def test_VRS_env_output_oriented_weakly_disposable_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example2_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelVRSDecorator(
EnvelopmentModelBase(
data,
EnvelopmentModelOutputOriented(
generate_lower_bound_for_efficiency_score),
DisposableVarsConstraintCreator(set(['I3', 'O1']))))
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 1 / 1.4107143, 1, 1,
1 / 1.821256,
1, 1, 1, 1, 1 / 1.1407407,
1], model_solution, data)
clean_up_pickled_files()
def test_VRS_env_output_oriented_small(model, data):
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 1, 1, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'C', 1, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'D', 1, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [0.25, 0, 1], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0, 0, 0.33333333], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0.33333333, 0.66666667, 1], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0, 0.125, 0.5], model_solution, data)
# B does not return the same value
utils_for_tests.check_VRS_duals(
['A', 'C', 'D', 'E'], [1.5, 1, -1.3333333, 0.75], model_solution, data)
def test_maximize_slacks_usual_weakly_disposable_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example2_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelVRSDecorator(
EnvelopmentModelBase(data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DisposableVarsConstraintCreator(
set(['I2', 'O1']))))
model = MaximizeSlacksModel(model)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.86998617, 1, 1, 1, 1,
1, 1, 1, 0.6386574, 1],
model_solution, data)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_max_slacks_weakly_disp_vars_output.xls')
clean_up_pickled_files()
def test_super_efficiency_medium(DEA_example2_data):
data = DEA_example2_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(generate_supper_efficiency_upper_bound),
DefaultConstraintCreator())
super_efficiency_model = SupperEfficiencyModel(model)
start_time = datetime.datetime.now()
solution = super_efficiency_model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(
dmus,
[1.652173892, 0.6485875732, 0.909247759,
1.116838534, 0.5490716156, 2.485294106,
1.244945494, 0.824120607, 2.299898658,
0.6267333816, 1.088235274],
solution, data, 1e-6)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(solution)
work_book.save('tests/test_super_efficiency_output.xls')
def test_VRS_multi_output_oriented_with_weakly_disposable_vars_small(data):
model = MultiplierModelVRSDecorator(MultiplierModelWithDisposableCategories(
MultiplierModelBase(data, 1e-12,
MultiplierOutputOrientedModel()), set(['q'])))
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 1, 1, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'C', 1, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'D', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [0.25, 0, 1], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'B', ['x1', 'x2', 'q'], [0.125, 0, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0.083333333, 0, 0.33333333], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0.33333333, 0.66666667, 1], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0, 0.125, 0.5], model_solution, data)
utils_for_tests.check_VRS_duals(
dmus, [1.5, 0.75, 0.5, -1.3333333, 0.75], model_solution, data)
def test_CRS_env_output_oriented_non_disc_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_output_category('ND1')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelOutputOrientedWithNonDiscVars(
set(['ND1']),
generate_lower_bound_for_efficiency_score),
DefaultConstraintCreator())
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(model_solution,
data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 1, 1 / 1.1698685, 1,
1 / 2.3749162, 1,
1, 1 / 1.3198374,
1 / 1.2115385,
0.5, 1], model_solution,
data)
clean_up_pickled_files()
def test_maximize_slacks_usual_weakly_disposable_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example2_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelVRSDecorator(
EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DisposableVarsConstraintCreator(set(['I2', 'O1']))))
