train_sequences = expr.generate_sequences(
nb_train_samples,
nb_variables=nb_variables,
- length=2 * sequence_length,
- randomize_length=True,
+ length=sequence_length,
+ # length=2 * sequence_length,
+ # randomize_length=True,
)
test_sequences = expr.generate_sequences(
nb_test_samples,
nb_total = input.size(0)
nb_correct = (input == result).long().min(1).values.sum()
+ values_input = expr.extract_results([self.seq2str(s) for s in input])
+ max_input = max([max(x.values()) for x in values_input])
+ values_result = expr.extract_results([self.seq2str(s) for s in result])
+ max_result = max(
+ [-1 if len(x) == 0 else max(x.values()) for x in values_result]
+ )
+
+ nb_missing, nb_predicted = torch.zeros(max_input + 1), torch.zeros(
+ max_input + 1, max_result + 1
+ )
+ for i, r in zip(values_input, values_result):
+ for n, vi in i.items():
+ vr = r.get(n)
+ if vr is None or vr < 0:
+ nb_missing[vi] += 1
+ else:
+ nb_predicted[vi, vr] += 1
+
return nb_total, nb_correct
test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])