def autoFindBestEpoch(c, evaluFun, sortkey=None, epochs=None, savefig=False):
'''遍历所有epoch的weight 并通过测试集评估项sortkey 找出最佳epoch
Parameters
----------
c : dicto
即configManager 生成的测试集的所有环境配置 c
包含args，数据配置，各类函数等
evaluFun : Funcation
用于评测的函数，用于Evalu类 需要返回dict对象
sortkey : str, default None
用于筛选时候的key 默认为df.columns[-1]
Return: DataFrame
每个参数组合及其评价的平均值
'''
args = c.args
if not isinstance(epochs, (tuple, list)):
pas = [
p[len(args.prefix):] for p in glob(args.prefix + '*')
if p[-4:] != 'json'
]
eps = map(lambda s: len(findints(s)) and findints(s)[-1], pas)
maxx = len(eps) and max(eps)
minn = len(eps) and min(eps)
if isinstance(epochs, int):
epochs = range(minn, maxx)[::epochs] + [maxx]
else:
epochs = range(minn, maxx + 1)
args['restore'] = ArgList(epochs)
# print epochs
df = autoFindBestParams(c,
args,
evaluFun,
sortkey=sortkey,
savefig=savefig)
return df
args.out = pathjoin(c.tmpdir,'val/png')
pr_out = pathjoin(c.tmpdir,'pr')
# =============================================================================
# config BEGIN
# =============================================================================
args.update(
restore=-1,
step=64,
)
# =============================================================================
# config END
# =============================================================================
if args.restore == -1:
pas = [p[len(args.prefix):] for p in glob(args.prefix+'*')]
args.restore = len(pas) and max(map(lambda s:len(findints(s)) and findints(s)[-1],pas))
makeValEnv(args)
#setMod('train')
if __name__ == '__main__':
import predictInterface
c.predictInterface = predictInterface
predict = predictInterface.predict
# c.predict = predict
# e = Evalu(diceEvalu,
## evaluName='restore-%s'%restore,
# valNames=c.names,
## loadcsv=1,
# logFormat='dice:{dice:.3f}, loss:{loss:.3f}',
# sortkey='loss',