def main(graph_name):
G = nx.read_gml(graph_name)
for_testing_fixed_set = "YES" # when YES, fixed values param, to get all statistics on final distances etc
# change the range for the parameters accordingly
envelopes = "YES"
Niter = 1000 # 100 iter seems to be enough (no big diff. with respect to 1000it)
percent_envelope = 95.
list_id_weekends_T3 = look_for_T3_weekends(
G
) # T3 doesnt share fellows in the weekend (but they are the exception)
Nbins = 20 # for the histogram of sum of distances
cutting_day = 175 # i use this only for the filenames
all_team = "NO" # as adopters or not
dir_real_data = '../Results/'
dir = "../Results/weight_shifts/infection/"
delta_end = 3. # >= than + or - dr difference at the end of the evolution (NO realization ends up closer than this!!!! if 2, i get and empty list!!!)
if for_testing_fixed_set == "NO":
output_file3 = "../Results/weight_shifts/Landscape_parameters_infection_" + str(
Niter) + "iter.dat"
file3 = open(output_file3, 'wt')
file3.close()
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
if all_team == "YES":
print "remember that now i use the file of adopters without fellows\n../Results/Actual_evolution_adopters_NO_fellows_only_attendings.dat"
exit()
else:
filename_actual_evol = "../Results/Actual_evolution_adopters_NO_fellows_only_attendings.dat"
file1 = open(
filename_actual_evol, 'r'
) ## i read the file: list_dates_and_names_current_adopters.txt (created with: extract_real_evolution_number_adopters.py)
list_lines_file = file1.readlines()
list_actual_evol = []
for line in list_lines_file: # [1:]: # i exclude the first row
num_adopters = float(line.split(" ")[1])
list_actual_evol.append(num_adopters)
##################################################################
#../Results/weight_shifts/infection/Average_time_evolution_Infection_training_p0.8_Immune0.3_1000iter_2012_avg_ic_day125.dat ESTOS VALORES SON EL OPTIMUM FIT THE 152-DIAS
prob_min = 0.1
prob_max = 0.101
delta_prob = 0.1
prob_Immune_min = 0.00
prob_Immune_max = 0.001
delta_prob_Immune = 0.1
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
prob_Immune = prob_Immune_min
while prob_Immune <= prob_Immune_max:
print "prom Immune:", prob_Immune
prob_infection = prob_min
while prob_infection <= prob_max:
print " p:", prob_infection
if for_testing_fixed_set == "YES":
output_file2 = dir + "Average_time_evolution_Infection_train_test_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_2012.dat"
else:
output_file2 = dir + "Average_time_evolution_Infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_2012.dat"
file2 = open(output_file2, 'wt')
file2.close()
# list_final_I_values_fixed_p=[] # i dont care about the final values right now, but about the whole time evol
list_lists_t_evolutions = []
list_dist_fixed_parameters = []
list_abs_dist_at_ending_point_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_final_num_infected = []
# list_abs_dist_at_cutting_day=[]
for iter in range(Niter):
#print " iter:",iter
#######OJO~!!!!!!!!!! COMENTAR ESTO CUANDO ESTOY BARRIENDO TOOOOOOOOOODO EL ESPACIO DE PARAMETROS
# file_name_indiv_evol=output_file2.strip("Average_").split('.dat')[0]+"_indiv_iter"+str(iter)+".dat"
# file4 = open(file_name_indiv_evol,'wt')
# file4.close()
##########################################
########### set I.C.
list_I = [] #list infected doctors
max_order = 0
for n in G.nodes():
G.node[n]["status"] = "S" # all nodes are Susceptible
if G.node[n]['type'] == "shift":
if G.node[n]['order'] > max_order:
max_order = G.node[n][
'order'] # to get the last shift-order for the time loop
else:
if G.node[n]['label'] == "Wunderink" or G.node[n][
"label"] == "Weiss":
G.node[n]["status"] = "I"
list_I.append(G.node[n]['label'])
list_single_t_evolution = []
list_single_t_evolution.append(
2.0) # I always start with TWO infected doctors!!
for n in G.nodes(
): # i make some DOCTORs INMUNE (anyone except Weiss and Wunderink)
if (G.node[n]['type'] == "A") or (G.node[n]['type']
== "F"):
if G.node[n]['label'] != "Wunderink" and G.node[n][
"label"] != "Weiss":
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
################# the dynamics starts:
t = 1
while t <= max_order: # loop over shifts, in order
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n][
'order'] == t:
shift_lenght = int(G.node[n]['shift_lenght'])
if shift_lenght == 2 and n not in list_id_weekends_T3:
shift_lenght = 1 # because during weekends, the fellow does rounds one day with Att1 and the other day with Att2. (weekend shifts for T3 are two day long, with no sharing fellows)
# print "one-day weekend", G.node[n]['label'],G.node[n]['shift_lenght']
flag_possible_infection = 0
for doctor in G.neighbors(
n
): #first i check if any doctor is infected in this shift
if G.node[doctor]["status"] == "I":
flag_possible_infection = 1
if flag_possible_infection:
for doctor in G.neighbors(
n
): # then the doctors in that shift, gets infected with prob_infection
for i in range(
shift_lenght
): # i repeat the infection process several times, to acount for shift lenght
if G.node[doctor]["status"] == "S":
rand = random.random()
if rand < prob_infection:
G.node[doctor]["status"] = "I"
if G.node[doctor][
"type"] == "A": # fellows participate in the dynamics, but i only consider the attendings as real adopters
list_I.append(
G.node[doctor]
["label"])
# if for_testing_fixed_set=="YES":
# if t==cutting_day:
# list_abs_dist_at_cutting_day.append(abs(float(list_actual_evol[-1])-float(len(list_I))))
# print abs(float(list_actual_evol[-1])-float(len(list_I))), float(list_actual_evol[t]),float(len(list_I))
list_single_t_evolution.append(float(len(list_I)))
t += 1
######## end t loop
########OJO~!!!!!!!!!! COMENTAR ESTO CUANDO ESTOY BARRIENDO TOOOOOOOOOODO EL ESPACIO DE PARAMETROS
# file4 = open(file_name_indiv_evol,'at')
#for i in range(len(list_single_t_evolution)): #time step by time step
# print >> file4, i,list_single_t_evolution[i], prob_infection, prob_Immune
#file4.close()
########################################################
list_lists_t_evolutions.append(list_single_t_evolution)
list_dist_fixed_parameters.append(
compare_real_evol_vs_simus_to_be_called.compare_two_curves(
list_actual_evol, list_single_t_evolution))
list_abs_dist_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] - list_actual_evol[-1])
) # i save the distance at the ending point between the current simu and actual evol
list_dist_at_ending_point_fixed_parameters.append(
list_single_t_evolution[-1] - list_actual_evol[-1]
) # i save the distance at the ending point between the current simu and actual evol
list_final_num_infected.append(list_single_t_evolution[-1])
######## end loop Niter
list_pair_dist_std_delta_end = []
list_pair_dist_std_delta_end.append(
numpy.mean(list_dist_fixed_parameters)
) # average dist between the curves over Niter
list_pair_dist_std_delta_end.append(
numpy.std(list_dist_fixed_parameters))
list_pair_dist_std_delta_end.append(
numpy.mean(list_abs_dist_at_ending_point_fixed_parameters))
if for_testing_fixed_set == "NO":
file3 = open(output_file3, 'at') # i print out the landscape
print >> file3, prob_infection, prob_Immune, numpy.mean(
list_abs_dist_at_ending_point_fixed_parameters
), numpy.mean(list_dist_fixed_parameters), numpy.mean(
list_final_num_infected), numpy.std(
list_final_num_infected)
file3.close()
if (
numpy.mean(list_abs_dist_at_ending_point_fixed_parameters)
) <= delta_end: # i only consider situations close enough at the ending point
dict_filenames_tot_distance[
output_file2] = list_pair_dist_std_delta_end
file2 = open(output_file2, 'at')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter):
list_fixed_t.append(list_lists_t_evolutions[iter][s])
print >> file2, s, numpy.mean(list_fixed_t)
file2.close()
print "printed out: ", output_file2
# raw_input()
if envelopes == "YES":
calculate_envelope_set_curves.calculate_envelope(
list_lists_t_evolutions, percent_envelope, "Infection",
[prob_infection, prob_Immune])
if for_testing_fixed_set == "YES":
num_valid_endings = 0.
for item in list_abs_dist_at_ending_point_fixed_parameters:
if item <= delta_end: # i count how many realizations i get close enough at the ending point
num_valid_endings += 1.
print "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters), numpy.std(
list_dist_fixed_parameters
), list_dist_fixed_parameters, "\n"
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
histogram_filename = "../Results/weight_shifts/histogr_raw_distances_ending_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + ".dat"
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters,
histogram_filename)
histogram_filename2 = "../Results/weight_shifts/histogr_sum_dist_traject_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + ".dat"
histograma_bines_gral.histograma_bins(
list_dist_fixed_parameters, Nbins, histogram_filename2)
output_file10 = "../Results/weight_shifts/Summary_results_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + ".dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from train-testing infection with", Niter, "iter, and with values for the parameters: prob_inf ", prob_infection, " prob immune: ", prob_Immune, "\n"
print >> file10, "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters), numpy.std(
list_dist_fixed_parameters
), list_dist_fixed_parameters, "\n"
print >> file10, "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
print >> file10, "written optimum train_test evolution file:", output_file2
print >> file10, "written histogram file: ", histogram_filename
file10.close()
print "written Summary file: ", output_file10
print "written histogram file: ", histogram_filename
print "written histogram file: ", histogram_filename2
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
if for_testing_fixed_set == "NO": # only if i am exploring the whole landscape, i need to call this function, otherwise, i already know the optimum
compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Infection_weight", all_team, Niter,
None) # last argument doesnt apply (cutting day)
if for_testing_fixed_set == "NO":
print "written landscape file:", output_file3
def main(graph_name):
G = nx.read_gml(graph_name)
cutting_day = 243 # i use this only for the filenames
for_testing_fixed_set = "YES" # when YES, fixed values param, to get all statistics on final distances etc
# change the range for the parameters accordingly
envelopes = "YES"
Niter = 1000 # 100 iter seems to be enough (no big diff. with respect to 1000it)
percent_envelope = 95.
list_id_weekends_T3 = look_for_T3_weekends(
G
) # T3 doesnt share fellows in the weekend (but they are the exception)
Nbins = 1000 # for the histogram of sum of distances
all_team = "NO" # as adopters or not
dir_real_data = '../Results/'
dir = "../Results/weight_shifts/infection/"
delta_end = 3. # >= than + or - dr difference at the end of the evolution (NO realization ends up closer than this!!!! if 2, i get and empty list!!!)
