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- import os
- from CertainTrust import Opinion
- from vendors.debian.CSVReader import CSVReader
- class Tests:
- @staticmethod
- def input_prediction_error_test(model):
- norm_param = 4
- model_filename = os.path.join(model.module_path, "models", "dummy_input_package_prediction_errorcompl.csv")
- model_set = model.gen_model_opinion_set(model_filename, 9, norm_param)
- system_filename = os.path.join(model.module_path, "inputs", "dummy_input_package_server.csv")
- system = CSVReader.read_csv_package_names(system_filename)
- # get system dict
- system_dict = dict()
- for key in system:
- print(key)
- new_key = model.unifySrcName(key)
- if new_key in model_set:
- system_dict[new_key] = model_set[new_key]
- print("Evaluating on: " + str(len(system_dict)) + " out of " + str(
- len(system)) + " packages, since they are not present in our model.")
- Tests.evaluate_expectation(system_dict, model, 9, norm_param)
- @staticmethod
- def evaluate_expectation(package_list, vendormodel, months, norm_val):
- """
- TODO:
- :param package_list: dictionary of opinions with names as key and opinion as value
- :param vendormodel: the vendor-model to compare the prediction with
- :return: TODO:
- """
- print("For normalization value = " + str(norm_val))
- # ANDed prediction
- system_and = Opinion._cum_and(list(package_list.values()))
- expectation = system_and.expectation_value()
- AND_prediction = (1 - expectation) * (norm_val * months * 30)
- # separate prediction
- sep_pred = 0
- for k in package_list:
- sep_pred = sep_pred + ((1 - package_list[k].expectation_value()) * (norm_val * months * 30))
- # src2month prediction gathering
- summ = 0
- for package in package_list:
- unified_package = vendormodel.unifySrcName(package)
- src2month = vendormodel.get_src2month()
- if unified_package in src2month:
- summ = summ + sum(src2month[package][-months - 3:-3])
- file_error = abs(summ - CSVReader.prediction_read) / summ
- print("Input file prediction = " + str(CSVReader.prediction_read) + ", ( error = " + str(file_error) + " )")
- sep_error = abs(summ - sep_pred) / summ
- print("Separate prediction = " + str(sep_pred) + ", ( error = " + str(sep_error) + " )")
- and_error = abs(summ - AND_prediction) / summ
- print("ANDed prediction = " + str(AND_prediction) + ", ( error = " + str(and_error) + " )")
- print("Src2month data = " + str(summ))
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