Merge branch 'main' of https://github.com/open-thought/reasoning-gym into fix/simplify-rotate-matrix

reasoning_gym/algorithmic/__init__.py

@@ -13,6 +13,7 @@ from .group_anagrams import GroupAnagramsConfig, GroupAnagramsDataset
 from .isomorphic_strings import IsomorphicStringsConfig, IsomorphicStringsDataset
 from .letter_counting import LetterCountingConfig, LetterCountingDataset
 from .letter_jumble import LetterJumbleConfig, LetterJumbleDataset
+from .manipulate_matrix import ManipulateMatrixConfig, ManipulateMatrixDataset
 from .number_filtering import NumberFilteringConfig, NumberFilteringDataset
 from .number_sorting import NumberSortingConfig, NumberSortingDataset
 from .palindrome_generation import PalindromeConfig, PalindromeDataset
@@ -61,6 +62,8 @@ __all__ = [
     "IsomorphicStringsDataset",
     "RotateMatrixConfig",
     "RotateMatrixDataset",
+    "ManipulateMatrixConfig",
+    "ManipulateMatrixDataset",
     "BinaryMatrixConfig",
     "BinaryMatrixDataset",
 ]

reasoning_gym/algorithmic/manipulate_matrix.py

@@ -0,0 +1,273 @@
+"""Manipulate matrices by performing augmentations such as rotations, flips, mapping, etc."""
+
+from copy import deepcopy
+from dataclasses import dataclass
+from random import Random
+from typing import Optional
+
+from ..factory import ProceduralDataset, register_dataset
+
+QUESTION_TEMPLATE = """For the following matrix:
+{matrix}
+
+Perform the following series of operations in order:
+- Identity transformation, i.e. no change
+{operations}
+"""
+
+
+def num_rows(matrix: list[list[int]]) -> int:
+    return len(matrix)
+
+
+def num_cols(matrix: list[list[int]]) -> int:
+    return len(matrix[0]) if matrix else 0
+
+
+@dataclass
+class ManipulateMatrixConfig:
+    """Configuration for Manipulate Matrix dataset generation"""
+
+    min_rows: int = 1  # Minimum number of rows
+    min_cols: int = 1  # Minimum number of columns
+    max_rows: int = 10  # Maximum number of rows
+    max_cols: int = 10  # Maximum number of columns
+    max_transforms: int = 5  # Maximum number of transformations to apply
+    p_rotate: float = 0.2  # Probability of rotating the matrix
+    p_hmirror: float = 0.2  # Probability of horizontally mirroring the matrix
+    p_vmirror: float = 0.2  # Probability of vertically mirroring the matrix
+    p_dmirror: float = 0.2  # Probability of mirroring along the diagonal
+    p_cmirror: float = 0.2  # Probability of mirroring along the counterdiagonal
+    p_map: float = 0.2  # Probability of mapping a certain value to another
+    p_crop: float = 0.2  # Probability of cropping the matrix
+    p_remove_every_nth_row: float = 0.2  # Probability of removing every nth row
+    p_remove_every_nth_col: float = 0.2  # Probability of removing every nth column
+    p_zero_divisible: float = 0.2  # Probability of setting elements divisible by some number to zero
+
+    size: int = 500  # Virtual dataset size
+    seed: Optional[int] = None
+
+    def validate(self):
+        """Validate configuration parameters"""
+        assert 1 <= self.min_rows, "min_rows must be at least 1"
+        assert 1 <= self.min_cols, "min_cols must be at least 1"
+        assert self.min_rows <= self.max_rows, "max_rows must be at least min_rows"
+        assert self.min_cols <= self.max_cols, "max_cols must be at least min_cols"
+        assert 0 <= self.max_transforms, "max_transforms must be non-negative"
+        assert 0 <= self.p_rotate <= 1, "p_rotate must be between 0 and 1"
+        assert 0 <= self.p_hmirror <= 1, "p_hmirror must be between 0 and 1"
+        assert 0 <= self.p_vmirror <= 1, "p_vmirror must be between 0 and 1"
+        assert 0 <= self.p_dmirror <= 1, "p_dmirror must be between 0 and 1"
+        assert 0 <= self.p_cmirror <= 1, "p_cmirror must be between 0 and 1"
+        assert 0 <= self.p_map <= 1, "p_map must be between 0 and 1"
+        assert 0 <= self.p_crop <= 1, "p_crop must be between 0 and 1"
+        assert 0 <= self.p_remove_every_nth_row <= 1, "p_remove_every_nth_row must be between 0 and 1"
+        assert 0 <= self.p_remove_every_nth_col <= 1, "p_remove_nth_col must be between 0 and 1"
+        assert 0 <= self.p_zero_divisible <= 1, "p_zero_divisible must be between 0 and 1"
+
+
+class ManipulateMatrixDataset(ProceduralDataset):
+    """Generates Manipulate Matrix exercises with configurable difficulty"""
+
+    def __init__(self, config: ManipulateMatrixConfig):
+        super().__init__(config=config, seed=config.seed, size=config.size)
+        self._rotations = {
+            "90": self._rot90,
