src/mnemocore/core/synapse_index.py

"""
Synapse Adjacency Index — O(1) Lookup (Phase 4.0)
=================================================
Provides a hardened, thread-safe adjacency data structure for synaptic
connections with guaranteed O(1) lookup, O(1) insert, and O(k) neighbour
enumeration (where k = degree of a node).

Design:
  - Primary store: Dict[Tuple[str,str], SynapticConnection]
    Key is always sorted(a, b) to ensure uniqueness regardless of direction.
  - Adjacency index: Dict[str, set[str]]
    Maps node_id → set of connected node_ids.  Lookup is O(1), iteration O(k).
  - The set-based adjacency replaces the previous list-based one to prevent
    duplicate edges and make removals O(1) instead of O(k).

Phase 4.0 additions:
  - Bayesian LTP state is serialised alongside Hebbian state on save.
  - adjacency_degree() exposes per-node connectivity (used by immunology sweep).
  - to_dict() / from_dict() for full serialisation round-trips.
  - compact() removes all edges below a strength threshold in O(E) time.

This module is intentionally dependency-light: it only imports stdlib +
SynapticConnection + BayesianLTPUpdater from this package.
"""

from __future__ import annotations

import json
import os
import math
from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Dict, Iterator, List, Optional, Set, Tuple
from loguru import logger


# ------------------------------------------------------------------ #
#  Deferred import to avoid circular deps at module level             #
# ------------------------------------------------------------------ #

def _get_bayesian_updater():
    from .bayesian_ltp import get_bayesian_updater
    return get_bayesian_updater()


# ------------------------------------------------------------------ #
#  Adjacency index                                                    #
# ------------------------------------------------------------------ #

class SynapseIndex:
    """
    O(1) synaptic adjacency index.

    Thread-safety: designed to be used under an external asyncio.Lock.
    The engine calls all mutating methods inside its `synapse_lock`.

    Internals:
        _edges: Dict[Tuple[str,str], SynapticConnection]   primary edge store
        _adj:   Dict[str, Set[str]]                        adjacency sets
    """

    def __init__(self):
        from .synapse import SynapticConnection  # local import
        self._SynapticConnection = SynapticConnection
        self._edges: Dict[Tuple[str, str], "SynapticConnection"] = {}
        self._adj: Dict[str, Set[str]] = {}

    # ---- Public API --------------------------------------------- #

    def register(self, syn: "SynapticConnection") -> None:
        """
        Register an already-constructed SynapticConnection into the index.

        Use this instead of poking at _edges/_adj directly when you already
        have a SynapticConnection object (e.g. during legacy-dict sync in
        cleanup_decay).  No Bayesian observation is made – the connection is
        accepted as-is.

        O(1).
        """
        key = _key(syn.neuron_a_id, syn.neuron_b_id)
        if key not in self._edges:
            self._edges[key] = syn
            self._adj.setdefault(key[0], set()).add(key[1])
            self._adj.setdefault(key[1], set()).add(key[0])

    def add_or_fire(self, id_a: str, id_b: str, success: bool = True, weight: float = 1.0) -> "SynapticConnection":
        """
        Create a synapse if it doesn't exist, then fire it.

        O(1) operation.
        Returns the (potentially new) SynapticConnection.
        """
        key = _key(id_a, id_b)
        if key not in self._edges:
            syn = self._SynapticConnection(key[0], key[1])
            self._edges[key] = syn
            self._adj.setdefault(key[0], set()).add(key[1])
            self._adj.setdefault(key[1], set()).add(key[0])
            logger.debug(f"Synapse created: {key[0][:8]} ↔ {key[1][:8]}")

        syn = self._edges[key]

        # Phase 4.0: Bayesian update first, then Hebbian fire
        upd = _get_bayesian_updater()
        upd.observe_synapse(syn, success=success)
        # Also call the Hebbian fire for backward compat (updates fire_count etc.)
        syn.fire(success=success, weight=weight)

        return syn

    def get(self, id_a: str, id_b: str) -> Optional["SynapticConnection"]:
        """O(1) edge lookup. Returns None if no edge exists."""
        return self._edges.get(_key(id_a, id_b))

    def neighbours(self, node_id: str) -> List["SynapticConnection"]:
        """
        Return all SynapticConnections adjacent to node_id.

