def getTrain4Test(self, indexCV=0):
dataTrain = [[], [], []]
dataValid = [[], [], []]
with open(
os.path.join(UtilPath.Datasets(), self.project,
self.typeDataset, self.release4Test,
"train" + str(indexCV) + ".csv")) as f:
train = csv.reader(f)
for i, row in enumerate(train):
dataTrain[0].append(row[0])
dataTrain[1].append([float(x) for x in row[2:]])
dataTrain[2].append(int(row[1]))
dataTrain[1] = np.array(dataTrain[1])
dataTrain[2] = np.array(dataTrain[2])
print(dataTrain[1].shape)
with open(
os.path.join(UtilPath.Datasets(), self.project,
self.typeDataset, self.release4Test,
"valid" + str(indexCV) + ".csv")) as f:
valid = csv.reader(f)
for i, row in enumerate(valid):
dataValid[0].append(row[0])
dataValid[1].append([float(x) for x in row[2:]])
dataValid[2].append(int(row[1]))
dataValid[1] = np.array(dataValid[1])
dataValid[2] = np.array(dataValid[2])
print(dataValid[1].shape)
return np.concatenate([dataTrain[1], dataValid[1]
]), np.concatenate([dataTrain[2], dataValid[2]],
0)
def __init__(self, project, typeDataset, release4Test, purpose):
self.project = project
self.typeDataset = typeDataset
self.release4Test = release4Test
self.porpose = purpose
print(UtilPath.Datasets(), self.project, self.typeDataset,
self.release4Test)
def getTest4Test(self, indexCV=0):
dataTest = [[], [], []]
with open(
os.path.join(UtilPath.Datasets(), self.project,
self.typeDataset, self.release4Test,
"test.csv")) as f:
test = csv.reader(f)
for i, row in enumerate(test):
dataTest[0].append(row[0])
dataTest[1].append([float(x) for x in row[2:]])
dataTest[2].append(int(row[1]))
dataTest[1] = np.array(dataTest[1])
dataTest[2] = np.array(dataTest[2])
print(dataTest[1].shape)
return dataTest[1], dataTest[2]
def getValid4Search(self, indexCV=0):
dataValid = [[], [], []]
with open(
os.path.join(UtilPath.Datasets(), self.project,
self.typeDataset, self.release4Test,
"valid" + str(indexCV) + ".csv")) as f:
valid = csv.reader(f)
for i, row in enumerate(valid):
dataValid[0].append(row[0])
dataValid[1].append([float(x) for x in row[2:]])
dataValid[2].append(int(row[1]))
dataValid[1] = np.array(dataValid[1])
dataValid[2] = np.array(dataValid[2])
#print(dataValid[1].shape)
#print(dataValid[2].shape)
return dataValid[1], dataValid[2]
def getTrain4Search(self, indexCV=0):
dataTrain = [[], [], []]
with open(
os.path.join(UtilPath.Datasets(), self.project,
self.typeDataset, self.release4Test,
"train" + str(indexCV) + ".csv")) as f:
train = csv.reader(f)
for i, row in enumerate(train):
#print(item["id"])
dataTrain[0].append(row[0])
dataTrain[1].append([float(x) for x in row[2:]])
dataTrain[2].append(int(row[1]))
dataTrain[1] = np.array(dataTrain[1])
dataTrain[2] = np.array(dataTrain[2])
#print(dataTrain[1].shape)
#print(dataTrain[2].shape)
return dataTrain[1], dataTrain[2]
from src.manager import Maneger
from src.utility import UtilPath
import datetime
option={
"date" : datetime.datetime.now().strftime('%Y%m%d%H%M%S'),
"project" : "linuxtools",
"release4test" : "2",
"variableDependent" : "isBuggy",
"purpose" : "test",
"modelAlgorithm" : "DNN",
"pathModel" : "",
"pathHP" : UtilPath.Datasets()+"/"+"linuxtools/isBuggy/2/hpDNN.json"
}
option["idExperiment"] = \
option["project"] + "_" \
+ option["variableDependent"] + "_" \
+ option["release4test"] + "_" \
+ option["modelAlgorithm"] + "_" \
+ option["date"]
maneger = Maneger(option)
maneger.do()