def main(graph_name):
cutting_day = 243 # to separate training-testing
Niter = 1000
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)
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.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)
list_actual_evol_training = list_actual_evol[:cutting_day]
# list_actual_evol_testing=list_actual_evol[(cutting_day-1):] #i dont use this
##################################################################
#../Results/network_final_schedule_withTeam3/infection/Average_time_evolution_Infection_p0.9_Immune0.5_1000iter_2012.dat
prob_min = 1.00
prob_max = 1.0
delta_prob = 0.1
prob_Immune_min = 0.0
prob_Immune_max = 0.0
delta_prob_Immune = 0.1
#######
infect_threshold_min = 1.0 # THIS IS FIXED, BECAUSE DOSE CAN BE DEFINED IN UNITS OF IT!!
infect_threshold_max = 1.01
delta_infect_threshold = 0.1
#######
# of a single encounter with an infected (it cant be zero or it doesnt make sense!)
dose_min = 0.200 #infect_threshold_min
dose_max = 0.201 #######infect_threshold_min/10.
delta_dose = 0.1 ##infect_threshold_min/10.
fixed_param = "" #FIXED_Pimm0_" # or "" # for the Results file that contains the sorted list of best parameters
print_landscape = "NO" #YES" # for the whole exploration
print_training_evol = "YES" # "NO" # once i know the best fit for the training segment, i run it again to get the curve
if print_landscape == "YES":
output_file3 = "../Results/weight_shifts/Landscape_parameters_infection_memory_train_" + fixed_param + "_" + str(
Niter) + "iter_Att_only_middle_day" + str(cutting_day) + ".dat"
file3 = open(output_file3, 'wt')
file3.close()
list_dist_at_ending_point_fixed_parameters = []
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
dict_filenames_prod_distances = {}
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
print " threshold:", infect_threshold
dose = dose_min
while dose <= dose_max:
print " dose:", dose
output_file2 = dir + "Average_time_evolution_Infection_memory_training_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_FIXED_threshold" + str(
infect_threshold) + "_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle.dat"
# I DONT NEED TO WRITE IT, COS I WILL USE THE WHOLE FILE FROM THE WHOLE FIT, WITH THE PARAMETER VALUES THAT THE TESTING-UP-TODAY-125 TELLS ME
output_file4 = dir + "List_adopters_fellows_descending_frequency_Infection_memory_training_p" + str(
prob_infection) + "_Immune" + str(
prob_Immune) + "_FIXED_threshold" + str(
infect_threshold) + "_dose" + str(
dose) + "_" + str(Niter) + "iter_day" + str(
cutting_day) + "_Att_only_middle.dat"
num_Att_adopters = 0.
num_F_adopters = 0.
dict_att_freq_adoption_end = {
} # to keep track of what fellow is an adopter at the end (to use along with the real ic)
dict_fellow_freq_adoption_end = {
} # to keep track of what fellow is an adopter at the end (to use along with the real ic)
for n in G.nodes():
doctor = G.node[n]["label"]
if G.node[n]['type'] == "F":
dict_fellow_freq_adoption_end[doctor] = 0.
elif G.node[n]['type'] == "A":
dict_att_freq_adoption_end[doctor] = 0.
list_lists_t_evolutions = []
list_dist_fixed_parameters = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_final_num_infected = []
for iter in range(Niter):
# print " iter:",iter
########### set I.C.
list_I = [] #list infected doctors
for n in G.nodes():
G.node[n]["status"] = "S" # all nodes are Susceptible
G.node[n]["infec_value"] = 0.
if G.node[n]['type'] == "shift":
pass
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!!
old_num_adopters = 2
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:
shift_length = 5 #i know the first shift (order 0) is of length 5
t = 0
while t < cutting_day: # loop over shifts, in order just until cutting day (training segment)
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'])
effective_shift_length = shift_length
if shift_length == 2 and n not in list_id_weekends_T3:
effective_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(
effective_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: # 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"])
new_num_adopters = len(list_I)
if shift_length == 5: # i estimate that adoption happens in the middle of the shift
if t + 5 < cutting_day:
list_single_t_evolution.append(
old_num_adopters)
if t + 4 < cutting_day:
list_single_t_evolution.append(
old_num_adopters)
if t + 3 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
if t + 2 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
if t + 1 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
t += 5
elif shift_length == 4:
if t + 4 < cutting_day:
list_single_t_evolution.append(
old_num_adopters)
if t + 3 < cutting_day:
list_single_t_evolution.append(
old_num_adopters)
if t + 2 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
if t + 1 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
t += 4
elif shift_length == 3:
if t + 3 < cutting_day:
list_single_t_evolution.append(
old_num_adopters)
if t + 2 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
if t + 1 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
t += 3
elif shift_length == 2:
if t + 2 < cutting_day:
list_single_t_evolution.append(
old_num_adopters)
if t + 1 < cutting_day:
list_single_t_evolution.append(
new_num_adopters)
t += 2
elif shift_length == 1:
if t + 1 < cutting_day:
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)
list_dist_fixed_parameters.append(
compare_real_evol_vs_simus_to_be_called.
compare_two_curves(list_actual_evol_training,
list_single_t_evolution))
list_dist_abs_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] -
list_actual_evol_training[-1])
) # i save the distance at the ending point between the current simu and actual evol
# print "actual:",len(list_actual_evol_training)," simu:",len(list_single_t_evolution) # 125, 125
list_final_num_infected.append(list_single_t_evolution[-1])
list_dist_at_ending_point_fixed_parameters.append(
list_single_t_evolution[-1] -
list_actual_evol_training[-1]
) # i save the distance at the ending point between the current simu and actual evol
for n in G.nodes():
doctor = G.node[n]["label"]
if G.node[n]['type'] != "shift":
if G.node[n]['status'] == "I":
if G.node[n]['type'] == "F":
dict_fellow_freq_adoption_end[doctor] += 1.
num_F_adopters += 1.
elif G.node[n]['type'] == "A":
dict_att_freq_adoption_end[doctor] += 1.
num_Att_adopters += 1.
######## end loop Niter for the training fase
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 print_landscape == "YES":
file3 = open(output_file3,
'at') # i print out the landscape
print >> file3, prob_infection, prob_Immune, infect_threshold, dose, numpy.mean(
list_dist_abs_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 print_training_evol == "YES":
file2 = open(output_file2, 'wt')
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)
last_adoption_value = numpy.mean(list_fixed_t)
file2.close()
print "written evolution file:", output_file2
print "\nFraction of times each fellow was an adopter at the end of the training segment:"
for n in G.nodes():
if G.node[n]['type'] == "F":
doctor = G.node[n]["label"]
dict_fellow_freq_adoption_end[
doctor] = dict_fellow_freq_adoption_end[
doctor] / float(Niter)
sorted_list_tuples = sorted(
dict_fellow_freq_adoption_end.iteritems(),
key=operator.itemgetter(1),
reverse=True)
file4 = open(output_file4, 'wt')
print >> file4, last_adoption_value, "(value adoption among Att at cutting day)", "Avg # F adopters", num_F_adopters / Niter, "Avg # A adopters", num_Att_adopters / Niter
for pair in sorted_list_tuples:
print >> file4, pair[0], pair[1]
file4.close()
print "written adoption frecuency file for fellows:", output_file4
value = numpy.mean(list_dist_fixed_parameters) * numpy.mean(
list_dist_abs_at_ending_point_fixed_parameters
) # if SD=0, it is a problem, because then that is the minimun value, but not the optimum i am looking for!!
dict_filenames_prod_distances[output_file2] = value
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_file2] = list_pair_dist_std_delta_end
dose += delta_dose
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
if print_training_evol == "NO": #if i am exploring the whole space
string_name = "infection_memory_training_" + fixed_param + str(
Niter
) + "iter_day" + str(
cutting_day
) + "_Att_only_middle.dat" # for the "Results" file with the sorted list of files
list_order_dict = compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, string_name, Niter, cutting_day)
# it returns a list of tuples like this : ('../Results/network_final_schedule_withTeam3_local/infection/Average_time_evolution_Infection_training_p0.7_Immune0.0_2iter_2012.dat', [2540.0, 208.0, 1.0]) the best set of parameters being the fist one of the elements in the list.
