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paper_plot_case_studies_gpr_only.py
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import json
import os
import matplotlib
from matplotlib import pyplot as plt
import numpy as np
import argparse
from natsort import natsorted
from matplotlib.ticker import ScalarFormatter
from matplotlib.ticker import AutoMinorLocator, MultipleLocator
def read_json_file(file_path):
with open(file_path, 'r') as file:
data = json.load(file)
return data
def find_files(folder_path):
file_list = []
for root, dirs, files in os.walk(folder_path):
for file in files:
file_path = os.path.join(root, file)
file_list.append(file_path)
return file_list
def main():
# Create the argument parser
parser = argparse.ArgumentParser(description='Plotting tool for analysis.')
# Add the argument
parser.add_argument('--name', type=str, help='Set name of the plot.', required=False)
parser.add_argument('--reps', type=int, default=4, help='Set the number of repetitions per measurement point.', required=False)
# Parse the command-line arguments
args = parser.parse_args()
reps = args.reps
if args.name:
plot_name = args.name+".pdf"
else:
plot_name = "paper_plot_case_studies_gpr_only.pdf"
###########
# FASTEST #
###########
path_1 = "fastest/filtered/analysis_results"
files_1 = find_files(path_1)
files_1 = natsorted(files_1)
x_values_fastest = []
full_values_fastest = []
generic_values_fastest = []
gpr_values_fastest = []
hybrid_values_fastest = []
generic_costs_fastest = []
gpr_costs_fastest = []
hybrid_costs_fastest = []
base_point_costs_fastest = []
points_generic_fastest = []
points_gpr_fastest = []
points_hybrid_fastest = []
base_points_fastest = []
all_points_fastest = []
nr_func_modeled_fastest = 0
for i in range(len(files_1)):
json_file_path = files_1[i]
json_data = read_json_file(json_file_path)
if i == len(files_1)-1:
nr_func_modeled_fastest = json_data["nr_func_modeled_gpr"]
x_values_fastest.append(json_data["budget"])
full_values_fastest.append(json_data["percentage_bucket_counter_full"]["10"])
generic_values_fastest.append(json_data["percentage_bucket_counter_generic"]["10"])
gpr_values_fastest.append(json_data["percentage_bucket_counter_gpr"]["10"])
hybrid_values_fastest.append(json_data["percentage_bucket_counter_hybrid"]["10"])
generic_costs_fastest.append(json_data["budget"]-json_data["mean_budget_generic"])
gpr_costs_fastest.append(json_data["budget"]-json_data["mean_budget_gpr"])
hybrid_costs_fastest.append(json_data["budget"]-json_data["mean_budget_hybrid"])
base_point_costs_fastest.append(json_data["base_point_cost"])
points_generic_fastest.append(json_data["mean_add_points_generic"])
points_gpr_fastest.append(json_data["mean_add_points_gpr"])
points_hybrid_fastest.append(json_data["mean_add_points_hybrid"])
all_points_fastest.append(25*5)
base_points_fastest.append(json_data["min_points"])
y_values_list_acc_fastest = [
full_values_fastest ,
generic_values_fastest ,
gpr_values_fastest ,
#hybrid_values_fastest
]
y_values_list_points_fastest = [
all_points_fastest,
points_generic_fastest,
points_gpr_fastest,
#points_hybrid_fastest,
]
y_values_list_costs_fastest = [
generic_costs_fastest,
gpr_costs_fastest,
#hybrid_costs_fastest,
]
print("")
print("### FASTEST ###")
B_NU_FASTEST_GENERIC = np.nanmax(generic_costs_fastest)
B_NU_FASTEST_GPR = np.nanmax(gpr_costs_fastest)
#print(B_NU_FASTEST_GENERIC,B_NU_FASTEST_GPR)
print("unused budget factor CPF/GPR:",B_NU_FASTEST_GENERIC/B_NU_FASTEST_GPR)
B_CPF_FASTEST = 0
maxv = 0
for i in range(len(generic_costs_fastest)):
if generic_costs_fastest[i] >= maxv:
maxv = generic_costs_fastest[i]
B_CPF_FASTEST = x_values_fastest[i]
B_GPR_FASTEST = 0
maxv = 0
for i in range(len(gpr_costs_fastest)):
if gpr_costs_fastest[i] >= maxv:
maxv = gpr_costs_fastest[i]
B_GPR_FASTEST = x_values_fastest[i]
full_B_max_FASTEST = 0
maxv = 0
for i in range(len(full_values_fastest)):
if full_values_fastest[i] >= maxv:
maxv = full_values_fastest[i]
full_B_max_FASTEST = x_values_fastest[i]
full_max_FASTEST = max(full_values_fastest)
print("Max. accuracy Full:",full_max_FASTEST,"% at B=",full_B_max_FASTEST,"%.")
cpf_B_max_FASTEST = 0
maxv = 0
for i in range(len(generic_values_fastest)):
if generic_values_fastest[i] >= maxv:
maxv = generic_values_fastest[i]
cpf_B_max_FASTEST = x_values_fastest[i]
cpf_max_FASTEST = max(generic_values_fastest)
print("Max. accuracy CPF:",cpf_max_FASTEST,"% at B=",cpf_B_max_FASTEST,"%.")
