class SpiderMan(object):
def __init__(self):
self.number = 100
self.urlManager = UrlManager()
self.downloader = Downloader()
self.parser = Parser()
self.dataManager = DataManager()
self.dbManager = MongoUtils()
def set_crawler_number(self, num):
if num is None or int(num) < 0:
return
self.number = int(num)
def crawler(self, root_url):
self.urlManager.add_new_url(root_url)
while (self.urlManager.has_new_url()
and self.urlManager.old_urls_size() < self.number):
try:
new_url = self.urlManager.get_new_url()
html = self.downloader.download(new_url)
result = self.parser.parser(new_url, html)
self.urlManager.add_new_urls(result[0])
self.dataManager.store_data(result[1])
except Exception as err:
print("crawl failed" + err)
self.dataManager.output_data()
datas = self.dataManager.get_data()
self.dbManager.insert_baike_many(datas)
def __init__(self):
self.number = 100
self.urlManager = UrlManager()
self.downloader = Downloader()
self.parser = Parser()
self.dataManager = DataManager()
self.dbManager = MongoUtils()
def __init__(self,
building,
zones,
start,
end,
window,
lambda_val,
non_controllable_data=None,
debug=False):
"""
initialize instance variables
:param building: (str) building name
:param zones: [str] zone names
:param start: (datetime timezone aware)
:param end: (datetime timezone aware)
:param window: (str) the interval in which to split the data.
:param lambda_val: (float) lambda value for opjective function
"""
self.DataManager = DataManager(building, zones, start, end, window,
non_controllable_data)
self.start = start
self.unix_start = start.timestamp() * 1e9
self.end = end
self.unix_end = end.timestamp() * 1e9
self.window = window # timedelta string
self.building = building
self.zones = zones
self.lambda_val = lambda_val
self.debug = debug
self.g = nx.DiGraph() # [TODO:Changed to MultiDiGraph... FIX print]
def __init__(self, urm_train, eurm=False):
super(HybridGenRecommender, self).__init__(urm_train)
self.data_folder = Path(__file__).parent.parent.absolute()
self.eurm = eurm
self.num_users = urm_train.shape[0]
data = DataManager()
urm_train = check_matrix(urm_train.copy(), 'csr')
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(shrink=40, topK=20, feature_weighting='BM25')
# recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
# recommender_2.fit(shrink=500, topK=1600, normalize=True)
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
def upload(request):
if request.method == "POST":
cfg_path = handle_uploaded_file(request.FILES['file'],
request.FILES['file'].name)
dm = DataManager()
dm.save_data(cfg_path)
return redirect(homepage)
def post(self, request, *args, **kwargs):
try:
Log.debug('Create momend request: '+str(request.POST))
_owner = request.user
if 'owner' in request.POST:
if request.user.is_superuser:
_owner = User.objects.get(pk=request.POST['owner'])
_momend_name = request.POST['momend_name']
_privacy = request.POST['privacy_type']
_theme = request.POST['momend_theme']
_theme = 1 # TODO remove after showing theme selection combo
_send_mail = request.POST.get('mail', True) # Send mail after create, default True
dm = DataManager(_owner)
try:
_args = dict()
if 'selected' in request.POST and request.POST['selected']:
_args['is_date'] = False
_args['selected'] = json.loads(request.POST['selected'])
else:
_args['is_date'] = True
_args['selected'] = False
_args['since'] = datetime.strptime(request.POST['start_date'], '%d %b, %Y').replace(tzinfo=pytz.UTC)
_args['until'] = datetime.strptime(request.POST['finish_date'], '%d %b, %Y').replace(tzinfo=pytz.UTC)
_create_params = request.POST.keys()
for _param in _create_params:
if '-active' in _param: # Provider disable requests
_args[_param] = request.POST[_param]
if 'friends' in request.POST and len(request.POST['friends']) > 0:
_args['friends'] = request.POST['friends'].split(',')
else:
_args['friends'] = False
if 'chronological' in request.POST:
_args['chronological'] = request.POST['chronological']
else:
_args['chronological'] = False
_cryptic_id = dm.create_momend(name=_momend_name, duration=60, privacy=_privacy,
theme=Theme.objects.get(pk=_theme), send_mail=_send_mail, **_args)
if _cryptic_id: # If created momend successfully
return _generate_json_response(True, 'Create momend request received', cid=_cryptic_id)
_error_msg = dm.get_last_status()
return _generate_json_response(False, 'Create momend failed with error message: '+str(_error_msg), _error_msg)
except Exception as e:
_log_critical_error('Exception while creating the momend', True, request.POST, request.user, traceback.format_exc())
return _generate_json_response(False, 'Error while creating momend: '+str(e), 'An error occurred. Please try again') # Could be any error
except KeyError as e:
Log.error(traceback.format_exc())
return _generate_json_response(False, 'Error while creating momend: '+str(e), str(e)) # One of the parameters is missing
except Exception as e:
_log_critical_error('Impossible exception occurred while creating momend', True, request.POST, request.user, traceback.format_exc())
return _generate_json_response(False, 'Error while creating momend (impossible): '+str(e), 'An error occurred. Please try again after a while')
def __init__(self,
building,
zones,
temperatures,
start,
end,
window,
last_temperatures=None,
suppress_not_enough_data_error=False,
non_controllable_data=None):
self.temperatures = temperatures
self.last_temperatures = last_temperatures
self.indoor_temperature_prediction_stub = xsg.get_indoor_temperature_prediction_stub(
)
self.error = {}
self.building = building
self.zones = zones
self.start = start
self.end = end
self.DataManager = DataManager(building, zones, start, end, window,
non_controllable_data)
self.timesteps = {iter_zone: 0 for iter_zone in zones}
for iter_zone in zones:
for action in [
xsg.NO_ACTION, xsg.HEATING_ACTION, xsg.COOLING_ACTION
]:
try:
END = datetime.datetime(year=2019, month=4, day=1).replace(
tzinfo=pytz.utc
) # datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
START = END - datetime.timedelta(days=365)
raise Exception("ERROR: Not Implemented.")
