Closed emrysshevek closed 5 years ago
Running compute_dataset_metafeatures.py now shows a breakdown of new, deleted, unchanged, and updated metafeatures to more easily compare when making changes to metafeatures.
compute_dataset_metafeatures.py
Sample output (not necessarily real values or metafeatures :) )
38_sick_train_data.csv [(y)es, (v)erbose, (n)o]: v UNCHANGED METAFEATURES { "StdevStdDevOfStringLengthOfTextFeatures": { "compute_time": 4.8173824325203896e-05, "value": NaN }, "kNN1NErrRate": { "compute_time": 0.2978278868831694, "value": 0.09093279580094993 }, "kNN1NKappa": { "compute_time": 0.2978278868831694, "value": 0.16702041294319114 } } DELETED METAFEATURES { "Useless Metafeaure": { "compute_time": 0.001676646017585881, "value": 52.35637003353562 } } NEW METAFEATURES { "Someone's new metafeature": { "compute_time": 1, "value": "Cool" } } UPDATED METAFEATURES { "StdevCardinalityOfCategoricalFeatures": { "computed": { "compute_time": 0.0033262099605053663, "value": 123423 }, "known": { "compute_time": 0.0033224879880435765, "value": 0.7101612523427369 } } } Runtime: 0.8915457725524902
Running
compute_dataset_metafeatures.py
now shows a breakdown of new, deleted, unchanged, and updated metafeatures to more easily compare when making changes to metafeatures.Sample output (not necessarily real values or metafeatures :) )