gpr_B_max_FASTEST = 0
maxv = 0
for i in range(len(gpr_values_fastest)):
if gpr_values_fastest[i] >= maxv:
maxv = gpr_values_fastest[i]
gpr_B_max_FASTEST = x_values_fastest[i]
gpr_max_FASTEST = max(gpr_values_fastest)
print("Max. accuracy GPR:",gpr_max_FASTEST,"% at B=",gpr_B_max_FASTEST,"%.")
maxv_FASTEST = 0
Bv_FASTEST = 0
cpfv_FASTEST = 0
gprv_FASTEST = 0
for i in range(len(gpr_values_fastest)):
diff = gpr_values_fastest[i] - generic_values_fastest[i]
if diff >= maxv_FASTEST:
maxv_FASTEST = diff
Bv_FASTEST = x_values_fastest[i]
cpfv_FASTEST = generic_values_fastest[i]
gprv_FASTEST = gpr_values_fastest[i]
print("max. difference GPR/CPF:",maxv_FASTEST,"at B=",Bv_FASTEST,"%.")
max_points_FASTEST = 0
B_points_FASTEST = 0
cpf_points_FASTEST = 0
gpr_points_FASTEST = 0
for i in range(len(points_gpr_fastest)):
diff = points_gpr_fastest[i] - points_generic_fastest[i]
if diff >= max_points_FASTEST:
max_points_FASTEST = diff
B_points_FASTEST = x_values_fastest[i]
cpf_points_FASTEST = points_generic_fastest[i]
gpr_points_FASTEST = points_gpr_fastest[i]
print("max. difference points GPR/CPF:",max_points_FASTEST,"at B=",B_points_FASTEST,"%.")
max_points_FASTEST_2 = 0
B_points_FASTEST_2 = 0
cpf_points_FASTEST_2 = 0
gpr_points_FASTEST_2 = 0
for i in range(len(points_gpr_fastest)):
diff = points_generic_fastest[i] - points_gpr_fastest[i]
if diff >= max_points_FASTEST_2:
max_points_FASTEST_2 = diff
B_points_FASTEST_2 = x_values_fastest[i]
cpf_points_FASTEST_2 = points_generic_fastest[i]
gpr_points_FASTEST_2 = points_gpr_fastest[i]
print("max. difference points GPR/CPF:",max_points_FASTEST_2,"at B=",B_points_FASTEST_2,"%.")
print("############")
print("")
###########
# KRIPKE #
###########
path_2 = "kripke/all/analysis_results"
files_2 = find_files(path_2)
files_2 = natsorted(files_2)
x_values_kripke = []
full_values_kripke = []
generic_values_kripke = []
gpr_values_kripke = []
hybrid_values_kripke = []
generic_costs_kripke = []
gpr_costs_kripke = []
hybrid_costs_kripke = []
base_point_costs_kripke = []
points_generic_kripke = []
points_gpr_kripke = []
points_hybrid_kripke = []
base_points_kripke = []
all_points_kripke = []
nr_func_modeled_kripke = 0
for i in range(len(files_2)):
json_file_path = files_2[i]
json_data = read_json_file(json_file_path)
if i == len(files_1)-1:
nr_func_modeled_kripke = json_data["nr_func_modeled_gpr"]
x_values_kripke.append(json_data["budget"])
full_values_kripke.append(json_data["percentage_bucket_counter_full"]["10"])
generic_values_kripke.append(json_data["percentage_bucket_counter_generic"]["10"])
gpr_values_kripke.append(json_data["percentage_bucket_counter_gpr"]["10"])
hybrid_values_kripke.append(json_data["percentage_bucket_counter_hybrid"]["10"])
generic_costs_kripke.append(json_data["budget"]-json_data["mean_budget_generic"])
gpr_costs_kripke.append(json_data["budget"]-json_data["mean_budget_gpr"])
hybrid_costs_kripke.append(json_data["budget"]-json_data["mean_budget_hybrid"])
base_point_costs_kripke.append(json_data["base_point_cost"])
points_generic_kripke.append(json_data["mean_add_points_generic"])
points_gpr_kripke.append(json_data["mean_add_points_gpr"])
points_hybrid_kripke.append(json_data["mean_add_points_hybrid"])
all_points_kripke.append((150-1)*5)
base_points_kripke.append(json_data["min_points"])
y_values_list_acc_kripke = [
full_values_kripke ,
generic_values_kripke ,
gpr_values_kripke ,
#hybrid_values_kripke
]
y_values_list_points_kripke = [
all_points_kripke,
points_generic_kripke,
points_gpr_kripke,
#points_hybrid_kripke,
]
y_values_list_costs_kripke = [
generic_costs_kripke,
gpr_costs_kripke,
#hybrid_costs_kripke,
]
print("")
print("### Kripke ###")
B_NU_KRIPKE_GENERIC = np.nanmax(generic_costs_kripke)
B_NU_KRIPKE_GPR = np.nanmax(gpr_costs_kripke)
#print(B_NU_KRIPKE_GENERIC,B_NU_KRIPKE_GPR)
print("unused budget factor CPF/GPR:",B_NU_KRIPKE_GENERIC/B_NU_KRIPKE_GPR)
B_CPF_KRIPKE = 0
maxv = 0
for i in range(len(generic_costs_kripke)):
if generic_costs_kripke[i] >= maxv:
maxv = generic_costs_kripke[i]
B_CPF_KRIPKE = x_values_kripke[i]
B_GPR_KRIPKE = 0
maxv = 0
for i in range(len(gpr_costs_kripke)):
if gpr_costs_kripke[i] >= maxv:
maxv = gpr_costs_kripke[i]
B_GPR_KRIPKE = x_values_kripke[i]
full_B_max_KRIPKE = 0
maxv = 0
for i in range(len(full_values_kripke)):
if full_values_kripke[i] >= maxv:
maxv = full_values_kripke[i]
full_B_max_KRIPKE = x_values_kripke[i]
full_max_KRIPKE = max(full_values_kripke)
print("Max. accuracy Full:",full_max_KRIPKE,"% at B=",full_B_max_KRIPKE,"%.")