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
filename_actual_evol = "../Data/Attendings_Orders_from_inference_list_adopters_day.dat"
file1 = open(
filename_actual_evol, 'r'
) ## i read the file: list_dates_and_names_current_adopters.txt (created with: extract_real_evolution_number_adopters.py)
list_lines_file = file1.readlines()
dict_days_list_empirical_adopters = {}
list_actual_evol = []
for line in list_lines_file: # [1:]: # i exclude the first row
day = int(line.split(" ")[0])
num_adopters = float(line.split(" ")[1])
list_actual_evol.append(num_adopters)
list_current_adopters = []
for element in line.split(
" "
)[2:]: # i need to ignore the empty columns from the original datafile
if element:
if element != '\n':
list_current_adopters.append(element.strip('\n'))
dict_days_list_empirical_adopters[day] = list_current_adopters
list_actual_evol_testing = list_actual_evol[cutting_day:]
##################################################################
prob_min = 0.8
prob_max = 0.801
delta_prob = 0.1
prob_Immune_min = 0.10
prob_Immune_max = 0.101
delta_prob_Immune = 0.1
# threshold is not personal, and set randomly to a value (0,1)
dose_min = 0.2 # of a single encounter with an infected (starting from zero doesnt make sense)
dose_max = 0.201
delta_dose = 0.101
prob_Immune = prob_Immune_min
while prob_Immune <= prob_Immune_max:
print "prom Immune:", prob_Immune
prob_infection = prob_min
while prob_infection <= prob_max:
print " p:", prob_infection
dose = dose_min
while dose <= dose_max:
print " dose:", dose
output_file2 = dir + "Average_time_evolution_Infection_memory_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune
) + "_FIXED_threshold_from_distrib_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_A_F_inferred_middle_real_ic.dat"
file2 = open(output_file2, 'wt')
file2.close()
# list_final_I_values_fixed_p=[] # i dont care about the final values right now, but about the whole time evol
list_lists_t_evolutions = []
list_dist_fixed_parameters_testing_segment = []
list_abs_dist_at_ending_point_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_final_num_infected = []
list_abs_dist_point_by_point_indiv_simus_to_actual = []
list_dist_point_by_point_indiv_simus_to_actual = []
# list_abs_dist_at_cutting_day=[]
for iter in range(Niter):
# print " iter:",iter
########### set I.C.
list_I = [] #list infected doctors
max_order = 0
for n in G.nodes():
G.node[n]["status"] = "S" # all nodes are Susceptible
G.node[n][
"infec_value"] = 0. # when this value goes over the infect_threshold, the dr is infected
G.node[n]["personal_threshold"] = random.random(
) # for a dr to become infected
if G.node[n]['type'] == "shift":
if G.node[n]['order'] > max_order:
max_order = G.node[n][
'order'] # to get the last shift-order for the time loop
else:
if G.node[n][
'label'] in dict_days_list_empirical_adopters[
cutting_day]:
G.node[n]["infec_value"] = G.node[n][
"personal_threshold"] + 1.
G.node[n]["status"] = "I"
list_I.append(G.node[n]['label'])
list_single_t_evolution = []
old_num_adopters = len(
dict_days_list_empirical_adopters[cutting_day])
list_single_t_evolution.append(
old_num_adopters
) # I always start with TWO infected doctors!!
for n in G.nodes(
): # i make some DOCTORs INMUNE (anyone except Weiss and Wunderink)
if (G.node[n]['type'] == "A") or (G.node[n]['type']
== "F"):
if G.node[n][
'label'] not in dict_days_list_empirical_adopters[
cutting_day]:
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
################# the dynamics starts:
shift_length = 5 #i know the first shift (order 0) is of length 5
t = cutting_day
while t <= max_order: # loop over shifts, in order
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n][
'order'] == t:
shift_length = int(G.node[n]['shift_length'])
if shift_length == 2 and n not in list_id_weekends_T3:
shift_length = 1 # because during weekends, the fellow does rounds one day with Att1 and the other day with Att2. (weekend shifts for T3 are two day long, with no sharing fellows)
flag_possible_infection = 0
for doctor in G.neighbors(
n
): #first i check if any doctor is infected in this shift
if G.node[doctor]["status"] == "I":
flag_possible_infection = 1
if flag_possible_infection:
for doctor in G.neighbors(
n
): # then the doctors in that shift, gets infected with prob_infection
for i in range(shift_length):
if G.node[doctor]["status"] == "S":
rand = random.random()
if rand < prob_infection: # with prob p the infection occurres
G.node[doctor][
"infec_value"] += dose # and bumps the infection_value of that susceptible dr
if G.node[doctor][
"infec_value"] >= G.node[
doctor][
"personal_threshold"]: # the threshold for infection is personal
G.node[doctor][
"status"] = "I"
# if G.node[doctor]["type"]=="A": # fellows participate in the dynamics, but i only consider the attendings as real adopters
list_I.append(
G.node[doctor]
["label"])
new_num_adopters = len(list_I)
if shift_length == 5: # i estimate that adoption happens in the middle of the shift
if t + 5 < max_order:
list_single_t_evolution.append(
old_num_adopters)
if t + 4 < max_order:
list_single_t_evolution.append(
old_num_adopters)
if t + 3 < max_order:
list_single_t_evolution.append(
new_num_adopters)
if t + 2 < max_order:
list_single_t_evolution.append(
new_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(
new_num_adopters)
t += 5
elif shift_length == 4:
if t + 4 < max_order:
list_single_t_evolution.append(
old_num_adopters)
if t + 3 < max_order:
list_single_t_evolution.append(
old_num_adopters)
if t + 2 < max_order:
list_single_t_evolution.append(
new_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(
new_num_adopters)
t += 4
elif shift_length == 3:
if t + 3 < max_order:
list_single_t_evolution.append(
old_num_adopters)
if t + 2 < max_order:
list_single_t_evolution.append(
new_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(
new_num_adopters)
t += 3
elif shift_length == 2:
if t + 2 < max_order:
list_single_t_evolution.append(
old_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(
new_num_adopters)
t += 2
elif shift_length == 1:
if t + 1 < max_order:
list_single_t_evolution.append(
new_num_adopters)
t += 1
old_num_adopters = new_num_adopters
######## end t loop
list_lists_t_evolutions.append(list_single_t_evolution)
# i only run the testing segment
list_dist_fixed_parameters_testing_segment.append(
compare_real_evol_vs_simus_to_be_called.
compare_two_curves(list_actual_evol_testing,
list_single_t_evolution))
list_abs_dist_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] -
list_actual_evol_testing[-1])
) # i save the distance at the ending point between the current simu and actual evol
list_dist_at_ending_point_fixed_parameters.append(
list_single_t_evolution[-1] -
list_actual_evol_testing[-1]
) # i save the distance at the ending point between the current simu and actual evol
list_final_num_infected.append(list_single_t_evolution[-1])
for index in range(len(list_single_t_evolution)):
list_abs_dist_point_by_point_indiv_simus_to_actual.append(
abs(list_single_t_evolution[index] -
list_actual_evol_testing[index]))
list_dist_point_by_point_indiv_simus_to_actual.append(
list_single_t_evolution[index] -
list_actual_evol_testing[index])
######## end loop Niter
file2 = open(output_file2, 'at')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter):
list_fixed_t.append(list_lists_t_evolutions[iter][s])
print >> file2, s + cutting_day, numpy.mean(list_fixed_t)
file2.close()
print "printed out: ", output_file2
# raw_input()
if envelopes == "YES":
calculate_envelope_set_curves.calculate_envelope(
list_lists_t_evolutions, percent_envelope, "Infection",
[prob_infection, prob_Immune])
num_valid_endings = 0.
for item in list_abs_dist_at_ending_point_fixed_parameters:
if item <= delta_end: # i count how many realizations i get close enough at the ending point
num_valid_endings += 1.
print "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters_testing_segment), numpy.std(
list_dist_fixed_parameters_testing_segment
), list_dist_fixed_parameters_testing_segment, "\n"
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
histogram_filename = "../Results/weight_shifts/histogr_raw_distances_ending_infection_memory_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_threshold_from_distrib_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day
) + "_A_F_inferred_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters,
histogram_filename)
# histogram_filename2="../Results/weight_shifts/histogr_sum_dist_traject_infection_memory_p"+str(prob_infection)+"_Immune"+str(prob_Immune)+"_threshold_from_distrib_dose"+str(dose)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred_middle.dat"
# histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,Nbins,histogram_filename2)
histogram_filename3 = "../Results/weight_shifts/histogr_sum_dist_testing_segment_infection_memory_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_threshold_from_distrib_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day
) + "_A_F_inferred_middle_real_ic.dat"
#print list_dist_fixed_parameters_testing_segment
histograma_bines_gral.histograma_bins_zero(
list_dist_fixed_parameters_testing_segment, Nbins,
histogram_filename3)
print min(list_dist_fixed_parameters_testing_segment), max(
list_dist_fixed_parameters_testing_segment)
histogram_filename4 = "../Results/weight_shifts/histogr_abs_dist_point_by_point_infection_memory_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_threshold_from_distrib_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day
) + "_A_F_inferred_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(
list_abs_dist_point_by_point_indiv_simus_to_actual,
histogram_filename4)
histogram_filename5 = "../Results/weight_shifts/histogr_dist_point_by_point_infection_memory_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_threshold_from_distrib_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day
) + "_A_F_inferred_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(
list_dist_point_by_point_indiv_simus_to_actual,
histogram_filename5)
output_file10 = "../Results/weight_shifts/Summary_results_infection_memory_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_threshold_from_distrib_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day
) + "_A_F_inferred_middle_real_ic.dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from best fit infection _memory with", Niter, "iter, and with values for the parameters: prob_inf ", prob_infection, " prob immune: ", prob_Immune, "\n"
print >> file10, "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters_testing_segment), numpy.std(
list_dist_fixed_parameters_testing_segment
), list_dist_fixed_parameters_testing_segment, "\n"
print >> file10, "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
print >> file10, "written optimum best fit evolution file:", output_file2
print >> file10, "written histogram file: ", histogram_filename
file10.close()
print "written Summary file: ", output_file10
dose += delta_dose
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
def main(graph_name):
G = nx.read_gml(
graph_name
) # about the "order" in the shift nodes: not all orders exist, only every 5/2 days. thats why shifts have length that we use to weight the interactions accordingly
list_id_weekends_T3 = look_for_T3_weekends(
G
) # T3 doesnt share fellows in the weekend (but they are the exception)
Niter = 1000
dir_real_data = '../Results/'
time_window_ahead = 4 # number of days in which there will be no Adopters on call
basic_intervention_start_day = 20 # and then plus minus a small random number
random_start = "YES" # if no, all iter with same initial re-seeding (intervention) day
num_reseeds = 1 # per intervention
min_bump = 0.0 # for the doctors that are re-seeded
max_bump = 1.0 #same scale as the status, and the adoption threshold
delta_bump = 0.03
all_team = "NO" # as adopters or not
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
if all_team == "YES":
print "remember that now i use the file of adopters without fellows\n../Results/Actual_evolution_adopters_NO_fellows_only_attendings.dat"
exit()
else:
filename_actual_evol = "../Results/Actual_evolution_adopters_NO_fellows_only_attendings.dat"
file1 = open(
filename_actual_evol, 'r'
) ## i read the file: list_dates_and_names_current_adopters.txt (created with: extract_real_evolution_number_adopters.py)
list_lines_file = file1.readlines()
list_actual_evol = []
for line in list_lines_file: # [1:]: # i exclude the first row
num_adopters = float(line.split(" ")[1])
list_actual_evol.append(num_adopters)
##################################################################
# i use the best fit (over the 250-day curve):
# from only Att counted as adopters: ../Results/weight_shifts/persuasion/alpha0.10_damping0.00/Time_evol_Persuasion_alpha0.1_damping0.0_mutual0.5_threshold0.3_1000iter.dat THIS IS THE THIRD BEST SOLUTION, BUT ENDS UP CLOSER, SO I PREFER TO USE THIS, TO CHECK THE PERFORMANCE WHEN BUMP=0
alpha_F = 0.10 # alpha=0: nobody changes their mind
alpha_A = alpha_F
damping = 0.0 #its harder to go back from YES to NO again. =1 means no effect, =0.5 half the movement from Y->N than the other way around, =0 never go back from Y to N
mutual_encouragement = 0.50 # when two Adopters meet, they convince each other even more
threshold = 0.50 # larger than, to be an Adopte
print "\n\nPersuasion process on network, with Niter:", Niter, "\n"
dir = "../Results/weight_shifts/persuasion/"
output_file2 = "../Results/weight_shifts/Final_distance_vs_bump_alpha" + str(
alpha_F) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_num_reseed_per_shift" + str(
num_reseeds) + "_" + str(Niter) + "iter_intervention_start" + str(
basic_intervention_start_day) + "_window" + str(
time_window_ahead) + ".dat"
file2 = open(output_file2, 'wt')
file2.close()
bump = min_bump
while bump <= max_bump:
print "bump:", bump
output_file = dir + "Time_evolutions_Persuasion_alpha" + str(
alpha_F) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_num_reseed_per_shift" + str(
num_reseeds) + "_" + str(
Niter) + "iter_intervention_start" + str(
basic_intervention_start_day) + "_window" + str(
time_window_ahead) + "_bump" + str(bump) + ".dat"
file = open(output_file, 'wt')
file.close()
list_distances_150day = []
list_distances_150day_Att_fellow = []
list_distances_200day = []
list_distances_200day_Att_fellow = []
list_ending_distances = []
list_ending_distances_Att_fellow = []
tot_number_interventions = 0
tot_number_interventions_Att = 0
tot_number_successful_interventions = 0
tot_number_successful_interventions_Att = 0
num_successfully_bumped = 0
num_successfully_bumped_Att = 0
time_evol_number_adopters_ITER = [
] # list of complete single realizations of the dynamics
for iter in range(Niter):
print " iter: ", iter
if random_start == "YES": # i pick the first intervention day
sign = random.random()
if sign < 0.5:
sign = -1.
