def create_conjunction_graph(self):
fallacy_map = {
unidecode(key): value
for (key, value) in
get_fallacy_types()
}
for contention in Contention.objects.all():
for premise in contention.premises.all():
fallacies = filter(None, premise.reports.values_list(
'fallacy_type', flat=True))
fallacies = [
fallacy_map[unidecode(_f)]
for _f in fallacies
]
fallacies_set = set(fallacies)
for fallacy in fallacies_set:
graph.add_edges_from(
[
(unidecode(self.normalize(fallacy)),
unidecode(self.normalize(_f)))
for _f in fallacies_set
if _f != fallacy
]
)
nx.write_gml(graph, 'conjunction.gml')
def create_conjunction_graph(self):
fallacy_map = {
unidecode(key): value
for (key, value) in get_fallacy_types()
}
for contention in Contention.objects.all():
for premise in contention.premises.all():
fallacies = filter(
None, premise.reports.values_list('fallacy_type',
flat=True))
fallacies = [fallacy_map[unidecode(_f)] for _f in fallacies]
fallacies_set = set(fallacies)
for fallacy in fallacies_set:
graph.add_edges_from([(unidecode(self.normalize(fallacy)),
unidecode(self.normalize(_f)))
for _f in fallacies_set
if _f != fallacy])
nx.write_gml(graph, 'conjunction.gml')
def create_report_graph(self):
for (fallacy_type, localized) in get_fallacy_types():
node = unidecode(self.normalize(localized))
graph.add_node(node, type="fallacy", Weight=10)
for premise in Premise.objects.filter(
reports__fallacy_type=fallacy_type):
#graph.add_node(premise.argument.pk, type="argument")
#graph.add_edge(premise.argument.pk, node, type="reported")
if premise.argument.channel:
channel_node = unidecode(premise.argument.channel.title)
graph.add_node(channel_node, type="channel",
Weight=premise.argument.channel.contentions.count() * 30)
graph.add_edge(channel_node, node, type="reported")
nx.write_gml(graph, 'reports.gml')
def create_report_graph(self):
for (fallacy_type, localized) in get_fallacy_types():
node = unidecode(self.normalize(localized))
graph.add_node(node, type="fallacy", Weight=10)
for premise in Premise.objects.filter(
reports__fallacy_type=fallacy_type):
#graph.add_node(premise.argument.pk, type="argument")
#graph.add_edge(premise.argument.pk, node, type="reported")
if premise.argument.channel:
channel_node = unidecode(premise.argument.channel.title)
graph.add_node(
channel_node,
type="channel",
Weight=premise.argument.channel.contentions.count() *
30)
graph.add_edge(channel_node, node, type="reported")
nx.write_gml(graph, 'reports.gml')
def humanize_fallacy_type(value):
return dict(get_fallacy_types()).get(value)
def humanize_fallacy_type(value):
return dict(get_fallacy_types()).get(value)
