def test_greedy0_n3(self):
arms, mab = self.predict(
arms=[1, 2, 3, 4],
decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3],
rewards=[0, 0, 0, 1, 1, 0, 0, 1, 1, 1],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.Clusters(3),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=1,
is_predict=True)
self.assertListEqual(arms, [2, 2])
self.assertEqual(mab._imp.kmeans.n_clusters, 3)
def test_greedy0_k2_exps(self):
exps, mab = self.predict(
arms=[1, 2, 3, 4],
decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3],
rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.KNearest(2),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=1,
is_predict=False)
self.assertDictEqual(exps[0], {1: 0.0, 2: 0.0, 3: 0, 4: 0})
self.assertDictEqual(exps[1], {1: 1.0, 2: 0.0, 3: 0, 4: 0})
def test_alpha0_nearest5(self):
arm, mab = self.predict(
arms=[1, 2, 3],
decisions=[1, 1, 1, 2, 2, 2, 3, 3, 3, 1],
rewards=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
learning_policy=LearningPolicy.LinUCB(alpha=0),
neighborhood_policy=NeighborhoodPolicy.KNearest(k=5),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=3,
is_predict=True)
self.assertEqual(len(arm), 3)
self.assertEqual(arm, [[3, 3], [3, 3], [3, 3]])
def test_greedy0_n2_mini(self):
arms, mab = self.predict(
arms=[1, 2, 3, 4],
decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3],
rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.Clusters(2, True),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=1,
is_predict=True)
self.assertListEqual(arms, [3, 1])
self.assertTrue(isinstance(mab._imp.kmeans, MiniBatchKMeans))
def test_greedy0_no_nhood_predict_random(self):
# 2nd, 3rd arm has bad rewards should not be selected
# Use small neighborhood size to force to no nhood
arms, mab = self.predict(arms=[1, 2, 3],
decisions=[1, 1, 1, 2, 2, 2],
rewards=[10, 10, 10, -10, -10, -10],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.LSHNearest(n_dimensions=25),
context_history=[[1, 1, 2, 3, 5], [1, 2, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0]],
contexts=[[0, -1, -2, -3, -5], [-1, -1, -1, -1, -1]],
seed=7,
num_run=2,
is_predict=True)
# 3rd arm was never seen but picked up by random neighborhood in both tests
self.assertListEqual(arms[0], [2, 3])
self.assertListEqual(arms[1], [2, 1])
def test_partial_fit_indices(self):
seed = 11
n_dimensions = 5
n_tables = 5
rng = np.random.RandomState(seed)
contexts = np.array([[rng.rand() for _ in range(7)] for _ in range(10)])
decisions = np.array([rng.randint(0, 2) for _ in range(10)])
rewards = np.array([rng.rand() for _ in range(10)])
lsh = MAB(arms=[0, 1], learning_policy=LearningPolicy.Softmax(),
neighborhood_policy=NeighborhoodPolicy.LSHNearest(n_dimensions, n_tables),
seed=seed)
lsh.fit(decisions, rewards, contexts)
contexts2 = np.array([[rng.rand() for _ in range(7)] for _ in range(10)])
decisions2 = np.array([rng.randint(0, 2) for _ in range(10)])
rewards2 = np.array([rng.rand() for _ in range(10)])
lsh.partial_fit(decisions2, rewards2, contexts2)
self.assertListEqual(lsh._imp.table_to_hash_to_index[0][4], [1, 15, 16])
self.assertListEqual(lsh._imp.table_to_hash_to_index[0][12], [9, 10, 11, 19])
def test_greedy0_no_nhood_expectation_nan(self):
# 2nd, 3rd arm has bad rewards should not be selected
# Use small neighborhood size to force to no nhoods
exps, mab = self.predict(arms=[1, 2, 3],
decisions=[1, 1, 1, 2, 2, 2],
rewards=[10, 10, 10, -10, -10, -10],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.LSHNearest(n_dimensions=25),
context_history=[[1, 1, 2, 3, 5], [1, 2, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0]],
contexts=[[0, -1, -2, -3, -5], [-1, -1, -1, -1, -1]],
seed=7,
