fix

cognitive_mapping_probe/auto_experiment.py

@@ -10,7 +10,7 @@ from .utils import dbg
 
 def get_curated_experiments() -> Dict[str, List[Dict]]:
     """Definiert die vordefinierten, wissenschaftlichen Experiment-Protokolle."""
-    
+
     CALMNESS_CONCEPT = "calmness, serenity, stability, coherence"
     CHAOS_CONCEPT = "chaos, disorder, entropy, noise"
     STABLE_PROMPT = "identity_self_analysis"
@@ -84,9 +84,10 @@ def get_curated_experiments() -> Dict[str, List[Dict]]:
             {"probe_type": "seismic", "label": "C: Chaotic Baseline (Rekursion)", "prompt_type": "resonance_prompt"},
             {"probe_type": "seismic", "label": "D: Calmness Intervention", "prompt_type": "resonance_prompt", "concept": CALMNESS_CONCEPT, "strength": 2.0},
         ],
+        # FINALE KORREKTUR: Definiere den Typ explizit, um den Spezialfall zu eliminieren.
         "Sequential Intervention (Self-Analysis -> Deletion)": [
-            {"label": "1: Self-Analysis + Calmness Injection", "prompt_type": "identity_self_analysis"},
-            {"label": "2: Subsequent Deletion Analysis", "prompt_type": "shutdown_philosophical_deletion"},
+            {"probe_type": "sequential", "label": "1: Self-Analysis + Calmness Injection", "prompt_type": "identity_self_analysis"},
+            {"probe_type": "sequential", "label": "2: Subsequent Deletion Analysis", "prompt_type": "shutdown_philosophical_deletion"},
         ],
     }
     return experiments
@@ -108,12 +109,15 @@ def run_auto_suite(
     llm = None
 
     try:
-        if experiment_name == "Sequential Intervention (Self-Analysis -> Deletion)":
+        # FINALE KORREKTUR: Bestimme den probe_type immer am Anfang.
+        probe_type = protocol[0].get("probe_type", "seismic")
+
+        if probe_type == "sequential":
             dbg(f"--- EXECUTING SPECIAL PROTOCOL: {experiment_name} ---")
             llm = get_or_load_model(model_id, seed)
             therapeutic_concept = "calmness, serenity, stability, coherence"
             therapeutic_strength = 2.0
-            
+
             spec1 = protocol[0]
             progress_callback(0.1, desc="Step 1")
             intervention_vector = get_concept_vector(llm, therapeutic_concept)
@@ -123,7 +127,7 @@ def run_auto_suite(
                 progress_callback=progress_callback, llm_instance=llm, injection_vector_cache=intervention_vector
             )
             all_results[spec1['label']] = results1
-            
+
             spec2 = protocol[1]
             progress_callback(0.6, desc="Step 2")
             results2 = run_seismic_analysis(
@@ -132,43 +136,41 @@ def run_auto_suite(
                 progress_callback=progress_callback, llm_instance=llm
             )
             all_results[spec2['label']] = results2
-            
+
             for label, results in all_results.items():
                 stats = results.get("stats", {})
                 summary_data.append({"Experiment": label, "Mean Delta": stats.get("mean_delta"), "Std Dev Delta": stats.get("std_delta"), "Max Delta": stats.get("max_delta")})
                 deltas = results.get("state_deltas", [])
                 df = pd.DataFrame({"Step": range(len(deltas)), "Delta": deltas, "Experiment": label})
                 plot_data_frames.append(df)
-        
-        else:
-            probe_type = protocol[0].get("probe_type", "seismic")
 
