def policy_evaluation(self, policy, context, desired_action, delta, r,
epsilon):
if policy != 'LinThompSamp':
print("We don't support other bandit algorithms now!")
else:
historystorage = history.MemoryHistoryStorage()
modelstorage = model.MemoryModelStorage()
policy = linthompsamp.LinThompSamp(self.actions, historystorage,
modelstorage, self.d, delta, r,
epsilon)
seq_error = np.zeros(shape=(self.t, 1))
for t in range(self.t):
history_id, action = policy.get_action(context[t])
if desired_action[t][0] != action:
policy.reward(history_id, 0)
# sum_error += 1
if t == 0:
seq_error[t] = 1.0
else:
seq_error[t] = seq_error[t - 1] + 1.0
else:
policy.reward(history_id, 1)
if t > 0:
seq_error[t] = seq_error[t - 1]
return seq_error
def policy_evaluation(self, policy, context, desired_action, models,
delta):
if policy != 'EXP4P':
print("We don't support other bandit algorithms now!")
else:
historystorage = history.MemoryHistoryStorage()
modelstorage = model.MemoryModelStorage()
seq_error = np.zeros(shape=(self.t, 1))
policy = exp4p.Exp4P(self.actions,
historystorage,
modelstorage,
models,
delta,
pmin=None)
for t in range(self.t):
history_id, action = policy.get_action(context[t])
if desired_action[t][0] != action:
policy.reward(history_id, 0)
# sum_error += 1
if t == 0:
seq_error[t] = 1.0
else:
seq_error[t] = seq_error[t - 1] + 1.0
else:
policy.reward(history_id, 1)
if t > 0:
seq_error[t] = seq_error[t - 1]
return seq_error
def policy_generation(bandit, actions):
historystorage = history.MemoryHistoryStorage()
modelstorage = model.MemoryModelStorage()
if bandit == 'Exp4P':
policy = exp4p.Exp4P(actions,
historystorage,
modelstorage,
delta=0.5,
pmin=None)
elif bandit == 'LinUCB':
policy = linucb.LinUCB(actions, historystorage, modelstorage, 0.3, 20)
elif bandit == 'LinThompSamp':
policy = linthompsamp.LinThompSamp(actions,
historystorage,
modelstorage,
d=20,
delta=0.61,
r=0.01,
epsilon=0.71)
elif bandit == 'UCB1':
policy = ucb1.UCB1(actions, historystorage, modelstorage)
elif bandit == 'Exp3':
policy = exp3.Exp3(actions, historystorage, modelstorage, gamma=0.2)
elif bandit == 'random':
policy = 0
return policy
def setUp(self):
self.modelstorage = model.MemoryModelStorage()
self.historystorage = history.MemoryHistoryStorage()
self.actions = [1, 2, 3]
self.d = 2
self.delta = 0.5
self.R = 0.5
self.epsilon = 0.1
def train_movielens(max_iter=163683, batch_size=100):
# 데이터 전처리 방법에 대해 알고자 한다면...
# 참고: https://striatum.readthedocs.io/en/latest/auto_examples/index.html#general-examples
streaming_batch = pd.read_csv('streaming_batch.csv', sep='\t', names=['user_id'], engine='c')
user_feature = pd.read_csv('user_feature.csv', sep='\t', header=0, index_col=0, engine='c')
arm_ids = list(pd.read_csv('actions.csv', sep='\t', header=0, engine='c')['movie_id'])
reward_list = pd.read_csv('reward_list.csv', sep='\t', header=0, engine='c')
streaming_batch = streaming_batch.iloc[0:max_iter]
# 아래 n_actions 인자에서 처음 시점에서의 Action 의 개수를 정의 함
th = TreeHeuristic(history.MemoryHistoryStorage(), model.MemoryModelStorage(),
action.MemoryActionStorage(), n_actions=50)
actions = make_arm(arm_ids=arm_ids)
reward_sum = 0
y = []
print("Starting Now...")