num_run=1,
is_predict=False)
# When there are no neighborhoods, expectations will be nan
self.assertDictEqual(exps[0], {1: np.nan, 2: np.nan, 3: np.nan})
self.assertDictEqual(exps[1], {1: np.nan, 2: np.nan, 3: np.nan})
def test_partial_fit_thompson_thresholds(self):
arm_to_threshold = {1: 1, 2: 5, 3: 2, 4: 3}
def binarize(arm, reward):
return reward >= arm_to_threshold[arm]
arms, mab = self.predict(
arms=[1, 2, 3, 4],
decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3],
rewards=[0, 1, 7, 0, 1, 9, 0, 2, 6, 11],
learning_policy=LearningPolicy.ThompsonSampling(binarize),
neighborhood_policy=NeighborhoodPolicy.KNearest(2),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=1,
is_predict=True)
self.assertTrue(mab._imp.lp.is_contextual_binarized)
self.assertListEqual(arms, [4, 4])
self.assertEqual(len(mab._imp.decisions), 10)
self.assertEqual(len(mab._imp.rewards), 10)
self.assertEqual(len(mab._imp.contexts), 10)
self.assertEqual(np.ndim(mab._imp.decisions), 1)
self.assertListEqual(list(set(mab._imp.rewards)), [0, 1])
decisions2 = [1, 2, 3]
rewards2 = [11, 1, 6]
context_history2 = [[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0]]
mab.partial_fit(decisions2, rewards2, context_history2)
self.assertEqual(len(mab._imp.decisions), 13)
self.assertEqual(len(mab._imp.rewards), 13)
self.assertEqual(len(mab._imp.contexts), 13)
self.assertEqual(np.ndim(mab._imp.decisions), 1)
arm = mab.predict([[0, 1, 2, 3, 5]])
self.assertEqual(arm, 3)
self.assertListEqual(list(set(mab._imp.rewards)), [0, 1])
def test_tables(self):
seed = 11
n_dimensions = 5
n_tables = 5
rng = np.random.RandomState(seed)
contexts = np.array([[rng.rand() for _ in range(7)] for _ in range(10)])
decisions = np.array([rng.randint(0, 2) for _ in range(10)])
rewards = np.array([rng.rand() for _ in range(10)])
lsh = MAB(arms=[0, 1], learning_policy=LearningPolicy.Softmax(),
neighborhood_policy=NeighborhoodPolicy.LSHNearest(n_dimensions, n_tables),
seed=seed)
for i in range(n_tables):
self.assertListEqual([], lsh._imp.table_to_plane[i])
lsh.fit(decisions, rewards, contexts)
self.assertListAlmostEqual(list(lsh._imp.table_to_plane[0][0]),
[1.74945474, -0.286073, -0.48456513, -2.65331856, -0.00828463])
self.assertListEqual(list(lsh._imp.table_to_hash_to_index[0].keys()), [1, 4, 5, 12, 13, 14, 15])
self.assertListEqual(lsh._imp.table_to_hash_to_index[0][1], [3])
self.assertListEqual(lsh._imp.table_to_hash_to_index[0][14], [0, 4, 8])
def test_greedy0_no_nhood_predict_weighted(self):
# 2nd, 3rd arm has bad rewards should not be selected
# Use small neighborhood size to force to no nhoods
arms, mab = self.predict(arms=[1, 2, 3],
decisions=[1, 1, 1, 2, 2, 2],
rewards=[10, 10, 10, -10, -10, -10],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.LSHNearest(
n_dimensions=25, no_nhood_prob_of_arm=[0, 0.8, 0.2]),
context_history=[[1, 1, 2, 3, 5], [1, 2, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0]],
contexts=[[0, -1, -2, -3, -5], [-1, -1, -1, -1, -1]],
seed=7,
num_run=2,
is_predict=True)
# 2nd arm is weighted highly but 3rd is picked too
self.assertListEqual(arms[0], [2, 2])
self.assertListEqual(arms[1], [2, 2])
def test_add_arm(self):
arms, mab = self.predict(arms=[1, 2, 3, 4],
decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3],
rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.LSHNearest(n_dimensions=2),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=1,
is_predict=True)
mab.add_arm(5)
self.assertTrue(5 in mab.arms)
self.assertTrue(5 in mab._imp.arms)
self.assertTrue(5 in mab._imp.lp.arms)
self.assertTrue(5 in mab._imp.lp.arm_to_expectation.keys())
def test_lints_knearest(self):
train_df = pd.DataFrame({