# mean, var, unit = xsg.get_indoor_temperature_prediction_error(self.indoor_temperature_prediction_stub,
# building,
# iter_zone,
# action, START, END)
except:
if not suppress_not_enough_data_error:
raise Exception(
"ERROR: Tstat for building: '{0}' and zone: '{1}' did not receive error data from "
"indoor_temperature_prediction microservice for action: '{2}'."
)
print(
"WARNING: Tstat for building: '{0}' and zone: '{1}' did not receive error data from "
"indoor_temperature_prediction microservice for action: '{2}' and is now using STANDARD error."
.format(building, iter_zone, action))
mean, var, unit = DigitalTwin.STANDARD_MEAN, DigitalTwin.STANDARD_VAR, DigitalTwin.STANDARD_UNIT
self.error[action] = {"mean": mean, "var": var}
def __init__(self, urm_train):
super(HybridZeroRecommender, self).__init__(urm_train)
urm_train = check_matrix(urm_train.copy(), 'csr')
data = DataManager()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = TopPop(urm_train)
recommender_1.fit()
#
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=500, topK=1600, similarity='tversky')
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
def __init__(self,
building,
zones,
lambda_val,
start,
end,
forecasting_horizon,
window,
tstats,
non_contrallable_data=None):
"""
:param building:
:param zones:
:param lambda_val:
:param start: datetime with timezone
:param end: datetime with timezone
:param forecasting_horizon:
:param window:
:param tstats:
:param non_contrallable_data:
"""
assert xsg.get_window_in_sec(
forecasting_horizon) % xsg.get_window_in_sec(window) == 0
self.building = building
self.zones = zones
self.window = window
self.lambda_val = lambda_val
self.forecasting_horizon = forecasting_horizon
self.delta_forecasting_horizon = datetime.timedelta(
seconds=xsg.get_window_in_sec(forecasting_horizon))
self.delta_window = datetime.timedelta(
seconds=xsg.get_window_in_sec(window))
# Simulation end is when current_time reaches end and end will become the end of our data.
self.simulation_end = end
end += self.delta_forecasting_horizon
self.DataManager = DataManager(building, zones, start, end, window,
non_contrallable_data)
self.tstats = tstats # dictionary of simulator object with key zone. has functions: current_temperature, next_temperature(action)
self.current_time = start
self.current_time_step = 0
self.actions = {iter_zone: []
for iter_zone in self.zones} # {zone: [ints]}
self.temperatures = {
iter_zone: [self.tstats[iter_zone].temperature]
for iter_zone in self.zones
} # {zone: [floats]}
def get(request):
# Check on uploading configuration file
if glob.glob(BASE_DIR + "/uploads/*.csv") == []:
raise Http404("Please, upload config file.")
if request.method == "GET":
# To manage the issuance of banners
dm = DataManager()
if "category" in request.GET:
categories = request.GET["category"]
good_pks = [] # pks are Primary Keys, actually database indexes
# We do not show banners that have run out of paid shows
for obj in Banner.objects.filter(
prepaid_shows_amount__gt=0).order_by(
"-prepaid_shows_amount"):
# If at least one category matches
if dm.matching_values(obj.categories.split(","),
categories) > 0:
# Save his database primary key
good_pks.append(obj.pk)
# Matching banners exist
if good_pks != []:
sorted_psa = [
Banner.objects.get(pk=good_pk).prepaid_shows_amount
for good_pk in good_pks
]
# Banner to be shown
banner_pk = dm.random_pick_from_given_distribution(
good_pks, sorted_psa)
# If used
banner_pk = dm.was_used(banner_pk, good_pks)
banner_to_show = Banner.objects.get(pk=banner_pk)
else:
# Select random banner
banner_to_show = dm.select_random_banner(Banner)
else:
# Select random banner
banner_to_show = dm.select_random_banner(Banner)
banner_to_show.prepaid_shows_amount -= 1
banner_to_show.save()
return render(request, 'get/get.html', {
"banner_url": banner_to_show.url,
"categories": banner_to_show.categories
})
from Hybrid.HybridGenRecommender import HybridGenRecommender
from Hybrid.HybridNormRecommender import HybridNormRecommender
from GraphBased.RP3betaRecommender import RP3betaRecommender
from FeatureWeighting.CFW_D_Similarity_Linalg import CFW_D_Similarity_Linalg
from Hybrid.HybridNorm1Recommender import HybridNorm1Recommender
from Hybrid.HybridNorm2Recommender import HybridNorm2Recommender
from Hybrid.HybridGen2Recommender import HybridGen2Recommender
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_BPR_Cython
data_folder = Path(__file__).parent.absolute()
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Hybrid.HybridNorm3Recommender import HybridNorm3Recommender
from MatrixFactorization.ALSRecommender import ALSRecommender
from MatrixFactorization.BPRRecommender import BPRRecommender
import similaripy as sim
data = DataManager()
urm_train = data.get_urm()
urm_train, urm_test = split_train_leave_k_out_user_wise(data.get_urm(),
temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train,
temperature='valid2')
urm_train_warm = data.create_test_warm_users(urm_train, threshold=10)
urm_test_warm = data.create_test_warm_users(urm_test, threshold=10)
evaluator_test_warm = EvaluatorHoldout(urm_test_warm, cutoff_list=[10])
recommender = UserKNNCFRecommender(urm_train)
recommender.fit(shrink=2, topK=600, normalize=True)
class DigitalTwin:
STANDARD_MEAN = 0
STANDARD_VAR = 0.05
STANDARD_UNIT = "F"
def __init__(self,
building,
zones,
temperatures,
start,
end,
window,
last_temperatures=None,
suppress_not_enough_data_error=False,
non_controllable_data=None):
self.temperatures = temperatures
self.last_temperatures = last_temperatures
self.indoor_temperature_prediction_stub = xsg.get_indoor_temperature_prediction_stub(
)
self.error = {}
self.building = building
self.zones = zones
self.start = start
self.end = end
self.DataManager = DataManager(building, zones, start, end, window,
non_controllable_data)
self.timesteps = {iter_zone: 0 for iter_zone in zones}
for iter_zone in zones:
for action in [
xsg.NO_ACTION, xsg.HEATING_ACTION, xsg.COOLING_ACTION
]:
try:
END = datetime.datetime(year=2019, month=4, day=1).replace(
tzinfo=pytz.utc
) # datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
START = END - datetime.timedelta(days=365)
raise Exception("ERROR: Not Implemented.")