+            "180": self._rot180,
+            "270": self._rot270,
+            "360": self._identity,
+        }
+        self._all_transforms = [
+            "rotate",
+            "hmirror",
+            "vmirror",
+            "dmirror",
+            "cmirror",
+            "map",
+            "zero_divisible",
+            "crop",
+            "remove_every_nth_row",
+            "remove_every_nth_col",
+        ]
+
+    def _get_matrix(self, rng: Random) -> list[list[int]]:
+        """Generate a random matrix"""
+        rows = rng.randint(self.config.min_rows, self.config.max_rows)
+        cols = rng.randint(self.config.min_cols, self.config.max_cols)
+        numbers = [rng.randint(0, 9) for _ in range(rows * cols)]
+        matrix = [numbers[i * cols : (i + 1) * cols] for i in range(rows)]
+        return matrix
+
+    def _matrix_to_str(self, matrix: list[list[int]]) -> str:
+        """Get a string representation of the matrix"""
+        return "\n".join(" ".join(str(x) for x in row) for row in matrix)
+
+    def _identity(self, matrix: list[list[int]]) -> list[list[int]]:
+        """Identity transformation"""
+        return matrix
+
+    def _rot90(self, matrix: list[list[int]]) -> list[list[int]]:
+        """quarter clockwise rotation"""
+        return [list(row) for row in zip(*matrix[::-1])]
+
+    def _rot180(self, matrix: list[list[int]]) -> list[list[int]]:
+        """half rotation"""
+        return [list(row[::-1]) for row in matrix[::-1]]
+
+    def _rot270(self, matrix: list[list[int]]) -> list[list[int]]:
+        """quarter anticlockwise rotation"""
+        return [list(row[::-1]) for row in zip(*matrix[::-1])][::-1]
+
+    def _hmirror(self, matrix: list[list[int]]) -> list[list[int]]:
+        """mirroring along horizontal"""
+        return matrix[::-1]
+
+    def _vmirror(self, matrix: list[list[int]]) -> list[list[int]]:
+        """mirroring along vertical"""
+        return [row[::-1] for row in matrix]
+
+    def _dmirror(self, matrix: list[list[int]]) -> list[list[int]]:
+        """mirroring along diagonal"""
+        return list(list(row) for row in zip(*matrix))
+
+    def _cmirror(self, matrix: list[list[int]]) -> list[list[int]]:
+        """mirroring along counterdiagonal"""
+        return list(list(row) for row in zip(*[r[::-1] for r in matrix[::-1]]))
+
+    def _map(self, matrix: list[list[int]], a: int, b: int) -> list[list[int]]:
+        """mapping a to b"""
+        return [[b if x == a else x for x in row] for row in matrix]
+
+    def _zero_divisible(self, matrix: list[list[int]], k: int) -> list[list[int]]:
+        """set elements divisible by k to zero"""
+        return [[0 if x % k == 0 else x for x in row] for row in matrix]
+
+    def _crop(
+        self, matrix: list[list[int]], row_start: int, row_end: int, col_start: int, col_end: int
+    ) -> list[list[int]]:
+        """crop the matrix (1-indexed)"""
+        return [row[col_start - 1 : col_end] for row in matrix[row_start - 1 : row_end]]
+
+    def _remove_every_nth_row(self, matrix: list[list[int]], n: int) -> list[list[int]]:
+        """remove every nth row (1-indexed)"""
+        return [row for i, row in enumerate(matrix, start=1) if i % n != 0]
+
+    def _remove_every_nth_col(self, matrix: list[list[int]], n: int) -> list[list[int]]:
+        """remove every nth column (1-indexed)"""
+        return [[col for i, col in enumerate(row, start=1) if i % n != 0] for row in matrix]
+
+    def __getitem__(self, idx: int) -> dict:
+        """Generate a single Manipulate Matrix question"""
+        rng = Random(self.seed + idx)
+
+        matrix = self._get_matrix(rng)
+        matrix_str = self._matrix_to_str(matrix)
+
+        num_transforms = rng.randint(0, self.config.max_transforms)
+        transforms = rng.sample(self._all_transforms, num_transforms)
+        operations = []
+
+        answer = deepcopy(matrix)
+
+        for transform in transforms:
+            # Rotate
+            if transform == "rotate" and rng.random() < self.config.p_rotate:
+                rotation = rng.choice(list(self._rotations.keys()))
+                answer = self._rotations[rotation](answer)
+                operations.append(
+                    {
+                        "transform": transform,
+                        "degrees": rotation,
+                        "instruction": f"- Rotate the matrix {rotation} degrees",
+                    }
+                )
+            # Horizontal mirror
+            if transform == "hmirror" and rng.random() < self.config.p_hmirror:
