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evaluation.py
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evaluation.py
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from sqlite3.dbapi2 import Cursor, connect
from sklearn import metrics
from architecture import load_cam_model
import torch
from sklearn.metrics import auc
import matplotlib.pyplot as plt
import sqlite3
import enlighten
import os
import glob
import datetime
import time
device = None
ticks = None
tocks = None
data_pbar = None
conn = None
def evaluate_model(device_in, uuid, ld_helper, cur):
global device
global ticks
global tocks
global data_pbar
device = device_in
manager = enlighten.get_manager()
ticks = manager.counter(total=5, desc='Fold------------------', unit='folds') #22 chars
tocks = manager.counter(total=10, desc='Threshold-------------', unit='notches')
data_pbar = manager.counter(total=0, desc='Data------------------', unit='batches')
make_folders()
log_path = "../logs/" + uuid + ".txt"
if (os.path.exists(log_path)):
filein = open(log_path, 'a')
else:
filein = open(log_path, 'w')
write_to_file(filein)
cursor = get_db_cursor()
task_str = ld_helper.get_task_string()
tot_acc = 0; tot_sens = 0; tot_spec = 0; tot_roc_auc = 0
fold = 0
srch_path = "../weights/{}/".format(task_str) + uuid + "/*"
for path in glob.glob(srch_path):
model = load_cam_model(path)
model.to(device)
test_dl = ld_helper.get_test_dl(fold)
data_pbar.total = len(test_dl)
if (not os.path.exists("../graphs/" + uuid)) : os.mkdir("../graphs/" + uuid)
metrics = get_roc_auc(model, test_dl, figure=True, path = "../graphs/" + uuid, fold=fold+1)
accuracy, sensitivity, specificity, roc_auc, you_thresh, you_max = [*metrics]
write_to_file(filein, metrics=[fold+1, accuracy, sensitivity, specificity, roc_auc, you_max, you_thresh])
tot_acc += accuracy; tot_sens += sensitivity; tot_spec += specificity; tot_roc_auc += roc_auc
fold += 1
ticks.update()
tocks.count = 0
avg_acc = (tot_acc / 5)
avg_sens = (tot_sens / 5)
avg_spec = (tot_spec / 5)
avg_roc_auc = (tot_roc_auc / 5)
params = (uuid, str(time.time()), task_str, avg_acc, avg_sens,
avg_spec, avg_roc_auc)
cursor.execute("INSERT INTO nn_perfomance VALUES (NULL,?, ?, ?, ?, ?, ?, ?)", params)
conn.commit()
write_to_file_footer(filein, [avg_acc, avg_sens, avg_spec, avg_roc_auc])
def evaluate_fold(device_in, uuid, ld_helper, fold_in, commit_to_db=True):
global device
global ticks
global tocks
global data_pbar
manager = enlighten.get_manager()
ticks = manager.counter(total=1, desc='Fold------------------', unit='folds')
tocks = manager.counter(total=10, desc='Threshold-------------', unit='notches')
data_pbar = manager.counter(total=0, desc='Data------------------', unit='batches')
fold = fold_in - 1 #This is done because python indexes by zero.
device = device_in
make_folders()
cursor = get_db_cursor()
task_str = ld_helper.get_task_string()
tot_acc = 0; tot_sens = 0; tot_spec = 0; tot_roc_auc = 0
srch_path = "../weights/{}/".format(task_str) + uuid + "/*"
folds_paths = glob.glob(srch_path)
model = load_cam_model(folds_paths[fold])
model.to(device)
test_dl = ld_helper.get_test_dl(fold)
data_pbar.total = len(test_dl)
if (not os.path.exists("../graphs/" + uuid)) : os.mkdir("../graphs/" + uuid)
metrics = get_roc_auc(model, test_dl, figure=True, path = "../graphs/" + uuid, fold=fold+1)
accuracy, sensitivity, specificity, roc_auc, you_thresh, you_max = [*metrics]
tot_acc += accuracy; tot_sens += sensitivity; tot_spec += specificity; tot_roc_auc += roc_auc
fold += 1
ticks.update()
tocks.count = 0
if commit_to_db == True:
params = (uuid, str(time.time()), task_str, str(accuracy), str(sensitivity),
str(specificity), str(roc_auc))
cursor.execute("INSERT INTO nn_perfomance VALUES (NULL,?, ?, ?, ?, ?, ?, ?)", params)
conn.commit()
#cursor.execute("INSERT INTO nn_perfomance (uuid, time, task, accuracy, sensitivity, specificity, roc_auc) VALUES (" + uuid + "," + str(time.time()) + "," + task_str + "," + str(accuracy) + "," + str(sensitivity) + "," + str(specificity) + "," + str(roc_auc) + ")")
def get_roc_auc(model_in, test_dl, figure=False, path=None, fold=1):
fpr = [] #1-specificity
tpr = []
youden_s_lst = []
