sklearn (scikit-learn) logistic regression package -- set trained coefficients for classification.

Question

So I read the scikit-learn package webpate:

http://scikit-learn.sourceforge.net/dev/modules/generated/sklearn.linear_model.LogisticRegression.html

I can use logistic regression to fit the data, and after I obtain an instance of LogisticRegression, I can use it to classify new data points. So far so good.

Is there a way to set the coefficients of LogisticRegression() instance though? Because after I obtain the trained coefficients, I want to use the same API to classify new data points.

Or perhaps someone else recommends another python machine learning package that have better APIs?

Thanks

Answer 1

The coefficients are attributes of the estimator object--that you created when you instantiated the Logistic Regression class--so you can access them in the normal python way:

>>> import numpy as NP
>>> from sklearn import datasets
>>> from sklearn import datasets as DS
>>> digits = DS.load_digits()
>>> D = digits.data
>>> T = digits.target

>>> # instantiate an estimator instance (classifier) of the Logistic Reg class
>>> clf = LR()
>>> # train the classifier
>>> clf.fit( D[:-1], T[:-1] )
    LogisticRegression(C=1.0, dual=False, fit_intercept=True, 
      intercept_scaling=1, penalty='l2', tol=0.0001)

>>> # attributes are accessed in the normal python way
>>> dx = clf.__dict__
>>> dx.keys()
    ['loss', 'C', 'dual', 'fit_intercept', 'class_weight_label', 'label_', 
     'penalty', 'multi_class', 'raw_coef_', 'tol', 'class_weight', 
     'intercept_scaling']

So that's how to get the coefficients, but if you are going to just use those for prediction, a more direct way is to use the estimator's predict method:

>>> # instantiate the L/R classifier, passing in norm used for penalty term 
>>> # and regularization strength
>>> clf = LR(C=.2, penalty='l1')
>>> clf
    LogisticRegression(C=0.2, dual=False, fit_intercept=True, 
      intercept_scaling=1, penalty='l1', tol=0.0001)

>>> # select some "training" instances from the original data
>>> # [of course the model should not have been trained on these instances]
>>> test = NP.random.randint(0, 151, 5)
>>> d = D[test,:]     # random selected data points w/o class labels
>>> t = T[test,:]     # the class labels that correspond to the points in d

>>> # generate model predictions for these 5 data points
>>> v = clf.predict(d)
>>> v
    array([0, 0, 2, 0, 2], dtype=int32)
>>> # how well did the model do?
>>> percent_correct = 100*NP.sum(t==v)/t.shape[0]
>>> percent_correct
    100