model = MaximizeSlacksModel(model)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(
dmus, [1, 0.86998617, 1, 1, 1, 1, 1, 1, 1, 0.6386574, 1],
model_solution, data)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_max_slacks_weakly_disp_vars_output.xls')
clean_up_pickled_files()
def test_price_ratio_restrictions_medium_env_model():
categories, data, dmu_name, sheet_name = read_data(
'tests/dataFromDEAbook_page181.xls')
coefficients, has_same_dmus = convert_to_dictionary(data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
print(data.categories)
data.add_input_category('Doctors')
data.add_input_category('Nurses')
data.add_output_category('Outpatients')
data.add_output_category('Inpatients')
model = MultiplierModelBase(data, 0,
MultiplierInputOrientedModel())
ratio_bounds = {('Nurses', 'Doctors'): (0.2, 5),
('Inpatients', 'Outpatients'): (0.2, 5)}
model = MultiplierModelWithPriceRatioConstraints(model, ratio_bounds)
model_solution = model.run()
utils_for_tests.check_if_category_is_within_price_ratio_constraints(
model_solution, ratio_bounds)
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7',
'H8', 'H9', 'H10', 'H11', 'H12', 'H13', 'H14']
utils_for_tests.check_efficiency_scores(dmus, [0.926, 1, 1, 0.634, 0.82, 1,
0.803, 0.872, 0.982, 1,
0.849,
0.93, 0.74, 0.929],
model_solution, data, 1e-3)
clean_up_pickled_files()
def test_CRS_env_input_oriented_small(model, data):
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 0.83333333, 0.71428571, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'B', 0.21428571,
model_solution, data)
utils_for_tests.check_lambda_variables('D', 'E', 0.28571429,
model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu('A', ['x1', 'x2', 'q'],
[0.5, 0, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu('B', ['x1', 'x2', 'q'],
[0.1, 0.2, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0.055555556, 0.11111111, 0.27777778],
model_solution, data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0.14285714, 0.28571429, 0.71428571],
model_solution, data)
utils_for_tests.check_categories_for_dmu('E', ['x1', 'x2', 'q'],
[0.1, 0.2, 0.5], model_solution,
data)
def test_super_efficiency_medium(DEA_example2_data):
data = DEA_example2_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(generate_supper_efficiency_upper_bound),
DefaultConstraintCreator())
super_efficiency_model = SupperEfficiencyModel(model)
start_time = datetime.datetime.now()
solution = super_efficiency_model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [
1.652173892, 0.6485875732, 0.909247759, 1.116838534, 0.5490716156,
2.485294106, 1.244945494, 0.824120607, 2.299898658, 0.6267333816,
1.088235274
], solution, data, 1e-6)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(solution)
work_book.save('tests/test_super_efficiency_output.xls')
def test_CRS_multi_output_oriented_small(model, data):
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 1 / 1.199999982, 1 / 1.4, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1.2, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.6, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'B', 0.3, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'E', 0.4, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [1, 0, 1], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'B', ['x1', 'x2', 'q'], [0.1, 0.2, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu('C', ['x1', 'x2', 'q'], [
0.066666667, 0.13333333,
0.33333333],
model_solution, data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0.2, 0.4, 1], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0.1, 0.2, 0.5], model_solution, data)
def test_CRS_env_output_oriented_small(model, data):
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(dmus,
[0.5, 1, 1 / 1.2, 1 / 1.4, 1],
model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1.2, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.6, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'B', 0.3, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'E', 0.4, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu('A', ['x1', 'x2', 'q'], [1, 0, 1],
model_solution, data)
utils_for_tests.check_categories_for_dmu('B', ['x1', 'x2', 'q'],
[0.1, 0.2, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0.066666667, 0.13333333, 0.33333333],
