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Result_anla.py
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Result_anla.py
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import pandas as pd
import matplotlib.pyplot as plt
from autorank import autorank, plot_stats, create_report, latex_table
from numpy import std, mean, sqrt
def cohen_d(x,y):
nx = len(x)
ny = len(y)
dof = nx + ny - 2
result = (mean(x) - mean(y)) / sqrt(((nx-1)*std(x, ddof=1) ** 2 + (ny-1)*std(y, ddof=1) ** 2) / dof)
size = print_cohen(abs(result))
return result, size
def print_star(pval):
if pval <0.001:
return "***"
elif pval <0.01:
return "**"
elif pval <0.05:
return "*"
else:
return "n.s."
def print_star2(pval):
if pval <0.05:
return "win"
else:
return "tie"
def print_cohen(d):
if d <0.2:
return "neg"
elif d >= 0.2 and d <=0.5:
return "small"
elif d > 0.5 and d <=0.8:
return "medium"
else:
return "large"
if __name__ == '__main__':
# pd.set_option('display.max_rows', None)
# pd.set_option('display.max_columns', None)
# # RQ1,RQ2
# warnings.filterwarnings("ignore")
# GIS = pd.read_csv('./outcome/RQ1,2/GIS13_.csv')
# FesCH = pd.read_csv('./outcome/RQ1,2/FesCH_.csv')
# CFPS = pd.read_csv('./outcome/RQ1,2/CFPS_.csv')
# CDE = pd.read_csv('./outcome/RQ1,2/CDE25_.csv')
# TPTL = pd.read_csv('./outcome/RQ1,2/TPTL_22.csv')
# ManualD = pd.read_csv('./outcome/RQ1,2/ManualDown20_.csv')
# LSKDSA = pd.read_csv('./outcome/RQ1,2/LSKDSA13_.csv')
# Camargo = pd.read_csv('./outcome/RQ1,2/Camargo22_.csv')
# journal = pd.read_csv('./outcome/RQ1,2/_first_NORM = box-cox__Is_SVD = True__GRID = True__37_.csv')
#
# project_list = sorted(list(set(journal.Target)))
# if 'avg' in project_list :
# project_list.remove('avg')
# print(project_list)
# proindex = [13,12,14,15,16,0,2,3,4,17,18,19,20,21,22,23,5,10,1,11,8,7,6,9]
# sort_list = list(range(len(proindex)))
# for idx, val in enumerate(proindex):
# sort_list[val] = project_list[idx]
# project_list = sort_list
# print(project_list)
# metric_list = ['cost']
# # metric_list = ['AUC']
# test_list = ['win','tie','lose','neg','small','medium','large']
#
# source_List = {'GIS': GIS,'FesCH': FesCH,'CFPS': CFPS,'CDE': CDE,'ManualD': ManualD,'LSKDSA': LSKDSA,'Camargo': Camargo,
# 'TPTL':TPTL}
#
# # result_list = {'none': none, 'qrs' : qrs, 'qrs_box' : qrs_box, 'journal' : journal}
# avg_list = ['project','FesCH','GIS','CDE','TPTL','CFPS','ManualD','LSKDSA','Camargo','journal']
#
# result_avg = pd.DataFrame(columns=avg_list)
# result_avg['project']=project_list
#
# result_var = pd.DataFrame(columns=avg_list)
# result_var['project'] = project_list
#
#
# target = journal
#
#
# for metric in metric_list:
# print(" =================== Metrics is {} ===================".format(metric))
# result_test = pd.DataFrame(columns=avg_list)
# result_test['project'] = test_list
# result_test = result_test.fillna(0)
# for Sname, source in source_List.items():
# source_by_metric = source[["Target",metric]]
# target_by_metric = target[["Target",metric]]
# for project_name in project_list:
# source_by_metric_project = source_by_metric.query("Target == @project_name")
# target_by_metric_project = target_by_metric.query("Target == @project_name")
#
# source_mean = mean(source_by_metric_project[metric])
# target_mean = mean(target_by_metric_project[metric])
# source_var = std(source_by_metric_project[metric])
# target_var = std(target_by_metric_project[metric])
