How to evaluate cost function for scikit learn LogisticRegression?

Question

After using sklearn.linear_model.LogisticRegression to fit a training data set, I would like to obtain the value of the cost function for the training data set and a cross validation data set.

Is it possible to have sklearn simply give me the value (at the fit minimum) of the function it minimized?

The function is stated in the documentation at http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression (depending on the regularization one has chosen). But I can't find how to get sklearn to give me the value of this function.

I would have thought this is what LogisticRegression.score does, but that simply returns the accuracy (the fraction of data points its prediction classifies correctly).

I have found sklearn.metrics.log_loss, but of course this is not the actual function being minimized.

Answer 1

Unfortunately there is no "nice" way to do so, but there is a private function _logistic_loss(w, X, y, alpha, sample_weight=None) in https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/logistic.py, thus you can call it by hand

from sklearn.linear_model.logistic import _logistic_loss
print _logistic_loss(clf.coef_, X, y, 1 / clf.C)

where clf is your learned LogisticRegression

Answer 2

I used below code to calculate cost value.

import numpy as np

cost = np.sum((reg.predict(x) - y) ** 2)

where reg is your learned LogisticRegression

Answer 3

I have the following suggestions. You can write the codes for the loss function of logistic regression as a function. After you get your predicted labels of data, you can revoke your defined function to calculate the cost values.