# -*- coding: utf-8 -*-
import random
import logging
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from tests.test_base import BaseTest
from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
from mabwiser.simulator import Simulator
logging.disable(logging.CRITICAL)
class ExampleTest(BaseTest):
def test_popularity(self):
list_of_arms = ['Arm1', 'Arm2']
decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
rewards = [20, 17, 25, 9]
mab = MAB(list_of_arms, LearningPolicy.Popularity())
mab.fit(decisions, rewards)
mab.predict()
self.assertEqual("Arm2", mab.predict())
self.assertDictEqual({'Arm1': 0.38016528925619836, 'Arm2': 0.6198347107438016},
mab.predict_expectations())
def test_random(self):
arm, mab = self.predict(arms=[1, 2],
decisions=[1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1],
rewards=[10, 17, 22, 9, 4, 0, 7, 8, 20, 9, 50, 5, 7, 12, 10],
learning_policy=LearningPolicy.Random(),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 2)
layout_partial = [1, 2, 1, 2]
revenue_partial = [0, 12, 7, 19]
mab.partial_fit(decisions=layout_partial, rewards=revenue_partial)
mab.add_arm(3)
self.assertTrue(3 in mab.arms)
self.assertTrue(3 in mab._imp.arm_to_expectation.keys())
def test_greedy15(self):
arm, mab = self.predict(arms=[1, 2],
decisions=[1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1],
rewards=[10, 17, 22, 9, 4, 0, 7, 8, 20, 9, 50, 5, 7, 12, 10],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.15),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 1)
layout_partial = [1, 2, 1, 2]
revenue_partial = [0, 12, 7, 19]
mab.partial_fit(decisions=layout_partial, rewards=revenue_partial)
mab.add_arm(3)
self.assertTrue(3 in mab.arms)
self.assertTrue(3 in mab._imp.arm_to_expectation.keys())
def test_linucb(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]})
test_df_revenue = pd.Series([7, 13])
# 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.LinUCB(alpha=1.25),
context_history=train,
contexts=test,
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arms, [5, 2])
mab.partial_fit(decisions=arms, rewards=test_df_revenue, contexts=test)
mab.add_arm(6)
self.assertTrue(6 in mab.arms)
self.assertTrue(6 in mab._imp.arm_to_expectation.keys())
def test_softmax(self):
arm, mab = self.predict(arms=[1, 2],
decisions=[1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1],
rewards=[10, 17, 22, 9, 4, 0, 7, 8, 20, 9, 50, 5, 7, 12, 10],
learning_policy=LearningPolicy.Softmax(tau=1),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 2)
layout_partial = [1, 2, 1, 2]
revenue_partial = [0, 12, 7, 19]
mab.partial_fit(decisions=layout_partial, rewards=revenue_partial)
mab.add_arm(3)
self.assertTrue(3 in mab.arms)
self.assertTrue(3 in mab._imp.arm_to_expectation.keys())
def test_lints(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]})
test_df_revenue = pd.Series([7, 13])
# 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=1.5),
context_history=train,
contexts=test,
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arms, [5, 2])
mab.partial_fit(decisions=arms, rewards=test_df_revenue, contexts=test)
mab.add_arm(6)
self.assertTrue(6 in mab.arms)
self.assertTrue(6 in mab._imp.arm_to_expectation.keys())
def test_ts(self):
dec_to_threshold = {1: 10, 2: 20}
def binarize(dec, value):
return value >= dec_to_threshold[dec]
arm, mab = self.predict(arms=[1, 2],
decisions=[1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1],
rewards=[10, 17, 22, 9, 4, 0, 7, 8, 20, 9, 50, 5, 7, 12, 10],
learning_policy=LearningPolicy.ThompsonSampling(binarizer=binarize),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 1)
layout_partial = [1, 2, 1, 2]
revenue_partial = [0, 12, 7, 19]
mab.partial_fit(decisions=layout_partial, rewards=revenue_partial)
# Updating of the model with new arm
def binary_func2(decision, reward):
if decision == 3:
return 1 if reward > 15 else 0
else:
return 1 if reward > 10 else 0
mab.add_arm(3, binary_func2)
self.assertTrue(3 in mab.arms)
self.assertTrue(3 in mab._imp.arm_to_expectation.keys())
def test_ts_binary(self):
arm, mab = self.predict(arms=[1, 2],
decisions=[1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1],
rewards=[1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1],
learning_policy=LearningPolicy.ThompsonSampling(),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 2)
layout_partial = [1, 2, 1, 2]
revenue_partial = [0, 1, 0, 1]
mab.partial_fit(decisions=layout_partial, rewards=revenue_partial)
mab.add_arm(3)
self.assertTrue(3 in mab.arms)
self.assertTrue(3 in mab._imp.arm_to_expectation.keys())
def test_ucb1(self):
arm, mab = self.predict(arms=[1, 2],
decisions=[1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1],
rewards=[10, 17, 22, 9, 4, 0, 7, 8, 20, 9, 50, 5, 7, 12, 10],
learning_policy=LearningPolicy.UCB1(alpha=1.25),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 2)
layout_partial = [1, 2, 1, 2]
revenue_partial = [0, 12, 7, 19]
mab.partial_fit(decisions=layout_partial, rewards=revenue_partial)
mab.add_arm(3)
self.assertTrue(3 in mab.arms)
self.assertTrue(3 in mab._imp.arm_to_expectation.keys())
