Question

我一直试图从头开始编写逻辑回归，我已经做了，但我正在使用我的乳腺癌数据集中的所有功能，我想选择一些功能（特别是我发现scikit-当我与它进行比较并使用其在数据上的特征选择时，学习已经选择了自己。但是，我不知道在我的代码中该怎么做，我现在拥有的是：

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包含哪些功能，这会起作用，但是我收到错误：AttributeError：'list'对象在initial_theta行没有属性'shape'。任何正确方向的帮助都将受到赞赏。

Answer 1

问题是X_train是一个列表和形状仅适用于数据帧。

你可以： - 保持列表，但改为使用len（X_train），OR - 将X_train类型更改为pandas数据帧，pandas.DataFrame（X_train）.shape [0]

如何在python中从头开始选择逻辑回归的功能？

1 个答案: