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Copy pathplot_sampling_unc.py
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179 lines (153 loc) · 5.98 KB
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import sys
import mysql.connector as db
import os
import math
import numpy as np
import matplotlib.pyplot as plt
local = True
base_dir = os.path.dirname(os.path.realpath(__file__))
pic_dir = f"{base_dir}/pic/sampling"
if not os.path.exists(pic_dir):
os.makedirs(pic_dir)
data_list = ["parkinsons", "vertebral","ionosphere", "climate", "blod", "breast","bank", "QSAR"] # ,"spambase", "madelon"
algo = "LR"
run_name = "Paper_results_main"
unc = "rl_e"
for data in data_list:
# prameters ############################################################################################################################################
plot_name = f"{data}_{algo}_unc"
# plot_name = run_name
query = f"SELECT results, id, prams, result_type FROM experiments Where dataset='Jdata/{data}' AND algo='{algo}' AND run_name='{run_name}' AND result_type='{unc}'" #
########################################################################################################################################################
max_run = 1000
xlabel = "Number of queried instances"
ylabel = "Uncertainty"
jobs = []
# get data from database based on the query above
mydb = db.connect(host="131.234.250.119", user="root", passwd="uncertainty", database="uncertainty")
mycursor = mydb.cursor()
mycursor.execute(query)
results = mycursor.fetchall()
for job in results:
jobs.append(job)
fig, ax1 = plt.subplots()
plot_list = []
for job in jobs:
dir = job[0]
if dir[0] == ".":
dir = base_dir + dir[1:]
if local:
dir = f"/home/mhshaker/Projects/Database/DB_files/job_{job[1]}"
isFile = os.path.isdir(dir)
if not isFile:
print("[Error] file does not exist")
print(dir)
exit()
plot_list.append(job[1])
legend = ""
prams = str(job[2])
pram_name = "batch_size"
search_pram = f"'{pram_name}': "
v_index_s = prams.index(search_pram)
v_index_e = prams.index("}", v_index_s)
batch_size = int(prams[v_index_s+len(search_pram) : v_index_e])
for text in job[3:]:
legend += " " +str(text)
# print(legend)
# exit()
# get the list of file names for unc_mean
dir_mean = dir + "/unc_mean"
file_list = []
for (dirpath, dirnames, filenames) in os.walk(dir_mean):
file_list.extend(filenames)
all_runs = []
run_count = 0
for f in file_list:
# print(f)
run_count += 1
run_result = np.loadtxt(dir_mean+"/"+f)
all_runs.append(run_result)
if run_count > max_run:
break
all_runs = np.array(all_runs)
run_mean = np.nanmean(all_runs, axis=0)
# get the list of file names for unc_std
dir_std = dir + "/unc_std"
file_list = []
for (dirpath, dirnames, filenames) in os.walk(dir_std):
file_list.extend(filenames)
all_runs = []
run_count = 0
for f in file_list:
# print(f)
run_count += 1
run_result = np.loadtxt(dir_std+"/"+f)
all_runs.append(run_result)
if run_count > max_run:
break
all_runs = np.array(all_runs)
run_std = np.nanmean(all_runs, axis=0)
# get the list of file names for unc_max
dir_max = dir + "/unc_max"
file_list = []
for (dirpath, dirnames, filenames) in os.walk(dir_max):
file_list.extend(filenames)
all_runs = []
run_count = 0
for f in file_list:
# print(f)
run_count += 1
run_result = np.loadtxt(dir_max+"/"+f)
all_runs.append(run_result)
if run_count > max_run:
break
all_runs = np.array(all_runs)
run_max = np.nanmean(all_runs, axis=0)
# get the list of file names for acc
dir_max = dir + "/acc"
file_list = []
for (dirpath, dirnames, filenames) in os.walk(dir_max):
file_list.extend(filenames)
all_runs = []
run_count = 0
for f in file_list:
# print(f)
run_count += 1
run_result = np.loadtxt(dir_max+"/"+f)
all_runs.append(run_result)
if run_count > max_run:
break
all_runs = np.array(all_runs)
run_acc = np.nanmean(all_runs, axis=0)
run_acc = run_acc * 100 # to have percentates and not decimals
legend = legend.replace("rl_e", "EU")
legend = legend.replace("rl_a", "AU")
legend = legend.replace("ent", "ENT")
legend = legend.replace("credal", "CU")
legend = legend.replace("random", "Rand")
legend = legend.replace("evid_e", "IEU")
legend = legend.replace("evid_a", "CEU")
steps = np.array(range(len(run_mean))) * batch_size
# steps = np.array(range(len(run_mean)))
low_std = run_mean - run_std
low_std = low_std.clip(min=0)
color = 'k'
ax1.set_xlabel(xlabel)
ax1.set_ylabel('Accuracy %', color=color)
ax1.plot(steps, run_acc, label= "Acc", color='k')
ax1.legend(loc="center left")
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
# color = 'tab:orange'
ax2.set_ylabel(ylabel, color=color) # we already handled the x-label with ax1
# ax2.plot(t, data2, color=color)
# ax2.tick_params(axis='y', labelcolor=color)
ax2.plot(steps, run_mean, label= legend +" Mean")
ax2.fill_between(steps, run_mean + run_std, low_std, alpha=0.2,label= legend +" std")
ax2.plot(steps, run_max, label=legend + " Max")
ax2.legend(loc="center right")
# plt.xlabel(xlabel)
# plt.ylabel(ylabel)
# plt.title(plot_list)
plt.legend(loc="center right")
fig.savefig(f"{pic_dir}/{plot_name}.png")
print(f"Plot {plot_name} Done")