{"action":"created","issue":{"url":"https://api.github.com/repos/tensorflow/ranking/issues/41","repository_url":"https://api.github.com/repos/tensorflow/ranking","labels_url":"https://api.github.com/repos/tensorflow/ranking/issues/41/labels{/name}","comments_url":"https://api.github.com/repos/tensorflow/ranking/issues/41/comments","events_url":"https://api.github.com/repos/tensorflow/ranking/issues/41/events","html_url":"https://github.com/tensorflow/ranking/pull/41","id":423064656,"node_id":"MDExOlB1bGxSZXF1ZXN0MjYyNzE4MjY0","number":41,"title":"support crossed feature, branched features","user":{"login":"cloverrose","id":1592208,"node_id":"MDQ6VXNlcjE1OTIyMDg=","avatar_url":"https://avatars1.githubusercontent.com/u/1592208?v=4","gravatar_id":"","url":"https://api.github.com/users/cloverrose","html_url":"https://github.com/cloverrose","followers_url":"https://api.github.com/users/cloverrose/followers","following_url":"https://api.github.com/users/cloverrose/following{/other_user}","gists_url":"https://api.github.com/users/cloverrose/gists{/gist_id}","starred_url":"https://api.github.com/users/cloverrose/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cloverrose/subscriptions","organizations_url":"https://api.github.com/users/cloverrose/orgs","repos_url":"https://api.github.com/users/cloverrose/repos","events_url":"https://api.github.com/users/cloverrose/events{/privacy}","received_events_url":"https://api.github.com/users/cloverrose/received_events","type":"User","site_admin":false},"labels":[{"id":1149277162,"node_id":"MDU6TGFiZWwxMTQ5Mjc3MTYy","url":"https://api.github.com/repos/tensorflow/ranking/labels/cla:%20yes","name":"cla: yes","color":"3690c1","default":false}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2019-03-20T04:45:38Z","updated_at":"2019-03-30T06:34:01Z","closed_at":null,"author_association":"NONE","pull_request":{"url":"https://api.github.com/repos/tensorflow/ranking/pulls/41","html_url":"https://github.com/tensorflow/ranking/pull/41","diff_url":"https://github.com/tensorflow/ranking/pull/41.diff","patch_url":"https://github.com/tensorflow/ranking/pull/41.patch"},"body":"Hi,\r\nI used this library and found I cannot use crossed feature.\r\nBecause crossed feature has one name (dict key) but it depends on 2 values.\r\nAlso I have to feed duplicate value if I want to use both numeric feature and its bucketized feature.\r\n\r\nI think it is not better to call get_feed_feature_names function each time.\r\nBut I have no idea about it without breaking encode_listwise_features I/F.\r\n"},"comment":{"url":"https://api.github.com/repos/tensorflow/ranking/issues/comments/478213275","html_url":"https://github.com/tensorflow/ranking/pull/41#issuecomment-478213275","issue_url":"https://api.github.com/repos/tensorflow/ranking/issues/41","id":478213275,"node_id":"MDEyOklzc3VlQ29tbWVudDQ3ODIxMzI3NQ==","user":{"login":"xuanhuiwang","id":43305893,"node_id":"MDQ6VXNlcjQzMzA1ODkz","avatar_url":"https://avatars2.githubusercontent.com/u/43305893?v=4","gravatar_id":"","url":"https://api.github.com/users/xuanhuiwang","html_url":"https://github.com/xuanhuiwang","followers_url":"https://api.github.com/users/xuanhuiwang/followers","following_url":"https://api.github.com/users/xuanhuiwang/following{/other_user}","gists_url":"https://api.github.com/users/xuanhuiwang/gists{/gist_id}","starred_url":"https://api.github.com/users/xuanhuiwang/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/xuanhuiwang/subscriptions","organizations_url":"https://api.github.com/users/xuanhuiwang/orgs","repos_url":"https://api.github.com/users/xuanhuiwang/repos","events_url":"https://api.github.com/users/xuanhuiwang/events{/privacy}","received_events_url":"https://api.github.com/users/xuanhuiwang/received_events","type":"User","site_admin":false},"created_at":"2019-03-30T06:34:01Z","updated_at":"2019-03-30T06:34:01Z","author_association":"COLLABORATOR","body":"Your example code looks fine to me. Another variant is to keep the example_feature_columns() return both s1 and s2 separately.\r\n\r\nIn the _transoform_fn, you can set the arg example_feature_columns as {'crossed1': tf.feature_column.embedding_column(tf.feature_column.crossed_column(['s1', 's2'], 10000), 8)}"}}
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