Many computer systems are becoming increasingly tailored to their users, customizing and optimizing their experience. However, most conversational agents do not follow this trend when it comes to humorous interactions. Instead, they employ pre-written answers regardless of whether the user liked previous similar interactions. While there already exist several computational humor systems that can successfully generate jokes, their joke generation models, parameters or even both are often fixed. In this paper, we propose GOOFER, a general framework for computational humor that learns joke structures and parameterizations from rated example jokes. This framework uses metrical schemas, a new notion we introduce, which are a generalization of several types of other schemas. This new type of schema makes regular schemas compatible with machine learning techniques. We also propose a strategy for identifying useful humor metrics based on humor theory, which can be used as features for the machine learning algorithm. The GOOFER framework uses these novel concepts to construct a pipeline with new components around previous generators. Using a mapping to our previous work on analogy jokes, we show that this framework cannot only generate this type of jokes well, but also find the importance of specific humor metrics for template values. This indicates that it is on the right track towards joke generation systems that can automatically learn new templates and schemas from rated examples. This work thus forms a stepping stone towards creating programs with a sense of humor that is adaptable to the user.