nb_total = input.size(0)
nb_correct = (input == result).long().min(1).values.sum()
+ #######################################################################
+ # Comput predicted vs. true variable values
+
+ nb_delta = torch.zeros(5, dtype=torch.int64)
+ nb_missed = 0
+
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
+ nb_missed += 1
else:
- nb_predicted[vi, vr] += 1
+ d = abs(vr - vi)
+ if d >= nb_delta.size(0):
+ nb_missed += 1
+ else:
+ nb_delta[d] += 1
- return nb_total, nb_correct
+ ######################################################################
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+ return nb_total, nb_correct, nb_delta, nb_missed
+
+ (
+ test_nb_total,
+ test_nb_correct,
+ test_nb_delta,
+ test_nb_missed,
+ ) = compute_nb_correct(self.test_input[:1000])
log_string(
f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
)
+ nb_total = test_nb_delta.sum() + test_nb_missed
+ for d in range(test_nb_delta.size(0)):
+ log_string(
+ f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
+ )
+ log_string(
+ f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+ )
+
##############################################################
# Log a few generated sequences
input = self.test_input[:10]