| class OieReader: | |
| def read(self, fn, includeNominal): | |
| ''' should set oie as a class member | |
| as a dictionary of extractions by sentence''' | |
| raise Exception("Don't run me") | |
| def count(self): | |
| ''' number of extractions ''' | |
| return sum([len(extractions) for _, extractions in self.oie.items()]) | |
| def split_to_corpus(self, corpus_fn, out_fn): | |
| """ | |
| Given a corpus file name, containing a list of sentences | |
| print only the extractions pertaining to it to out_fn in a tab separated format: | |
| sent, prob, pred, arg1, arg2, ... | |
| """ | |
| raw_sents = [line.strip() for line in open(corpus_fn)] | |
| with open(out_fn, 'w') as fout: | |
| for line in self.get_tabbed().split('\n'): | |
| data = line.split('\t') | |
| sent = data[0] | |
| if sent in raw_sents: | |
| fout.write(line + '\n') | |
| def output_tabbed(self, out_fn): | |
| """ | |
| Write a tabbed represenation of this corpus. | |
| """ | |
| with open(out_fn, 'w') as fout: | |
| fout.write(self.get_tabbed()) | |
| def get_tabbed(self): | |
| """ | |
| Get a tabbed format representation of this corpus (assumes that input was | |
| already read). | |
| """ | |
| return "\n".join(['\t'.join(map(str, | |
| [ex.sent, | |
| ex.confidence, | |
| ex.pred, | |
| '\t'.join(ex.args)])) | |
| for (sent, exs) in self.oie.iteritems() | |
| for ex in exs]) | |