【发布时间】：2018-07-02 08:59:37
【问题描述】：
我一直在尝试从头开始编写逻辑回归，我已经完成了，但我正在使用我的乳腺癌数据集中的所有特征，并且我想选择一些特征（特别是我发现 scikit-当我与它进行比较并在数据上使用它的特征选择时，learn 已经为自己选择了）。但是，我不确定在我的代码中在哪里执行此操作，我目前拥有的是：
X_train = ['texture_mean', 'smoothness_mean', 'compactness_mean', 'symmetry_mean', 'radius_se', 'symmetry_se'
'fractal_dimension_se', 'radius_worst', 'texture_worst', 'area_worst', 'smoothness_worst', 'compactness_worst']
X_test = ['texture_mean', 'smoothness_mean', 'compactness_mean', 'symmetry_mean', 'radius_se', 'symmetry_se'
'fractal_dimension_se', 'radius_worst', 'texture_worst', 'area_worst', 'smoothness_worst', 'compactness_worst']
def Sigmoid(z):
return 1/(1 + np.exp(-z))
def Hypothesis(theta, X):
return Sigmoid(X @ theta)
def Cost_Function(X,Y,theta,m):
hi = Hypothesis(theta, X)
_y = Y.reshape(-1, 1)
J = 1/float(m) * np.sum(-_y * np.log(hi) - (1-_y) * np.log(1-hi))
return J
def Cost_Function_Derivative(X,Y,theta,m,alpha):
hi = Hypothesis(theta,X)
_y = Y.reshape(-1, 1)
J = alpha/float(m) * X.T @ (hi - _y)
return J
def Gradient_Descent(X,Y,theta,m,alpha):
new_theta = theta - Cost_Function_Derivative(X,Y,theta,m,alpha)
return new_theta
def Accuracy(theta):
correct = 0
length = len(X_test)
prediction = (Hypothesis(theta, X_test) > 0.5)
_y = Y_test.reshape(-1, 1)
correct = prediction == _y
my_accuracy = (np.sum(correct) / length)*100
print ('LR Accuracy: ', my_accuracy, "%")
def Logistic_Regression(X,Y,alpha,theta,num_iters):
m = len(Y)
for x in range(num_iters):
new_theta = Gradient_Descent(X,Y,theta,m,alpha)
theta = new_theta
if x % 100 == 0:
print #('theta: ', theta)
print #('cost: ', Cost_Function(X,Y,theta,m))
Accuracy(theta)
ep = .012
initial_theta = np.random.rand(X_train.shape[1],1) * 2 * ep - ep
alpha = 0.5
iterations = 10000
Logistic_Regression(X_train,Y_train,alpha,initial_theta,iterations)
我假设如果我手动更改 X_train 和 X_test 包含的哪些功能会起作用，但我收到一个错误：AttributeError: 'list' object has no attribute 'shape' at the initial_theta 行。任何正确方向的帮助将不胜感激。
【问题讨论】：
标签： python machine-learning logistic-regression feature-selection