        O(k) where k is the degree.
        """
        neighbour_ids = self._adj.get(node_id, set())
        result = []
        for nid in neighbour_ids:
            syn = self._edges.get(_key(node_id, nid))
            if syn:
                result.append(syn)
        return result

    def get_multi_hop_neighbors(self, node_id: str, depth: int = 2) -> Dict[str, float]:
        """
        Phase 12.1: Traverse graph up to `depth` hops away.
        Returns a mapping of node_id -> maximum cumulative connection strength path.
        """
        visited = {node_id: 1.0}
        current_layer = {node_id: 1.0}
        
        for _ in range(depth):
            next_layer = {}
            for curr_node, cum_weight in current_layer.items():
                for syn in self.neighbours(curr_node):
                    neighbor_id = syn.neuron_b_id if syn.neuron_a_id == curr_node else syn.neuron_a_id
                    if neighbor_id == node_id:
                        continue
                    
                    edge_weight = syn.get_current_strength()
                    new_weight = cum_weight * edge_weight
                    
                    # Store the strongest path to the node
                    if neighbor_id not in visited or new_weight > visited[neighbor_id]:
                        visited[neighbor_id] = new_weight
                    
                    if neighbor_id not in next_layer or new_weight > next_layer[neighbor_id]:
                        next_layer[neighbor_id] = new_weight
            current_layer = next_layer
            
        visited.pop(node_id, None)
        return visited

    def neighbour_ids(self, node_id: str) -> Set[str]:
        """O(1) set of connected node IDs."""
        return self._adj.get(node_id, set()).copy()

    def remove_node(self, node_id: str) -> int:
        """
        Remove all edges involving node_id.

        O(k) where k is the degree.
        Returns number of edges removed.
        """
        neighbours = self._adj.pop(node_id, set())
        removed = 0
        for nid in neighbours:
            key = _key(node_id, nid)
            if self._edges.pop(key, None) is not None:
                removed += 1
            # Remove the reverse adjacency entry
            if nid in self._adj:
                self._adj[nid].discard(node_id)
                if not self._adj[nid]:
                    del self._adj[nid]
        return removed

    def remove_edge(self, id_a: str, id_b: str) -> bool:
        """
        Remove a single edge.

        O(1).  Returns True if the edge existed.
        """
        key = _key(id_a, id_b)
        syn = self._edges.pop(key, None)
        if syn is None:
            return False
        # Clean adjacency sets
        if key[0] in self._adj:
            self._adj[key[0]].discard(key[1])
            if not self._adj[key[0]]:
                del self._adj[key[0]]
        if key[1] in self._adj:
            self._adj[key[1]].discard(key[0])
            if not self._adj[key[1]]:
                del self._adj[key[1]]
        return True

    def compact(self, threshold: float = 0.05) -> int:
        """
        Remove all edges whose decayed strength is below `threshold`.

        O(E) where E = total edge count.
        Returns number of edges removed.
        """
        dead_keys = [
            k for k, s in self._edges.items()
            if s.get_current_strength() < threshold
        ]
        for key in dead_keys:
            syn = self._edges.pop(key)
            if syn.neuron_a_id in self._adj:
                self._adj[syn.neuron_a_id].discard(syn.neuron_b_id)
                if not self._adj[syn.neuron_a_id]:
                    del self._adj[syn.neuron_a_id]
            if syn.neuron_b_id in self._adj:
                self._adj[syn.neuron_b_id].discard(syn.neuron_a_id)
                if not self._adj[syn.neuron_b_id]:
                    del self._adj[syn.neuron_b_id]

        if dead_keys:
            logger.info(f"SynapseIndex.compact: removed {len(dead_keys)} dead edges.")
        return len(dead_keys)

    def adjacency_degree(self, node_id: str) -> int:
        """O(1) degree query."""
        return len(self._adj.get(node_id, set()))

    def boost(self, node_id: str) -> float:
        """
        Compute synaptic boost multiplier for a node (used in scoring).

        boost = ∏ (1 + strength_i)  over all edges i adjacent to node_id.

