From: François Fleuret Date: Wed, 5 Jul 2023 07:25:40 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=87c9333e7800e62911cd4299500d4824d29a1ce1;p=culture.git Update. --- diff --git a/main.py b/main.py index 15e6d99..32447bf 100755 --- a/main.py +++ b/main.py @@ -1119,33 +1119,40 @@ class TaskExpr(Task): ####################################################################### # 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 = torch.zeros(max_input + 1) - nb_predicted = 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 + return nb_total, nb_correct, nb_delta, nb_missed - test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000]) + 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]