else:
sign = 1.
delta_day = random.random() * 5.
start_intervention = int(
basic_intervention_start_day + sign * delta_day
) # i let the system evolve freely for a some time before i start re-seeding
else:
start_intervention = basic_intervention_start_day
list_t = []
time_evol_number_adopters = [
] # for a single realization of the dynamics ONLY ATTENDING ADOPTERS
time_evol_number_tot_adopters = [
] # Attendings and fellows as adopters
num_adopters, seed_shift, max_shift = set_ic(
G, threshold
) # i establish who is Adopter and NonAdopter initially, and count how many shifts i have total
time_evol_number_adopters.append(float(num_adopters))
list_t.append(0)
time_evol_number_tot_adopters.append(
float(num_adopters)) # Attendings and fellows as adopters
next_intervention_day = start_intervention
# the dynamics starts:
t = int(seed_shift) + 1
while t <= max_shift: # loop over shifts, in chronological order (the order is the day index since seeding_day)
old_num_Att_Adopters = 0
old_num_Adopters = 0 #count number of adopters before an intervention
for n in G.nodes():
try:
if G.node[n]["status"] == "Adopter":
old_num_Adopters += 1.
if G.node[n]["type"] == "A":
old_num_Att_Adopters += 1.
except KeyError:
pass #to ignore the shift-nodes
flag_future = look_ahead(
G, time_window_ahead, t
) # i evaluate the next few days, to see if any adopter will be on call: if not, i re-seed some more
if flag_future == "YES" and t >= start_intervention and t == next_intervention_day:
flag_Att = intervention(
G, t, bump, threshold,
num_reseeds) # num of Att intervened
next_intervention_day += time_window_ahead # i dont reseed everyday of the look_ahead window, just the first day of it
tot_number_interventions += 1
tot_number_interventions_Att += flag_Att
num_Att_Adopters = 0
num_Adopters = 0
for n in G.nodes():
try:
if G.node[n][
"status"] == "Adopter": #first i check if any doctor is an adopter in this shift
num_Adopters += 1.
if G.node[n][
"type"] == "A": #first i check if any doctor is an adopter in this shift
num_Att_Adopters += 1.
except KeyError:
pass
if old_num_Adopters < num_Adopters:
tot_number_successful_interventions += 1
if num_Adopters - old_num_Adopters > num_reseeds:
print "how did i bumped more doctors than", num_reseed, "?? ", t, ": ", num_Adopters - old_num_Adopters
if old_num_Att_Adopters < num_Att_Adopters:
tot_number_successful_interventions_Att += 1
list_t.append(t)
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n][
'order'] == t: # i look for the shift corresponding to that time step
shift_lenght = int(G.node[n]['shift_lenght'])
if shift_lenght == 2 and n not in list_id_weekends_T3:
shift_lenght = 1 # because during weekends, the fellow does rounds one day with Att1 and the other day with Att2. (weekend shifts for T3 are two day long, with no sharing fellows)
flag_possible_persuasion = 0
for doctor in G.neighbors(n):
if G.node[doctor][
"status"] == "Adopter": #first i check if any doctor is an adopter in this shift
flag_possible_persuasion = 1
break
if flag_possible_persuasion == 1:
list_doctors = []
for doctor in G.neighbors(
n): # for all drs in that shift
list_doctors.append(doctor)
pairs = itertools.combinations(
list_doctors,
2) # cos the shift can be 2 but also 3 doctors
for pair in pairs:
doctor1 = pair[0]
doctor2 = pair[1]
if G.node[doctor1]['status'] != G.node[doctor2][
'status']: # if they think differently,
# there will be persuasion
persuasion(
G, damping, doctor1, doctor2, alpha_A,
alpha_F, threshold, shift_lenght
) # i move their values of opinion
update_opinions(
G, threshold, doctor1, doctor2
) # i update status and make sure the values of the vectors stay between [0,1]
else: # if two Adopters meet, they encourage each other (if two NonAdopters, nothing happens)
mutual_reinforcement(
G, mutual_encouragement, doctor1,
doctor2, shift_lenght)
list_Att_Adopters = []
list_Adopters = [] #count how many i have at this time
for n in G.nodes():
try:
if G.node[n]["status"] == "Adopter":
list_Adopters.append(G.node[n]["label"])
if G.node[n]["type"] == "A":
list_Att_Adopters.append(G.node[n]["label"])
except:
pass # if the node is a shift, it doesnt have a 'status' attribute
time_evol_number_adopters.append(float(len(list_Att_Adopters)))
time_evol_number_tot_adopters.append(float(
len(list_Adopters))) # Attendings and fellows as adopters
t += 1
############# end while loop over t
time_evol_number_adopters_ITER.append(time_evol_number_adopters)
list_distances_150day.append(
time_evol_number_adopters[-93] -
list_actual_evol[-93]) # because last day is 243
list_distances_150day_Att_fellow.append(
time_evol_number_tot_adopters[-93] - list_actual_evol[-93])
list_distances_200day.append(time_evol_number_adopters[-43] -
list_actual_evol[-43])
list_distances_200day_Att_fellow.append(
time_evol_number_tot_adopters[-43] - list_actual_evol[-43])
list_ending_distances.append(
time_evol_number_adopters[-1] -
list_actual_evol[-1]) # ONLY att adopters
list_ending_distances_Att_fellow.append(
time_evol_number_tot_adopters[-1] -
list_actual_evol[-1]) # att + fellow adopters
print iter, t, time_evol_number_tot_adopters[
-1], time_evol_number_adopters[-1], list_actual_evol[-1]
print "diff. at day 200:", time_evol_number_adopters[
-43] - list_actual_evol[-43], time_evol_number_adopters[
-43], list_actual_evol[
-43], "diff. at the end:", time_evol_number_adopters[
-1] - list_actual_evol[-1], time_evol_number_adopters[
-1], list_actual_evol[
-1], " if i count tot Att+Fellows at end:", time_evol_number_tot_adopters[
-1], "for bump:", bump
##############end loop Niter
parameters = [
alpha_F, damping, mutual_encouragement, threshold, bump,
time_window_ahead
]
calculate_envelope_set_curves.calculate_envelope(
time_evol_number_adopters_ITER, 95, "Persuasion_intervention",
parameters)
file = open(output_file, 'wt')
for i in range(
len(time_evol_number_adopters)): #time step by time step
list_fixed_t = []
for iteracion in range(
Niter): #loop over all independent iter of the process
list_fixed_t.append(
time_evol_number_adopters_ITER[iteracion]
[i]) # i collect all values for the same t, different iter
print >> file, list_t[i], numpy.mean(list_fixed_t), numpy.std(
list_fixed_t), alpha_F, damping, mutual_encouragement
file.close()
try:
fraction_success_interv = float(
tot_number_successful_interventions) / float(
tot_number_interventions) #averages over Niter
except ZeroDivisionError:
fraction_success_interv = 0
try:
fraction_success_interv_Att = float(
tot_number_successful_interventions_Att) / float(
tot_number_interventions_Att) #averages over Niter
except ZeroDivisionError:
fraction_success_interv_Att = 0
file2 = open(output_file2, 'at')
print >> file2, bump, numpy.mean(list_distances_150day),numpy.std(list_distances_150day), \
numpy.mean(list_distances_150day_Att_fellow),numpy.std(list_distances_150day_Att_fellow), \
numpy.mean(list_distances_200day),numpy.std(list_distances_200day), \
numpy.mean(list_distances_200day_Att_fellow),numpy.std(list_distances_200day_Att_fellow),\
numpy.mean(list_ending_distances),numpy.std(list_ending_distances), \
numpy.mean(list_ending_distances_Att_fellow), numpy.std(list_ending_distances_Att_fellow),\
float(tot_number_interventions)/float(Niter) , float(tot_number_interventions_Att)/float(Niter) ,fraction_success_interv, fraction_success_interv_Att,bump/float(threshold)
file2.close()
print "fraction successful interventions", fraction_success_interv, "fraction successful interventions on Att", fraction_success_interv_Att
print " written:", output_file
bump += delta_bump
########################## end loop over bump
print "\nwritten:", output_file2
def main(graph_name):
G = nx.read_gml(graph_name)
list_id_weekends_T3=look_for_T3_weekends(G) # T3 doesnt share fellows in the weekend (but they are the exception)
percent_envelope=95.
