def ex11_short(mdl_typ):
if mdl_typ == 'nml':
prms = ['TCchar', 'CountLineCode', 'CountLineComment' , 'N', 'NN', 'NF','SumCyclomatic']
elif mdl_typ == 'rfn':
prms = ['TCchar', 'CountLineCode', 'CountLineComment', 'N', 'NN', 'NF','SumCyclomatic','chum','relatedChum','delectChum','ncdChum']
elif mdl_typ == 'chrn':
prms = ['chum','relatedChum','delectChum','ncdChum']
THRESHOLD = 0.5
vers = version.get_version_short_list()
ev_values = []
prm_note = []
for curr_ver in vers :
next_ver = version.get_next_version(curr_ver)
print curr_ver
print next_ver
if curr_ver != '4.5.0':
curr_df = mmm.create_df(curr_ver)
# get paramaters that has lowest aic value
best_prms = get_best_paramaters(prms, curr_df, curr_ver, mdl_typ)
# dependent value
dv_data = curr_df['fault']
# explanatory value
ev_data = curr_df[list(best_prms)]
# create mdl
logit = sm.Logit(dv_data, ev_data)
result = logit.fit()
# get coefficients
params = result.params.values
coef = pd.Series(params, index=best_prms)
# create model used evaluatopn_ex
next_df = mmm.create_df(next_ver)
# explanatory value
ev_data = next_df[list(best_prms)]
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [curr_ver,]
ev_value.extend( ex01.evaluate_ex(next_df, evals) )
ev_values.append(ev_value)
prm_note.append(curr_ver)
prm_note.append(best_prms)
df = pd.DataFrame(ev_values)
df.columns = ['version','nm','np','nf','nc','f_value']
df = df.sort_index(ascending=False)
df.to_csv( './../result/ex11/record_ex11_' + mdl_typ + '_' + str(THRESHOLD) + '.csv', index=False, cols=None)
df = pd.DataFrame(prm_note)
df.to_csv( './../result/ex11/prm_note_'+mdl_typ+'.csv', index=False, cols=None)
def ex1_short(mdl_typ, threshold):
if mdl_typ == 'nml':
prms = ['TCchar', 'CountLineCode', 'CountLineComment' , 'N', 'NN', 'NF','SumCyclomatic']
elif mdl_typ == 'rfn':
prms = ['TCchar', 'CountLineCode', 'CountLineComment', 'N', 'NN', 'NF','SumCyclomatic','chum','relatedChum','delectChum','ncdChum']
elif mdl_typ == 'chrn':
prms = ['chum','relatedChum','delectChum','ncdChum']
THRESHOLD = threshold
vers = version.get_version_short_list()
ev_values = []
print 'operation starts'
for curr_ver in vers :
next_ver = version.get_next_version(curr_ver)
print curr_ver
print next_ver
if curr_ver != '4.5.0':
curr_df = mmm.create_df(curr_ver)
# dependent value
dv_data = curr_df['fault']
# explanatory value
ev_data = curr_df[prms]
# create mdl
logit = sm.Logit(dv_data, ev_data)
result = logit.fit()
# get coefficients
params = result.params.values
coef = pd.Series(params, index=prms)
print coef
# create model used evaluatopn_ex
next_df = mmm.create_df(next_ver)
ev_data = next_df[prms]
print ev_data
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
# print logit_odds
evals = logit.cdf(logit_odds)
print evals
ev_value = [curr_ver,]
# ev_value.extend( evaluate_ex_report(next_df, evals, mdl_typ,curr_ver) )
ev_value.extend( evaluate_ex(next_df, evals) )
ev_values.append(ev_value)
df = pd.DataFrame(ev_values)
df.columns = ['version','nm','np','nf','nc','f_value']
df = df.sort_index(ascending=False)
df.to_csv( './../result/ex1/record_ex1_' + mdl_typ + '_' + str(THRESHOLD) + '.csv', index=False, cols=None)
def create_df_used_all_predate_nml(curr_ver):
vers = version.get_all_previous_version_list(curr_ver)
marged_df = pd.DataFrame([])
print vers
