def bias_evaluations_svm():
logging.info("APP: " + str(datetime.datetime.now()) + " Bias Evaluation with SVM-Classifier scores started")
content = request.get_json()
bar = request.args.to_dict()
response, status_code = evaluation_controller.evaluation('svm', content, bar)
return response, status_code
def bias_evaluations_wordsim():
logging.info("APP: " + str(datetime.datetime.now()) + " Semantic Quality Test WordSim started")
content = request.get_json()
bar = request.args.to_dict()
response, status_code = evaluation_controller.evaluation('wordsim', content, bar)
return response, status_code
def bias_evaluations_kmeans():
logging.info("APP: " + str(datetime.datetime.now()) + " Bias Evaluation with KMEANS scores started")
content = request.get_json()
bar = request.args.to_dict()
response, status_code = evaluation_controller.evaluation('kmeans', content, bar)
return response, status_code
def bias_evaluation_config(configfile):
global space
global uploaded
global json_value
global lower
global scores
global specification_data
with open(configfile) as json_file:
config_data = json.load(json_file)
if 'space' in config_data:
space = config_data['space']
else:
space = 'fasttext'
if 'uploaded' in config_data:
uploaded = config_data['uploaded']
else:
uploaded = "false"
if 'json' in config_data:
json_value = config_data['json']
if 'lower' in config_data:
lower = config_data['lower']
if 'scores' in config_data:
scores = config_data['scores']
else:
scores = 'all'
specification_data = config_data
print("\nDEBIE -- Bias Evaluation with " + scores + " scores started at " +
str(datetime.now()))
bar = {
"space": space,
"lower": lower,
"uploaded": uploaded,
"json": json_value
}
scores, used_space, used_lower, not_found, deleted = evaluation_controller.evaluation(
scores, specification_data, bar)
output = scores_to_output(scores, used_space, used_lower, not_found,
deleted)
print(output)
return output
def bias_evaluation():
global space
global uploaded
global specification_data
global lower
global scores
global json_value
print("\nDEBIE -- Bias Evaluation with " + scores + " scores started at " +
str(datetime.now()))
bar = {
"space": space,
"lower": lower,
"uploaded": uploaded,
"json": json_value
}
scores, used_space, used_lower, not_found, deleted = evaluation_controller.evaluation(
scores, specification_data, bar)
output = scores_to_output(scores, used_space, used_lower, not_found,
deleted)
print(output)
return select_method()
