| """Shared base utilities for per-area generators. |
| |
| This module is domain-agnostic. Area-specific logic lives in |
| `aba_<area>.py` modules. |
| """ |
|
|
| import random |
| import re |
| from pathlib import Path |
|
|
| import yaml |
|
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| |
| |
| |
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|
|
| def load_yaml(path: Path) -> dict: |
| """Load a single YAML file.""" |
| with open(path) as f: |
| return yaml.safe_load(f) |
|
|
|
|
| def load_shared(config_dir: Path) -> dict: |
| """Load the shared primitives used across all areas. |
| |
| Returns a dict keyed by primitive name: |
| learner_profiles, mastery_states, prompt_types |
| """ |
| shared_dir = Path(config_dir) / "shared" |
| return { |
| "learner_profiles": load_yaml(shared_dir / "learner_profiles.yaml"), |
| "mastery_states": load_yaml(shared_dir / "mastery_states.yaml"), |
| "prompt_types": load_yaml(shared_dir / "prompt_types.yaml"), |
| } |
|
|
|
|
| def load_area(config_dir: Path, area: str) -> dict: |
| """Load an area's self-contained config: taxonomy + template + compatibility.""" |
| area_dir = Path(config_dir) / area |
| if not area_dir.is_dir(): |
| raise FileNotFoundError(f"Area config directory not found: {area_dir}") |
| return { |
| "taxonomy": load_yaml(area_dir / "taxonomy.yaml"), |
| "template": load_yaml(area_dir / "template.yaml"), |
| "compatibility": load_yaml(area_dir / "compatibility.yaml"), |
| } |
|
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| |
| |
| |
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|
|
| def article(word: str) -> str: |
| """Return 'a' or 'an' appropriate for the following word.""" |
| if not word: |
| return "a" |
| return "an" if word[0].lower() in "aeiou" else "a" |
|
|
|
|
| def strip_trailing_period(text: str) -> str: |
| """Remove a single trailing period — for safe mid-sentence embedding.""" |
| return text.rstrip().rstrip(".") |
|
|
|
|
| def render_prompt_sequence(hierarchy_sequence: list, prompt_types: list) -> str: |
| """Render a prompt-hierarchy sequence as a human-readable arrow chain. |
| |
| Looks up each item in the canonical prompt_types list; falls back to |
| underscore-to-space conversion for composite IDs like '0s_delay'. |
| """ |
| id_to_name = {pt["id"]: pt["name"] for pt in prompt_types} |
| rendered = [] |
| for seq_id in hierarchy_sequence: |
| if seq_id in id_to_name: |
| rendered.append(id_to_name[seq_id].lower()) |
| else: |
| rendered.append(seq_id.replace("_", " ")) |
| return " -> ".join(rendered) |
|
|
|
|
| def parse_skill_numeric_range(skill_text: str): |
| """Extract (min, max) numeric range from skill text. |
| |
| Handles patterns like '1-5', '1–5' (en-dash), 'to 10', '1-20'. |
| Returns None if no range is present. |
| """ |
| s = skill_text.lower() |
| m = re.search(r"(\d+)\s*[-–]\s*(\d+)", s) |
| if m: |
| lo, hi = int(m.group(1)), int(m.group(2)) |
| return (min(lo, hi), max(lo, hi)) |
| m = re.search(r"to\s+(\d+)", s) |
| if m: |
| return (1, int(m.group(1))) |
| return None |
|
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| |
| |
| |
|
|
| STIMULUS_POOLS = { |
| "objects": [ |
| "ball", "cup", "shoe", "spoon", "book", "car", "block", "brush", "key", |
| "phone", "hat", "sock", "apple", "crayon", "scissors", "glue", "pencil", |
| "napkin", "plate", "towel", "jacket", "backpack", "bottle", "blanket", |
| "toothbrush", |
| ], |