Niter=1000
cutting_day=125
min_sum_dist=20 # to compute number of realizations that have a sum of distances smaller than this
Nbins=200 # for the histogram of sum of distances
envelopes="NO"
delta_end=3. # >= than + or - dr difference at the end of the evolution
dir_real_data='../Results/'
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
filename_actual_evol="../Data/Attendings_Orders_from_inference_list_adopters_day.dat" # "../Results/Actual_evolution_adopters_from_inference.dat"
file1=open(filename_actual_evol,'r') ## i read the file: list_dates_and_names_current_adopters.txt (created with: extract_real_evolution_number_adopters.py)
list_lines_file=file1.readlines()
dict_days_list_empirical_adopters={}
list_actual_evol=[]
for line in list_lines_file: # [1:]: # i exclude the first row
day=int(line.split(" ")[0])
num_adopters= float(line.split(" ")[1])
list_actual_evol.append(num_adopters)
list_current_adopters=[]
for element in line.split(" ")[2:]: # i need to ignore the empty columns from the original datafile
if element:
if element != '\n':
list_current_adopters.append(element.strip('\n'))
dict_days_list_empirical_adopters[day]=list_current_adopters
list_actual_evol_testing=list_actual_evol[cutting_day:]
##################################################################
#../Results/weight_shifts/persuasion/Time_evolutions_Persuasion_training_alpha0.5_damping0.4_mutual_encourg0.5_threshold0.5_unif_distr_1000iter_2012_seed31Oct_finalnetwork_day125.dat
#OJO!!! NECESITO DOS DECIMALES SIEMPRE, PARA QUE CUADRE CON EL NOMBRE DE LOS SUB-DIRECTORIOS DONDE LO GUARDO
alpha_F_min=0.50 #0.15 # alpha=0: nobody changes their mind
alpha_F_max=0.501 #0.351
delta_alpha_F=0.10 #AVOID 1.0 OR THE DYNAMICS GETS TOTALLY STUCK AND IT IS NOT ABLE TO PREDICT SHIT!
min_damping=0.600 #0.0 #its harder to go back from YES to NO again. =1 means no effect, =0.5 half the movement from Y->N than the other way around, =0 never go back from Y to N
max_damping=0.601 #0.451
delta_damping=0.10
min_mutual_encouragement=0.40 #0.50 # when two Adopters meet, they convince each other even more
max_mutual_encouragement=0.401 # 0.51 # KEEP THIS FIXED VALUES FOR NOW
delta_mutual_encouragement=0.10
threshold_min=0.50 #0.50 # larger than, to be an Adopte
threshold_max=0.501 # 0.51 # KEEP THIS FIXED VALUES FOR NOW
delta_threshold=0.10 # AVOID 1.0 OR THE DYNAMICS GETS TOTALLY STUCK AND IT IS NOT ABLE TO PREDICT SHIT
print "\n\nPersuasion process on network, with Niter:",Niter
threshold=threshold_min
while threshold<= threshold_max:
print "thershold:",threshold
alpha_F=alpha_F_min
while alpha_F<= alpha_F_max: # i explore all the parameter space, and create a file per each set of valuesllkl
alpha_A=1.0*alpha_F
print " alpha_F:",alpha_F
mutual_encouragement=min_mutual_encouragement
while mutual_encouragement <= max_mutual_encouragement:
print " mutual_encouragement:",mutual_encouragement
damping=min_damping
while damping <= max_damping:
print " damping:",damping
dir="../Results/weight_shifts/persuasion/alpha%.2f_damping%.2f/" % (alpha_F, damping )
output_file=dir+"Time_evol_Persuasion_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_"+str(cutting_day)+"_A_F_inferred_middle_real_ic.dat"
file = open(output_file,'wt')
file.close()
time_evol_number_adopters_ITER=[] # list of complete single realizations of the dynamics
list_dist_fixed_parameters_testing_segment=[]
list_dist_abs_at_ending_point_fixed_parameters=[]
list_dist_at_ending_point_fixed_parameters=[]
list_final_num_adopt=[]
list_abs_dist_point_by_point_indiv_simus_to_actual=[]
list_dist_point_by_point_indiv_simus_to_actual=[]
#list_abs_dist_at_cutting_day=[]
for iter in range(Niter):
# print " ",iter
num_realizations_sum_dist_small=0.
time_evol_number_adopters=[] # for a single realization of the dynamics
num_adopters , max_shift= set_ic(G,threshold,cutting_day,dict_days_list_empirical_adopters)
time_evol_number_adopters.append(float(num_adopters))
old_num_adopters=num_adopters
# the dynamics starts:
shift_length=5 #i know the first shift (order 0) is of length 5
t=cutting_day
while t<= max_shift: # loop over shifts, in chronological order (the order is the day index since seeding_day)
# print 't:',t
for n in G.nodes():
if G.node[n]['type']=="shift" and G.node[n]['order']==t: # i look for the shift corresponding to that time step
shift_length=int(G.node[n]['shift_length'])
if shift_length==2 and n not in list_id_weekends_T3:
shift_length=1 # because during weekends, the fellow does rounds one day with Att1 and the other day with Att2. (weekend shifts for T3 are two day long, with no sharing fellows)
# print "one-day weekend", G.node[n]['label'],G.node[n]['shift_length']
flag_possible_persuasion=0
for doctor in G.neighbors(n):
if G.node[doctor]["status"]=="Adopter": #first i check if any doctor is an adopter in this shift
flag_possible_persuasion=1
break
if flag_possible_persuasion==1:
list_doctors=[]
for doctor in G.neighbors(n): # for all drs in that shift
list_doctors.append(doctor)
pairs=itertools.combinations(list_doctors,2) # cos the shift can be 2 but also 3 doctors
for pair in pairs:
doctor1=pair[0]
doctor2=pair[1]
if G.node[doctor1]['status'] != G.node[doctor2]['status']: # if they think differently,
# there will be persuasion
persuasion(G,damping,doctor1,doctor2,alpha_A,alpha_F,threshold,shift_length) # i move their values of opinion
update_opinions(G,threshold,doctor1,doctor2) # i update status and make sure the values of the vectors stay between [0,1]
else: # if two Adopters meet, they encourage each other (if two NonAdopters, nothing happens)
mutual_reinforcement(G,mutual_encouragement,doctor1,doctor2,shift_length)
# else:
# print " no persuasion possible during shift (no adopters present)!"
list_Adopters=[]
for n in G.nodes():
try:
if G.node[n]["status"]=="Adopter":
if G.node[n]["label"] not in list_Adopters :#and G.node[n]["type"]=="A":
list_Adopters.append(G.node[n]["label"])
except: pass # if the node is a shift, it doesnt have a 'status' attribute
new_num_adopters=len(list_Adopters)
if shift_length==5: # i estimate that adoption happens in the middle of the shift
if t+5 < max_shift:
time_evol_number_adopters.append(old_num_adopters)
if t+4 < max_shift:
time_evol_number_adopters.append(old_num_adopters)
if t+3 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
if t+2 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
if t+1 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
t+=5
elif shift_length==4:
if t+4 < max_shift:
time_evol_number_adopters.append(old_num_adopters)
if t+3 < max_shift:
time_evol_number_adopters.append(old_num_adopters)
if t+2 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
if t+1 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
t+=4
elif shift_length==3:
if t+3 < max_shift:
time_evol_number_adopters.append(old_num_adopters)
if t+2 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
if t+1 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
t+=3
elif shift_length==2:
if t+2 < max_shift:
time_evol_number_adopters.append(old_num_adopters)
if t+1 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
t+=2
elif shift_length==1:
if t+1 < max_shift:
time_evol_number_adopters.append(new_num_adopters)
t+=1
old_num_adopters=new_num_adopters
############## end while loop over t
time_evol_number_adopters_ITER.append(time_evol_number_adopters)
# now i only run the testing segment!
dist=compare_real_evol_vs_simus_to_be_called.compare_two_curves( list_actual_evol_testing,time_evol_number_adopters)
list_dist_fixed_parameters_testing_segment.append(dist)
if dist < min_sum_dist:
num_realizations_sum_dist_small+=1
list_dist_abs_at_ending_point_fixed_parameters.append( abs(time_evol_number_adopters[-1]-list_actual_evol_testing[-1]) )
list_dist_at_ending_point_fixed_parameters.append(time_evol_number_adopters[-1]-list_actual_evol_testing[-1])
list_final_num_adopt.append(time_evol_number_adopters[-1])
for index in range(len(time_evol_number_adopters)):
list_abs_dist_point_by_point_indiv_simus_to_actual.append(abs(time_evol_number_adopters[index]-list_actual_evol_testing[index]))
list_dist_point_by_point_indiv_simus_to_actual.append(time_evol_number_adopters[index]-list_actual_evol_testing[index])
#######################end loop over Niter
file = open(output_file,'wt')
for i in range(len(time_evol_number_adopters)): #time step by time step
list_fixed_t=[]
for iteracion in range (Niter): #loop over all independent iter of the process
list_fixed_t.append(time_evol_number_adopters_ITER[iteracion][i]) # i collect all values for the same t, different iter
print >> file,i+cutting_day,numpy.mean(list_fixed_t),numpy.std(list_fixed_t), alpha_F,damping,mutual_encouragement
file.close()
print "printed out: ",output_file
if envelopes=="YES":
calculate_envelope_set_curves.calculate_envelope(time_evol_number_adopters_ITER,percent_envelope,"Persuasion",[alpha_F,damping,mutual_encouragement,threshold])
num_valid_endings=0.
for item in list_dist_abs_at_ending_point_fixed_parameters:
if item <= delta_end: # i count how many realizations i get close enough at the ending point
num_valid_endings+=1.
print "average distance of the optimum in the testing segment:",numpy.mean(list_dist_fixed_parameters_testing_segment),numpy.std(list_dist_fixed_parameters_testing_segment),list_dist_fixed_parameters_testing_segment,"\n"
print "fraction of realizations that end within delta_doctor:",num_valid_endings/Niter,"mean ending dist:",numpy.mean(list_dist_at_ending_point_fixed_parameters), "SD final dist",numpy.std(list_dist_at_ending_point_fixed_parameters),list_dist_at_ending_point_fixed_parameters
histogram_filename="../Results/weight_shifts/histogr_raw_distances_ending_persuasion_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(list_dist_at_ending_point_fixed_parameters, histogram_filename)
# histogram_filename2="../Results/weight_shifts/histogr_sum_dist_traject_persuasion_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_alphaA_eq_alphaF_day"+str(cutting_day)+"_A_F_inferred_middle.dat"
# histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,Nbins,histogram_filename2)
histogram_filename3="../Results/weight_shifts/histogr_sum_dist_testing_segment_persuasion_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred_middle_real_ic.dat"
histograma_bines_gral.histograma_bins_zero(list_dist_fixed_parameters_testing_segment,Nbins,histogram_filename3)
print min(list_dist_fixed_parameters_testing_segment),max(list_dist_fixed_parameters_testing_segment)
histogram_filename4="../Results/weight_shifts/histogr_abs_dist_point_by_point_persuasion_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(list_abs_dist_point_by_point_indiv_simus_to_actual, histogram_filename4)
histogram_filename5="../Results/weight_shifts/histogr_dist_point_by_point_persuasion_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(list_dist_point_by_point_indiv_simus_to_actual, histogram_filename5)
output_file10="../Results/weight_shifts/Summary_results_persuasion_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred_middle_real_ic.dat"
file10 = open(output_file10,'wt')
print >> file10, "Summary results from best fit persuasion with",Niter, "iter, and with values for the parameters: alpha ",alpha_F," damping: ",damping," mutual_encourg: ",mutual_encouragement," threshold:",threshold
print >> file10, "average distance of the optimum in the testing segment:",numpy.mean(list_dist_fixed_parameters_testing_segment),numpy.std(list_dist_fixed_parameters_testing_segment),list_dist_fixed_parameters_testing_segment
print >> file10, "fraction of realizations that end within delta_doctor:",num_valid_endings/Niter,"mean ending dist:",numpy.mean(list_dist_at_ending_point_fixed_parameters), "SD final dist",numpy.std(list_dist_at_ending_point_fixed_parameters),list_dist_at_ending_point_fixed_parameters
print >> file10, "written optimum train_test evolution file:",output_file
print >> file10,"written histogram file: ",histogram_filename
# print >> file10,"written histogram file: ",histogram_filename2
file10.close()
print "written optimum train_test evolution file:",output_file
print "written summary file: ",output_file10
damping += delta_damping
mutual_encouragement += delta_mutual_encouragement
alpha_F += delta_alpha_F
threshold += delta_threshold
def main(graph_name):
G = nx.read_gml(graph_name)
list_id_weekends_T3 = look_for_T3_weekends(
G
) # T3 doesnt share fellows in the weekend (but they are the exception)
percent_envelope = 95.
