diff --git a/benchmark/Experiments.py b/benchmark/Experiments.py index 42906f6..be2be4f 100644 --- a/benchmark/Experiments.py +++ b/benchmark/Experiments.py @@ -42,15 +42,11 @@ class DatasetsTanveer: def load(self, name): file_name = os.path.join(self.folder(), self.dataset_names(name)) - try: - data = pd.read_csv( - file_name, - sep="\t", - index_col=0, - ) - except FileNotFoundError: - print(f"Couldn't open data file {file_name}") - exit(1) + data = pd.read_csv( + file_name, + sep="\t", + index_col=0, + ) X = data.drop("clase", axis=1).to_numpy() y = data["clase"].to_numpy() return X, y @@ -67,14 +63,10 @@ class DatasetsSurcov: def load(self, name): file_name = os.path.join(self.folder(), self.dataset_names(name)) - try: - data = pd.read_csv( - file_name, - index_col=0, - ) - except FileNotFoundError: - print(f"Couldn't open data file {file_name}") - exit(1) + data = pd.read_csv( + file_name, + index_col=0, + ) data.dropna(axis=0, how="any", inplace=True) self.columns = data.columns X = data.drop("class", axis=1).to_numpy() @@ -92,12 +84,8 @@ class Datasets: self.dataset = class_name() if dataset_name is None: file_name = os.path.join(self.dataset.folder(), Files.index) - try: - with open(file_name) as f: - self.data_sets = f.read().splitlines() - except FileNotFoundError: - print(f"Couldn't open index file {file_name}") - exit(1) + with open(file_name) as f: + self.data_sets = f.read().splitlines() else: self.data_sets = [dataset_name] @@ -109,10 +97,11 @@ class Datasets: class BestResults: - def __init__(self, score, model, datasets): + def __init__(self, score, model, datasets, quiet=False): self.score_name = score self.datasets = datasets self.model = model + self.quiet = quiet self.data = {} def _get_file_name(self): @@ -154,7 +143,9 @@ class BestResults: score=self.score_name, model=self.model ) all_files = sorted(list(os.walk(Folders.results))) - for root, _, files in tqdm(all_files, desc="files"): + for root, _, files in tqdm( + all_files, desc="files", disable=self.quiet + ): for name in files: if name.startswith(init_suffix) and name.endswith(end_suffix): file_name = os.path.join(root, name) @@ -164,7 +155,7 @@ class BestResults: # Build best results json file output = {} datasets = Datasets() - for name in tqdm(list(datasets), desc="datasets"): + for name in tqdm(list(datasets), desc="datasets", disable=self.quiet): output[name] = ( results[name]["score"], results[name]["hyperparameters"], diff --git a/benchmark/tests/.env.dist b/benchmark/tests/.env.dist new file mode 100644 index 0000000..819f93f --- /dev/null +++ b/benchmark/tests/.env.dist @@ -0,0 +1,7 @@ +score=accuracy +platform=iMac27 +n_folds=5 +model=ODTE +stratified=0 +# Source of data Tanveer/Surcov +source_data=Tanveer diff --git a/benchmark/tests/.env.surcov b/benchmark/tests/.env.surcov new file mode 100644 index 0000000..01deb63 --- /dev/null +++ b/benchmark/tests/.env.surcov @@ -0,0 +1,7 @@ +score=accuracy +platform=iMac27 +n_folds=5 +model=ODTE +stratified=0 +# Source of data Tanveer/Surcov +source_data=Surcov diff --git a/benchmark/tests/BestResults_test.py b/benchmark/tests/BestResults_test.py new file mode 100644 index 0000000..15154a6 --- /dev/null +++ b/benchmark/tests/BestResults_test.py @@ -0,0 +1,72 @@ +import os +import unittest +from ..Models import Models +from ..Experiments import BestResults, Datasets + + +class BestResultTest(unittest.TestCase): + def __init__(self, *args, **kwargs): + os.chdir(os.path.dirname(os.path.abspath(__file__))) + super().__init__(*args, **kwargs) + + def tearDown(self) -> None: + return super().tearDown() + + def test_load(self): + expected = { + "balance-scale": [ + 0.98, + {"splitter": "iwss", "max_features": "auto"}, + "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json", + ], + "balloons": [ + 0.86, + { + "C": 7, + "gamma": 0.1, + "kernel": "rbf", + "max_iter": 10000.0, + "multiclass_strategy": "ovr", + }, + "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json", + ], + } + dt = Datasets() + model = "STree" + best = BestResults( + score="accuracy", model=model, datasets=dt, quiet=True + ) + best.build() + self.assertSequenceEqual(best.load({}), expected) + + def test_load_error(self): + dt = Datasets() + model = "STree" + best = BestResults( + score="accuracy", model=model, datasets=dt, quiet=True + ) + file_name = best._get_file_name() + os.rename(file_name, file_name + ".bak") + try: + best.load({}) + except ValueError: + pass + else: + self.fail("BestResults.load() should raise ValueError") + finally: + os.rename(file_name + ".bak", file_name) + + def test_fill(self): + dt = Datasets() + model = "STree" + best = BestResults( + score="accuracy", model=model, datasets=dt, quiet=True + ) + self.assertSequenceEqual( + best.fill({"test": "test"}, {"balloons": []}), + {"balloons": [], "balance-scale": (0.0, {"test": "test"}, "")}, + ) + self.assertSequenceEqual( + best.fill({}), + {"balance-scale": (0.0, {}, ""), "balloons": (0.0, {}, "")}, + ) diff --git a/benchmark/tests/Dataset_test.py b/benchmark/tests/Dataset_test.py new file mode 100644 index 0000000..b1d99e2 --- /dev/null +++ b/benchmark/tests/Dataset_test.py @@ -0,0 +1,71 @@ +import os +import shutil +import unittest + +from ..Experiments import Randomized, Datasets + + +class DatasetTest(unittest.TestCase): + def __init__(self, *args, **kwargs): + os.chdir(os.path.dirname(os.path.abspath(__file__))) + self.datasets_values = { + "balance-scale": (625, 4, 3), + "balloons": (16, 4, 2), + "iris": (150, 4, 3), + "wine": (178, 13, 3), + } + super().__init__(*args, **kwargs) + + def tearDown(self) -> None: + self.set_env(".env.dist") + return super().tearDown() + + @staticmethod + def set_env(env): + shutil.copy(env, ".env") + + def test_Randomized(self): + expected = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] + self.assertSequenceEqual(Randomized.seeds, expected) + + def test_Datasets_iterator(self): + test = { + ".env.dist": ["balance-scale", "balloons"], + ".env.surcov": ["iris", "wine"], + } + for key, value in test.items(): + self.set_env(key) + dt = Datasets() + computed = [] + for dataset in dt: + computed.append(dataset) + X, y = dt.load(dataset) + m, n = X.shape + c = max(y) + 1 + # Check dataset integrity + self.assertSequenceEqual( + (m, n, c), self.datasets_values[dataset] + ) + self.assertSequenceEqual(computed, value) + self.set_env(".env.dist") + + def test_Datasets_subset(self): + test = { + ".env.dist": "balloons", + ".env.surcov": "wine", + } + for key, value in test.items(): + self.set_env(key) + dt = Datasets(value) + computed = [] + for dataset in dt: + computed.append(dataset) + X, y = dt.load(dataset) + m, n = X.shape + c = max(y) + 1 + # Check dataset integrity + self.assertSequenceEqual( + (m, n, c), self.datasets_values[dataset] + ) + self.assertSequenceEqual(computed, [value]) + self.set_env(".env.dist") diff --git a/benchmark/tests/Models_test.py b/benchmark/tests/Models_test.py index de54763..f4ab7c3 100644 --- a/benchmark/tests/Models_test.py +++ b/benchmark/tests/Models_test.py @@ -16,9 +16,6 @@ from ..Models import Models class ModelTest(unittest.TestCase): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - def test_Models(self): test = { "STree": Stree, diff --git a/benchmark/tests/Util_test.py b/benchmark/tests/Util_test.py index 4910cc8..c40753b 100644 --- a/benchmark/tests/Util_test.py +++ b/benchmark/tests/Util_test.py @@ -114,15 +114,16 @@ class UtilTest(unittest.TestCase): def test_Files_get_results(self): os.chdir(os.path.dirname(os.path.abspath(__file__))) - self.assertSequenceEqual( + self.assertCountEqual( Files().get_all_results(hidden=False), [ "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json", + "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json", "results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0." "json", ], ) - self.assertSequenceEqual( + self.assertCountEqual( Files().get_all_results(hidden=True), ["results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json"], ) diff --git a/benchmark/tests/__init__.py b/benchmark/tests/__init__.py index 897ae22..c1b6f20 100644 --- a/benchmark/tests/__init__.py +++ b/benchmark/tests/__init__.py @@ -1,4 +1,6 @@ from .Util_test import UtilTest from .Models_test import ModelTest +from .Dataset_test import DatasetTest +from .BestResults_test import BestResultTest -all = ["UtilTest", "ModelTest"] +all = ["UtilTest", "ModelTest", "DatasetTest", "BestResultTest"] diff --git a/benchmark/tests/data/all.txt b/benchmark/tests/data/all.txt new file mode 100644 index 0000000..f52c8ec --- /dev/null +++ b/benchmark/tests/data/all.txt @@ -0,0 +1,2 @@ +balance-scale +balloons diff --git a/benchmark/tests/data/balance-scale_R.dat b/benchmark/tests/data/balance-scale_R.dat new file mode 100755 index 0000000..655e0e0 --- /dev/null +++ b/benchmark/tests/data/balance-scale_R.dat @@ -0,0 +1,626 @@ + f1 f2 f3 f4 clase +1 -1.41308 -1.41308 -1.41308 -1.41308 0 +2 -1.41308 -1.41308 -1.41308 -0.706541 2 +3 -1.41308 -1.41308 -1.41308 0 2 +4 -1.41308 -1.41308 -1.41308 0.706541 