# mean, var, unit = xsg.get_indoor_temperature_prediction_error(self.indoor_temperature_prediction_stub,
# building,
# iter_zone,
# action, START, END)
except:
if not suppress_not_enough_data_error:
raise Exception(
"ERROR: Tstat for building: '{0}' and zone: '{1}' did not receive error data from "
"indoor_temperature_prediction microservice for action: '{2}'."
)
print(
"WARNING: Tstat for building: '{0}' and zone: '{1}' did not receive error data from "
"indoor_temperature_prediction microservice for action: '{2}' and is now using STANDARD error."
.format(building, iter_zone, action))
mean, var, unit = DigitalTwin.STANDARD_MEAN, DigitalTwin.STANDARD_VAR, DigitalTwin.STANDARD_UNIT
self.error[action] = {"mean": mean, "var": var}
def next_temperature(self, action, zone):
# data prep for updating temperatures
curr_other_zone_temperatures = self.DataManager.all_zone_temperature_data.iloc[
self.timesteps[zone]]
for iter_zone_2 in self.zones:
curr_other_zone_temperatures[iter_zone_2] = self.temperatures[
iter_zone_2]
for i in range(3):
# new_temperature = xsg.get_indoor_temperature_prediction(self.DataManager.indoor_temperature_prediction_stub,
# self.building,
# zone,
# self.start,
# action,
# self.temperatures[zone],
# self.DataManager.outdoor_temperature.iloc[
# self.timesteps[zone]],
# self.last_temperatures[zone],
# curr_other_zone_temperatures)[0]
new_temperature = self.DataManager.get_indoor_temperature_prediction(
self.building, zone, self.start, action,
self.temperatures[zone],
self.DataManager.outdoor_temperature.iloc[
self.timesteps[zone]], self.last_temperatures[zone],
curr_other_zone_temperatures)[0]
self.last_temperatures[zone] = self.temperatures[zone]
self.temperatures[zone] = new_temperature
self.temperatures[zone] += np.random.normal(self.error[action]["mean"],
self.error[action]["var"])
self.timesteps[zone] += 1
return self.temperatures[zone]
def reset(self, temperature, zone, last_temperature=None):
self.temperatures[zone] = temperature
self.last_temperatures[zone] = last_temperature
self.timesteps[zone] = 0
from GraphBased.RP3betaRecommender import RP3betaRecommender
from FeatureWeighting.CFW_D_Similarity_Linalg import CFW_D_Similarity_Linalg
from Hybrid.HybridNorm1Recommender import HybridNorm1Recommender
from Hybrid.HybridNorm2Recommender import HybridNorm2Recommender
from Hybrid.HybridGen2Recommender import HybridGen2Recommender
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_BPR_Cython
data_folder = Path(__file__).parent.absolute()
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Hybrid.HybridNorm3Recommender import HybridNorm3Recommender
from MatrixFactorization.ALSRecommender import ALSRecommender
from MatrixFactorization.BPRRecommender import BPRRecommender
import similaripy as sim
test = False
threshold = 1
temperature = 'normal'
Data = DataManager()
urm_train = Data.get_urm()
valid = False
multiple_test = True
num_test = 3
if multiple_test:
# res = np.zeros(num_test)
# for i in np.arange(num_test):
# Data = DataManager()
# urm_train = Data.get_urm()
# urm_train, urm_test = split_train_leave_k_out_user_wise(urm_train, threshold=threshold, temperature=temperature)
# urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train, threshold=threshold, temperature='valid')
# evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
# evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
#
def __init__(self, env_config):
self.DataManager = DataManager(env_config["building"],
env_config["zones"],
env_config["start"], env_config["end"],
env_config["window"])
self.start = start
self.unix_start = start.timestamp() * 1e9
self.end = end
self.unix_end = end.timestamp() * 1e9
self.window = window # timedelta string
self.building = building
self.zones = zones
self.lambda_val = env_config["lambda_val"]