Niter = 1000
cutting_day = 125
Nbins = 200 # for the histogram of sum of distances
for_testing_fixed_set = "YES" # when YES, fixed values param and get all statistics on final distances etc
envelopes = "NO"
delta_end = 3. # >= than + or - dr difference at the end of the evolution
dir_real_data = '../Results/'
all_team = "NO" # as adopters or not NO now means i use the file without fellows, only attendings
if for_testing_fixed_set == "NO":
output_file3 = "../Results/weight_shifts/Landscape_parameters_persuasion_" + str(
Niter) + "iter_A_F_inferred.dat"
file3 = open(output_file3, 'wt')
file3.close()
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
if all_team == "YES":
print "remember that now i use the file of adopters without fellows\n../Results/Actual_evolution_adopters_NO_fellows_only_attendings.dat"
exit()
else:
filename_actual_evol = "../Results/Actual_evolution_adopters_from_inference.dat"
file1 = open(
filename_actual_evol, 'r'
) ## i read the file: list_dates_and_names_current_adopters.txt (created with: extract_real_evolution_number_adopters.py)
list_lines_file = file1.readlines()
list_actual_evol = []
for line in list_lines_file: # [1:]: # i exclude the first row
num_adopters = float(line.split("\t")[1])
list_actual_evol.append(num_adopters)
##################################################################
#../Results/weight_shifts/persuasion/Time_evolutions_Persuasion_training_alpha0.2_damping0.0_mutual_encourg0.5_threshold0.7_unif_distr_1000iter_2012_seed31Oct_finalnetwork_day125.dat
#../Results/weight_shifts/persuasion/Time_evolutions_Persuasion_training_alpha0.5_damping0.4_mutual_encourg0.5_threshold0.5_unif_distr_1000iter_2012_seed31Oct_finalnetwork_day125.dat
#OJO!!! NECESITO DOS DECIMALES SIEMPRE, PARA QUE CUADRE CON EL NOMBRE DE LOS SUB-DIRECTORIOS DONDE LO GUARDO
alpha_F_min = 0.10 #0.15 # alpha=0: nobody changes their mind
alpha_F_max = 0.101 #0.351
delta_alpha_F = 0.10 #AVOID 1.0 OR THE DYNAMICS GETS TOTALLY STUCK AND IT IS NOT ABLE TO PREDICT SHIT!
min_damping = 0.00 #0.0 #its harder to go back from YES to NO again. =1 means no effect, =0.5 half the movement from Y->N than the other way around, =0 never go back from Y to N
max_damping = 0.001 #0.451
delta_damping = 0.10
min_mutual_encouragement = 0.000 #0.50 # when two Adopters meet, they convince each other even more
max_mutual_encouragement = 0.001 # 0.51 # KEEP THIS FIXED VALUES FOR NOW
delta_mutual_encouragement = 0.10
threshold_min = 0.50 #0.50 # larger than, to be an Adopte
threshold_max = 0.501 # 0.51 # KEEP THIS FIXED VALUES FOR NOW
delta_threshold = 0.10 # AVOID 1.0 OR THE DYNAMICS GETS TOTALLY STUCK AND IT IS NOT ABLE TO PREDICT SHIT
print "\n\nPersuasion process on network, with Niter:", Niter
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
threshold = threshold_min
while threshold <= threshold_max:
print "thershold:", threshold
alpha_F = alpha_F_min
while alpha_F <= alpha_F_max: # i explore all the parameter space, and create a file per each set of valuesllkl
alpha_A = 1.0 * alpha_F
print " alpha_F:", alpha_F
mutual_encouragement = min_mutual_encouragement
while mutual_encouragement <= max_mutual_encouragement:
print " mutual_encouragement:", mutual_encouragement
damping = min_damping
while damping <= max_damping:
print " damping:", damping
dir = "../Results/weight_shifts/persuasion/alpha%.2f_damping%.2f/" % (
alpha_F, damping)
if for_testing_fixed_set == "YES":
output_file = dir + "Time_evol_Persuasion_alpha" + str(
alpha_F
) + "_damping" + str(damping) + "_mutual" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_A_F_inferred.dat"
else:
output_file = dir + "Time_evol_Persuasion_alpha" + str(
alpha_F
) + "_damping" + str(damping) + "_mutual" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_A_F_inferred.dat"
file = open(output_file, 'wt')
file.close()
time_evol_number_adopters_ITER = [
] # list of complete single realizations of the dynamics
list_dist_fixed_parameters = []
list_dist_fixed_parameters_testing_segment = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_final_num_adopt = []
list_abs_dist_point_by_point_indiv_simus_to_actual = []
list_dist_point_by_point_indiv_simus_to_actual = []
#list_abs_dist_at_cutting_day=[]
for iter in range(Niter):
print " ", iter
list_t = []
time_evol_number_adopters = [
] # for a single realization of the dynamics
num_adopters, seed_shift, max_shift = set_ic(
G, threshold
) # i establish who is Adopter and NonAdopter initially, and count how many shifts i have total
time_evol_number_adopters.append(float(num_adopters))
# print "initial number of adopters:", num_adopters
list_t.append(0)
########OJO~!!!!!!!!!! COMENTAR ESTO CUANDO ESTOY BARRIENDO TOOOOOOOOOODO EL ESPACIO DE PARAMETROS
# file4 = open(output_file.split('.dat')[0]+"_indiv_iter"+str(iter)+".dat",'wt')
# file4.close()
##########################################
# the dynamics starts:
t = int(seed_shift
) + 1 # the first time step is just IC.???
while t <= max_shift: # loop over shifts, in chronological order (the order is the day index since seeding_day)
# print 't:',t
list_t.append(t)
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n][
'order'] == t: # i look for the shift corresponding to that time step
shift_length = int(
G.node[n]['shift_length'])
if shift_length == 2 and n not in list_id_weekends_T3:
shift_length = 1 # because during weekends, the fellow does rounds one day with Att1 and the other day with Att2. (weekend shifts for T3 are two day long, with no sharing fellows)
# print "one-day weekend", G.node[n]['label'],G.node[n]['shift_length']
flag_possible_persuasion = 0
for doctor in G.neighbors(n):
if G.node[doctor][
"status"] == "Adopter": #first i check if any doctor is an adopter in this shift
flag_possible_persuasion = 1
break
if flag_possible_persuasion == 1:
list_doctors = []
for doctor in G.neighbors(
n
): # for all drs in that shift
list_doctors.append(doctor)
pairs = itertools.combinations(
list_doctors, 2
) # cos the shift can be 2 but also 3 doctors
for pair in pairs:
doctor1 = pair[0]
doctor2 = pair[1]
if G.node[doctor1][
'status'] != G.node[doctor2][
'status']: # if they think differently,
# there will be persuasion
persuasion(
G, damping, doctor1,
doctor2, alpha_A, alpha_F,
threshold, shift_length
) # i move their values of opinion
update_opinions(
G, threshold, doctor1,
doctor2
) # i update status and make sure the values of the vectors stay between [0,1]
else: # if two Adopters meet, they encourage each other (if two NonAdopters, nothing happens)
mutual_reinforcement(
G, mutual_encouragement,
doctor1, doctor2,
shift_length)
# else:
# print " no persuasion possible during shift (no adopters present)!"
list_Adopters = [
] #count how many i have at this time
for n in G.nodes():
try:
if G.node[n]["status"] == "Adopter":
if G.node[n][
"label"] not in list_Adopters: # and G.node[n]["type"]=="A":
list_Adopters.append(
G.node[n]["label"])
except:
pass # if the node is a shift, it doesnt have a 'status' attribute
# if for_testing_fixed_set=="YES":
# if t==cutting_day:
# list_abs_dist_at_cutting_day.append(abs(float(list_actual_evol[-1])-float(len(list_Adopters))))
# print abs(float(list_actual_evol[-1])-float(len(list_Adopters))), float(list_actual_evol[t]),float(len(list_Adopters))
time_evol_number_adopters.append(
float(len(list_Adopters)))
t += 1
############## end while loop over t
time_evol_number_adopters_ITER.append(
time_evol_number_adopters)
list_dist_fixed_parameters.append(
compare_real_evol_vs_simus_to_be_called.
compare_two_curves(list_actual_evol,
time_evol_number_adopters))
list_dist_fixed_parameters_testing_segment.append(
compare_real_evol_vs_simus_to_be_called.
compare_two_curves_testing_segment(
list_actual_evol, time_evol_number_adopters,
cutting_day))
list_dist_abs_at_ending_point_fixed_parameters.append(
abs(time_evol_number_adopters[-1] -
list_actual_evol[-1]))
list_dist_at_ending_point_fixed_parameters.append(
time_evol_number_adopters[-1] -
list_actual_evol[-1])
list_final_num_adopt.append(
time_evol_number_adopters[-1])
########OJO~!!!!!!!!!! COMENTAR ESTO CUANDO ESTOY BARRIENDO TOOOOOOOOOODO EL ESPACIO DE PARAMETROS
# file4 = open(output_file.split('.dat')[0]+"_indiv_iter"+str(iter)+".dat",'at')
# for i in range(len(time_evol_number_adopters)): #ime step by time step
# print >> file4, i,time_evol_number_adopters[i], alpha_F,damping,mutual_encouragement
#file4.close()
########################################################
for index in range(len(time_evol_number_adopters)):
list_abs_dist_point_by_point_indiv_simus_to_actual.append(
abs(time_evol_number_adopters[index] -
list_actual_evol[index]))
list_dist_point_by_point_indiv_simus_to_actual.append(
time_evol_number_adopters[index] -
list_actual_evol[index])
#######################end loop over Niter
list_pair_dist_std_delta_end = []
list_pair_dist_std_delta_end.append(
numpy.mean(list_dist_fixed_parameters)
) # average dist between the curves over Niter
list_pair_dist_std_delta_end.append(
numpy.std(list_dist_fixed_parameters))
list_pair_dist_std_delta_end.append(
numpy.mean(
list_dist_abs_at_ending_point_fixed_parameters))
if for_testing_fixed_set == "NO":
file3 = open(output_file3,
'at') # i print out the landscape
print >> file3, alpha_F, damping, mutual_encouragement, threshold, numpy.mean(
list_dist_abs_at_ending_point_fixed_parameters
), numpy.mean(list_dist_fixed_parameters), numpy.mean(
list_final_num_adopt), numpy.std(
list_final_num_adopt
), numpy.std(list_final_num_adopt) / numpy.mean(
list_final_num_adopt)
file3.close()
if (
numpy.mean(
list_dist_abs_at_ending_point_fixed_parameters)
) <= delta_end: # i only consider situations close enough at the ending point
dict_filenames_tot_distance[
output_file] = list_pair_dist_std_delta_end
file = open(output_file, 'wt')
for i in range(len(time_evol_number_adopters)
): #time step by time step
list_fixed_t = []
for iteracion in range(
Niter
): #loop over all independent iter of the process
list_fixed_t.append(
time_evol_number_adopters_ITER[iteracion][i]
) # i collect all values for the same t, different iter
print >> file, list_t[i], numpy.mean(
list_fixed_t), numpy.std(
list_fixed_t
), alpha_F, damping, mutual_encouragement
file.close()
print "printed out: ", output_file
if envelopes == "YES":
calculate_envelope_set_curves.calculate_envelope(
time_evol_number_adopters_ITER, percent_envelope,
"Persuasion", [
alpha_F, damping, mutual_encouragement,
threshold
])
if for_testing_fixed_set == "YES":
num_valid_endings = 0.
