def split_logs_to_two_and_write(traces):
first_half, second_half = split_trace_in_half_by_time(traces)
first_half_traces = []
for tr in first_half:
first_half_traces.append([ev.label for ev in tr])
second_half_traces = []
for tr in second_half:
second_half_traces.append([ev.label for ev in tr])
from log_writer import LogWriter
LogWriter.write_log(first_half_traces,
'../../data/bear/first_half_traces.log')
LogWriter.write_log(second_half_traces,
'../../data/bear/last_half_traces.log')
def split_traces_by_months_and_write(traces):
months = {}
for trace in traces:
month_year = (trace[0].time.year, trace[0].time.month)
if month_year not in months:
months[month_year] = []
trace_labels = [ev.label for ev in trace]
months[month_year].append(trace_labels)
from log_writer import LogWriter
for m in months:
print('month/year', m, 'had', len(months[m]), 'traces')
LogWriter.write_log(
months[m],
'../../data/bear/' + str(m[0]) + '_' + str(m[1]) + '.log')
def split_traces_by_quarters_and_write(traces):
quarters = {'Q1': [], 'Q2': [], 'Q3': [], 'Q4': []}
for trace in traces:
if trace[0].time.year == 2011:
continue
trace_labels = [ev.label for ev in trace]
if trace[0].time.month <= 3:
quarters['Q1'].append(trace_labels)
elif trace[0].time.month <= 6:
quarters['Q2'].append(trace_labels)
elif trace[0].time.month <= 9:
quarters['Q3'].append(trace_labels)
elif trace[0].time.month <= 12:
quarters['Q4'].append(trace_labels)
from log_writer import LogWriter
for q in quarters:
print('month/year', q, 'had', len(quarters[q]), 'traces')
LogWriter.write_log(quarters[q], '../../data/bear/' + str(q) + '.log')
def produce_logs():
MODEL_TO_PRODUCE = 6
TRACE2PRODUCE = 100000
first_model = ProtocolModel(MODELS_PATH, 0, assign_transtion_probs=True)
second_model = ProtocolModel(MODELS_PATH, 0, assign_transtion_probs=True)
for instance_id in range(MODEL_TO_PRODUCE):
print('processing instance:', instance_id, MODELS_PATH)
## read model & add transition probabilities
# model_generator = ProtocolModel(MODELS_PATH, instance_id, assign_transtion_probs=True)
model_generator = first_model if instance_id < 3 else second_model
log = LogGenerator.produce_log_from_model(
model_generator.graph,
transition_probability_attribute=TRANSITION_PROBABILITY_ATTRIBUTE,
traces2produce=TRACE2PRODUCE)
## generate transition probabilities
model_generator.write_transitions_probabilities(LOGS_OUTPUT_PATH)
## produce k-Tail model
LogWriter.write_log(log,
LOGS_OUTPUT_PATH + 'l' + str(instance_id) + ".log")
def bear_based_experiments():
## read log
# k = 11
# ks = [20, 40, 80]
# ks = [1, 2, 3, 4, 6, 8, 10]
LOG_SUFFIX = '.log'
MODEL_SUFFIX = '_model.dot'
LOG_PATH = '../../data/bear/findyourhouse_long.log'
LOG_OUT_PATH = '../../data/bear/filtered_logs/'
GRAPH_OUTPUT = "../../data/bear_models/bear_models"
ks = [1, 2, 3, 4]
log_parser = BearLogParser(LOG_PATH)
traces = log_parser.process_log(True)
# log1_traces = log_parser.get_traces_of_browser(traces, "Mozilla/4.0")
# log2_traces = log_parser.get_traces_of_browser(traces, "Mozilla/5.0")
# log1_filename = 'mozzila4'
# log2_filename = 'mozzila5'
log1_filename = 'desktop'
log2_filename = 'mobile'
log1_traces = log_parser.get_desktop_traces(traces)
log2_traces = log_parser.get_mobile_traces(traces)