opt_acc = 0; opt_sens = 0; opt_spec = 0
youdens_s_max = 0
optimal_thresh = 0
for t in range(0, 10, 1):
thresh = t/10
acc, sens, spec = get_metrics(model_in, test_dl, thresh)
tpr.append(sens)
fpr.append(1 - spec)
youdens_s = sens + spec - 1
if (youdens_s > youdens_s_max):
youdens_s_max = youdens_s;
optimal_thresh = thresh
opt_acc = acc; opt_sens = sens; opt_spec = spec
tocks.update()
roc_auc = -1
try:
roc_auc = auc(fpr, tpr)
except Exception as e:
print(e)
metrics = [opt_acc, opt_sens, opt_spec, roc_auc, youdens_s_max, optimal_thresh]
if(figure):
if (path == None):
path = "../graphs/auc-{date:%Y-%m-%d_%H-%M-%S}.png".format(date=datetime.datetime.now())
else:
#append dir
path = path + "/auc-fold{}-{date:%Y-%m-%d_%H-%M-%S}.png".format(fold, date=datetime.datetime.now())
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve - Fold {}'.format(fold))
plt.legend(loc="lower right")
plt.savefig(path)
return metrics
def get_metrics(model_in, test_dl, thresh=0.5, param_count=False):
correct = 0; total = 0
model_in.eval()
TP = 0.000001; TN = 0.000001; FP = 0.000001; FN = 0.000001
with torch.no_grad():
for i_batch, sample_batched in enumerate(test_dl):
batch_X = sample_batched['mri'].to(device)
batch_Xb = sample_batched['clin_t'].to(device)
batch_y = sample_batched['label'].to(device)
for i in range(4): #hard coded batch size of 4
real_class = batch_y[i].item()
X = batch_X[i].view(-1, 1, 110, 110, 110)
Xb = batch_Xb[i].view(1, 21)
net_out = model_in((batch_X[i].view(-1, 1, 110, 110, 110), batch_Xb[i].view(1, 21)))
predicted_class = 1 if net_out > thresh else 0
if (predicted_class == real_class):
correct += 1
if (real_class == 0):
TN += 1
elif (real_class == 1):
TP += 1
else:
if (real_class == 0):
FP += 1
elif (real_class == 1):
FN += 1
total += 1
data_pbar.update()
accuracy = round(correct/total, 3)
sensitivity = round((TP / (TP + FN)), 3)
specificity = round((TN / (TN + FP)), 3)
data_pbar.count = 0
return (accuracy, sensitivity, specificity)
def get_db_cursor():
global conn
conn = sqlite3.connect("../weights/neural-network.db")
cursor = conn.cursor()
return cursor
def create_db():
conn = sqlite3.connect("../weights/neural-network.db")
cursor = conn.cursor()
cursor.execute('''CREATE TABLE "nn_perfomance" (
"uuid" TEXT NOT NULL UNIQUE,
"time" REAL,
"task" TEXT DEFAULT 0,
"accuracy" INTEGER,
"sensitivity" INTEGER,
"specificity" INTEGER,
"roc_auc" INTEGER,
PRIMARY KEY("uuid")
)''')
conn.commit()
conn.close()
def make_folders():
if (not os.path.exists("../logs/")):
os.mkdir("../logs/")
if (not os.path.exists("../graphs/")):
os.mkdir("../graphs/")
if (not os.path.exists("../weights/neural-network.db")):
fp = open('neural-network.db', 'x')
fp.close()
create_db()
def write_to_file(filein, metrics=None):
if (metrics == None):
filein.write("\n")
filein.write("==========================\n")
filein.write("===== Log for camull =====\n")
filein.write("==========================\n")
filein.write("\n")
filein.write("----- Date: {date:%Y-%m-%d_%H:%M:%S} -----\n".format(date=datetime.datetime.now()))
filein.write("\n")
filein.write("\n")
else:
fold, accuracy, sensitivity, specificity, roc_auc, you_thresh, you_max = [*metrics]
filein.write("===== Fold {} =====".format(fold+1)) #22 chars
filein.write("\n")
filein.write("-----Threshold {}-----".format(you_thresh))
filein.write("\n")
filein.write("--- Accuracy : {}\n".format(accuracy))
filein.write("--- Sensitivity : {}\n".format(sensitivity))
filein.write("--- Specificity : {}\n".format(specificity))
filein.write("--- Youdens stat : {}\n".format(you_max))
filein.write("\n")
filein.write("(Variable Threshold)")
filein.write("--- ROC AUC : {}\n".format(roc_auc))
filein.write("\n")
def write_to_file_footer(filein, avg_lst):
avg_acc, avg_sens, avg_spec, avg_roc_auc = [*avg_lst]
filein.write("\n")
filein.write("===== Average Across 5 folds =====")
filein.write("\n")
filein.write("--- Accuracy : {}\n".format(avg_acc))
filein.write("--- Sensitivity : {}\n".format(avg_sens))
filein.write("--- Specificity : {}\n".format(avg_spec))
filein.write("\n")
filein.write("(Variable Threshold)")
filein.write("--- ROC AUC : {}\n".format(avg_roc_auc))
filein.write("\n")