for item in list_dist_abs_at_ending_point_fixed_parameters:
if item <= delta_end: # i count how many realizations i get close enough at the ending point
num_valid_endings += 1.
print "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters), numpy.std(
list_dist_fixed_parameters
), list_dist_fixed_parameters, "\n"
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters
histogram_filename = "../Results/weight_shifts/histogr_raw_distances_ending_persuasion_alpha" + str(
alpha_F) + "_damping" + str(
damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_day" + str(
cutting_day) + "_A_F_inferred.dat"
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters,
histogram_filename)
histogram_filename2 = "../Results/weight_shifts/histogr_sum_dist_traject_persuasion_alpha" + str(
alpha_F) + "_damping" + str(
damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_day" + str(
cutting_day) + "_A_F_inferred.dat"
histograma_bines_gral.histograma_bins(
list_dist_fixed_parameters, Nbins,
histogram_filename2)
histogram_filename3 = "../Results/weight_shifts/histogr_sum_dist_testing_segment_persuasion_alpha" + str(
alpha_F) + "_damping" + str(
damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_day" + str(
cutting_day) + "_A_F_inferred.dat"
histograma_bines_gral.histograma_bins_zero(
list_dist_fixed_parameters_testing_segment, Nbins,
histogram_filename3)
histogram_filename4 = "../Results/weight_shifts/histogr_abs_dist_point_by_point_persuasion_alpha" + str(
alpha_F) + "_damping" + str(
damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_day" + str(
cutting_day) + "_A_F_inferred.dat"
histograma_gral_negv_posit.histograma(
list_abs_dist_point_by_point_indiv_simus_to_actual,
histogram_filename4)
histogram_filename5 = "../Results/weight_shifts/histogr_dist_point_by_point_persuasion_alpha" + str(
alpha_F) + "_damping" + str(
damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_day" + str(
cutting_day) + "_A_F_inferred.dat"
histograma_gral_negv_posit.histograma(
list_dist_point_by_point_indiv_simus_to_actual,
histogram_filename5)
output_file10 = "../Results/weight_shifts/Summary_results_persuasion_alpha" + str(
alpha_F) + "_damping" + str(
damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_threshold" + str(threshold) + "_" + str(
Niter) + "iter_alphaA_eq_alphaF_day" + str(
cutting_day) + "_A_F_inferred.dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from best fit persuasion with", Niter, "iter, and with values for the parameters: alpha ", alpha_F, " damping: ", damping, " mutual_encourg: ", mutual_encouragement, " threshold:", threshold
print >> file10, "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters), numpy.std(
list_dist_fixed_parameters
), list_dist_fixed_parameters
print >> file10, "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters
print >> file10, "written optimum train_test evolution file:", output_file
print >> file10, "written histogram file: ", histogram_filename
print >> file10, "written histogram file: ", histogram_filename2
file10.close()
print "written optimum train_test evolution file:", output_file
print "written summary file: ", output_file10
damping += delta_damping
mutual_encouragement += delta_mutual_encouragement
alpha_F += delta_alpha_F
threshold += delta_threshold
if for_testing_fixed_set == "NO": # only if i am exploring the whole landscape, i need to call this function, otherwise, i already know the optimum
compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Persuasion_weight", all_team, Niter,
None) #last argument, cutting day (it doesnt apply)
if for_testing_fixed_set == "NO":
print "written landscape file:", output_file3
def main(graph_name):
G = nx.read_gml(graph_name)
percent_envelope = 95.
#../Results/save/Average_time_evolution_Infection_p0.3_Immune0.15_1000iter_2012.dat
prob_infection = 0.2
prob_Immune = 0.1
Niter = 1000
# i create the empty list of list for the Niter temporal evolutions
num_shifts = 0
for n in G.nodes():
G.node[n]["status"] = "S"
if G.node[n]['type'] == "shift":
num_shifts += 1
# list_final_I_values_fixed_p=[] # i dont care about the final values right now, but about the whole time evol
list_lists_t_evolutions = []
for iter in range(Niter):
print " iter:", iter
list_I = [] #list infected doctors
list_ordering = []
list_s = []
list_A = []
list_F = []
########### set I.C.
max_order = 0
for n in G.nodes():
G.node[n]["status"] = "S" # all nodes are Susceptible
if G.node[n]['type'] == "shift":
list_s.append(n)
if G.node[n]['order'] > max_order:
max_order = G.node[n]['order']
else:
if G.node[n]['label'] == "Wunderink" or G.node[n][
"label"] == "Weiss":
G.node[n]["status"] = "I"
list_I.append(G.node[n]['label'])
######################## WHAT ABOUT SMITH AND SPORN???
if G.node[n]['type'] == "A":
list_A.append(n)
if G.node[n]['type'] == "F":
list_F.append(n)
list_single_t_evolution = []
list_single_t_evolution.append(
2.0) # I always start with TWO infected doctors!!
for n in G.nodes(
): # i make some DOCTORs INMUNE (anyone except Weiss and Wunderink)
if (G.node[n]['type'] == "A") or (G.node[n]['type'] == "F"):
if G.node[n]['label'] != "Wunderink" and G.node[n][
"label"] != "Weiss": # these particular two cant be immune
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
# print max_order
################# the dynamics starts:
t = 1
while t <= max_order: # loop over shifts, in order
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n]['order'] == t:
flag_possible_infection = 0
for doctor in G.neighbors(
n
): #first i check if any doctor is infected in this shift
if G.node[doctor]["status"] == "I":
flag_possible_infection = 1
if flag_possible_infection:
for doctor in G.neighbors(
n
): # then the doctors in that shift, gets infected with prob_infection
if G.node[doctor]["status"] == "S":
rand = random.random()
if rand < prob_infection:
G.node[doctor]["status"] = "I"
list_I.append(G.node[doctor]["label"])
list_single_t_evolution.append(float(
len(list_I))) #/(len(list_A)+len(list_F)))
t += 1
list_lists_t_evolutions.append(list_single_t_evolution)
######## end Niter
calculate_envelope_set_curves.calculate_envelope(
list_lists_t_evolutions, percent_envelope, "Infection",
[prob_infection, prob_Immune])
def main(graph_name):
G = nx.read_gml(graph_name)
percent_envelope = 95.
Niter = 1000
#"../Results/save/Time_evolutions_Persuasion_alpha0.25_damping0.0_mutual_encourg0.5_threshold0.5_unif_distr_1000iter_2012_seed31Oct_finalnetwork.dat"
alpha_F = 0.1 # alpha=0: nobody changes their mind
alpha_A = 1. * alpha_F
damping = 0.0 #its harder to go back from YES to NO again. =1 means no effect, =0.5 half the movement from Y->N than the other way around, =0 never go back from Y to N
mutual_encouragement = 0.0 # when two Adopters meet, they convince each other even more
threshold = 0.5 # larger than, to be an Adopte
print "\n\nPersuasion process on network, with Niter:", Niter
dir = "../Results/weight_shifts/"
output_file = dir + "Time_evolutions_Persuasion_alpha" + str(
alpha_F) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement) + "_threshold" + str(
threshold) + "_unif_distr_" + str(
Niter) + "iter_2012_seed31Oct_finalnetwork.dat"
file = open(output_file, 'wt')
file.close()
time_evol_number_adopters_ITER = [
] # list of complete single realizations of the dynamics
for iter in range(Niter):
print "iter:", iter
list_t = []
time_evol_number_adopters = [
] # for a single realization of the dynamics
num_adopters, seed_shift, max_shift = set_ic(
G, threshold
) # i establish who is Adopter and NonAdopter initially, and count how many shifts i have total
time_evol_number_adopters.append(float(num_adopters))
# print "initial number of adopters:", num_adopters
list_t.append(0)
########OJO~!!!!!!!!!! COMENTAR ESTO CUANDO ESTOY BARRIENDO TOOOOOOOOOODO EL ESPACIO DE PARAMETROS
# file2 = open(output_file.split('.dat')[0]+"_indiv_iter"+str(iter)+".dat",'wt')
# file2.close()
##########################################
# the dynamics starts:
t = int(seed_shift) + 1 # the first time step is just IC.???
while t <= max_shift: # loop over shifts, in chronological order (the order is the day index since seeding_day)
list_t.append(t)
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n][
'order'] == t: # i look for the shift corresponding to that time step
flag_possible_persuasion = 0
for doctor in G.neighbors(n):
if G.node[doctor][
"status"] == "Adopter": #first i check if any doctor is an adopter in this shift
flag_possible_persuasion = 1
break
if flag_possible_persuasion == 1:
list_doctors = []
for doctor in G.neighbors(
n): # for all drs in that shift
list_doctors.append(doctor)
pairs = itertools.combinations(
list_doctors,
2) # cos the shift can be 2 but also 3 doctors
for pair in pairs:
doctor1 = pair[0]
doctor2 = pair[1]
if G.node[doctor1]['status'] != G.node[doctor2][
'status']: # if they think differently,
# there will be persuasion
persuasion(G, damping, doctor1, doctor2,
alpha_A, alpha_F, threshold
) # i move their values of opinion
update_opinions(
G, threshold, doctor1, doctor2
) # i update status and make sure the values of the vectors stay between [0,1]
else: # if two Adopters meet, they encourage each other (if two NonAdopters, nothing happens)
mutual_reinforcement(G, mutual_encouragement,
doctor1, doctor2)
list_Adopters = [] #count how many i have at this time
for n in G.nodes():
try:
if G.node[n]["status"] == "Adopter":
if G.node[n]["label"] not in list_Adopters:
list_Adopters.append(G.node[n]["label"])
except:
pass # if the node is a shift, it doesnt have a 'status' attribute
time_evol_number_adopters.append(float(len(list_Adopters)))
t += 1
############## end while loop over t
########OJO~!!!!!!!!!! COMENTAR ESTO CUANDO ESTOY BARRIENDO TOOOOOOOOOODO EL ESPACIO DE PARAMETROS
#file2 = open(output_file.split('.dat')[0]+"_indiv_iter"+str(iter)+".dat",'at')
#for i in range(len(time_evol_number_adopters)): #ime step by time step
# print >> file2, i,time_evol_number_adopters[i], alpha_F,damping,mutual_encouragement
#file.close()
########################################################
time_evol_number_adopters_ITER.append(time_evol_number_adopters)
#end loop over Niter
file = open(output_file, 'wt')
for i in range(len(time_evol_number_adopters)): #time step by time step
list_fixed_t = []
for iteracion in range(
Niter): #loop over all independent iter of the process
list_fixed_t.append(
time_evol_number_adopters_ITER[iteracion]
[i]) # i collect all values for the same t, different iter
print >> file, list_t[i], numpy.mean(list_fixed_t), numpy.std(
list_fixed_t), alpha_F, damping, mutual_encouragement
file.close()
calculate_envelope_set_curves.calculate_envelope(
time_evol_number_adopters_ITER, percent_envelope, "Persuasion",
[alpha_F, damping, mutual_encouragement, threshold])
def main(graph_name):
G = nx.read_gml(graph_name)
cutting_day = 175 # i use this only for the filenames
for_testing_fixed_set = "YES" # when YES, fixed values param, to get all statistics on final distances etc
# change the range for the parameters accordingly
envelopes = "YES"
Niter = 1000 # 100 iter seems to be enough (no big diff. with respect to 1000it)
percent_envelope = 95.
list_id_weekends_T3 = look_for_T3_weekends(
G
) # T3 doesnt share fellows in the weekend (but they are the exception)
Nbins = 1000 # for the histogram of sum of distances
all_team = "NO" # as adopters or not
dir_real_data = '../Results/'
dir = "../Results/weight_shifts/infection/"
delta_end = 3. # >= than + or - dr difference at the end of the evolution (NO realization ends up closer than this!!!! if 2, i get and empty list!!!)
