def treePredict():
#build the decision tree model label, the label 0 means choose 4G and 1 means choose 5G
label_list = []
predict_label_list = []
data_list = []
total_num = 0
for i in webSet_test:
if (not ((i, '4G') in fileStatistics.keys())) or (not ((i, '5G') in fileStatistics.keys())):
continue
#ipdb.set_trace()
if ((len(fileStatistics[(i, "4G")]) <= 1) or (len(fileStatistics[(i, "5G")]) <= 1)):
continue
final_statistics[(i, "4G")] = mergeList(fileStatistics[(i, '4G')])
final_statistics[(i, "5G")] = mergeList(fileStatistics[(i, '5G')])
dict_4g = final_statistics[(i, "4G")]
dict_5g = final_statistics[(i, "5G")]
dict_4g, dict_5g = treeFeatureGeneration(alpha, beta, dict_4g, dict_5g, standard_tuple, mn_tuple)
#construct the decision tree dataset
if (dict_4g['comparison_metric'] < dict_5g['comparison_metric']):
label_list.append(0)
else:
label_list.append(1)
merge_feature_dict = mergeList(fileStatistics[(i, '4G')] + fileStatistics[(i, '5G')])
feature_list = [merge_feature_dict["pageSize"], merge_feature_dict["objectNumber"], merge_feature_dict["averageObjectSize"],
merge_feature_dict["memeCounter"][0], merge_feature_dict["memeCounter"][1],
merge_feature_dict["dynamicObjratio"], merge_feature_dict["dynamic-size-ratio"]]
data_list.append(feature_list)
predict_label = clf.predict([feature_list])[0]
predict_label_list.append(predict_label)
clf_list = [clf1, clf2, clf3, clf4, clf5]
for (index, alpha_beta) in enumerate(alpha_beta_list):
clf = clf_list[index]
alpha, beta = alpha_beta
label_list = []
data_list = []
for i in webSet_train:
if (not ((i, '4G') in fileStatistics.keys())) or (not ((i, '5G') in fileStatistics.keys())):
continue
#ipdb.set_trace()
if ((len(fileStatistics[(i, "4G")]) <= webset_threshold) or (len(fileStatistics[(i, "5G")]) <= webset_threshold)):
continue
final_statistics[(i, "4G")] = mergeList(fileStatistics[(i, '4G')])
final_statistics[(i, "5G")] = mergeList(fileStatistics[(i, '5G')])
dict_4g = final_statistics[(i, "4G")]
dict_5g = final_statistics[(i, "5G")]
dict_4g, dict_5g = treeFeatureGeneration(alpha, beta, dict_4g, dict_5g, standard_tuple, mn_tuple)
#construct the decision tree dataset
if (dict_4g['comparison_metric'] < dict_5g['comparison_metric']):
label_list.append(0)
else:
label_list.append(1)
merge_feature_dict = mergeList(fileStatistics[(i, '4G')] + fileStatistics[(i, '5G')])
feature_list = [merge_feature_dict["pageSize"], merge_feature_dict["objectNumber"], merge_feature_dict["averageObjectSize"],
merge_feature_dict["memeCounter"][0], merge_feature_dict["memeCounter"][1],
merge_feature_dict["dynamicObjratio"], merge_feature_dict["dynamic-size-ratio"]]