human
Write me a the Python program you requested given the following:
I am working on the file "README.ipynb".
The first task is:
Regression
Here, we introduce a nonconformity for regression, where
$\alpha_i = ln \left(\left| y_i - \hat{y}_i \right|\right)$
and 
$\Gamma_i^{\epsilon} = \hat{y}i \pm e^{\alpha{\epsilon}}$
(This example is provided only for illustration, as it is practically identical to simply taking the absolute error as nonconformity.)
Can you write Python program for it?
assistant
#Here is the Python program you requested.

from sklearn.svm import SVR
from nonconformist.nc import NcFactory, RegressionErrFunc

class MyRegErrFunc(RegressionErrFunc):
    def __init__(self):
        super(MyRegErrFunc, self).__init__()
        
    def apply(self, prediction, y):
        '''
            y is a vector of (true) labels
            prediction is a vector of (predicted) labels
        '''
        return np.log(np.abs(prediction - y))

    def apply_inverse(self, nc, significance):
        '''
            apply_inverse() is the (partial) inverse of apply()
            apply_inverse(...)[0] is subtracted from the prediction of the
                underlying model to create the lower boundary of the
                prediction interval
            apply_inverse(...)[1] is added to the prediction of the
                underlying model to create the upper boundary of the
                prediction interval
        '''
        nc = np.sort(nc)[::-1]
        border = int(np.floor(significance * (nc.size + 1))) - 1
        border = min(max(border, 0), nc.size - 1)
        return np.vstack([np.exp(nc[border]), np.exp(nc[border])])

    
model = SVR()
nc = NcFactory.create_nc(model, MyRegErrFunc())