import time

import math

import json

import random

from hyperboard import Agent

agent = Agent()

metric2scale = { 'cross entropy': 10, 'accuracy': 1, 'BLEU': 100 }

criteria2metric = {

'train loss': 'cross entropy',

'valid loss': 'cross entropy',

'train accu': 'accuracy',

'valid accu': 'accuracy',

'test BELU': 'BLEU'

}

name_list = []

criteria_list = []

offset_list = []

for learning_rate in [0.1, 0.01, 0.001]:

for batch_size in [128, 256]:

for optimizer in ['SGD', 'Adam']:

for criteria in criteria2metric.keys():

for corpus in ['wikipedia', 'PennTreeBank']:

hyperparameters = {

'learning rate': learning_rate,

'batch size': batch_size,

'criteria': criteria,

'corpus': corpus,

'optimizer': optimizer,

'momentum': 0.9,

'num hidden': 300,

}

metric = criteria2metric[criteria]

print('register criteria <%s> as metric <%s>' % (criteria, metric))

name = agent.register(hyperparameters, metric)

name_list.append(name)

criteria_list.append(criteria)

offset_list.append(abs(random.random() * 0.5))

for i in range(10000):

print('append %d' % i)

for name, criteria, offset in zip(name_list, criteria_list, offset_list):

value = math.exp(- i / 10.0) + offset + random.random() * 0.05

metric = criteria2metric[criteria]

if metric == 'accuracy': value = 1 - value

scale = metric2scale[metric]

value *= scale

agent.append(name, i, value)

time.sleep(0.1)