Add "partial prediction" (initially at the factor level)
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调研方向
issue 中没有指定任何文件、测试或入口点。首先阅读 issue #63 以及它指出的前置条件——因子预测工作；完成意味着支持因子级别的部分预测，检查每个请求的假设是否定义明确，并生成相应的设计矩阵差异。
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描述
In many cases when working with linear models, one has a model like y ~ 1 + a + b:c and you want to test a hypothesis like "y at a = a1 is significantly different from y at a = a2 (with the other predictors set to arbitrary values)". In this particular case, for a linear model, this is a well-defined and can be expressed as a linear hypothesis test. By contrast, a hypothesis like "y at b = b1 is significantly different from y at b = b2", because the difference between these is undefined without knowing c. OTOH, a hypothesis like "y at b = b1 and c = c1 is significantly different from y at b = b2 and c = c2" is also well-defined.
So we want some way for users to request a "prediction" of the difference between the design matrices at these sorts of partially-specified locations, and then patsy would (a) check that this actually makes sense, and (b) figure it out.
Initially, for simplicity, this should probably be done for "factor prediction" (so #63 would be a precondition). It would be nice to have it for "data-level prediction" too, but this will require more metadata about which data variables are referred to in which factors. Possibly we will get that soon because we need somewhat similar information to allow pickling (#25).
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