# index = result_avg.index[(result_avg['project'] == project_name)]
#
#
# result_avg.loc[index, Sname] = round(source_mean,3)
# result_avg.loc[index, 'journal'] = round(target_mean,3)
# result_var.loc[index, Sname] = round(source_var,3)
# result_var.loc[index, 'journal'] = round(target_var,3)
#
# # cliffs_result = cliffs_delta.cliffs_delta(target_by_metric_project[metric],source_by_metric_project[metric])
# c_result, c_size = cohen_d(target_by_metric_project[metric], source_by_metric_project[metric])
#
# if metric in ['cost', 'PF']:
# # result = ranksums(target_by_metric_project[metric], source_by_metric_project[metric], alternative='less')
# result= wilcoxon(target_by_metric_project[metric], source_by_metric_project[metric], zero_method='wilcox', alternative='less')
# if c_result > 0:
# index = result_test.index[(result_test['project'] == 'neg')]
# result_test.loc[index, Sname] += 1
# else :
# index = result_test.index[(result_test['project'] == c_size)]
# result_test.loc[index, Sname] += 1
# else :
# # result = ranksums(target_by_metric_project[metric],source_by_metric_project[metric], alternative='greater')
# result= wilcoxon(target_by_metric_project[metric], source_by_metric_project[metric], zero_method='wilcox', alternative='greater')
#
# if c_result < 0:
# index = result_test.index[(result_test['project'] == 'neg')]
# result_test.loc[index, Sname] += 1
# else :
# index = result_test.index[(result_test['project'] == c_size)]
# result_test.loc[index, Sname] += 1
#
# if result.pvalue < 0.05:
# index = result_test.index[(result_test['project'] == 'win')]
# result_test.loc[index, Sname] += 1
# else:
# result= wilcoxon(target_by_metric_project[metric], source_by_metric_project[metric], zero_method='wilcox')
# if result.pvalue < 0.05 :
# index = result_test.index[(result_test['project'] == 'lose')]
# result_test.loc[index, Sname] += 1
# else:
# index = result_test.index[(result_test['project'] == 'tie')]
# result_test.loc[index, Sname] += 1
#
#
#
# avg_filepath = "./outcome/RQ1,2/temp/metic_{}__avg.csv".format(metric)
# var_filepath = "./outcome/RQ1,2/temp/metic_{}__var.csv".format(metric)
# test_filepath = "./outcome/RQ1,2/temp/metic_{}__test.csv".format(metric)
#
# result_avg.to_csv(avg_filepath, index=False)
# result_var.to_csv(var_filepath, index=False)
# result_test.to_csv(test_filepath, index=False)
# RQ3
# warnings.filterwarnings("ignore")
# NB = pd.read_csv('./outcome/RQ3/_NB_30.csv')
# NN = pd.read_csv('./outcome/RQ3/_NN_30_.csv')
# RF = pd.read_csv('./outcome/RQ3/_RF_30.csv')
# SVC = pd.read_csv('./outcome/RQ3/_SVC_30.csv')
# JN = pd.read_csv('./outcome/RQ3/_first_NORM = box-cox__Is_SVD = True__GRID = True__37_.csv')
# # metric_list = ['AUC', 'Gmean', 'Fmeasure', 'Balance', 'MCC','FIR','cost']
# metric_list = ['cost']
# source_List = {'NB': NB, 'NN':NN, 'RF' : RF, 'SVC' : SVC, 'LR':JN}
# # source_List = {'NB': NB, 'NN':NN, 'RF' : RF}
#
# # result_list = {'none': none, 'qrs' : qrs, 'qrs_box' : qrs_box, 'journal' : journal}
# avg_list = ['NB','NN','RF','SVC','LR']
#
# for metric in metric_list:
# print('processing {}'.format(metric))
# dataset = pd.DataFrame(columns=avg_list)
# for Sname, source in source_List.items():
# value=source[[metric]]
# if metric == 'cost':
# dataset[Sname]=-value
# else:
# dataset[Sname] = value
# result_bayesian = autorank(dataset, alpha=0.05, order='ascending',verbose=False,effect_size='cohen_d')
# print(result_bayesian)
# plot_stats(result_bayesian)
# plt.title(metric)
# plt.show()
#Discussion-1