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
filename_actual_evol = "../Data/Actual_evolution_adopters_NO_fellows_only_attendings_with_list_names.csv" # "../Results/Actual_evolution_adopters_from_inference.dat"
file1 = open(
filename_actual_evol, 'r'
) ## i read the file: list_dates_and_names_current_adopters.txt (created with: extract_real_evolution_number_adopters.py)
list_lines_file = file1.readlines()
dict_days_list_empirical_att_adopters = {}
list_actual_evol = []
for line in list_lines_file: # [1:]: # i exclude the first row
day = int(line.split(" ")[0])
num_adopters = float(line.split(" ")[1])
list_actual_evol.append(num_adopters)
list_current_att_adopters = []
for element in line.split(
" "
)[2:]: # i need to ignore the empty columns from the original datafile
if element:
if element != '\n':
list_current_att_adopters.append(
element.strip('\n').title())
dict_days_list_empirical_att_adopters[day] = list_current_att_adopters
list_actual_evol_testing = list_actual_evol[cutting_day:]
##################################################################
#../Results/weight_shifts/infection/Average_time_evolution_Infection_training_p0.8_Immune0.3_1000iter_2012_avg_ic_day125.dat ESTOS VALORES SON EL OPTIMUM FIT THE 152-DIAS
prob_min = 1.00
prob_max = 1.001
delta_prob = 0.1
prob_Immune_min = 0.700
prob_Immune_max = 0.7001
delta_prob_Immune = 0.1
prob_Immune = prob_Immune_min
while prob_Immune <= prob_Immune_max:
print "prom Immune:", prob_Immune
prob_infection = prob_min
while prob_infection <= prob_max:
print " p:", prob_infection
output_file2 = dir + "Average_time_evolution_Infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle_real_ic.dat"
file2 = open(output_file2, 'wt')
file2.close()
########## i read the list of frequent adopters from simulations, to estimate the ic for fellows
filename_list_simu_adopt = "../Results/weight_shifts/infection/List_adopters_fellows_descending_frequency_Infection_training_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_1000iter_2012_avg_ic_day" + str(
cutting_day) + "_Att_only_middle.dat"
# print filename_list_simu_adopt
file_list_simu_adopt = open(filename_list_simu_adopt, 'r')
list_lines_file = file_list_simu_adopt.readlines()
list_sorted_fellow_adopters = []
for line in list_lines_file[1:]: # i exclude the first row
adopter = line.split(" ")[0]
list_sorted_fellow_adopters.append(adopter)
# print "list sorted fellows:",list_sorted_fellow_adopters
num_simu_Fellow_adopters_cutting_day = int(
round(
float(list_lines_file[0].split("Avg # F adopters ")
[1].split(" ")[0])))
#print "avg simu number Fellow adopters:",num_simu_Fellow_adopters_cutting_day,int( round(num_simu_Fellow_adopters_cutting_day))
################
list_lists_t_evolutions = []
list_dist_fixed_parameters_testing_segment = []
list_abs_dist_at_ending_point_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_final_num_infected = []
list_abs_dist_point_by_point_indiv_simus_to_actual = []
list_dist_point_by_point_indiv_simus_to_actual = []
for iter in range(Niter):
# print " iter:",iter
########### set I.C. according to the empirical data
dict_name_node = {}
list_I = []
max_order = 0
for n in G.nodes():
G.node[n]["status"] = "S" # all nodes are Susceptible
if G.node[n]['type'] == "shift":
if G.node[n]['order'] > max_order:
max_order = G.node[n][
'order'] # to get the last shift-order for the time loop
else:
dict_name_node[G.node[n]["label"]] = n
if G.node[n][
'label'] in dict_days_list_empirical_att_adopters[
cutting_day]:
G.node[n]["status"] = "I"
list_I.append(G.node[n]['label'])
list_fellows = []
for i in range(num_simu_Fellow_adopters_cutting_day):
fellow_adopter = list_sorted_fellow_adopters[
i] # this list is sorted from more to less frequent adopter
node = dict_name_node[fellow_adopter]
G.node[node]["status"] = "I"
list_fellows.append(fellow_adopter)
list_single_t_evolution = []
old_num_adopters = len(list_I)
list_single_t_evolution.append(
old_num_adopters
) # I always start with TWO infected doctors!!
for n in G.nodes(
): # i make some DOCTORs INMUNE (anyone except Weiss and Wunderink)
if (G.node[n]['type'] == "A") or (G.node[n]['type']
== "F"):
if (G.node[n]['label']
not in dict_days_list_empirical_att_adopters[
cutting_day]) and (G.node[n]['label']
not in list_fellows):
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
################# the dynamics starts:
shift_length = 5 #i know the first shift (order 0) is of length 5
t = cutting_day
while t <= max_order: # loop over shifts, in order
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n][
'order'] == t:
shift_length = int(G.node[n]['shift_length'])
if shift_length == 2 and n not in list_id_weekends_T3:
shift_length = 1 # because during weekends, the fellow does rounds one day with Att1 and the other day with Att2. (weekend shifts for T3 are two day long, with no sharing fellows)
# print "one-day weekend", G.node[n]['label'],G.node[n]['shift_length']
flag_possible_infection = 0
for doctor in G.neighbors(
n
): #first i check if any doctor is infected in this shift
if G.node[doctor]["status"] == "I":
flag_possible_infection = 1
if flag_possible_infection:
for doctor in G.neighbors(
n
): # then the doctors in that shift, gets infected with prob_infection
for i in range(
shift_length
): # i repeat the infection process several times, to acount for shift length
if G.node[doctor]["status"] == "S":
rand = random.random()
if rand < prob_infection:
G.node[doctor]["status"] = "I"
if G.node[doctor][
"type"] == "A": # fellows participate in the dynamics, but i only consider the attendings as real adopters
list_I.append(
G.node[doctor]
["label"])
new_num_adopters = len(list_I)
if shift_length == 5: # i estimate that adoption happens in the middle of the shift
if t + 5 < max_order:
list_single_t_evolution.append(old_num_adopters)
if t + 4 < max_order:
list_single_t_evolution.append(old_num_adopters)
if t + 3 < max_order:
list_single_t_evolution.append(new_num_adopters)
if t + 2 < max_order:
list_single_t_evolution.append(new_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(new_num_adopters)
t += 5
elif shift_length == 4:
if t + 4 < max_order:
list_single_t_evolution.append(old_num_adopters)
if t + 3 < max_order:
list_single_t_evolution.append(old_num_adopters)
if t + 2 < max_order:
list_single_t_evolution.append(new_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(new_num_adopters)
t += 4
elif shift_length == 3:
if t + 3 < max_order:
list_single_t_evolution.append(old_num_adopters)
if t + 2 < max_order:
list_single_t_evolution.append(new_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(new_num_adopters)
t += 3
elif shift_length == 2:
if t + 2 < max_order:
list_single_t_evolution.append(old_num_adopters)
if t + 1 < max_order:
list_single_t_evolution.append(new_num_adopters)
t += 2
elif shift_length == 1:
if t + 1 < max_order:
list_single_t_evolution.append(new_num_adopters)
t += 1
old_num_adopters = new_num_adopters
######## end t loop
list_lists_t_evolutions.append(list_single_t_evolution)
# now i only run the testing segment!
list_dist_fixed_parameters_testing_segment.append(
compare_real_evol_vs_simus_to_be_called.compare_two_curves(
list_actual_evol_testing, list_single_t_evolution))
list_abs_dist_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] -
list_actual_evol_testing[-1])
) # i save the distance at the ending point between the current simu and actual evol
list_dist_at_ending_point_fixed_parameters.append(
list_single_t_evolution[-1] - list_actual_evol_testing[-1]
) # i save the distance at the ending point between the current simu and actual evol
list_final_num_infected.append(list_single_t_evolution[-1])
for index in range(len(list_single_t_evolution)):
list_abs_dist_point_by_point_indiv_simus_to_actual.append(
abs(list_single_t_evolution[index] -
list_actual_evol_testing[index]))
list_dist_point_by_point_indiv_simus_to_actual.append(
list_single_t_evolution[index] -
list_actual_evol_testing[index])
######## end loop Niter
file2 = open(output_file2, 'at')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter):
list_fixed_t.append(list_lists_t_evolutions[iter][s])
print >> file2, s + cutting_day, numpy.mean(list_fixed_t)
file2.close()
print "printed out: ", output_file2
if envelopes == "YES":
calculate_envelope_set_curves.calculate_envelope(
list_lists_t_evolutions, percent_envelope, "Infection",
[prob_infection, prob_Immune])
num_valid_endings = 0.
for item in list_abs_dist_at_ending_point_fixed_parameters:
if item <= delta_end: # i count how many realizations i get close enough at the ending point
num_valid_endings += 1.
print "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters_testing_segment), numpy.std(
list_dist_fixed_parameters_testing_segment
), list_dist_fixed_parameters_testing_segment, "\n"
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
histogram_filename = "../Results/weight_shifts/histogr_raw_distances_ending_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters, histogram_filename)
histogram_filename3 = "../Results/weight_shifts/histogr_sum_dist_testing_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle_real_ic.dat"
histograma_bines_gral.histograma_bins_zero(
list_dist_fixed_parameters_testing_segment, Nbins,
histogram_filename3)
print min(list_dist_fixed_parameters_testing_segment), max(
list_dist_fixed_parameters_testing_segment)
histogram_filename4 = "../Results/weight_shifts/histogr_abs_dist_point_by_point_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(
list_abs_dist_point_by_point_indiv_simus_to_actual,
histogram_filename4)
histogram_filename5 = "../Results/weight_shifts/histogr_dist_point_by_point_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle_real_ic.dat"
histograma_gral_negv_posit.histograma(
list_dist_point_by_point_indiv_simus_to_actual,
histogram_filename5)
output_file10 = "../Results/weight_shifts/Summary_results_infection_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle_real_ic.dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from best fit infection with", Niter, "iter, and with values for the parameters: prob_inf ", prob_infection, " prob immune: ", prob_Immune, "\n"
print >> file10, "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters_testing_segment), numpy.std(
list_dist_fixed_parameters_testing_segment
), list_dist_fixed_parameters_testing_segment, "\n"
print >> file10, "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
print >> file10, "written optimum best fit evolution file:", output_file2
print >> file10, "written histogram file: ", histogram_filename
file10.close()
print "written Summary file: ", output_file10
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
def main(graph_name):
G = nx.read_gml(graph_name)
for_testing_fixed_set = "YES" # when YES, fixed values param, to get all statistics on final distances etc
# change the range for the parameters accordingly
envelopes = "NO"
Niter = 1000
percent_envelope = 95.
list_id_weekends_T3 = look_for_T3_weekends(
G
) # T3 doesnt share fellows in the weekend (but they are the exception)
cutting_day = 175
all_team = "NO" # as adopters or not
dir_real_data = '../Results/'
dir = "../Results/weight_shifts/infection/"
delta_end = 3. # >= than + or - dr difference at the end of the evolution (NO realization ends up closer than this!!!! if 2, i get and empty list!!!)
