{"action":"created","issue":{"url":"https://api.github.com/repos/dkpro/dkpro-tc/issues/516","repository_url":"https://api.github.com/repos/dkpro/dkpro-tc","labels_url":"https://api.github.com/repos/dkpro/dkpro-tc/issues/516/labels{/name}","comments_url":"https://api.github.com/repos/dkpro/dkpro-tc/issues/516/comments","events_url":"https://api.github.com/repos/dkpro/dkpro-tc/issues/516/events","html_url":"https://github.com/dkpro/dkpro-tc/issues/516","id":367573929,"node_id":"MDU6SXNzdWUzNjc1NzM5Mjk=","number":516,"title":"Add a learning curve 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a new experiment type which runs a learning curve experiment.\r\n\r\nThe experiment should support large and small sized experiments, i.e. averaging all combinations on a lc-stage might become extremely expensive for large Ns, so a small-sized version that skips some steps is necessary. The smaller version lead to a less steady curve but runs a lot faster for larger datasets."},"comment":{"url":"https://api.github.com/repos/dkpro/dkpro-tc/issues/comments/427829792","html_url":"https://github.com/dkpro/dkpro-tc/issues/516#issuecomment-427829792","issue_url":"https://api.github.com/repos/dkpro/dkpro-tc/issues/516","id":427829792,"node_id":"MDEyOklzc3VlQ29tbWVudDQyNzgyOTc5Mg==","user":{"login":"Horsmann","id":8234699,"node_id":"MDQ6VXNlcjgyMzQ2OTk=","avatar_url":"https://avatars3.githubusercontent.com/u/8234699?v=4","gravatar_id":"","url":"https://api.github.com/users/Horsmann","html_url":"https://github.com/Horsmann","followers_url":"https://api.github.com/users/Horsmann/followers","following_url":"https://api.github.com/users/Horsmann/following{/other_user}","gists_url":"https://api.github.com/users/Horsmann/gists{/gist_id}","starred_url":"https://api.github.com/users/Horsmann/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Horsmann/subscriptions","organizations_url":"https://api.github.com/users/Horsmann/orgs","repos_url":"https://api.github.com/users/Horsmann/repos","events_url":"https://api.github.com/users/Horsmann/events{/privacy}","received_events_url":"https://api.github.com/users/Horsmann/received_events","type":"User","site_admin":false},"created_at":"2018-10-08T13:20:13Z","updated_at":"2018-10-08T13:20:13Z","author_association":"MEMBER","body":"The bucketing is resolved at the point in the code you pasted. I have all .bin files later on but I do not know to how many buckets the files correspond. I think I will have to introduce a new information piece at this point in code and import a new dimension in the `MLA` adapters.\r\ni.e.\r\n```\r\n     Map<String, Collection<String>> data = new HashMap<String, Collection<String>>();\r\n        data.put(getName() + \"_training\", trainingData);\r\n        data.put(getName() + \"_validation\", buckets[learningCurveRun.test]);\r\n        data.put(getName() + \"_numTrainingFolds\", bucketCount);\r\n```\r\n\r\nThis requires an (unused) import in each MLA TestTask, just to make this bucketing information available during reporting. This works probably but is also a pretty ugly hack imo. "}}
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