list_order_dict2 = compare_real_evol_vs_simus_to_be_called.pick_minimum_prod_distances(
dict_filenames_prod_distances, string_name, Niter, cutting_day)
if print_landscape == "YES":
print "printed out landscape file:", output_file3
def main(graph_name):
G = nx.read_gml(graph_name)
cutting_day = 125 # to separate training-testing
Niter_training = 100
Niter_testing = 100
delta_end = 3 # >= than + or - dr difference at the end of the evolution
dir_real_data = '../Results/'
all_team = "NO" # as adopters or not
# output_file3=dir_real_data+"Landscape_parameters_persuasion_"+str(Niter)+"iter.dat"
#file3 = open(output_file3,'wt')
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
if all_team == "YES":
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_all_team_as_adopters_SIMPLER.csv"
else:
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_SIMPLER.csv"
#ya no necesito CAMBIAR TB EL NOMBRE DEL ARCHIVO EN EL CODIGO PARA COMPARAR CURVAs
list_actual_evol = []
result_actual_file = csv.reader(open(filename_actual_evol, 'rb'),
delimiter=',')
cont = 0
for row in result_actual_file:
if cont > 0: # i ignore the first line with the headers
num_adopters = row[3]
list_actual_evol.append(float(num_adopters))
cont += 1
list_actual_evol_training = list_actual_evol[:cutting_day]
list_actual_evol_testing = list_actual_evol[(cutting_day - 1):]
##################################################################
#../Results/network_final_schedule_withTeam3/Time_evolutions_Persuasion_alpha0.2_damping0.0_mutual_encourg0.7_threshold0.4_unif_distr_50iter_2012_seed31Oct_finalnetwork.dat
alpha_F_min = 0.0 # alpha=0: nobody changes their mind
alpha_F_max = 1.001
delta_alpha_F = 0.1
min_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
max_damping = 1.01
delta_damping = 0.1
min_mutual_encouragement = 0.0 # when two Adopters meet, they convince each other even more
max_mutual_encouragement = 1.01
delta_mutual_encouragement = 0.1
print "\n\nPersuasion process on network, with Niter:", Niter_training
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
dict_filenames_list_dict_network_states = {}
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 values
alpha_A = 0.5 * 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/network_final_schedule_withTeam3_local/"
output_file = dir + "Time_evolutions_Persuasion_training_alpha" + str(
alpha_F
) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement) + "_" + str(
Niter_training) + "iter_distributed_thresholds.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_abs_at_ending_point_fixed_parameters = []
list_dict_network_states = []
list_networks_at_cutting_day = []
for iter in range(Niter_training):
print " ", iter
list_t = []
time_evol_number_adopters = [
] # for a single realization of the dynamics
dict_network_states = {}
num_adopters, seed_shift, max_shift = set_ic(
G
) # 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)
########### the dynamics starts:
t = int(
seed_shift) + 1 # the first time step is just IC.???
while t < cutting_day: # 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
) # i move their values of opinion
update_opinions(
G, 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
for n in G.nodes():
if G.node[n]['type'] != "shift":
dict_network_states[
G.node[n]["label"]] = G.node[n]["status"]
list_dict_network_states.append(dict_network_states)
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_training,
time_evol_number_adopters))
list_dist_abs_at_ending_point_fixed_parameters.append(
abs(time_evol_number_adopters[-1] -
list_actual_evol_training[-1]))
####################### end loop Niter for the training fase
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 (
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
#print >> file3, alpha_F,damping,mutual_encouragement,threshold,dict_filenames_tot_distance[output_file][0],dict_filenames_tot_distance[output_file][1]
dict_filenames_list_dict_network_states[
output_file] = list_dict_network_states
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_training
): #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()
damping += delta_damping
mutual_encouragement += delta_mutual_encouragement
alpha_F += delta_alpha_F
list_order_dict = compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance,
"Persuasion_training_distributed_thresholds", all_team, Niter_training)
#./Results/network_final_schedule_withTeam3_local/Time_evolutions_Persuasion_alpha0.4_damping0.4_mutual_encourg0.6_threshold0.5_unif_distr_2iter_2012_seed31Oct_finalnetwork.dat
optimum_filename = list_order_dict[0][0]
alpha_F = float(list_order_dict[0][0].split("_alpha")[1][0:3])
alpha_A = 0.5 * alpha_F
damping = float(list_order_dict[0][0].split("_damping")[1][0:3])
mutual_encouragement = float(
list_order_dict[0][0].split("_mutual_encourg")[1][0:3])
# raw_input()
print "starting testing fase with:"
print "alpha=", alpha_F, " damping=", damping, " mutual encourag=", mutual_encouragement, " distributed threshold"
# i already know the optimum, now i run the dynamics with those values, starting from the average state on the cutting point, and test:
time_evol_number_adopters_ITER = [
] # list of complete single realizations of the dynamics
list_dict_network_states = []
list_dist_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_lists_t_evolutions = []
lista_num_adopters = []
lista_Adopters = []
dict_tot_Adopters = {}
for dictionary in dict_filenames_list_dict_network_states[
optimum_filename]:
# dictionary={Dr1:status, Dr2:status,} # one dict per iteration
num_Adopters = 0.
for key in dictionary:
if dictionary[key] == "Adopter":
num_Adopters += 1.
if key not in lista_Adopters:
lista_Adopters.append(key)
dict_tot_Adopters[key] = 1.
else:
dict_tot_Adopters[key] += 1.
lista_num_adopters.append(num_Adopters)
avg_adopters = int(
numpy.mean(lista_num_adopters)) # i find out the average num Adopters
print numpy.mean(lista_num_adopters), avg_adopters, numpy.std(
lista_num_adopters)
if numpy.mean(lista_num_adopters) - avg_adopters >= 0.5:
avg_adopters += 1.0
print avg_adopters
# i sort the list from more frequently infected to less
list_sorted_dict = sorted(dict_tot_Adopters.iteritems(),
key=operator.itemgetter(1))
new_list_sorted_dict = list_sorted_dict
new_list_sorted_dict.reverse()
print "Adopters:", new_list_sorted_dict
#list_sorted_dict=[(u'Weiss', 5.0), (u'Wunderink', 5.0), (u'Keller', 4.0), (u'Go', 3.0), (u'Cuttica', 3.0), (u'Rosario', 2.0), (u'Radigan', 2.0), (u'Smith', 2.0), (u'RosenbergN', 2.0), (u'Gillespie', 1.0), (u'Osher', 1.0), (u'Mutlu', 1.0), (u'Dematte', 1.0), (u'Hawkins', 1.0), (u'Gates', 1.0)]
lista_avg_Adopters = [
] # i create the list of Drs that on average are most likely infected by the cutting day
i = 1
for item in new_list_sorted_dict:
if (item[0] not in lista_avg_Adopters) and (i <= avg_adopters):
lista_avg_Adopters.append(item[0])
i += 1
print lista_avg_Adopters
for iter in range(Niter_testing):
print " ", iter
dict_dr_status_current_iter = dict_filenames_list_dict_network_states[
optimum_filename][iter]
time_evol_number_adopters = [
] # for a single realization of the dynamics
dict_network_states = {}
list_t = []
###############
# NECESITO GUARDAR RECORD DE LOS THERSHOLDS PERSONALES PARA USARLOS LUEGO aki???
########
list_Adopters = [] #set initial conditions
for node in G.nodes():
if G.node[node]['type'] != "shift":
# personal threshold have been established for each dr at the beginning of the simu: set_ic()
G.node[node][
"status"] = "NonAdopter" # by default, non-Adopters
label = G.node[node]['label']
if label in lista_avg_Adopters:
G.node[node]["status"] = "Adopter"
G.node[node]["adoption_vector"] = random.random() * (
1.0 -
G.node[node]["personal_threshold"]) + G.node[node][
"personal_threshold"] #values from (threshold,1]
if G.node[node]["adoption_vector"] > 1.0:
G.node[node]["adoption_vector"] = 1.0
list_Adopters.append(G.node[node]["label"])
time_evol_number_adopters.append(float(len(list_Adopters)))
list_t.append(cutting_day)
################# the dynamics starts for the testing fase:
t = cutting_day
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) # i move their values of opinion
update_opinions(
G, 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)))
print t, len(list_Adopters)
t += 1
############## end while loop over t
#raw_input()
for n in G.nodes():
if G.node[n]['type'] != "shift":
dict_network_states[G.node[n]["label"]] = G.node[n]["status"]
list_dict_network_states.append(dict_network_states)
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_testing, time_evol_number_adopters))
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])
#######################end loop over Niter for the testing fase
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 (
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
dict_filenames_list_dict_network_states[
output_file] = list_dict_network_states
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
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter_testing, list_dist_abs_at_ending_point_fixed_parameters
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters,
"../Results/histogr_raw_distances_ending_test_train_persuasion_avg_ic_"
+ str(Niter_testing) + "iter_distributed_thresholds.dat")
output_file8 = "../Results/List_tot_distances_training_segment_persuasion_alpha" + str(
alpha_F) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement) + "_" + str(
Niter_training) + "iter_avg_ic_distributed_thresholds.dat"
file8 = open(output_file8, 'wt')
for item in list_dist_fixed_parameters:
print >> file8, item
file8.close()
output_file9 = "../Results/List_distances_ending_training_segment_persuasion_alpha" + str(
alpha_F) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement) + "_" + str(
Niter_training) + "iter_avg_ic_distributed_thresholds.dat"
file9 = open(output_file9, 'wt')
for item in list_dist_abs_at_ending_point_fixed_parameters:
print >> file9, item
file9.close()
output_file = dir + "Time_evolutions_Persuasion_testing_avg_ic_alpha" + str(
alpha_F
) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement
) + "_unif_distr_" + str(
Niter_training
) + "iter_2012_seed31Oct_finalnetwork_avg_ic_distributed_thresholds.dat"
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_training
): #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 "written training segment file:", optimum_filename
print "written testing segment file:", output_file
output_file10 = "../Results/Summary_results_train_test_persuasion_alpha" + str(
alpha_F) + "_damping" + str(damping) + "_mutual_encourg" + str(
mutual_encouragement) + "_" + str(
Niter_training) + "iter_avg_ic_distributed_thresholds.dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from train-testing persuasion with", Niter_training, Niter_testing, "iter (respectively), using the avg of the cutting points as IC, and with values for the parameters: alpha ", alpha_F, " damping: ", damping, " mutual_encourg: ", mutual_encouragement, " distributed 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_testing, list_dist_at_ending_point_fixed_parameters
print >> file10, "written training segment file:", optimum_filename
print >> file10, "written testing segment file:", output_file
file10.close()
def main(graph_name):
cutting_day=175 # to separate training-testing
G = nx.read_gml(graph_name)
all_team="NO" # as adopters or not
list_id_weekends_T3=look_for_T3_weekends(G) # T3 doesnt share fellows in the weekend (but they are the exception)
dir_real_data='../Results/'
Nbins=20 # for the histogram of sum of distances
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!!!)