cpf_B_max_KRIPKE = 0
maxv = 0
for i in range(len(generic_values_kripke)):
if generic_values_kripke[i] >= maxv:
maxv = generic_values_kripke[i]
cpf_B_max_KRIPKE = x_values_kripke[i]
cpf_max_KRIPKE = max(generic_values_kripke)
print("Max. accuracy CPF:",cpf_max_KRIPKE,"% at B=",cpf_B_max_KRIPKE,"%.")
gpr_B_max_KRIPKE = 0
maxv = 0
for i in range(len(gpr_values_kripke)):
if gpr_values_kripke[i] >= maxv:
maxv = gpr_values_kripke[i]
gpr_B_max_KRIPKE = x_values_kripke[i]
gpr_max_KRIPKE = max(gpr_values_kripke)
print("Max. accuracy GPR:",gpr_max_KRIPKE,"% at B=",gpr_B_max_KRIPKE,"%.")
maxv_KRIPKE = 0
Bv_KRIPKE = 0
cpfv_KRIPKE = 0
gprv_KRIPKE = 0
for i in range(len(gpr_values_kripke)):
diff = gpr_values_kripke[i] - generic_values_kripke[i]
if diff >= maxv_KRIPKE:
maxv_KRIPKE = diff
Bv_KRIPKE = x_values_kripke[i]
cpfv_KRIPKE = generic_values_kripke[i]
gprv_KRIPKE = gpr_values_kripke[i]
print("max. difference GPR/CPF:",maxv_KRIPKE,"at B=",Bv_KRIPKE,"%.")
max_points_KRIPKE = 0
B_points_KRIPKE = 0
cpf_points_KRIPKE = 0
gpr_points_KRIPKE = 0
for i in range(len(points_generic_kripke)):
diff = points_generic_kripke[i] - points_gpr_kripke[i]
if diff >= max_points_KRIPKE:
max_points_KRIPKE = diff
B_points_KRIPKE = x_values_kripke[i]
cpf_points_KRIPKE = points_generic_kripke[i]
gpr_points_KRIPKE = points_gpr_kripke[i]
print("max. difference points GPR/CPF:",max_points_KRIPKE,"at B=",B_points_KRIPKE,"%.")
print("############")
print("")
###########
# LULESH #
###########
path_3 = "lulesh/lichtenberg/all/analysis_results"
files_3 = find_files(path_3)
files_3 = natsorted(files_3)
x_values_lulesh = []
full_values_lulesh = []
generic_values_lulesh = []
gpr_values_lulesh = []
hybrid_values_lulesh = []
generic_costs_lulesh = []
gpr_costs_lulesh = []
hybrid_costs_lulesh = []
base_point_costs_lulesh = []
points_generic_lulesh = []
points_gpr_lulesh = []
points_hybrid_lulesh = []
base_points_lulesh = []
all_points_lulesh = []
nr_func_modeled_lulesh = 0
for i in range(len(files_3)):
json_file_path = files_3[i]
json_data = read_json_file(json_file_path)
if i == len(files_1)-1:
nr_func_modeled_lulesh = json_data["nr_func_modeled_gpr"]
x_values_lulesh.append(json_data["budget"])
full_values_lulesh.append(json_data["percentage_bucket_counter_full"]["10"])
generic_values_lulesh.append(json_data["percentage_bucket_counter_generic"]["10"])
gpr_values_lulesh.append(json_data["percentage_bucket_counter_gpr"]["10"])
hybrid_values_lulesh.append(json_data["percentage_bucket_counter_hybrid"]["10"])
generic_costs_lulesh.append(json_data["budget"]-json_data["mean_budget_generic"])
gpr_costs_lulesh.append(json_data["budget"]-json_data["mean_budget_gpr"])
hybrid_costs_lulesh.append(json_data["budget"]-json_data["mean_budget_hybrid"])
base_point_costs_lulesh.append(json_data["base_point_cost"])
points_generic_lulesh.append(json_data["mean_add_points_generic"])
points_gpr_lulesh.append(json_data["mean_add_points_gpr"])
points_hybrid_lulesh.append(json_data["mean_add_points_hybrid"])
all_points_lulesh.append(25*5)
base_points_lulesh.append(json_data["min_points"])
y_values_list_acc_lulesh = [
full_values_lulesh ,
generic_values_lulesh ,
gpr_values_lulesh ,
#hybrid_values_lulesh
]
y_values_list_points_lulesh = [
all_points_lulesh,
points_generic_lulesh,
points_gpr_lulesh,
#points_hybrid_lulesh,
]
y_values_list_costs_lulesh = [
generic_costs_lulesh,
gpr_costs_lulesh,
#hybrid_costs_lulesh,
]
print("")
print("### LULESH ###")
B_NU_LULESH_GENERIC = np.nanmax(generic_costs_lulesh)
B_NU_LULESH_GPR = np.nanmax(gpr_costs_lulesh)
#print(B_NU_LULESH_GENERIC,B_NU_LULESH_GPR)
print("unused budget factor CPF/GPR:",B_NU_LULESH_GENERIC/B_NU_LULESH_GPR)
B_CPF_LULESH = 0
maxv = 0
for i in range(len(generic_costs_lulesh)):
if generic_costs_lulesh[i] >= maxv:
maxv = generic_costs_lulesh[i]
B_CPF_LULESH = x_values_lulesh[i]
B_GPR_LULESH = 0
maxv = 0
for i in range(len(gpr_costs_lulesh)):
if gpr_costs_lulesh[i] >= maxv:
maxv = gpr_costs_lulesh[i]
B_GPR_LULESH = x_values_lulesh[i]
full_B_max_LULESH = 0
maxv = 0
for i in range(len(full_values_lulesh)):
if full_values_lulesh[i] >= maxv:
maxv = full_values_lulesh[i]
full_B_max_LULESH = x_values_lulesh[i]
full_max_LULESH = max(full_values_lulesh)
print("Max. accuracy Full:",full_max_LULESH,"% at B=",full_B_max_LULESH,"%.")
