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# evo_transformer.py
import random
import json
class EvoTransformer:
def __init__(self, config=None):
self.config = config or {
"layers": 4,
"attention_heads": 4,
"ffn_dim": 1024,
"dropout": 0.1,
"memory": False,
}
self.history = [self.config.copy()]
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":
new_config[trait] = random.choice([2, 4, 6, 8])
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)
elif trait == "memory":
new_config[trait] = not new_config[trait]
self.config = new_config
self.history.append(new_config.copy())
def evolve(self, generations=3):
for _ in range(generations):
self.mutate()
def get_history(self):
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()
}
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)
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