def test_ts_series(self):
df = pd.DataFrame({'layouts': [1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1],
'revenues': [10, 17, 22, 9, 4, 0, 7, 8, 20, 9, 50, 5, 7, 12, 10]})
arm, mab = self.predict(arms=[1, 2],
decisions=df['layouts'],
rewards=df['revenues'],
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.15),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 1)
def test_ts_numpy(self):
arm, mab = self.predict(arms=[1, 2],
decisions=np.array([1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1]),
rewards=np.array([10, 17, 22, 9, 4, 0, 7, 8, 20, 9, 50, 5, 7, 12, 10]),
learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.15),
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arm, 1)
def test_radius(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]})
test_df_revenue = pd.Series([7, 13])
# 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.UCB1(alpha=1.25),
neighborhood_policy=NeighborhoodPolicy.Radius(radius=5),
context_history=train,
contexts=test,
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arms, [4, 4])
mab.partial_fit(decisions=arms, rewards=test_df_revenue, contexts=test)
mab.add_arm(6)
self.assertTrue(6 in mab.arms)
self.assertTrue(6 in mab._imp.arm_to_expectation.keys())
def test_nearest(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]})
test_df_revenue = pd.Series([7, 13])
# 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.UCB1(alpha=1.25),
neighborhood_policy=NeighborhoodPolicy.KNearest(k=5),
context_history=train,
contexts=test,
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arms, [5, 1])
mab.partial_fit(decisions=arms, rewards=test_df_revenue, contexts=test)
mab.add_arm(6)
self.assertTrue(6 in mab.arms)
self.assertTrue(6 in mab._imp.arm_to_expectation.keys())
def test_linucb_radius(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.LinUCB(alpha=1.25),
neighborhood_policy=NeighborhoodPolicy.Radius(radius=1),
context_history=train,
contexts=test,
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arms, [1, 2])
def test_linucb_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.LinUCB(alpha=1.25),
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_lints_radius(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=0.5),
neighborhood_policy=NeighborhoodPolicy.Radius(radius=1),
context_history=train,
contexts=test,
seed=123456,
num_run=1,
is_predict=True)
self.assertEqual(arms, [1, 2])
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_simulator_contextual(self):
size = 100
decisions = [random.randint(0, 2) for _ in range(size)]
rewards = [random.randint(0, 1000) for _ in range(size)]
contexts = [[random.random() for _ in range(50)] for _ in range(size)]
def binarize(decision, reward):
if decision == 0:
return reward <= 50
else:
return reward >= 220
n_jobs = 1
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))]
sim = Simulator(contextual_mabs, decisions, rewards, contexts,
scaler=StandardScaler(), test_size=0.5, is_ordered=False, batch_size=0, seed=123456)
sim.run()
self.assertTrue(sim.bandit_to_confusion_matrices)
self.assertTrue(sim.bandit_to_predictions)
def test_simulator_context_free(self):
size = 100
decisions = [random.randint(0, 2) for _ in range(size)]
rewards = [random.randint(0, 1000) for _ in range(size)]
def binarize(decision, reward):
if decision == 0:
return reward <= 50
else:
return reward >= 220
n_jobs = 1
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))]
sim = Simulator(context_free_mabs, decisions, rewards, contexts=None,
scaler=None, test_size=0.5, is_ordered=False, batch_size=1, seed=123456)
sim.run()
self.assertTrue(sim.bandit_to_confusion_matrices)
self.assertTrue(sim.bandit_to_predictions)
def test_simulator_mixed(self):
size = 100
decisions = [random.randint(0, 2) for _ in range(size)]
rewards = [random.randint(0, 1000) for _ in range(size)]
contexts = [[random.random() for _ in range(50)] for _ in range(size)]
n_jobs = 1
mixed = [('RandomRadius', MAB([0, 1], LearningPolicy.Random(), NeighborhoodPolicy.Radius(10), n_jobs=n_jobs)),
('Random', MAB([0, 1], LearningPolicy.Random(), n_jobs=n_jobs))]
sim = Simulator(mixed, decisions, rewards, contexts,
scaler=StandardScaler(), test_size=0.5, is_ordered=False, batch_size=0, seed=123456)
sim.run()
self.assertTrue(sim.bandit_to_confusion_matrices)
self.assertTrue(sim.bandit_to_predictions)
def test_simulator_hyper_parameter(self):
size = 100
decisions = [random.randint(0, 2) for _ in range(size)]
rewards = [random.randint(0, 1000) for _ in range(size)]
contexts = [[random.random() for _ in range(50)] for _ in range(size)]
n_jobs = 1
hyper_parameter_tuning = []
for radius in range(6, 10):
hyper_parameter_tuning.append(('Radius' + str(radius),
MAB([0, 1], LearningPolicy.UCB1(1), NeighborhoodPolicy.Radius(radius),
n_jobs=n_jobs)))
sim = Simulator(hyper_parameter_tuning, decisions, rewards, contexts,
scaler=StandardScaler(), test_size=0.5, is_ordered=False, batch_size=0, seed=123456,
is_quick=True)
sim.run()
self.assertTrue(sim.bandit_to_confusion_matrices)
self.assertTrue(sim.bandit_to_predictions)
