def test_build_models_two_models():
params, model_input = _construct_params_and_input()
params.update_parameter('RETURN_TO_SCALE', 'both')
models, all_params = build_models(params, model_input)
assert len(models) == 2 and len(all_params) == 2
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'VRS'
or all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'VRS')
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'CRS'
or all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'CRS')
_check_params_are_the_same(all_params[0], params, ['RETURN_TO_SCALE'])
_check_params_are_the_same(all_params[1], params, ['RETURN_TO_SCALE'])
def test_build_models_two_models():
params, model_input = _construct_params_and_input()
params.update_parameter('RETURN_TO_SCALE', 'both')
models, all_params = build_models(params, model_input)
assert len(models) == 2 and len(all_params) == 2
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'VRS' or
all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'VRS')
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'CRS' or
all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'CRS')
_check_params_are_the_same(all_params[0], params, ['RETURN_TO_SCALE'])
_check_params_are_the_same(all_params[1], params, ['RETURN_TO_SCALE'])
def test_build_models_four_models():
params, model_input = _construct_params_and_input()
params.update_parameter('RETURN_TO_SCALE', 'both')
params.update_parameter('ORIENTATION', 'both')
models, all_params = build_models(params, model_input)
assert len(models) == 4 and len(all_params) == 4
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'VRS' or
all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'VRS')
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'CRS' or
all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'CRS')
assert (all_params[0].get_parameter_value('ORIENTATION') == 'input' or
all_params[1].get_parameter_value('ORIENTATION') == 'input')
assert (all_params[0].get_parameter_value('ORIENTATION') == 'output' or
all_params[1].get_parameter_value('ORIENTATION') == 'output')
for params_to_check in all_params:
_check_params_are_the_same(params_to_check, params, [
'RETURN_TO_SCALE', 'ORIENTATION'])
def test_build_models_four_models():
params, model_input = _construct_params_and_input()
params.update_parameter('RETURN_TO_SCALE', 'both')
params.update_parameter('ORIENTATION', 'both')
models, all_params = build_models(params, model_input)
assert len(models) == 4 and len(all_params) == 4
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'VRS'
or all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'VRS')
assert (all_params[0].get_parameter_value('RETURN_TO_SCALE') == 'CRS'
or all_params[1].get_parameter_value('RETURN_TO_SCALE') == 'CRS')
assert (all_params[0].get_parameter_value('ORIENTATION') == 'input'
or all_params[1].get_parameter_value('ORIENTATION') == 'input')
assert (all_params[0].get_parameter_value('ORIENTATION') == 'output'
or all_params[1].get_parameter_value('ORIENTATION') == 'output')
for params_to_check in all_params:
_check_params_are_the_same(params_to_check, params,
['RETURN_TO_SCALE', 'ORIENTATION'])
def test_abs_restrictions_env_model_output(data):
filename = 'tests/DEA_Harish_parameters.txt'
params = parse_parameters_from_file(filename)
categories, data, dmu_name, sheet_name = read_data(
params.get_parameter_value('DATA_FILE'))
coefficients, has_same_dmus = convert_to_dictionary(data)
model_input = construct_input_data_instance(categories, coefficients)
models, all_params = build_models(params, model_input)
assert len(models) == 1 and len(all_params) == 1
model = models[0]
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
bounds = {'Urban Roads (%)': (None, 0.003)}
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, bounds)
work_book = 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_abs_constraints_env_outoriented_output.xls')
def test_abs_restrictions_env_model_output(data):
filename = 'tests/DEA_Harish_parameters.txt'
params = parse_parameters_from_file(filename)
categories, data, dmu_name, sheet_name = read_data(
params.get_parameter_value('DATA_FILE'))
coefficients, has_same_dmus = convert_to_dictionary(data)
model_input = construct_input_data_instance(categories, coefficients)
models, all_params = build_models(params, model_input)
assert len(models) == 1 and len(all_params) == 1
model = models[0]
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
bounds = {'Urban Roads (%)': (None, 0.003)}
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, bounds)
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_abs_constraints_env_outoriented_output.xls')
def test_build_models_one_model():
params, model_input = _construct_params_and_input()
models, all_params = build_models(params, model_input)
assert len(models) == 1 and len(all_params) == 1
_check_params_are_the_same(all_params[0], params, [])
def test_build_models_one_model():
params, model_input = _construct_params_and_input()
models, all_params = build_models(params, model_input)
assert len(models) == 1 and len(all_params) == 1
_check_params_are_the_same(all_params[0], params, [])
def run(self, params):
''' Executes solution routine - create data instance,
solve LPs, post-process solutions.
