def param_study(n_bandits=2000,
n_steps=1000,
title='Figure 2.6',
fn='fig2_6',
nonstat=False,
print_freq=10,
start_timestep=np.inf):
results = {(method, hyper): 0
for (method, hyperparams) in HYPERPARMS.items()
for hyper in hyperparams}
y_label = (f"Average Reward over last {n_steps-start_timestep} steps"
if nonstat else f"Average Reward over first {n_steps} steps")
for t in range(1, n_bandits + 1):
print(f"{t}/{n_bandits}")
bandit = Bandit()
for method, hyperparams in HYPERPARMS.items():
for hyper in hyperparams:
results[(method,
hyper)] += apply_method(bandit, n_steps, method,
hyper, nonstat,
start_timestep)[-1]
bandit.reset() # need to reset q values after random walk
if (t % print_freq == 0):
plot_current(n_steps, results, t, title, fn, y_label)
class PredictionMarketEnv(object):
def __init__(self, predict_market, num_bids, trials,
label='Multi-Armed Prediction Market Bandit'):
self.predict_market = predict_market
self.n_arms = predict_market.arms
self.agents = predict_market.agents
self.data = predict_market.dataframe
self.num_bids = num_bids
self.label = label
self.bandit = Bandit(self.n_arms, self.data)
self.trials = trials
self.scores = None
self.optimal = None
def run(self, experiments=1, market=True):
"""Run the trial with or without the prediction market"""
scores = np.zeros((self.trials, len(self.agents)))
if market is False:
for _ in range(experiments):
for trial in range(self.trials):
self.bandit.reset()
for i, agent in enumerate(self.agents):
action = agent.choose()
reward, max_reward = self.bandit.pull(action)
agent.observe(reward, max_reward, update=True)
scores[trial, i] += reward
else:
for _ in range(experiments):
for trial in range(self.trials):
self.predict_market.reset()
for i, agent in enumerate(self.agents):
bids = []
for i in range(self.num_bids):
bids.append(agent.bid())
bid = np.mean(bids, axis=0) # column-wise mean
self.predict_market.get_bids(bid, agent.id)
normal_params = self.predict_market.settle_market()
arm_samples = []
for i, params in enumerate(normal_params):
arm_samples.append(np.random.normal(params[0], params[1]**0.5))
action = np.argmax(arm_samples)
reward, max_reward = self.bandit.pull(action)
for i, agent in enumerate(self.agents):
agent.current_action = action
agent.observe(reward, max_reward, update=True)
scores[trial, i] += reward
self.scores = scores / experiments
return self.scores
def plot_results(self, market=True):
scores = self.scores
fig = plt.figure()
ax = plt.subplot(111)
if market:
ax.set_title('Multi-Armed Bandit Market Reward')
ax.plot(scores, 'b.')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.set_ylabel('Average Reward')
ax.legend(['Prediction Market'], loc='center left',
bbox_to_anchor=(1, 0.5))
plt.show()
else:
ax.set_title('Multi-Armed Bandit Rewards')
ax.plot(scores, '.')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.set_ylabel('Average Reward')
ax.legend(['Prediction Market'], loc='center left',
bbox_to_anchor=(1, 0.5))
plt.show()
