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164 lines (128 loc) · 6.48 KB
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"""
Utilitiy functions for RL algorithm
"""
from sklearn.neural_network import MLPRegressor
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
class BatchRL_env(object):
def __init__(self, environment, mlp_max_iter=100, envir_type='real'):
"""
:param mlp_max_iter: maximum number of iteration in multilayer perceptrons.
:param envir_type: the environment, choosen from 'simu' for simulated data and 'real' for calibrated real data.
"""
self.environment = environment
self.envir_type = envir_type
self.mlp_max_iter = mlp_max_iter
self.state1_observe = []
self.action_taken = []
self.reward_received = []
self.state2_observe = []
return
def initialize_training(self, train_data):
"""
Initialize training process
"""
self.Cost_Dict = {}
self.Q_nn = {}
self.train_data = train_data
return
def get_cost(self, l, r, seed=1):
"""
Collect the cost function.
:return: the cost
"""
if self.Cost_Dict.get(str(l) + ':' + str(r)) == None:
if l == r:
self.Cost_Dict[str(l) + ':' + str(r)] = 0
else:
subdata = self.train_data[(self.train_data['at'] >= l / self.m) & (self.train_data['at'] <= r / self.m)]
if len(subdata) == 0:
self.Cost_Dict[str(l) + ':' + str(r)] = 0
else:
regr = MLPRegressor(hidden_layer_sizes=(10,10), random_state=seed, max_iter=self.mlp_max_iter).fit(np.array([x for x in subdata['xt']]), subdata['yt'])
y_fit = regr.predict(np.array([x for x in subdata['xt']]))
self.Cost_Dict[str(l) + ':' + str(r)] = sum((y_fit - subdata['yt']) ** 2)
self.Q_nn[str(l) + ':' + str(r)] = regr
return self.Cost_Dict.get(str(l) + ':' + str(r))
def get_prop_score(self, l, r, seed=1, act_method='logistic'):
"""
Calculate the propensity score function for each interval.
:return: the propensity score function
"""
self.train_data[str(l) + ':' + str(r)] = 1 * ((self.train_data['at'] >= l / self.m) & (self.train_data['at'] <= r / self.m))
regr = MLPRegressor(hidden_layer_sizes=(10,10), random_state=seed, max_iter= self.mlp_max_iter, activation=act_method).fit(np.array([x for x in self.train_data['xt']]), self.train_data[str(l) + ':' + str(r)])
return regr
def least_square_loss(self, tau, test_data, seed=1):
"""
Use the left k-fold to calculate the least square loss function.
:return: the Estimated Value
"""
self.test_data = test_data
ls_loss = 0
for i in range(len(tau)):
l = tau[i]
r = tau[i + 1] if i < len(tau) - 1 else self.m
subdata = self.test_data[(self.test_data['at'] >= l / self.m) &
(self.test_data['at'] < r / self.m)] if i < len(tau) - 1 else self.test_data[(self.test_data['at'] >= l / self.m) & (self.test_data['at'] <= r / self.m)]
if len(subdata) > 0:
fitted_Q = self.Q_nn[str(l) + ':' + str(r)].predict(np.array([x for x in subdata['xt']]))
ls_loss += sum((subdata['yt'] - fitted_Q) ** 2)
return ls_loss
def evaluate(self, tau, test_data, seed=1):
"""
Value Evaluation
:return: the Estimated Value
"""
self.test_data = test_data
V_hat = 0
for i in range(len(tau)):
l = tau[i]
r = tau[i + 1] if i < len(tau) - 1 else self.m
#print('Processing interval: (', l / self.m, ',', r / self.m, ')...')
subdata = self.test_data[(self.test_data['at'] >= l / self.m) &
(self.test_data['at'] < r / self.m)] if i < len(tau) - 1 else self.test_data[(self.test_data['at'] >= l / self.m) & (self.test_data['at'] <= r / self.m)]
if len(subdata) > 0:
prop_score = self.get_prop_score(l, r)
if prop_score == 1:
prob_fit = 1
else:
prob_fit = prop_score.predict(np.array([x for x in subdata['xt']]))
prob_fit = np.minimum(np.maximum(prob_fit, len(subdata['yt']) / len(self.test_data['yt'])), 1.)
#print('fitted behavior prob: ', prob_fit)
pi_star_ind = np.array([(self.policy_evaluate(x) >= l / self.m) * (self.policy_evaluate(x) < r / self.m) for x in subdata['xt']]) if i < len(tau) - 1 else np.array([(self.policy_evaluate(x) >= l / self.m) * (self.policy_evaluate(x) <= r / self.m) for x in subdata['xt']])
fitted_Q = self.Q_nn[str(l) + ':' + str(r)].predict(np.array([x for x in subdata['xt']]))
#print('fitted Q: ', fitted_Q)
V_hat += sum(pi_star_ind / prob_fit * (subdata['yt'] - fitted_Q) + fitted_Q)
#print('diff: ', (subdata['yt'] - fitted_Q))
V_hat = V_hat / len(self.test_data['at'])
#print('Estimated Value: ', V_hat)
return V_hat
def sample(self):
"""
sample one traj from environment based on behavior policy.
:return:
"""
if self.envir_type == 'simu':
context = self.environment.get_context()
action = self.policy_behavior(context)
reward = self.environment.get_reward(context, action)
elif self.envir_type == 'real':
state1, action, reward, state2 = self.environment.get_onesample()
self.state1_observe.append(state1)
self.action_taken.append(action)
self.reward_received.append(reward)
self.state2_observe.append(state2)
def get_dataset(self, n):
"""
generate data from environment based on behavior policy.
:return: the dataset
"""
for i in range(n):
self.sample()
dataset = pd.DataFrame(columns=['s1', 'a', 'r', 's2'])
dataset['s1'] = self.state1_observe
dataset['a'] = self.action_taken
dataset['r'] = self.reward_received
dataset['s2'] = self.state2_observe
return dataset