def group_test(Data, Label, Ds, Dl, train_index_start, num_of_train, model, threshold_value, result_number, winner_number, all_group_top_precision, all_group_recall, all_group_top_exact_accuracy, all_group_exact_accuracy, record):
for group_index_start in range(len(Ds)):
if group_index_start<train_index_start:
group_start = Ds[group_index_start]
elif (group_index_start>=train_index_start and group_index_start<train_index_start+num_of_train):
continue
else:
group_start = Ds[group_index_start]
length = Dl[group_index_start]
group_end = group_start + length
group_data = Data[group_start:group_end, :]
group_label = Label[group_start:group_end]
reference = [t for t in range(1, length + 1)]
random.shuffle(reference)
predict_rank = rank_the_group(group_data, reference, model, threshold_value, record)
group_rank = handle_data.exchange(group_label)
# reference = [t for t in range(1, length + 1)]
# record_rank_reference(reference, group_rank, predict_rank, record)
# --------------------------------------------------------------------
# group_test_relative(group_data, group_label, length, model, threshold_value, record)
# --------------------------------------------------------------------
group_top_precision, group_recall, group_top_exact_accuracy = count_top(group_rank, predict_rank, result_number, winner_number)
group_exact_accuracy = calacc(group_rank, predict_rank)
all_group_top_precision.append(group_top_precision)
all_group_recall.append(group_recall)
all_group_top_exact_accuracy.append(group_top_exact_accuracy)
all_group_exact_accuracy.append(group_exact_accuracy)
# record.write("the group top precision is {0}\n".format(group_top_precision))
# record.write("the group top exact accuracy is {0}\n".format(group_top_exact_accuracy))
# record.write("the group accuracy is {0}\n".format(group_exact_accuracy))
# record.write("-------------------------------------------------------------------------------------------\n")
return all_group_top_precision, all_group_recall, all_group_top_exact_accuracy, all_group_exact_accuracy
def group_test(Data, model, threshold_value):
length = len(Data)
reference = [t for t in range(1, length + 1)]
# random.shuffle(reference)
predict_rank = rank_the_group(Data, reference, model, threshold_value)
predict_rank = handle_data.exchange(predict_rank)
return predict_rank
def group_test(Data, Label, Ds, Dl, train_index_start, num_of_train, model,
record):
for group_index_start in range(1, len(Ds) + 1):
if group_index_start < train_index_start:
group_start = Ds[group_index_start]
elif (group_index_start >= train_index_start
and group_index_start < train_index_start + num_of_train):
continue
else:
group_start = Ds[group_index_start]
length = Dl[group_index_start]
group_end = group_start + length
group_data = Data[group_start:group_end, :]
group_label = Label[group_start:group_end]
# reference = [t for t in range(1, length + 1)]
# reference1 = rank_the_group(group_data, length, reference, model)
# group_test_relative(group_start, length, Data, Label, model)
for time in range(100):
reference = [t for t in range(1, length + 1)]
random.shuffle(reference)
record.write("the random order is ")
for t in reference:
record.write(f"{int(t):2d}\t")
record.write("\n")
# print(reference)
reference = rank_the_group(group_data, length, reference, model)
# if not operator.eq(reference,reference1):
# # print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
# record.write("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n")
# record.write("the predict rank is ")
# for t in reference:
# record.write(f"{int(t):2d}\t")
# record.write('\n')
# record.write("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n")
# reference1 = reference
# print(reference)
# record.write("the predict rank is ")
# for t in reference:
# record.write(f"{int(t):2d}\t")
# record.write("\n")
# print(reference)
group_rank = handle_data.exchange(group_label)
# print(group_rank)
record_rank_reference(group_rank, reference)
group_precision, group_top_exact_accuracy = count_top(
group_rank, reference)
group_accuracy = calacc(group_rank, reference)
all_group_top_precision.append(group_precision)
all_group_top_exact_accuracy.append(group_top_exact_accuracy)
all_group_accuracy.append(group_accuracy)
record.write(f"the group top precision is {group_precision}\n")
record.write(
f"the group top exact accuracy is {group_top_exact_accuracy} \n")
record.write(f"the group accuracy is {group_accuracy}\n")
record.write(
"-------------------------------------------------------------------------------------------\n"
)
def analyse_group_result(true_label, predict_label, result_number,
winner_number, all_group_top_precision,
all_group_recall, all_group_top_exact_accuracy,
all_group_exact_accuracy, all_group_auc):
group_true_label = handle_data.exchange(true_label)
group_predict_label = handle_data.exchange(predict_label)
group_top_precision, group_recall, group_top_exact_accuracy = count_top(
group_true_label, group_predict_label, result_number, winner_number)
group_exact_accuracy = calacc(group_true_label, group_predict_label,
result_number, winner_number)
change_true_label = change_to_0_1(group_true_label, winner_number)
change_predict_label = change_to_0_1(group_predict_label, result_number)
change_true_label = np.array(change_true_label)
change_predict_label = np.array(change_predict_label)
all_group_auc.append(
skmet.roc_auc_score(change_true_label, change_predict_label))
all_group_top_precision.append(group_top_precision)
all_group_recall.append(group_recall)
all_group_top_exact_accuracy.append(group_top_exact_accuracy)
all_group_exact_accuracy.append(group_exact_accuracy)
return all_group_top_precision, all_group_recall, all_group_top_exact_accuracy, all_group_exact_accuracy, all_group_auc