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feat_extractor.py
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feat_extractor.py
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import util
import numpy as np
from state import EnvironmentState
from datetime import datetime
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from state import AgentState
class FeatureExtractor:
def __init__(self):
print("Instantiating feature extractor...")
self._train()
def _train(self):
train_x = np.zeros(shape=[365 * 48, 2])
for i in range (0, (365 * 48)):
train_x[i][0] = i%48
for i in range (0, (365 * 48)):
train_x[i][1] = i%7
# for i in range (0, (365 * 48)):
# train_x[i][2] = i%12
self.ohe_time = OneHotEncoder(sparse=False)
self.ohe_time.fit(train_x)
self.lb_actions = LabelEncoder()
actions_trans = self.lb_actions.fit_transform(AgentState.actions)
self.ohe_actions = OneHotEncoder(sparse=False)
self.ohe_actions.fit(actions_trans.reshape(-1,1))
def get_features(self, state, action):
'''
Compute the features from the state to extract the q-value
:param state:
:param action:
:return: a list of feature values
'''
features = self.encode_state(state)
# ---------------- ENCODING ACTIONS ----------------
# Modelling energy request data
if action['action'] == 'grant' or action['action'] == 'deny_request':
# TODO: Discritize by observing the values of data
features.append(int(action['data']/0.2))
else:
features.append(0)
action_trans = self.ohe_actions.transform(self.lb_actions.transform([action['action']]).reshape(1,-1))
for f in action_trans[0]:
features.append(f)
# ------------------------------------------------
#return self.__encode_features_to_Counter(features)
return features
def encode_state(self, state):
'''
Encode the state variable into n features
:param state:
:return:
'''
time_feat = util.Counter()
time_feat['hour'] = (state.time.time().hour * 60 + state.time.time().minute) // 30
time_feat['dayofweek'] = state.time.weekday() # monday = 0
# time_feat['month'] = state.time.month - 1
# Transform and avoid the dummy variable trap
features = self.ohe_time.transform(np.array([time_feat['hour'], time_feat['dayofweek']])
.reshape(1, -1))[:, :-1]
features = list(features[0])
features.append(self.__encode_energy(state.energy_consumption))
features.append(self.__encode_energy(state.energy_generation))
features.append(self.__encode_energy(state.battery_curr))
return features
def __encode_features_to_Counter(self, features):
# Transforming into apt data structure
feat_dict = util.Counter()
for i in range(len(features)):
feat_dict['f_' + str(i)] = float(features[i])
# print(feat_dict)
return feat_dict
def get_n_features(self):
'''
Simulates a fake agent state and returns the numbers of features.
:param state:
:return:
'''
environment_state = EnvironmentState(0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
fake_agent_state = AgentState(name='Test', iter =0, energy_consumption=0.0, energy_generation=0.0,
battery_curr=float(5), time=datetime.now(),
environment_state=environment_state,
cg_http_service=None)
action = {}
action['action'] = 'consume_and_store'
features = self.get_features(fake_agent_state, action)
return len(features)
def __encode_energy(self, energy):
if energy == 0.0:
return 0
elif energy < 1.0:
return 1.0
elif energy < 2.88:
return 2.0
else:
return 3