cpf_B_max_LULESH = 0
maxv = 0
for i in range(len(generic_values_lulesh)):
if generic_values_lulesh[i] >= maxv:
maxv = generic_values_lulesh[i]
cpf_B_max_LULESH = x_values_lulesh[i]
cpf_max_LULESH = max(generic_values_lulesh)
print("Max. accuracy CPF:",cpf_max_LULESH,"% at B=",cpf_B_max_LULESH,"%.")
gpr_B_max_LULESH = 0
maxv = 0
for i in range(len(gpr_values_lulesh)):
if gpr_values_lulesh[i] >= maxv:
maxv = gpr_values_lulesh[i]
gpr_B_max_LULESH = x_values_lulesh[i]
gpr_max_LULESH = max(gpr_values_lulesh)
print("Max. accuracy GPR:",gpr_max_LULESH,"% at B=",gpr_B_max_LULESH,"%.")
maxv_LULESH = 0
Bv_LULESH = 0
cpfv_LULESH = 0
gprv_LULESH = 0
for i in range(len(gpr_values_lulesh)):
diff = gpr_values_lulesh[i] - generic_values_lulesh[i]
if diff >= maxv_LULESH:
maxv_LULESH = diff
Bv_LULESH = x_values_lulesh[i]
cpfv_LULESH = generic_values_lulesh[i]
gprv_LULESH = gpr_values_lulesh[i]
print("max. difference GPR/CPF:",maxv_LULESH,"at B=",Bv_LULESH,"%.")
max_points_LULESH = 0
B_points_LULESH = 0
cpf_points_LULESH = 0
gpr_points_LULESH = 0
for i in range(len(points_generic_lulesh)):
diff = points_generic_lulesh[i] - points_gpr_lulesh[i]
if diff >= max_points_LULESH:
max_points_LULESH = diff
B_points_LULESH = x_values_lulesh[i]
cpf_points_LULESH = points_generic_lulesh[i]
gpr_points_LULESH = points_gpr_lulesh[i]
print("max. difference points GPR/CPF:",max_points_LULESH,"at B=",B_points_LULESH,"%.")
print("############")
print("")
###########
# MiniFE #
###########
path_4 = "minife/lichtenberg/all/analysis_results"
files_4 = find_files(path_4)
files_4 = natsorted(files_4)
x_values_minife = []
full_values_minife = []
generic_values_minife = []
gpr_values_minife = []
hybrid_values_minife = []
generic_costs_minife = []
gpr_costs_minife = []
hybrid_costs_minife = []
base_point_costs_minife = []
points_generic_minife = []
points_gpr_minife = []
points_hybrid_minife = []
base_points_minife = []
all_points_minife = []
nr_func_modeled_minife = 0
for i in range(len(files_4)):
json_file_path = files_4[i]
json_data = read_json_file(json_file_path)
if i == len(files_1)-1:
nr_func_modeled_minife = json_data["nr_func_modeled_gpr"]
x_values_minife.append(json_data["budget"])
full_values_minife.append(json_data["percentage_bucket_counter_full"]["10"])
generic_values_minife.append(json_data["percentage_bucket_counter_generic"]["10"])
gpr_values_minife.append(json_data["percentage_bucket_counter_gpr"]["10"])
hybrid_values_minife.append(json_data["percentage_bucket_counter_hybrid"]["10"])
generic_costs_minife.append(json_data["budget"]-json_data["mean_budget_generic"])
gpr_costs_minife.append(json_data["budget"]-json_data["mean_budget_gpr"])
hybrid_costs_minife.append(json_data["budget"]-json_data["mean_budget_hybrid"])
base_point_costs_minife.append(json_data["base_point_cost"])
points_generic_minife.append(json_data["mean_add_points_generic"])
points_gpr_minife.append(json_data["mean_add_points_gpr"])
points_hybrid_minife.append(json_data["mean_add_points_hybrid"])
all_points_minife.append(25*5)
base_points_minife.append(json_data["min_points"])
y_values_list_acc_minife = [
full_values_minife ,
generic_values_minife ,
gpr_values_minife ,
#hybrid_values_minife
]
y_values_list_points_minife = [
all_points_minife,
points_generic_minife,
points_gpr_minife,
#points_hybrid_minife,
]
y_values_list_costs_minife = [
generic_costs_minife,
gpr_costs_minife,
#hybrid_costs_minife,
]
print("")
print("### MiniFE ###")
B_NU_MINIFE_GENERIC = np.nanmax(generic_costs_minife)
B_NU_MINIFE_GPR = np.nanmax(gpr_costs_minife)
#print(B_NU_MINIFE_GENERIC,B_NU_MINIFE_GPR)
print("unused budget factor CPF/GPR:",B_NU_MINIFE_GENERIC/B_NU_MINIFE_GPR)
B_CPF_MINIFE = 0
maxv = 0
for i in range(len(generic_costs_minife)):
if generic_costs_minife[i] >= maxv:
maxv = generic_costs_minife[i]
B_CPF_MINIFE = x_values_minife[i]
B_GPR_MINIFE = 0
maxv = 0
for i in range(len(gpr_costs_minife)):
if gpr_costs_minife[i] >= maxv:
maxv = gpr_costs_minife[i]
B_GPR_MINIFE = x_values_minife[i]
full_B_max_MINIFE = 0
maxv = 0
for i in range(len(full_values_minife)):
if full_values_minife[i] >= maxv:
maxv = full_values_minife[i]
full_B_max_MINIFE = x_values_minife[i]
full_max_MINIFE = max(full_values_minife)
print("Max. accuracy Full:",full_max_MINIFE,"% at B=",full_B_max_MINIFE,"%.")
