ValueError：形状（54,2）和（54,2）不对齐：2（dim 1）！= 54（dim 0）

Question

我正在尝试从GitHub运行一些代码，并出现错误：

ValueError: shapes (54,2) and (54,2) not aligned: 2 (dim 1) != 54 (dim 0)

我正在使用以下代码：

df = pd.read_csv(r'C:/Desktop/Noise/Area/Test/AD.csv')

df.columns

def firth_likelihood(beta, logit):
    return -(logit.loglike(beta) + 0.5*np.log(np.linalg.det(-logit.hessian(beta))))


def fit_firth(y, X, start_vec, step_limit=1000, convergence_limit=0.0001):

    logit_model = sm.Logit(y, X)

    if start_vec is None:
        start_vec = np.zeros(X.shape[1])

    beta_iterations = []
    beta_iterations.append(start_vec)
    for i in range(0, step_limit):
        pi = logit_model.predict(beta_iterations[i])
        W = np.diagflat(np.multiply(pi, 1-pi))
        var_covar_mat = np.linalg.pinv(-logit_model.hessian(beta_iterations[i]))

        # build hat matrix
        rootW = np.sqrt(W)
        H = np.dot(np.transpose(X), np.transpose(rootW))
        H = np.matmul(var_covar_mat, H)
        H = np.matmul(np.dot(rootW, X), H)

        # penalised score
        U = np.matmul(np.transpose(X), y - pi + np.multiply(np.diagonal(H), 0.5 - pi))
        new_beta = beta_iterations[i] + np.matmul(var_covar_mat, U)

        # step halving
        j = 0
        while firth_likelihood(new_beta, logit_model) > firth_likelihood(beta_iterations[i], logit_model):
            new_beta = beta_iterations[i] + 0.5*(new_beta - beta_iterations[i])
            j = j + 1
            if (j > step_limit):
                sys.stderr.write('Firth regression failed\n')
                return None

        beta_iterations.append(new_beta)
        if i > 0 and (np.linalg.norm(beta_iterations[i] - beta_iterations[i-1]) < convergence_limit):
            break

    return_fit = None
    if np.linalg.norm(beta_iterations[i] - beta_iterations[i-1]) >= convergence_limit:
        sys.stderr.write('Firth regression failed\n')
    else:
        # Calculate stats
        fitll = -firth_likelihood(beta_iterations[-1], logit_model)
        intercept = beta_iterations[-1][0]
        beta = beta_iterations[-1][1:].tolist()
        bse = np.sqrt(np.diagonal(-logit_model.hessian(beta_iterations[-1])))

        return_fit = intercept, beta, bse, fitll

    return return_fit

if __name__ == "__main__":



    # create X and y here. Make sure X has an intercept term (column of ones)
    # ...
    X = df[['RwG', 'vpdmax (hPa)']]
    X = np.array(X)


    y = df['Occurred']
    y = np.array(y)
    sv = np.ones((54, 2))
    sv = np.array(sv)
    start_vec = df['SV']

    # How to call and calculate p-values
    (intercept, beta, bse, fitll) = fit_firth(y, X, sv)

    # Wald test
    waldp = 2 * (1 - stats.norm.cdf(abs(beta[0]/bse[0])))

    # LRT
    null_X = np.delete(X, 1, axis=1)
    (null_intercept, null_beta, null_bse, null_fitll) = fit_firth(y, X, sv)
    lrstat = -2*(null_fitll - fitll)
    lrt_pvalue = 1
    if lrstat > 0: # non-convergence
        lrt_pvalue = stats.chi2.sf(lrstat, 1)

基本上，当我在分配变量的代码中运行代码时，我遇到了ValueError。另外，我不太清楚应在start_vector变量中添加什么内容，但由于它是适合健身的变量，因此需要包括在内。所以我的问题是如何删除错误，以便我可以处理此错误？