# assert self.zones == all zones in building. this is because of the thermal model needing other zone temperatures.
self.curr_timestep = 0
self.indoor_starting_temperatures = env_config[
"indoor_starting_temperatures"] # to get starting temperatures [last, current]
self.outdoor_starting_temperature = env_config[
"outdoor_starting_temperature"]
self.tstats = {}
for iter_zone in self.zones:
self.tstats[iter_zone] = Tstat(
self.building,
iter_zone,
self.indoor_starting_temperatures[iter_zone]["current"],
last_temperature=self.indoor_starting_temperatures[iter_zone]
["last"])
assert 60 * 60 % xsg.get_window_in_sec(
self.window) == 0 # window divides an hour
assert (self.end - self.start).total_seconds() % xsg.get_window_in_sec(
self.window) == 0 # window divides the timeframe
# the number of timesteps
self.num_timesteps = int((self.end - self.start).total_seconds() /
xsg.get_window_in_sec(self.window))
self.unit = env_config["unit"]
assert self.unit == "F"
# all zones current and last temperature = 2*num_zones
# building outside temperature -> make a class for how this behaves = 1
# timestep -> do one hot encoding of week, day, hour, window \approx 4 + 7 + 24 + 60*60 / window
low_bound = [32] * 2 * len(
self.zones
) # we could use parametric temperature bounds... for now we will give negative inft reward
low_bound += [-100] # for outside temperature we cannot gurantee much
high_bound = [100] * 2 * len(self.zones)
high_bound += [200] # for outside temperature we cannot gurantee much
low_bound += [0] * (
self.num_timesteps + 1
) # total timesteps plus the final timestep which wont be executed
high_bound += [1] * (
self.num_timesteps + 1
) # total timesteps plus the final timestep which wont be executed
self.observation_space = Box(low=np.array(low_bound),
high=np.array(high_bound),
dtype=np.float32)
self.action_space = Tuple((Discrete(3), ) * len(self.zones))
self.reset()
from ParameterTuning.SearchBayesianSkopt import SearchBayesianSkopt
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
from skopt.space import Real, Integer, Categorical
from KNN.ItemKNNCFRecommender import ItemKNNCFRecommender
from KNN.ItemKNNCBFRecommender import ItemKNNCBFRecommender
from KNN.UserKNNCBFRecommender import UserKNNCBFRecommender
from KNN.UserKNNCFRecommender import UserKNNCFRecommender
import numpy as np
import scipy.sparse as sps
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Hybrid.HybridGen2Recommender import HybridGen2Recommender
from Hybrid.HybridNormRecommender import HybridNormRecommender
from Hybrid.HybridNorm1Recommender import HybridNorm1Recommender
from Hybrid.HybridNorm2Recommender import HybridNorm2Recommender
Data = DataManager()
urm_train, urm_test = split_train_leave_k_out_user_wise(Data.get_urm(), threshold=10, temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train, threshold=10, temperature='valid')
evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
recommender = HybridNorm1Recommender
# recommender_3 = UserKNNCFRecommender(urm_train)
# recommender_3.fit(shrink=2, topK=600, normalize=True)
# w_sparse = recommender_3.W_sparse
parameterSearch = SearchBayesianSkopt(recommender,
evaluator_validation=evaluator_valid,
from skopt.space import Real, Integer, Categorical
from KNN.ItemKNNCFRecommender import ItemKNNCFRecommender
from KNN.ItemKNNCBFRecommender import ItemKNNCBFRecommender
from KNN.UserKNNCBFRecommender import UserKNNCBFRecommender
from KNN.UserKNNCFRecommender import UserKNNCFRecommender
import numpy as np
import scipy.sparse as sps
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Hybrid.HybridGen2Recommender import HybridGen2Recommender
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_BPR_Cython
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_AsySVD_Cython
from MatrixFactorization.Cython.MatrixFactorization_Cython import MatrixFactorization_FunkSVD_Cython
from Hybrid.HybridNormRecommender import HybridNormRecommender
from GraphBased.RP3betaRecommender import RP3betaRecommender
from SLIM_ElasticNet.SLIMElasticNetRecommender2 import MultiThreadSLIM_ElasticNet
Data = DataManager()
urm_train, urm_test = split_train_leave_k_out_user_wise(Data.get_urm(),
threshold=10,
temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train,
threshold=10,
temperature='valid')
urm_valid = Data.create_test_warm_users(urm_valid, threshold=3)
evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
recommender = MultiThreadSLIM_ElasticNet
# recommender = MatrixFactorization_FunkSVD_Cython
# recommender = MatrixFactorization_AsySVD_Cython
# recommender = MatrixFactorization_BPR_Cython
from lightfm import LightFM
from DataManager.DataManager import DataManager
from lightfm.evaluation import auc_score
from DataManager.split_train_validation_leave_k_out import split_train_leave_k_out_user_wise
from Base.Evaluation.Evaluator import EvaluatorHoldout
from Hybrid.LighFm import LighFMRecommender
import matplotlib.pyplot as pyplot
from sklearn import metrics, ensemble
import numpy as np
data = DataManager()
urm = data.get_urm()
threshold = 10
temperature = 'normal'
ucm_age, ucm_region, ucm_all = data.get_ucm()
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
urm_train, urm_test = split_train_leave_k_out_user_wise(
urm, threshold=threshold, temperature=temperature)
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train,
threshold=threshold,
temperature='valid')
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
# recommender = LighFMRecommender(urm_train,
# no_components=150,
# loss='warp',
# learning_rate=0.09,
# random_state=2019)
#
"""
This file demonstrates writing tests using the unittest module. These will pass
when you run "manage.py test".
Replace this with more appropriate tests for your application.