Niter=1000
fixed_param=""#FIXED_Pimm0_" # or "" # for the Results file that contains the sorted list of best parameters
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
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)
list_actual_evol_training=list_actual_evol[:cutting_day]
# list_actual_evol_testing=list_actual_evol[(cutting_day-1):] # i dont need this one
##################################################################
prob_min=0.10
prob_max=1.01
delta_prob=0.1
prob_Immune_min=0.0
prob_Immune_max=1.001
delta_prob_Immune=0.1
# threshold is not personal, and set randomly to a value (0,1)
# of a single encounter with an infected (it cant be zero or it doesnt make sense!)
dose_min=0.05 #infect_threshold_min
dose_max=1.001 #######infect_threshold_min/10.
delta_dose=0.05 ##infect_threshold_min/10.
dir="../Results/weight_shifts/infection/"
dict_filenames_tot_distance={} # i will save the filename as key and the tot distance from that curve to the original one
dict_filenames_prod_distances={}
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_training_p"+str(prob_infection)+"_"+"Immune"+str(prob_Immune)+"_threshold_from_distrib_dose"+str(dose)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred.dat"
# file2 = open(output_file2,'wt')
# file2.close()
list_lists_t_evolutions=[] # i create the empty list of list for the Niter temporal evolutions
list_dist_fixed_parameters=[]
list_dist_at_ending_point_fixed_parameters=[]
list_dist_abs_at_ending_point_fixed_parameters=[]
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.
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']=="Wunderink" or G.node[n]["label"]=="Weiss":
G.node[n]["status"]="I"
G.node[n]["infec_value"]=G.node[n]["personal_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< cutting_day: # 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): # 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: # 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"
list_I.append(G.node[doctor]["label"])
list_single_t_evolution.append(float(len(list_I)))
t+=1
######## end t loop
list_lists_t_evolutions.append(list_single_t_evolution)
#print "actual:",len(list_actual_evol_training)," simu:",len(list_single_t_evolution)
list_dist_fixed_parameters.append(compare_real_evol_vs_simus_to_be_called.compare_two_curves( list_actual_evol_training,list_single_t_evolution))
list_dist_abs_at_ending_point_fixed_parameters.append( abs(list_single_t_evolution[-1]-list_actual_evol_training[-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_training[-1]) # i save the distance at the ending point between the current simu and actual evol
######## end loop Niter for the training fase
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))
value=numpy.mean(list_dist_fixed_parameters) *numpy.mean(list_dist_abs_at_ending_point_fixed_parameters)# if SD=0, it is a problem, because then that is the minimun value, but not the optimum i am looking for!!
dict_filenames_prod_distances[output_file2]= value
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_file2]=list_pair_dist_std_delta_end
histogram_filename="../Results/weight_shifts/histogr_raw_distances_ending_infection_memory_training_p"+str(prob_infection)+"_"+"Immune"+str(prob_Immune)+"_threshold_from_distrib_dose"+str(dose)+"_"+str(Niter)+"iter_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_infection_memory_training_p"+str(prob_infection)+"_"+"Immune"+str(prob_Immune)+"_threshold_from_distrib_dose"+str(dose)+"_"+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred.dat"
histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,Nbins,histogram_filename2)
print "written histogram file: ",histogram_filename
print "written histogram file: ",histogram_filename2
dose+= delta_dose
prob_infection+= delta_prob
prob_Immune+= delta_prob_Immune
string_name="infection_memory_training_"+fixed_param+str(Niter)+"iter_day"+str(cutting_day)+"_A_F_inferred.dat" # for the "Results" file with the sorted list of files
list_order_dict= compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(dict_filenames_tot_distance,string_name,Niter,cutting_day)
# it returns a list of tuples like this : ('../Results/network_final_schedule_withTeam3_local/infection/Average_time_evolution_Infection_training_p0.7_Immune0.0_2iter_2012.dat', [2540.0, 208.0, 1.0]) the best set of parameters being the fist one of the elements in that list.
list_order_dict2= compare_real_evol_vs_simus_to_be_called.pick_minimum_prod_distances(dict_filenames_prod_distances,string_name,Niter,cutting_day)
prob_infection=float(list_order_dict[0][0].split("_p")[1].split("_")[0])
prob_Immune=float(list_order_dict[0][0].split("_Immune")[1].split("_")[0])
dose=float(list_order_dict[0][0].split("_dose")[1].split("_")[0])
print "\nOptimum parameters (old method) at day",cutting_day," are: p=",prob_infection," Pimmune=",prob_Immune," infection threshold from distribution, and dose=",dose
# optimum_filename=list_order_dict2[0][0]
prob_infection=float(list_order_dict2[0][0].split("_p")[1].split("_")[0])
prob_Immune=float(list_order_dict2[0][0].split("_Immune")[1].split("_")[0])
dose=float(list_order_dict2[0][0].split("_dose")[1].split("_")[0])
print "Optimum parameters (product of distances and SDs) at day",cutting_day," are: p=",prob_infection," Pimmune=",prob_Immune," infection threshold from distribution, and dose=",dose
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):
cutting_day = 125 # to separate training-testing
G = nx.read_gml(graph_name)
all_team = "NO" # as adopters or not
dir_real_data = '../Results/'
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!!!)
Niter_training = 100
Niter_testing = 100
output_file3 = dir_real_data + "Landscape_parameters_infection_train_test_" + str(
Niter_training) + "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":
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_all_team_as_adopters_SIMPLER.csv"
else:
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_SIMPLER.csv"
#ya no necesito CAMBIAR TB EL NOMBRE DEL ARCHIVO EN EL CODIGO PARA COMPARAR CURVAs
list_actual_evol = []
result_actual_file = csv.reader(open(filename_actual_evol, 'rb'),
delimiter=',')
cont = 0
for row in result_actual_file:
if cont > 0: # i ignore the first line with the headers
num_adopters = row[3]
list_actual_evol.append(float(num_adopters))
cont += 1
list_actual_evol_training = list_actual_evol[:cutting_day]
list_actual_evol_testing = list_actual_evol[(cutting_day - 1):]
##################################################################
#../Results/network_final_schedule_withTeam3/infection/Average_time_evolution_Infection_p0.9_Immune0.5_1000iter_2012.dat
prob_min = 0.0
prob_max = 1.001
delta_prob = 0.01
prob_Immune_min = 0.00
prob_Immune_max = 1.001
delta_prob_Immune = 0.01
dir = "../Results/network_final_schedule_withTeam3_local/infection/"
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
dict_filenames_list_dict_network_states = {
} # i will save the filename as key and the list of networks at cutting day as value
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_training_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(
Niter_training) + "iter_2012_avg_ic_day" + str(
cutting_day) + ".dat"
file2 = open(output_file2, 'wt')
file2.close()
# i create the empty list of list for the Niter temporal evolutions
num_shifts = 0
num_Drs = 0.
for n in G.nodes():
G.node[n]["status"] = "S"
if G.node[n]['type'] == "shift":
num_shifts += 1
else:
num_Drs += 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 = []
list_dist_fixed_parameters = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_final_num_infected = []
list_dict_network_states = []
for iter in range(Niter_training):
print " iter:", iter
dict_network_states = {}
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'])
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":
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
# print max_order
################# the dynamics starts:
t = 1
while t < cutting_day: # loop over shifts, in order just until cutting day (training segment)
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
######## end t loop
for n in G.nodes():
if G.node[n]['type'] != "shift":
dict_network_states[G.node[n]
["label"]] = G.node[n]["status"]
list_dict_network_states.append(dict_network_states)
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_training, list_single_t_evolution))
list_dist_abs_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] -
list_actual_evol_training[-1])
) # i save the distance at the ending point between the current simu and actual evol
# print "actual:",len(list_actual_evol_training)," simu:",len(list_single_t_evolution) # 125, 125
list_final_num_infected.append(list_single_t_evolution[-1])
######## end loop Niter for the training fase
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))
file3 = open(output_file3, 'at') # i print out the landscape
print >> file3, prob_infection, prob_Immune, numpy.mean(
list_dist_abs_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_dist_abs_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
dict_filenames_list_dict_network_states[
output_file2] = list_dict_network_states
file2 = open(output_file2, 'at')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter_training):
list_fixed_t.append(list_lists_t_evolutions[iter][s])
print >> file2, s, numpy.mean(list_fixed_t)
file2.close()
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
list_order_dict = compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Infection_training", all_team,
Niter_training, cutting_day)
# it returns a list of tuples like this : ('../Results/network_final_schedule_withTeam3_local/infection/Average_time_evolution_Infection_training_p0.7_Immune0.0_2iter_2012.dat', [2540.0, 208.0, 1.0]) the best set of parameters being the fist one of the elements in the list.
optimum_filename = list_order_dict[0][0]
prob_infection = float(list_order_dict[0][0].split("_p")[1].split("_")[0])
prob_Immune = float(
list_order_dict[0][0].split("_Immune")[1].split("_")[0])
print "starting testing fase with:"
print "p=", prob_infection, " and Pimmune=", prob_Immune
# i already know the optimum, now i run the dynamics with those values, starting from the average state on the cutting point, and test:
list_dist_fixed_parameters = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_lists_t_evolutions = []
lista_num_infect = []
lista_I_drs = []
dict_tot_I_doctors = {}
lista_num_imm = []
lista_Imm_drs = []
dict_tot_Imm_doctors = {}
for dictionary in dict_filenames_list_dict_network_states[
optimum_filename]:
# dictionary={Dr1:status, Dr2:status,} # one dict per iteration
num_I = 0.