+                answer = self._hmirror(answer)
+                operations.append({"transform": transform, "instruction": "- Horizontally mirror the matrix"})
+            # Vertical mirror
+            if transform == "vmirror" and rng.random() < self.config.p_vmirror:
+                answer = self._vmirror(answer)
+                operations.append({"transform": transform, "instruction": "- Vertically mirror the matrix"})
+            # Diagonal mirror
+            if transform == "dmirror" and rng.random() < self.config.p_dmirror:
+                answer = self._dmirror(answer)
+                operations.append({"transform": transform, "instruction": "- Mirror the matrix along the diagonal"})
+            # Counterdiagonal mirror
+            if transform == "cmirror" and rng.random() < self.config.p_cmirror:
+                answer = self._cmirror(answer)
+                operations.append(
+                    {"transform": transform, "instruction": "- Mirror the matrix along the counterdiagonal"}
+                )
+            # Map a value to another
+            if transform == "map" and rng.random() < self.config.p_map:
+                a, b = rng.sample(range(10), 2)
+                answer = self._map(answer, a, b)
+                operations.append(
+                    {"transform": transform, "from": a, "to": b, "instruction": f"- Map each occurrence of {a} to {b}"}
+                )
+            # Set elements divisible by k to zero
+            if transform == "zero_divisible" and rng.random() < self.config.p_zero_divisible:
+                k = rng.randint(1, 9)
+                answer = self._zero_divisible(answer, k)
+                operations.append(
+                    {"transform": transform, "k": k, "instruction": f"- Set all elements divisible by {k} to zero"}
+                )
+            # Crop the matrix
+            if transform == "crop" and rng.random() < self.config.p_crop:
+                row_start = rng.randint(1, num_rows(answer))
+                row_end = rng.randint(row_start, num_rows(answer))
+                col_start = rng.randint(1, num_cols(answer))
+                col_end = rng.randint(col_start, num_cols(answer))
+                answer = self._crop(answer, row_start, row_end, col_start, col_end)
+                operations.append(
+                    {
+                        "transform": transform,
+                        "row_start": row_start,
+                        "row_end": row_end,
+                        "col_start": col_start,
+                        "col_end": col_end,
+                        "instruction": f"- Crop the matrix to rows {row_start}-{row_end} and columns {col_start}-{col_end} (1-indexed)",
+                    }
+                )
+            # Remove every nth row
+            if (
+                transform == "remove_every_nth_row"
+                and rng.random() < self.config.p_remove_every_nth_row
+                and num_rows(answer) > 1
+            ):
+                n = rng.randint(2, num_rows(answer))
+                answer = self._remove_every_nth_row(answer, n)
+                formatting = "st" if n == 1 else "nd" if n == 2 else "th"
+                operations.append(
+                    {"transform": transform, "n": n, "instruction": f"- Remove every {n}-{formatting} row (1-indexed)"}
+                )
+            # Remove every nth column
+            if (
+                transform == "remove_every_nth_col"
+                and rng.random() < self.config.p_remove_every_nth_col
+                and num_cols(answer) > 1
+            ):
+                n = rng.randint(2, num_cols(answer))
+                answer = self._remove_every_nth_col(answer, n)
+                formatting = "st" if n == 1 else "nd" if n == 2 else "th"
+                operations.append(
+                    {
+                        "transform": transform,
+                        "n": n,
+                        "instruction": f"- Remove every {n}-{formatting} column (1-indexed)",
+                    }
+                )
+
+        answer_str = self._matrix_to_str(answer)
+
+        return {
+            "question": QUESTION_TEMPLATE.format(
+                matrix=matrix_str, operations="\n".join(op["instruction"] for op in operations)
+            ),
+            "answer": answer_str,
+            "metadata": {"matrix": matrix, "solution": answer, "operations": operations},
+        }
+
+
+register_dataset("manipulate_matrix", ManipulateMatrixDataset, ManipulateMatrixConfig)