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retkowski opened this issue Jan 29, 2017 · 4 comments
Open

Using Several Parameters with GridSearchCV #8243

retkowski opened this issue Jan 29, 2017 · 4 comments
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@retkowski
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When using (for example) the following transformators

  • CountVectorizer
  • TruncatedSVD
  • SelectKBest

with GridSearchCV it happens that it chooses a number for n_features for CountVectorizer that is less than n_components for TruncatedSVD or k for SelectKBest.

This leads to an error: ValueError: n_components must be < n_features

For SelectKBest I found a temporary solution:

class SelectAtMostKBest(SelectKBest):
    def _check_params(self, X, y):
        if not (self.k == "all" or 0 <= self.k <= X.shape[1]):
            self.k = "all"

But there is no equivalent for TruncatedSVD.
Is this behaviour intended? If yes, what can I do about this?
@jnothman jnothman added the API label Jan 30, 2017
@jnothman
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Yes, this is a limitation of our API currently. One option is to use a parameter grid that only allows valid combinations, by using a list of parameter dicts wherein each setting is valid. A more robust forward-thinking solution might consider n_components and k optionally being functions of X and y.

@retkowski
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@jnothman Thank you, can you please elaborate a little bit more on the function-based approach? I am also considering to use the RandomizedSearchCV, but will probably face the same problem there, right?

@jnothman
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jnothman commented Jan 30, 2017 via email

@amueller
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amueller commented Mar 4, 2017

How about using SelectPercentile instead? Not sure if we allow covered variance in TruncatedSVD but it would be nice to have an option that is relative to the input feature size, not absolute.

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@jnothman @amueller @retkowski
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