在 python 中编码逻辑回归时出现 AttributeError

Question

你好，我正在尝试学习 python 和使用威斯康星乳腺癌数据集的机器学习，我正在尝试从头开始编写逻辑回归代码，但一直出现属性错误 ('AttributeError: 'DataFrame' object has no attribute 'target') 在我的代码的第一部分（Y = data.target），我无法在网上找到解决方案。我知道它的其余部分可能也不适用于数据，我从我在网上看到的不同事物中获取它，但我目前只是试图一次解决一个问题以了解逻辑回归的工作原理。如果能提供任何帮助，我将不胜感激。

这是我的代码：

data = pd.read_csv("C:\Users\Hannah\Desktop\Research Project\data.csv",header=0)
    X = data.values[:, :30] 
    Y = data.target['diagnosis']

X = X[:500,:]
Y = Y[:500]

def sigmoid(z):
    s= 1/(1 + np.exp(-z))
    return s

def propagate(w, b, X, Y):

    m = X.shape[1]

    A = sigmoid(np.dot(w.T,X)+b)                                

    cost = -1/m * np.sum(Y * np.log(A) + (1-Y) * (np.log(1-A)))

    dz= (1/m)*(A - Y)
    dw = np.dot(X, dz.T)
    db = np.sum(dz)


    cost = np.squeeze(cost)
    grads = {"dw": dw,
             "db": db}

    return grads, cost


def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):

    costs = []

    for i in range(num_iterations):
        m = X.shape[1]
        grads,cost = propagate(w, b, X, Y)
        b = b - learning_rate*grads["db"]
        w = w - learning_rate*grads["dw"]
         if i % 100 == 0:
            costs.append(cost)
        if print_cost and i % 100 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))

    params = {"w": w,
              "b": b}
    return params, grads, costs



def predict(w, b, X):
    m = X.shape[1]
    Y_prediction = np.zeros((1,m))
    w = w.reshape(X.shape[0], 1)
    A = sigmoid(np.dot(w.T,X)+ b)

    for i in range(A.shape[1]):
        x_exp = np.exp(A)
        x_sum = np.sum(x_exp,axis=1,keepdims=True)
        s = np.divide(x_exp,x_sum)

    Y_prediction = 1. * (A > 0.5)

    return Y_prediction



def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
    w, b = initialize_with_zeros(X_train.shape[0])

    print("learning rate:",learning_rate)

    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost = False)

    w = parameters["w"]
    b = parameters["b"]

    Y_prediction_train = predict(w,b,X_train)
    Y_prediction_test = predict(w,b,X_test)
    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test, 
         "Y_prediction_train" : Y_prediction_train, 
         "w" : w, 
         "b" : b,
         "learning_rate" : learning_rate,
         "num_iterations": num_iterations}

    return d

d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)

Answer 1

据我了解，您正在使用 pandas 读取 csv 并希望将列 diagnosis 中的值分配给变量 Y。在这种情况下，您不需要 target。根据错误，DataFrame 根本没有属性。 data['diagnosis'] 应该 return 你需要的。

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