cpf_B_max_MINIFE = 0
maxv = 0
for i in range(len(generic_values_minife)):
if generic_values_minife[i] >= maxv:
maxv = generic_values_minife[i]
cpf_B_max_MINIFE = x_values_minife[i]
cpf_max_MINIFE = max(generic_values_minife)
print("Max. accuracy CPF:",cpf_max_MINIFE,"% at B=",cpf_B_max_MINIFE,"%.")
gpr_B_max_MINIFE = 0
maxv = 0
for i in range(len(gpr_values_minife)):
if gpr_values_minife[i] >= maxv:
maxv = gpr_values_minife[i]
gpr_B_max_MINIFE = x_values_minife[i]
gpr_max_MINIFE = max(gpr_values_minife)
print("Max. accuracy GPR:",gpr_max_MINIFE,"% at B=",gpr_B_max_MINIFE,"%.")
maxv_MINIFE = 0
Bv_MINIFE = 0
cpfv_MINIFE = 0
gprv_MINIFE = 0
for i in range(len(gpr_values_minife)):
diff = gpr_values_minife[i] - generic_values_minife[i]
if diff >= maxv_MINIFE:
maxv_MINIFE = diff
Bv_MINIFE = x_values_minife[i]
cpfv_MINIFE = generic_values_minife[i]
gprv_MINIFE = gpr_values_minife[i]
print("max. difference GPR/CPF:",maxv_MINIFE,"at B=",Bv_MINIFE,"%.")
max_points_MINIFE = 0
B_points_MINIFE = 0
cpf_points_MINIFE = 0
gpr_points_MINIFE = 0
for i in range(len(points_generic_minife)):
diff = points_generic_minife[i] - points_gpr_minife[i]
if diff >= max_points_MINIFE:
max_points_MINIFE = diff
B_points_MINIFE = x_values_minife[i]
cpf_points_MINIFE = points_generic_minife[i]
gpr_points_MINIFE = points_gpr_minife[i]
print("max. difference points GPR/CPF:",max_points_MINIFE,"at B=",B_points_MINIFE,"%.")
print("############")
print("")
###########
# Quicksilver #
###########
path_5 = "quicksilver/lichtenberg/filtered/analysis_results"
files_5 = find_files(path_5)
files_5 = natsorted(files_5)
x_values_quicksilver = []
full_values_quicksilver = []
generic_values_quicksilver = []
gpr_values_quicksilver = []
hybrid_values_quicksilver = []
generic_costs_quicksilver = []
gpr_costs_quicksilver = []
hybrid_costs_quicksilver = []
base_point_costs_quicksilver = []
points_generic_quicksilver = []
points_gpr_quicksilver = []
points_hybrid_quicksilver = []
base_points_quicksilver = []
all_points_quicksilver = []
nr_func_modeled_quicksilver = 0
for i in range(len(files_5)):
json_file_path = files_5[i]
json_data = read_json_file(json_file_path)
if i == len(files_1)-1:
nr_func_modeled_quicksilver = json_data["nr_func_modeled_gpr"]
x_values_quicksilver.append(json_data["budget"])
full_values_quicksilver.append(json_data["percentage_bucket_counter_full"]["10"])
generic_values_quicksilver.append(json_data["percentage_bucket_counter_generic"]["10"])
gpr_values_quicksilver.append(json_data["percentage_bucket_counter_gpr"]["10"])
hybrid_values_quicksilver.append(json_data["percentage_bucket_counter_hybrid"]["10"])
generic_costs_quicksilver.append(json_data["budget"]-json_data["mean_budget_generic"])
gpr_costs_quicksilver.append(json_data["budget"]-json_data["mean_budget_gpr"])
hybrid_costs_quicksilver.append(json_data["budget"]-json_data["mean_budget_hybrid"])
base_point_costs_quicksilver.append(json_data["base_point_cost"])
points_generic_quicksilver.append(json_data["mean_add_points_generic"])
points_gpr_quicksilver.append(json_data["mean_add_points_gpr"])
points_hybrid_quicksilver.append(json_data["mean_add_points_hybrid"])
all_points_quicksilver.append(503)