# events2keep = set(['search','sales_anncs',
# 'sales_page, facebook',
# 'sales_page, page_1',
# 'sales_page, page_2',
# 'sales_page, page_3',
# 'sales_page, page_4',
# 'sales_page, page_5',
# 'sales_page, page_6',
# 'sales_page, page_7',
# 'sales_page, page_8',
# 'sales_page, page_9',
# ])
# filter_traces_mozilla4 = log_parser.filter_events(events2keep, mozilla4_traces, True)
# filter_traces_mozilla5 = log_parser.filter_events(events2keep, mozilla5_traces, True)
new_name_mapping = {
'sales_page, page_1': 'sales_page',
'sales_page, page_2': 'sales_page',
'sales_page, page_3': 'sales_page',
'sales_page, page_4': 'sales_page',
'sales_page, page_5': 'sales_page',
'sales_page, page_6': 'sales_page',
'sales_page, page_7': 'sales_page',
'sales_page, page_8': 'sales_page',
'sales_page, page_9': 'sales_page',
'renting_page, page_1': 'renting_page',
'renting_page, page_2': 'renting_page',
'contacts_requested': 'contact_requested'
}
filter_traces_log1 = log_parser.abstract_events(new_name_mapping,
log1_traces)
filter_traces_log2 = log_parser.abstract_events(new_name_mapping,
log2_traces)
log1_traces = log_parser.get_traces_as_lists_of_event_labels(
filter_traces_log1)
log2_traces = log_parser.get_traces_as_lists_of_event_labels(
filter_traces_log2)
from log_writer import LogWriter
LogWriter.write_log(log1_traces, LOG_OUT_PATH + log1_filename + LOG_SUFFIX)
LogWriter.write_log(log2_traces, LOG_OUT_PATH + log2_filename + LOG_SUFFIX)
# mozilla4_traces = change_tuples_to_list(mozilla4_traces)
# mozilla5_traces = change_tuples_to_list(mozilla5_traces)
# traces = log_parser.get_traces_as_lists_of_event_labels
log1_traces_tups = []
for tr in log1_traces:
log1_traces_tups.append(tuple(tr))
log1_traces = log1_traces_tups
log2_traces_tups = []
for tr in log2_traces:
log2_traces_tups.append(tuple(tr))
log2_traces = log2_traces_tups
for k in ks:
ktail_runner_4 = kTailsRunner(log1_traces, k)
ktail_runner_5 = kTailsRunner(log2_traces, k)
ktail_runner_4_past = kTailsRunner(log1_traces, k)
ktail_runner_5_past = kTailsRunner(log2_traces, k)
ktail_runner_4.run_ktails(add_dummy_init=False,
add_dummy_terminal=False)
ktail_runner_5.run_ktails(add_dummy_init=False,
add_dummy_terminal=False)
ktail_runner_4_past.run_ktails(add_dummy_init=False,
add_dummy_terminal=False,
graph_simplification=1)
ktail_runner_5_past.run_ktails(add_dummy_init=False,
add_dummy_terminal=False,
graph_simplification=1)
g4 = ktail_runner_4.get_graph()
g5 = ktail_runner_5.get_graph()
g4_past = ktail_runner_4_past.get_graph()
g5_past = ktail_runner_5_past.get_graph()
print(len(g4.nodes()), len(g4_past.nodes()), len(g5.nodes()),
len(g5_past.nodes()))
continue
filtering_str = ""
low_probability_filter = None ## 0.05
# if low_probability_filter:
# print("FILTER APPLIED: low prob filter!")
# g4 = graph_filtering.filter_low_probability_transitions(g4, low_probability_filter)
# g5 = graph_filtering.filter_low_probability_transitions(g5, low_probability_filter)
# filtering_str += "_lp_" + str(low_probability_filter)
#
# simple_filter = 20
# if simple_filter:
# print("FILTER APPLIED: simple filter!")
# g4 = graph_filtering.simple_filter_graph(g4, simple_filter, False)
# g5 = graph_filtering.simple_filter_graph(g5, simple_filter, False)
# filtering_str += "_sim_" + str(simple_filter)
ktail_runner_4.write2file(GRAPH_OUTPUT + log1_filename +
filtering_str + '_k' + str(k) + DOT_SUFFIX)
ktail_runner_5.write2file(GRAPH_OUTPUT + log2_filename +
filtering_str + '_k' + str(k) + DOT_SUFFIX)
print("done running with k=", k)