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import pandas as pd
from .utils import predict_dict
class PolicyArgMax():
'''Choose input for which max reward is obtained. Pseudocode:
* Determine the input features of the models a.k.a. state variables
* Determine all possible state combinations
* For each state, run the model, obtain the output and calculate reward
* Return the state giving the maximum reward
#### Formula
$$ \max_{state} reward(model(state)) $$
'''
def __init__(self):
self.label = """PolicyArgMax: pursue max reward """
def choose(self, model, ds, input_ranges, cur_state, cur_metrics, reward_fn):
#print(input_ranges)
input_df, output_df = predict_dict(model, ds, input_ranges)
io_df = pd.concat([input_df, output_df],axis=1)
io_df['reward'] = io_df.apply(lambda row: reward_fn.calculate(row), axis=1)
max_reward_index = io_df['reward'].argmax()
next_state = io_df.loc[max_reward_index].to_dict()
#print('next state', next_state)
return next_state
class PolicyHPA():
'''Increase/decrease replica if cpu usage is above/below threshold
**Remarks**
* reward functions are not used.
'''
def __init__(self, threshold = 0.4):
self.label = f"""Kubernetes HPA (cpu threshold={threshold})"""
self.threshold = threshold
def choose(self, model, ds, input_ranges, cur_state, cur_metrics, reward_fn):
next_state = cur_state.copy()
if cur_metrics['cpu_usage'] > self.threshold:
next_state['replica'] = min(cur_state['replica'] + 1, max(input_ranges['replica']))
elif cur_metrics['cpu_usage'] < self.threshold:
next_state['replica'] = max(cur_state['replica'] - 1, min(input_ranges['replica']))
return next_state |