| { | |
| "name": "CPMP-2015-regression", | |
| "n_num_features": 23, | |
| "n_cat_features": 2, | |
| "train_size": 1348, | |
| "val_size": 338, | |
| "test_size": 422, | |
| "source": "https://www.openml.org/search?type=data&status=active&id=41700&sort=runs", | |
| "task_intro": "source: An Algorithm Selection Benchmark for the Container Pre-Marshalling Problem (CPMP)\nauthors: K. Tierney and Y. Malitsky (features) / K. Tierney and D. Pacino and S. Voss (algorithms)\ntranslator in coseal format: K. Tierney\n\nThis is an extension of the 2013 premarshalling dataset that includes more features and a set of test instances. \n\nThere are three sets of features:\n\nfeature_values.arff contains the full set of features from iteration 2 of our latent feature analysis (LFA) process (see paper)\nfeature_values_itr1.arff contains only the features after iteration 1 of LFA\nfeature_values_orig.arff containers the features used in PREMARHSALLING-ASTAR-2013\n\nWe also provide test data with an identical naming scheme (see _test). \n\nThe features for the pre-marshalling problem are all extremely easy and fast to\ncompute, thus the feature_costs.arff file has been omitted, as it would be time\n0 for every feature (regardless of using original, iteration 1 or iteration 2\nfeatures).\n\nThe feature computation code is available at https://bitbucket.org/eusorpb/cpmp-as\n\nNote: previously the scenario was called PREMARSHALLING-ASTAR-2015. To save same space, we renamed the scenario.", | |
| "task_type": "regression", | |
| "openml_id": 41700, | |
| "n_classes": 1, | |
| "num_feature_intro": { | |
| "repetition": "repetition", | |
| "stacks": "stacks", | |
| "tiers": "tiers", | |
| "stack.tier.ratio": "stack.tier.ratio", | |
| "container.density": "container.density", | |
| "empty.stack.pct": "empty.stack.pct", | |
| "overstowing.stack.pct": "overstowing.stack.pct", | |
| "overstowing.2cont.stack.pct": "overstowing.2cont.stack.pct", | |
| "group.same.min": "group.same.min", | |
| "group.same.max": "group.same.max", | |
| "group.same.mean": "group.same.mean", | |
| "group.same.stdev": "group.same.stdev", | |
| "top.good.min": "top.good.min", | |
| "top.good.max": "top.good.max", | |
| "top.good.mean": "top.good.mean", | |
| "top.good.stdev": "top.good.stdev", | |
| "overstowage.pct": "overstowage.pct", | |
| "bflb": "bflb", | |
| "left.density": "left.density", | |
| "tier.weighted.groups": "tier.weighted.groups", | |
| "avg.l1.top.left.lg.group": "avg.l1.top.left.lg.group", | |
| "cont.empty.grt.estack": "cont.empty.grt.estack", | |
| "pct.bottom.pct.on.top": "pct.bottom.pct.on.top" | |
| }, | |
| "cat_feature_intro": { | |
| "algorithm": "algorithm", | |
| "runstatus": "runstatus" | |
| } | |
| } |