model_solution, data)
utils_for_tests.check_categories_for_dmu('D', ['x1', 'x2', 'q'],
[0.2, 0.4, 1], model_solution,
data)
utils_for_tests.check_categories_for_dmu('E', ['x1', 'x2', 'q'],
[0.1, 0.2, 0.5], model_solution,
data)
def test_CRS_env_output_oriented_non_disc_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_output_category('ND1')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelOutputOrientedWithNonDiscVars(
set(['ND1']), generate_lower_bound_for_efficiency_score),
DefaultConstraintCreator())
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [
1, 1, 1 / 1.1698685, 1, 1 / 2.3749162, 1, 1, 1 / 1.3198374,
1 / 1.2115385, 0.5, 1
], model_solution, data)
clean_up_pickled_files()
def test_CRS_env_input_oriented_small(model, data):
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 0.83333333, 0.71428571, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables(
'D', 'B', 0.21428571, model_solution, data)
utils_for_tests.check_lambda_variables(
'D', 'E', 0.28571429, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [0.5, 0, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'B', ['x1', 'x2', 'q'], [0.1, 0.2, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu('C', ['x1', 'x2', 'q'], [
0.055555556, 0.11111111,
0.27777778],
model_solution, data)
utils_for_tests.check_categories_for_dmu('D', ['x1', 'x2', 'q'], [
0.14285714, 0.28571429,
0.71428571],
model_solution, data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0.1, 0.2, 0.5], model_solution, data)
def test_run_gui(main_frame):
filename = 'tests/params_new_format.txt'
def get_params():
return filename
main_frame.params_frame._get_filename_for_load = get_params
def get_sheet_name(names):
return 'Sheet1'
main_frame.data_frame.data_tab.call_dialog = get_sheet_name
main_frame.params_frame.load_file()
main_frame.run()
model_solutions = main_frame.data_frame.solution_tab.model_solutions
assert len(model_solutions) == 1
model_solution = model_solutions[0]
model_input = model_solution._input_data
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, model_input)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [
1, 0.86998617, 1, 0.95561335, 0.85, 1, 1, 0.84507042, 1, 0.524,
0.89058524
], model_solution, model_input)
def test_VRS_env_input_oriented_weakly_disposable_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example2_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelVRSDecorator(
EnvelopmentModelBase(data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DisposableVarsConstraintCreator(
set(['I2', 'O1']))))
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.86998617, 1, 1, 1, 1,
1, 1, 1, 0.6386574, 1],
model_solution,
data)
clean_up_pickled_files()
def test_CRS_multi_output_oriented_with_weakly_disposable_vars_small(data):
model = MultiplierModelWithDisposableCategories(
MultiplierModelBase(data, 1e-12,
MultiplierOutputOrientedModel()), set(['x1']))
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 0.8333333458, 0.7142857143, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1.2, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.6, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'B', 0.3, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'E', 0.4, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [1, 0, 1], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'B', ['x1', 'x2', 'q'], [0.5, 0, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu('C', ['x1', 'x2', 'q'], [
0.066666667, 0.13333333,
0.33333333], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0.2, 0.4, 1], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0.1, 0.2, 0.5], model_solution, data)
def test_CRS_multi_input_oriented_with_weakly_disposable_vars_small(data):
model = MultiplierModelWithDisposableCategories(
MultiplierModelBase(data, 1e-12,
MultiplierInputOrientedModel()), ['x1', 'q'])
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 0.83333334, 0.71428571, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables(
'D', 'B', 0.21428571, model_solution, data)
utils_for_tests.check_lambda_variables(
