From f439a2f1fec8d67495471dd60f3d1c084e8d7037 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 12 Feb 2016 22:29:49 +0100 Subject: [PATCH] ENH allow meta-learning for regression --- .../algorithm_runs.arff | 120 ++++++++++++++++++ .../configurations.csv | 88 +++++++++++++ .../a_metric_regression_dense/description.txt | 68 ++++++++++ .../feature_costs.arff | 83 ++++++++++++ .../feature_runstatus.arff | 83 ++++++++++++ .../feature_values.arff | 77 +++++++++++ .../a_metric_regression_dense/readme.txt | 0 .../algorithm_runs.arff | 120 ++++++++++++++++++ .../configurations.csv | 58 +++++++++ .../description.txt | 68 ++++++++++ .../feature_costs.arff | 83 ++++++++++++ .../feature_runstatus.arff | 83 ++++++++++++ .../feature_values.arff | 77 +++++++++++ .../a_metric_regression_sparse/readme.txt | 0 .../algorithm_runs.arff | 120 ++++++++++++++++++ .../configurations.csv | 79 ++++++++++++ .../description.txt | 68 ++++++++++ .../feature_costs.arff | 83 ++++++++++++ .../feature_runstatus.arff | 83 ++++++++++++ .../feature_values.arff | 77 +++++++++++ .../r2_metric_regression_dense/readme.txt | 0 .../algorithm_runs.arff | 120 ++++++++++++++++++ .../configurations.csv | 52 ++++++++ .../description.txt | 68 ++++++++++ .../feature_costs.arff | 83 ++++++++++++ .../feature_runstatus.arff | 83 ++++++++++++ .../feature_values.arff | 77 +++++++++++ .../r2_metric_regression_sparse/readme.txt | 0 autosklearn/metalearning/mismbo.py | 50 ++++++-- autosklearn/smbo.py | 14 +- .../03_autosklearn_retrieve_metadata.py | 41 ++++-- .../05_autosklearn_create_aslib_files.py | 17 ++- test/metalearning/test_metalearning.py | 88 ++++++++----- 33 files changed, 2153 insertions(+), 58 deletions(-) create mode 100644 autosklearn/metalearning/files/a_metric_regression_dense/algorithm_runs.arff create mode 100644 autosklearn/metalearning/files/a_metric_regression_dense/configurations.csv create mode 100644 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autosklearn/metalearning/files/a_metric_regression_sparse/feature_values.arff create mode 100644 autosklearn/metalearning/files/a_metric_regression_sparse/readme.txt create mode 100644 autosklearn/metalearning/files/r2_metric_regression_dense/algorithm_runs.arff create mode 100644 autosklearn/metalearning/files/r2_metric_regression_dense/configurations.csv create mode 100644 autosklearn/metalearning/files/r2_metric_regression_dense/description.txt create mode 100644 autosklearn/metalearning/files/r2_metric_regression_dense/feature_costs.arff create mode 100644 autosklearn/metalearning/files/r2_metric_regression_dense/feature_runstatus.arff create mode 100644 autosklearn/metalearning/files/r2_metric_regression_dense/feature_values.arff create mode 100644 autosklearn/metalearning/files/r2_metric_regression_dense/readme.txt create mode 100644 autosklearn/metalearning/files/r2_metric_regression_sparse/algorithm_runs.arff create mode 100644 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+65,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,1.1462353774599199,rbf,56,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.743394673354396e-06,True,0.00847857431871685,0.09971388475732029,True,0.00033284954626829543,optimal,huber,46,elasticnet,,normalize +66,median,0.0968556998482573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,8,4,1.0,30,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0641375748610752,,,0.23204376605619673,,linear,-1,True,0.008566344762428551,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,most_frequent,0.005607066924937062,True,extra_trees_preproc_for_regression,True,mse,None,2.6112604563622552,12,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.045789159051221,,5,0.004784503543114743,4.627298382526854,rbf,-1,False,2.8494792916708405e-05,,,,,,,,,,,,,,,,,,,,,,,,min/max +68,median,,False,extra_trees_preproc_for_regression,True,mse,None,0.993528657198836,10,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1933.5929428405466,,2,0.015939473931353206,0.7858707127149234,rbf,-1,False,0.0037322094857651553,,,,,,,,,,,,,,,,,,,,,,,,min/max +69,most_frequent,0.0014457709291250823,True,kernel_pca,,,,,,,,,,,,,,,,,-0.01416546049641254,3,0.23060510905667822,poly,1413,,,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0017837392449809443,True,0.00345075877020118,0.09774090746458727,True,,optimal,epsilon_insensitive,195,l2,,none +70,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.683440436880492,,4,0.008766678436592141,0.10578131451502429,rbf,-1,False,0.08678739387617436,,,,,,,,,,,,,,,,,,,,,,,,none +71,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.5828695478528918,,1.2846036338050844,sigmoid,355,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.793413587557181e-06,False,2.7540645871255865e-05,0.006462379282943462,True,,optimal,huber,325,l1,,min/max +72,mean,0.08562251742396482,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,3.5905557599097153,rbf,74,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.052990481379682e-06,False,2.2842654098672913e-05,0.09620557628291702,True,0.012660036055398494,optimal,huber,209,elasticnet,,none +73,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.010148480388225148,0.00016676399867236993,3.16342061142116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,min/max +74,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.007642369343100022,1.8419258971411445e-05,0.9833146696212234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,mean,0.00016629738514881763,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,4.592230471191443,None,4,6,0.0,100,,,,,,,,,,,,,,,min/max +76,median,0.00548806117992972,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,3.0972538714361577,rbf,5291,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6864007905723228e-06,True,0.006559405529560316,0.07194784725205487,True,,constant,squared_epsilon_insensitive,89,l2,,min/max +77,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.1566448365058719,5,0.7004476388937543,poly,956,,,,,,,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02849145741142527,True,0.001397013116831936,,,,,,,,,,,,min/max +78,median,0.0005361882641678341,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.9516292646600342,5226,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.844327967977615e-05,True,0.010965543419019197,0.02393286766186689,True,,constant,squared_epsilon_insensitive,502,l2,,min/max +79,mean,0.007129891763108858,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,222.52007103508296,,2,0.0014545049235116735,0.8961440468564937,rbf,-1,False,0.005206529915661272,,,,,,,,,,,,,,,,,,,,,,,,min/max +80,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,3.8792789945671258,None,10,18,0.0,100,,,,,,,,,,,,,,,min/max +81,median,0.05987995402504049,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,4.663594806506735,18,17,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +82,most_frequent,0.00234992390287966,True,extra_trees_preproc_for_regression,False,mse,None,0.8044960404154922,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,286.5841951417159,,4,0.17570677855266975,0.0999999999999999,rbf,-1,True,0.001,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,most_frequent,,False,extra_trees_preproc_for_regression,True,mse,None,2.5830935657278076,19,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.962812951051284,,4,0.01204802523960763,0.1821781494294054,rbf,-1,False,1.3160253217209612e-05,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,mean,0.15213222665693707,True,feature_agglomeration,,,,,,,,,,,,,manhattan,average,294,median,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,3.9189947880940927,3,4,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,mean,0.08567185090735716,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,2.3981698229031436,None,19,20,0.0,100,,,,,,,,,,,,,,,min/max +86,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.12583724307049,False,0.0018926648667342925,True,1,squared_epsilon_insensitive,0.008267080450623797,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,min/max +87,median,0.003145909671422029,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1154751316101943e-06,False,0.006276424097548576,0.038108446269937486,True,9.434769722730874e-09,invscaling,squared_epsilon_insensitive,305,elasticnet,0.45306002466009593,min/max diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/description.txt b/autosklearn/metalearning/files/a_metric_regression_dense/description.txt new file mode 100644 index 0000000000..78ac6f12b1 --- /dev/null +++ b/autosklearn/metalearning/files/a_metric_regression_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step ClassEntropy: ClassEntropy +feature_step SymbolsMin: SymbolsMin +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step LandmarkLDA: LandmarkLDA +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step KurtosisMax: KurtosisMax +feature_step Landmark1NN: Landmark1NN +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step SkewnessMin: SkewnessMin +feature_step KurtosisMean: KurtosisMean +feature_step SkewnessMean: SkewnessMean +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step NumberOfClasses: NumberOfClasses +feature_step SymbolsSum: SymbolsSum +feature_step SymbolsMean: SymbolsMean +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step KurtosisSTD: KurtosisSTD +feature_step SkewnessSTD: SkewnessSTD +feature_step KurtosisMin: KurtosisMin +feature_step SymbolsSTD: SymbolsSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step LogDatasetRatio: LogDatasetRatio +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step SymbolsMax: SymbolsMax +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step SkewnessMax: SkewnessMax +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstancesWithMissingValues, LogInverseDatasetRatio, PCASkewnessFirstPC, ClassEntropy, SymbolsMin, NumberOfFeatures, NumberOfCategoricalFeatures, PercentageOfMissingValues, PCA, LandmarkLDA, ClassProbabilityMax, KurtosisMax, Landmark1NN, LandmarkNaiveBayes, NumberOfMissingValues, NumSymbols, PercentageOfFeaturesWithMissingValues, PCAKurtosisFirstPC, NumberOfNumericFeatures, Kurtosisses, RatioNumericalToNominal, SkewnessMin, KurtosisMean, SkewnessMean, LogNumberOfFeatures, LandmarkDecisionNodeLearner, NumberOfClasses, SymbolsSum, SymbolsMean, PCAFractionOfComponentsFor95PercentVariance, NumberOfFeaturesWithMissingValues, ClassProbabilitySTD, LandmarkRandomNodeLearner, LogNumberOfInstances, Skewnesses, RatioNominalToNumerical, ClassProbabilityMean, ClassOccurences, KurtosisSTD, SkewnessSTD, KurtosisMin, SymbolsSTD, NumberOfInstances, InverseDatasetRatio, ClassProbabilityMin, LogDatasetRatio, LandmarkDecisionTree, PercentageOfInstancesWithMissingValues, SymbolsMax, MissingValues, SkewnessMax, DatasetRatio + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87 +performance_type: solution_quality +performance_measures: a_metric +algorithms_stochastic: + +scenario_id: openml +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/a_metric_regression_dense/feature_costs.arff new file mode 100644 index 0000000000..7674ae9051 --- /dev/null +++ b/autosklearn/metalearning/files/a_metric_regression_dense/feature_costs.arff @@ -0,0 +1,83 @@ +@RELATION openml_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC + +@DATA +1028_a_metric,1.0,0.00023,1e-05,0.00048,0.00029,0.0,1e-05,2e-05,1e-05,0.00084,0.01935,1e-05,4e-05,0.02096,0.01456,5e-05,0.00065,1e-05,0.00034,2e-05,0.00027,5e-05,3e-05,7e-05,8e-05,1e-05,0.00751,5e-05,4e-05,0.00013,3e-05,0.0001,8e-05,0.00799,1e-05,0.00028,7e-05,5e-05,0.00016,0.00013,0.00015,3e-05,0.00047,1e-05,3e-05,2e-05,1e-05,0.01481,1e-05,0.0,0.00035,3e-05,2e-05 +1028_r2_metric,1.0,0.00024,1e-05,0.00048,0.00032,0.0,1e-05,2e-05,0.0,0.00084,0.01908,1e-05,4e-05,0.0209,0.01453,6e-05,0.00065,1e-05,0.00033,2e-05,0.00027,5e-05,3e-05,7e-05,8e-05,1e-05,0.00752,6e-05,4e-05,0.00013,3e-05,0.00011,8e-05,0.0083,1e-05,0.00028,6e-05,4e-05,0.00015,0.00013,0.00015,3e-05,0.00047,1e-05,3e-05,2e-05,1e-05,0.01465,1e-05,0.0,0.00038,3e-05,3e-05 +1030_a_metric,1.0,0.00022,1e-05,0.00047,0.0003,0.0,1e-05,2e-05,0.0,0.00082,0.02092,1e-05,4e-05,0.01925,0.0215,3e-05,0.00064,1e-05,0.00033,2e-05,0.00027,4e-05,3e-05,7e-05,8e-05,1e-05,0.00896,6e-05,4e-05,0.00013,2e-05,8e-05,9e-05,0.00974,1e-05,0.00028,7e-05,4e-05,0.00016,0.00013,0.00014,3e-05,0.00046,1e-05,3e-05,2e-05,1e-05,0.01136,1e-05,0.0,0.00031,3e-05,2e-05 