def main(_):
y_eval, logits_list = eval()
# analyse result:
each_result = len(logits_list) / 4
i = 0
true_positive = 0
true_negative = 0
false_positive = 0
false_negative = 0
for rst in logits_list:
lab = 0
rst = rst.tolist()
rst = rst.index(max(rst))
if i < each_result:
lab = 0
elif i < each_result * 2:
lab = 1
elif i < each_result * 3:
lab = 2
else:
lab = 3
if lab == 3 and rst == 3:
true_positive += 1
elif lab != 3 and rst == 3:
false_positive += 1
elif lab == 3 and rst != 3:
true_negative += 1
i += 1
with open("result", "a") as outfile:
outfile.write("NN Result:\n")
outfile.write("total number:" + str(len(logits_list)) + "\n")
outfile.write("true_positive:" + str(true_positive) + "(" +
str(round(true_positive / len(logits_list) * 100, 2)) +
"%)\n")
outfile.write("false_positive:" + str(false_positive) + "(" +
str(round(false_positive / len(logits_list) * 100, 2)) +
"%)\n")
outfile.write("true_negative:" + str(true_negative) + "(" +
str(round(true_negative / len(logits_list) * 100, 2)) +
"%)\n")
outfile.close()
print("NN result:")
print("total number:" + str(len(logits_list)))
print("true_positive:" + str(true_positive))
print("false_positive:" + str(false_positive))
print("true_negative:" + str(true_negative))
# ensemble with SVM
SVM_pred_list = SVM.SVM_eval()
ratio_neural, ratio_SVM, ensemble_pred_list = change_ensemble_weight(
logits_list, SVM_pred_list, y_eval)
with open("runs/ratio", "w") as outfile:
outfile.write(str(ratio_neural))
outfile.write(" ")
outfile.write(str(ratio_SVM))
outfile.close()
# enhance result by keywords
layer_list, weight_list, word_num_test, headlines_test, labels_test, keywords_list = Keywords_analysis.analyze_low_to_high(
)
prediction_list = create_keyword_prediction(layer_list, weight_list,
word_num_test, headlines_test,
labels_test, keywords_list)
for neural_pred, keywords_pred, label, headlines in zip(
ensemble_pred_list, prediction_list, y_eval, headlines_test):
keyword_num = 0
First3_num = 0
word_num = 0
i = 1
for word in headlines:
if word in keywords_list:
keyword_num += 1
if i <= 3:
First3_num += 1
word_num += 1
i += 1
weight_list = change_keyword_weight(weight_list, keywords_pred,
neural_pred, word_num, keyword_num,
First3_num, label)
np_weight_list = np.array(weight_list)
np.save("runs/weight_list", np_weight_list)
np.save("runs/keywords_layers", layer_list)
weight_list = np.load("runs/weight_list.npy")
final_pred(ensemble_pred_list, prediction_list, weight_list, y_eval,
headlines_test, keywords_list)
def main(_):
y_eval, logits_list = eval()
# analyse result:
each_result = len(logits_list) / 4
i = 0
true_positive = 0
true_negative = 0
false_positive = 0
false_negative = 0
for rst in logits_list:
lab = 0
rst = rst.tolist()
rst = rst.index(max(rst))
if i < each_result:
lab = 0
elif i < each_result * 2:
lab = 1
elif i < each_result * 3:
lab = 2
else:
lab = 3
if lab == 3 and rst == 3:
true_positive += 1
elif lab != 3 and rst == 3:
false_positive += 1
elif lab == 3 and rst != 3:
true_negative += 1
i += 1
with open("result", "a") as outfile:
outfile.write("NN Result:\n")
outfile.write("total number:" + str(len(logits_list)) + "\n")
outfile.write("true_positive:" + str(true_positive) + "(" +
str(round(true_positive / len(logits_list) * 100, 2)) +
"%)\n")
outfile.write("false_positive:" + str(false_positive) + "(" +
str(round(false_positive / len(logits_list) * 100, 2)) +
"%)\n")
outfile.write("true_negative:" + str(true_negative) + "(" +
str(round(true_negative / len(logits_list) * 100, 2)) +
"%)\n")
outfile.close()
print("NN result:")
print("total number:" + str(len(logits_list)))
print("true_positive:" + str(true_positive))
print("false_positive:" + str(false_positive))
print("true_negative:" + str(true_negative))
# ensemble with SVM
SVM_pred_list = SVM.SVM_eval()
ratio_neural, ratio_SVM, ensemble_pred_list = change_ensemble_weight(
logits_list, SVM_pred_list, y_eval)
with open("runs/ratio", "w") as outfile:
outfile.write(str(ratio_neural))
outfile.write(" ")
outfile.write(str(ratio_SVM))
outfile.close()
