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pomegranate假设我使用当时可用的数据拟合模型。一旦有更多数据进入，我想相应地更新模型。换句话说，是否可以在pomegranate不覆盖先前参数的情况下用新数据更新现有模型？需要明确的是：我不是指核外学习，因为我的问题与在不同时间点可用的数据有关，而不是在单个时间点提供过大的内存数据。

这是我尝试过的：

>>> from pomegranate.distributions import BetaDistribution

>>> # suppose a coin generated the following data, where 1 is head and 0 is tail
>>> data1 = [0, 0, 0, 1, 0, 1, 0, 1, 0, 0]

>>> # as usual, we fit a Beta distribution to infer the bias of the coin
>>> model = BetaDistribution(1, 1)
>>> model.summarize(data1)  # compute sufficient statistics

>>> # presume we have seen all the data available so far,
>>> # we can now estimate the parameters
>>> model.from_summaries()

>>> # this results in the following model (so far so good)
>>> model
{
    "class" :"Distribution",
    "name" :"BetaDistribution",
    "parameters" :[
        3.0,
        7.0
    ],
    "frozen" :false
}

>>> # now suppose the coin is flipped a few more times, getting the following data
>>> data2 = [0, 1, 0, 0, 1]

>>> # we would like to update the model parameters accordingly
>>> model.summarize(data2)

>>> # but this fits only data2, overriding the previous parameters
>>> model.from_summaries()
>>> model
{
    "class" :"Distribution",
    "name" :"BetaDistribution",
    "parameters" :[
        2.0,
        3.0
    ],
    "frozen" :false
}


>>> # however I want to get the result that corresponds to the following,
>>> # but ideally without having to "drag along" data1
>>> data3 = data1 + data2
>>> model.fit(data3)
>>> model  # this should be the final model
{
    "class" :"Distribution",
    "name" :"BetaDistribution",
    "parameters" :[
        5.0,
        10.0
    ],
    "frozen" :false
}

编辑：

提出问题的另一种方式：是否pomegranate支持增量学习或在线学习？基本上，我正在寻找与scikit-learn's类似的东西partial_fit()，你可以在这里找到。

鉴于它pomegranate支持核心外学习，我觉得我忽略了一些东西。有什么帮助吗？

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1 回答 1

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实际上from_summaries这就是问题所在。在 Beta 发行版的情况下，它会：self.summaries = [0, 0]. 所有的from_summaries方法都是破坏性的。他们用分布中的参数替换摘要。摘要总是可以针对其他观察结果进行更新，而参数则不能。

我认为这是一个糟糕的设计。最好将它们视为观察值的累加器，并将参数作为派生的缓存值。

如果你这样做：

model = BetaDistribution(1, 1)
model.summarize(data1)
model.summarize(data2)
model.from_summaries()
model

您会发现它确实产生了与model.summarize(data1 + data2)使用过的结果相同的结果。

于 2019-12-11T20:28:36.500 回答