'D', 'E', 0.28571429, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [0.5, 0, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'B', ['x1', 'x2', 'q'], [0.5, 0, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu('C', ['x1', 'x2', 'q'], [
0.055555556, 0.11111111,
0.27777778], model_solution,
data)
utils_for_tests.check_categories_for_dmu('D', ['x1', 'x2', 'q'], [
0.14285714, 0.28571429,
0.71428571], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0.1, 0.2, 0.5], model_solution, data)
def test_VRS_multi_input_oriented_small(model, data):
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(dmus, [1, 1, 1, 1, 1],
model_solution, data)
utils_for_tests.check_lambda_variables('A', 'A', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'C', 1, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'D', 1, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu('A', ['x1', 'x2', 'q'],
[0.5, 0, 0], model_solution, data)
utils_for_tests.check_categories_for_dmu('B', ['x1', 'x2', 'q'],
[0.25, 0.125, 0], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0.055555556, 0.11111111, 0.44444444],
model_solution, data)
utils_for_tests.check_categories_for_dmu('D', ['x1', 'x2', 'q'],
[0.25, 0.125, 0], model_solution,
data)
utils_for_tests.check_categories_for_dmu('E', ['x1', 'x2', 'q'],
[0, 0.5, 0], model_solution, data)
utils_for_tests.check_VRS_duals(dmus, [1, 1, -0.33333333, 1, 1],
model_solution, data)
def test_VRS_multi_input_oriented_small(model, data):
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [1, 1, 1, 1, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'A', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'C', 1, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'D', 1, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [0.5, 0, 0], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'B', ['x1', 'x2', 'q'], [0.25, 0.125, 0], model_solution, data)
utils_for_tests.check_categories_for_dmu('C', ['x1', 'x2', 'q'], [
0.055555556, 0.11111111,
0.44444444],
model_solution, data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0.25, 0.125, 0], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0, 0.5, 0], model_solution, data)
utils_for_tests.check_VRS_duals(
dmus, [1, 1, -0.33333333, 1, 1], model_solution, data)
def test_CRS_multi_output_oriented_with_weakly_disposable_vars_small(data):
model = MultiplierModelWithDisposableCategories(
MultiplierModelBase(data, 1e-12, MultiplierOutputOrientedModel()),
set(['x1']))
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 0.8333333458, 0.7142857143, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1.2, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.6, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'B', 0.3, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'E', 0.4, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu('A', ['x1', 'x2', 'q'], [1, 0, 1],
model_solution, data)
utils_for_tests.check_categories_for_dmu('B', ['x1', 'x2', 'q'],
[0.5, 0, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0.066666667, 0.13333333, 0.33333333],
model_solution, data)
utils_for_tests.check_categories_for_dmu('D', ['x1', 'x2', 'q'],
[0.2, 0.4, 1], model_solution,
data)
utils_for_tests.check_categories_for_dmu('E', ['x1', 'x2', 'q'],
[0.1, 0.2, 0.5], model_solution,
data)
def _check_large_model(model_solution, data):
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [
1, 0.64858757, 0.90924776, 1, 0.54907162, 1, 1, 0.8241206, 1,
0.62673338, 1
], model_solution, data)
def _check_large_model_CRS_multi_output_oriented_with_non_discretionary(
model_solution, data):
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [
1, 0.3085412442, 0.8305229413, 1, 0.5294117616, 1, 1, 0.1538535738, 1,
0.2627956766, 1
], model_solution, data)
def _check_large_model_CRS_multi_output_oriented_with_non_discretionary(
model_solution, data):
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.3085412442,
0.8305229413,
1, 0.5294117616,
1, 1, 0.1538535738, 1,
0.2627956766, 1],
model_solution, data)
def _check_large_model(model_solution, data):
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.64858757,
0.90924776, 1,
0.54907162, 1, 1,
0.8241206, 1,
0.62673338, 1],
model_solution, data)
def test_peel_the_onion_VRS_multi(categorical_data):
categorical_data.add_input_category('rein')
categorical_data.add_output_category('aus')