2 +5 -1.41308 -1.41308 -1.41308 1.41308 2 +6 -1.41308 -1.41308 -0.706541 -1.41308 2 +7 -1.41308 -1.41308 -0.706541 -0.706541 2 +8 -1.41308 -1.41308 -0.706541 0 2 +9 -1.41308 -1.41308 -0.706541 0.706541 2 +10 -1.41308 -1.41308 -0.706541 1.41308 2 +11 -1.41308 -1.41308 0 -1.41308 2 +12 -1.41308 -1.41308 0 -0.706541 2 +13 -1.41308 -1.41308 0 0 2 +14 -1.41308 -1.41308 0 0.706541 2 +15 -1.41308 -1.41308 0 1.41308 2 +16 -1.41308 -1.41308 0.706541 -1.41308 2 +17 -1.41308 -1.41308 0.706541 -0.706541 2 +18 -1.41308 -1.41308 0.706541 0 2 +19 -1.41308 -1.41308 0.706541 0.706541 2 +20 -1.41308 -1.41308 0.706541 1.41308 2 +21 -1.41308 -1.41308 1.41308 -1.41308 2 +22 -1.41308 -1.41308 1.41308 -0.706541 2 +23 -1.41308 -1.41308 1.41308 0 2 +24 -1.41308 -1.41308 1.41308 0.706541 2 +25 -1.41308 -1.41308 1.41308 1.41308 2 +26 -1.41308 -0.706541 -1.41308 -1.41308 1 +27 -1.41308 -0.706541 -1.41308 -0.706541 0 +28 -1.41308 -0.706541 -1.41308 0 2 +29 -1.41308 -0.706541 -1.41308 0.706541 2 +30 -1.41308 -0.706541 -1.41308 1.41308 2 +31 -1.41308 -0.706541 -0.706541 -1.41308 0 +32 -1.41308 -0.706541 -0.706541 -0.706541 2 +33 -1.41308 -0.706541 -0.706541 0 2 +34 -1.41308 -0.706541 -0.706541 0.706541 2 +35 -1.41308 -0.706541 -0.706541 1.41308 2 +36 -1.41308 -0.706541 0 -1.41308 2 +37 -1.41308 -0.706541 0 -0.706541 2 +38 -1.41308 -0.706541 0 0 2 +39 -1.41308 -0.706541 0 0.706541 2 +40 -1.41308 -0.706541 0 1.41308 2 +41 -1.41308 -0.706541 0.706541 -1.41308 2 +42 -1.41308 -0.706541 0.706541 -0.706541 2 +43 -1.41308 -0.706541 0.706541 0 2 +44 -1.41308 -0.706541 0.706541 0.706541 2 +45 -1.41308 -0.706541 0.706541 1.41308 2 +46 -1.41308 -0.706541 1.41308 -1.41308 2 +47 -1.41308 -0.706541 1.41308 -0.706541 2 +48 -1.41308 -0.706541 1.41308 0 2 +49 -1.41308 -0.706541 1.41308 0.706541 2 +50 -1.41308 -0.706541 1.41308 1.41308 2 +51 -1.41308 0 -1.41308 -1.41308 1 +52 -1.41308 0 -1.41308 -0.706541 1 +53 -1.41308 0 -1.41308 0 0 +54 -1.41308 0 -1.41308 0.706541 2 +55 -1.41308 0 -1.41308 1.41308 2 +56 -1.41308 0 -0.706541 -1.41308 1 +57 -1.41308 0 -0.706541 -0.706541 2 +58 -1.41308 0 -0.706541 0 2 +59 -1.41308 0 -0.706541 0.706541 2 +60 -1.41308 0 -0.706541 1.41308 2 +61 -1.41308 0 0 -1.41308 0 +62 -1.41308 0 0 -0.706541 2 +63 -1.41308 0 0 0 2 +64 -1.41308 0 0 0.706541 2 +65 -1.41308 0 0 1.41308 2 +66 -1.41308 0 0.706541 -1.41308 2 +67 -1.41308 0 0.706541 -0.706541 2 +68 -1.41308 0 0.706541 0 2 +69 -1.41308 0 0.706541 0.706541 2 +70 -1.41308 0 0.706541 1.41308 2 +71 -1.41308 0 1.41308 -1.41308 2 +72 -1.41308 0 1.41308 -0.706541 2 +73 -1.41308 0 1.41308 0 2 +74 -1.41308 0 1.41308 0.706541 2 +75 -1.41308 0 1.41308 1.41308 2 +76 -1.41308 0.706541 -1.41308 -1.41308 1 +77 -1.41308 0.706541 -1.41308 -0.706541 1 +78 -1.41308 0.706541 -1.41308 0 1 +79 -1.41308 0.706541 -1.41308 0.706541 0 +80 -1.41308 0.706541 -1.41308 1.41308 2 +81 -1.41308 0.706541 -0.706541 -1.41308 1 +82 -1.41308 0.706541 -0.706541 -0.706541 0 +83 -1.41308 0.706541 -0.706541 0 2 +84 -1.41308 0.706541 -0.706541 0.706541 2 +85 -1.41308 0.706541 -0.706541 1.41308 2 +86 -1.41308 0.706541 0 -1.41308 1 +87 -1.41308 0.706541 0 -0.706541 2 +88 -1.41308 0.706541 0 0 2 +89 -1.41308 0.706541 0 0.706541 2 +90 -1.41308 0.706541 0 1.41308 2 +91 -1.41308 0.706541 0.706541 -1.41308 0 +92 -1.41308 0.706541 0.706541 -0.706541 2 +93 -1.41308 0.706541 0.706541 0 2 +94 -1.41308 0.706541 0.706541 0.706541 2 +95 -1.41308 0.706541 0.706541 1.41308 2 +96 -1.41308 0.706541 1.41308 -1.41308 2 +97 -1.41308 0.706541 1.41308 -0.706541 2 +98 -1.41308 0.706541 1.41308 0 2 +99 -1.41308 0.706541 1.41308 0.706541 2 +100 -1.41308 0.706541 1.41308 1.41308 2 +101 -1.41308 1.41308 -1.41308 -1.41308 1 +102 -1.41308 1.41308 -1.41308 -0.706541 1 +103 -1.41308 1.41308 -1.41308 0 1 +104 -1.41308 1.41308 -1.41308 0.706541 1 +105 -1.41308 1.41308 -1.41308 1.41308 0 +106 -1.41308 1.41308 -0.706541 -1.41308 1 +107 -1.41308 1.41308 -0.706541 -0.706541 1 +108 -1.41308 1.41308 -0.706541 0 2 +109 -1.41308 1.41308 -0.706541 0.706541 2 +110 -1.41308 1.41308 -0.706541 1.41308 2 +111 -1.41308 1.41308 0 -1.41308 1 +112 -1.41308 1.41308 0 -0.706541 2 +113 -1.41308 1.41308 0 0 2 +114 -1.41308 1.41308 0 0.706541 2 +115 -1.41308 1.41308 0 1.41308 2 +116 -1.41308 1.41308 0.706541 -1.41308 1 +117 -1.41308 1.41308 0.706541 -0.706541 2 +118 -1.41308 1.41308 0.706541 0 2 +119 -1.41308 1.41308 0.706541 0.706541 2 +120 -1.41308 1.41308 0.706541 1.41308 2 +121 -1.41308 1.41308 1.41308 -1.41308 0 +122 -1.41308 1.41308 1.41308 -0.706541 2 +123 -1.41308 1.41308 1.41308 0 2 +124 -1.41308 