Restructure into nima_unified package + add model card

nima_unified/training/goal_formulator.py

@@ -0,0 +1,79 @@
+"""
+Goal Formulator — analyzes capabilities, identifies gaps, defines improvement goals.
+"""
+
+import logging
+import time
+from collections import deque
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger("nima_unified.training.goal_formulator")
+
+
+class GoalFormulator:
+    """
+    Formulates improvement goals by analyzing capabilities, identifying gaps,
+    and defining objectives based on system performance and feedback.
+    """
+
+    def __init__(self):
+        self.performance_history: deque = deque(maxlen=100)
+        self.capability_map: Dict[str, float] = {}
+        self.gap_analysis: List[Dict[str, Any]] = []
+        self.goals: List[Dict[str, Any]] = []
+        logger.info("GoalFormulator initialized")
+
+    def analyze_capabilities(self, capabilities: Dict[str, float]) -> Dict[str, Any]:
+        """Analyze current capabilities and identify gaps."""
+        self.capability_map.update(capabilities)
+
+        gaps = {}
+        priorities = []
+
+        for cap, score in capabilities.items():
+            if score < 0.5:
+                gaps[cap] = {"current": score, "priority": "HIGH"}
+                priorities.append((cap, score, "HIGH"))
+            elif score < 0.8:
+                gaps[cap] = {"current": score, "priority": "MEDIUM"}
+                priorities.append((cap, score, "MEDIUM"))
+            else:
+                gaps[cap] = {"current": score, "priority": "LOW"}
+
+        priorities.sort(key=lambda x: (x[2] != "HIGH", x[2] != "MEDIUM", x[1]))
+
+        analysis = {
+            "timestamp": time.time(),
+            "total_capabilities": len(capabilities),
+            "average_maturity": sum(capabilities.values()) / len(capabilities) if capabilities else 0.0,
+            "gaps": gaps,
+            "priority_order": [p[0] for p in priorities],
+        }
+
+        self.gap_analysis.append(analysis)
+        logger.debug(f"Capability analysis: {len(priorities)} gaps identified")
+        return analysis
+
+    def formulate_goals(
+        self,
+        gap_analysis: Dict[str, Any],
+        performance_feedback: Optional[Dict[str, Any]] = None,
+    ) -> List[Dict[str, Any]]:
+        """Formulate improvement goals from gap analysis and feedback."""
+        goals = []
+        for gap in gap_analysis.get("priority_order", []):
+            gap_info = gap_analysis["gaps"][gap]
+            current_score = gap_info["current"]
+            goal = {
+                "capability": gap,
+                "current_score": current_score,
+                "target_score": min(1.0, current_score + 0.3),
+                "priority": gap_info["priority"],
+                "formulated_at": time.time(),
+                "target_completion": time.time() + (3600 if gap_info["priority"] == "HIGH" else 7200),
+            }
+            goals.append(goal)
+
+        self.goals.extend(goals)
+        logger.info(f"Formulated {len(goals)} improvement goals")
+        return goals