{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2034","repository_url":"https://api.github.com/repos/scikit-learn/scikit-learn","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2034/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2034/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2034/events","html_url":"https://github.com/scikit-learn/scikit-learn/issues/2034","id":15158663,"number":2034,"title":"Extend `FeatureUnion` to better handle heterogeneous data","user":{"login":"jnothman","id":78827,"avatar_url":"https://avatars.githubusercontent.com/u/78827?v=3","gravatar_id":"","url":"https://api.github.com/users/jnothman","html_url":"https://github.com/jnothman","followers_url":"https://api.github.com/users/jnothman/followers","following_url":"https://api.github.com/users/jnothman/following{/other_user}","gists_url":"https://api.github.com/users/jnothman/gists{/gist_id}","starred_url":"https://api.github.com/users/jnothman/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jnothman/subscriptions","organizations_url":"https://api.github.com/users/jnothman/orgs","repos_url":"https://api.github.com/users/jnothman/repos","events_url":"https://api.github.com/users/jnothman/events{/privacy}","received_events_url":"https://api.github.com/users/jnothman/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/labels/Moderate","name":"Moderate","color":"e102d8"},{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/labels/New%20Feature","name":"New Feature","color":"02e10c"}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":25,"created_at":"2013-06-05T08:13:59Z","updated_at":"2016-09-20T13:02:27Z","closed_at":null,"body":"`FeatureUnion` currently passes identical data to each constituent transformer. Often one wants to differentiate between groups of features in how they are transformed. While this is possible by making each stacked transformer a `Pipeline` consisting of a pre-determined feature selector and another transformer, this is cumbersome.\r\n\r\nA parameter should be added to specify which features are routed to which constituents. This is not necessarily trivial to design, particularly because the input `X` to `FeatureUnion.transform` need not be a conventional 2d feature array (it may be a list/array of dicts, texts, or other objects)."},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/248294727","html_url":"https://github.com/scikit-learn/scikit-learn/issues/2034#issuecomment-248294727","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2034","id":248294727,"user":{"login":"jnothman","id":78827,"avatar_url":"https://avatars.githubusercontent.com/u/78827?v=3","gravatar_id":"","url":"https://api.github.com/users/jnothman","html_url":"https://github.com/jnothman","followers_url":"https://api.github.com/users/jnothman/followers","following_url":"https://api.github.com/users/jnothman/following{/other_user}","gists_url":"https://api.github.com/users/jnothman/gists{/gist_id}","starred_url":"https://api.github.com/users/jnothman/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jnothman/subscriptions","organizations_url":"https://api.github.com/users/jnothman/orgs","repos_url":"https://api.github.com/users/jnothman/repos","events_url":"https://api.github.com/users/jnothman/events{/privacy}","received_events_url":"https://api.github.com/users/jnothman/received_events","type":"User","site_admin":false},"created_at":"2016-09-20T13:02:27Z","updated_at":"2016-09-20T13:02:27Z","body":"You're right that we're somewhat abstracted from use cases here. It would\nbe great to have a proposal where people (including core devs) could\nactually give us things they would like to do with it. Your points are a\ngood starting point.\n\nI agree with @amueller that this is a bit out-of-place in Pipeline, at\nleast when you're not assuming that some input columns always extraneous\n(which it usually is, I suppose, for Pandas DataFrames)\n\nOn 20 September 2016 at 22:50, Mats Julian Olsen <notifications@github.com>\nwrote:\n\n> Well, you could always just drop the parameter when using it outside a\n> FeatureUnion.\n>\n> Did you give any consideration to the case when you have several columns\n> that you would treat the same way? Or a transformer that uses more than one\n> column? For the dictionary-approach the only way I'd imagine this is\n> specifying a tuple of the field names as a key in the dictionary.\n>\n> I think a common use-case is having separate pipelines for numerical and\n> categorical features, and I'd love to have better support for this in\n> scikit-learn.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/scikit-learn/scikit-learn/issues/2034#issuecomment-248291847>,\n> or mute the thread\n> <https://github.com/notifications/unsubscribe-auth/AAEz62FxwQGgunBKUzeYgVqDz5wrj9fuks5qr9atgaJpZM4AtZy5>\n> .\n>\n"}}
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