How to Trigger Model Retraining on Drift in Python

import random
import time

class MockModel:
    def __init__(self, name):
        self.name = name
        self.accuracy = 0.85
        self.version = 1

    def train(self, data_size):
        # Simulate training time and accuracy improvement
        time.sleep(0.1)
        drift = random.uniform(-0.02, 0.02)
        self.accuracy = min(0.99, max(0.50, self.accuracy + drift))
        self.version += 1
        return self.accuracy

    def predict(self, inputs):
        # Simulate prediction based on current model version
        return [self.accuracy * x for x in inputs]

def trigger_retrain(model, current_accuracy, threshold=0.80):
    if current_accuracy < threshold:
        print(f"DRIFT DETECTED for {model.name}: accuracy dropped to {current_accuracy:.2f}")
        new_acc = model.train(data_size=1000)
        print(f"Retrained {model.name} v{model.version}: new accuracy {new_acc:.2f}")
        return True
    else:
        print(f"No drift for {model.name}: accuracy {current_accuracy:.2f} >= {threshold}")
        return False

if __name__ == "__main__":
    random.seed(42)
    model = MockModel("sentiment-analysis")
    
    # Simulate streaming predictions and periodic drift checks
    for window in range(5):
        # Mock monitoring: simulate accuracy degradation over time
        predicted = model.predict([1, 2, 3])
        current_acc = model.accuracy - (window * 0.03)
        
        if trigger_retrain(model, current_acc):
            predicted = model.predict([1, 2, 3])
        
        print(f"Window {window}: predictions={[round(p, 3) for p in predicted]}\n")