Update.

[picoclvr.git] / main.py

diff --git a/main.py b/main.py
index b907e60..9dee679 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -170,10 +170,10 @@ default_args = {
         "nb_test_samples": 1000,
     },
     "expr": {
-        "nb_epochs": 5,
+        "nb_epochs": 50,
         "batch_size": 25,
-        "nb_train_samples": 100000,
-        "nb_test_samples": 1000,
+        "nb_train_samples": 250000,
+        "nb_test_samples": 10000,
     },
 }
 
@@ -1028,10 +1028,16 @@ class TaskExpr(Task):
         self.device = device
 
         train_sequences = expr.generate_sequences(
-            nb_train_samples, nb_variables=nb_variables, length=sequence_length
+            nb_train_samples,
+            nb_variables=nb_variables,
+            length=sequence_length,
+            # length=2 * sequence_length,
+            # randomize_length=True,
         )
         test_sequences = expr.generate_sequences(
-            nb_test_samples, nb_variables=nb_variables, length=sequence_length
+            nb_test_samples,
+            nb_variables=nb_variables,
+            length=sequence_length,
         )
         self.char2id = dict(
             [
@@ -1042,7 +1048,10 @@ class TaskExpr(Task):
             ]
         )
         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-        len_max = max([len(x) for x in train_sequences + test_sequences])
+
+        self.filler, self.space = self.char2id["#"], self.char2id[" "]
+
+        len_max = max([len(x) for x in train_sequences])
         self.train_input = torch.cat(
             [
                 torch.tensor(
@@ -1054,6 +1063,8 @@ class TaskExpr(Task):
             ],
             0,
         ).to(device)
+
+        len_max = max([len(x) for x in test_sequences])
         self.test_input = torch.cat(
             [
                 torch.tensor(
@@ -1065,6 +1076,7 @@ class TaskExpr(Task):
             ],
             0,
         ).to(device)
+
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
     def batches(self, split="train", nb_to_use=-1, desc=None):
@@ -1077,11 +1089,17 @@ class TaskExpr(Task):
         for batch in tqdm.tqdm(
             input.split(self.batch_size), dynamic_ncols=True, desc=desc
         ):
+            if split == "train":
+                last = (batch != self.filler).max(0).values.nonzero().max() + 1
+                batch = batch[:, :last]
             yield batch
 
     def vocabulary_size(self):
         return self.nb_codes
 
+    def seq2str(self, s):
+        return "".join([self.id2char[k.item()] for k in s])
+
     def produce_results(self, n_epoch, model):
         with torch.autograd.no_grad():
             t = model.training
@@ -1089,43 +1107,76 @@ class TaskExpr(Task):
 
             def compute_nb_correct(input):
                 result = input.clone()
-                filler, space = self.char2id["#"], self.char2id[" "]
-                ar_mask = (result == space).long().cumsum(dim=1).clamp(max=1)
-                result = (1 - ar_mask) * result + ar_mask * filler
+                ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
+                result = (1 - ar_mask) * result + ar_mask * self.filler
                 masked_inplace_autoregression(
                     model, self.batch_size, result, ar_mask, device=self.device
                 )
 
-                nb_total = ar_mask.sum()
-                nb_correct = ((input == result).long() * ar_mask).sum()
+                nb_total = input.size(0)
+                nb_correct = (input == result).long().min(1).values.sum()
 
-                return nb_total, nb_correct
+                #######################################################################
+                # Comput predicted vs. true variable values
 
-            test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+                nb_delta = torch.zeros(5, dtype=torch.int64)
+                nb_missed = 0
+
+                values_input = expr.extract_results([self.seq2str(s) for s in input])
+                values_result = expr.extract_results([self.seq2str(s) for s in result])
+
+                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_missed += 1
+                        else:
+                            d = abs(vr - vi)
+                            if d >= nb_delta.size(0):
+                                nb_missed += 1
+                            else:
+                                nb_delta[d] += 1
+
+                ######################################################################
+
+                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]
             result = input.clone()
-            filler, space = self.char2id["#"], self.char2id[" "]
-            ar_mask = (result == space).long().cumsum(dim=1).clamp(max=1)
-            result = (1 - ar_mask) * result + ar_mask * filler
+            ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
+            result = (1 - ar_mask) * result + ar_mask * self.filler
             for n in range(result.size(0)):
-                s = "".join([self.id2char[k.item()] for k in result[n]])
-                log_string(f"test_before {s}")
+                log_string(f"test_before {self.seq2str(result[n])}")
             masked_inplace_autoregression(
                 model, self.batch_size, result, ar_mask, device=self.device
             )
-            correct = (1 - ar_mask) * space + ar_mask * input
+            correct = (1 - ar_mask) * self.space + ar_mask * input
             for n in range(result.size(0)):
-                s = "".join([self.id2char[k.item()] for k in result[n]])
-                log_string(f"test_after  {s}")
-                s = "".join([self.id2char[k.item()] for k in correct[n]])
-                log_string(f"correct     {s}")
+                comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
+                log_string(f"test_after  {self.seq2str(result[n])} {comment}")
+                log_string(f"correct     {self.seq2str(correct[n])}")
             ##############################################################
 
             model.train(t)