model = MultiplierModelVRSDecorator(
MultiplierModelBase(categorical_data, 0,
MultiplierInputOrientedModel()))
solution, ranks, state = peel_the_onion_method(model)
utils_for_tests.check_optimal_solution_status_and_sizes(
solution, categorical_data)
dmus = ['B', 'C', 'D', 'E', 'F', 'G']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.5, 1, 0.99999998,
0.75, 0.50000001],
solution, categorical_data, 1e-7)
expected_ranks = [1, 2, 1, 1, 2, 2]
utils_for_tests.check_onion_ranks(
model.input_data, dmus, expected_ranks, ranks)
def test_peel_the_onion_VRS_multi(categorical_data):
categorical_data.add_input_category('rein')
categorical_data.add_output_category('aus')
model = MultiplierModelVRSDecorator(
MultiplierModelBase(categorical_data, 0,
MultiplierInputOrientedModel()))
solution, ranks, state = peel_the_onion_method(model)
utils_for_tests.check_optimal_solution_status_and_sizes(
solution, categorical_data)
dmus = ['B', 'C', 'D', 'E', 'F', 'G']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.5, 1, 0.99999998,
0.75, 0.50000001],
solution, categorical_data, 1e-7)
expected_ranks = [1, 2, 1, 1, 2, 2]
utils_for_tests.check_onion_ranks(
model.input_data, dmus, expected_ranks, ranks)
def test_CRS_multi_input_oriented_with_non_discretionary_vars(data):
model = MultiplierModelInputOrientedWithNonDiscVars(
MultiplierModelBase(data, 1e-12, MultiplierInputOrientedModel()),
set(['x1']))
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(dmus,
[0.3, 1, 0.750000012, 0.5, 1],
model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 0.25, model_solution,
data)
utils_for_tests.check_lambda_variables('A', 'E', 0.25, model_solution,
data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 0.75, model_solution,
data)
utils_for_tests.check_lambda_variables('C', 'E', 0.75, model_solution,
data)
utils_for_tests.check_lambda_variables('D', 'E', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu('A', ['x1', 'x2', 'q'],
[0.1, 0.2, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu('B', ['x1', 'x2', 'q'],
[0.125, 0.25, 0.625],
model_solution, data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0.083333333, 0.16666667, 0.41666667],
model_solution, data)
utils_for_tests.check_categories_for_dmu('D', ['x1', 'x2', 'q'],
[0, 0.5, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu('E', ['x1', 'x2', 'q'],
[0, 0.5, 0.5], model_solution,
data)
def test_CRS_env_input_oriented_non_disc_vars(DEA_example_data):
data = DEA_example_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('ND1')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOrientedWithNonDiscVars(
set(['ND1']), generate_upper_bound_for_efficiency_score),
DefaultConstraintCreator())
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [
1, 0.62939304, 0.95095971, 1, 0.42525889, 1, 1, 0.82672274, 1,
0.53385106, 1
], model_solution, data)
def test_VRS_from_params_file():
params = parse_parameters_from_file('tests/params_new_format.txt')
categories, xls_data, dmu_name, sheet_name = read_data(
params.get_parameter_value('DATA_FILE'))
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
model_input = construct_input_data_instance(categories, coefficients)
model_factory.add_input_and_output_categories(params, model_input)
model = model_factory.create_model(params, model_input)
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, model_input)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.86998617, 1, 0.95561335,
0.85,
1, 1, 0.84507042, 1,
0.524, 0.89058524],
model_solution, model_input)
clean_up_pickled_files()
def test_CRS_multi_input_oriented_with_weakly_disposable_vars_small(data):
model = MultiplierModelWithDisposableCategories(
MultiplierModelBase(data, 1e-12, MultiplierInputOrientedModel()),
['x1', 'q'])
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.5, 1, 0.83333334, 0.71428571, 1], model_solution, data)
utils_for_tests.check_lambda_variables('A', 'B', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables('C', 'E', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'B', 0.21428571,
model_solution, data)
utils_for_tests.check_lambda_variables('D', 'E', 0.28571429,
model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu('A', ['x1', 'x2', 'q'],