'ad': [1, 1, 1, 2, 4, 5, 3, 3, 2, 1, 4, 5, 3, 2, 5],
'revenues': [10, 17, 22, 9, 4, 20, 7, 8, 20, 9, 50, 5, 7, 12, 10],
'age':
[22, 27, 39, 48, 21, 20, 19, 37, 52, 26, 18, 42, 55, 57, 38],
'click_rate': [
0.2, 0.6, 0.99, 0.68, 0.15, 0.23, 0.75, 0.17, 0.33, 0.65, 0.56,
0.22, 0.19, 0.11, 0.83
],
'subscriber': [1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0]
})
# Test data to for new prediction
test_df = pd.DataFrame({
'age': [37, 52],
'click_rate': [0.5, 0.6],
'subscriber': [0, 1]
})
# Scale the data
scaler = StandardScaler()
train = scaler.fit_transform(
np.asarray(train_df[['age', 'click_rate', 'subscriber']],
dtype='float64'))
test = scaler.transform(np.asarray(test_df, dtype='float64'))
arms, mab = self.predict(
arms=[1, 2, 3, 4, 5],
decisions=train_df['ad'],
rewards=train_df['revenues'],
learning_policy=LearningPolicy.LinTS(alpha=1),
neighborhood_policy=NeighborhoodPolicy.KNearest(k=4),
context_history=train,
contexts=test,
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arms, [1, 2])
def test_copy_arms(self):
arms, mab = self.predict(
arms=[1, 2, 3, 4],
decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3],
rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0),
neighborhood_policy=NeighborhoodPolicy.Clusters(2),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=1,
is_predict=True)
self.assertTrue(mab.arms is mab._imp.lp_list[0].arms)
self.assertTrue(mab.arms is mab._imp.lp_list[1].arms)
mab.add_arm(5)
self.assertTrue(mab.arms is mab._imp.lp_list[0].arms)
self.assertTrue(mab.arms is mab._imp.lp_list[1].arms)
def test_add_arm(self):
arms, mab = self.predict(
arms=[1, 2, 3, 4],
decisions=[1, 1, 1, 2, 2, 3, 3, 3, 3, 3],
rewards=[0, 1, 1, 0, 0, 0, 0, 1, 1, 1],
learning_policy=LearningPolicy.Popularity(),
neighborhood_policy=NeighborhoodPolicy.Clusters(2),
context_history=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1], [0, 0, 1, 0, 0],
[0, 2, 2, 3, 5], [1, 3, 1, 1, 1], [0, 0, 0, 0, 0],
[0, 1, 4, 3, 5], [0, 1, 2, 4, 5], [1, 2, 1, 1, 3],
[0, 2, 1, 0, 0]],
contexts=[[0, 1, 2, 3, 5], [1, 1, 1, 1, 1]],
seed=123456,
num_run=1,
is_predict=True)
mab.add_arm(5)
self.assertTrue(5 in mab.arms)
self.assertTrue(5 in mab._imp.arms)
self.assertTrue(5 in mab._imp.lp_list[0].arms)
self.assertTrue(5 in mab._imp.lp_list[0].arm_to_expectation.keys())
test_df_revenue = pd.Series([7, 13])
# Scale the training and test data
scaler = StandardScaler()
train = scaler.fit_transform(train_df[['age', 'click_rate',
'subscriber']].values.astype('float64'))
test = scaler.transform(test_df.values.astype('float64'))
########################################################
# Radius Neighborhood Policy with UCB1 Learning Policy
########################################################
# Radius contextual policy with radius equals to 5 and ucb1 learning with alpha 1.25
radius = MAB(arms=ads,
learning_policy=LearningPolicy.UCB1(alpha=1.25),
neighborhood_policy=NeighborhoodPolicy.Radius(radius=5))
# Learn from previous ads shown and revenues generated
radius.fit(decisions=train_df['ad'],
rewards=train_df['revenues'],
contexts=train)
# Predict the next best ad to show
prediction = radius.predict(test)
# Expectation of each ad based on learning from past ad revenues
expectations = radius.predict_expectations(test)
# Results
print("Radius: ", prediction, " ", expectations)
assert (prediction == [4, 4])
def test_invalid_n_tables_type(self):
with self.assertRaises(TypeError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.LSHNearest(n_tables='string'))
def test_invalid_learning_policy(self):
with self.assertRaises(TypeError):
MAB([0, 1], NeighborhoodPolicy.Radius(radius=12))
def test_invalid_clusters_num(self):
