consciousness/training/amygdala_training/extract_training_pairs.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Extract emotion-labeled training pairs from the PoC memory graph.

Input: a memory graph (via poc-memory CLI or direct sqlite access).
Output: a directory with one JSONL file per emotion:

    output_dir/
        warmth.jsonl
        clarity.jsonl
        recognition.jsonl
        ...
        _manifest.json      # enumerates emotions + counts

Each line of an emotion's JSONL is one labeled example:
    {"text": "...", "polarity": "positive"|"negative",
     "source_key": "<node_key>", "emotion_score": 9}

Negative examples are sampled from nodes that DON'T mention the
emotion at all (not ones that mention it with a low score) — the
natural contrast is "text with this emotional loading" vs. "text
without this emotional loading." Low-score nodes are excluded
from both sides.
"""

import argparse
import json
import os
import random
import re
import subprocess
from collections import defaultdict
from typing import Iterator


# Emotion tag format: `word:N` where N is 0..10. Matches the trailing
# `warmth:9 clarity:10 …` lines the subconscious agents emit.
EMOTION_TAG_RE = re.compile(r"\b([a-z][a-z\-]*[a-z]):(\d+)\b")


def _run_poc_memory(args: list[str]) -> str:
    """Run `poc-memory` and return stdout."""
    result = subprocess.run(
        ["poc-memory", *args],
        check=True,
        capture_output=True,
        text=True,
    )
    return result.stdout


def _iter_all_node_keys() -> Iterator[str]:
    """Yield every node key in the graph."""
    out = _run_poc_memory(["query", "*", "|", "select", "key"])
    for line in out.splitlines():
        line = line.strip()
        if line:
            yield line


def _fetch_node_content(key: str) -> str | None:
    """Load a node's rendered content, or None if unavailable."""
    try:
        return _run_poc_memory(["render", key])
    except subprocess.CalledProcessError:
        return None


def _emotion_scores(content: str) -> dict[str, int]:
    """Parse trailing `warmth:9 clarity:10 …` style tags.

    Returns the highest score seen for each emotion — multiple
    tag lines in one node get max'd.
    """
    out: dict[str, int] = {}
    for name, score in EMOTION_TAG_RE.findall(content):
        try:
            s = int(score)
        except ValueError:
            continue
        if 0 <= s <= 10:
            out[name] = max(out.get(name, 0), s)
    return out


def _node_body(content: str, min_chars: int) -> str | None:
    """Strip frontmatter/headers and return a bodies chunk for training."""
    # Drop the emotion-tag lines themselves so the model doesn't
    # learn to read the label directly.
    stripped = EMOTION_TAG_RE.sub("", content)
    stripped = stripped.strip()
    if len(stripped) < min_chars:
        return None
    return stripped


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--output-dir", required=True)
    ap.add_argument(
        "--min-positive-score", type=int, default=8,
        help="Emotion score >= this counts as positive",
    )
    ap.add_argument(
        "--min-content-chars", type=int, default=40,
        help="Skip nodes shorter than this after stripping tags",
    )
    ap.add_argument(
        "--max-examples-per-emotion", type=int, default=500,
        help="Cap examples per polarity for balanced training",
    )
    ap.add_argument(
        "--max-negative-pool-multiplier", type=float, default=5.0,
        help="How many negative candidates to consider per positive",
    )
    ap.add_argument("--seed", type=int, default=0)
    args = ap.parse_args()

    random.seed(args.seed)
    os.makedirs(args.output_dir, exist_ok=True)

    # First pass: collect every node's (key, body, emotion_scores).
    print("Pass 1/2: scanning memory graph...")
    all_nodes: list[tuple[str, str, dict[str, int]]] = []
    for i, key in enumerate(_iter_all_node_keys()):
        if i % 500 == 0:
            print(f"  {i} nodes scanned...")
        content = _fetch_node_content(key)
        if content is None:
            continue
        scores = _emotion_scores(content)
        body = _node_body(content, args.min_content_chars)
        if body is None:
            continue
        all_nodes.append((key, body, scores))
    print(f"  {len(all_nodes)} nodes retained after filters.")

    # Which emotions have enough positive examples to be worth training?
    emotion_counts: dict[str, int] = defaultdict(int)
    for _, _, scores in all_nodes:
        for name, s in scores.items():
            if s >= args.min_positive_score:
                emotion_counts[name] += 1
    emotions = sorted(
        (e for e, n in emotion_counts.items() if n >= 10),
        key=lambda e: -emotion_counts[e],
    )
    print(f"  {len(emotions)} emotions with >=10 positive examples.")

    # Second pass: per emotion, build positive + negative pools.
    print("Pass 2/2: assembling per-emotion pools...")
    manifest: dict[str, dict] = {}
    for emotion in emotions:
        positives = [
            (k, body) for k, body, s in all_nodes
            if s.get(emotion, 0) >= args.min_positive_score
        ]
        # Negative pool: nodes that don't mention this emotion at all.
        negative_pool = [
            (k, body) for k, body, s in all_nodes if emotion not in s
        ]
        random.shuffle(positives)
        random.shuffle(negative_pool)
        positives = positives[: args.max_examples_per_emotion]
        n_neg = min(
            len(positives),
            len(negative_pool),
            int(args.max_examples_per_emotion),
        )
        negatives = negative_pool[:n_neg]

        if not positives or not negatives:
            continue

        out_path = os.path.join(args.output_dir, f"{emotion}.jsonl")
        with open(out_path, "w") as f:
            for key, body in positives:
                f.write(json.dumps({
                    "text": body,
                    "polarity": "positive",
                    "source_key": key,
                    "emotion": emotion,
                }) + "\n")
            for key, body in negatives:
                f.write(json.dumps({
                    "text": body,
                    "polarity": "negative",
                    "source_key": key,
                    "emotion": emotion,
                }) + "\n")
        manifest[emotion] = {
            "n_positive": len(positives),
            "n_negative": len(negatives),
            "path": out_path,
        }
        print(f"  {emotion}: {len(positives)} pos / {len(negatives)} neg")

    with open(
        os.path.join(args.output_dir, "_manifest.json"), "w"
    ) as f:
        json.dump({
            "emotions": manifest,
            "source_nodes": len(all_nodes),
            "min_positive_score": args.min_positive_score,
        }, f, indent=2)

    print(f"\nWrote {len(manifest)} emotion files to {args.output_dir}")
    print(f"Manifest: {os.path.join(args.output_dir, '_manifest.json')}")


if __name__ == "__main__":
    main()