[0.5, 0, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu('B', ['x1', 'x2', 'q'],
[0.5, 0, 0.5], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'C', ['x1', 'x2', 'q'], [0.055555556, 0.11111111, 0.27777778],
model_solution, data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0.14285714, 0.28571429, 0.71428571],
model_solution, data)
utils_for_tests.check_categories_for_dmu('E', ['x1', 'x2', 'q'],
[0.1, 0.2, 0.5], model_solution,
data)
def test_price_ratio_restrictions_medium_env_model():
categories, data, dmu_name, sheet_name = read_data(
'tests/dataFromDEAbook_page181.xls')
coefficients, has_same_dmus = convert_to_dictionary(data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
print(data.categories)
data.add_input_category('Doctors')
data.add_input_category('Nurses')
data.add_output_category('Outpatients')
data.add_output_category('Inpatients')
model = MultiplierModelBase(data, 0, MultiplierInputOrientedModel())
ratio_bounds = {
('Nurses', 'Doctors'): (0.2, 5),
('Inpatients', 'Outpatients'): (0.2, 5)
}
model = MultiplierModelWithPriceRatioConstraints(model, ratio_bounds)
model_solution = model.run()
utils_for_tests.check_if_category_is_within_price_ratio_constraints(
model_solution, ratio_bounds)
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = [
'H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7', 'H8', 'H9', 'H10', 'H11',
'H12', 'H13', 'H14'
]
utils_for_tests.check_efficiency_scores(dmus, [
0.926, 1, 1, 0.634, 0.82, 1, 0.803, 0.872, 0.982, 1, 0.849, 0.93, 0.74,
0.929
], model_solution, data, 1e-3)
clean_up_pickled_files()
def test_run_gui(main_frame):
filename = 'tests/params_new_format.txt'
def get_params():
return filename
main_frame.params_frame._get_filename_for_load = get_params
def get_sheet_name(names):
return 'Sheet1'
main_frame.data_frame.data_tab.call_dialog = get_sheet_name
main_frame.params_frame.load_file()
main_frame.run()
model_solutions = main_frame.data_frame.solution_tab.model_solutions
assert len(model_solutions) == 1
model_solution = model_solutions[0]
model_input = model_solution._input_data
utils_for_tests.check_optimal_solution_status_and_sizes(model_solution,
model_input)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.86998617, 1, 0.95561335,
0.85,
1, 1, 0.84507042, 1,
0.524, 0.89058524],
model_solution, model_input)
def test_CRS_multi_input_oriented_with_non_discretionary_vars(data):
model = MultiplierModelInputOrientedWithNonDiscVars(
MultiplierModelBase(data, 1e-12,
MultiplierInputOrientedModel()), set(['x1']))
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E']
utils_for_tests.check_efficiency_scores(
dmus, [0.3, 1, 0.750000012, 0.5, 1], model_solution, data)
utils_for_tests.check_lambda_variables(
'A', 'B', 0.25, model_solution, data)
utils_for_tests.check_lambda_variables(
'A', 'E', 0.25, model_solution, data)
utils_for_tests.check_lambda_variables('B', 'B', 1, model_solution, data)
utils_for_tests.check_lambda_variables(
'C', 'B', 0.75, model_solution, data)
utils_for_tests.check_lambda_variables(
'C', 'E', 0.75, model_solution, data)
utils_for_tests.check_lambda_variables('D', 'E', 0.5, model_solution, data)
utils_for_tests.check_lambda_variables('E', 'E', 1, model_solution, data)
utils_for_tests.check_categories_for_dmu(
'A', ['x1', 'x2', 'q'], [0.1, 0.2, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'B', ['x1', 'x2', 'q'], [0.125, 0.25, 0.625], model_solution, data)
utils_for_tests.check_categories_for_dmu('C', ['x1', 'x2', 'q'], [
0.083333333, 0.16666667,
0.41666667], model_solution,
data)
utils_for_tests.check_categories_for_dmu(
'D', ['x1', 'x2', 'q'], [0, 0.5, 0.5], model_solution, data)
utils_for_tests.check_categories_for_dmu(
'E', ['x1', 'x2', 'q'], [0, 0.5, 0.5], model_solution, data)
def test_CRS_env_input_oriented_non_disc_vars(DEA_example_data):
data = DEA_example_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('ND1')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOrientedWithNonDiscVars(
set(['ND1']),
generate_upper_bound_for_efficiency_score),
DefaultConstraintCreator())
model_solution = model.run()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.62939304, 0.95095971,
1, 0.42525889,
1, 1, 0.82672274, 1,
0.53385106, 1],
model_solution, data)