"""
from django.test import TestCase
from django.contrib.auth.models import User
from DataManager.DataManager import DataManager
from DataManager.models import Provider, RawData
from DataManager.DataEnrich.DataEnrichManager import DataEnrichManager
from datetime import datetime
ert = User.objects.get(username='******')
dm = DataManager(ert)
start = datetime.strptime('2012-01-01','%Y-%m-%d')
end = datetime.strptime('2012-05-01','%Y-%m-%d')
dm.create_momend(start,end,50)
from KNN.UserKNNCBFRecommender import UserKNNCBFRecommender
from KNN.ItemKNNCFRecommender import ItemKNNCFRecommender
from KNN.ItemKNNCBFRecommender import ItemKNNCBFRecommender
import pandas as pd
from DataManager.split_train_validation_leave_k_out import split_train_leave_k_out_user_wise
from FeatureWeighting.CFW_D_Similarity_Linalg import CFW_D_Similarity_Linalg
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Utils.s_plus import dot_product
import scipy.sparse as sps
from Base.Evaluation.Evaluator import EvaluatorHoldout
from GraphBased.RP3betaRecommender import RP3betaRecommender
from ParameterTuning.SearchBayesianSkopt import SearchBayesianSkopt
from skopt.space import Real, Integer, Categorical
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
data = DataManager()
ucm_age, ucm_region, ucm_all = data.get_ucm()
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
recommender_4 = UserKNNCFRecommender(data.get_urm())
recommender_4.fit(shrink=2, topK=600, normalize=True)
W_sparse_CF = recommender_4.W_sparse
cfw = User_CFW_D_Similarity_Linalg(URM_train=data.get_urm(),
UCM=ucm_all.copy(),
S_matrix_target=W_sparse_CF
)
from KNN.UserKNNCBFRecommender import UserKNNCBFRecommender
from KNN.ItemKNNCFRecommender import ItemKNNCFRecommender
from KNN.ItemKNNCBFRecommender import ItemKNNCBFRecommender
import pandas as pd
from DataManager.split_train_validation_leave_k_out import split_train_leave_k_out_user_wise
from FeatureWeighting.CFW_D_Similarity_Linalg import CFW_D_Similarity_Linalg
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Utils.s_plus import dot_product
import scipy.sparse as sps
from Base.Evaluation.Evaluator import EvaluatorHoldout
from GraphBased.RP3betaRecommender import RP3betaRecommender
from ParameterTuning.SearchBayesianSkopt import SearchBayesianSkopt
from skopt.space import Real, Integer, Categorical
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
Data = DataManager()
ucm_age, ucm_region, ucm_all = Data.get_ucm()
icm_price, icm_asset, icm_sub, icm_all = Data.get_icm()
urm_train, urm_test = split_train_leave_k_out_user_wise(Data.get_urm(),
temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train,
temperature='valid')
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
recommender_4 = RP3betaRecommender(urm_train)
recommender_4.fit(topK=16,
alpha=0.03374950051351756,
def read_data_split_and_search():
"""
This function provides a simple example on how to tune parameters of a given algorithm
The BayesianSearch object will save:
- A .txt file with all the cases explored and the recommendation quality
- A _best_model file which contains the trained model and can be loaded with recommender.load_model()
- A _best_parameter file which contains a dictionary with all the fit parameters, it can be passed to recommender.fit(**_best_parameter)
- A _best_result_validation file which contains a dictionary with the results of the best solution on the validation
- A _best_result_test file which contains a dictionary with the results, on the test set, of the best solution chosen using the validation set
"""
# dataReader = Movielens10MReader()
#
# URM_train = dataReader.get_URM_train()
# URM_validation = dataReader.get_URM_validation()
# URM_test = dataReader.get_URM_test()
Data = DataManager()
urm_temp, urm_test = split_train_leave_k_out_user_wise(
Data.get_urm(), use_validation_set=False, leave_random_out=True)
urm_train, urm_valid = split_train_leave_k_out_user_wise(
urm_temp, use_validation_set=False, leave_random_out=True)
output_folder_path = "result_experiments/"
# If directory does not exist, create
if not os.path.exists(output_folder_path):
os.makedirs(output_folder_path)
collaborative_algorithm_list = [
# Random,
# TopPop,
# P3alphaRecommender,
# RP3betaRecommender,
# ItemKNNCFRecommender,
# UserKNNCFRecommender,
# MatrixFactorization_BPR_Cython,
# MatrixFactorization_FunkSVD_Cython,
# PureSVDRecommender,
# SLIM_BPR_Cython,
# SLIMElasticNetRecommender
SLIMElasticNetRecommender
]
from Base.Evaluation.Evaluator import EvaluatorHoldout
evaluator_validation = EvaluatorHoldout(urm_valid, cutoff_list=[10])
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
runParameterSearch_Collaborative_partial = partial(
runParameterSearch_Collaborative,
URM_train=urm_train,
metric_to_optimize="MAP",
n_cases=10,
evaluator_validation_earlystopping=evaluator_validation,
evaluator_validation=evaluator_validation,
evaluator_test=evaluator_test,
output_folder_path=output_folder_path,
similarity_type_list=["cosine"],
parallelizeKNN=False)
# pool = multiprocessing.Pool(processes=int(multiprocessing.cpu_count()), maxtasksperchild=1)
# pool.map(runParameterSearch_Collaborative_partial, collaborative_algorithm_list)
for recommender_class in collaborative_algorithm_list:
try:
runParameterSearch_Collaborative_partial(recommender_class)
except Exception as e:
print("On recommender {} Exception {}".format(
recommender_class, str(e)))
traceback.print_exc()
import numpy as np
from DataManager.DataManager import DataManager
import scipy.sparse as sps
data = DataManager()
_, ucm_all, _ = data.get_ucm()
features_per_user = np.ediff1d(ucm_all.indptr)
ucm_all = sps.csc_matrix(ucm_all)
users_per_feature = np.ediff1d(ucm_all.indptr)
features_per_users = np.sort(features_per_user)
# users_per_feature = np.sort(users_per_feature)
print(features_per_users.shape)
print(users_per_feature.shape)
import matplotlib.pyplot as pyplot
pyplot.plot(users_per_feature, 'ro')
pyplot.ylabel('Num features ')
pyplot.xlabel('Users Index')
pyplot.show()
class MPC:
"""MPC Optimizer.
No Demand Charges and Two Stage actions implemented."""
def __init__(self,
building,
zones,
start,
end,
window,
lambda_val,
non_controllable_data=None,
debug=False):
"""
initialize instance variables
:param building: (str) building name
:param zones: [str] zone names
:param start: (datetime timezone aware)
:param end: (datetime timezone aware)
:param window: (str) the interval in which to split the data.