num_Imm = 0.
for key in dictionary:
if dictionary[key] == "I":
num_I += 1.
if key not in lista_I_drs:
lista_I_drs.append(key)
dict_tot_I_doctors[key] = 1.
else:
dict_tot_I_doctors[key] += 1.
elif dictionary[key] == "Immune":
num_Imm += 1.
if key not in lista_Imm_drs:
lista_Imm_drs.append(key)
dict_tot_Imm_doctors[key] = 1.
else:
dict_tot_Imm_doctors[key] += 1.
lista_num_infect.append(num_I)
lista_num_imm.append(num_Imm)
avg_inf_drs = int(
numpy.mean(lista_num_infect)) # i find out the average num I
print "I", numpy.mean(lista_num_infect), numpy.mean(
lista_num_infect) / num_Drs, avg_inf_drs, numpy.std(lista_num_infect)
if numpy.mean(lista_num_infect
) - avg_inf_drs >= 0.5: # correccion de truncamiento
avg_inf_drs += 1.0
# print avg_inf_drs
avg_imm_drs = int(
numpy.mean(lista_num_imm)) # i find out the average num Immune
print "Imm", numpy.mean(lista_num_imm), numpy.mean(
lista_num_imm) / num_Drs, avg_imm_drs, numpy.std(lista_num_imm)
if numpy.mean(
lista_num_imm) - avg_imm_drs >= 0.5: # correccion de truncamiento
avg_imm_drs += 1.0
# print avg_imm_drs
# i sort the list from more frequently infected to less
list_sorted_dict = sorted(dict_tot_I_doctors.iteritems(),
key=operator.itemgetter(1))
new_list_sorted_dict = list_sorted_dict
new_list_sorted_dict.reverse()
print "I:", new_list_sorted_dict
# i sort the list from more frequently imm to less
list_sorted_dict_imm = sorted(dict_tot_Imm_doctors.iteritems(),
key=operator.itemgetter(1))
new_list_sorted_dict_imm = list_sorted_dict_imm
new_list_sorted_dict_imm.reverse()
print "Immunes:", new_list_sorted_dict_imm
# raw_input()
#list_sorted_dict=[(u'Weiss', 5.0), (u'Wunderink', 5.0), (u'Keller', 4.0), (u'Go', 3.0), (u'Cuttica', 3.0), (u'Rosario', 2.0), (u'Radigan', 2.0), (u'Smith', 2.0), (u'RosenbergN', 2.0), (u'Gillespie', 1.0), (u'Osher', 1.0), (u'Mutlu', 1.0), (u'Dematte', 1.0), (u'Hawkins', 1.0), (u'Gates', 1.0)]
lista_avg_I_drs = [
] # i create the list of Drs that on average are most likely infected by the cutting day
i = 1
for item in new_list_sorted_dict:
if (item[0] not in lista_avg_I_drs) and (i <= avg_inf_drs):
lista_avg_I_drs.append(item[0])
i += 1
print lista_avg_I_drs, len(lista_avg_I_drs), float(
len(lista_avg_I_drs)) / num_Drs
lista_avg_Imm_drs = [
] # i create the list of Drs that on average are most likely immune by the cutting day
i = 1
for item in new_list_sorted_dict_imm:
if (item[0] not in lista_avg_Imm_drs) and (i <= avg_imm_drs) and (
item[0] not in lista_avg_I_drs):
lista_avg_Imm_drs.append(item[0])
i += 1
print lista_avg_Imm_drs, len(lista_avg_Imm_drs), float(
len(lista_avg_Imm_drs)) / num_Drs
# raw_input()
for iter in range(Niter_testing):
# i establish the initial conditions (as the average of the cutting point)
list_I = [] #list infected doctors
list_Immune = []
for node in G.nodes():
if G.node[node]['type'] != "shift":
label = G.node[node]['label']
G.node[node]["status"] = "S" #by default, all are susceptible
if label in lista_avg_I_drs:
G.node[node]["status"] = "I"
list_I.append(label)
elif label in lista_avg_Imm_drs:
G.node[node]["status"] = "Immune"
list_Immune.append(label)
if label in lista_avg_I_drs and label in lista_avg_Imm_drs:
print label, "is in the top most infected AND immune!"
raw_input()
print "# I at the beginning of the testing fase:", len(list_I), float(
len(list_I)) / num_Drs, " and # Immune:", len(list_Immune), float(
len(list_Immune)) / num_Drs
# print " iter:",iter, len(list_I)
list_single_t_evolution = []
list_single_t_evolution.append(len(list_I))
t = cutting_day
while t <= max_order: # loop over shifts, in order just until cutting day (training segment)
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)))
# print t, len(list_I)
t += 1
list_lists_t_evolutions.append(list_single_t_evolution)
list_I = []
list_Immune = []
for node in G.nodes():
if G.node[node]['type'] != "shift":
label = G.node[node]['label']
if G.node[node]["status"] == "I":
list_I.append(label)
elif G.node[node]["status"] == "Immune":
list_Immune.append(label)
print " # I at the END of the testing fase:", len(list_I), float(
len(list_I)) / num_Drs, " and # Immune:", len(list_Immune), float(
len(list_Immune)) / num_Drs, "\n"
list_dist_fixed_parameters.append(
compare_real_evol_vs_simus_to_be_called.compare_two_curves(
list_actual_evol_testing, list_single_t_evolution))
# print " dist:",list_dist_fixed_parameters[-1]
list_dist_abs_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
############### end loop Niter for the testing
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
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter_testing, list_dist_abs_at_ending_point_fixed_parameters
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters,
"../Results/histogr_raw_distances_ending_test_train_infection_p" +
str(prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" +
str(Niter_training) + "iter_avg_ic_day" + str(cutting_day) + ".dat")
output_file8 = "../Results/List_tot_distances_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter_avg_ic_day" + str(cutting_day) + ".dat"
file8 = open(output_file8, 'wt')
for item in list_dist_fixed_parameters:
print >> file8, item
file8.close()
output_file9 = "../Results/List_distances_ending_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter_avg_ic_day" + str(cutting_day) + ".dat"
file9 = open(output_file9, 'wt')
for item in list_dist_abs_at_ending_point_fixed_parameters:
print >> file9, item
file9.close()
output_file5 = dir + "Average_time_evolution_Infection_testing_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_testing) + "iter_2012_avg_ic_day" + str(cutting_day) + ".dat"
file5 = open(output_file5, 'wt')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter_testing):
list_fixed_t.append(list_lists_t_evolutions[iter][s])
print >> file5, s + cutting_day, numpy.mean(list_fixed_t)
# print s+cutting_day,numpy.mean(list_fixed_t)
file5.close()
print "written training segment file:", optimum_filename
print "written testing segment file:", output_file5
print "printed out landscape file:", output_file3
output_file10 = "../Results/Summary_results_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter_avg_ic_day" + str(cutting_day) + ".dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from train-testing persuasion with", Niter_training, Niter_testing, "iter (respectively), using all the individual cutting points as IC, 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_testing, list_dist_abs_at_ending_point_fixed_parameters, "\n"
print >> file10, "written training segment file:", optimum_filename
print >> file10, "written testing segment file:", output_file5
file10.close()
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)
cutting_day=243 # to separate training-testing
Niter=1000
delta_end=3 # >= than + or - dr difference at the end of the evolution
dir_real_data='../Results/'
dir="../Results/weight_shifts/persuasion/"
Nbins=20 # for the histogram of sum of distances
######################################################################################
# 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.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)
list_actual_evol_training=list_actual_evol[:cutting_day]
##################################################################
#../Results/network_final_schedule_withTeam3/Time_evolutions_Persuasion_alpha0.2_damping0.0_mutual_encourg0.7_threshold0.4_unif_distr_50iter_2012_seed31Oct_finalnetwork.dat
alpha_F_min=0.100 # # alpha=0: nobody changes their mind
alpha_F_max=0.1001
delta_alpha_F=0.10 #AVOID 1.0 OR THE DYNAMICS GETS TOTALLY STUCK AND IT IS NOT ABLE TO PREDICT SHIT!
min_damping=0.000 #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=0.0001 #0.451
delta_damping=0.10
min_mutual_encouragement=0.300 # # when two Adopters meet, they convince each other even more
max_mutual_encouragement=0.301
delta_mutual_encouragement=0.10
threshold_min=0.500 # # larger than, to be an Adopter
threshold_max=0.501
delta_threshold=0.10 # AVOID 1.0 OR THE DYNAMICS GETS TOTALLY STUCK AND IT IS NOT ABLE TO PREDICT SHIT
fixed_param="FIXED_threshold0.5_damping0_" # or "" # for the Results file that contains the sorted list of best parameters
print_landscape="NO"#NO" # for the whole exploration
print_training_evol= "YES"#"#YES" # once i know the best fit for the training segment, i run it again to get the curve
if print_landscape =="YES":
output_file3="../Results/weight_shifts/Landscape_parameters_persuasion_train_"+fixed_param+str(Niter)+"iter_Att_only_middle_day"+str(cutting_day)+".dat"
file3 = open(output_file3,'wt')
file3.close()
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
dict_filenames_prod_distances={}
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 values
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_evolutions_Persuasion_training_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_"+str(cutting_day)+"_Att_only_middle.dat"
output_file4=dir+"List_adopters_fellows_descending_frequency_persuasion_training_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_"+str(Niter)+"iter_"+str(cutting_day)+"_Att_only_middle.dat"
num_Att_adopters=0.
