def mabp_random(self):
df = pd.read_pickle('../data/profit_df.pkl')
df.fillna(df.mean())
scaler = RobustScaler().fit(df)
df = scaler.transform(df)
N = df.shape[0]
d = df.shape[1]
selected = []
total_reward = 0
total_profit = 0
for n in range(0, N):
item = random.randrange(d)
selected.append(item)
reward = df[n, item]
profit = scaler.inverse_tranform(df)[n, item]
total_reward = total_reward + reward
total_profit = total_profit + profit
return pd.Series(selected).value_counts(normalize=True)
def mapb_ucb(self):
df = pd.read_pickle('../data/profit_df.pkl')
df.fillna(df.mean())
scaler = RobustScaler().fit(df)
df = scaler.transform(df)
N = df.shape[0]
d = df.shape[1]
selected = []
numbers_of_selections = [0] * d
sums_of_reward = [0] * d
total_reward = 0
total_profit = 0
for n in range(0, N):
item = 0
max_upper_bound = 0
for i in range(0, d):
if (numbers_of_selections[i] > 0):
average_reward = sums_of_reward[i] / numbers_of_selections[
i]
delta_i = math.sqrt(2 * math.log(n + 1) /
numbers_of_selections[i])
upper_bound = average_reward + delta_i
else:
upper_bound = 1e400
if upper_bound > max_upper_bound:
max_upper_bound = upper_bound
item = i
selected.append(item)
numbers_of_selections[item] += 1
reward = df[n, item]
profit = scaler.inverse_tranform(df)[n, item]
sums_of_reward[item] += reward
total_reward += reward
total_profit += profit
return pd.Series(selected).value_counts(normalize=True)
