X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=problems.py;h=d7dbc542aa14b0c77999b7b7a55ece9394e57c90;hb=HEAD;hp=78bb64e601785bf2e116be86bd14faa4d7021696;hpb=db7cefe4fefb381e56f1292d5bbe4a18c76afb47;p=picoclvr.git

diff --git a/problems.py b/problems.py
index 78bb64e..446e1a1 100755
--- a/problems.py
+++ b/problems.py
@@ -17,14 +17,194 @@ class Problem:
     def seq2str(self, seq):
         return "[NOT IMPLEMENTED]"
 
+    def compute_nb_correct(self, input, ar_mask, result):
+        nb_total = ar_mask.sum().item()
+        nb_correct = ((result == input).long() * ar_mask).sum().item()
+        return nb_total, nb_correct
+
+
+####################
+
+
+class ProblemDegradation(Problem):
+    def __init__(self, nb_state_tokens=5, nb_time_steps=12, value_max=25, hard=False):
+        assert value_max // nb_state_tokens >= 2
+        self.nb_state_tokens = nb_state_tokens
+        self.nb_time_steps = nb_time_steps
+        self.value_max = value_max
+        self.hard = hard
+
+    def generate_sequences(self, nb):
+        x = (
+            torch.rand(nb, self.nb_state_tokens).sort(dim=-1).indices == 0
+        ).long() * self.value_max
+        seq = [x]
+
+        for t in range(self.nb_time_steps - 1):
+            v = (torch.rand(x.size()).sort(dim=-1).indices + 1) * (x >= 2).long()
+            u = (v.max(dim=-1, keepdim=True).values == v).long()
+            n = (
+                (u * x)
+                .minimum(2 + torch.randint(self.value_max // 4 - 2, x.size()))
+                .sum(dim=-1, keepdim=True)
+            )
+            m = 1 + ((n - 1) * torch.rand(n.size())).long()
+            x = (
+                x
+                + m * u.roll(shifts=-1, dims=-1)
+                - n * u
+                + (n - m) * u.roll(shifts=1, dims=-1)
+            )
+            seq.append(x)
+
+        if self.hard:
+            seq.reverse()
+
+        seq = torch.cat(seq, dim=1)
+        return seq, seq.new_full(seq.size(), 1, dtype=torch.int64)
+
+    def compute_nb_correct(self, input, ar_mask, result):
+        nb_total = result.size(0)
+        nb_correct = 0
+        e = result.new_zeros(self.nb_state_tokens)
+
+        for seq in result:
+            states = list(seq.split(self.nb_state_tokens))
+            if self.hard:
+                states.reverse()
+
+            d = states[0]
+            j = d.sort(descending=True).indices[0]
+            e.zero_()
+            e[j] = self.value_max
+            if (d - e).abs().sum() == 0:
+                nb_errors = 0
+                for k in range(len(states) - 1):
+                    d = states[k + 1] - states[k]
+                    j = d.sort(descending=False).indices[0]
+                    if (
+                        d[j] == 0
+                        or d[j] > self.value_max // 4
+                        or d[(j + 1) % e.size(0)] <= 0
+                        or d[(j + 1) % e.size(0)] >= -d[j]
+                    ):
+                        nb_errors += 1
+                    else:
+                        e.zero_()
+                        e[j] = d[j]
+                        e[(j + 1) % e.size(0)] = d[(j + 1) % e.size(0)]
+                        e[(j - 1) % e.size(0)] = -d[(j + 1) % e.size(0)] - d[j]
+                        if (d - e).abs().sum() > 0:
+                            nb_errors += 1
+                if nb_errors == 0:
+                    nb_correct += 1
+
+        return nb_total, nb_correct
+
+    def seq2str(self, seq):
+        return " | ".join(
+            [" ".join([f"{x:02d}" for x in s]) for s in seq.split(self.nb_state_tokens)]
+        )
+
+
+####################
+
+
+class ProblemMemory(Problem):
+    def __init__(self, len_total=25):
+        self.len_total = len_total
+        self.max_len_pattern = 5
+        self.nb_noise_tokens = 10
+        self.start_pattern_token = 0
+        self.end_pattern_token = 1
+        self.start_result_token = 2
+        self.end_result_token = 3
+        self.token_string = "[]<>" + "".join(
+            [chr(ord("a") + k) for k in range(self.nb_noise_tokens)]
+        )
+
+    def generate_sequences(self, nb):
+        sequences = (
+            torch.randint(self.nb_noise_tokens, (nb, self.len_total))
+            + self.end_result_token
+            + 1
+        )
+        len_patterns = torch.randint(self.max_len_pattern, (nb,)) + 1
+        pattern_positions = torch.randint(
+            self.len_total - (5 + 2 * self.max_len_pattern), (nb,)
+        )
+        k = self.len_total - (3 + self.max_len_pattern)
+        for i in range(nb):
+            l = len_patterns[i]
+            j = pattern_positions[i]
+            sequences[i, j] = self.start_pattern_token
+            sequences[i, j + l + 2] = self.end_pattern_token
+            sequences[i, k] = self.start_result_token
+            sequences[i, k + l + 2] = self.end_result_token
+            sequences[i, k + 1 : k + 2 + l] = sequences[i, j + 1 : j + 2 + l]
+
+        j = torch.arange(self.len_total)[None, :]
+        ar_mask = (j > k).long() * (j <= k + 1 + len_patterns[:, None]).long()
+
+        return sequences, ar_mask
+
+    def seq2str(self, seq):
+        return "".join(self.token_string[x.item()] for x in seq)
+
+
+class ProblemTwoTargets(Problem):
+    def __init__(self, len_total=10, len_targets=3):
+        assert len_targets >= 3
+        assert len_total >= 3 * len_targets - 1
+        self.len_total = len_total
+        self.len_targets = len_targets
+
+    def generate_sequences(self, nb):
+        k = torch.arange(self.len_total)[None, :]
+        s = torch.randint(10, (nb, self.len_total))
+        l = torch.rand(nb, self.len_total)
+        l = l * (k <= self.len_total - self.len_targets).long()
+        k1 = l.argmax(dim=1, keepdim=True)
+        m = (k != k1).long() * (k != k1 + self.len_targets - 1).long()
+        s = s * m + 10 * (1 - m)
+        l = l * (
+            1
+            - (k + self.len_targets - 1 >= k1).long()
+            * (k < k1 + self.len_targets).long()
+        )
+        k2 = l.argmax(dim=1, keepdim=True)
+        m = (k != k2).long() * (k != k2 + self.len_targets - 1).long()
+        s = s * m + 11 * (1 - m)
+        a1 = s.gather(dim=1, index=k1 + 1 + torch.arange(self.len_targets - 2)[None, :])
+        a2 = s.gather(dim=1, index=k2 + 1 + torch.arange(self.len_targets - 2)[None, :])
+        sequences = torch.cat(
+            (
+                s,
+                torch.full((nb, 1), 12),
+                a1,
+                torch.full((nb, 1), 12),
+                a2,
+                torch.full((nb, 1), 12),
+            ),
+            1,
+        )
+        ar_mask = (sequences == 12).long()
+        ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+        return sequences, ar_mask
+
+    def seq2str(self, seq):
+        return "".join("0123456789-+|"[x.item()] for x in seq)
+
 