num_F_adopters=0.
dict_att_freq_adoption_end={} # to keep track of what fellow is an adopter at the end (to use along with the real ic)
dict_fellow_freq_adoption_end={} # to keep track of what fellow is an adopter at the end (to use along with the real ic)
for n in G.nodes():
doctor=G.node[n]["label"]
if G.node[n]['type'] =="F":
dict_fellow_freq_adoption_end[doctor]=0.
elif G.node[n]['type'] =="A":
dict_att_freq_adoption_end[doctor]=0.
time_evol_number_adopters_ITER=[] # list of complete single realizations of the dynamics
list_dist_fixed_parameters=[]
list_dist_at_ending_point_fixed_parameters=[]
list_dist_abs_at_ending_point_fixed_parameters=[]
list_networks_at_cutting_day=[]
list_final_num_adopt=[]
for iter in range(Niter):
# print " ",iter
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))
old_num_adopters=num_adopters
########### the dynamics starts:
shift_length=5 #i know the first shift (order 0) is of length 5
t=0
while t< cutting_day: # loop over shifts, in chronological order (the order is the day index since seeding_day)
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 (not all t's exists as 'orders' in the network!! just the days corresponding to the beginning of each shift)
shift_length=int(G.node[n]['shift_length'])
effective_shift_length=shift_length
if shift_length==2 and n not in list_id_weekends_T3:
effective_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_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,effective_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)
list_ALL_Adopters=[]
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"])
list_ALL_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 < cutting_day:
time_evol_number_adopters.append(old_num_adopters)
if t+4 < cutting_day:
time_evol_number_adopters.append(old_num_adopters)
if t+3 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
if t+2 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
if t+1 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
t+=5
elif shift_length==4:
if t+4 < cutting_day:
time_evol_number_adopters.append(old_num_adopters)
if t+3 < cutting_day:
time_evol_number_adopters.append(old_num_adopters)
if t+2 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
if t+1 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
t+=4
elif shift_length==3:
if t+3 < cutting_day:
time_evol_number_adopters.append(old_num_adopters)
if t+2 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
if t+1 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
t+=3
elif shift_length==2:
if t+2 < cutting_day:
time_evol_number_adopters.append(old_num_adopters)
if t+1 < cutting_day:
time_evol_number_adopters.append(new_num_adopters)
t+=2
elif shift_length==1:
if t+1 < cutting_day:
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)
list_final_num_adopt.append(time_evol_number_adopters[-1])
list_dist_fixed_parameters.append(compare_real_evol_vs_simus_to_be_called.compare_two_curves( list_actual_evol_training,time_evol_number_adopters))
list_dist_abs_at_ending_point_fixed_parameters.append( abs(time_evol_number_adopters[-1]-list_actual_evol_training[-1]) )
list_dist_at_ending_point_fixed_parameters.append( time_evol_number_adopters[-1]-list_actual_evol_training[-1])
for n in G.nodes():
doctor= G.node[n]["label"]
if G.node[n]['type'] != "shift":
if G.node[n]['status'] =="Adopter":
if G.node[n]['type'] =="F":
dict_fellow_freq_adoption_end[doctor] += 1.
num_F_adopters+=1.
elif G.node[n]['type'] =="A":
dict_att_freq_adoption_end[doctor] += 1.
num_Att_adopters+=1.
####################### end loop Niter for the training fase
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))
value=numpy.mean(list_dist_fixed_parameters) *numpy.mean(list_dist_abs_at_ending_point_fixed_parameters) # if SD=0, it is a problem, because then that is the minimun value, but not the optimum i am looking for!!
dict_filenames_prod_distances[output_file]= value
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
if print_landscape =="YES":
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 print_training_evol=="YES":
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,numpy.mean(list_fixed_t),numpy.std(list_fixed_t), alpha_F,damping,mutual_encouragement
last_adoption_value=numpy.mean(list_fixed_t)
file.close()
print "written evolution file:", output_file
print "\nFraction of times each fellow was an adopter at the end of the training segment:"
for n in G.nodes():
if G.node[n]['type'] =="F":
doctor= G.node[n]["label"]
dict_fellow_freq_adoption_end[doctor]= dict_fellow_freq_adoption_end[doctor]/float(Niter)
print doctor,"\t", dict_fellow_freq_adoption_end[doctor]
print "(out of",len(dict_fellow_freq_adoption_end),"fellows)\n"
sorted_list_tuples=sorted(dict_fellow_freq_adoption_end.iteritems(), key=operator.itemgetter(1),reverse=True)
file4 = open(output_file4,'wt')
print >> file4,last_adoption_value, "(value adoption among Att at cutting day)","Avg # F adopters",num_F_adopters/Niter, "Avg # A adopters",num_Att_adopters/Niter
for pair in sorted_list_tuples:
print >> file4, pair[0], pair[1]
file4.close()
print "written adoption frecuency file for fellows:", output_file4
damping += delta_damping
mutual_encouragement += delta_mutual_encouragement
alpha_F += delta_alpha_F
threshold += delta_threshold
string_name="persuasion_training_"+fixed_param+str(Niter)+"iter_"+str(cutting_day)+"_Att_only_middle.dat" # for the "Results" file with the sorted list of files
list_order_dict= compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(dict_filenames_tot_distance,string_name,Niter,cutting_day)
list_order_dict2= compare_real_evol_vs_simus_to_be_called.pick_minimum_prod_distances(dict_filenames_prod_distances,string_name,Niter,cutting_day)
if print_landscape =="YES":
print "printed out landscape file:",output_file3
print "\n\n"
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)
all_team = "NO" # as adopters or not
Niter = 100
dir_real_data = '../Results/'
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!!!)
# NO RESTRICTION, TO OBTAIN THE COMPLETE PARAMETER LANDSCAPE
output_file3 = dir_real_data + "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":
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_all_team_as_adopters_SIMPLER.csv"
else:
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_SIMPLER.csv"
#ya no necesito CAMBIAR TB EL NOMBRE DEL ARCHIVO EN EL CODIGO PARA COMPARAR CURVAs
list_actual_evol = []
result_actual_file = csv.reader(open(filename_actual_evol, 'rb'),
delimiter=',')
cont = 0
for row in result_actual_file:
if cont > 0: # i ignore the first line with the headers
num_adopters = row[3]
list_actual_evol.append(float(num_adopters))
cont += 1
##################################################################
#../Results/network_final_schedule_withTeam3/infection/Average_time_evolution_Infection_p0.9_Immune0.5_1000iter_2012.dat
prob_min = 0.00
prob_max = 1.001
delta_prob = 0.01
prob_Immune_min = 0.0
prob_Immune_max = 1.001
delta_prob_Immune = 0.01
dir = "../Results/network_final_schedule_withTeam3_local/infection/"
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
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()
# 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 = []
list_dist_fixed_parameters = []
list_abs_dist_at_ending_point_fixed_parameters = []
list_final_num_infected = []
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()
##########################################
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"
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"
# 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)))
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)): #ime 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_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))
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()
# file = open(output_file,'at')
#print >> file, prob_infection, numpy.mean(list_final_I_values_fixed_p)
#file.close()
# print list_dist_fixed_parameters
# histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,50, "../Results/histogr_distances_indiv_infect_simus_to_the_average_curve_p"+str(prob_infection)+"_"+"Immune"+str(prob_Immune)+"_"+str(Niter)+"iter.dat") # Nbins=100
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Infection", all_team, Niter, None)
# file3.close()
print "printed out landscape file:", output_file3
def main(graph_name):
cutting_day = 175 # to separate training-testing
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)
all_team = "NO" # as adopters or not
Nbins = 20 # for the histogram of sum of distances
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!!!)
Niter_training = 1000
fixed_param = "" #"FIXED_Pimm0_" # or "" # for the Results file that contains the sorted list of best parameters
output_file3 = "../Results/weight_shifts/Landscape_parameters_infection_train_test_" + str(
Niter_training) + "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)
list_actual_evol_training = list_actual_evol[:cutting_day]
# list_actual_evol_testing=list_actual_evol[(cutting_day-1):] #i dont use this
##################################################################
#../Results/network_final_schedule_withTeam3/infection/Average_time_evolution_Infection_p0.9_Immune0.5_1000iter_2012.dat
prob_min = 0.0
prob_max = 1.01
delta_prob = 0.1
prob_Immune_min = 0.00
prob_Immune_max = 1.01
delta_prob_Immune = 0.1
list_dist_at_ending_point_fixed_parameters = []
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
dict_filenames_prod_distances = {}
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_training_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(
Niter_training) + "iter_2012_avg_ic_day" + str(
cutting_day) + ".dat"
# file2 = open(output_file2,'wt') I DONT NEED TO WRITE IT, COS I WILL USE THE WHOLE FILE FROM THE WHOLE FIT, WITH THE PARAMETER VALUES THAT THE TESTING-UP-TODAY-125 TELLS ME
# file2.close()
# i create the empty list of list for the Niter temporal evolutions
num_shifts = 0
num_Drs = 0.