:param lambda_val: (float) lambda value for opjective function
"""
self.DataManager = DataManager(building, zones, start, end, window,
non_controllable_data)
self.start = start
self.unix_start = start.timestamp() * 1e9
self.end = end
self.unix_end = end.timestamp() * 1e9
self.window = window # timedelta string
self.building = building
self.zones = zones
self.lambda_val = lambda_val
self.debug = debug
self.g = nx.DiGraph() # [TODO:Changed to MultiDiGraph... FIX print]
def safety_check(self, node):
for iter_zone in self.zones:
curr_temperature = node.temperatures[iter_zone]
curr_safety = self.DataManager.do_not_exceed[iter_zone].iloc[
node.timestep]
if not (curr_safety["t_low"] <= curr_temperature <=
curr_safety["t_high"]):
return False
return True
def timestep_to_datetime(self, timestep):
return self.start + timestep * datetime.timedelta(
seconds=xsg.get_window_in_sec(self.window))
# the shortest path algorithm
def shortest_path(self, root):
"""
Creates the graph using DFS and calculates the shortest path
:param root: node being examined right now and needs to be added to graph.
:return: root Node if root added else return None.
"""
if root is None:
return None
if root in self.g:
return root
# stop if node is past predictive horizon
if self.timestep_to_datetime(root.timestep) >= self.end:
self.g.add_node(root,
objective_cost=0,
best_action=None,
best_successor=None) # no cost as leaf node
return root
# check if valid node
if not self.safety_check(root):
return None
self.g.add_node(root,
objective_cost=np.inf,
best_action=None,
best_successor=None)
# creating children, adding corresponding edge and updating root's objective cost
for action in itertools.product(
[xsg.NO_ACTION, xsg.HEATING_ACTION, xsg.COOLING_ACTION],
repeat=len(self.zones)):
# TODO Compute temperatures properly
temperatures = {}
zone_actions = {}
for i in range(len(self.zones)):
zone_actions[self.zones[i]] = action[i]
temperatures[self.zones[i]] = root.temperatures[self.zones[i]] + \
1 * (action[i] == 1) - 1 * (action[i] == 2)
# Create child node and call the shortest_path recursively on it
child_node = Node(temperatures=temperatures,
timestep=root.timestep + 1)
child_node = self.shortest_path(child_node)
if child_node is None:
continue
# get discomfort across edge
discomfort = {}
for iter_zone in self.zones:
curr_comfortband = self.DataManager.comfortband[
iter_zone].iloc[root.timestep]
curr_occupancy = self.DataManager.occupancy[iter_zone].iloc[
root.timestep]
average_edge_temperature = (
root.temperatures[iter_zone] +
child_node.temperatures[iter_zone]) / 2.
discomfort[iter_zone] = self.DataManager.get_discomfort(
self.building, average_edge_temperature,
curr_comfortband["t_low"], curr_comfortband["t_high"],
curr_occupancy)
# Get consumption across edge
price = 1 # self.prices.iloc[root.timestep] TODO also add right unit conversion, and duration
consumption_cost = {
self.zones[i]: price *
self.DataManager.hvac_consumption[self.zones[i]][action[i]]
for i in range(len(self.zones))
}
# add edge
self.g.add_edge(root,
child_node,
action=zone_actions,
discomfort=discomfort,
consumption_cost=consumption_cost)
# update root node to contain the best child.
total_edge_cost = ((1 - self.lambda_val) *
(sum(consumption_cost.values()))) + (
self.lambda_val *
(sum(discomfort.values())))
objective_cost = self.g.node[child_node][
"objective_cost"] + total_edge_cost
if objective_cost < self.g.node[root]["objective_cost"]:
self.g.node[root]["objective_cost"] = objective_cost
self.g.node[root]["best_action"] = zone_actions
self.g.node[root]["best_successor"] = child_node
return root
def reconstruct_path(self, root):
"""
Util function that reconstructs the best action path
Parameters
----------
graph : networkx graph
Returns
-------
List
"""
graph = self.g
if root not in self.g:
raise Exception("Root does not exist in MPC graph.")
path = [root]
while graph.node[root]['best_successor'] is not None:
root = graph.node[root]['best_successor']
path.append(root)
return path
# def g_plot(self, zone):
# try:
# os.remove('mpc_graph_' + zone + '.html')
# except OSError:
# pass
# fig = plotly_figure(self.advise_unit.g, path=self.path)
# py.plot(fig, filename='mpc_graph_' + zone + '.html', auto_open=False)
def advise(self, starting_temperatures):
"""Call this function to get best action.
:param starting_temperatures: dict {zone: float temperature}
:return: action, err
"""
root = Node(starting_temperatures, 0)
root = self.shortest_path(root)
if root is None:
return None, "Could not find feasible action."
return self.g.node[root]["best_action"], None
def __init__(self, urm_train, eurm=False):
super(HybridGen2Recommender, self).__init__(urm_train)
self.eurm = eurm
self.data_folder = Path(__file__).parent.parent.absolute()
self.num_users = urm_train.shape[0]
urm_train = check_matrix(urm_train.copy(), 'csr')
data = DataManager()
if Path(self.data_folder /
'Data/icm_sparse.npz').is_file() and self.eurm:
print("Previous icm_sparse found")
else:
_, _, _, icm_all = data.get_icm()
args = {
'topK': 6,
'shrink': 5,
'feature_weighting': 'TF-IDF',
'similarity': 'cosine',
'normalize': False
}
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(**args)
self.recommender_1 = recommender_1
if Path(self.data_folder /
'Data/ucm_sparse.npz').is_file() and self.eurm:
print("Previous ucm_sparse found")
else:
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
self.recommender_2 = recommender_2
if self.eurm:
beta = 0.6
if Path(self.data_folder / 'Data/icm_sparse.npz').is_file():
self.score_matrix_1 = sps.load_npz(self.data_folder /
'Data/icm_sparse.npz')
else:
print("icm_sparse not found, create new one...")
self.score_matrix_1 = self.recommender_1._compute_item_matrix_score(
np.arange(self.num_users))
user_score_matrix_1 = normalize(self.score_matrix_1,
norm='max',
axis=1)
item_score_matrix_1 = normalize(self.score_matrix_1.tocsc(),
norm='max',
axis=0)
self.score_matrix_1 = item_score_matrix_1 * beta + user_score_matrix_1.tocsr(
) * (1 - beta)
sps.save_npz(self.data_folder / 'Data/icm_sparse.npz',
self.score_matrix_1)
if Path(self.data_folder / 'Data/ucm_sparse.npz').is_file():
self.score_matrix_2 = sps.load_npz(self.data_folder /
'Data/ucm_sparse.npz')
else:
print("ucm_sparse not found, create new one...")