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timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} + +@DATA 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ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE 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+54,mean,0.007129891763108858,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,222.52007103508296,,2,0.0014545049235116735,0.8961440468564937,rbf,-1,False,0.005206529915661272,,,,,,,,,,,,,,,,,,,,,,,,min/max +55,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,3.8792789945671258,None,10,18,0.0,100,,,,,,,,,,,,,,,min/max +56,most_frequent,0.00234992390287966,True,extra_trees_preproc_for_regression,False,mse,None,0.8044960404154922,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,286.5841951417159,,4,0.17570677855266975,0.0999999999999999,rbf,-1,True,0.001,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,most_frequent,,False,extra_trees_preproc_for_regression,True,mse,None,2.5830935657278076,19,16,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.962812951051284,,4,0.01204802523960763,0.1821781494294054,rbf,-1,False,1.3160253217209612e-05,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/description.txt b/autosklearn/metalearning/files/a_metric_regression_sparse/description.txt new file mode 100644 index 0000000000..5a08e210f3 --- /dev/null +++ b/autosklearn/metalearning/files/a_metric_regression_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step ClassEntropy: ClassEntropy +feature_step SymbolsMin: SymbolsMin +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step LandmarkLDA: LandmarkLDA +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step KurtosisMax: KurtosisMax +feature_step Landmark1NN: Landmark1NN +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step SkewnessMin: SkewnessMin +feature_step KurtosisMean: KurtosisMean +feature_step SkewnessMean: SkewnessMean +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step NumberOfClasses: NumberOfClasses +feature_step SymbolsSum: SymbolsSum +feature_step SymbolsMean: SymbolsMean +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step KurtosisSTD: KurtosisSTD +feature_step SkewnessSTD: SkewnessSTD +feature_step KurtosisMin: KurtosisMin +feature_step SymbolsSTD: SymbolsSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step LogDatasetRatio: LogDatasetRatio +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step SymbolsMax: SymbolsMax +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step SkewnessMax: SkewnessMax +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstancesWithMissingValues, LogInverseDatasetRatio, PCASkewnessFirstPC, ClassEntropy, SymbolsMin, NumberOfFeatures, NumberOfCategoricalFeatures, PercentageOfMissingValues, PCA, LandmarkLDA, ClassProbabilityMax, KurtosisMax, Landmark1NN, LandmarkNaiveBayes, NumberOfMissingValues, NumSymbols, PercentageOfFeaturesWithMissingValues, PCAKurtosisFirstPC, NumberOfNumericFeatures, Kurtosisses, RatioNumericalToNominal, SkewnessMin, KurtosisMean, SkewnessMean, LogNumberOfFeatures, LandmarkDecisionNodeLearner, NumberOfClasses, SymbolsSum, SymbolsMean, PCAFractionOfComponentsFor95PercentVariance, NumberOfFeaturesWithMissingValues, ClassProbabilitySTD, LandmarkRandomNodeLearner, LogNumberOfInstances, Skewnesses, RatioNominalToNumerical, ClassProbabilityMean, ClassOccurences, KurtosisSTD, SkewnessSTD, KurtosisMin, SymbolsSTD, NumberOfInstances, InverseDatasetRatio, ClassProbabilityMin, LogDatasetRatio, LandmarkDecisionTree, PercentageOfInstancesWithMissingValues, SymbolsMax, MissingValues, SkewnessMax, DatasetRatio + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57 +performance_type: solution_quality +performance_measures: a_metric +algorithms_stochastic: + +scenario_id: openml +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_costs.arff new file mode 100644 index 0000000000..7674ae9051 --- /dev/null +++ b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_costs.arff @@ -0,0 +1,83 @@ +@RELATION openml_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC + +@DATA 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+287_a_metric,1.0,0.00115,1e-05,0.00059,0.00122,0.0,1e-05,2e-05,0.0,0.00106,0.0471,1e-05,3e-05,0.19388,0.03374,0.0001,0.00062,1e-05,0.00045,2e-05,0.00027,5e-05,3e-05,7e-05,8e-05,1e-05,0.0509,0.00015,4e-05,0.00013,3e-05,0.00026,9e-05,0.01752,1e-05,0.00029,7e-05,4e-05,0.00016,0.00013,0.00015,3e-05,0.00044,1e-05,3e-05,2e-05,1e-05,0.36272,1e-05,0.0,0.00148,3e-05,2e-05 +287_r2_metric,1.0,0.00115,1e-05,0.00059,0.00125,0.0,1e-05,2e-05,1e-05,0.00106,0.07657,1e-05,3e-05,0.19379,0.03377,0.0001,0.00069,1e-05,0.00045,2e-05,0.00027,5e-05,3e-05,8e-05,8e-05,1e-05,0.05094,0.00016,4e-05,0.00013,3e-05,0.00026,9e-05,0.0175,1e-05,0.00029,7e-05,4e-05,0.00017,0.00013,0.00015,3e-05,0.00051,1e-05,4e-05,2e-05,1e-05,0.36236,1e-05,0.0,0.00149,4e-05,2e-05 +562_a_metric,1.0,0.00154,1e-05,0.0007,0.00157,0.0,1e-05,2e-05,1e-05,0.00124,0.11888,1e-05,4e-05,0.39691,0.15881,0.00012,0.00065,1e-05,0.00051,2e-05,0.00029,5e-05,3e-05,8e-05,8e-05,1e-05,1.08946,0.00028,4e-05,0.00013,3e-05,0.00032,0.00011,0.08654,1e-05,0.00035,7e-05,5e-05,0.0002,0.00014,0.00019,3e-05,0.00048,1e-05,4e-05,3e-05,1e-05,1.08761,1e-05,0.0,0.00196,4e-05,2e-05 +562_r2_metric,1.0,0.0015,1e-05,0.00073,0.00159,0.0,1e-05,2e-05,0.0,0.00124,0.11795,2e-05,3e-05,0.39723,0.15862,0.00012,0.0007,1e-05,0.00049,2e-05,0.00029,5e-05,3e-05,8e-05,9e-05,1e-05,1.08546,0.00027,4e-05,0.00013,3e-05,0.00031,0.00011,0.08744,1e-05,0.00043,7e-05,5e-05,0.00021,0.00015,0.00027,3e-05,0.00052,1e-05,4e-05,3e-05,1e-05,1.08323,1e-05,0.0,0.0019,4e-05,2e-05 +573_a_metric,1.0,0.0015,1e-05,0.00092,0.00162,0.0,1e-05,2e-05,1e-05,0.00153,0.14625,1e-05,4e-05,0.94202,0.17899,0.00013,0.00078,1e-05,0.00058,2e-05,0.00031,5e-05,3e-05,8e-05,9e-05,1e-05,1.51126,0.00026,4e-05,0.0002,3e-05,0.00035,9e-05,0.05692,1e-05,0.00041,7e-05,5e-05,0.00018,0.00016,0.00025,4e-05,0.00053,1e-05,4e-05,3e-05,1e-05,1.69676,1e-05,1e-05,0.00196,4e-05,2e-05 +573_r2_metric,1.0,0.00151,1e-05,0.00092,0.00194,0.0,1e-05,2e-05,1e-05,0.00153,0.14663,1e-05,4e-05,1.09208,0.17819,0.00013,0.00083,1e-05,0.00057,2e-05,0.00028,5e-05,3e-05,8e-05,0.0001,1e-05,1.55181,0.00027,4e-05,0.00017,3e-05,0.00036,0.00011,0.0667,1e-05,0.00053,7e-05,5e-05,0.0002,0.00013,0.00034,3e-05,0.0006,1e-05,4e-05,3e-05,1e-05,1.78851,1e-05,0.0,0.00196,5e-05,3e-05 +574_a_metric,1.0,0.00395,1e-05,0.0012,0.00551,0.0,1e-05,2e-05,1e-05,0.00211,3.37802,9e-05,3e-05,5.47426,7.419,0.00025,0.00064,1e-05,0.00088,2e-05,0.00027,5e-05,3e-05,7e-05,8e-05,1e-05,152.67116,0.00095,4e-05,0.00013,3e-05,0.00061,0.00023,8.86999,1e-05,0.00033,7e-05,0.00018,0.00063,0.00013,0.00018,3e-05,0.00046,1e-05,3e-05,0.00012,1e-05,84.10332,1e-05,0.0,0.00478,4e-05,2e-05 +574_r2_metric,1.0,0.00398,1e-05,0.00128,0.00471,0.0,1e-05,2e-05,1e-05,0.0022,3.38485,0.0001,3e-05,5.49499,7.42585,0.00025,0.00067,2e-05,0.00089,2e-05,0.00027,5e-05,3e-05,7e-05,9e-05,1e-05,152.47855,0.00099,5e-05,0.00013,3e-05,0.00062,0.00026,8.90127,1e-05,0.00039,8e-05,0.00019,0.00066,0.00013,0.00023,3e-05,0.00049,1e-05,4e-05,0.00011,1e-05,85.99908,1e-05,0.0,0.00482,4e-05,2e-05 +% +% +% \ No newline at end of file diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_runstatus.arff new file mode 100644 index 0000000000..c5380cc36c --- /dev/null +++ b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_runstatus.arff @@ -0,0 +1,83 @@ +@RELATION openml_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} + +@DATA +1028_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +1028_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +1030_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +1030_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +1414_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +1414_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +197_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +197_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +201_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +201_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +218_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +218_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +227_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +227_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +287_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +287_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +562_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +562_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +573_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +573_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +574_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +574_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_values.arff new file mode 100644 index 0000000000..0beae3e25b --- /dev/null +++ b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_values.arff @@ -0,0 +1,77 @@ +@RELATION openml_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE 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+67,median,0.00548806117992972,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,3.0972538714361577,rbf,5291,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6864007905723228e-06,True,0.006559405529560316,0.07194784725205487,True,,constant,squared_epsilon_insensitive,89,l2,,min/max +68,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.1566448365058719,5,0.7004476388937543,poly,956,,,,,,,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02849145741142527,True,0.001397013116831936,,,,,,,,,,,,min/max +69,median,0.0005361882641678341,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.9516292646600342,5226,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.844327967977615e-05,True,0.010965543419019197,0.02393286766186689,True,,constant,squared_epsilon_insensitive,502,l2,,min/max 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+76,mean,0.08567185090735716,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,2.3981698229031436,None,19,20,0.0,100,,,,,,,,,,,,,,,min/max +77,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.12583724307049,False,0.0018926648667342925,True,1,squared_epsilon_insensitive,0.008267080450623797,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,min/max +78,median,0.003145909671422029,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1154751316101943e-06,False,0.006276424097548576,0.038108446269937486,True,9.434769722730874e-09,invscaling,squared_epsilon_insensitive,305,elasticnet,0.45306002466009593,min/max diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/description.txt b/autosklearn/metalearning/files/r2_metric_regression_dense/description.txt new file mode 100644 index 0000000000..316820cab0 --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step ClassEntropy: ClassEntropy +feature_step SymbolsMin: SymbolsMin +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step LandmarkLDA: LandmarkLDA +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step KurtosisMax: KurtosisMax +feature_step Landmark1NN: Landmark1NN +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step SkewnessMin: SkewnessMin +feature_step KurtosisMean: KurtosisMean +feature_step SkewnessMean: SkewnessMean +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step NumberOfClasses: NumberOfClasses +feature_step SymbolsSum: SymbolsSum +feature_step SymbolsMean: SymbolsMean +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step KurtosisSTD: KurtosisSTD +feature_step SkewnessSTD: SkewnessSTD +feature_step KurtosisMin: KurtosisMin +feature_step SymbolsSTD: SymbolsSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step LogDatasetRatio: LogDatasetRatio +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step SymbolsMax: SymbolsMax +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step SkewnessMax: SkewnessMax +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstancesWithMissingValues, LogInverseDatasetRatio, PCASkewnessFirstPC, ClassEntropy, SymbolsMin, NumberOfFeatures, NumberOfCategoricalFeatures, PercentageOfMissingValues, PCA, LandmarkLDA, ClassProbabilityMax, KurtosisMax, Landmark1NN, LandmarkNaiveBayes, NumberOfMissingValues, NumSymbols, PercentageOfFeaturesWithMissingValues, PCAKurtosisFirstPC, NumberOfNumericFeatures, Kurtosisses, RatioNumericalToNominal, SkewnessMin, KurtosisMean, SkewnessMean, LogNumberOfFeatures, LandmarkDecisionNodeLearner, NumberOfClasses, SymbolsSum, SymbolsMean, PCAFractionOfComponentsFor95PercentVariance, NumberOfFeaturesWithMissingValues, ClassProbabilitySTD, LandmarkRandomNodeLearner, LogNumberOfInstances, Skewnesses, RatioNominalToNumerical, ClassProbabilityMean, ClassOccurences, KurtosisSTD, SkewnessSTD, KurtosisMin, SymbolsSTD, NumberOfInstances, InverseDatasetRatio, ClassProbabilityMin, LogDatasetRatio, LandmarkDecisionTree, PercentageOfInstancesWithMissingValues, SymbolsMax, MissingValues, SkewnessMax, DatasetRatio + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78 +performance_type: solution_quality +performance_measures: r2_metric +algorithms_stochastic: + +scenario_id: openml +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_costs.arff new file mode 100644 index 0000000000..7674ae9051 --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_costs.arff @@ -0,0 +1,83 @@ +@RELATION openml_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC + +@DATA +1028_a_metric,1.0,0.00023,1e-05,0.00048,0.00029,0.0,1e-05,2e-05,1e-05,0.00084,0.01935,1e-05,4e-05,0.02096,0.01456,5e-05,0.00065,1e-05,0.00034,2e-05,0.00027,5e-05,3e-05,7e-05,8e-05,1e-05,0.00751,5e-05,4e-05,0.00013,3e-05,0.0001,8e-05,0.00799,1e-05,0.00028,7e-05,5e-05,0.00016,0.00013,0.00015,3e-05,0.00047,1e-05,3e-05,2e-05,1e-05,0.01481,1e-05,0.0,0.00035,3e-05,2e-05 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+574_r2_metric,1.0,0.00398,1e-05,0.00128,0.00471,0.0,1e-05,2e-05,1e-05,0.0022,3.38485,0.0001,3e-05,5.49499,7.42585,0.00025,0.00067,2e-05,0.00089,2e-05,0.00027,5e-05,3e-05,7e-05,9e-05,1e-05,152.47855,0.00099,5e-05,0.00013,3e-05,0.00062,0.00026,8.90127,1e-05,0.00039,8e-05,0.00019,0.00066,0.00013,0.00023,3e-05,0.00049,1e-05,4e-05,0.00011,1e-05,85.99908,1e-05,0.0,0.00482,4e-05,2e-05 +% +% +% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_runstatus.arff new file mode 100644 index 0000000000..c5380cc36c --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_runstatus.arff @@ -0,0 +1,83 @@ +@RELATION openml_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} + +@DATA 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+574_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +574_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_values.arff b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_values.arff new file mode 100644 index 0000000000..0beae3e25b --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_values.arff @@ -0,0 +1,77 @@ +@RELATION openml_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA 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+% +% +% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/readme.txt b/autosklearn/metalearning/files/r2_metric_regression_dense/readme.txt new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/r2_metric_regression_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..6ac7ba41bc --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_sparse/algorithm_runs.arff @@ -0,0 +1,120 @@ +@RELATION openml_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE r2_metric NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +344_r2_metric,1.0,29,0.000174939632416,ok +344_r2_metric,1.0,1,0.00122278928757,ok +422_r2_metric,1.0,1,0.960557281971,ok +422_r2_metric,1.0,10,0.945026278496,ok 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+39,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,5,None,2,8,1.0,99,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.144233734895743,True,0.05071592767365497,,,,,,,,,,,,standardize +40,most_frequent,0.004535038763972565,True,extra_trees_preproc_for_regression,True,mse,None,4.347325260732746,10,14,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23914.174724642544,,4,0.002004566226002717,0.8634952558838467,rbf,-1,False,0.024899956992494608,,,,,,,,,,,,,,,,,,,,,,,,min/max +41,median,,False,extra_trees_preproc_for_regression,True,mse,None,0.993528657198836,10,20,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1933.5929428405466,,2,0.015939473931353206,0.7858707127149234,rbf,-1,False,0.0037322094857651553,,,,,,,,,,,,,,,,,,,,,,,,min/max +42,median,,False,extra_trees_preproc_for_regression,True,mse,None,3.296750799496171,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,2.5317611636176207,1,10,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.683440436880492,,4,0.008766678436592141,0.10578131451502429,rbf,-1,False,0.08678739387617436,,,,,,,,,,,,,,,,,,,,,,,,none +44,median,0.00548806117992972,True,nystroem_sampler,,,,,,,,,,,,,,,,,,3.0972538714361577,rbf,5291,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6864007905723228e-06,True,0.006559405529560316,0.07194784725205487,True,,constant,squared_epsilon_insensitive,89,l2,,min/max +45,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,0.1566448365058719,5,0.7004476388937543,poly,956,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02849145741142527,True,0.001397013116831936,,,,,,,,,,,,min/max +46,median,0.0005361882641678341,True,kitchen_sinks,,,,,,,,,,,,,,1.9516292646600342,5226,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.844327967977615e-05,True,0.010965543419019197,0.02393286766186689,True,,constant,squared_epsilon_insensitive,502,l2,,min/max +47,mean,0.007129891763108858,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,222.52007103508296,,2,0.0014545049235116735,0.8961440468564937,rbf,-1,False,0.005206529915661272,,,,,,,,,,,,,,,,,,,,,,,,min/max +48,mean,0.011362042214560027,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,4.6530295512971644,None,12,10,0.0,100,,,,,,,,,,,,,,,standardize +49,median,0.010427843341691877,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,1.397114088844996,15,12,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +50,most_frequent,0.00234992390287966,True,extra_trees_preproc_for_regression,False,mse,None,0.8044960404154922,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,286.5841951417159,,4,0.17570677855266975,0.0999999999999999,rbf,-1,True,0.001,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,most_frequent,,False,extra_trees_preproc_for_regression,True,mse,None,2.5830935657278076,19,16,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.962812951051284,,4,0.01204802523960763,0.1821781494294054,rbf,-1,False,1.3160253217209612e-05,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/description.txt