Custom transformer for sklearn Pipeline that alters both X and y

Question

I want to create my own transformer for use with the sklearn Pipeline. Hence I am creating a class that implements both fit and transform methods. The purpose of the transfo

Answer 1

You can solve this easily by using the sklearn.preprocessing.FunctionTransformer method (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html)

You just need to put your alternations to X in a function

def drop_nans(X, y=None):
    total = X.shape[1]                                           
    new_thresh = total - thresh
    df = pd.DataFrame(X)
    df.dropna(thresh=new_thresh, inplace=True)
    return df.values

then you get your transformer by calling

transformer = FunctionTransformer(drop_nans, validate=False)

which you can use in the pipeline. The threshold can be set outside the drop_nans function.