1.41308 1.41308 0.706541 2 +125 -1.41308 1.41308 1.41308 1.41308 2 +126 -0.706541 -1.41308 -1.41308 -1.41308 1 +127 -0.706541 -1.41308 -1.41308 -0.706541 0 +128 -0.706541 -1.41308 -1.41308 0 2 +129 -0.706541 -1.41308 -1.41308 0.706541 2 +130 -0.706541 -1.41308 -1.41308 1.41308 2 +131 -0.706541 -1.41308 -0.706541 -1.41308 0 +132 -0.706541 -1.41308 -0.706541 -0.706541 2 +133 -0.706541 -1.41308 -0.706541 0 2 +134 -0.706541 -1.41308 -0.706541 0.706541 2 +135 -0.706541 -1.41308 -0.706541 1.41308 2 +136 -0.706541 -1.41308 0 -1.41308 2 +137 -0.706541 -1.41308 0 -0.706541 2 +138 -0.706541 -1.41308 0 0 2 +139 -0.706541 -1.41308 0 0.706541 2 +140 -0.706541 -1.41308 0 1.41308 2 +141 -0.706541 -1.41308 0.706541 -1.41308 2 +142 -0.706541 -1.41308 0.706541 -0.706541 2 +143 -0.706541 -1.41308 0.706541 0 2 +144 -0.706541 -1.41308 0.706541 0.706541 2 +145 -0.706541 -1.41308 0.706541 1.41308 2 +146 -0.706541 -1.41308 1.41308 -1.41308 2 +147 -0.706541 -1.41308 1.41308 -0.706541 2 +148 -0.706541 -1.41308 1.41308 0 2 +149 -0.706541 -1.41308 1.41308 0.706541 2 +150 -0.706541 -1.41308 1.41308 1.41308 2 +151 -0.706541 -0.706541 -1.41308 -1.41308 1 +152 -0.706541 -0.706541 -1.41308 -0.706541 1 +153 -0.706541 -0.706541 -1.41308 0 1 +154 -0.706541 -0.706541 -1.41308 0.706541 0 +155 -0.706541 -0.706541 -1.41308 1.41308 2 +156 -0.706541 -0.706541 -0.706541 -1.41308 1 +157 -0.706541 -0.706541 -0.706541 -0.706541 0 +158 -0.706541 -0.706541 -0.706541 0 2 +159 -0.706541 -0.706541 -0.706541 0.706541 2 +160 -0.706541 -0.706541 -0.706541 1.41308 2 +161 -0.706541 -0.706541 0 -1.41308 1 +162 -0.706541 -0.706541 0 -0.706541 2 +163 -0.706541 -0.706541 0 0 2 +164 -0.706541 -0.706541 0 0.706541 2 +165 -0.706541 -0.706541 0 1.41308 2 +166 -0.706541 -0.706541 0.706541 -1.41308 0 +167 -0.706541 -0.706541 0.706541 -0.706541 2 +168 -0.706541 -0.706541 0.706541 0 2 +169 -0.706541 -0.706541 0.706541 0.706541 2 +170 -0.706541 -0.706541 0.706541 1.41308 2 +171 -0.706541 -0.706541 1.41308 -1.41308 2 +172 -0.706541 -0.706541 1.41308 -0.706541 2 +173 -0.706541 -0.706541 1.41308 0 2 +174 -0.706541 -0.706541 1.41308 0.706541 2 +175 -0.706541 -0.706541 1.41308 1.41308 2 +176 -0.706541 0 -1.41308 -1.41308 1 +177 -0.706541 0 -1.41308 -0.706541 1 +178 -0.706541 0 -1.41308 0 1 +179 -0.706541 0 -1.41308 0.706541 1 +180 -0.706541 0 -1.41308 1.41308 1 +181 -0.706541 0 -0.706541 -1.41308 1 +182 -0.706541 0 -0.706541 -0.706541 1 +183 -0.706541 0 -0.706541 0 0 +184 -0.706541 0 -0.706541 0.706541 2 +185 -0.706541 0 -0.706541 1.41308 2 +186 -0.706541 0 0 -1.41308 1 +187 -0.706541 0 0 -0.706541 0 +188 -0.706541 0 0 0 2 +189 -0.706541 0 0 0.706541 2 +190 -0.706541 0 0 1.41308 2 +191 -0.706541 0 0.706541 -1.41308 1 +192 -0.706541 0 0.706541 -0.706541 2 +193 -0.706541 0 0.706541 0 2 +194 -0.706541 0 0.706541 0.706541 2 +195 -0.706541 0 0.706541 1.41308 2 +196 -0.706541 0 1.41308 -1.41308 1 +197 -0.706541 0 1.41308 -0.706541 2 +198 -0.706541 0 1.41308 0 2 +199 -0.706541 0 1.41308 0.706541 2 +200 -0.706541 0 1.41308 1.41308 2 +201 -0.706541 0.706541 -1.41308 -1.41308 1 +202 -0.706541 0.706541 -1.41308 -0.706541 1 +203 -0.706541 0.706541 -1.41308 0 1 +204 -0.706541 0.706541 -1.41308 0.706541 1 +205 -0.706541 0.706541 -1.41308 1.41308 1 +206 -0.706541 0.706541 -0.706541 -1.41308 1 +207 -0.706541 0.706541 -0.706541 -0.706541 1 +208 -0.706541 0.706541 -0.706541 0 1 +209 -0.706541 0.706541 -0.706541 0.706541 0 +210 -0.706541 0.706541 -0.706541 1.41308 2 +211 -0.706541 0.706541 0 -1.41308 1 +212 -0.706541 0.706541 0 -0.706541 1 +213 -0.706541 0.706541 0 0 2 +214 -0.706541 0.706541 0 0.706541 2 +215 -0.706541 0.706541 0 1.41308 2 +216 -0.706541 0.706541 0.706541 -1.41308 1 +217 -0.706541 0.706541 0.706541 -0.706541 0 +218 -0.706541 0.706541 0.706541 0 2 +219 -0.706541 0.706541 0.706541 0.706541 2 +220 -0.706541 0.706541 0.706541 1.41308 2 +221 -0.706541 0.706541 1.41308 -1.41308 1 +222 -0.706541 0.706541 1.41308 -0.706541 2 +223 -0.706541 0.706541 1.41308 0 2 +224 -0.706541 0.706541 1.41308 0.706541 2 +225 -0.706541 0.706541 1.41308 1.41308 2 +226 -0.706541 1.41308 -1.41308 -1.41308 1 +227 -0.706541 1.41308 -1.41308 -0.706541 1 +228 -0.706541 1.41308 -1.41308 0 1 +229 -0.706541 1.41308 -1.41308 0.706541 1 +230 -0.706541 1.41308 -1.41308 1.41308 1 +231 -0.706541 1.41308 -0.706541 -1.41308 1 +232 -0.706541 1.41308 -0.706541 -0.706541 1 +233 -0.706541 1.41308 -0.706541 0 1 +234 -0.706541 1.41308 -0.706541 0.706541 1 +235 -0.706541 1.41308 -0.706541 1.41308 0 +236 -0.706541 1.41308 0 -1.41308 1 +237 -0.706541 1.41308 0 -0.706541 1 +238 -0.706541 1.41308 0 0 1 +239 -0.706541 1.41308 0 0.706541 2 +240 -0.706541 1.41308 0 1.41308 2 +241 -0.706541 1.41308 0.706541 -1.41308 1 +242 -0.706541 1.41308 0.706541 -0.706541 1 +243 -0.706541 1.41308 0.706541 0 2 +244 -0.706541 1.41308 0.706541 0.706541 2 +245 -0.706541 1.41308 0.706541 1.41308 2 +246 -0.706541 1.41308 1.41308 -1.41308 1 +247 -0.706541 1.41308 1.41308 -0.706541 0 +248 -0.706541 1.41308 1.41308 0 2 +249 -0.706541 1.41308 1.41308 0.706541 2 +250 -0.706541 1.41308 1.41308 1.41308 2 +251 0 -1.41308 -1.41308 -1.41308 1 +252 0 -1.41308 -1.41308 -0.706541 1 +253 0 -1.41308 -1.41308 0 0 +254 0 -1.41308 -1.41308 0.706541 2 +255 0 -1.41308 -1.41308 1.41308 2 +256 0 -1.41308 -0.706541 -1.41308 1 +257 0 -1.41308 -0.706541 -0.706541 2 +258 0 -1.41308 -0.706541 0 2 +259 0 -1.41308 -0.706541 0.706541 2 +260 0 -1.41308 -0.706541 1.41308 2 +261 0 -1.41308 0 -1.41308 0 +262 0 -1.41308 0 -0.706541 2 +263 0 -1.41308 0 0 2 +264 0 -1.41308 0 0.706541 2 +265 0 -1.41308 0 1.41308 2 +266 0 -1.41308 0.706541 -1.41308 2 +267 0 -1.41308 0.706541 -0.706541 2 +268 0 -1.41308 0.706541 0 2 +269 0 -1.41308 0.706541 0.706541 2 +270 0 -1.41308 0.706541 1.41308 2 +271 0 -1.41308 1.41308 -1.41308 2 +272 0 -1.41308 1.41308 -0.706541 2 +273 0 -1.41308 1.41308 0 2 +274 0 -1.41308 1.41308 0.706541 2 +275 0 -1.41308 1.41308 1.41308 2 +276 0 -0.706541 -1.41308 -1.41308 1 +277 0 -0.706541 -1.41308 -0.706541 1 +278 0 -0.706541 -1.41308 0 1 +279 0 -0.706541 -1.41308 0.706541 1 +280 0 -0.706541 -1.41308 1.41308 1 +281 0 -0.706541 -0.706541 -1.41308 1 +282 0 -0.706541 -0.706541 -0.706541 1 +283 0 -0.706541 -0.706541 0 0 +284 0 -0.706541 -0.706541 0.706541 2 +285 0 -0.706541 -0.706541 1.41308 2 +286 0 -0.706541 0 -1.41308 1 +287 0 -0.706541 0 -0.706541 0 +288 0 -0.706541 0 0 2 +289 0 -0.706541 0 0.706541 2 +290 0 -0.706541 0 1.41308 2 +291 0 -0.706541 0.706541 -1.41308 1 +292 0 -0.706541 0.706541 -0.706541 2 +293 0 -0.706541 0.706541 0 2 +294 0 -0.706541 0.706541 0.706541 2 +295 0 -0.706541 0.706541 1.41308 2 +296 0 -0.706541 1.41308 -1.41308 1 +297 0 -0.706541 1.41308 -0.706541 2 +298 0 -0.706541 1.41308 0 2 +299 0 -0.706541 1.41308 0.706541 2 +300 0 -0.706541 1.41308 1.41308 2 +301 0 0 -1.41308 -1.41308 1 +302 0 0 -1.41308 -0.706541 1 +303 0 0 -1.41308 0 1 +304 0 0 -1.41308 0.706541 1 +305 0 0 -1.41308 1.41308 1 +306 0 0 -0.706541 -1.41308 1 +307 0 0 -0.706541 -0.706541 1 +308 0 0 -0.706541 0 1 +309 0 0 -0.706541 0.706541 1 +310 0 0 -0.706541 1.41308 2 +311 0 0 0 -1.41308 1 +312 0 0 0 -0.706541 1 +313 0 0 0 0 0 +314 0 0 0 0.706541 2 +315 0 0 0 1.41308 2 +316 0 0 0.706541 -1.41308 1 +317 0 0 0.706541 -0.706541 1 +318 0 0 0.706541 0 2 +319 0 0 0.706541 0.706541 2 +320 0 0 0.706541 1.41308 2 +321 0 0 1.41308 -1.41308 1 +322 0 0 1.41308 -0.706541 2 +323 0 0 1.41308 0 2 +324 0 0 1.41308 0.706541 2 +325 0 0 1.41308 1.41308 2 +326 0 0.706541 -1.41308 -1.41308 1 +327 0 0.706541 -1.41308 -0.706541 1 +328 0 0.706541 -1.41308 0 1 +329 0 0.706541 -1.41308 0.706541 1 +330 0 0.706541 -1.41308 1.41308 1 +331 0 0.706541 -0.706541 -1.41308 1 +332 0 0.706541 -0.706541 -0.706541 1 +333 0 0.706541 -0.706541 0 1 +334 0 0.706541 -0.706541 0.706541 1 +335 0 0.706541 -0.706541 1.41308 1 +336 0 0.706541 0 -1.41308 1 +337 0 0.706541 0 -0.706541 1 +338 0 0.706541 0 0 1 +339 0 0.706541 0 0.706541 0 +340 0 0.706541 0 1.41308 2 +341 0 0.706541 0.706541 -1.41308 1 +342 0 0.706541 0.706541 -0.706541 1 +343 0 0.706541 0.706541 0 0 +344 0 0.706541 0.706541 0.706541 2 +345 0 0.706541 0.706541 1.41308 2 +346 0 0.706541 1.41308 -1.41308 1 +347 0 0.706541 1.41308 -0.706541 1 +348 0 0.706541 1.41308 0 2 +349 0 0.706541 1.41308 0.706541 2 +350 0 0.706541 1.41308 1.41308 2 +351 0 1.41308 -1.41308 -1.41308 1 +352 0 1.41308 -1.41308 -0.706541 1 +353 0 1.41308 -1.41308 0 1 +354 0 1.41308 -1.41308 0.706541 1 +355 0 1.41308 -1.41308 1.41308 1 +356 0 1.41308 -0.706541 -1.41308 1 +357 0 1.41308 -0.706541 -0.706541 1 +358 0 1.41308 -0.706541 0 1 +359 0 1.41308 -0.706541 0.706541 1 +360 0 1.41308 -0.706541 1.41308 1 +361 0 1.41308 0 -1.41308 1 +362 0 1.41308 0 -0.706541 1 +363 0 1.41308 0 0 1 +364 0 1.41308 0 0.706541 1 +365 0 1.41308 0 1.41308 0 +366 0 1.41308 0.706541 -1.41308 1 +367 0 1.41308 0.706541 -0.706541 1 +368 0 1.41308 0.706541 0 1 +369 0 1.41308 0.706541 0.706541 2 +370 0 1.41308 0.706541 1.41308 2 +371 0 1.41308 1.41308 -1.41308 1 +372 0 1.41308 1.41308 -0.706541 1 +373 0 1.41308 1.41308 0 0 +374 0 1.41308 1.41308 0.706541 2 +375 0 1.41308 1.41308 1.41308 2 +376 0.706541 -1.41308 -1.41308 -1.41308 1 +377 0.706541 -1.41308 -1.41308 -0.706541 1 +378 0.706541 -1.41308 -1.41308 0 1 +379 0.706541 -1.41308 -1.41308 0.706541 0 +380 0.706541 -1.41308 -1.41308 1.41308 2 +381 0.706541 -1.41308 -0.706541 -1.41308 1 +382 0.706541 -1.41308 -0.706541 -0.706541 0 +383 0.706541 -1.41308 -0.706541 0 2 +384 0.706541 -1.41308 -0.706541 0.706541 2 +385 0.706541 -1.41308 -0.706541 1.41308 2 +386 0.706541 -1.41308 0 -1.41308 1 +387 0.706541 -1.41308 0 -0.706541 2 +388 0.706541 -1.41308 0 0 2 +389 0.706541 -1.41308 0 0.706541 2 +390 0.706541 -1.41308 0 1.41308 2 +391 0.706541 -1.41308 0.706541 -1.41308 0 +392 0.706541 -1.41308 0.706541 -0.706541 2 +393 0.706541 -1.41308 0.706541 0 2 +394 0.706541 -1.41308 0.706541 0.706541 2 +395 0.706541 -1.41308 0.706541 1.41308 2 +396 0.706541 -1.41308 1.41308 -1.41308 2 +397 0.706541 -1.41308 1.41308 -0.706541 2 +398 0.706541 -1.41308 1.41308 0 2 +399 0.706541 -1.41308 1.41308 0.706541 2 +400 0.706541 -1.41308 1.41308 1.41308 2 +401 0.706541 -0.706541 -1.41308 -1.41308 1 +402 0.706541 -0.706541 -1.41308 -0.706541 1 +403 0.706541 -0.706541 -1.41308 0 1 +404 0.706541 -0.706541 -1.41308 0.706541 1 +405 0.706541 -0.706541 -1.41308 1.41308 1 +406 0.706541 -0.706541 -0.706541 -1.41308 1 +407 0.706541 -0.706541 -0.706541 -0.706541 1 +408 0.706541 -0.706541 -0.706541 0 1 +409 0.706541 -0.706541 -0.706541 0.706541 0 +410 0.706541 -0.706541 -0.706541 1.41308 2 +411 0.706541 -0.706541 0 -1.41308 1 +412 0.706541 -0.706541 0 -0.706541 1 +413 0.706541 -0.706541 0 0 2 +414 0.706541 -0.706541 0 0.706541 2 +415 0.706541 -0.706541 0 1.41308 2 +416 0.706541 -0.706541 0.706541 -1.41308 1 +417 0.706541 -0.706541 0.706541 -0.706541 0 +418 0.706541 -0.706541 0.706541 0 2 +419 0.706541 -0.706541 0.706541 0.706541 2 +420 0.706541 -0.706541 0.706541 1.41308 2 +421 0.706541 -0.706541 1.41308 -1.41308 1 +422 0.706541 -0.706541 1.41308 -0.706541 2 +423 0.706541 -0.706541 1.41308 0 2 +424 0.706541 -0.706541 1.41308 0.706541 2 +425 0.706541 -0.706541 1.41308 1.41308 2 +426 0.706541 0 -1.41308 -1.41308 1 +427 0.706541 0 -1.41308 -0.706541 1 +428 0.706541 0 -1.41308 0 1 +429 0.706541 0 -1.41308 0.706541 1 +430 0.706541 0 -1.41308 1.41308 1 +431 0.706541 0 -0.706541 -1.41308 1 +432 0.706541 0 -0.706541 -0.706541 1 +433 0.706541 0 -0.706541 0 1 +434 0.706541 0 -0.706541 0.706541 1 +435 0.706541 0 -0.706541 1.41308 1 +436 0.706541 0 0 -1.41308 1 +437 0.706541 0 0 -0.706541 1 +438 0.706541 0 0 0 1 +439 0.706541 0 0 0.706541 0 +440 0.706541 0 0 1.41308 2 +441 0.706541 0 0.706541 -1.41308 1 +442 0.706541 0 0.706541 -0.706541 1 +443 0.706541 0 0.706541 0 0 +444 0.706541 0 0.706541 0.706541 2 +445 0.706541 0 0.706541 1.41308 2 +446 0.706541 0 1.41308 -1.41308 1 +447 0.706541 0 1.41308 -0.706541 1 +448 0.706541 0 1.41308 0 2 +449 0.706541 0 1.41308 0.706541 2 +450 0.706541 0 1.41308 1.41308 2 +451 0.706541 0.706541 -1.41308 -1.41308 1 +452 0.706541 0.706541 -1.41308 -0.706541 1 +453 0.706541 0.706541 -1.41308 0 1 +454 0.706541 0.706541 -1.41308 0.706541 1 +455 0.706541 0.706541 -1.41308 1.41308 1 +456 0.706541 0.706541 -0.706541 -1.41308 1 +457 0.706541 0.706541 -0.706541 -0.706541 1 +458 0.706541 0.706541 -0.706541 0 1 +459 0.706541 0.706541 -0.706541 0.706541 1 +460 0.706541 0.706541 -0.706541 1.41308 1 +461 0.706541 0.706541 0 -1.41308 1 +462 0.706541 0.706541 0 -0.706541 1 +463 0.706541 0.706541 0 0 1 +464 0.706541 0.706541 0 0.706541 1 +465 0.706541 0.706541 0 1.41308 1 +466 0.706541 0.706541 0.706541 -1.41308 1 +467 0.706541 0.706541 0.706541 -0.706541 1 +468 0.706541 0.706541 0.706541 0 1 +469 0.706541 0.706541 0.706541 0.706541 0 +470 0.706541 0.706541 0.706541 1.41308 2 +471 0.706541 0.706541 1.41308 -1.41308 1 +472 0.706541 0.706541 1.41308 -0.706541 1 +473 0.706541 0.706541 1.41308 0 1 +474 0.706541 0.706541 1.41308 0.706541 2 +475 0.706541 0.706541 1.41308 1.41308 2 +476 0.706541 1.41308 -1.41308 -1.41308 1 +477 0.706541 1.41308 -1.41308 -0.706541 1 +478 0.706541 1.41308 -1.41308 0 1 +479 0.706541 1.41308 -1.41308 0.706541 1 +480 0.706541 1.41308 -1.41308 1.41308 1 +481 0.706541 1.41308 -0.706541 -1.41308 1 +482 0.706541 1.41308 -0.706541 -0.706541 1 +483 0.706541 1.41308 -0.706541 0 1 +484 0.706541 1.41308 -0.706541 0.706541 1 +485 0.706541 1.41308 -0.706541 1.41308 1 +486 0.706541 1.41308 0 -1.41308 1 +487 0.706541 1.41308 0 -0.706541 1 +488 0.706541 1.41308 0 0 1 +489 0.706541 1.41308 0 0.706541 1 +490 0.706541 1.41308 0 1.41308 1 +491 0.706541 1.41308 0.706541 -1.41308 1 +492 0.706541 1.41308 0.706541 -0.706541 1 +493 