base_points_quicksilver.append(json_data["min_points"])
y_values_list_acc_quicksilver = [
full_values_quicksilver ,
generic_values_quicksilver ,
gpr_values_quicksilver ,
#hybrid_values_quicksilver
]
y_values_list_points_quicksilver = [
all_points_quicksilver,
points_generic_quicksilver,
points_gpr_quicksilver,
#points_hybrid_quicksilver,
]
y_values_list_costs_quicksilver = [
generic_costs_quicksilver,
gpr_costs_quicksilver,
#hybrid_costs_quicksilver,
]
print("")
print("### Quicksilver ###")
B_NU_QUICKSILVER_GENERIC = np.nanmax(generic_costs_quicksilver)
B_NU_QUICKSILVER_GPR = np.nanmax(gpr_costs_quicksilver)
#print(B_NU_QUICKSILVER_GENERIC,B_NU_QUICKSILVER_GPR)
print("unused budget factor CPF/GPR:",B_NU_QUICKSILVER_GENERIC/B_NU_QUICKSILVER_GPR)
B_CPF_QUICKSILVER = 0
maxv = 0
for i in range(len(generic_costs_quicksilver)):
if generic_costs_quicksilver[i] >= maxv:
maxv = generic_costs_quicksilver[i]
B_CPF_QUICKSILVER = x_values_quicksilver[i]
B_GPR_QUICKSILVER = 0
maxv = 0
for i in range(len(gpr_costs_quicksilver)):
if gpr_costs_quicksilver[i] >= maxv:
maxv = gpr_costs_quicksilver[i]
B_GPR_QUICKSILVER = x_values_quicksilver[i]
full_B_max_QUICKSILVER = 0
maxv = 0
for i in range(len(full_values_quicksilver)):
if full_values_quicksilver[i] >= maxv:
maxv = full_values_quicksilver[i]
full_B_max_QUICKSILVER = x_values_quicksilver[i]
full_max_QUICKSILVER = max(full_values_quicksilver)
print("Max. accuracy Full:",full_max_QUICKSILVER,"% at B=",full_B_max_QUICKSILVER,"%.")
cpf_B_max_QUICKSILVER = 0
maxv = 0
for i in range(len(generic_values_quicksilver)):
if generic_values_quicksilver[i] >= maxv:
maxv = generic_values_quicksilver[i]
cpf_B_max_QUICKSILVER = x_values_quicksilver[i]
cpf_max_QUICKSILVER = max(generic_values_quicksilver)
print("Max. accuracy CPF:",cpf_max_QUICKSILVER,"% at B=",cpf_B_max_QUICKSILVER,"%.")
gpr_B_max_QUICKSILVER = 0
maxv = 0
for i in range(len(gpr_values_quicksilver)):
if gpr_values_quicksilver[i] >= maxv:
maxv = gpr_values_quicksilver[i]
gpr_B_max_QUICKSILVER = x_values_quicksilver[i]
gpr_max_QUICKSILVER = max(gpr_values_quicksilver)
print("Max. accuracy GPR:",gpr_max_QUICKSILVER,"% at B=",gpr_B_max_QUICKSILVER,"%.")
maxv_QUICKSILVER = 0
Bv_QUICKSILVER = 0
cpfv_QUICKSILVER = 0
gprv_QUICKSILVER = 0
for i in range(len(gpr_values_quicksilver)):
diff = gpr_values_quicksilver[i] - generic_values_quicksilver[i]
if diff >= maxv_QUICKSILVER:
maxv_QUICKSILVER = diff
Bv_QUICKSILVER = x_values_quicksilver[i]
cpfv_QUICKSILVER = generic_values_quicksilver[i]
gprv_QUICKSILVER = gpr_values_quicksilver[i]
print("max. difference GPR/CPF:",maxv_QUICKSILVER,"at B=",Bv_QUICKSILVER,"%.")
max_points_QUICKSILVER = 0
B_points_QUICKSILVER = 0
cpf_points_QUICKSILVER = 0
gpr_points_QUICKSILVER = 0
for i in range(len(points_generic_quicksilver)):
diff = points_generic_quicksilver[i] - points_gpr_quicksilver[i]
if diff >= max_points_QUICKSILVER:
max_points_QUICKSILVER = diff
B_points_QUICKSILVER = x_values_quicksilver[i]
cpf_points_QUICKSILVER = points_generic_quicksilver[i]
gpr_points_QUICKSILVER = points_gpr_quicksilver[i]
print("max. difference points GPR/CPF:",max_points_QUICKSILVER,"at B=",B_points_QUICKSILVER,"%.")
