How to select features for logistic regression from scratch in python?

Question

I have been trying to code logistic regression from scratch, which I have done, but I am using all the features in my breast cancer dataset, and I would like to select some features (specifically ones that I've found scikit-learn has selected for itself when I compare with it and use its feature selection on the data). However, I am not sure where to do this in my code, what I currently have is this:

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)

I was assuming that if I manually change what features X_train and X_test consist of this would work, but I get an error: AttributeError: 'list' object has no attribute 'shape' at the initial_theta line. Any help in the right direction would be appreciated.

Francisco Sepulveda Francisco Sepulveda · Accepted Answer · 2018-01-23T14:31:33

the problem is that X_train is a list and shape only work for dataframes.

you could either: -keep the list but use len(X_train) instead, OR -change the X_train type to a pandas dataframe, pandas.DataFrame(X_train).shape[0]

How to select features for logistic regression from scratch in python?

1 Answers