Nbins = 20 # for the histogram of sum of distances
if for_testing_fixed_set == "NO":
output_file3 = "../Results/weight_shifts/Landscape_parameters_infection_memory_fixed_dose_thr_" + str(
Niter) + "iterFIXED_Thr0.2_Imm0.0.dat"
file3 = open(output_file3, 'wt')
file3.close()
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
if all_team == "YES":
print "remember that now i use the file of adopters without fellows\n../Results/Actual_evolution_adopters_NO_fellows_only_attendings.dat"
exit()
else:
filename_actual_evol = "../Results/Actual_evolution_adopters_NO_fellows_only_attendings.dat"
file1 = open(
filename_actual_evol, 'r'
) ## i read the file: list_dates_and_names_current_adopters.txt (created with: extract_real_evolution_number_adopters.py)
list_lines_file = file1.readlines()
list_actual_evol = []
for line in list_lines_file: # [1:]: # i exclude the first row
num_adopters = float(line.split(" ")[1])
list_actual_evol.append(num_adopters)
################################################################################
prob_min = 0.3
prob_max = 0.301
delta_prob = 0.1
prob_Immune_min = 0.00
prob_Immune_max = 0.001
delta_prob_Immune = 0.1
dose_min = 0.7 # of a single encounter with an infected (starting from zero doesnt make sense)
dose_max = 0.701
delta_dose = 0.01
########## KEEP FIXED TO ONE
infect_threshold_min = 1.00 # i can define the dose in units of the threshold
infect_threshold_max = 1.001
delta_infect_threshold = 0.1
############
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
prob_Immune = prob_Immune_min
while prob_Immune <= prob_Immune_max:
print "prom Immune:", prob_Immune
prob_infection = prob_min
while prob_infection <= prob_max:
print " p:", prob_infection
infect_threshold = infect_threshold_min
while infect_threshold <= infect_threshold_max:
print " threshold:", infect_threshold
dose = dose_min
while dose <= dose_max:
print " dose:", dose
if for_testing_fixed_set == "YES":
output_file2 = dir + "Average_time_evolution_Infection_memory_train_test_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_FIXED_threshold" + str(
infect_threshold) + "_dose" + str(
dose) + "_" + str(Niter) + "iter.dat"
else:
output_file2 = dir + "Average_time_evolution_Infection_memory_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_FIXED_threshold" + str(
infect_threshold) + "_dose" + str(
dose) + "_" + str(Niter) + "iter.dat"
file2 = open(output_file2, 'wt')
file2.close()
num_shifts = 0
for n in G.nodes():
G.node[n]["status"] = "S"
G.node[n][
"infec_value"] = 0. # when this value goes over the infect_threshold, the dr is infected
if G.node[n]['type'] == "shift":
num_shifts += 1
list_lists_t_evolutions = [
] # i create the empty list of list for the Niter temporal evolutions
list_dist_fixed_parameters = []
list_abs_dist_at_ending_point_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_final_num_infected = []
for iter in range(Niter):
# print " iter:",iter
list_I = [] #list infected doctors
list_ordering = []
list_s = []
########### set I.C.
max_order = 0
for n in G.nodes():
G.node[n][
"status"] = "S" # all nodes are Susceptible
if G.node[n]['type'] == "shift":
list_s.append(n)
if G.node[n]['order'] > max_order:
max_order = G.node[n]['order']
else:
if G.node[n]['label'] == "Wunderink" or G.node[
n]["label"] == "Weiss":
G.node[n]["status"] = "I"
G.node[n][
"infec_value"] = infect_threshold + 1.
list_I.append(G.node[n]['label'])
list_single_t_evolution = []
list_single_t_evolution.append(
2.0) # I always start with TWO infected doctors!!
for n in G.nodes(
): # i make some DOCTORs INMUNE (anyone except Weiss and Wunderink)
if (G.node[n]['type'] == "A") or (G.node[n]['type']
== "F"):
if G.node[n]['label'] != "Wunderink" and G.node[
n]["label"] != "Weiss":
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
################# the dynamics starts:
t = 1
while t <= max_order: # loop over shifts, in order
for n in G.nodes():
if G.node[n]['type'] == "shift" and G.node[n][
'order'] == t:
shift_lenght = int(
G.node[n]['shift_lenght'])
if shift_lenght == 2 and n not in list_id_weekends_T3:
shift_lenght = 1 # because during weekends, the fellow does rounds one day with Att1 and the other day with Att2. (weekend shifts for T3 are two day long, with no sharing fellows)
flag_possible_infection = 0
for doctor in G.neighbors(
n
): #first i check if any doctor is infected in this shift
if G.node[doctor]["status"] == "I":
flag_possible_infection = 1
if flag_possible_infection:
for doctor in G.neighbors(
n
): # then the doctors in that shift, gets infected with prob_infection
for i in range(shift_lenght):
if G.node[doctor][
"status"] == "S":
rand = random.random()
if rand < prob_infection: # with prob p the infection occurres
G.node[doctor][
"infec_value"] += dose # and bumps the infection_value of that susceptible dr
if G.node[doctor][
"infec_value"] >= infect_threshold: # becomes infected
G.node[doctor][
"status"] = "I"
if G.node[doctor][
"type"] == "A": # fellows participate in the dynamics, but i only consider the attendings as real adopters
list_I.append(
G.node[
doctor]
["label"])
# for node in G.nodes():
# if G.node[node]['type']!="shift":
# print t, G.node[node]['label'], G.node[node]["infec_value"]
#raw_input()
list_single_t_evolution.append(float(len(list_I)))
t += 1
######## end t loop
list_lists_t_evolutions.append(list_single_t_evolution)
list_dist_fixed_parameters.append(
compare_real_evol_vs_simus_to_be_called.
compare_two_curves(list_actual_evol,
list_single_t_evolution))
list_abs_dist_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] -
list_actual_evol[-1])
) # i save the distance at the ending point between the current simu and actual evol
list_dist_at_ending_point_fixed_parameters.append(
list_single_t_evolution[-1] - list_actual_evol[-1]
) # i save the distance at the ending point between the current simu and actual evol
list_final_num_infected.append(
list_single_t_evolution[-1])
######## end loop Niter
list_pair_dist_std_delta_end = []
list_pair_dist_std_delta_end.append(
numpy.mean(list_dist_fixed_parameters)
) # average dist between the curves over Niter
list_pair_dist_std_delta_end.append(
numpy.std(list_dist_fixed_parameters))
list_pair_dist_std_delta_end.append(
numpy.mean(
list_abs_dist_at_ending_point_fixed_parameters))
if for_testing_fixed_set == "NO":
file3 = open(output_file3,
'at') # i print out the landscape
print >> file3, prob_infection, prob_Immune, numpy.mean(
list_abs_dist_at_ending_point_fixed_parameters
), numpy.mean(list_dist_fixed_parameters), numpy.mean(
list_final_num_infected), numpy.std(
list_final_num_infected
), numpy.std(list_final_num_infected) / numpy.mean(
list_final_num_infected)
file3.close()
if (
numpy.mean(
list_abs_dist_at_ending_point_fixed_parameters)
) <= delta_end: # i only consider situations close enough at the ending point
dict_filenames_tot_distance[
output_file2] = list_pair_dist_std_delta_end
file2 = open(output_file2, 'at')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter):
list_fixed_t.append(
list_lists_t_evolutions[iter][s])
print >> file2, s, numpy.mean(list_fixed_t)
file2.close()
print "printed out: ", output_file2
if for_testing_fixed_set == "YES":
num_valid_endings = 0.
for item in list_abs_dist_at_ending_point_fixed_parameters:
if item <= delta_end: # i count how many realizations i get close enough at the ending point
num_valid_endings += 1.
print "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters), numpy.std(
list_dist_fixed_parameters
), list_dist_fixed_parameters, "\n"
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
histogram_filename = "../Results/weight_shifts/histogr_raw_distances_ending_infection_memory_p" + str(
prob_infection
) + "_Immune" + str(prob_Immune) + "_threshold" + str(
infect_threshold) + "_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + ".dat"
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters,
histogram_filename)
histogram_filename2 = "../Results/weight_shifts/histogr_sum_dist_traject_infection_memory_p" + str(
prob_infection
) + "_Immune" + str(prob_Immune) + "_threshold" + str(
infect_threshold) + "_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + ".dat"
histograma_bines_gral.histograma_bins(
list_dist_fixed_parameters, Nbins,
histogram_filename2)
output_file10 = "../Results/weight_shifts/Summary_results_training_segment_infection_memory_distrib_p" + str(
prob_infection) + "_" + "FIXED_Immune" + str(
prob_Immune) + "_FIXED_threshold" + str(
infect_threshold
) + "_dose" + str(dose) + "_" + str(
Niter) + "iter_day" + str(cutting_day) + ".dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from train-testing infection with", Niter, "iter, and with values for the parameters: prob_inf ", prob_infection, " prob immune: ", prob_Immune, "infect. threshold:", infect_threshold, "dose:", dose, "\n"
print >> file10, "average distance of the optimum in the testing segment:", numpy.mean(
list_dist_fixed_parameters), numpy.std(
list_dist_fixed_parameters
), list_dist_fixed_parameters, "\n"
print >> file10, "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter, "mean ending dist:", numpy.mean(
list_dist_at_ending_point_fixed_parameters
), "SD final dist", numpy.std(
list_dist_at_ending_point_fixed_parameters
), list_dist_at_ending_point_fixed_parameters, "\n"
print >> file10, "written optimum train_test evolution file:", output_file2
print >> file10, "written histogram file: ", histogram_filename
file10.close()
print "written Summary file: ", output_file10
print "written histogram file: ", histogram_filename
if envelopes == "YES":
calculate_envelope_set_curves.calculate_envelope(
list_lists_t_evolutions, percent_envelope,
"Infection_memory_fixed", [
prob_infection, prob_Immune, infect_threshold,
dose
])
dose += delta_dose
infect_threshold += delta_infect_threshold
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
if for_testing_fixed_set == "NO": # only if i am exploring the whole landscape, i need to call this function, otherwise, i already know the optimum
compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Infection_memory", all_team,
Niter, None)
print "written landscape file:", output_file3