        Returns 1.0 for isolated nodes.
        """
        # Phase 4.5 Hotfix (Robin's Score Bug e+195):
        # Instead of exponential product scaling which explodes for hub nodes,
        # we aggregate the strengths and bound the multiplier logarithmically.
        total_strength = sum(syn.get_current_strength() for syn in self.neighbours(node_id))
        return 1.0 + math.log1p(total_strength)

    def __len__(self) -> int:
        return len(self._edges)

    def __iter__(self) -> Iterator[Tuple[Tuple[str, str], "SynapticConnection"]]:
        yield from self._edges.items()

    def items(self):
        return self._edges.items()

    def values(self):
        return self._edges.values()

    # ---- Persistence -------------------------------------------- #

    def to_jsonl(self) -> List[str]:
        """
        Serialise all edges to JSONL records (Phase 4.0: includes Bayesian state).
        """
        lines = []
        upd = _get_bayesian_updater()
        for syn in self._edges.values():
            rec = {
                "neuron_a_id": syn.neuron_a_id,
                "neuron_b_id": syn.neuron_b_id,
                "strength": syn.strength,
                "fire_count": syn.fire_count,
                "success_count": syn.success_count,
                "last_fired": syn.last_fired.isoformat() if syn.last_fired else None,
                "bayes": upd.synapse_to_dict(syn),  # Phase 4.0
            }
            lines.append(json.dumps(rec))
        return lines

    def load_jsonl(self, lines: List[str]) -> None:
        """
        Restore edges from JSONL records (Phase 4.0: restores Bayesian state).
        """
        self._edges.clear()
        self._adj.clear()
        upd = _get_bayesian_updater()

        for line in lines:
            line = line.strip()
            if not line:
                continue
            try:
                rec = json.loads(line)
                syn = self._SynapticConnection(
                    rec["neuron_a_id"], rec["neuron_b_id"], rec["strength"]
                )
                syn.fire_count = rec.get("fire_count", 0)
                syn.success_count = rec.get("success_count", 0)
                if rec.get("last_fired"):
                    syn.last_fired = datetime.fromisoformat(rec["last_fired"])

                # Phase 4.0: restore Bayesian state
                if "bayes" in rec:
                    upd.synapse_from_dict(syn, rec["bayes"])

                key = _key(syn.neuron_a_id, syn.neuron_b_id)
                self._edges[key] = syn
                self._adj.setdefault(key[0], set()).add(key[1])
                self._adj.setdefault(key[1], set()).add(key[0])
            except Exception as exc:
                logger.warning(f"SynapseIndex.load_jsonl: skipping bad record: {exc}")

    def save_to_file(self, path: str) -> None:
        """Save index to a JSONL file."""
        try:
            os.makedirs(os.path.dirname(path), exist_ok=True)
            lines = self.to_jsonl()
            with open(path, "w", encoding="utf-8") as f:
                f.write("\n".join(lines) + ("\n" if lines else ""))
        except Exception as exc:
            logger.error(f"SynapseIndex.save_to_file failed: {exc}")

    def load_from_file(self, path: str) -> None:
        """Load index from a JSONL file."""
        if not os.path.exists(path):
            return
        try:
            with open(path, "r", encoding="utf-8") as f:
                lines = f.readlines()
            self.load_jsonl(lines)
            logger.info(
                f"SynapseIndex loaded {len(self._edges)} edges from {path}"
            )
        except Exception as exc:
            logger.error(f"SynapseIndex.load_from_file failed: {exc}")

    @property
    def stats(self) -> Dict:
        return {
            "edge_count": len(self._edges),
            "node_count": len(self._adj),
            "avg_degree": (
                sum(len(v) for v in self._adj.values()) / len(self._adj)
                if self._adj else 0.0
            ),
        }


# ------------------------------------------------------------------ #
#  Helper                                                             #
# ------------------------------------------------------------------ #

def _key(a: str, b: str) -> Tuple[str, str]:
    """Canonical, order-independent edge key."""
    return (a, b) if a < b else (b, a)