def main():
#attr_type_flag, full_labels, data_value_list, data_label_list = data_processing.data_processing(DATASET_NAME, 0)
#Djk_label_list, Djv_label_list = D_matrix_generate.D_matrix_generate(full_labels, data_label_list)
#model, max_and_min = Bayes.Naive_Bayes(attr_type_flag, data_value_list, full_labels, Djk_label_list, Djv_label_list)
#attr_type_flag, full_labels, data_value_list, data_label_list = data_processing.data_processing(DATASET_NAME, 0)
#accuracy = Bayes.classify(model, max_and_min, attr_type_flag, data_value_list, full_labels, data_label_list)
#return
attr_type_flag, full_labels, data_value_list, data_label_list = data_processing.data_processing(
DATASET_NAME, 0)
max_and_min = {}
for i in range(len(attr_type_flag)):
if (attr_type_flag[str(i)] == "1"):
for data in data_value_list:
if data.__contains__(str(i)):
value = float(data[str(i)])
max = value
min = value
break
for data in data_value_list:
if data.__contains__(str(i)):
value = float(data[str(i)])
if value > max:
max = value
if value < min:
min = value
max_and_min["%s_max" % str(i)] = str(max)
max_and_min["%s_min" % str(i)] = str(min)
print(max_and_min)
minus = float(max_and_min["102_max"]) - float(max_and_min["102_min"])
print(minus)
datasets_value, datasets_label = cross_validation.construct_cv_folds(
N_FOLDS, data_value_list, data_label_list)
#print(datasets_label)
Accuracy_scores = []
HamLosses = []
Precisions = []
Recalls = []
F1s = []
AVGPRECs = []
RANKLOSSes = []
avg_Accuracy_score = 0
avg_HamLoss = 0
avg_Precision = 0
avg_Recall = 0
avg_F1 = 0
avg_AVGPREC = 0
avg_RANKLOSS = 0
std_Accuracy_score = 0
std_HamLoss = 0
std_Precision = 0
std_Recall = 0
std_F1 = 0
std_AVGPREC = 0
std_RANKLOSS = 0
for i in range(N_FOLDS):
training_set_value = []
training_set_label = []
for j in range(N_FOLDS):
if i != j:
training_set_value = training_set_value + datasets_value[j]
training_set_label = training_set_label + datasets_label[j]
testing_set_value = datasets_value[i]
testing_set_label = datasets_label[i]
Djk_label_list, Djv_label_list = D_matrix_generate.D_matrix_generate(
full_labels, training_set_label)
model, max_and_min = Bayes.Naive_Bayes(attr_type_flag,
training_set_value, full_labels,
Djk_label_list, Djv_label_list,
max_and_min)
Accuracy_score, HamLoss, Precision, Recall, F1, AVGPREC, RANKLOSS = Bayes.classify(
model, max_and_min, attr_type_flag, testing_set_value, full_labels,
testing_set_label)
Accuracy_scores.append(Accuracy_score)
HamLosses.append(HamLoss)
Precisions.append(Precision)
Recalls.append(Recall)
F1s.append(F1)
AVGPRECs.append(AVGPREC)
RANKLOSSes.append(RANKLOSS)
avg_Accuracy_score += Accuracy_score
avg_HamLoss += HamLoss
avg_Precision += Precision
avg_Recall += Recall
avg_F1 += F1
avg_AVGPREC += AVGPREC
avg_RANKLOSS += RANKLOSS
avg_Accuracy_score /= N_FOLDS
avg_HamLoss /= N_FOLDS
avg_Precision /= N_FOLDS
avg_Recall /= N_FOLDS
avg_F1 /= N_FOLDS
avg_AVGPREC /= N_FOLDS
avg_RANKLOSS /= N_FOLDS
for i in range(N_FOLDS):
std_Accuracy_score += (Accuracy_scores[i] - avg_Accuracy_score)**2
std_HamLoss += (HamLosses[i] - avg_HamLoss)**2
std_Precision += (Precisions[i] - avg_Precision)**2
std_Recall += (Recalls[i] - avg_Recall)**2
std_F1 += (F1s[i] - avg_F1)**2
std_AVGPREC += (AVGPRECs[i] - avg_AVGPREC)**2
std_RANKLOSS += (RANKLOSSes[i] - avg_RANKLOSS)**2
std_Accuracy_score = math.sqrt(std_Accuracy_score / N_FOLDS)
std_HamLoss = math.sqrt(std_HamLoss / N_FOLDS)
std_Precision = math.sqrt(std_Precision / N_FOLDS)
std_Recall = math.sqrt(std_Recall / N_FOLDS)
std_F1 = math.sqrt(std_F1 / N_FOLDS)
std_AVGPREC = math.sqrt(std_AVGPREC / N_FOLDS)
std_RANKLOSS = math.sqrt(std_RANKLOSS / N_FOLDS)
print(
"AVG:\nAccuracy_score=%.4f %.4f\nHamLoss=%.4f %.4f\nPrecision=%.4f %.4f\nRecall=%.4f %.4f\nF1=%.4f %.4f\nAVGPREC=%.4f %.4f\nRANKLOSS=%.4f %.4f"
% (avg_Accuracy_score, std_Accuracy_score, avg_HamLoss, std_HamLoss,
avg_Precision, std_Precision, avg_Recall, std_Recall, avg_F1,
std_F1, avg_AVGPREC, std_AVGPREC, avg_RANKLOSS, std_RANKLOSS))
