def funcWrite2File(file_name):
with open(file_name) as fileSmt:
smt_file_content = fileSmt.readlines()
smt_file_content = [x.strip() for x in smt_file_content]
smt_file_lines = util.file_len(file_name)
#print(smt_file_lines)
fileTogFeSmt = open('ToggleFeatureSmt.smt2', 'w')
for i in range(smt_file_lines):
fileTogFeSmt.write(smt_file_content[i])
fileTogFeSmt.write("\n")
fileTogFeSmt.close()
def funcPrunBranch(dfOrig, tree_model):
noPathCond = 0
#data set to hold set of candidate counter examples, refer to cand-set of prunBranch algorithm
with open('CandidateSetBranch.csv', 'w', newline='') as csvfile:
fieldnames = dfOrig.columns.values
writer = cv.writer(csvfile)
writer.writerow(fieldnames)
with open('param_dict.csv') as csv_file:
reader = cv.reader(csv_file)
paramDict = dict(reader)
dfRead = pd.read_csv('TestDataSMTMain.csv')
for row in range(0, dfRead.shape[0]):
if (paramDict['multi_label'] == 'True'):
funcgetPath4multiLbl(tree_model, dfOrig, row,
int(paramDict['no_of_params']))
else:
funcgetPath(tree_model, dfOrig, row)
fileCond = open('TreeOutput.txt', 'r')
first = fileCond.read(1)
if not first:
print('No Branch')
else:
noPathCond = util.file_len('ConditionFile.txt')
if (noPathCond == 'empty'):
return
for i in range(noPathCond):
funcAddCond2File(i)
os.system(r"z3 ToggleBranchSmt.smt2 > FinalOutput.txt")
satFlag = ReadZ3Output.funcConvZ3OutToData(dfOrig)
if (satFlag == True):
dfSmt = pd.read_csv('TestDataSMT.csv')
dataAppend = dfSmt.values
with open('CandidateSetBranch.csv', 'a',
newline='') as csvfile:
writer = cv.writer(csvfile)
writer.writerows(dataAppend)
def funcAddCond2File(index):
temp_cond_content = ''
with open('ConditionFile.txt') as fileCond:
condition_file_content = fileCond.readlines()
condition_file_content = [x.strip() for x in condition_file_content]
with open('DecSmt.smt2') as fileSmt:
smt_file_content = fileSmt.readlines()
smt_file_content = [x.strip() for x in smt_file_content]
smt_file_lines = util.file_len('DecSmt.smt2')
fileCondSmt = open('ToggleBranchSmt.smt2', 'w')
for i in range(smt_file_lines):
fileCondSmt.write(smt_file_content[i])
fileCondSmt.write("\n")
fileCondSmt.close()
with open('ToggleBranchSmt.smt2', 'r') as fileCondSmt:
text = fileCondSmt.read()
text = text.replace("(check-sat)", '')
text = text.replace("(get-model)", '')
with open('ToggleBranchSmt.smt2', 'w') as fileCondSmt:
fileCondSmt.write(text)
fileCondSmt = open('ToggleBranchSmt.smt2', 'a')
temp_cond_content = condition_file_content[index]
#print(temp_cond_content)
fileCondSmt.write("(assert (not " + temp_cond_content + "))")
fileCondSmt.write("\n")
fileCondSmt.write("(check-sat) \n")
fileCondSmt.write("(get-model) \n")
fileCondSmt.close()
def funcConvZ3OutToData(df):
fe_flag = False
with open('param_dict.csv') as csv_file:
reader = cv.reader(csv_file)
paramDict = dict(reader)
no_of_params = int(paramDict['no_of_params'])
testMatrix = np.zeros(((no_of_params), df.shape[1]))
if (os.stat('FinalOutput.txt').st_size > 0):
with open('FinalOutput.txt') as f1:
file_content = f1.readlines()
file_content = [x.strip() for x in file_content]
noOfLines = util.file_len('FinalOutput.txt')
with open('TestDataSMT.csv', 'w', newline='') as csvfile:
fieldnames = df.columns.values
writer = cv.writer(csvfile)
writer.writerow(fieldnames)
writer.writerows(testMatrix)
dfAgain = pd.read_csv('TestDataSMT.csv')
nums = re.compile(r"[+-]?\d+(?:\.\d+)?")
if ('unknown' in file_content[0]):
raise Exception('Encoding problem')
sys.exit(1)
if ('model is not available' in file_content[1]):
return False
else:
i = 1
while (i < noOfLines):
minus_flag = False
fe_flag = False
if ("(model" == file_content[i]):
i = i + 1
elif (")" == file_content[i]):
i = i + 1
else:
for j in range(0, df.columns.values.shape[0]):
for param_no in range(0, no_of_params):
if (paramDict['multi_label'] == 'True'
and no_of_params == 1
and paramDict['white_box_model']
== 'Decision tree'):
fe_add = ' '
else:
fe_add = str(param_no)
if (df.columns.values[j] + fe_add
in file_content[i]):
feature_name = df.columns.values[j]
fe_flag = True
if ('Int' in file_content[i]):
i = i + 1
digit = int(
re.search(r'\d+',
file_content[i]).group(0))
if ('-' in file_content[i]):
digit = 0 - digit
elif ('Real' in file_content[i]):
i = i + 1
if ("(/" in file_content[i]):
if ('-' in file_content[i]):
minus_flag = True
multi_digits = re.findall(
'\d*?\.\d+', file_content[i])
if (len(multi_digits) == 1):
i = i + 1
multi_digits.append(
float(
re.search(
r'\d+',
file_content[i]).group(
0)))
digit = float(multi_digits[0]) / float(
multi_digits[1])
if (minus_flag == True):
digit = 0 - digit
else:
#digit = round(float(re.search(r'\d+', file_content[i]).group(0)), 2)
digit = float(
re.search(
r'\d+',
file_content[i]).group(0))
if ('-' in file_content[i]):
digit = 0 - digit
dfAgain.loc[param_no, feature_name] = digit
i = i + 1
if (fe_flag == False):
i = i + 2
dfAgain.to_csv('TestDataSMT.csv', index=False, header=True)
return True
else:
raise Exception("There is no solver installed in your system")