with self.assertRaises(ValueError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.Clusters(n_clusters=1))
def test_invalid_k(self):
with self.assertRaises(ValueError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.KNearest(k=0))
def test_invalid_minibatch(self):
with self.assertRaises(TypeError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.Clusters(minibatch=0))
def test_invalid_radius(self):
with self.assertRaises(ValueError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.Radius(radius=-1))
def test_invalid_radius_no_nhood_sum(self):
with self.assertRaises(ValueError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.Radius(radius=1,
no_nhood_prob_of_arm=[0, 0]))
class BaseTest(unittest.TestCase):
# A list of valid learning policies
lps = [
LearningPolicy.EpsilonGreedy(),
LearningPolicy.EpsilonGreedy(epsilon=0),
LearningPolicy.EpsilonGreedy(epsilon=0.0),
LearningPolicy.EpsilonGreedy(epsilon=0.5),
LearningPolicy.EpsilonGreedy(epsilon=1),
LearningPolicy.EpsilonGreedy(epsilon=1.0),
LearningPolicy.Random(),
LearningPolicy.Softmax(),
LearningPolicy.Softmax(tau=0.1),
LearningPolicy.Softmax(tau=0.5),
LearningPolicy.Softmax(tau=1),
LearningPolicy.Softmax(tau=1.0),
LearningPolicy.Softmax(tau=5.0),
LearningPolicy.ThompsonSampling(),
LearningPolicy.UCB1(),
LearningPolicy.UCB1(alpha=0),
LearningPolicy.UCB1(alpha=0.0),
LearningPolicy.UCB1(alpha=0.5),
LearningPolicy.UCB1(alpha=1),
LearningPolicy.UCB1(alpha=1.0),
LearningPolicy.UCB1(alpha=5)
]
para_lps = [
LearningPolicy.LinTS(alpha=0.00001, l2_lambda=1),
LearningPolicy.LinTS(alpha=0.5, l2_lambda=1),
LearningPolicy.LinTS(alpha=1, l2_lambda=1),
LearningPolicy.LinTS(alpha=0.00001, l2_lambda=0.5),
LearningPolicy.LinTS(alpha=0.5, l2_lambda=0.5),
LearningPolicy.LinTS(alpha=1, l2_lambda=0.5),
LearningPolicy.LinUCB(alpha=0, l2_lambda=1),
LearningPolicy.LinUCB(alpha=0.5, l2_lambda=1),
LearningPolicy.LinUCB(alpha=1, l2_lambda=1),
LearningPolicy.LinUCB(alpha=0, l2_lambda=0.5),
LearningPolicy.LinUCB(alpha=0.5, l2_lambda=0.5),
LearningPolicy.LinUCB(alpha=1, l2_lambda=0.5)
]
# A list of valid context policies
nps = [
NeighborhoodPolicy.KNearest(),
NeighborhoodPolicy.KNearest(k=1),
NeighborhoodPolicy.KNearest(k=3),
NeighborhoodPolicy.Radius(),
NeighborhoodPolicy.Radius(2.5),
NeighborhoodPolicy.Radius(5)
]
cps = [
NeighborhoodPolicy.Clusters(),
NeighborhoodPolicy.Clusters(n_clusters=3),
NeighborhoodPolicy.Clusters(is_minibatch=True),
NeighborhoodPolicy.Clusters(n_clusters=3, is_minibatch=True)
]
@staticmethod
def predict(
arms: List[Arm],
decisions: Union[List, np.ndarray, pd.Series],
rewards: Union[List, np.ndarray, pd.Series],
learning_policy: Union[LearningPolicy.EpsilonGreedy,
LearningPolicy.Random, LearningPolicy.Softmax,
LearningPolicy.ThompsonSampling,
LearningPolicy.UCB1, LearningPolicy.LinTS,
LearningPolicy.LinUCB],
neighborhood_policy: Union[None, NeighborhoodPolicy.Clusters,
NeighborhoodPolicy.Radius,
NeighborhoodPolicy.KNearest] = None,
context_history: Union[None, List[Num], List[List[Num]], np.ndarray,
pd.DataFrame, pd.Series] = None,
contexts: Union[None, List[Num], List[List[Num]], np.ndarray,
pd.DataFrame, pd.Series] = None,
seed: Optional[int] = 123456,
num_run: Optional[int] = 1,
is_predict: Optional[bool] = True,
n_jobs: Optional[int] = 1,
backend: Optional[str] = None
) -> (Union[Arm, List[Arm], List[float], List[List[float]]], MAB):
"""Sets up a MAB model and runs the given configuration.
Return list of predictions or prediction and the mab instance, when is_predict is true
Return list of expectations or expectation and the mab instance, when is predict is false
Calls the predict or predict_expectation method num_run number of times.