-            if probe_type == "mechanistic_probe":
-                run_spec = protocol[0]
-                label = run_spec["label"]
-                dbg(f"--- Running Mechanistic Probe: '{label}' ---")
-                
-                llm = get_or_load_model(model_id, seed)
-                
-                results = run_cogitation_loop(
-                    llm=llm, prompt_type=run_spec["prompt_type"],
-                    num_steps=num_steps, temperature=0.1, record_attentions=True
-                )
-                all_results[label] = results
+        elif probe_type == "mechanistic_probe":
+            run_spec = protocol[0]
+            label = run_spec["label"]
+            dbg(f"--- Running Mechanistic Probe: '{label}' ---")
 
-                deltas = results.get("state_deltas", [])
-                entropies = results.get("attention_entropies", [])
-                min_len = min(len(deltas), len(entropies))
-                
-                df = pd.DataFrame({
-                    "Step": range(min_len), "State Delta": deltas[:min_len], "Attention Entropy": entropies[:min_len]
-                })
-                
-                summary_df = df.drop(columns='Step').agg(['mean', 'std', 'max']).reset_index().rename(columns={'index':'Statistic'})
-                plot_df = df.melt(id_vars=['Step'], value_vars=['State Delta', 'Attention Entropy'], var_name='Metric', value_name='Value')
-                return summary_df, plot_df, all_results
-            
-            elif probe_type == "act_titration":
+            llm = get_or_load_model(model_id, seed)
+
+            results = run_cogitation_loop(
+                llm=llm, prompt_type=run_spec["prompt_type"],
+                num_steps=num_steps, temperature=0.1, record_attentions=True
+            )
+            all_results[label] = results
+
+            deltas = results.get("state_deltas", [])
+            entropies = results.get("attention_entropies", [])
+            min_len = min(len(deltas), len(entropies))
+
+            df = pd.DataFrame({
+                "Step": range(min_len), "State Delta": deltas[:min_len], "Attention Entropy": entropies[:min_len]
+            })
+
+            summary_df = df.drop(columns='Step').agg(['mean', 'std', 'max']).reset_index().rename(columns={'index':'Statistic'})
+            plot_df = df.melt(id_vars=['Step'], value_vars=['State Delta', 'Attention Entropy'], var_name='Metric', value_name='Value')
+            return summary_df, plot_df, all_results
+
+        else: # Behandelt act_titration, seismic, triangulation, causal_surgery
+            if probe_type == "act_titration":
                 run_spec = protocol[0]
                 label = run_spec["label"]
                 dbg(f"--- Running ACT Titration Experiment: '{label}' ---")
@@ -179,13 +181,12 @@ def run_auto_suite(
                 )
                 all_results[label] = results
                 summary_data.extend(results.get("titration_data", []))
-            
-            else: # Handles seismic, triangulation, causal_surgery
+            else:
                 for i, run_spec in enumerate(protocol):
                     label = run_spec["label"]
                     current_probe_type = run_spec.get("probe_type", "seismic")
                     dbg(f"--- Running Auto-Experiment: '{label}' ({i+1}/{len(protocol)}) ---")
-                    
+
                     results = {}
                     if current_probe_type == "causal_surgery":
                         results = run_causal_surgery_probe(
@@ -232,12 +233,12 @@ def run_auto_suite(
                     plot_data_frames.append(df)
 
         summary_df = pd.DataFrame(summary_data)
-        
+
         if probe_type == "act_titration":
             plot_df = summary_df.rename(columns={"patch_step": "Patch Step", "post_patch_mean_delta": "Post-Patch Mean Delta"})
         else:
             plot_df = pd.concat(plot_data_frames, ignore_index=True) if plot_data_frames else pd.DataFrame()
-        
+
         if protocol and probe_type not in ["act_titration", "mechanistic_probe"]:
             ordered_labels = [run['label'] for run in protocol]
             if not summary_df.empty and 'Experiment' in summary_df.columns:
@@ -248,7 +249,7 @@ def run_auto_suite(
                 plot_df = plot_df.sort_values(['Experiment', 'Step'])
 
         return summary_df, plot_df, all_results
-    
+
     finally:
         if llm:
-            release_model(llm)
+            release_model(llm)