async def run(self, main_func_name):
reqs = os.listdir(self.req_dir)
if reqs:
req_name = reqs[0]
req_path = os.path.join(self.req_dir, req_name)
req_content = read_json(req_path)
os.remove(req_path) # 읽은 후 바로 삭제
res = getattr(self.worker, main_func_name)(**req_content)
res_path = os.path.join(self.res_dir, req_name)
write_json(res, res_path) # res 파일 생성
def webhook():
if request.method == 'POST':
r = request.get_json()
for command in command_list:
for regular in command.keys:
if re.match(regular, r['message']['text']) is not None:
send_message(r['message']['chat']['id'],
command.process(r))
return jsonify(r)
send_message(r['message']['chat']['id'], 'unknown command')
utility.write_json(r)
return jsonify(r)
return render_template('landing.html')
def my_custom_loss_func(ground_truth, p, n, json):
# Calculate Precision-Recall-F-Score
precision, recall, fscore, _ = precision_recall_fscore_support(
ground_truth, p, labels=[1, 0])
# Calculate Precision-Recall Curve
prc, rec, _ = precision_recall_curve(ground_truth, p, pos_label=1)
# Calculate AUPRC
auprc = average_precision_score(ground_truth, p)
# Calculate False Positive Rate (FPR) and True Positive Rate (TPR)
# [FPR, TNR] [TPR, FNR]
fpr, tpr, _ = roc_curve(ground_truth, p)
# Calculate AUROC
auroc = auc(fpr, tpr)
# Save the metrics as a ordered dictionary
scores = [
("class", n), # class number
("positives", np.count_nonzero(ground_truth)),
("precision0", precision[1]),
("precision1", precision[0]),
("recall0", recall[1]),
("recall1", recall[0]),
("fscore0", fscore[1]),
("fscore1", fscore[0]),
("prc10", prc.tolist()),
("rec10", rec.tolist()),
("auprc", auprc),
("fpr10", fpr.tolist()),
("tpr10", tpr.tolist()),
("auroc", auroc)
]
# Write scores into the json file
write_json(json, scores)
return 0
# Add Json extension
f_temp = p_sim + f_temp + ".json"
# Classifier
c = cl[x]
# Save the header as a dictionary
header = [
("Ontology", sys.argv[1]),
("Classifier", x),
#("Parameters", c.get_params()),
("Start_Time", time.strftime("%c"))
]
# Write the header into the json file
write_json(f_temp, header)
# Different branches for linearly and non-linearly separable classes
if switch:
for j in range(Y.shape[1]):
if j in lc:
metrics(c, X, Y.getdensecol(j), j, f_temp)
else:
for j in range(0, Y.shape[1], 5):
metrics(c, X, Y.getdensecol(j), j, f_temp)
# Save the footer as a dictionary
footer = [("End_Time", time.strftime("%c"))]
# Write the footer into the json file
write_json(f_temp, footer)
def _send_request(self, content):
write_json(content, self.req_path)
# Path to the simulation directory of the chosen ontology
p_sim = "Simulation/" + onto_name + "/"
# Part of the filenames associated to the Json files
f_sim = p_sim + "M_" + onto_name + "_"
# Read Annotation Matrix
Y = load_annotation(f_ann)
for classname, classifier in classifiers.items():
filename_out = f_sim + classname + '.json'
# Save the header as a dictionary
header = [
("Ontology", onto_name),
("Classifier", classname),
#("Parameters", classifier.get_params()),
("Start_Time", time.strftime("%c"))
]
# Write the header into the json file
write_json(filename_out, header)
for x in range(0, Y.shape[1], 5):
metrics(classifier, X, Y.getdensecol(x), x, filename_out)
# Save the footer as a dictionary
footer = [("End_Time", time.strftime("%c"))]
# Write the footer into the json file
write_json(filename_out, footer)