self.score_matrix_2 = self.recommender_2._compute_item_matrix_score(
np.arange(self.num_users))
user_score_matrix_2 = normalize(self.score_matrix_2,
norm='max',
axis=1)
item_score_matrix_2 = normalize(self.score_matrix_2.tocsc(),
norm='max',
axis=0)
self.score_matrix_2 = item_score_matrix_2 * beta + user_score_matrix_2.tocsr(
) * (1 - beta)
sps.save_npz(self.data_folder / 'Data/ucm_sparse.npz',
self.score_matrix_2)
import numpy as np
import h5py
from DataManager.DataManager import DataManager
from Utilities.utilities import extract_NIFTI, extract_FigShare
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot as plt
from DataManager.FeatureExtractor import *
dataManager = DataManager(
r'C:/Users/eee/workspace_python/Image Reconstruction/data/', ['ADNI'])
'''
#Example to extract FigShare Dataset
dataManager = DataManager(r'C:/Users/eee/workspace_python/Image Reconstruction/data/', ['FigShare'])
params = {'database_name': 'fig_share_data',
'dataset': 'FigShare',
'feature_option': 'image_and_k_space',
'img_shape': 128,
'num_subjects': 'all'}
print(len(dataManager.dataCollection['FigShare']))
print(len(dataManager.data_splits['FigShare'][0]))
print(len(dataManager.data_splits['FigShare'][1]))
print(len(dataManager.data_splits['FigShare'][2]))
dataManager.compile_dataset(params)
'''
#Example to extract BRATS Dataset
# dataManager = DataManager(r'C:/Users/eee/workspace_python/Image Reconstruction/data/', ['BRATS'])
verbose=False)
MAP_per_k_valid = []
recommender = HybridNorm3Recommender(urm_train)
for alpha in tqdm(x_tick):
recommender.fit(beta=alpha)
result_dict, res_str = evaluator_valid.evaluateRecommender(recommender)
MAP_per_k_valid.append(result_dict[10]["MAP"])
return MAP_per_k_valid
data = DataManager()
my_input = []
for i in np.arange(num_test):
urm_train, urm_test = split_train_leave_k_out_user_wise(
data.get_urm(), temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(
urm_train, temperature='valid')
# urm_test = data.create_test_warm_users(urm_test, threshold=5)
urm_valid = data.create_test_warm_users(urm_valid, threshold=3)
my_input.append([urm_train, urm_test, urm_valid, x_tick])
from multiprocessing import Pool
def __init__(self, urm_train, eurm=False):
super(HybridNormOrigRecommender, self).__init__(urm_train)
self.data_folder = Path(__file__).parent.parent.absolute()
self.eurm = eurm
self.num_users = urm_train.shape[0]
data = DataManager()
urm_train = check_matrix(urm_train.copy(), 'csr')
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(shrink=40, topK=20, feature_weighting='BM25')
recommender_7 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_7.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
# recommender_1 = HybridGenRecommender(urm_train, eurm=self.eurm)
# recommender_1.fit()
# recommender_2 = ItemKNNCFRecommender(urm_train)
# recommender_2.fit(shrink=30, topK=20)
recommender_2 = ItemKNNCFRecommender(urm_train)
recommender_2.fit(topK=5,
shrink=500,
feature_weighting='BM25',
similarity='tversky',
normalize=False,
tversky_alpha=0.0,
tversky_beta=1.0)
recommender_3 = UserKNNCFRecommender(urm_train)
recommender_3.fit(shrink=2, topK=600, normalize=True)
# recommender_3 = UserKNNCFRecommender(urm_train)
# recommender_3.fit(topK=697, shrink=1000, feature_weighting='TF-IDF', similarity='tversky', normalize=False,
# tversky_alpha=1.0, tversky_beta=1.0)
recommender_4 = RP3betaRecommender(urm_train)
recommender_4.fit(topK=16,
alpha=0.03374950051351756,
beta=0.24087176329409027,
normalize_similarity=False)
recommender_5 = SLIM_BPR_Cython(urm_train)
recommender_5.fit(lambda_i=0.0926694015,
lambda_j=0.001697250,
learning_rate=0.002391,
epochs=65,
topK=200)
recommender_6 = ALSRecommender(urm_train)
recommender_6.fit(alpha=5, iterations=40, reg=0.3)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
self.recommender_3 = recommender_3
self.recommender_4 = recommender_4
self.recommender_5 = recommender_5
self.recommender_6 = recommender_6
self.recommender_7 = recommender_7
if self.eurm:
if Path(self.data_folder / 'Data/uicm_orig_sparse.npz').is_file():
print("Previous uicm_sparse found")
self.score_matrix_1 = sps.load_npz(self.data_folder /
'Data/uicm_sparse.npz')
else:
print("uicm_sparse not found, create new one...")