print("############")
print("")
###########
# relearn #
###########
path_6 = "relearn/all/analysis_results"
files_6 = find_files(path_6)
files_6 = natsorted(files_6)
x_values_relearn = []
full_values_relearn = []
generic_values_relearn = []
gpr_values_relearn = []
hybrid_values_relearn = []
generic_costs_relearn = []
gpr_costs_relearn = []
hybrid_costs_relearn = []
base_point_costs_relearn = []
points_generic_relearn = []
points_gpr_relearn = []
points_hybrid_relearn = []
base_points_relearn = []
all_points_relearn = []
nr_func_modeled_relearn = 0
for i in range(len(files_6)):
json_file_path = files_6[i]
json_data = read_json_file(json_file_path)
if i == len(files_6)-1:
nr_func_modeled_relearn = json_data["nr_func_modeled_gpr"]
x_values_relearn.append(json_data["budget"])
full_values_relearn.append(json_data["percentage_bucket_counter_full"]["10"])
generic_values_relearn.append(json_data["percentage_bucket_counter_generic"]["10"])
gpr_values_relearn.append(json_data["percentage_bucket_counter_gpr"]["10"])
hybrid_values_relearn.append(json_data["percentage_bucket_counter_hybrid"]["10"])
generic_costs_relearn.append(json_data["budget"]-json_data["mean_budget_generic"])
gpr_costs_relearn.append(json_data["budget"]-json_data["mean_budget_gpr"])
hybrid_costs_relearn.append(json_data["budget"]-json_data["mean_budget_hybrid"])
base_point_costs_relearn.append(json_data["base_point_cost"])
points_generic_relearn.append(json_data["mean_add_points_generic"])
points_gpr_relearn.append(json_data["mean_add_points_gpr"])
points_hybrid_relearn.append(json_data["mean_add_points_hybrid"])
all_points_relearn.append((25-1)*2)
base_points_relearn.append(json_data["min_points"])
y_values_list_acc_relearn = [
full_values_relearn ,
generic_values_relearn ,
gpr_values_relearn ,
#hybrid_values_relearn
]
y_values_list_points_relearn = [
all_points_relearn,
points_generic_relearn,
points_gpr_relearn,
#points_hybrid_relearn,
]
y_values_list_costs_relearn = [
generic_costs_relearn,
gpr_costs_relearn,
#hybrid_costs_relearn,
]
print("")
print("### RELEARN ###")
B_NU_RELEARN_GENERIC = np.nanmax(generic_costs_relearn)
B_NU_RELEARN_GPR = np.nanmax(gpr_costs_relearn)
#print(B_NU_RELEARN_GENERIC,B_NU_RELEARN_GPR)
print("unused budget factor CPF/GPR:",B_NU_RELEARN_GENERIC/B_NU_RELEARN_GPR)
B_CPF_RELEARN = 0
maxv = 0
for i in range(len(generic_costs_relearn)):
if generic_costs_relearn[i] >= maxv:
maxv = generic_costs_relearn[i]
B_CPF_RELEARN = x_values_relearn[i]
B_GPR_RELEARN = 0
maxv = 0
for i in range(len(gpr_costs_relearn)):
if gpr_costs_relearn[i] >= maxv:
maxv = gpr_costs_relearn[i]
B_GPR_RELEARN = x_values_relearn[i]
full_B_max_RELEARN = 0
maxv = 0
for i in range(len(full_values_relearn)):
if full_values_relearn[i] >= maxv:
maxv = full_values_relearn[i]
full_B_max_RELEARN = x_values_relearn[i]
full_max_RELEARN = max(full_values_relearn)
print("Max. accuracy Full:",full_max_RELEARN,"% at B=",full_B_max_RELEARN,"%.")
cpf_B_max_RELEARN = 0
maxv = 0
for i in range(len(generic_values_relearn)):
if generic_values_relearn[i] >= maxv:
maxv = generic_values_relearn[i]
cpf_B_max_RELEARN = x_values_relearn[i]
cpf_max_RELEARN = max(generic_values_relearn)
print("Max. accuracy CPF:",cpf_max_RELEARN,"% at B=",cpf_B_max_RELEARN,"%.")
gpr_B_max_RELEARN = 0
maxv = 0
for i in range(len(gpr_values_relearn)):
if gpr_values_relearn[i] >= maxv:
maxv = gpr_values_relearn[i]
gpr_B_max_RELEARN = x_values_relearn[i]
gpr_max_RELEARN = max(gpr_values_relearn)
print("Max. accuracy GPR:",gpr_max_RELEARN,"% at B=",gpr_B_max_RELEARN,"%.")
maxv_RELEARN = 0
Bv_RELEARN = 0
cpfv_RELEARN = 0
gprv_RELEARN = 0
for i in range(len(gpr_values_relearn)):
diff = gpr_values_relearn[i] - generic_values_relearn[i]
if diff >= maxv_RELEARN:
maxv_RELEARN = diff
Bv_RELEARN = x_values_relearn[i]
cpfv_RELEARN = generic_values_relearn[i]
gprv_RELEARN = gpr_values_relearn[i]
print("max. difference GPR/CPF:",maxv_RELEARN,"at B=",Bv_RELEARN,"%.")
max_points_RELEARN = 0
B_points_RELEARN = 0
cpf_points_RELEARN = 0
gpr_points_RELEARN = 0
for i in range(len(points_gpr_relearn)):
diff = points_gpr_relearn[i] - points_generic_relearn[i]
if diff >= max_points_RELEARN:
max_points_RELEARN = diff
B_points_RELEARN = x_values_relearn[i]
cpf_points_RELEARN = points_generic_relearn[i]
gpr_points_RELEARN = points_gpr_relearn[i]
print("max. difference points GPR/CPF:",max_points_RELEARN,"at B=",B_points_RELEARN,"%.")