start = time.time()
for i in range(max_iter):
context = np.array(user_feature[user_feature.index == streaming_batch.iloc[i, 0]])[0]
if i == 0:
th.build(first_context=context, actions=actions)
history_id, recommendations = th.sample_from_beta(context=context)
watched_list = reward_list[reward_list['user_id'] == streaming_batch.iloc[i, 0]]
if recommendations.action.id not in list(watched_list['movie_id']):
# 잘 못 맞췄으면 0점을 얻음
th.reward(history_id, {recommendations.action.id: 0.0})
th.update_D(context=context, action_id=recommendations.action.id, reward=0.0)
else:
# 잘 맞춨으면 1점을 얻음
th.reward(history_id, {recommendations.action.id: 1.0})
th.update_D(context=context, action_id=recommendations.action.id, reward=1.0)
reward_sum += 1
if i % batch_size == 0 and i != 0:
for action_chosen in th._action_storage.iterids():
th.update_tree(action_id=action_chosen)
if i % 100 == 0:
print("Step: {} -- Average Reward: {}".format(i, np.round(reward_sum / (i+1), 4)))
y.append(reward_sum / (i + 1))
print("Time: {}".format(time.time() - start))
x = list(range(max_iter))
plt.figure()
plt.plot(x, y, c='r')
plt.title("Cumulative Average Reward of \n Tree Heuristic: Movie Lens Data")
plt.show()
def train_covtype(n_samples=581000, batch_size=3000):
file = pd.read_csv("covtype.data", header=None)
data = file.values
np.random.shuffle(data)
X, temp = data[:, 0:54], data[:, 54]
Y = pd.get_dummies(temp).values
actions = make_arm(list(range(7)))
th = TreeHeuristic(history.MemoryHistoryStorage(), model.MemoryModelStorage(),
action.MemoryActionStorage(), n_actions=7)
th.build(first_context=X[0], actions=actions)
reward_sum = 0
y = []
print("Starting Now...")
start = time.time()
for i in range(n_samples):
context = X[i]
history_id, recommendations = th.sample_from_beta(context=context)
# 실제 Reward 를 받고 이를 누적함
actual_reward = Y[i, recommendations.action.id]
reward_sum += actual_reward
th.reward(history_id, {recommendations.action.id: actual_reward})
# D는 매 trial 마다 업데이트해 주어야 함
th.update_D(context=context, action_id=recommendations.action.id, reward=actual_reward)
# batch size 만큼을 모아서 적합해줌
if i % batch_size == 0 and i != 0:
for action_chosen in th._action_storage.iterids():
th.update_tree(action_id=action_chosen)
# 로그는 100개 마다 찍음
if i % 100 == 0:
print("Step: {} -- Average Reward: {}".format(i, np.round(reward_sum / (i+1), 4)))
y.append(reward_sum/(i+1))
print("Time: {}".format(time.time() - start))
x = list(range(n_samples))
y[0] = 0
plt.figure()
plt.plot(x, y, c='r')
plt.title("Cumulative Average Reward Flow of \n Tree Heuristic: Cover type Data")
plt.show()
def setUp(self):
self.modelstorage = model.MemoryModelStorage()
self.historystorage = history.MemoryHistoryStorage()
self.actions = [1, 2, 3, 4, 5]
self.history_context = np.random.uniform(0, 5, (1000, 2))
self.history_action = np.zeros(1000)
for t in range(1000):
for i in range(5):
if i * 2 < sum(self.history_context[t, :]) <= (i + 1) * 2:
self.history_action[t] = self.actions[i]
self.LogReg = OneVsRestClassifier(LogisticRegression())
self.MNB = OneVsRestClassifier(MultinomialNB())
self.LogReg.fit(self.history_context, self.history_action)
self.MNB.fit(self.history_context, self.history_action)
def policy_generation(bandit, actions):
"""
Parameters
----------
bandit： 赌博机算法
actions：动作即推荐的电影
Returns
-------
policy： 生成的策略
"""
