def read_files(instance_config_filename, config_filename):
if instance_config_filename is None:
instance = Instance.load_test()
else:
instance = Instance.load_from_file(instance_config_filename)
# print_instance(instance)
# print("")
if config_filename is None:
config = Config.load_test()
else:
config = Config.load_from_file(config_filename)
return (instance, config)
class FitnessValues:
file = os.path.join(dir, '..', 'instances', 'real', 'instance.txt')
default_instance = Instance.load_from_file(file)
def __init__(self, fitness_list):
self.concepts_covered = fitness_list[0]
self.difficulty = fitness_list[1]
self.total_time = fitness_list[2]
self.materials_balancing = fitness_list[3]
self.learning_style = fitness_list[4]
self.total = fitness_list[5]
def __repr__(self):
return f'---- FitnessValues ----\n concepts_covered: {self.concepts_covered}, difficulty: {self.difficulty}, total_time: {self.total_time}, materials_balancing: {self.materials_balancing}, learning_style: {self.learning_style}, total: {self.total} \n'
@staticmethod
def get_Instance(materials_concepts):
individual = list()
for mat in materials_concepts:
individual.append(False if mat.sum() == 0 else True)
return individual
@classmethod
def fitness_fn(cls, student_id, materials_concepts):
individual = FitnessValues.get_Instance(materials_concepts)
concepts_covered_objective = objective.concepts_covered_function(
individual, cls.default_instance, student_id)
materials_balancing_objective = objective.materials_balancing_function(
individual, cls.default_instance, student_id)
sum_objective = (
cls.default_instance.concepts_covered_weight *
concepts_covered_objective +
FitnessValues.default_instance.materials_balancing_weight *
materials_balancing_objective)
return sum_objective
parser_de = subparsers.add_parser('de')
create_base_parser(parser_de)
parser_de.add_argument('-m', '--max-velocity', type=float)
parser_de.add_argument('--mutation-chance', type=float)
parser_de.add_argument('-c', '--crossover-rate', type=float)
parser_de.add_argument('-a', '--evaluator', choices=['RANDOM', 'FIXED'])
args = parser.parse_args()
if not args.config and not args.cost_budget and not args.max_stagnation and not args.num_iterations:
raise Exception("No end condition specified. Use '-b', '-s' or '-i'.")
if args.algorithm == 'nsga_ii' and args.num_objectives > 5:
raise Exception("The problem can only have at most five objectives")
instance = Instance.load_from_file(args.instance_file)
config_class = config_class_dict[args.algorithm]
if args.config:
config = config_class.load_from_file(args.config)
else:
config = config_class()
config.update_from_args(args)
results = {
'info': {
'command':
os.path.basename(sys.argv[0]) + " " + " ".join(sys.argv[1:]),
'datetime': str(datetime.datetime.now()),
'instance': instance,
('andre_900', 'andre/900/instance.txt'),
('andre_950', 'andre/950/instance.txt'),
('andre_1000', 'andre/1000/instance.txt'),
('real', 'real/test_instance.txt'),
]
population_size = 1000
quant_instances = len(filenames)
results = np.empty((quant_instances, 24, population_size, 5))
np.random.seed(0)
for (i, (base, filename)) in enumerate(filenames):
print('Reading %s' % (folder + filename))
instance = Instance.load_from_file(folder + filename)
timer = Timer()
for student in range(instance.num_learners):
print('%d / %d' % (student, instance.num_learners))
population = np.random.randint(2,
size=(population_size,
instance.num_materials),
dtype=bool)
survival_values = []
np.apply_along_axis(fitness,
1,
population,
instance,
student,
interactivity_type_frequency = Counter(instance.materials_interactivity_type)
############################################################################
return {
'instance': instance,
'concepts_materials': instance.concepts_materials,
'concepts_name': concepts_name,
'concepts_quant': concepts_quant,
'concepts_difficulty': concepts_difficulty,
'count_histogram': count_histogram,
'n_coocurrence_matrix': n_coocurrence_matrix,
'coocurrence_set': coocurrence_set,
'coocurrence_dict': coocurrence_dict,
'quant_resource_types': quant_resource_types,
'quant_resource_types_histogram': quant_resource_types_histogram,
'resource_types_frequency': resource_types_frequency,
'interactivity_level_frequency': interactivity_level_frequency,
'interactivity_type_frequency': interactivity_type_frequency,
}
if __name__ == '__main__':
instance = Instance.load_from_file('instances/real/instance.txt')
instance_data = extract_data(instance)
with open('results/instance_stats.pickle', 'wb') as file:
pickle.dump(instance_data, file)
def instance(self, instance_file): self.__instance = Instance.load_from_file(instance_file)
import numpy as np
from acs.instance import Instance
from acs.objective import materials_balancing_function
import os
dir = os.path.dirname(__file__)
instance_file = os.path.join(dir,'instances', 'real', 'instance.txt')
individual5 = np.array([False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,
False, False, True, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False])
individual23 = np.array([False, False, False, False, False, True, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, True, False, False, False, False, False, False, False, True, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, True, False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, True, False, False, False, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False])
instance = Instance.load_from_file(instance_file)
materials_balancing1 = materials_balancing_function(individual5, instance, 5)
print("Aluno 5 materials_balancing: ", materials_balancing1, "\n")
# materials_balancing = materials_balancing_function(individual23, instance, 23)
# print("Aluno 23 materials_balancing: ", materials_balancing)
'instances/andre/500/instance.txt',
'instances/real/instance.txt',
# 'instances/test/instance_config.txt',
]
difficulty_list = [1, 2, 3, 4, 5]
style_range = np.arange(-6.5, 0, 3)
# style_range = np.concatenate((style_range, -style_range[::-1]))
style_range = np.concatenate((style_range, -style_range[::-1]))
np.set_printoptions(precision=2)
if True:
instances = []
for instance_file in instance_list:
instances.append(Instance.load_from_file(instance_file))
with open('results/instances.pickle', 'wb') as file:
pickle.dump(instances, file)
else:
with open('results/instances.pickle', 'rb') as file:
instances = pickle.load(file)
show_general = True
show_difficulty = True
show_style = True
show_concepts = True
show_coocurrence = True
for (num, instance) in enumerate(instances):
print(instance_list[num])
import os
from acs.instance import Instance
from read.algorithm import open_results
dir = os.path.dirname(__file__)
instance = Instance.load_from_file(
os.path.join(dir, 'instances', 'real', 'instance.txt'))
concept_coverage = instance.concepts_materials.T
objectives = instance.objectives
_acs_results = open_results('2020-01-16_real_5_100000.pickle')
_algorithm = 'de'
_repetition = 0
recommendation = _acs_results[_algorithm][0][_repetition].T # 284x24
num_materials = concept_coverage.shape[0]
num_concepts = concept_coverage.shape[1]
num_students = recommendation.shape[1]