for ver in vers:
df = mmm.create_df(ver)
# print df
marged_df = pd.concat([marged_df, df])
return marged_df
def ex12_short(mdl_typ):
if mdl_typ == 'nml':
prms = [
'TCchar', 'CountLineCode', 'CountLineComment', 'N', 'NN', 'NF',
'SumCyclomatic'
]
elif mdl_typ == 'rfn':
prms = [
'TCchar', 'CountLineCode', 'CountLineComment', 'N', 'NN', 'NF',
'SumCyclomatic', 'chum', 'relatedChum', 'delectChum', 'ncdChum'
]
elif mdl_typ == 'chrn':
prms = ['chum', 'relatedChum', 'delectChum', 'ncdChum']
THRESHOLD = 0.5
vers = version.get_version_short_list()
ev_values = []
prm_note = []
for curr_ver in vers:
next_ver = version.get_next_version(curr_ver)
print curr_ver
print next_ver
if curr_ver != '4.5.0':
curr_df = ex02.create_df_used_all_predate_nml(curr_ver)
# get paramaters that has lowest aic value
best_prms = ex11.get_best_paramaters(prms, curr_df, curr_ver,
mdl_typ)
# dependent value
dv_data = curr_df['fault']
# explanatory value
ev_data = curr_df[list(best_prms)]
# create mdl
logit = sm.Logit(dv_data, ev_data)
result = logit.fit()
# get coefficients
params = result.params.values
coef = pd.Series(params, index=best_prms)
# create model used evaluatopn_ex
next_df = mmm.create_df(next_ver)
# explanatory value
ev_data = next_df[list(best_prms)]
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [
curr_ver,
]
ev_value.extend(ex01.evaluate_ex(next_df, evals))
ev_values.append(ev_value)
prm_note.append(curr_ver)
prm_note.append(best_prms)
df = pd.DataFrame(ev_values)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df = df.sort_index(ascending=False)
df.to_csv('./../result/ex12/record_ex12_' + mdl_typ + '_' +
str(THRESHOLD) + '.csv',
index=False,
cols=None)
df = pd.DataFrame(prm_note)
df.to_csv('./../result/ex12/prm_note_' + mdl_typ + '.csv',
index=False,
cols=None)
def ex1_short(mdl_typ, threshold):
if mdl_typ == 'nml':
prms = [
'TCchar', 'CountLineCode', 'CountLineComment', 'N', 'NN', 'NF',
'SumCyclomatic'
]
elif mdl_typ == 'rfn':
prms = [
'TCchar', 'CountLineCode', 'CountLineComment', 'N', 'NN', 'NF',
'SumCyclomatic', 'chum', 'relatedChum', 'delectChum', 'ncdChum'
]
elif mdl_typ == 'chrn':
prms = ['chum', 'relatedChum', 'delectChum', 'ncdChum']
THRESHOLD = threshold
vers = version.get_version_short_list()
ev_values = []
print 'operation starts'
for curr_ver in vers:
next_ver = version.get_next_version(curr_ver)
print curr_ver
print next_ver
if curr_ver != '4.5.0':
curr_df = mmm.create_df(curr_ver)
# dependent value
dv_data = curr_df['fault']
# explanatory value
ev_data = curr_df[prms]
# create mdl
logit = sm.Logit(dv_data, ev_data)
result = logit.fit()
# get coefficients
params = result.params.values
coef = pd.Series(params, index=prms)
print coef
# create model used evaluatopn_ex
next_df = mmm.create_df(next_ver)
ev_data = next_df[prms]
print ev_data
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
# print logit_odds
evals = logit.cdf(logit_odds)
print evals
ev_value = [
curr_ver,
]
# ev_value.extend( evaluate_ex_report(next_df, evals, mdl_typ,curr_ver) )
ev_value.extend(evaluate_ex(next_df, evals))
ev_values.append(ev_value)
df = pd.DataFrame(ev_values)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df = df.sort_index(ascending=False)
df.to_csv('./../result/ex1/record_ex1_' + mdl_typ + '_' + str(THRESHOLD) +
'.csv',
index=False,
cols=None)