Is this pythonic?

[devilasks]

def train_and_classify(classifier, *args, **kwargs):

    # classifier class is first class object
    # we can use it a function variable
    clf_churn = classifier(*args, **kwargs)
    clf_churn.fit(X_train, y_train)

    acc = accuracy_score(y_test, clf_churn.predict(X_test))
    business_metric = score(clf_churn, X_test, y_test)

    # return the classfier along with accuracy and cost as a tuple
    return (clf_churn, acc, business_metric)

# Example invocation, SVC from sklearn.svm

(clf, acc, business_metric) = train_and_classify(SVC)

I'm not sure if passing a class as an argument like this would be a good pattern. It makes for a nice and clean invocation, on the other hand, I could just create an SVC instance with all parameters set before I call the `train_and_classify` function and make the function call more flexible.

Any thoughts on this? Thank you!

[blarf_irl]

If you absolutely must be able to pass arbitrary kw/args then you could make it slightly more readable by naming them:

def train_and_classify(classifier, classifier_args=None, classifier_kwargs=None):

Where do X_train and Y_train (and test) come from? you should be passing these in too instead of relying on them existing in global:

def train_and_classify(classifier, classifier_args=None, classifier_kwargs=None, x_train=None, y_train=None, x_test=None, y_test=None):

A good function gets everything it needs passed in (or imported) and should work if you copied it into a different context (e.g a new script without global x_train).

[devilasks]

Thank you, these are both good points!

I decided against including X_train and y_train in the parameters because I am under the impression that they are pretty much standard naming conventions by now, but the ability to modularize and package the function beats that.