feat: Add letter counting algorithmic task with configurable text span generation

reasoning_gym/algorithmic/letter_counting.py

@@ -0,0 +1,93 @@
+"""Letter counting task generator"""
+from dataclasses import dataclass
+import re
+from random import Random
+from typing import List, Optional
+
+from reasoning_gym.data import read_data_file
+
+@dataclass
+class LetterCountingConfig:
+    """Configuration for letter counting task generation"""
+    min_words: int = 5          # Minimum words in span
+    max_words: int = 15         # Maximum words in span
+    seed: Optional[int] = None
+    size: int = 500            # Virtual dataset size
+
+    def validate(self):
+        """Validate configuration parameters"""
+        assert self.min_words > 0, "min_words must be positive"
+        assert self.max_words >= self.min_words, "max_words must be >= min_words"
+
+
+class LetterCountingDataset:
+    """Generates letter counting tasks from text spans"""
+
+    def __init__(self, config: LetterCountingConfig):
+        self.config = config
+        self.config.validate()
+        self.seed = config.seed if config.seed is not None else Random().randint(0, 2**32)
+        
+        # Load and preprocess text
+        text = read_data_file("in_the_year_2889.txt")
+        self.words = [word for word in re.findall(r'\b\w+\b', text)]
+
+    def __len__(self) -> int:
+        return self.config.size
+
+    def __iter__(self):
+        self._current_idx = 0
+        return self
+
+    def __next__(self):
+        if self._current_idx >= self.config.size:
+            raise StopIteration
+        item = self[self._current_idx]
+        self._current_idx += 1
+        return item
+
+    def __getitem__(self, idx: int) -> dict:
+        """Generate a single letter counting task"""
+        rng = Random(self.seed + idx)
+        
+        # Select random span of words
+        span_length = rng.randint(self.config.min_words, self.config.max_words)
+        start_idx = rng.randint(0, len(self.words) - span_length)
+        span = self.words[start_idx:start_idx + span_length]
+        
+        # Get all unique letters from span
+        letters = set(''.join(span).lower())
+        if not letters:
+            letters = {'a'}  # Fallback if span has no letters
+            
+        # Select random letter that appears in the span
+        target_letter = rng.choice(list(letters))
+        
+        # Count occurrences
+        count = sum(word.lower().count(target_letter) for word in span)
+        
+        return {
+            "question": f'How many times does the letter "{target_letter}" appear in the text: "{" ".join(span)}"?',
+            "answer": str(count),
+            "metadata": {
+                "span_length": span_length,
+                "target_letter": target_letter,
+                "span": span
+            }
+        }
+
+
+def letter_counting_dataset(
+    min_words: int = 5,
+    max_words: int = 15,
+    seed: Optional[int] = None,
+    size: int = 500,
+) -> LetterCountingDataset:
+    """Create a LetterCountingDataset with the given configuration."""
+    config = LetterCountingConfig(
+        min_words=min_words,
+        max_words=max_words,
+        seed=seed,
+        size=size,
+    )
+    return LetterCountingDataset(config)