Args:
params (Parameters): parameters of a given problem instance
'''
logger = get_logger()
logger.info('Started solving given DEA model(s).')
logger.info('Parameters: %s', params.get_all_params_as_string())
categories = self.get_categories()
coefficients, has_same_dmus = self.get_coefficients()
if has_same_dmus:
self.show_error('Some DMUs have the same name')
else:
if validate_data(categories, coefficients):
try:
self.validate_weights_if_needed() # MUST be called
# before model_builder, because it might
# update parameters
model_input = construct_input_data_instance(
categories, coefficients)
models, all_params = model_builder.build_models(
params, model_input)
self.init_before_run(len(models), coefficients)
solutions = []
state = True
param_strs = []
all_ranks = []
run_date = datetime.datetime.today()
start_time = datetime.datetime.now()
for count, model_obj in enumerate(models):
model = self.decorate_model(model_obj)
call_peel_the_onion = params.get_parameter_value(
'PEEL_THE_ONION')
if call_peel_the_onion:
model_solution, ranks, state = (
peel_the_onion_method(model))
all_ranks.append(ranks)
else:
model_solution = model.run()
str_to_write = create_params_str(all_params[count])
param_strs.append(str_to_write)
solutions.append(model_solution)
try:
solutions.append(model.second_solution)
param_strs.append(str_to_write + ' second phase')
except AttributeError:
pass
end_time = datetime.datetime.now()
diff = end_time - start_time
total_seconds = diff.total_seconds()
if params.get_parameter_value('RETURN_TO_SCALE') == 'both':
derive_returns_to_scale_classification(
param_strs, solutions)
self.post_process_solutions(solutions, params, param_strs,
all_ranks, run_date,
total_seconds)
if state is False:
self.show_error('For one of the runs of the '
'peel-the-onion problem is infeasible'
' or unbounded')
self.show_success()
except Exception as excinfo:
self.show_error(excinfo)
else:
logger.info('Given DEA model(s) successfully solved.')
else:
self.show_error('Some of the input data is not correct')
def run(self, params):
''' Executes solution routine - create data instance,
solve LPs, post-process solutions.
Args:
params (Parameters): parameters of a given problem instance
'''
logger = get_logger()
logger.info('Started solving given DEA model(s).')
logger.info('Parameters: %s', params.get_all_params_as_string())
categories = self.get_categories()
coefficients, has_same_dmus = self.get_coefficients()
if has_same_dmus:
self.show_error('Some DMUs have the same name')
else:
if validate_data(categories, coefficients):
try:
self.validate_weights_if_needed() # MUST be called
# before model_builder, because it might
# update parameters
model_input = construct_input_data_instance(categories,
coefficients)
models, all_params = model_builder.build_models(params,
model_input)
self.init_before_run(len(models), coefficients)
solutions = []
state = True
param_strs = []
all_ranks = []
run_date = datetime.datetime.today()
start_time = datetime.datetime.now()
for count, model_obj in enumerate(models):
model = self.decorate_model(model_obj)
call_peel_the_onion = params.get_parameter_value(
'PEEL_THE_ONION')
if call_peel_the_onion:
model_solution, ranks, state = (
peel_the_onion_method(model))
all_ranks.append(ranks)
else:
model_solution = model.run()
str_to_write = create_params_str(all_params[count])
param_strs.append(str_to_write)
solutions.append(model_solution)
try:
solutions.append(model.second_solution)
param_strs.append(str_to_write + ' second phase')
except AttributeError:
pass
end_time = datetime.datetime.now()
diff = end_time - start_time
total_seconds = diff.total_seconds()
if params.get_parameter_value('RETURN_TO_SCALE') == 'both':
derive_returns_to_scale_classification(param_strs, solutions)
self.post_process_solutions(solutions, params, param_strs,
all_ranks, run_date,
total_seconds)
if state is False:
self.show_error('For one of the runs of the '
'peel-the-onion problem is infeasible'
' or unbounded')
self.show_success()
except Exception as excinfo:
self.show_error(excinfo)
else:
logger.info('Given DEA model(s) successfully solved.')
else:
self.show_error('Some of the input data is not correct')