def load_Q(self):
"""
This function opens the file containing the matrix R, whose name is defined by private variable rfile_name,
it changes the contains of the file to a matrix and stores the contains in the private variable R.
"""
Q_str = fct.read_file(self.__qfile_name)
Q_list = Q_str.strip().split('\n')
self.__Q = np.zeros(((self.__nb_states,self.__nb_actions)))
for i in range(self.__nb_states):
Q_sublist = Q_list[i].strip().split(',')
for j in range(len(Q_sublist)):
self.__Q[i,j] = int(Q_sublist[j])
print("Matrix Q loaded ! ")
return self.__Q
def load_list_grid(self):
"""
This function opens the file containing the matrix of all grid, whose name is defined by private variable list_grid_name,
it changes the contains of the file to a matrix and stores the contains in the private variable list_grid.
"""
list_grid_str = fct.read_file(self.__list_grid_name)[1:-2]
list_grid_list = list_grid_str.strip().split('], ')
self.__list_grid = []
for i in range(len(list_grid_list)-1):
list_grid_sublist = list_grid_list[i][1:].split(', ')
sublist = []
for j in range(len(list_grid_sublist)):
sublist.append(int(list_grid_sublist[j]))
self.__list_grid.append(sublist)
self.__nb_states = len(self.__list_grid)
print("List of all grid loaded !")
return self.__list_grid
def extract_blocks(input_file, k_base):
r_input = [r for r in F.read_file(input_file)]
blocks = []
for raw_terms in r_input:
blocks.append(build_blocks(raw_terms, k_base))
return blocks
def evaluate_results_per_record(results, reference_file, attributes):
reference = F.read_file(reference_file)
record_evaluation = []
for result_record, ref in zip(results, reference):
results_stats = {}
reference_stats = {}
right_answers = {}
attr_evaluation = {}
reference_record = ET.fromstring('<record>' + ref + '</record>')
for reference_block in reference_record:
if reference_block.tag not in reference_stats:
reference_stats[reference_block.tag] = len(
reference_block.text.split())
else:
reference_stats[reference_block.tag] += len(
reference_block.text.split())
for result_block in result_record:
if result_block.attr != 'none' and result_block.attr not in results_stats:
results_stats[result_block.attr] = len(
result_block.value.split())
else:
results_stats[result_block.attr] += len(
result_block.value.split())
for result_block in result_record:
for reference_block in reference_record:
if result_block.value in F.normalize_str(
reference_block.text
) and result_block.attr == reference_block.tag:
if result_block.attr not in right_answers:
right_answers[result_block.attr] = len(
result_block.value.split())
else:
right_answers[result_block.attr] += len(
result_block.value.split())
break
for attr in attributes:
if attr in results_stats and attr in reference_stats and attr in right_answers:
attr_evaluation[attr] = Metrics()
attr_evaluation[
attr].precision = right_answers[attr] / results_stats[attr]
attr_evaluation[
attr].recall = right_answers[attr] / reference_stats[attr]
attr_evaluation[attr].calculate_f_measure()
elif attr in results_stats and attr not in reference_stats:
attr_evaluation[attr] = Metrics()
record = Metrics()
for attr in attr_evaluation:
record.precision += attr_evaluation[attr].precision
record.recall += attr_evaluation[attr].recall
record.f_measure += attr_evaluation[attr].f_measure
record.precision /= len(attr_evaluation)
record.recall /= len(attr_evaluation)
record.f_measure /= len(attr_evaluation)
record_evaluation.append(record)
final_metrics = Metrics()
for record in record_evaluation:
final_metrics.precision += record.precision
final_metrics.recall += record.recall
final_metrics.f_measure += record.f_measure
final_metrics.precision /= len(record_evaluation)
final_metrics.recall /= len(record_evaluation)
final_metrics.f_measure /= len(record_evaluation)
print('---------- Results Evaluation Per Record ----------')
print('{:<20} {:<20} {:<18}'.format('Precision', 'Recall', 'F-Measure'))
print('{:<20} {:<20} {:<18}'.format(final_metrics.precision,
final_metrics.recall,
final_metrics.f_measure))
print()
def evaluate_results_per_attribute(results, reference_file, attributes):
reference = F.read_file(reference_file)
results_stats = {}
reference_stats = {}
right_answers = {}
attr_evaluation = {}
record_evaluation = []
for attr in attributes:
attr_evaluation[attr] = Metrics()
for result_record, ref in zip(results, reference):
reference_record = ET.fromstring('<record>' + ref + '</record>')
for reference_block in reference_record:
if reference_block.tag not in reference_stats:
reference_stats[reference_block.tag] = len(
reference_block.text.split())
else:
reference_stats[reference_block.tag] += len(
reference_block.text.split())
for result_block in result_record:
if result_block.attr != 'none':
if result_block.attr not in results_stats:
results_stats[result_block.attr] = len(
result_block.value.split())
else:
results_stats[result_block.attr] += len(
result_block.value.split())
for result_block in result_record:
for reference_block in reference_record:
if result_block.value in F.normalize_str(
reference_block.text
) and result_block.attr == reference_block.tag:
if result_block.attr not in right_answers:
right_answers[result_block.attr] = len(
result_block.value.split())
else:
right_answers[result_block.attr] += len(
result_block.value.split())
break
for attr in attributes:
if attr in results_stats and attr in reference_stats and attr in right_answers:
attr_evaluation[
attr].precision = right_answers[attr] / results_stats[attr]
attr_evaluation[
attr].recall = right_answers[attr] / reference_stats[attr]
attr_evaluation[attr].calculate_f_measure()
print()
print('---------- Results Evaluation Per Attribute ----------')
print('{:<15} {:<20} {:<20} {:<18}'.format('Attribute', 'Precision',
'Recall', 'F-Measure'))
total_metrics = Metrics()
non_zero_attrs = 0
for k, v in attr_evaluation.items():
if v.f_measure > 0:
print('{:<15} {:<20} {:<20} {:<18}'.format(k, v.precision,
v.recall, v.f_measure))
total_metrics.precision += v.precision
total_metrics.recall += v.recall
total_metrics.f_measure += v.f_measure
non_zero_attrs += 1
total_metrics.precision /= non_zero_attrs
total_metrics.recall /= non_zero_attrs
total_metrics.f_measure /= non_zero_attrs
print()
print('{:<15} {:<20} {:<20} {:<18}'.format("Total",
total_metrics.precision,
total_metrics.recall,
total_metrics.f_measure))
print()