 ####################
 
 
-class ProblemLevel0(Problem):
-    def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
-        self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result))
-        self.seq[:, len_prompt] = 10
+class ProblemByHeart(Problem):
+    def __init__(self, nb_sentences=100, len_prompt=8, len_result=8, separation=1):
+        self.seq = torch.randint(
+            10, (nb_sentences, len_prompt + separation + len_result)
+        )
+        self.seq[:, len_prompt : len_prompt + separation] = 10
 
     def generate_sequences(self, nb):
         sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
@@ -32,9 +212,15 @@ class ProblemLevel0(Problem):
         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
         return sequences, ar_mask
 
+    def seq2str(self, seq):
+        return "".join("0123456789|"[x.item()] for x in seq)
+
+
+####################
+
 
-class ProblemLevel1(Problem):
-    def __init__(self, nb_operators=100, len_source=5, len_result=8):
+class ProblemLearnOperator(Problem):
+    def __init__(self, nb_operators=100, len_source=6, len_result=9):
         self.len_source = len_source
         self.len_result = len_result
         self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
@@ -51,7 +237,6 @@ class ProblemLevel1(Problem):
             // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
         ) % 10
         marker1 = torch.full((nb, 1), 10)
-        # source = torch.randint(10, (nb, self.len_source))
         source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
         marker2 = torch.full((nb, 1), 11)
         result = operators.bmm(source[:, :, None]).squeeze(-1)
@@ -64,7 +249,10 @@ class ProblemLevel1(Problem):
         return "".join("0123456789|>"[x.item()] for x in seq)
 
 
-class ProblemLevel2(Problem):
+####################
+
+
+class ProblemGuessOperator(Problem):
     def __init__(self, len_source=5, len_result=8):
         self.len_source = len_source
         self.len_result = len_result
@@ -141,19 +329,164 @@ class ProblemAddition(Problem):
         return "".join(self.id2char[x.item()] for x in seq)
 