# warnings.filterwarnings("ignore")
# none = pd.read_csv('./outcome/_first_NORM = None__Is_SVD = False__GRID = False__12_.csv')
# znorm = pd.read_csv('./outcome/_first_NORM = Znorm__Is_SVD = False__GRID = False__11_.csv')
# qrs = pd.read_csv('./outcome/_first123_NORM = Znorm__Is_SVD = True__GRID = False__49_.csv')
# qrs_box = pd.read_csv('./outcome/_first_NORM = box-cox__Is_SVD = True__GRID = False__16_.csv')
# journal = pd.read_csv('./outcome/_first_NORM = box-cox__Is_SVD = True__GRID = True__37_.csv')
#
# project_list = list(set(none.Target))
# project_list.remove('avg')
# metric_list = ['AUC', 'PD', 'PF', 'Gmean', 'Fmeasure', 'Balance', 'MCC','FIR','cost']
# # metric_list = ['AUC']
# test_list = ['win','tie','lose','neg','small','medium','large']
#
# source_List = {'none': none, 'znorm':znorm, 'qrs' : qrs, 'qrs_box' : qrs_box}
#
# # result_list = {'none': none, 'qrs' : qrs, 'qrs_box' : qrs_box, 'journal' : journal}
# avg_list = ['project','none','znorm', 'qrs','qrs_box','journal']
#
# result_avg = pd.DataFrame(columns=avg_list)
# result_avg['project']=project_list
#
# result_var = pd.DataFrame(columns=avg_list)
# result_var['project'] = project_list
#
#
# target = journal
#
#
# for metric in metric_list:
# print(" =================== Metrics is {} ===================".format(metric))
# result_test = pd.DataFrame(columns=avg_list)
# result_test['project'] = test_list
# result_test = result_test.fillna(0)
# for Sname, source in source_List.items():
# # print(" =================== Compare with {} ===================".format(Sname))
# source_by_metric = source[["Target",metric]]
# target_by_metric = target[["Target",metric]]
# for project_name in project_list:
# source_by_metric_project = source_by_metric.query("Target == @project_name")
# target_by_metric_project = target_by_metric.query("Target == @project_name")
#
# source_mean = mean(source_by_metric_project[metric])
# target_mean = mean(target_by_metric_project[metric])
# source_var = std(source_by_metric_project[metric])
# target_var = std(target_by_metric_project[metric])
# index = result_avg.index[(result_avg['project'] == project_name)]
#
#
# result_avg.loc[index, Sname] = round(source_mean,3)
# result_avg.loc[index, 'journal'] = round(target_mean,3)
# result_var.loc[index, Sname] = round(source_var,3)
# result_var.loc[index, 'journal'] = round(target_var,3)
#
# cliffs_result = cliffs_delta.cliffs_delta(target_by_metric_project[metric],source_by_metric_project[metric])
# c_result, c_size = cohen_d(target_by_metric_project[metric], source_by_metric_project[metric])
#
# if metric in ['cost', 'PF']:
# # result = ranksums(target_by_metric_project[metric], source_by_metric_project[metric], alternative='less')
# result= wilcoxon(target_by_metric_project[metric], source_by_metric_project[metric], zero_method='wilcox', alternative='less')
# if c_result > 0:
# index = result_test.index[(result_test['project'] == 'neg')]
# result_test.loc[index, Sname] += 1
# else :
# index = result_test.index[(result_test['project'] == c_size)]
# result_test.loc[index, Sname] += 1
# else :
# # result = ranksums(target_by_metric_project[metric],source_by_metric_project[metric], alternative='greater')
