import time

import argparse

from collections import defaultdict,namedtuple

import os

from link_server import *

from math import log

#import cProfile, pstats,StringIO

from bfs_seq import sequential_estimation

from link_server import LinkServerCP

import cPickle as cp

from common_tools import removeFile

parser = argparse.ArgumentParser()

parser.add_argument('-dataset', type = str, default = 'input/datasets/wiki',

help = '-dataset : dataset to use. Default: input/datasets/wiki')

parser.add_argument('-seeds_file', type = str, default = 'experiments/wiki/seeds/wiki-seeds-71.cp',

help = '-edge_prob_type : method of generating edge probabilities. 0 - uniform. 1 - 1/in-deg(v), 2 - uid from [0.1,0.01]')

parser.add_argument('-results_file', type = str, default = "experiments/wiki/results-seq-71.txt",

help = '-results_file : file name of the results output file')

parser.add_argument('-get_n_reached', type = int, default = 0)

parser.add_argument('-reached_nodes_file', type = str, default = "experiments/wiki/wiki-reached_nodes.txt")

parser.add_argument('-output_mode', type = int, default = 2,

help = '-output_mode: 0 - print to screen only, 1 - output results to file, 2 - output to both, 3 - no output')

parser.add_argument('-cores', type = int, default = -1,

help = '-cores: number of concurrent processes to start every time. Default : -1 (# of cores)')

parser.add_argument('-nSamples', type = int, default = 0,

help = '-nSamples: Number of BFS samples to take (0 = take theoretic n*log(n) samples. Default : 0')

parser.add_argument('-min_samples', type = int, default = -1)

parser.add_argument('-min_relative_standard_error', type = float, default = -1)

def print_out(text):

f_output.close()

if __name__ == "__main__":

parameters = parser.parse_args()

dataset, seeds_file, results_file, output_mode, nSamples = parameters.dataset, parameters.seeds_file, parameters.results_file,\

parameters.output_mode, parameters.nSamples

L = LinkServerCP(dataset)

f = open(seeds_file, 'r')

assert (nSamples <= 0 or parameters.min_relative_standard_error <= 0)

seeds_list = cp.load(f)

f.close()

removeFile(parameters.reached_nodes_file)

if output_mode in [1,2]:

f = open(results_file, 'w')

for i, seed_set in enumerate(seeds_list):

if output_mode in [0,2]:

print "Sample ", i

if parameters.get_n_reached == 1:

try:

avg, total_samples,l_n_reached = sequential_estimation(L, seed_set, max_samples_cap=nSamples, \

nCores=parameters.cores,bReturnValues = True,\

min_samples = parameters.min_samples, \

min_relative_standard_error = parameters.min_relative_standard_error)

except:

r = open('test.txt','w')

r.write('cannot run bfs\n')

r.close()

raise Exception()

else:

avg, total_samples = sequential_estimation(L, seed_set, max_samples_cap=nSamples, \

nCores=parameters.cores,bReturnValues = False, \

min_samples = parameters.min_samples,\

min_relative_standard_error = parameters.min_relative_standard_error)

if output_mode in [1,2]:

f.write(str(avg) + "\n")

f.write(str(total_samples) + '\n')

if output_mode in [0,2]:

print "Average = %.3f, total # of samples needed = %d"%(avg, total_samples)

if parameters.get_n_reached == 1:

g = open(parameters.reached_nodes_file, 'a')

g.write('\t'.join(str(v) for v in l_n_reached) + '\n')

g.close()

if output_mode in [1,2]:

f.close()