How to duplicate an estimator in order to use it on multiple data sets?

Question

Here is an example that creates two data sets:

from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_classification  # data set 1 X1, y1 = make_classification(n_classes=2, n_features=5, random_state=1) # data set 2 X2, y2 = make_classification(n_classes=2, n_features=5, random_state=2) 

I want to use the LogisticRegression estimator with the same parameter values to fit a classifier on each data set:

lr = LogisticRegression()  clf1 = lr.fit(X1, y1) clf2 = lr.fit(X2, y2)  print "Classifier for data set 1: " print "  - intercept: ", clf1.intercept_ print "  - coef_: ", clf1.coef_  print "Classifier for data set 2: " print "  - intercept: ", clf2.intercept_ print "  - coef_: ", clf2.coef_ 

The problem is that both classifiers are the same:

Classifier for data set 1:    - intercept:  [ 0.05191729]   - coef_:  [[ 0.06704494  0.00137751 -0.12453698 -0.05999127  0.05798146]] Classifier for data set 2:    - intercept:  [ 0.05191729]   - coef_:  [[ 0.06704494  0.00137751 -0.12453698 -0.05999127  0.05798146]] 

For this simple example, I could use something like:

lr1 = LogisticRegression() lr2 = LogisticRegression()  clf1 = lr1.fit(X1, y1) clf2 = lr2.fit(X2, y2) 

to avoid the problem. However, the question remains: How to duplicate / copy an estimator with its particular parameter values in general?

Answer 1

from sklearn.base import clone  lr1 = LogisticRegression() lr2 = clone(lr1)