line[1:6])
if(nick_to_search != nick):
nick_receiver = util.get_nick_sen_rec(len(nicks), nick, conn_comp_list, nick_receiver)
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if (nick_sender in conversations[i] and nick_receiver in conversations[i]):
conversations[i].append(line[1:6])
break
if(len(conversations[i]) == 0):
conversations[i].append(nick_sender)
conversations[i].append(nick_receiver)
conversations[i].append(line[1:6])
break
return conversations, nick_receiver, send_time
for day_content_all_channels in log_dict.values():
for day_content in day_content_all_channels:
day_log = day_content["log_data"]
send_time = [] # list of all the times a user sends a message to another user
meanstd_list = []
totalmeanstd_list = []
x_axis = []
y_axis = []
real_y_axis = []
conversations = [[] for i in range(config.MAX_RESPONSE_CONVERSATIONS)]
# code for making relation map between clients
for line in day_log:
flag_comma = 0
if(util.check_if_msg_line (line)):
nick_sender = ""
nick_receiver = ""
m = re.search(r"\<(.*?)\>", line)
nick_to_search = util.correctLastCharCR(m.group(0)[1:-1])
nick_sender = util.get_nick_sen_rec(len(nicks), nick_to_search, conn_comp_list, nick_sender)
for nick in nicks:
rec_list = [e.strip() for e in line.split(':')]
util.rec_list_splice(rec_list)
if not rec_list[1]:
break
rec_list = util.correct_last_char_list(rec_list)
for name in rec_list:
conversations, nick_receiver, send_time = resp_helper(name, nick, send_time, nick_to_search, nick_receiver, nick_sender, conversations, conn_comp_list)
if "," in rec_list[1]:
flag_comma = 1
rec_list_2 = [e.strip() for e in rec_list[1].split(',')]
rec_list_2 = util.correct_last_char_list(rec_list_2)
for name in rec_list_2:
conversations, nick_receiver, send_time = resp_helper(name, nick, send_time, nick_to_search, nick_receiver, nick_sender, conversations, conn_comp_list)
if(flag_comma == 0):
rec = util.splice_find(line, ">", ", ",1)
conversations, nick_receiver, send_time = resp_helper(rec, nick, send_time, nick_to_search, nick_receiver, nick_sender, conversations, conn_comp_list)
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
for j in range(2, len(conversations[i]) - 1):
conversations[i][j]=(int(conversations[i][j+1][0:2])*config.MINS_PER_HOUR+int(conversations[i][j+1][3:5])) - (int(conversations[i][j][0:2])*config.MINS_PER_HOUR+int(conversations[i][j][3:5]))
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
if(len(conversations[i]) == 3):
conversations[i][2] = int(conversations[i][2][0:2])*config.MINS_PER_HOUR+int(conversations[i][2][3:5])
else:
del conversations[i][-1]
# Explanation provided in parser-CL+CRT.py
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
totalmeanstd_list = build_mean_list(conversations, i, totalmeanstd_list)
if(len(totalmeanstd_list) != 0):
for i in range(max(totalmeanstd_list) + 1):
x_axis.append(i)
for i in x_axis:
y_axis.append(float(totalmeanstd_list.count(i)) / float(len(totalmeanstd_list)))
# finding the probability of each RT to occur=No. of occurence/total occurences.
real_y_axis.append(y_axis[0])
for i in range(len(y_axis)):
real_y_axis.append(float(real_y_axis[i-1]) + float(y_axis[i]))
# to find cumulative just go on adding the current value to previously cumulated value till sum becomes 1 for last entry.
for i in range(len(totalmeanstd_list)):
graph_cumulative.append(totalmeanstd_list[i])
if len(totalmeanstd_list) > 0:
totalmeanstd_list.append(numpy.mean(totalmeanstd_list))
totalmeanstd_list.append(numpy.mean(totalmeanstd_list)+2*numpy.std(totalmeanstd_list))
for i in range(config.MAX_RESPONSE_CONVERSATIONS):
if(len(conversations[i]) != 0):
meanstd_list = build_mean_list(conversations, i, meanstd_list)
conversations[i].append(numpy.mean(meanstd_list))
conversations[i].append(numpy.mean(meanstd_list)+(2*numpy.std(meanstd_list)))
meanstd_list[:] = []
graph_cumulative.sort()
truncated_rt = None
rt_cutoff_time = None
if graph_cumulative:
for i in range(graph_cumulative[len(graph_cumulative)-1] + 1):
graph_y_axis.append(graph_cumulative.count(i)) # problem when ti=0 count is unexpectedly large
graph_x_axis.append(i)
# Finally storing the RT values along with their frequencies in a csv file; no need to invoke build_stat_dist() function
rows_rt = zip(graph_x_axis, graph_y_axis)
truncated_rt, rt_cutoff_time = truncate_table(rows_rt, cutoff_percentile)
if config.CUTOFF_TIME_STRATEGY == "TWO_SIGMA":
resp_time, resp_frequency_tuple = zip(*truncated_rt)
resp_frequency = list(resp_frequency_tuple)
rt_cutoff_time_frac = numpy.mean(resp_frequency) + 2*numpy.std(resp_frequency)
rt_cutoff_time = int(numpy.ceil(rt_cutoff_time_frac))
return truncated_rt, rt_cutoff_time
def build_stat_dist(number_list):
"""
Summarize a list into a statistical distribution.
An empty input list generates an empty output list.
Args:
number_list (List): List containing positive integers
Returns:
rows_table(zip List): A tuple with two items in each element,
in the (number, frequency) format
"""
# check for an empty input list
if not number_list:
return []
graph_x = []
graph_y = []
for i in range(max(number_list)+1):
graph_x.append(i)
graph_y.append(number_list.count(i))
# print zip(graph_x, graph_y)
return zip(graph_x, graph_y)
def truncate_table(table, cutoff_percentile):
"""
The calculations of conversation characteristics, namely RT, CL and CRT, are
based on the cutoff values estimated for RT and CL. This generic function takes
a two column table and truncates the same to a required percentile value. Usually
the RT followed by CL tables are processed through this function.
cutoff_percentile (float) : Cutoff indicating the statistical significance of
observations on conversation characteristics. The value is expressed as a
floating point number.
Args:
table (zip List): List containing 2-tuple elements, ex: [(0,10),(1,5)]
Returns:
truncated_table (zip List): A truncated version of table provided as input
argument. The table is truncated to the level of statistical significance
mentioned in the cutoff_percentile parameter.
cutoff_time (int): Cutoff time value corresponding to the chosen level of
statistical significance.
"""
truncated_table = None
cutoff_time = None
if table:
times, values = zip(*table)
total_value = 0
for value in values:
total_value = total_value + value
index = 0
cutoff_index = 0
cumulative_value = 0
while (index < len(values)):
if (values[index] != 0):
cumulative_value = cumulative_value + values[index]
if (cumulative_value <= (1-cutoff_percentile/100.0) * total_value):
cutoff_index = index
else:
break
index = index + 1
# slice counts the number of elements, which will be one greater than the index
truncated_table = zip(times[:cutoff_index+1], values[:cutoff_index+1])
cutoff_time = times[cutoff_index]
return truncated_table, cutoff_time