More documentation of generalize_knowledge.
In generalize_knowledge, output scores from knowledge (1 for the seed facts, an over-threshold classifier score for learnt facts) and not scores from input evidence (0.5 for seeds in the first iteration, and the scores given by the last classifier starting from the second iteration). This guarantees high scores for questions about knowledge.
In generate_questions, now also add questions for undecided evidence with scores from last classifier. Rename evidence parameter to knowledge_evidence, to show that this evidence comes from known facts. In generalize_knowledge also rename fact_evidence to knowledge_evidence.
Ask questions in decreasing score order, instead of decreasing certainty order.
Save classifier scores into self.evaluate so they can be use to sort the question in the following step.
The actual input in generalize_knowledge should be knowledge, not evidence. This propagates changes to start (that launches the pipeline), and to the last pipeline steps filter_facts and evaluate. The information flow looks more natural now.
Merge branch '153-bootstrap-questions' into develop