"""CSC148 Assignment 1 === CSC148 Winter 2023 === Department of Computer Science, University of Toronto This code is provided solely for the personal and private use of students taking the CSC148 course at the University of Toronto. Copying for purposes other than this use is expressly prohibited. All forms of distribution of this code, whether as given or with any changes, are expressly prohibited. Authors: Misha Schwartz, Mario Badr, Christine Murad, Diane Horton, Sophia Huynh, Jaisie Sin, Tom Ginsberg, Jonathan Calver, and Jacqueline Smith All of the files in this directory and all subdirectories are: Copyright (c) 2023 Misha Schwartz, Mario Badr, Diane Horton, Sophia Huynh, Jonathan Calver, and Jacqueline Smith === Module Description === This file contains classes that describe different types of criteria used to evaluate a group of answers to a survey question. """ from __future__ import annotations from typing import TYPE_CHECKING if TYPE_CHECKING: from survey import Question, Answer class InvalidAnswerError(Exception): """Error that should be raised when an answer is invalid for a given question. """ class Criterion: """An abstract class representing a criterion used to evaluate the quality of a group based on the group members' answers for a given question. """ def score_answers(self, question: Question, answers: list[Answer]) -> float: """Return score between 0.0 and 1.0 indicating how well the group of <answers> to the question <question> satisfy this Criterion. Raise InvalidAnswerError if any answer in <answers> is not a valid answer to <question>. Each implementation of this abstract class will measure satisfaction of a criterion differently. """ for answer in answers: if (answer is None) or (not answer.is_valid(question)): raise InvalidAnswerError return 0.0 class HomogeneousCriterion(Criterion): """A criterion used to evaluate the quality of a group based on the group members' answers for a given question. This criterion gives a higher score to answers that are more similar. """ def score_answers(self, question: Question, answers: list[Answer]) -> float: """Return a score between 0.0 and 1.0 indicating how similar the answers in <answers> are. This score is calculated by finding the similarity of every combination of two answers in <answers> and taking the average of all of these similarity scores. * Don't include a pair of answers twice while finding the similarity scores. For example, don't compare answer 1 and answer 2, then later compare answer 2 and answer 1. * Don't compare an answer with itself while computing the similarity scores. * Don't do any rounding. If there is only one answer in <answers> and it is valid, return 1.0 since a single answer is always identical to itself. Raise InvalidAnswerError if any answer in <answers> is not a valid answer to <question>. Preconditions: - len(answers) > 0 """ result = Criterion.score_answers(self, question, answers) similarities = [] if len(answers) == 1: result = result + 1.0 else: for i in range(len(answers)): for j in range(i + 1, len(answers)): similarities.append(question.get_similarity(answers[i], answers[j])) result = sum(similarities) / len(similarities) return result class HeterogeneousCriterion(HomogeneousCriterion): """A criterion used to evaluate the quality of a group based on the group members' answers for a given question. This criterion gives a higher score to answers that are more different. """ def score_answers(self, question: Question, answers: list[Answer]) -> float: """Return a score between 0.0 and 1.0 indicating how different the answers in <answers> are. This score is calculated by finding the similarity of every combination of two answers in <answers>, finding the average of all of these similarity scores, and then subtracting this average from 1.0 * Don't include a pair of answers twice while finding the similarity scores. For example, don't compare answer 1 and answer 2, then later compare answer 2 and answer 1. * Don't compare an answer with itself while computing the similarity scores. * Don't do any rounding. If there is only one answer in <answers> and it is valid, return 0.0 since a single answer is never different from itself. Raise InvalidAnswerError if any answer in <answers> is not a valid answer to <question>. Preconditions: - len(answers) > 0 """ result = HomogeneousCriterion.score_answers(self, question, answers) return 1.0 - result class LonelyMemberCriterion(Criterion): """A criterion used to measure the quality of a group of students according to the group members' answers to a question. This criterion gives a higher score to a group if no member of the group gives a unique answer to a question, that is, an answer that no other member gave. This criterion could be used, for example, to avoid putting a student into a group where they are the only one from their college. """ def score_answers(self, question: Question, answers: list[Answer]) -> float: """Return score between 0.0 and 1.0 indicating the quality of the group of <answers> to the question <question>. The score returned will be 0.0 iff there are any unique answers in <answers> and will be 1.0 otherwise. An answer is unique if there is no other answer in <answers> with identical content. If there is only one answer in <answers> and it is valid, return 0.0 since the student with that answer is by definition the only one with that answer in the group. Raise InvalidAnswerError if any answer in <answers> is not a valid answer to <question>. Preconditions: - len(answers) > 0 """ result = Criterion.score_answers(self, question, answers) similarities = [] if len(answers) == 1: return result else: for i in range(len(answers)): for j in range(i + 1, len(answers)): similarities.append(question.get_similarity(answers[i], answers[j])) if 0.0 in similarities: return result else: return result + 1.0 if __name__ == '__main__': import python_ta python_ta.check_all(config={'extra-imports': ['typing', 'survey', 'E9992'], 'disable': ['E9992']})