class Topic:
def __init__(self, topic_settings={}):
if topic_settings == {}:
print("Topic: __init__: No settings given. Creating empty object.")
return
if 'topic-name' not in topic_settings:
print("Topic: Provide a topic name")
exit(-12312)
self.topic_name = topic_settings['topic-name']
if 'procon' not in topic_settings:
print('Topic: Provide procon settings')
exit(-124)
procon_settings = topic_settings['procon']
if 'reddit' not in topic_settings:
print('Topic: Provide reddit settings')
exit(-3324)
reddit_settings = topic_settings['reddit']
procon_settings['topic'] = self.topic_name
reddit_settings['topic'] = self.topic_name
self.procon = Procon(procon_settings)
self.reddit = Reddit(reddit_settings)
self.similarity_matrices = {}
for name, similarity_matrix_algorithm in similarity_matrix_algorithms.items(
):
self.similarity_matrices[name] = similarity_matrix_algorithm.match(
[comment.text for comment in self.get_all_comments()],
self.get_pros(), self.get_cons())
def to_dict(self):
dic = {}
dic['topic_name'] = self.topic_name
dic['procon'] = self.procon.to_dict()
dic['reddit'] = self.reddit.to_dict()
dic['similarity_matrices'] = self.similarity_matrices
return dic
def from_dict(self, dic):
self.topic_name = dic['topic_name']
procon_dict = dic['procon']
procon = Procon()
procon.from_dict(procon_dict)
self.procon = procon
reddit_dict = dic['reddit']
reddit = Reddit()
reddit.from_dict(reddit_dict)
self.reddit = reddit
self.similarity_matrices = dic['similarity_matrices']
def get_similarity_matrices(self, similarity_matrix_algorithm):
similarity_matrix_pro, similarity_matrix_con = self.similarity_matrices[
similarity_matrix_algorithm]
return similarity_matrix_pro, similarity_matrix_con
def get_similarity_matrix(self, similarity_matrix_algorithm, polarity):
similarity_matrix_pro, similarity_matrix_con = self.get_similarity_matrices(
similarity_matrix_algorithm)
if polarity == 'pro':
return similarity_matrix_pro
elif polarity == 'con':
return similarity_matrix_con
else:
print("Topic: getSimilarityMatrix: Unknown polarity:", polarity)
def get_comment_rankings(self, similarity_matrix_algorithm, polarity):
similarity_matrix = self.get_similarity_matrix(
similarity_matrix_algorithm, polarity)
comment_rankings = []
for similarity_vector_argument in similarity_matrix:
comment_rankings.append(
sorted(similarity_vector_argument,
key=lambda tup: tup[0],
reverse=True))
return comment_rankings
def get_comment_rankings_text(self, similarity_matrix_algorithm, polarity):
comment_rankings = self.get_comment_rankings(
similarity_matrix_algorithm, polarity)
comment_rankings_text = []
for comment_ranking in comment_rankings:
comment_ranking_text = []
for comment_score, comment_idx in comment_ranking:
comment_text = self.get_all_comments()[comment_idx].text
comment_ranking_text.append((comment_score, comment_text))
comment_rankings_text.append(comment_ranking_text)
return comment_rankings_text
def transpose(self, matrix):
transposed_matrix = [[] for _ in matrix[0]]
for vector in matrix:
for i in range(len(vector)):
transposed_matrix[i].append(vector[i])
return transposed_matrix
def get_aggregated_scores_comments(self, similarity_matrix_algorithm,
aggregation):
similarity_matrix_pro, similarity_matrix_con = self.get_similarity_matrices(
similarity_matrix_algorithm)
combined_matrix = similarity_matrix_pro + similarity_matrix_con
transposed_combined_matrix = self.transpose(combined_matrix)
aggregated_comment_scores = []
for similarity_vector_comment in transposed_combined_matrix:
if aggregation == 'max':
comment_score = max(similarity_vector_comment,
key=itemgetter(0))
else:
print(
"Topic: get_aggregated_score_comments: Unknown aggregation method:",
aggregation)
exit(0)
aggregated_comment_scores.append(comment_score)
return aggregated_comment_scores
def get_data_points_comment_score_author_delta(self,
similarity_matrix_algorithm,
aggregation='max'):
data_points = {}
aggregated_comment_scores = self.get_aggregated_scores_comments(
similarity_matrix_algorithm, aggregation)
for score, idx in aggregated_comment_scores:
deltas_author = self.get_all_comments()[idx].author_delta
if deltas_author not in data_points:
data_points[deltas_author] = []
data_points[deltas_author].append(score)
return data_points
def get_all_comments(self):
return self.reddit.getAllComments()
def get_pros(self):
return self.procon.pros
def get_cons(self):
return self.procon.cons