 
-# class ProblemUnion(Problem):
-# problems = [ProblemByheart()]
-# nb_common_codes = 100
+####################
+
+
+class ProblemMixing(Problem):
+    def __init__(
+        self, height=4, width=4, nb_time_steps=9, hard=False, random_start=True
+    ):
+        self.height = height
+        self.width = width
+        self.nb_time_steps = nb_time_steps
+        self.hard = hard
+        self.random_start = random_start
+
+    def start_random(self, nb):
+        y = torch.arange(self.height * self.width).reshape(1, -1).expand(nb, -1)
+
+        if self.random_start:
+            i = (
+                torch.arange(self.height)
+                .reshape(1, -1, 1)
+                .expand(nb, self.height, self.width)
+            )
+            j = (
+                torch.arange(self.width)
+                .reshape(1, 1, -1)
+                .expand(nb, self.height, self.width)
+            )
+
+            ri = torch.randint(self.height, (nb,)).reshape(nb, 1, 1)
+            rj = torch.randint(self.width, (nb,)).reshape(nb, 1, 1)
+
+            m = 1 - torch.logical_or(i == ri, j == rj).long().flatten(1)
+
+            y = y * m + self.height * self.width * (1 - m)
+
+        y = y.reshape(nb, self.height, self.width)
+
+        return y
+
+    def start_error(self, x):
+        if self.random_start:
+            i = (
+                torch.arange(self.height, device=x.device)
+                .reshape(1, -1, 1)
+                .expand_as(x)
+            )
+            j = torch.arange(self.width, device=x.device).reshape(1, 1, -1).expand_as(x)
+
+            ri = (
+                (x == self.height * self.width)
+                .long()
+                .sum(dim=-1)
+                .argmax(-1)
+                .view(-1, 1, 1)
+            )
+            rj = (
+                (x == self.height * self.width)
+                .long()
+                .sum(dim=-2)
+                .argmax(-1)
+                .view(-1, 1, 1)
+            )
+
+            m = 1 - torch.logical_or(i == ri, j == rj).long().flatten(1)
+        else:
+            m = 1
+
+        x = x.flatten(1)
+        u = torch.arange(self.height * self.width, device=x.device).reshape(1, -1)
+
+        d = (x - (m * u + (1 - m) * self.height * self.width)).abs().sum(-1)
+
+        return d
+
+    def moves(self, x):
+        y = (
+            x[:, None, :, :]
+            .expand(-1, self.height * 2 + self.width * 2, -1, -1)
+            .clone()
+        )
+        k = 0
+
+        for i in range(self.height):
+            y[:, k, i, :] = y[:, k, i, :].roll(dims=-1, shifts=-1)
+            k += 1
+            y[:, k, i, :] = y[:, k, i, :].roll(dims=-1, shifts=1)
+            k += 1
+
+        for j in range(self.width):
+            y[:, k, :, j] = y[:, k, :, j].roll(dims=-1, shifts=-1)
+            k += 1
+            y[:, k, :, j] = y[:, k, :, j].roll(dims=-1, shifts=1)
+            k += 1
+
+        return y
+
+    def generate_sequences(self, nb):
+        x = self.start_random(nb)
+
+        seq = [x.flatten(1)]
 
-# def generate_sequences(nb_samples):
-# problem_indexes = torch.randint(len(problems), (nb_samples,))
-# nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
-# print(f"{nb_samples_per_problem}")
-# all_seq = []
-# for nb, p in zip(nb_samples_per_problem, problems):
-# all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
-# return all_seq
+        for t in range(self.nb_time_steps - 1):
+            y = self.moves(x)
+            x = y[torch.arange(nb), torch.randint(y.size(1), (nb,))]
+            seq.append(x.flatten(1))
+
+        if self.hard:
+            seq.reverse()
+
+        seq = torch.cat(seq, dim=1)
+        return seq, seq.new_full(seq.size(), 1, dtype=torch.int64)
+
+    def compute_nb_correct(self, input, ar_mask, result):
+        a = [
+            x.reshape(result.size(0), self.height, self.width)
+            for x in result.split(self.height * self.width, dim=1)
+        ]
+        if self.hard:
+            a.reverse()
+
+        x = a[0]
+
+        d = self.start_error(x)
+
+        for t in range(self.nb_time_steps - 1):
+            x0, x = a[t], a[t + 1]
+            y = self.moves(x0)
+            d = d + (x[:, None] - y).abs().sum((-1, -2)).min(dim=-1).values
+
+        nb_total, nb_correct = result.size(0), (d == 0).long().sum().item()
+
+        return nb_total, nb_correct
+
+    def seq2str(self, seq):
+        return " | ".join(
+            [
+                " ".join(
+                    [
+                        "-".join(
+                            [
+                                f"{x:02d}" if x < self.height * self.width else "**"
+                                for x in s
+                            ]
+                        )
+                        for s in r.split(self.width)
+                    ]
+                )
+                for r in seq.split(self.height * self.width)
+            ]
+        )
+
+
+####################
 
-# for strain, stest in zip(train_seq, test_seq):
-# s = torch.cat((strain, stest), 0)
+if __name__ == "__main__":
+    p = ProblemMixing(height=3, width=3, random_start=False)
 
+    s, m = p.generate_sequences(10000)
+    for x in s[:5]:
+        print(p.seq2str(x))
+    print(p.compute_nb_correct(None, None, s))