for n in G.nodes():
G.node[n]["status"] = "S"
if G.node[n]['type'] == "shift":
num_shifts += 1
else:
num_Drs += 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 = []
list_dist_fixed_parameters = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_final_num_infected = []
for iter in range(Niter_training):
# 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'])
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":
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
# print max_order
################# the dynamics starts:
t = 1
while t < cutting_day: # loop over shifts, in order just until cutting day (training segment)
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):
if G.node[doctor]["status"] == "S":
rand = random.random()
if rand < prob_infection:
G.node[doctor]["status"] = "I"
if G.node[doctor][
"type"] == "A":
list_I.append(
G.node[doctor]
["label"])
list_single_t_evolution.append(float(
len(list_I))) #/(len(list_A)+len(list_F)))
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_training, list_single_t_evolution))
list_dist_abs_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] -
list_actual_evol_training[-1])
) # i save the distance at the ending point between the current simu and actual evol
# print "actual:",len(list_actual_evol_training)," simu:",len(list_single_t_evolution) # 125, 125
list_final_num_infected.append(list_single_t_evolution[-1])
list_dist_at_ending_point_fixed_parameters.append(
list_single_t_evolution[-1] - list_actual_evol_training[-1]
) # i save the distance at the ending point between the current simu and actual evol
######## end loop Niter for the training fase
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))
file3 = open(output_file3, 'at') # i print out the landscape
print >> file3, prob_infection, prob_Immune, numpy.mean(
list_dist_abs_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()
histogram_filename = "../Results/weight_shifts/histogr_raw_distances_ending_test_train_infection_p" + str(
prob_infection) + "_Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "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_training_p" + str(
prob_infection
) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter_day" + str(cutting_day) + ".dat"
histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,
Nbins, histogram_filename2)
print "written histogram file: ", histogram_filename
print "written histogram file: ", histogram_filename2
value = numpy.mean(list_dist_fixed_parameters) * numpy.mean(
list_dist_abs_at_ending_point_fixed_parameters
) # if SD=0, it is a problem, because then that is the minimun value, but not the optimum i am looking for!!
dict_filenames_prod_distances[output_file2] = value
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_file2] = list_pair_dist_std_delta_end
print numpy.mean(list_dist_abs_at_ending_point_fixed_parameters
), "added scenario:", output_file2
# file2 = open(output_file2,'at')
#for s in range(len(list_single_t_evolution)):
# list_fixed_t=[]
# for iter in range (Niter_training):
# list_fixed_t.append(list_lists_t_evolutions[iter][s])
#print >> file2, s,numpy.mean(list_fixed_t)
#file2.close()
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
list_order_dict = compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Infection_training_weight", all_team,
Niter_training, cutting_day)
# it returns a list of tuples like this : ('../Results/network_final_schedule_withTeam3_local/infection/Average_time_evolution_Infection_training_p0.7_Immune0.0_2iter_2012.dat', [2540.0, 208.0, 1.0]) the best set of parameters being the fist one of the elements in the list.
string_name = "infection_training_" + fixed_param + str(
Niter_training) + "iter_day" + str(
cutting_day
) + ".dat" # for the "Results" file with the sorted list of files
list_order_dict2 = compare_real_evol_vs_simus_to_be_called.pick_minimum_prod_distances(
dict_filenames_prod_distances, string_name, all_team, Niter_training,
cutting_day)
optimum_filename = list_order_dict[0][0]
prob_infection = float(list_order_dict[0][0].split("_p")[1].split("_")[0])
prob_Immune = float(
list_order_dict[0][0].split("_Immune")[1].split("_")[0])
print "Optimum parameters (old method) at day", cutting_day, " are: p=", prob_infection, " and Pimmune=", prob_Immune
# i already know the optimum, now i run the dynamics with those values, starting from the average state on the cutting point, and test:
optimum_filename = list_order_dict2[0][0]
prob_infection = float(list_order_dict2[0][0].split("_p")[1].split("_")[0])
prob_Immune = float(
list_order_dict2[0][0].split("_Immune")[1].split("_")[0])
print "Optimum parameters (product of distances along_traject and at the end) at day", cutting_day, " are: p=", prob_infection, " and Pimmune=", prob_Immune
print "Run that simulation with the optimum parameter set:", optimum_filename
print "printed out landscape file:", output_file3
output_file10 = "../Results/weight_shifts/Summary_results_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter_avg_ic_day" + str(cutting_day) + ".dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from train-testing persuasion with", Niter_training, "iter , using all the individual cutting points as IC, and with values for the parameters: prob_inf ", prob_infection, " prob immune: ", prob_Immune, "\n"
print >> file10, "Look for the file (or run that simulation) with the optimum parameter set:", optimum_filename
file10.close()
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)
cutting_day=175 # to separate training-testing
Niter_training=1000
delta_end=3 # >= than + or - dr difference at the end of the evolution
dir_real_data='../Results/'
dir="../Results/weight_shifts/persuasion/"
all_team="NO" # as adopters or not
Nbins=20 # for the histogram of sum of distances
fixed_param="FIXED_mutual0.5_damping.5_" # or "" # for the Results file that contains the sorted list of best parameters
# fixed_parameters="mutual_encoug0.5_threshold0.5" # for the Landscape text file CHANGE PARAMETERS ACCORDINGLY!!!
#output_file3="../Results/weight_shifts/Landscape_parameters_persuasion_train_test_"+str(fixed_parameters)+"_"+str(Niter_training)+"iter.dat"
output_file3="../Results/weight_shifts/Landscape_parameters_persuasion_train_FIXED_damping0.1_threshold0.7_"+str(Niter_training)+"iter_alphaA_eq_alphaF.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)
list_actual_evol_training=list_actual_evol[:cutting_day]
##################################################################
#../Results/network_final_schedule_withTeam3/Time_evolutions_Persuasion_alpha0.2_damping0.0_mutual_encourg0.7_threshold0.4_unif_distr_50iter_2012_seed31Oct_finalnetwork.dat
alpha_F_min=0.10 # # alpha=0: nobody changes their mind
alpha_F_max=0.9
delta_alpha_F=0.10 #AVOID 1.0 OR THE DYNAMICS GETS TOTALLY STUCK AND IT IS NOT ABLE TO PREDICT SHIT!
min_damping=0.500 #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=0.501 #0.451
delta_damping=0.10
min_mutual_encouragement=0.50 # # when two Adopters meet, they convince each other even more
max_mutual_encouragement=0.501
delta_mutual_encouragement=0.10
threshold_min=0.10 # # larger than, to be an Adopter
threshold_max=0.901
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_training
dict_filenames_tot_distance={} # i will save the filename as key and the tot distance from that curve to the original one
dict_filenames_prod_distances={}
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 values
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_evolutions_Persuasion_training_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_unif_distr_"+str(Niter_training)+"iter_alphaA_eq_alphaF"+"_"+str(cutting_day)+".dat"
# file = open(output_file,'wt') # i am not saving the train file, because i will just want to know
#file.close() # the optimum parameter set and go look for the whole-250-day file
time_evol_number_adopters_ITER=[] # list of complete single realizations of the dynamics
list_dist_fixed_parameters=[]
list_dist_at_ending_point_fixed_parameters=[]
list_dist_abs_at_ending_point_fixed_parameters=[]
list_networks_at_cutting_day=[]
list_final_num_adopt=[]
for iter in range(Niter_training):
# 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))
list_t.append(0)
########### the dynamics starts:
t=int(seed_shift)+1 # the first time step is just IC.???
while t< cutting_day: # 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
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_all_Adopters=[] #including fellows
list_Adopters=[] #NOT including fellows
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
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_final_num_adopt.append(time_evol_number_adopters[-1])
list_dist_fixed_parameters.append(compare_real_evol_vs_simus_to_be_called.compare_two_curves( list_actual_evol_training,time_evol_number_adopters))
list_dist_abs_at_ending_point_fixed_parameters.append( abs(time_evol_number_adopters[-1]-list_actual_evol_training[-1]) )
list_dist_at_ending_point_fixed_parameters.append( time_evol_number_adopters[-1]-list_actual_evol_training[-1])
####################### end loop Niter for the training fase
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))
value=numpy.mean(list_dist_fixed_parameters) *numpy.mean(list_dist_abs_at_ending_point_fixed_parameters) # if SD=0, it is a problem, because then that is the minimun value, but not the optimum i am looking for!!