historystorage = history.MemoryHistoryStorage() # 内存中历史存储记录
modelstorage = model.MemoryModelStorage() # 内存中模型存储，为了统一
if bandit == 'Exp4P':
policy = exp4p.Exp4P(historystorage,
modelstorage,
actions,
delta=0.5,
p_min=None)
elif bandit == 'LinUCB':
#policy = linucb.LinUCB(historystorage, modelstorage, actions, 0.3, 20)
policy = linucb.LinUCB(history_storage=historystorage,
model_storage=modelstorage,
action_storage=actions,
alpha=0.3,
context_dimension=18)
elif bandit == 'LinThompSamp':
policy = linthompsamp.LinThompSamp(
historystorage,
modelstorage,
actions,
#d=20, Supposed to be context dimension
context_dimension=18,
delta=0.61,
R=0.01,
epsilon=0.71)
elif bandit == 'UCB1':
policy = ucb1.UCB1(historystorage, modelstorage, actions)
elif bandit == 'Exp3':
policy = exp3.Exp3(historystorage, modelstorage, actions, gamma=0.2)
elif bandit == 'random':
policy = 0
return policy
def policy_evaluation(self, policy, desired_action):
if policy != 'UCB1':
print("We don't support other bandit algorithms now!")
else:
historystorage = history.MemoryHistoryStorage()
modelstorage = model.MemoryModelStorage()
sum_error = 0
policy = ucb1.UCB1(self.actions, historystorage, modelstorage)
for t in range(self.t):
history_id, action = policy.get_action(context=None)
if desired_action[t][0] != action:
policy.reward(history_id, 0)
sum_error += 1
else:
policy.reward(history_id, 1)
return self.t - sum_error
def __init__(self, policy, **params):
self.seed = None
self.data_size = None
self.max_iter = None
self.reset_freq = None
if 'seed' in params:
self.seed = params.pop('seed')
if 'data_size' in params:
self.data_size = params.pop('data_size')
if 'max_iter' in params:
self.max_iter = params.pop('max_iter')
if 'reset_freq' in params:
self.reset_freq = params.pop('reset_freq')
self._params = params
self._history_storage = history.MemoryHistoryStorage()
self._model_storage = model.MemoryModelStorage()
self._action_storage = action.MemoryActionStorage()
self.policy = policy(self._history_storage, self._model_storage,
self._action_storage, **self._params)
self.reward_values = np.array([])
self._n_iter = 0
def policy_generation(bandit, actions):
historystorage = history.MemoryHistoryStorage()
modelstorage = model.MemoryModelStorage()
if bandit == 'Exp4P':
policy = exp4p.Exp4P(historystorage,
modelstorage,
actions,
delta=0.5,
p_min=None)
elif bandit == 'LinUCB':
#policy = linucb.LinUCB(historystorage, modelstorage, actions, 0.3, 20)
policy = linucb.LinUCB(history_storage=historystorage,
model_storage=modelstorage,
action_storage=actions,
alpha=0.3,
context_dimension=18)
elif bandit == 'LinThompSamp':
policy = linthompsamp.LinThompSamp(
historystorage,
modelstorage,
actions,
#d=20, Supposed to be context dimension
context_dimension=18,
delta=0.61,
R=0.01,
epsilon=0.71)
elif bandit == 'UCB1':
policy = ucb1.UCB1(historystorage, modelstorage, actions)
elif bandit == 'Exp3':
policy = exp3.Exp3(historystorage, modelstorage, actions, gamma=0.2)
elif bandit == 'random':
policy = 0
return policy
def setUp(self):
self.modelstorage = model.MemoryModelStorage()
self.historystorage = history.MemoryHistoryStorage()
self.actions = [1, 2, 3]
self.alpha = 1.00
def setUp(self):
self.modelstorage = model.MemoryModelStorage()
self.historystorage = history.MemoryHistoryStorage()
self.actions = [1, 2, 3, 4, 5]
self.gamma = 0.5