max_points_RELEARN_2 = 0
B_points_RELEARN_2 = 0
cpf_points_RELEARN_2 = 0
gpr_points_RELEARN_2 = 0
for i in range(len(points_gpr_relearn)):
diff = points_generic_relearn[i] - points_gpr_relearn[i]
if diff >= max_points_RELEARN_2:
max_points_RELEARN_2 = diff
B_points_RELEARN_2 = x_values_relearn[i]
cpf_points_RELEARN_2 = points_generic_relearn[i]
gpr_points_RELEARN_2 = points_gpr_relearn[i]
print("max. difference points GPR/CPF:",max_points_RELEARN_2,"at B=",B_points_RELEARN_2,"%.")
print("############")
print("")
labels_acc = ['Full matrix', 'CPF strategy', 'GPR strategy', 'Hybrid strategy']
labels_points = ['Full matrix', 'CPF strategy', 'GPR strategy', 'Hybrid strategy', 'Minimum points required $\\bar{p}_{min}$']
labels_cost = ['CPF strategy', 'GPR strategy', 'Hybrid strategy', 'Min. modeling requirement $\\bar{b}_{min}$']
SMALL_SIZE = 8
MEDIUM_SIZE = 10
BIGGER_SIZE = 12
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=8) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=8) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
#"font.sans-serif": "Helvetica",
"font.size": 12
})
# create the figure environment including subplots
cm = 1/2.54
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9), (ax10, ax11, ax12), (ax13, ax14, ax15), (ax16, ax17, ax18)) = plt.subplots(6, 3, figsize=(20*cm, 20*cm))
ls=["-",'dotted','--',':','-']
lw = [1,1.5,1.5,4,1.5]
ax1.set_xlim(x_values_fastest[0], 103)
ax4.set_ylim(0, np.max(y_values_list_acc_fastest)+5)
style_counter = 0
zorders=[5,4,3,2,1]
colors=["gray", "blue", "red", "orange", "yellow"]
for y_values, label, color in zip(y_values_list_acc_fastest, labels_acc, colors):
if style_counter == 0:
ax1.scatter(100, y_values[len(y_values)-1], s=15, label=label, marker="D", linestyle = 'None', zorder=10, edgecolor="black", facecolor=colors[style_counter])
else:
ax1.plot(x_values_fastest, y_values, label=label, linestyle=ls[style_counter], linewidth=lw[style_counter], alpha=0.7, color=colors[style_counter], zorder=zorders[style_counter])
style_counter += 1
ax1.plot([Bv_FASTEST,Bv_FASTEST], [cpfv_FASTEST,gprv_FASTEST], color = "black", linewidth=1, marker="_")
ytemp = gprv_FASTEST - (maxv_FASTEST/2)
strtemp = "+"+str('{0:.2f}'.format(maxv_FASTEST))+"\% for \nB="+str('{0:.0f}'.format(Bv_FASTEST))+"\%"
ax1.annotate(strtemp, xy=(Bv_FASTEST, ytemp), xycoords='data', xytext=(-50, 10), textcoords='offset points', arrowprops=dict(arrowstyle="->", lw=0.5,), fontsize=7)
strtemp = str('{0:.2f}'.format(cpf_max_FASTEST))+"\% \nB="+str('{0:.0f}'.format(cpf_B_max_FASTEST))+"\%"
ax1.annotate(strtemp, xy=(cpf_B_max_FASTEST, cpf_max_FASTEST), xycoords='data', xytext=(-10, -40), textcoords='offset points', arrowprops=dict(arrowstyle="->", lw=0.5, color="blue"), fontsize=7, color="blue")
strtemp = str('{0:.2f}'.format(gpr_max_FASTEST))+"\% \nB="+str('{0:.0f}'.format(gpr_B_max_FASTEST))+"\%"
ax1.annotate(strtemp, xy=(gpr_B_max_FASTEST, gpr_max_FASTEST), xycoords='data', xytext=(0, -55), textcoords='offset points', arrowprops=dict(arrowstyle="->", lw=0.5, color="red"), fontsize=7, color="red")
strtemp = str('{0:.2f}'.format(full_max_FASTEST))+"\% \nB="+str('{0:.0f}'.format(full_B_max_FASTEST))+"\%"
ax1.annotate(strtemp, xy=(full_B_max_FASTEST, full_max_FASTEST), xycoords='data', xytext=(-25, -60), textcoords='offset points', arrowprops=dict(arrowstyle="->", lw=0.5, color="dimgray"), fontsize=7, color="dimgray")
ax1.grid(alpha=0.3, which='major')
ax1.grid(alpha=0.3, which='minor')
ax1.set_yticks(np.arange(0, np.max(y_values_list_acc_fastest)+5, 10))
ax1.set_xticks([20,30,40,50,60,70,80,90,100])
ax1.set_ylabel('Models within\n $\pm10\%$ at $P_{+}$ [\%]')
#ax1.tick_params(axis='x', labelsize=7)
#ax1.tick_params(axis='y', labelsize=7)
ls=['dotted','--',':','dashdot']
lw = [1.5,1.5,4,1.5]
colors = ["blue", "red", "orange", "dimgray"]
zorders=[7,6,5,4]
style_counter = 0
for y_values, label in zip(y_values_list_costs_fastest, labels_cost):
ax2.plot(x_values_fastest, y_values, label=label, linestyle=ls[style_counter], linewidth=lw[style_counter], alpha=0.7, zorder=zorders[style_counter], color=colors[style_counter])