dict_filenames_prod_distances[output_file]= value
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()
histogram_filename="../Results/weight_shifts/histogr_raw_distances_ending_test_train_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_unif_distr_"+str(Niter_training)+"iter_alphaA_eq_alphaF"+"_"+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_training_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_unif_distr_"+str(Niter_training)+"iter_alphaA_eq_alphaF"+"_"+str(cutting_day)+".dat"
histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,Nbins,histogram_filename2)
print "written histogram file: ",histogram_filename
print "written histogram file: ",histogram_filename2
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_training): #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()
damping += delta_damping
mutual_encouragement += delta_mutual_encouragement
alpha_F += delta_alpha_F
threshold += delta_threshold
list_order_dict= compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(dict_filenames_tot_distance,"Persuasion_training_land_weight",all_team,Niter_training,cutting_day)
string_name="_persuasion_training_"+fixed_param+str(Niter_training)+"iter_"+str(cutting_day)+".dat" # for the "Results" file with the sorted list of files
list_order_dict2= compare_real_evol_vs_simus_to_be_called.pick_minimum_prod_distances(dict_filenames_prod_distances,string_name,all_team,Niter_training,cutting_day)
#./Results/network_final_schedule_withTeam3_local/Time_evolutions_Persuasion_alpha0.4_damping0.4_mutual_encourg0.6_threshold0.5_unif_distr_2iter_2012_seed31Oct_finalnetwork.dat
optimum_filename=list_order_dict[0][0]
print optimum_filename
alpha_F=float(list_order_dict[0][0].split("_alpha")[1].split("_")[0])
alpha_A=0.5*alpha_F
damping=float(list_order_dict[0][0].split("_damping")[1].split("_")[0])
mutual_encouragement=float(list_order_dict[0][0].split("_mutual_encourg")[1].split("_")[0])
threshold=float(list_order_dict[0][0].split("_threshold")[1].split("_")[0])
print "Optimum (old method) alpha=", alpha_F, " damping=",damping," mutual encourag=",mutual_encouragement," threshold",threshold
optimum_filename=list_order_dict2[0][0]
print optimum_filename
alpha_F=float(list_order_dict2[0][0].split("_alpha")[1].split("_")[0])
alpha_A=0.5*alpha_F
damping=float(list_order_dict2[0][0].split("_damping")[1].split("_")[0])
mutual_encouragement=float(list_order_dict2[0][0].split("_mutual_encourg")[1].split("_")[0])
threshold=float(list_order_dict2[0][0].split("_threshold")[1].split("_")[0])
print "Optimum (product distances and SDs) alpha=", alpha_F, " damping=",damping," mutual encourag=",mutual_encouragement," threshold",threshold
output_file10="../Results/weight_shifts/Summary_results_train_test_persuasion_alpha"+str(alpha_F)+"_FIXED_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_FIXED_threshold"+str(threshold)+"_"+str(Niter_training)+"iter_alphaA_eq_alphaF_day"+str(cutting_day)+".dat"
file10 = open(output_file10,'wt')
print >> file10, "Summary results from train-testing persuasion with",Niter_training, "iter, using the avg of the cutting points as IC, and with values for the parameters: alpha ",alpha_F," damping: ",damping," mutual_encourg: ",mutual_encouragement," threshold:",threshold
print >> file10, "Look for optimum the file set of parameters (or run those simulations):",optimum_filename
file10.close()
print "Look for optimum the file set of parameters (or run those simulations):",optimum_filename
print "printed out landscape file:",output_file3
def main(graph_name):
G = nx.read_gml(graph_name)
Niter = 1000
delta_end = 3 # >= than + or - dr difference at the end of the evolution
dir_real_data = '../Results/'
all_team = "NO" # as adopters or not
# output_file3=dir_real_data+"Landscape_parameters_persuasion_"+str(Niter)+"iter.dat
#file3 = open(output_file3,'wt')
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
if all_team == "YES":
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_all_team_as_adopters_SIMPLER.csv"
else:
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_SIMPLER.csv"
#ya no necesito CAMBIAR TB EL NOMBRE DEL ARCHIVO EN EL CODIGO PARA COMPARAR CURVAs
list_actual_evol = []
result_actual_file = csv.reader(open(filename_actual_evol, 'rb'),
delimiter=',')
cont = 0
for row in result_actual_file:
if cont > 0: # i ignore the first line with the headers
num_adopters = row[3]
list_actual_evol.append(float(num_adopters))
cont += 1
##################################################################
#../Results/network_final_schedule_withTeam3/Time_evolutions_Persuasion_alpha0.2_damping0.0_mutual_encourg0.7_threshold0.4_unif_distr_50iter_2012_seed31Oct_finalnetwork.dat
alpha_F_min = 0.0 # alpha=0: nobody changes their mind
alpha_F_max = 1.001
delta_alpha_F = 0.1
min_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
max_damping = 1.01
delta_damping = 0.1
min_mutual_encouragement = 0.0 # when two Adopters meet, they convince each other even more
max_mutual_encouragement = 1.01
delta_mutual_encouragement = 0.1
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
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 values
alpha_A = 0.5 * 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/network_final_schedule_withTeam3_local/"
output_file = dir + "Time_evolutions_Persuasion_alpha" + str(
alpha_F) + "_damping" + str(
damping) + "_mutual_encourg" + str(
mutual_encouragement) + "_" + str(
Niter) + "iter_distributed_thresholds.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_at_ending_point_fixed_parameters = []
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
) # 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)
# 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
) # i move their values of opinion
update_opinions(
G, 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
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_at_ending_point_fixed_parameters.append(
abs(time_evol_number_adopters[-1] -
list_actual_evol[-1]))
#######################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_at_ending_point_fixed_parameters))
if (
numpy.mean(list_dist_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 len(list_t), len(time_evol_number_adopters)
# histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,50, "../Results/histogr_distances_indiv_pers_simus_to_the_average_curve_alpha"+str(alpha_F)+"_damping"+str(damping)+"_mutual_encourg"+str(mutual_encouragement)+"_threshold"+str(threshold)+"_unif_distr_"+str(Niter)+"iter.dat") # Nbins=100
damping += delta_damping
mutual_encouragement += delta_mutual_encouragement
alpha_F += delta_alpha_F
compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Persuasion_distributed_thresholds",
all_team, Niter)
def main(graph_name):
G = nx.read_gml(graph_name)
all_team = "NO" # as adopters or not
dir_real_data = '../Results/'
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!!!)
Niter = 100
list_id_weekends_T3 = look_for_T3_weekends(
G
) # T3 doesnt share fellows in the weekend (but they are the exception)
output_file3 = "../Results/weight_shifts/Landscape_parameters_infection_memory_fixed_dose_thr_" + 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)
################################################################################
prob_min = 0.00
prob_max = 1.01
delta_prob = 0.1
prob_Immune_min = 0.0
prob_Immune_max = 1.0
delta_prob_Immune = 0.1
infect_threshold_min = 0.0 # for someone to become infected
infect_threshold_max = 1.01
delta_infect_threshold = 0.1
dose_min = 0.1 # of a single encounter with an infected (starting from zero doesnt make sense)
dose_max = 1.01
delta_dose = 0.1
dir = "../Results/weight_shifts/infection/"
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
output_file2 = dir + "Average_time_evolution_Infection_memory_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_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_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"
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"])
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_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))
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
# print >> file3, prob_infection,prob_Immune,dict_filenames_tot_distance[output_file2][0],dict_filenames_tot_distance[output_file2][1]
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 list_dist_fixed_parameters
# histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,50, "../Results/histogr_distances_indiv_infect_memory_simus_to_the_average_curve_p"+str(prob_infection)+"_"+"Immune"+str(prob_Immune)+"_threshold"+str(infect_threshold)+"_dose"+str(dose)+"_"+str(Niter)+"iter.dat" ) # Nbins=100
dose += delta_dose
infect_threshold += delta_infect_threshold
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
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
print >> file2, s,numpy.mean(list_fixed_t)
file2.close()
# print list_dist_fixed_parameters
# histograma_bines_gral.histograma_bins(list_dist_fixed_parameters,50, "../Results/weight_shifts/histogr_distances_indiv_infect_simus_to_the_average_curve_p"+str(prob_infection)+"_"+"Immune"+str(prob_Immune)+"_"+str(Niter)+"iter.dat") # Nbins=100
prob_infection+= delta_prob
prob_Immune+= delta_prob_Immune
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)
print "written landscape file:",output_file3
######################################
######################################
def look_for_T3_weekends(G):
list_ids_T3_weekends=[]
for n in G.nodes():
def main(graph_name):
cutting_day=125 # to separate training-testing
G = nx.read_gml(graph_name)
all_team="NO" # as adopters or not
list_id_weekends_T3=look_for_T3_weekends(G) # T3 doesnt share fellows in the weekend (but they are the exception)
dir_real_data='../Results/'
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!!!)
Niter=1000
######################################################################################
# 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)
list_actual_evol_training=list_actual_evol[:cutting_day]
list_actual_evol_testing=list_actual_evol[(cutting_day-1):]
##################################################################
#../Results/network_final_schedule_withTeam3/infection/Average_time_evolution_Infection_p0.9_Immune0.5_1000iter_2012.dat
prob_min=0.00
prob_max=1.01
delta_prob=0.1
prob_Immune_min=0.0 # no immune people
prob_Immune_max=0.0
delta_prob_Immune=0.1
range_distrib_threshold=0.2 # for the uniform distrib to go : 0 to ...
range_distrib_dose=0.2 # for the uniform distrib to go : 0 to ...
range_distrib_dose=0.2 # for the uniform distrib to go : 0 to ...
range_distrib_dose=0.2 # for the uniform distrib to go : 0 to ...
dir="../Results/weight_shifts/infection/"
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
output_file2=dir+"Average_time_evolution_Infection_memory_dose_thr_from_distr_training_p"+str(prob_infection)+"_"+"FIXED_Immune"+str(prob_Immune)+"_"+str(Niter)+"iter_day"+str(cutting_day)+".dat"
# file2 = open(output_file2,'wt')
# file2.close()
num_Drs=0.
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
G.node[n]["personal_threshold"]=random.random() # for a dr to become infected
if G.node[n]['type']=="shift":
num_shifts+=1
else:
num_Drs+=1.
list_lists_t_evolutions=[] # i create the empty list of list for the Niter temporal evolutions
list_dist_fixed_parameters=[]
list_dist_abs_at_ending_point_fixed_parameters=[]
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"
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< cutting_day: # 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: # with prob p the infection occurres
G.node[doctor]["infec_value"]+=random.random() # and bumps the infection_value of that susceptible dr (the dose is random, not fixed)
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"])
list_single_t_evolution.append(float(len(list_I)))
t+=1
######## end t loop
list_lists_t_evolutions.append(list_single_t_evolution)
#print "actual:",len(list_actual_evol_training)," simu:",len(list_single_t_evolution)
list_dist_fixed_parameters.append(compare_real_evol_vs_simus_to_be_called.compare_two_curves( list_actual_evol_training,list_single_t_evolution))
list_dist_abs_at_ending_point_fixed_parameters.append( abs(list_single_t_evolution[-1]-list_actual_evol_training[-1]) ) # i save the distance at the ending point between the current simu and actual evol
######## end loop Niter for the training fase
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 (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_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 list_dist_fixed_parameters
prob_infection+= delta_prob
prob_Immune+= delta_prob_Immune
list_order_dict= compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(dict_filenames_tot_distance,"Infection_memory_distrib_training",all_team,Niter,cutting_day)
# it returns a list of tuples like this : ('../Results/network_final_schedule_withTeam3_local/infection/Average_time_evolution_Infection_training_p0.7_Immune0.0_2iter_2012.dat', [2540.0, 208.0, 1.0]) the best set of parameters being the fist one of the elements in that list.