| "colors": ["red", "blue", "green", "yellow", "orange", "purple", "pink", "brown", "black", "white"], |
| "shapes": ["circle", "square", "triangle", "rectangle", "star", "diamond", "oval", "heart"], |
| "animals": [ |
| "dog", "cat", "bird", "fish", "horse", "cow", "pig", "sheep", "rabbit", |
| "frog", "bear", "lion", "elephant", "monkey", "duck", "chicken", |
| ], |
| "actions": [ |
| "running", "jumping", "eating", "sleeping", "reading", "writing", |
| "drawing", "singing", "clapping", "washing", "brushing", "pouring", |
| ], |
| "letters": list("ABCDEFGHIJKLMNOPQRSTUVWXYZ"), |
| "numbers": [str(n) for n in range(1, 21)], |
| } |
|
|
| SKILL_TO_POOL = [ |
| ("color", "colors"), |
| ("shape", "shapes"), |
| ("animal", "animals"), |
| ("letter", "letters"), |
| ("number", "numbers"), |
| ("numeral", "numbers"), |
| ("count", "numbers"), |
| ("action", "actions"), |
| ] |
|
|
|
|
| def choose_stimulus_pool(skill_target: str) -> str: |
| """Pick the stimulus pool whose keyword appears in the skill name; default 'objects'.""" |
| s = skill_target.lower() |
| for keyword, pool in SKILL_TO_POOL: |
| if keyword in s: |
| return pool |
| return "objects" |
|
|
|
|
| def sample_stimuli( |
| skill_target: str, array_size_n: int, rng: random.Random |
| ) -> dict: |
| """Pick target + distractor stimuli, respecting any numeric range in the skill text.""" |
| pool_name = choose_stimulus_pool(skill_target) |
| pool = list(STIMULUS_POOLS[pool_name]) |
|
|
| rng_tuple = parse_skill_numeric_range(skill_target) |
| if rng_tuple is not None: |
| lo, hi = rng_tuple |
| if pool_name == "numbers": |
| pool = [str(n) for n in range(lo, hi + 1)] |
| elif pool_name == "letters": |
| pool = pool[: max(hi, 3)] |
|
|
| n_targets = min(3, len(pool)) |
| targets = rng.sample(pool, n_targets) |
| remaining = [x for x in pool if x not in targets] |
| n_distractors = min(max(array_size_n - 1, 0), len(remaining)) |
| distractors = rng.sample(remaining, n_distractors) if n_distractors > 0 else [] |
| return {"targets": targets, "distractors": distractors} |
|
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| |
| |
| |
|
|
|
|
| import hashlib |
|
|
|
|
| def make_example_envelope( |
| *, |
| system_content: str, |
| user_content: str, |
| assistant_content: str, |
| task_type: str, |
| gold_labels: dict, |
| provenance: dict, |
| ) -> dict: |
| """Assemble the final JSONL example dict with id + meta envelope. |
| |
| Envelope is a deterministic function of its inputs: identical inputs produce |
| an identical envelope, including the example_id. The example_id is |
| ``sha256(user_content + assistant_content)[:16]`` computed on the *published* |
| (stripped) message content, so any consumer can verify it directly from the |
| JSONL row. Per-run wall-clock timestamps are deliberately NOT included so |
| that running the generator twice with the same seed produces byte-identical |
| output. |
| """ |
| system_content = system_content.strip() |
| user_content = user_content.strip() |
| assistant_content = assistant_content.strip() |
| example_hash = hashlib.sha256( |
| (user_content + assistant_content).encode() |
| ).hexdigest()[:16] |
| return { |
| "messages": [ |
| {"role": "system", "content": system_content}, |
| {"role": "user", "content": user_content}, |
| {"role": "assistant", "content": assistant_content}, |
| ], |
| "meta": { |
| "task_type": task_type, |
| "example_id": example_hash, |
| "gold_labels": gold_labels, |
| "provenance": provenance, |
| }, |
| } |
|
|
