def PREDICT():
json = request.get_json(force=True)
if json["lang"] == "python":
methods = parse_py(json['f'])
else:
methods = parse_clike(json['f'])
m = []
if json['model'] == "model1": #dynamic analysis
for i in methods:
func_obj = i[0]
original_func_name = func_obj.__name__
func_source_code = i[1]
m.append([original_func_name, mp1.predict(func_obj, json['lang'])])
elif json['model'] == "model2": #static analysis
for i in methods:
func_obj = i[0]
original_func_name = func_obj.__name__
func_source_code = i[1]
m.append([
original_func_name,
mp2.predict(func_source_code, json['lang'])
])
else: #default is dynamic analysis
for i in methods:
func_obj = i[0]
original_func_name = func_obj.__name__
func_source_code = i[1]
m.append([original_func_name, mp1.predict(func_obj, json['lang'])])
return jsonify(m)
def execute(func_str, label):
cppyy.cppdef("using namespace std;")
cppyy.cppdef(func_str)
a = cp.Adaptor()
#print(getattr(a,label))
res = mp.predict(getattr(a, label), "C++")
return res
def test_performance():
# labels = os.listdir('dataset_working')
# # labels.remove(os.path.basename(__file__))
# labels.remove('isAnagram')
# labels.remove('myPow')
# labels.remove('isPalindrome')
# labels.remove('fib')
map = json.load(open("mapping.json"))
accuracy = json.load(open("label_accuracy.json"))
labels = ["isAnagram","strStr"]
# done = ["sortArray","rotate","fib","isPalindrome","myPow","isUgly","countPrimes","mySqrt","reverse","numTrees","isAnagram","strStr","canJump","coinChange","numIslands","canFinish","hasCycle","reverseList","inorderTraversal","isValidBST","maxDepth","maxPathSum","levelOrder","solveSudoku"]
# print(map["canFinish"][0])
total = 0
correct = 0
for label in labels:
prgs = os.listdir('dataset'+'/'+label)
prgs.sort()
if '__pycache__' in prgs:
prgs.remove('__pycache__')
ltotal = 0
lcorrect = 0
frontend_label = map[label][0]
for prg in prgs:
total += 1
ltotal += 1
print("Evaluating " + prg)
loader = importlib.machinery.SourceFileLoader('dataset'+'/'+label, 'dataset'+'/'+label+'/'+prg)
handle = loader.load_module('dataset'+'/'+label)
s = handle.Solution()
a = Adaptor()
global f
f = getattr(s, label)
res = mp.predict(getattr(a,label),"python")
oplabel = res
if(oplabel==map[label][0]):
lcorrect += 1
print(res,label)
# print("Accuracy till "+ str(total)+" prgs: " + str((float(correct)/total)*100) )
val = (float(lcorrect)*(accuracy[frontend_label]/100)/ltotal)*100
correct += float(lcorrect)*(accuracy[frontend_label]/100)
with open("results.txt",'a') as f:
f.write("Accuracy of "+ frontend_label + " " + str(lcorrect)+"/"+str(ltotal)+" prgs: " + str(val)+"\n")
with open("results.txt",'a') as f:
f.write("Overall Accuracy: " + str((float(correct)/total)*100)+"\n")
return (float(correct)/total)*100
import model1.predict as mp1
import model1.optimize as op1
import model1.dataset as g1
def bubbleSort(arr: list):
n = len(arr)
# Traverse through all array elements
for i in range(n - 1):
# range(n) also work but outer loop will repeat one time more than needed.
# Last i elements are already in place
for j in range(0, n - i - 1):
# traverse the array from 0 to n-i-1
# Swap if the element found is greater
# than the next element
if arr[j] > arr[j + 1]:
arr[j], arr[j + 1] = arr[j + 1], arr[j]
return arr
def timSort(arr: list):
return sorted(arr)
print(mp1.predict(bubbleSort))
#print(op1.optimize(bubbleSort,weights=[1,1,0,0]))
#print(op1.compare([timSort,bubbleSort]))
#print(op1.rank([timSort]))