#Average_time_evolution_Infection_memory_train_test_p"+str(prob_infection)+"_"+"Immune"+str(prob_Immune)+"_threshold"+str(infect_threshold)+"_dose"+str(dose)+"_"+str(Niter)+"iter_avg_ic.dat
optimum_filename=list_order_dict[0][0]
prob_infection=float(list_order_dict[0][0].split("_p")[1].split("_")[0])
prob_Immune=float(list_order_dict[0][0].split("_Immune")[1].split("_")[0])
print "Optimum parameters at day",cutting_day," are: p=",prob_infection," Pimmune=",prob_Immune
def main(graph_name):
cutting_day = 125 # to separate training-testing
G = nx.read_gml(graph_name)
all_team = "NO" # as adopters or not
dir_real_data = '../Results/'
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!!!)
Niter_training = 1000
Niter_testing = 1000
######################################################################################
# I read the file of the actual evolution of the idea spreading in the hospital: ##
######################################################################################
if all_team == "YES":
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_all_team_as_adopters_SIMPLER.csv"
else:
filename_actual_evol = dir_real_data + "HospitalModel_august1_adoption_counts_SIMPLER.csv"
#ya no necesito CAMBIAR TB EL NOMBRE DEL ARCHIVO EN EL CODIGO PARA COMPARAR CURVAs
list_actual_evol = []
result_actual_file = csv.reader(open(filename_actual_evol, 'rb'),
delimiter=',')
cont = 0
for row in result_actual_file:
if cont > 0: # i ignore the first line with the headers
num_adopters = row[3]
list_actual_evol.append(float(num_adopters))
cont += 1
list_actual_evol_training = list_actual_evol[:cutting_day]
list_actual_evol_testing = list_actual_evol[(cutting_day - 1):]
##################################################################
#../Results/network_final_schedule_withTeam3/infection/Average_time_evolution_Infection_p0.9_Immune0.5_1000iter_2012.dat
prob_min = 0.09
prob_max = 1.01
delta_prob = 0.1
prob_Immune_min = 0.00
prob_Immune_max = 1.01
delta_prob_Immune = 0.1
dir = "../Results/network_final_schedule_withTeam3_local/infection/"
dict_filenames_tot_distance = {
} # i will save the filename as key and the tot distance from that curve to the original one
dict_filenames_list_dict_network_states = {
} # i will save the filename as key and the list of networks at cutting day as value
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_training_p" + str(
prob_infection) + "_" + "Immune" + str(
prob_Immune) + "_" + str(Niter_training) + "iter_2012.dat"
file2 = open(output_file2, 'wt')
file2.close()
# 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 = []
list_dist_fixed_parameters = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_dict_network_states = []
for iter in range(Niter_training):
print " iter:", iter
dict_network_states = {}
#######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()
##########################################
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":
rand = random.random()
if rand < prob_Immune:
G.node[n]["status"] = "Immune"
# print max_order
################# the dynamics starts:
t = 1
while t < cutting_day: # loop over shifts, in order just until cutting day (training segment)
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
######## end t loop
for n in G.nodes():
if G.node[n]['type'] != "shift":
dict_network_states[G.node[n]
["label"]] = G.node[n]["status"]
list_dict_network_states.append(dict_network_states)
# print "number infected at the cutting point:", len(list_I), list_I
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_training, list_single_t_evolution))
list_dist_abs_at_ending_point_fixed_parameters.append(
abs(list_single_t_evolution[-1] -
list_actual_evol_training[-1])
) # i save the distance at the ending point between the current simu and actual evol
# print "distance at ending point:", abs(list_single_t_evolution[-1]-list_actual_evol_training[-1])
######## end loop Niter for the training fase
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 (
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_file2] = list_pair_dist_std_delta_end
dict_filenames_list_dict_network_states[
output_file2] = list_dict_network_states
file2 = open(output_file2, 'at')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter_training):
list_fixed_t.append(list_lists_t_evolutions[iter][s])
print >> file2, s, numpy.mean(list_fixed_t)
file2.close()
prob_infection += delta_prob
prob_Immune += delta_prob_Immune
list_order_dict = compare_real_evol_vs_simus_to_be_called.pick_minimum_same_end(
dict_filenames_tot_distance, "Infection_training", all_team,
Niter_training)
# it returns a list of tuples like this : ('../Results/network_final_schedule_withTeam3_local/infection/Average_time_evolution_Infection_training_p0.7_Immune0.0_2iter_2012.dat', [2540.0, 208.0, 1.0]) the best set of parameters being the fist one of the elements in the list.
optimum_filename = list_order_dict[0][0]
prob_infection = float(list_order_dict[0][0].split("_p")[1][0:3])
prob_Immune = float(list_order_dict[0][0].split("_Immune")[1][0:3])
# raw_input()
print "starting testing fase with:"
print "p=", prob_infection, " and Pimmune=", prob_Immune
# i already know the optimum, now i run the dynamics with those values, starting from the average state on the cutting point, and test:
list_dist_fixed_parameters = []
list_dist_at_ending_point_fixed_parameters = []
list_dist_abs_at_ending_point_fixed_parameters = []
list_lists_t_evolutions = []
for iter in range(Niter_testing):
dict_dr_status_current_iter = dict_filenames_list_dict_network_states[
optimum_filename][iter]
# print dict_dr_status_current_iter
list_I = [] #list infected doctors
list_Immune = []
for node in G.nodes():
if G.node[node]['type'] != "shift":
label = G.node[node]['label']
G.node[node]["status"] = "S" #by default, all are susceptible
G.node[node]["status"] = dict_dr_status_current_iter[label]
if G.node[node]["status"] == "I":
list_I.append(G.node[node]["label"])
elif G.node[node]["status"] == "Immune":
list_Immune.append(G.node[node]["label"])
print "# I at the beginning of the testing fase:", len(list_I), float(
len(list_I)) / 36., " and # Immune:", len(list_Immune), float(
len(list_Immune)) / 36.
# print " iter:",iter
list_single_t_evolution = []
list_single_t_evolution.append(len(list_I))
t = cutting_day
while t <= max_order: # loop over shifts, in order just until cutting day (training segment)
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)))
t += 1
list_lists_t_evolutions.append(list_single_t_evolution)
list_I = [] #list infected doctors
list_Immune = []
for node in G.nodes():
if G.node[node]['type'] != "shift":
label = G.node[node]['label']
if G.node[node]["status"] == "I":
list_I.append(G.node[node]["label"])
elif G.node[node]["status"] == "Immune":
list_Immune.append(G.node[node]["label"])
print " # I at the END of the testing fase:", len(list_I), float(
len(list_I)) / 36., " and # Immune:", len(list_Immune), float(
len(list_Immune)) / 36., "\n"
list_dist_fixed_parameters.append(
compare_real_evol_vs_simus_to_be_called.compare_two_curves(
list_actual_evol_testing, list_single_t_evolution))
list_dist_abs_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
############### end loop Niter for the testing
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
print "fraction of realizations that end within delta_doctor:", num_valid_endings / Niter_testing, list_dist_abs_at_ending_point_fixed_parameters
histograma_gral_negv_posit.histograma(
list_dist_at_ending_point_fixed_parameters,
"../Results/histogr_raw_distances_ending_test_train_infection_p" +
str(prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" +
str(Niter_training) + "iter_end_point.dat")
output_file8 = "../Results/List_tot_distances_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter_end_point.dat"
file8 = open(output_file8, 'wt')
for item in list_dist_fixed_parameters:
print >> file8, item
file8.close()
output_file9 = "../Results/List_distances_ending_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter_end_point.dat"
file9 = open(output_file9, 'wt')
for item in list_dist_abs_at_ending_point_fixed_parameters:
print >> file9, item
file9.close()
output_file5 = dir + "Average_time_evolution_Infection_testing_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_testing) + "iter_2012.dat"
file5 = open(output_file5, 'wt')
for s in range(len(list_single_t_evolution)):
list_fixed_t = []
for iter in range(Niter_testing):
list_fixed_t.append(list_lists_t_evolutions[iter][s])
print >> file5, s + cutting_day, numpy.mean(list_fixed_t)
# print s+cutting_day,numpy.mean(list_fixed_t)
file5.close()
print "written training segment file:", optimum_filename
print "written testing segment file:", output_file5
output_file10 = "../Results/Summary_results_training_segment_infection_p" + str(
prob_infection) + "_" + "Immune" + str(prob_Immune) + "_" + str(
Niter_training) + "iter.dat"
file10 = open(output_file10, 'wt')
print >> file10, "Summary results from train-testing persuasion with", Niter_training, Niter_testing, "iter (respectively), using all the individual cutting points as IC, and with values for the parameters: prob_inf ", prob_infection, " prob immune: ", prob_Immune
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_testing, list_dist_at_ending_point_fixed_parameters
print >> file10, "written training segment file:", optimum_filename
print >> file10, "written testing segment file:", output_file5
file10.close()
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
