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()