def _evaluate_individual(self, ind_params):
param_dict = self._to_param_dict(ind_params)
learner_obj = self.learner(**param_dict)
score = 0
for train_ind, val_ind in self.indices:
x_train, y_train = IMAHAKIL().fit_sample(self.X[train_ind, :], self.y[train_ind])
learner_obj.fit(x_train,y_train)
score += self.fitness_func(learner_obj.predict(self.X[val_ind, :]), self.y[val_ind])
return score / self.num_folds
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
from sklearn import naive_bayes
from sklearn import linear_model
from Imahakil import IMAHAKIL
dataset = np.loadtxt('I:\\tools\\SoftwarePrediction\\dataset\\pc1.txt',
delimiter=",")
length = len(dataset[0])
x = dataset[:, 0:length - 1]
y = dataset[:, length - 1]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4)
x_train, y_train = IMAHAKIL().fit_sample(x_train, y_train)
clf = linear_model.LogisticRegression(solver='liblinear')
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
# count=0
# # for i in range(len(result)):
# # if result[i]==y_test[i]:
# # count=count+1
# # print(count/len(result))
print("acc", accuracy_score(y_test, y_pred))
for i in range(len(y_pred)):
if y_test[i] == 1 and y_pred[i] == 1:
print(i)
print("precision", precision_score(y_test, y_pred))
print("f1", f1.mean())
print("gMean", gMean.mean())
#################################################################################
acc = []
precision = []
recall = []
f1 = []
gMean = []
matrix = []
#random_state 3,8
#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4,random_state=20)
#x_train, y_train = BorderlineOverSampling(x_train, y_train, 0.5, 10).sampling()
#x_train, y_train = Adasyn(x_train, y_train, 0.5, 10).sampling()
#x_train, y_train = MAHAKIL().fit_sample(x_train, y_train)
x_train, y_train = IMAHAKIL(pfp).fit_sample(before_x_train, before_y_train)
doc.write(
"\n" + "\n" + "\n" + "\n" + "\n" +
"##################################IMAHAKIL############################################"
+ "\n")
for i in range(len(x_train)):
for j in range(len(x_train[i])):
doc.write(str(round(x_train[i][j], 2)) + ",")
doc.write(str(y_train[i]) + "\n")
#x_train, y_train = Smote(x_train, y_train, 50, 10).over_sampling()
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)