b/autosklearn/metalearning/files/r2_metric_regression_sparse/description.txt new file mode 100644 index 0000000000..cfdb9a9996 --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step ClassEntropy: ClassEntropy +feature_step SymbolsMin: SymbolsMin +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step LandmarkLDA: LandmarkLDA +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step KurtosisMax: KurtosisMax +feature_step Landmark1NN: Landmark1NN +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step SkewnessMin: SkewnessMin +feature_step KurtosisMean: KurtosisMean +feature_step SkewnessMean: SkewnessMean +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step NumberOfClasses: NumberOfClasses +feature_step SymbolsSum: SymbolsSum +feature_step SymbolsMean: SymbolsMean +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step KurtosisSTD: KurtosisSTD +feature_step SkewnessSTD: SkewnessSTD +feature_step KurtosisMin: KurtosisMin +feature_step SymbolsSTD: SymbolsSTD +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step LogDatasetRatio: LogDatasetRatio +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step SymbolsMax: SymbolsMax +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step SkewnessMax: SkewnessMax +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstancesWithMissingValues, LogInverseDatasetRatio, PCASkewnessFirstPC, ClassEntropy, SymbolsMin, NumberOfFeatures, NumberOfCategoricalFeatures, PercentageOfMissingValues, PCA, LandmarkLDA, ClassProbabilityMax, KurtosisMax, Landmark1NN, LandmarkNaiveBayes, NumberOfMissingValues, NumSymbols, PercentageOfFeaturesWithMissingValues, PCAKurtosisFirstPC, NumberOfNumericFeatures, Kurtosisses, RatioNumericalToNominal, SkewnessMin, KurtosisMean, SkewnessMean, LogNumberOfFeatures, LandmarkDecisionNodeLearner, NumberOfClasses, SymbolsSum, SymbolsMean, PCAFractionOfComponentsFor95PercentVariance, NumberOfFeaturesWithMissingValues, ClassProbabilitySTD, LandmarkRandomNodeLearner, LogNumberOfInstances, Skewnesses, RatioNominalToNumerical, ClassProbabilityMean, ClassOccurences, KurtosisSTD, SkewnessSTD, KurtosisMin, SymbolsSTD, NumberOfInstances, InverseDatasetRatio, ClassProbabilityMin, LogDatasetRatio, LandmarkDecisionTree, PercentageOfInstancesWithMissingValues, SymbolsMax, MissingValues, SkewnessMax, DatasetRatio + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51 +performance_type: solution_quality +performance_measures: r2_metric +algorithms_stochastic: + +scenario_id: openml +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_costs.arff new file mode 100644 index 0000000000..7674ae9051 --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_costs.arff @@ -0,0 +1,83 @@ +@RELATION openml_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC + +@DATA +1028_a_metric,1.0,0.00023,1e-05,0.00048,0.00029,0.0,1e-05,2e-05,1e-05,0.00084,0.01935,1e-05,4e-05,0.02096,0.01456,5e-05,0.00065,1e-05,0.00034,2e-05,0.00027,5e-05,3e-05,7e-05,8e-05,1e-05,0.00751,5e-05,4e-05,0.00013,3e-05,0.0001,8e-05,0.00799,1e-05,0.00028,7e-05,5e-05,0.00016,0.00013,0.00015,3e-05,0.00047,1e-05,3e-05,2e-05,1e-05,0.01481,1e-05,0.0,0.00035,3e-05,2e-05 +1028_r2_metric,1.0,0.00024,1e-05,0.00048,0.00032,0.0,1e-05,2e-05,0.0,0.00084,0.01908,1e-05,4e-05,0.0209,0.01453,6e-05,0.00065,1e-05,0.00033,2e-05,0.00027,5e-05,3e-05,7e-05,8e-05,1e-05,0.00752,6e-05,4e-05,0.00013,3e-05,0.00011,8e-05,0.0083,1e-05,0.00028,6e-05,4e-05,0.00015,0.00013,0.00015,3e-05,0.00047,1e-05,3e-05,2e-05,1e-05,0.01465,1e-05,0.0,0.00038,3e-05,3e-05 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index 0000000000..c5380cc36c --- /dev/null +++ b/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_runstatus.arff @@ -0,0 +1,83 @@ +@RELATION openml_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} + +@DATA +1028_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +1028_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +1030_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok 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+574_a_metric,1.0,9.04497047259,0.137700622339,0.000551571980143,6.55093350802e-05,0.00328835593734,0.00104814936128,954.0625,5700.10169126,375.641420077,-1.09652172508,1374.91657088,0.0701239209057,0.150698073096,0.0766614923485,0.121671657544,0.00678418630643,0.150698073096,-6.86072918293,6.86072918293,2.77258872224,9.63331790517,0.0,1813.0,16.0,0.0,15265.0,0.0,0.0,16.0,0.75,0.998236,0.316971331835,0.0,0.0,0.0,0.0,0.0,66.714881897,5.79417816247,-5.77428817749,15.9991434181,0.0,0.0,0.0,0.0,0.0 +574_r2_metric,1.0,9.04497047259,0.137700622339,0.000551571980143,6.55093350802e-05,0.00328835593734,0.00104814936128,954.0625,5700.10169126,375.641420077,-1.09652172508,1374.91657088,0.0701239209057,0.150698073096,0.0766614923485,0.121671657544,0.00678418630643,0.150698073096,-6.86072918293,6.86072918293,2.77258872224,9.63331790517,0.0,1813.0,16.0,0.0,15265.0,0.0,0.0,16.0,0.75,0.998236,0.316971331835,0.0,0.0,0.0,0.0,0.0,66.714881897,5.79417816247,-5.77428817749,15.9991434181,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/readme.txt b/autosklearn/metalearning/files/r2_metric_regression_sparse/readme.txt new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/metalearning/mismbo.py b/autosklearn/metalearning/mismbo.py index a285243f5e..dcc893abb1 100644 --- a/autosklearn/metalearning/mismbo.py +++ b/autosklearn/metalearning/mismbo.py @@ -26,7 +26,7 @@ SENTINEL = 'uiaeo' -EXCLUDE_META_FUTURES = { +EXCLUDE_META_FEATURES_CLASSIFICATION = { 'Landmark1NN', 'LandmarkDecisionNodeLearner', 'LandmarkDecisionTree', @@ -34,12 +34,33 @@ 'LandmarkNaiveBayes', 'PCAFractionOfComponentsFor95PercentVariance', 'PCAKurtosisFirstPC', - 'PCASkewnessFirstPC' + 'PCASkewnessFirstPC', + 'PCA' +} + +EXCLUDE_META_FEATURES_REGRESSION = { + 'Landmark1NN', + 'LandmarkDecisionNodeLearner', + 'LandmarkDecisionTree', + 'LandmarkLDA', + 'LandmarkNaiveBayes', + 'PCAFractionOfComponentsFor95PercentVariance', + 'PCAKurtosisFirstPC', + 'PCASkewnessFirstPC', + 'NumberOfClasses', + 'ClassOccurences', + 'ClassProbabilityMin', + 'ClassProbabilityMax', + 'ClassProbabilityMean', + 'ClassProbabilitySTD', + 'ClassEntropy', + 'LandmarkRandomNodeLearner', + 'PCA', } -def