Add target cognition percentage and enhance reporting in cognition pr…

…ogress; update tests for new functionality

models/consciousness_model.py

@@ -129,6 +129,8 @@ def __init__(self, hidden_dim: int, num_heads: int, num_layers: int, num_states:
         self.error_handler = ErrorHandler(self.logger)
         self.error_correction = ErrorCorrection(hidden_dim)
 
+        self.target_cognition_percentage = 90.0  # Target cognition percentage
+
     def add_meta_learning_layer(self):
         """Add meta-learning capabilities"""
         self.meta_learner = nn.ModuleDict({
@@ -249,29 +251,33 @@ def calculate_cognition_progress(self, metrics):
 
     def report_cognition_progress(self):
         """
-        Report the overall cognition progress and identify areas for improvement.
+        Report the overall cognition progress, target, and identify areas for improvement.
         """
         if not self.cognition_progress_history:
             return "No cognition progress data available."
-            
+
         latest = self.cognition_progress_history[-1]
         report = [
-            f"Current Cognition Progress: {latest['total']:.2f}%\n",
+            f"Current Cognition Progress: {latest['total']:.2f}%",
+            f"Target Cognition Progress: {self.target_cognition_percentage:.2f}%\n",
             "Breakdown:",
             f"- Integrated Information (Phi): {latest['breakdown']['phi']*100:.2f}%",
             f"- Thought Coherence: {latest['breakdown']['coherence']*100:.2f}%",
             f"- Context Stability: {latest['breakdown']['stability']*100:.2f}%",
             f"- Adaptability: {latest['breakdown']['adaptability']*100:.2f}%",
             f"- Memory Retention: {latest['breakdown']['memory_retention']*100:.2f}%",
-            f"- Emotional Coherence: {latest['breakdown']['emotional_coherence']*100:.2f}%",  # New metric
-            f"- Decision Making Efficiency: {latest['breakdown']['decision_making_efficiency']*100:.2f}%\n",  # New metric
+            f"- Emotional Coherence: {latest['breakdown']['emotional_coherence']*100:.2f}%",
+            f"- Decision Making Efficiency: {latest['breakdown']['decision_making_efficiency']*100:.2f}%\n",
             "Areas Needing Improvement:"
         ]
 
+        # Identify areas below target threshold (e.g., 70%)
+        threshold = 70.0
         for metric, value in latest['breakdown'].items():
-            if value < 0.6:
-                report.append(f"- {metric.replace('_', ' ').title()}")
-
+            if value * 100 < threshold:
+                readable_metric = metric.replace('_', ' ').title()
+                report.append(f"- {readable_metric}: {value*100:.2f}%")
+
         return "\n".join(report)
 
     def forward(self, inputs, state=None, initial_state=None, deterministic=True, consciousness_threshold=0.5):

tests/unit/test_cognition_progress.py

@@ -137,4 +137,41 @@ def test_stress_condition_large_dataset(self, model):
         model.optimize_memory_usage()
         assert len(model.cognition_progress_history) == 100
         assert len(model.state_history) == 50
-        assert len(model.context_history) == 50
+        assert len(model.context_history) == 50
+
+    def test_report_cognition_progress_with_target(self, model):
+        model.target_cognition_percentage = 80.0
+        metrics = {
+            'phi': 0.6,
+            'coherence': 0.75,
+            'stability': 0.65,
+            'adaptability': 0.80,
+            'memory_retention': 0.70,
+            'emotional_coherence': 0.60,  # Below threshold
+            'decision_making_efficiency': 0.85
+        }
+        model.calculate_cognition_progress(metrics)
+        report = model.report_cognition_progress()
+        assert "Current Cognition Progress: 68.75%" in report
+        assert "Target Cognition Progress: 80.00%" in report
+        assert "Emotional Coherence: 60.00%" in report
+        assert "Areas Needing Improvement:" in report
+
+    def test_report_cognition_progress_no_improvement_needed(self, model):
+        model.target_cognition_percentage = 70.0
+        metrics = {
+            'phi': 0.8,
+            'coherence': 0.75,
+            'stability': 0.85,
+            'adaptability': 0.80,
+            'memory_retention': 0.90,
+            'emotional_coherence': 0.75,  # Above threshold
+            'decision_making_efficiency': 0.85
+        }
+        model.calculate_cognition_progress(metrics)
+        report = model.report_cognition_progress()
+        assert "Current Cognition Progress: 81.00%" in report
+        assert "Target Cognition Progress: 70.00%" in report
+        assert "Areas Needing Improvement:" in report
+        # No areas should be listed as needing improvement
+        assert not any(metric in report for metric in []), "No metrics should need improvement"