Update evo_transformer.py

evo_transformer.py

@@ -1,5 +1,4 @@
 # evo_transformer.py
-
 import random
 import json
 
@@ -25,8 +24,7 @@ class EvoTransformer:
         elif trait == "ffn_dim":
             new_config[trait] = random.choice([512, 1024, 2048])
         elif trait == "dropout":
-            change = round(random.uniform(-0.05, 0.05), 2)
-            new_config[trait] = round(min(max(0.0, new_config[trait] + change), 0.5), 2)
+            new_config[trait] = round(min(max(0.0, new_config[trait] + random.uniform(-0.05, 0.05)), 0.5), 2)
         elif trait == "memory":
             new_config[trait] = not new_config[trait]
 
@@ -41,22 +39,19 @@ class EvoTransformer:
         return self.history
 
     def evaluate(self):
-        # Simulated accuracy and parameter count
-        accuracy = round(0.8 + random.uniform(0.01, 0.15), 4)
-        return {
-            "accuracy": accuracy,
-            "params": self.estimate_params()
-        }
+        score = round(random.uniform(0.85, 0.95), 4)
+        return {"accuracy": score, "params": self.estimate_params()}
 
     def estimate_params(self):
-        # Lightweight param estimate formula (not real count)
-        multiplier = 1.0 + (0.5 if self.config["memory"] else 0.0)
-        return int(self.config["layers"] * self.config["ffn_dim"] * multiplier)
-
-    def export_csv(self):
-        import pandas as pd
-        df = pd.DataFrame(self.history)
-        return df.to_csv(index=False)
-
-    def export_json(self):
-        return json.dumps(self.history, indent=2)
+        return round(self.config["layers"] * self.config["ffn_dim"] * 0.001, 2)
+
+    def save_history(self):
+        csv_path = "evo_history.csv"
+        json_path = "evo_history.json"
+        with open(csv_path, "w") as f:
+            f.write("generation,layers,attention_heads,ffn_dim,dropout,memory\n")
+            for i, cfg in enumerate(self.history):
+                f.write(f"{i+1},{cfg['layers']},{cfg['attention_heads']},{cfg['ffn_dim']},{cfg['dropout']},{cfg['memory']}\n")
+        with open(json_path, "w") as f:
+            json.dump(self.history, f, indent=2)
+        return csv_path, json_path