calc_meta_features(X_train, Y_train, categorical, dataset_name): +def calc_meta_features(X_train, Y_train, categorical, dataset_name, task): """ Calculate meta features with label :param X_train: @@ -48,12 +69,16 @@ def calc_meta_features(X_train, Y_train, categorical, dataset_name): :param dataset_name: :return: """ + EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \ + if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION + return calculate_all_metafeatures_with_labels( X_train, Y_train, categorical, dataset_name + SENTINEL, - dont_calculate=EXCLUDE_META_FUTURES) + dont_calculate=EXCLUDE_META_FEATURES) -def calc_meta_features_encoded(X_train, Y_train, categorical, dataset_name): +def calc_meta_features_encoded(X_train, Y_train, categorical, dataset_name, + task): """ Calculate meta features with encoded labels :param X_train: @@ -62,12 +87,15 @@ def calc_meta_features_encoded(X_train, Y_train, categorical, dataset_name): :param dataset_name: :return: """ + EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \ + if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION + if np.sum(categorical) != 0: raise ValueError("Training matrix doesn't look OneHotEncoded!") return calculate_all_metafeatures_encoded_labels( X_train, Y_train, categorical, dataset_name + SENTINEL, - dont_calculate=EXCLUDE_META_FUTURES) + dont_calculate=EXCLUDE_META_FEATURES) def convert_conf2smac_string(configuration): @@ -99,7 +127,10 @@ def suggest_via_metalearning( configuration_space, dataset_name, metric, task, sparse, num_initial_configurations, metadata_directory): logger = get_logger('autosklearn.metalearning.mismbo') - task = task if task != MULTILABEL_CLASSIFICATION else MULTICLASS_CLASSIFICATION + + if task == MULTILABEL_CLASSIFICATION: + task = MULTICLASS_CLASSIFICATION + task = TASK_TYPES_TO_STRING[task] if metafeatures_encoded_labels is None or \ @@ -121,8 +152,11 @@ def suggest_via_metalearning( mf.metafeature_values.update( metafeatures_encoded_labels.metafeature_values) + EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \ + if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION + metafeatures_subset = subsets['all'] - metafeatures_subset.difference_update(EXCLUDE_META_FUTURES) + metafeatures_subset.difference_update(EXCLUDE_META_FEATURES) metafeatures_subset = list(metafeatures_subset) start = time.time() diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index d428e7d33d..43202e4264 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -57,11 +57,11 @@ def _calculate_metafeatures(data_feat_type, data_info_task, basename, if metalearning_cnt <= 0: result = None - elif data_info_task in \ - [MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION]: + elif data_info_task in [MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION, + MULTILABEL_CLASSIFICATION, REGRESSION]: logger.info('Start calculating metafeatures for %s', basename) result = calc_meta_features(x_train, y_train, categorical=categorical, - dataset_name=basename) + dataset_name=basename, task=data_info_task) else: result = None logger.info('Metafeatures not calculated') @@ -73,12 +73,12 @@ def _calculate_metafeatures(data_feat_type, data_info_task, basename, def _calculate_metafeatures_encoded(basename, x_train, y_train, watcher, - logger): + task, logger): task_name = 'CalculateMetafeaturesEncoded' watcher.start_task(task_name) result = calc_meta_features_encoded(X_train=x_train, Y_train=y_train, categorical=[False] * x_train.shape[1], - dataset_name=basename) + dataset_name=basename, task=task) watcher.stop_task(task_name) logger.info( 'Calculating Metafeatures (encoded attributes) took %5.2fsec', @@ -292,13 +292,15 @@ def collect_metalearning_suggestions(self): have_metafeatures = meta_features is not None known_task = self.datamanager.info['task'] in [MULTICLASS_CLASSIFICATION, BINARY_CLASSIFICATION, - MULTILABEL_CLASSIFICATION] + MULTILABEL_CLASSIFICATION, + REGRESSION] if have_metafeatures and known_task : meta_features_encoded = _calculate_metafeatures_encoded( self.dataset_name, self.datamanager.data['X_train'], self.datamanager.data['Y_train'], self.watcher, + self.datamanager.info['task'], self.logger) metalearning_configurations = _get_metalearning_configurations( diff --git a/scripts/update_metadata/03_autosklearn_retrieve_metadata.py b/scripts/update_metadata/03_autosklearn_retrieve_metadata.py index decccb2e54..fd86e0dce3 100644 --- a/scripts/update_metadata/03_autosklearn_retrieve_metadata.py +++ b/scripts/update_metadata/03_autosklearn_retrieve_metadata.py @@ -32,6 +32,8 @@ def retrieve_matadata(validation_directory, metric, configuration_space, if not os.path.exists(ped) or not os.path.isdir(ped): continue + print("Going through directory %s" % ped) + smac_output_dir = ped validation_files = [] validation_configuration_files = [] @@ -75,6 +77,9 @@ def retrieve_matadata(validation_directory, metric, configuration_space, for validation_file, validation_configuration_file, validation_run_results_file in \ zip(validation_files, validation_configuration_files, validation_run_results_files): + + print("\t%s" % validation_file) + configuration_to_time = dict() with open(validation_file) as fh: reader = csv.reader(fh) @@ -102,9 +107,12 @@ def retrieve_matadata(validation_directory, metric, configuration_space, metric_ = metric_.replace(":", "").strip() value = value.strip() - if metric_ == metric: - value = float(value) - best.append((value, i + 1)) + try: + if int(metric_) == STRING_TO_METRIC[metric]: + value = float(value) + best.append((value, i + 1)) + except ValueError: + pass best.sort() for test_performance, validation_configuration_id in best: @@ -132,8 +140,10 @@ def retrieve_matadata(validation_directory, metric, configuration_space, hyperparameter = \ configuration_space.get_hyperparameter( hp_name) + del configuration[key] except KeyError: break + value = value.strip("'") if isinstance(hyperparameter, @@ -157,7 +167,10 @@ def retrieve_matadata(validation_directory, metric, configuration_space, elif hyperparameter is None: value = '' else: - raise ValueError((hp_name, )) + raise ValueError((hp_name, value, + hyperparameter, + type(hyperparameter), + configuration, configuration_space)) configuration[hp_name] = value @@ -167,7 +180,7 @@ def retrieve_matadata(validation_directory, metric, configuration_space, except Exception as e: print("Configuration %s not applicable " \ "because of %s!" \ - % (row[1], e)) + % (configuration, e)) break if str(configuration) in \ @@ -287,10 +300,19 @@ def main(): for sparse, task in [(1, BINARY_CLASSIFICATION), (1, MULTICLASS_CLASSIFICATION), (0, BINARY_CLASSIFICATION), - (0, MULTICLASS_CLASSIFICATION)]: + (0, MULTICLASS_CLASSIFICATION), + (1, REGRESSION), + (0, REGRESSION)]: for metric in ['acc_metric', 'auc_metric', 'bac_metric', 'f1_metric', - 'pac_metric']: + 'pac_metric', 'a_metric', 'r2_metric']: + + if STRING_TO_METRIC[metric] not in REGRESSION_METRICS and task in\ + REGRESSION_TASKS: + continue + if STRING_TO_METRIC[metric] not in CLASSIFICATION_METRICS and \ + task in CLASSIFICATION_TASKS: + continue output_dir_ = os.path.join(output_dir, '%s_%s_%s' % ( metric, TASK_TYPES_TO_STRING[task], 'sparse' if sparse else 'dense')) @@ -313,7 +335,10 @@ def main(): only_best=args.only_best) if len(outputs) == 0: - raise ValueError("Nothing found!") + print("No output found for %s, %s, %s" % + (metric, TASK_TYPES_TO_STRING[task], + 'sparse' if sparse else 'dense')) + continue write_output(outputs, configurations, output_dir_, configuration_space, metric) diff --git a/scripts/update_metadata/05_autosklearn_create_aslib_files.py b/scripts/update_metadata/05_autosklearn_create_aslib_files.py index 7ea69b6b3f..e737f5c246 100644 --- a/scripts/update_metadata/05_autosklearn_create_aslib_files.py +++ b/scripts/update_metadata/05_autosklearn_create_aslib_files.py @@ -38,16 +38,29 @@ for sparse, task in [(1, BINARY_CLASSIFICATION), (1, MULTICLASS_CLASSIFICATION), (0, BINARY_CLASSIFICATION), - (0, MULTICLASS_CLASSIFICATION)]: + (0, MULTICLASS_CLASSIFICATION), + (1, REGRESSION), + (0, REGRESSION)]: for metric in ['acc_metric', 'auc_metric', 'bac_metric', 'f1_metric', - 'pac_metric']: + 'pac_metric', 'a_metric', 'r2_metric']: + + if STRING_TO_METRIC[metric] not in REGRESSION_METRICS and task in \ + REGRESSION_TASKS: + continue + if STRING_TO_METRIC[metric] not in CLASSIFICATION_METRICS and \ + task in CLASSIFICATION_TASKS: + continue dir_name = '%s_%s_%s' % (metric, TASK_TYPES_TO_STRING[task], 'sparse' if sparse else 'dense') output_dir_ = os.path.join(output_directory, dir_name) results_dir_ = os.path.join(results_dir, dir_name) + if not os.path.exists(results_dir_): + print("Results directory %s does not exist!") % results_dir_ + continue + try: os.makedirs(output_dir_) except Exception: diff --git a/test/metalearning/test_metalearning.py b/test/metalearning/test_metalearning.py index 73a8dd175e..c23115d660 100644 --- a/test/metalearning/test_metalearning.py +++ b/test/metalearning/test_metalearning.py @@ -31,15 +31,14 @@ def setUp(self): self.Y_train = self.Y_train[eliminate_class_two] def test_metalearning(self): - dataset_name = 'digits' - - initial_challengers = { + dataset_name_classification = 'digits' + initial_challengers_classification = { ACC_METRIC: "--initial-challengers \" " "-balancing:strategy 'weighting' " "-classifier:__choice__ 'proj_logit'", AUC_METRIC: "--initial-challengers \" " - "-balancing:strategy 'none' " - "-classifier:__choice__ 'random_forest'", + "-balancing:strategy 'weighting' " + "-classifier:__choice__ 'liblinear_svc'", BAC_METRIC: "--initial-challengers \" " "-balancing:strategy 'weighting' " "-classifier:__choice__ 'proj_logit'", @@ -51,32 +50,53 @@ def test_metalearning(self): "-classifier:__choice__ 'random_forest'" } - for metric in initial_challengers: - configuration_space = get_configuration_space( - { - 'metric': metric, - 'task': MULTICLASS_CLASSIFICATION, - 'is_sparse': False - }, - include_preprocessors=['no_preprocessing']) - - X_train, Y_train, X_test, Y_test = get_dataset(dataset_name) - categorical = [False] * X_train.shape[1] - - meta_features_label = calc_meta_features(X_train, Y_train, - categorical, dataset_name) - meta_features_encoded_label = calc_meta_features_encoded(X_train, - Y_train, - categorical, - dataset_name) - initial_configuration_strings_for_smac = \ - create_metalearning_string_for_smac_call( - meta_features_label, - meta_features_encoded_label, - configuration_space, dataset_name, metric, - MULTICLASS_CLASSIFICATION, False, 1, None) - - print(metric) - print(initial_configuration_strings_for_smac[0]) - self.assertTrue(initial_configuration_strings_for_smac[ - 0].startswith(initial_challengers[metric])) + dataset_name_regression = 'diabetes' + initial_challengers_regression = { + A_METRIC: "--initial-challengers \" " + "-imputation:strategy 'mean' " + "-one_hot_encoding:minimum_fraction '0.01' " + "-one_hot_encoding:use_minimum_fraction 'True' " + "-preprocessor:__choice__ 'no_preprocessing' " + "-regressor:__choice__ 'random_forest'", + R2_METRIC: "--initial-challengers \" " + "-imputation:strategy 'mean' " + "-one_hot_encoding:minimum_fraction '0.01' " + "-one_hot_encoding:use_minimum_fraction 'True' " + "-preprocessor:__choice__ 'no_preprocessing' " + "-regressor:__choice__ 'random_forest'", + } + + for dataset_name, task, initial_challengers in [ + (dataset_name_regression, REGRESSION, + initial_challengers_regression), + (dataset_name_classification, MULTICLASS_CLASSIFICATION, + initial_challengers_classification) + ]: + for metric in initial_challengers: + configuration_space = get_configuration_space( + { + 'metric': metric, + 'task': task, + 'is_sparse': False + }, + include_preprocessors=['no_preprocessing']) + + X_train, Y_train, X_test, Y_test = get_dataset(dataset_name) + categorical = [False] * X_train.shape[1] + + meta_features_label = calc_meta_features( + X_train, Y_train, categorical, dataset_name, task) + meta_features_encoded_label = calc_meta_features_encoded( + X_train, Y_train, categorical, dataset_name, task) + + initial_configuration_strings_for_smac = \ + create_metalearning_string_for_smac_call( + meta_features_label, + meta_features_encoded_label, + configuration_space, dataset_name, metric, + task, False, 1, None) + + print(METRIC_TO_STRING[metric]) + print(initial_configuration_strings_for_smac[0]) + self.assertTrue(initial_configuration_strings_for_smac[ + 0].startswith(initial_challengers[metric]))