self.score_matrix_1 = self.recommender_1._compute_item_matrix_score(
np.arange(self.num_users))
sps.save_npz(self.data_folder / 'Data/uicm_orig_sparse.npz',
self.score_matrix_1)
self.score_matrix_2 = self.recommender_2._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_3 = self.recommender_3._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_4 = self.recommender_4._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_5 = self.recommender_5._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_6 = self.recommender_6._compute_item_score(
np.arange(self.num_users))
self.score_matrix_1 = normalize(self.score_matrix_2,
norm='max',
axis=1)
self.score_matrix_2 = normalize(self.score_matrix_2,
norm='max',
axis=1)
self.score_matrix_3 = normalize(self.score_matrix_3,
norm='max',
axis=1)
self.score_matrix_4 = normalize(self.score_matrix_4,
norm='max',
axis=1)
self.score_matrix_5 = normalize(self.score_matrix_5,
norm='max',
axis=1)
self.score_matrix_6 = normalize(self.score_matrix_6,
norm='max',
axis=1)
class BuildingEnv(gym.Env):
def __init__(self, env_config):
self.DataManager = DataManager(env_config["building"],
env_config["zones"],
env_config["start"], env_config["end"],
env_config["window"])
self.start = start
self.unix_start = start.timestamp() * 1e9
self.end = end
self.unix_end = end.timestamp() * 1e9
self.window = window # timedelta string
self.building = building
self.zones = zones
self.lambda_val = env_config["lambda_val"]
# assert self.zones == all zones in building. this is because of the thermal model needing other zone temperatures.
self.curr_timestep = 0
self.indoor_starting_temperatures = env_config[
"indoor_starting_temperatures"] # to get starting temperatures [last, current]
self.outdoor_starting_temperature = env_config[
"outdoor_starting_temperature"]
self.tstats = {}
for iter_zone in self.zones:
self.tstats[iter_zone] = Tstat(
self.building,
iter_zone,
self.indoor_starting_temperatures[iter_zone]["current"],
last_temperature=self.indoor_starting_temperatures[iter_zone]
["last"])
assert 60 * 60 % xsg.get_window_in_sec(
self.window) == 0 # window divides an hour
assert (self.end - self.start).total_seconds() % xsg.get_window_in_sec(
self.window) == 0 # window divides the timeframe
# the number of timesteps
self.num_timesteps = int((self.end - self.start).total_seconds() /
xsg.get_window_in_sec(self.window))
self.unit = env_config["unit"]
assert self.unit == "F"
# all zones current and last temperature = 2*num_zones
# building outside temperature -> make a class for how this behaves = 1
# timestep -> do one hot encoding of week, day, hour, window \approx 4 + 7 + 24 + 60*60 / window
low_bound = [32] * 2 * len(
self.zones
) # we could use parametric temperature bounds... for now we will give negative inft reward
low_bound += [-100] # for outside temperature we cannot gurantee much
high_bound = [100] * 2 * len(self.zones)
high_bound += [200] # for outside temperature we cannot gurantee much
low_bound += [0] * (
self.num_timesteps + 1
) # total timesteps plus the final timestep which wont be executed
high_bound += [1] * (
self.num_timesteps + 1
) # total timesteps plus the final timestep which wont be executed
self.observation_space = Box(low=np.array(low_bound),
high=np.array(high_bound),
dtype=np.float32)
self.action_space = Tuple((Discrete(3), ) * len(self.zones))
self.reset()
def reset(self):
self.curr_timestep = 0
for iter_zone in self.zones:
self.tstats[iter_zone].reset(
self.indoor_starting_temperatures[iter_zone]["current"],
last_temperature=self.indoor_starting_temperatures[iter_zone]
["last"])
self.outdoor_temperature = self.outdoor_starting_temperature
return self.create_curr_obs() # obs
def step(self, action):
self.curr_timestep += 1
# if we reach the end time.
if self.curr_timestep == self.num_timesteps:
return self.create_curr_obs(), 0, True, {}
# find what new temperature would be. use thermal model with uncertainty. use reset if exceeding
# do_not_exceed. can't force it to take a different action anymore.
# update temperatures
for i, iter_zone in enumerate(self.zones):
self.tstats[iter_zone].next_temperature(action[i])
self.outdoor_temperature += np.random.normal(
) # TODO we should make a thermostat for the outdoor temperature.
# check that in saftey temperature band
for iter_zone in self.zones:
curr_safety = self.DataManager.do_not_exceed[iter_zone].iloc[
self.curr_timestep]
if not (curr_safety["t_low"] <= self.tstats[iter_zone].temperature
<= curr_safety["t_high"]):
return self.create_curr_obs(), -INF_REWARD, True, {
} # TODO do we want to add info?
# get reward by calling discomfort and consumption model ...
reward = self.get_reward(action)
return self.create_curr_obs(), reward, False, {
} # obs, reward, done, info
def get_reward(self, action):
"""Get the reward for the given action with the current observation parameters."""
# get discomfort across edge
discomfort = {}
for iter_zone in self.zones:
# TODO Check this again since we are a timestep ahead and we want average comfortband and average occupancy over the edge.
curr_comfortband = self.DataManager.comfortband[iter_zone].iloc[
self.curr_timestep]
curr_occupancy = self.DataManager.occupancy[iter_zone].iloc[
self.curr_timestep]
curr_tstat = self.tstats[iter_zone]
average_edge_temperature = (curr_tstat.temperature +
curr_tstat.last_temperature) / 2.
discomfort[iter_zone] = self.DataManager.get_discomfort(
self.building, average_edge_temperature,
curr_comfortband["t_low"], curr_comfortband["t_high"],
curr_occupancy)
# Get consumption across edge
price = 1 # self.prices.iloc[root.timestep] TODO also add right unit conversion, and duration
consumption_cost = {
self.zones[i]:
price * self.DataManager.hvac_consumption[self.zones[i]][action[i]]
for i in range(len(self.zones))
}
cost = (
(1 - self.lambda_val) *
(sum(consumption_cost.values()))) + (self.lambda_val *
(sum(discomfort.values())))
return -cost
def create_curr_obs(self):
return self._create_obs(self.tstats, self.outdoor_temperature,
self.curr_timestep)
def _create_obs(self, tstats, outdoor_temperature, curr_timestep):
obs = np.zeros(self.observation_space.low.shape)
idx = 0
for iter_zone in self.zones:
obs[idx] = tstats[iter_zone].last_temperature
idx += 1
obs[idx] = tstats[iter_zone].temperature
idx += 1
obs[idx] = outdoor_temperature
idx += 1
obs[idx + curr_timestep] = 1
return obs