Update evo_transformer.py

evo_transformer.py

@@ -1,5 +1,6 @@
 # evo_transformer.py
 import random
+import pandas as pd
 import json
 
 class EvoTransformer:
@@ -16,7 +17,7 @@ class EvoTransformer:
     def mutate(self):
         new_config = self.config.copy()
         trait = random.choice(list(new_config.keys()))
-        
+
         if trait == "layers":
             new_config[trait] = max(1, new_config[trait] + random.choice([-1, 1]))
         elif trait == "attention_heads":
@@ -31,27 +32,24 @@ class EvoTransformer:
         self.config = new_config
         self.history.append(new_config.copy())
 
-    def evolve(self, generations=3):
+    def evolve(self, generations=5):
         for _ in range(generations):
             self.mutate()
 
-    def get_history(self):
-        return self.history
-
     def evaluate(self):
-        score = round(random.uniform(0.85, 0.95), 4)
-        return {"accuracy": score, "params": self.estimate_params()}
+        # Simulated accuracy and parameter estimate
+        accuracy = round(random.uniform(0.85, 0.95), 4)
+        params = self.estimate_params()
+        return accuracy, params
 
     def estimate_params(self):
         return round(10 + self.config["layers"] * self.config["ffn_dim"] * 0.001, 2)
 
-    def export_csv(self):
-        headers = list(self.history[0].keys())
-        lines = [",".join(headers)]
-        for config in self.history:
-            line = ",".join([str(config[h]) for h in headers])
-            lines.append(line)
-        return "\n".join(lines)
+    def get_history_df(self):
+        return pd.DataFrame(self.history)
 
-    def export_json(self):
+    def get_history_json(self):
         return json.dumps(self.history, indent=2)
+
+    def get_final_config(self):
+        return self.config