# result= wilcoxon(target_by_metric_project[metric], source_by_metric_project[metric], zero_method='wilcox', alternative='greater')
#
# if c_result < 0:
# index = result_test.index[(result_test['project'] == 'neg')]
# result_test.loc[index, Sname] += 1
# else :
# index = result_test.index[(result_test['project'] == c_size)]
# result_test.loc[index, Sname] += 1
#
# if result.pvalue < 0.05:
# index = result_test.index[(result_test['project'] == 'win')]
# result_test.loc[index, Sname] += 1
# else:
# result= wilcoxon(target_by_metric_project[metric], source_by_metric_project[metric], zero_method='wilcox')
# if result.pvalue < 0.05 :
# index = result_test.index[(result_test['project'] == 'lose')]
# result_test.loc[index, Sname] += 1
# else:
# index = result_test.index[(result_test['project'] == 'tie')]
# result_test.loc[index, Sname] += 1
#
#
#
# avg_filepath = "./outcome/Dis/metic_{}__avg.csv".format(metric)
# var_filepath = "./outcome/Dis/metic_{}__var.csv".format(metric)
# test_filepath = "./outcome/Dis/metic_{}__test.csv".format(metric)
#
# result_avg.to_csv(avg_filepath, index=False)
# result_var.to_csv(var_filepath, index=False)
# result_test.to_csv(test_filepath, index=False)
# # print("Compare with " + Sname + ", typr of metric " + metric + ", target_project is " + project_name)
# # print("journal : {}({}) / {} : {}({})".format(target_mean,target_var,project_name,source_mean,source_var))
# # print("p_val : " + str(result.pvalue) + "(" + print_star(result.pvalue) + ")")
# # print(cliffs_result)
# # result_string = "({}, {})".format(c_result, c_size)
# # print(result_string)
# #
# # print(" ======================================")
# Discussion-2
# warnings.filterwarnings("ignore")
rate_10 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 2_0_.csv')
rate_20 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 4_22_.csv')
rate_30 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 6_5_.csv')
rate_40 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 8_59_.csv')
rate_50 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 10_13_.csv')
rate_60 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 12_39_.csv')
rate_70 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 14_23_.csv')
rate_80 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 16_20_.csv')
rate_90 = pd.read_csv('./outcome/Dis/2/_first_NORM = box-cox__Is_SVD = True__GRID = True____C_RATE = 18_35_.csv')
rate_100 =pd.read_csv('./outcome/RQ1,2/_first_NORM = box-cox__Is_SVD = True__GRID = True__37_.csv')
promise_List = ['ant1_7', 'poi2_0', 'camel1_4', 'ivy2_0', 'jedit4_0', 'log1_0', 'xal2_4', 'vel1_6','tom6_0', 'xer1_3', 'luc2_4', 'syn1_2']
data_list = {'rate_10':rate_10,'rate_20':rate_20,'rate_30':rate_30,'rate_40':rate_40,
'rate_50':rate_50,'rate_60':rate_60,'rate_70':rate_70,'rate_80':rate_80,
'rate_90':rate_90,'rate_100':rate_100}
metric_list = ['AUC', 'Gmean', 'Fmeasure', 'Balance', 'MCC']
# promise - 0:360
# jira - 361:570
# aeeem - 351:719
collist= list(data_list.keys())
df = pd.DataFrame(columns=collist)
for metric in metric_list:
for col_name , data in data_list.items() :
data_promise = data.iloc[0:360]
data_promise_by_metric = data_promise[metric]
df[col_name] = data_promise_by_metric
print("===================",metric,"===================")
result_bayesian = autorank(df, alpha=0.05, order='ascending',verbose=False,effect_size='cliff_delta')
print(result_bayesian)
plot_stats(result_bayesian)
plt.show()