"""
# Model
mab = MAB(arms, learning_policy, neighborhood_policy, seed, n_jobs,
backend)
# Train
mab.fit(decisions, rewards, context_history)
# Test
if is_predict:
# Return: prediction(s) and the MAB instance
predictions = [mab.predict(contexts) for _ in range(num_run)]
return predictions[0] if num_run == 1 else predictions, mab
else:
# Return: expectations(s) and the MAB instance
expectations = [
mab.predict_expectations(contexts) for _ in range(num_run)
]
return expectations[0] if num_run == 1 else expectations, mab
def assertListAlmostEqual(self, list1, list2):
"""
Asserts that floating values in the given lists (almost) equals to each other
"""
if not isinstance(list1, list):
list1 = list(list1)
if not isinstance(list2, list):
list2 = list(list2)
self.assertEqual(len(list1), len(list2))
for index, val in enumerate(list1):
self.assertAlmostEqual(val, list2[index])
def test_invalid_metric(self):
with self.assertRaises(ValueError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.Radius(metric='linear'))
####################################
# Different Bandits for Simulation
####################################
print('Starting simulation 1\n')
def binarize(decision, reward):
if decision == 0:
return reward <= 50
else:
return reward >= 220
n_jobs=2
contextual_mabs = [('Random', MAB([0, 1], LearningPolicy.Random(), NeighborhoodPolicy.Radius(10), n_jobs=n_jobs)),
('UCB1', MAB([0, 1], LearningPolicy.UCB1(1), NeighborhoodPolicy.Radius(10), n_jobs=n_jobs)),
('ThompsonSampling', MAB([0, 1], LearningPolicy.ThompsonSampling(binarize),
NeighborhoodPolicy.Radius(10), n_jobs=n_jobs)),
('EpsilonGreedy', MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=.15),
NeighborhoodPolicy.Radius(10), n_jobs=n_jobs)),
('Softmax', MAB([0, 1], LearningPolicy.Softmax(), NeighborhoodPolicy.Radius(10), n_jobs=n_jobs))]
context_free_mabs = [('Random', MAB([0, 1], LearningPolicy.Random(), n_jobs=n_jobs)),
('UCB1', MAB([0, 1], LearningPolicy.UCB1(1), n_jobs=n_jobs)),
('ThompsonSampling', MAB([0, 1], LearningPolicy.ThompsonSampling(binarize), n_jobs=n_jobs)),
('EpsilonGreedy', MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=.15), n_jobs=n_jobs)),
('Softmax', MAB([0, 1], LearningPolicy.Softmax(), n_jobs=n_jobs))]
mixed = [('RandomRadius', MAB([0, 1], LearningPolicy.Random(), NeighborhoodPolicy.Radius(10), n_jobs=n_jobs)),
('Random', MAB([0, 1], LearningPolicy.Random(), n_jobs=n_jobs))]
def test_invalid_radius_no_nhood_type_ann(self):
with self.assertRaises(TypeError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.LSHNearest(no_nhood_prob_of_arm={}))
def test_invalid_n_dimensions_value(self):
with self.assertRaises(ValueError):
MAB([0, 1], LearningPolicy.EpsilonGreedy(epsilon=0),
NeighborhoodPolicy.LSHNearest(n_dimensions=0))
# Online update of model
linucb.partial_fit(decisions=prediction,
rewards=test_df_revenue,
contexts=test)
# Update the model with new arm
linucb.add_arm(6)
###################################################################
# LinUCB Learning Policy combined with Radius Neighborhood Policy
###################################################################
# Radius context policy with radius equals to 1 and LinUCB learning with alpha of 1
radius = MAB(arms=ads,
learning_policy=LearningPolicy.LinUCB(alpha=1),
neighborhood_policy=NeighborhoodPolicy.Radius(radius=1))
# Learn from previous ads shown and revenues generated
radius.fit(decisions=train_df['ad'],
rewards=train_df['revenues'],
contexts=train)
# Predict the next best ad to show
prediction = radius.predict(test)
# Expectation of each ad based on learning from past ad revenues
expectations = radius.predict_expectations(test)
# Results
print("Radius: ", prediction, " ", expectations)
assert (prediction == [1, 2])
test_df_revenue = pd.Series([7, 13])
# Scale the training and test data
scaler = StandardScaler()
train = scaler.fit_transform(train_df[['age', 'click_rate',
'subscriber']].values.astype('float64'))
test = scaler.transform(test_df.values.astype('float64'))
########################################################
# Radius Neighborhood Policy with UCB1 Learning Policy
########################################################
# Radius contextual policy with radius equals to 5 and ucb1 learning with alpha 1.25
radius = MAB(arms=ads,
learning_policy=LearningPolicy.UCB1(alpha=1.25),
neighborhood_policy=NeighborhoodPolicy.Radius(radius=5))
# Learn from previous ads shown and revenues generated
radius.fit(decisions=train_df['ad'],
rewards=train_df['revenues'],
contexts=train)
# Predict the next best ad to show
prediction = radius.predict(test)
# Expectation of each ad based on learning from past ad revenues
expectations = radius.predict_expectations(test)
# Results
print("Radius: ", prediction, " ", expectations)
assert (prediction == [4, 4])