0.706541 1.41308 0.706541 0 1 +494 0.706541 1.41308 0.706541 0.706541 1 +495 0.706541 1.41308 0.706541 1.41308 0 +496 0.706541 1.41308 1.41308 -1.41308 1 +497 0.706541 1.41308 1.41308 -0.706541 1 +498 0.706541 1.41308 1.41308 0 1 +499 0.706541 1.41308 1.41308 0.706541 0 +500 0.706541 1.41308 1.41308 1.41308 2 +501 1.41308 -1.41308 -1.41308 -1.41308 1 +502 1.41308 -1.41308 -1.41308 -0.706541 1 +503 1.41308 -1.41308 -1.41308 0 1 +504 1.41308 -1.41308 -1.41308 0.706541 1 +505 1.41308 -1.41308 -1.41308 1.41308 0 +506 1.41308 -1.41308 -0.706541 -1.41308 1 +507 1.41308 -1.41308 -0.706541 -0.706541 1 +508 1.41308 -1.41308 -0.706541 0 2 +509 1.41308 -1.41308 -0.706541 0.706541 2 +510 1.41308 -1.41308 -0.706541 1.41308 2 +511 1.41308 -1.41308 0 -1.41308 1 +512 1.41308 -1.41308 0 -0.706541 2 +513 1.41308 -1.41308 0 0 2 +514 1.41308 -1.41308 0 0.706541 2 +515 1.41308 -1.41308 0 1.41308 2 +516 1.41308 -1.41308 0.706541 -1.41308 1 +517 1.41308 -1.41308 0.706541 -0.706541 2 +518 1.41308 -1.41308 0.706541 0 2 +519 1.41308 -1.41308 0.706541 0.706541 2 +520 1.41308 -1.41308 0.706541 1.41308 2 +521 1.41308 -1.41308 1.41308 -1.41308 0 +522 1.41308 -1.41308 1.41308 -0.706541 2 +523 1.41308 -1.41308 1.41308 0 2 +524 1.41308 -1.41308 1.41308 0.706541 2 +525 1.41308 -1.41308 1.41308 1.41308 2 +526 1.41308 -0.706541 -1.41308 -1.41308 1 +527 1.41308 -0.706541 -1.41308 -0.706541 1 +528 1.41308 -0.706541 -1.41308 0 1 +529 1.41308 -0.706541 -1.41308 0.706541 1 +530 1.41308 -0.706541 -1.41308 1.41308 1 +531 1.41308 -0.706541 -0.706541 -1.41308 1 +532 1.41308 -0.706541 -0.706541 -0.706541 1 +533 1.41308 -0.706541 -0.706541 0 1 +534 1.41308 -0.706541 -0.706541 0.706541 1 +535 1.41308 -0.706541 -0.706541 1.41308 0 +536 1.41308 -0.706541 0 -1.41308 1 +537 1.41308 -0.706541 0 -0.706541 1 +538 1.41308 -0.706541 0 0 1 +539 1.41308 -0.706541 0 0.706541 2 +540 1.41308 -0.706541 0 1.41308 2 +541 1.41308 -0.706541 0.706541 -1.41308 1 +542 1.41308 -0.706541 0.706541 -0.706541 1 +543 1.41308 -0.706541 0.706541 0 2 +544 1.41308 -0.706541 0.706541 0.706541 2 +545 1.41308 -0.706541 0.706541 1.41308 2 +546 1.41308 -0.706541 1.41308 -1.41308 1 +547 1.41308 -0.706541 1.41308 -0.706541 0 +548 1.41308 -0.706541 1.41308 0 2 +549 1.41308 -0.706541 1.41308 0.706541 2 +550 1.41308 -0.706541 1.41308 1.41308 2 +551 1.41308 0 -1.41308 -1.41308 1 +552 1.41308 0 -1.41308 -0.706541 1 +553 1.41308 0 -1.41308 0 1 +554 1.41308 0 -1.41308 0.706541 1 +555 1.41308 0 -1.41308 1.41308 1 +556 1.41308 0 -0.706541 -1.41308 1 +557 1.41308 0 -0.706541 -0.706541 1 +558 1.41308 0 -0.706541 0 1 +559 1.41308 0 -0.706541 0.706541 1 +560 1.41308 0 -0.706541 1.41308 1 +561 1.41308 0 0 -1.41308 1 +562 1.41308 0 0 -0.706541 1 +563 1.41308 0 0 0 1 +564 1.41308 0 0 0.706541 1 +565 1.41308 0 0 1.41308 0 +566 1.41308 0 0.706541 -1.41308 1 +567 1.41308 0 0.706541 -0.706541 1 +568 1.41308 0 0.706541 0 1 +569 1.41308 0 0.706541 0.706541 2 +570 1.41308 0 0.706541 1.41308 2 +571 1.41308 0 1.41308 -1.41308 1 +572 1.41308 0 1.41308 -0.706541 1 +573 1.41308 0 1.41308 0 0 +574 1.41308 0 1.41308 0.706541 2 +575 1.41308 0 1.41308 1.41308 2 +576 1.41308 0.706541 -1.41308 -1.41308 1 +577 1.41308 0.706541 -1.41308 -0.706541 1 +578 1.41308 0.706541 -1.41308 0 1 +579 1.41308 0.706541 -1.41308 0.706541 1 +580 1.41308 0.706541 -1.41308 1.41308 1 +581 1.41308 0.706541 -0.706541 -1.41308 1 +582 1.41308 0.706541 -0.706541 -0.706541 1 +583 1.41308 0.706541 -0.706541 0 1 +584 1.41308 0.706541 -0.706541 0.706541 1 +585 1.41308 0.706541 -0.706541 1.41308 1 +586 1.41308 0.706541 0 -1.41308 1 +587 1.41308 0.706541 0 -0.706541 1 +588 1.41308 0.706541 0 0 1 +589 1.41308 0.706541 0 0.706541 1 +590 1.41308 0.706541 0 1.41308 1 +591 1.41308 0.706541 0.706541 -1.41308 1 +592 1.41308 0.706541 0.706541 -0.706541 1 +593 1.41308 0.706541 0.706541 0 1 +594 1.41308 0.706541 0.706541 0.706541 1 +595 1.41308 0.706541 0.706541 1.41308 0 +596 1.41308 0.706541 1.41308 -1.41308 1 +597 1.41308 0.706541 1.41308 -0.706541 1 +598 1.41308 0.706541 1.41308 0 1 +599 1.41308 0.706541 1.41308 0.706541 0 +600 1.41308 0.706541 1.41308 1.41308 2 +601 1.41308 1.41308 -1.41308 -1.41308 1 +602 1.41308 1.41308 -1.41308 -0.706541 1 +603 1.41308 1.41308 -1.41308 0 1 +604 1.41308 1.41308 -1.41308 0.706541 1 +605 1.41308 1.41308 -1.41308 1.41308 1 +606 1.41308 1.41308 -0.706541 -1.41308 1 +607 1.41308 1.41308 -0.706541 -0.706541 1 +608 1.41308 1.41308 -0.706541 0 1 +609 1.41308 1.41308 -0.706541 0.706541 1 +610 1.41308 1.41308 -0.706541 1.41308 1 +611 1.41308 1.41308 0 -1.41308 1 +612 1.41308 1.41308 0 -0.706541 1 +613 1.41308 1.41308 0 0 1 +614 1.41308 1.41308 0 0.706541 1 +615 1.41308 1.41308 0 1.41308 1 +616 1.41308 1.41308 0.706541 -1.41308 1 +617 1.41308 1.41308 0.706541 -0.706541 1 +618 1.41308 1.41308 0.706541 0 1 +619 1.41308 1.41308 0.706541 0.706541 1 +620 1.41308 1.41308 0.706541 1.41308 1 +621 1.41308 1.41308 1.41308 -1.41308 1 +622 1.41308 1.41308 1.41308 -0.706541 1 +623 1.41308 1.41308 1.41308 0 1 +624 1.41308 1.41308 1.41308 0.706541 1 +625 1.41308 1.41308 1.41308 1.41308 0 diff --git a/benchmark/tests/data/balloons_R.dat b/benchmark/tests/data/balloons_R.dat new file mode 100755 index 0000000..1579461 --- /dev/null +++ b/benchmark/tests/data/balloons_R.dat @@ -0,0 +1,17 @@ + f1 f2 f3 f4 clase +1 0.968246 -0.968246 0.968246 0.968246 1 +2 0.968246 -0.968246 0.968246 -0.968246 1 +3 0.968246 -0.968246 -0.968246 0.968246 1 +4 0.968246 -0.968246 -0.968246 -0.968246 1 +5 0.968246 0.968246 0.968246 0.968246 1 +6 0.968246 0.968246 0.968246 -0.968246 0 +7 0.968246 0.968246 -0.968246 0.968246 0 +8 0.968246 0.968246 -0.968246 -0.968246 0 +9 -0.968246 -0.968246 0.968246 0.968246 1 +10 -0.968246 -0.968246 0.968246 -0.968246 0 +11 -0.968246 -0.968246 -0.968246 0.968246 0 +12 -0.968246 -0.968246 -0.968246 -0.968246 0 +13 -0.968246 0.968246 0.968246 0.968246 1 +14 -0.968246 0.968246 0.968246 -0.968246 0 +15 -0.968246 0.968246 -0.968246 0.968246 0 +16 -0.968246 0.968246 -0.968246 -0.968246 0 diff --git a/benchmark/tests/datasets/all.txt b/benchmark/tests/datasets/all.txt new file mode 100644 index 0000000..16d4d76 --- /dev/null +++ b/benchmark/tests/datasets/all.txt @@ -0,0 +1,2 @@ +iris +wine diff --git a/benchmark/tests/datasets/iris.csv b/benchmark/tests/datasets/iris.csv new file mode 100644 index 0000000..7d35d6f --- /dev/null +++ b/benchmark/tests/datasets/iris.csv @@ -0,0 +1,151 @@ +,sepal length (cm),sepal width (cm),petal length (cm),petal width (cm),class +0,5.1,3.5,1.4,0.2,0 +1,4.9,3.0,1.4,0.2,0 +2,4.7,3.2,1.3,0.2,0 +3,4.6,3.1,1.5,0.2,0 +4,5.0,3.6,1.4,0.2,0 +5,5.4,3.9,1.7,0.4,0 +6,4.6,3.4,1.4,0.3,0 +7,5.0,3.4,1.5,0.2,0 +8,4.4,2.9,1.4,0.2,0 +9,4.9,3.1,1.5,0.1,0 +10,5.4,3.7,1.5,0.2,0 +11,4.8,3.4,1.6,0.2,0 +12,4.8,3.0,1.4,0.1,0 +13,4.3,3.0,1.1,0.1,0 +14,5.8,4.0,1.2,0.2,0 +15,5.7,4.4,1.5,0.4,0 +16,5.4,3.9,1.3,0.4,0 +17,5.1,3.5,1.4,0.3,0 +18,5.7,3.8,1.7,0.3,0 +19,5.1,3.8,1.5,0.3,0 +20,5.4,3.4,1.7,0.2,0 +21,5.1,3.7,1.5,0.4,0 +22,4.6,3.6,1.0,0.2,0 +23,5.1,3.3,1.7,0.5,0 +24,4.8,3.4,1.9,0.2,0 +25,5.0,3.0,1.6,0.2,0 +26,5.0,3.4,1.6,0.4,0 +27,5.2,3.5,1.5,0.2,0 +28,5.2,3.4,1.4,0.2,0 +29,4.7,3.2,1.6,0.2,0 +30,4.8,3.1,1.6,0.2,0 +31,5.4,3.4,1.5,0.4,0 +32,5.2,4.1,1.5,0.1,0 +33,5.5,4.2,1.4,0.2,0 +34,4.9,3.1,1.5,0.2,0 +35,5.0,3.2,1.2,0.2,0 +36,5.5,3.5,1.3,0.2,0 +37,4.9,3.6,1.4,0.1,0 +38,4.4,3.0,1.3,0.2,0 +39,5.1,3.4,1.5,0.2,0 +40,5.0,3.5,1.3,0.3,0 +41,4.5,2.3,1.3,0.3,0 +42,4.4,3.2,1.3,0.2,0 +43,5.0,3.5,1.6,0.6,0 +44,5.1,3.8,1.9,0.4,0 +45,4.8,3.0,1.4,0.3,0 +46,5.1,3.8,1.6,0.2,0 +47,4.6,3.2,1.4,0.2,0 +48,5.3,3.7,1.5,0.2,0 +49,5.0,3.3,1.4,0.2,0 +50,7.0,3.2,4.7,1.4,1 +51,6.4,3.2,4.5,1.5,1 +52,6.9,3.1,4.9,1.5,1 +53,5.5,2.3,4.0,1.3,1 +54,6.5,2.8,4.6,1.5,1 +55,5.7,2.8,4.5,1.3,1 +56,6.3,3.3,4.7,1.6,1 +57,4.9,2.4,3.3,1.0,1 +58,6.6,2.9,4.6,1.3,1 +59,5.2,2.7,3.9,1.4,1 +60,5.0,2.0,3.5,1.0,1 +61,5.9,3.0,4.2,1.5,1 +62,6.0,2.2,4.0,1.0,1 +63,6.1,2.9,4.7,1.4,1 +64,5.6,2.9,3.6,1.3,1 +65,6.7,3.1,4.4,1.4,1 +66,5.6,3.0,4.5,1.5,1 +67,5.8,2.7,4.1,1.0,1 +68,6.2,2.2,4.5,1.5,1 +69,5.6,2.5,3.9,1.1,1 +70,5.9,3.2,4.8,1.8,1 +71,6.1,2.8,4.0,1.3,1 +72,6.3,2.5,4.9,1.5,1 +73,6.1,2.8,4.7,1.2,1 +74,6.4,2.9,4.3,1.3,1 +75,6.6,3.0,4.4,1.4,1 +76,6.8,2.8,4.8,1.4,1 +77,6.7,3.0,5.0,1.7,1 +78,6.0,2.9,4.5,1.5,1 +79,5.7,2.6,3.5,1.0,1 +80,5.5,2.4,3.8,1.1,1 +81,5.5,2.4,3.7,1.0,1 +82,5.8,2.7,3.9,1.2,1 +83,6.0,2.7,5.1,1.6,1 +84,5.4,3.0,4.5,1.5,1 +85,6.0,3.4,4.5,1.6,1 +86,6.7,3.1,4.7,1.5,1 +87,6.3,2.3,4.4,1.3,1 +88,5.6,3.0,4.1,1.3,1 +89,5.5,2.5,4.0,1.3,1 +90,5.5,2.6,4.4,1.2,1 +91,6.1,3.0,4.6,1.4,1 +92,5.8,2.6,4.0,1.2,1 +93,5.0,2.3,3.3,1.0,1 +94,5.6,2.7,4.2,1.3,1 +95,5.7,3.0,4.2,1.2,1 +96,5.7,2.9,4.2,1.3,1 +97,6.2,2.9,4.3,1.3,1 +98,5.1,2.5,3.0,1.1,1 +99,5.7,2.8,4.1,1.3,1 +100,6.3,3.3,6.0,2.5,2 +101,5.8,2.7,5.1,1.9,2 +102,7.1,3.0,5.9,2.1,2 +103,6.3,2.9,5.6,1.8,2 +104,6.5,3.0,5.8,2.2,2 +105,7.6,3.0,6.6,2.1,2 +106,4.9,2.5,4.5,1.7,2 +107,7.3,2.9,6.3,1.8,2 +108,6.7,2.5,5.8,1.8,2 +109,7.2,3.6,6.1,2.5,2 +110,6.5,3.2,5.1,2.0,2 +111,6.4,2.7,5.3,1.9,2 +112,6.8,3.0,5.5,2.1,2 +113,5.7,2.5,5.0,2.0,2 +114,5.8,2.8,5.1,2.4,2 +115,6.4,3.2,5.3,2.3,2 +116,6.5,3.0,5.5,1.8,2 +117,7.7,3.8,6.7,2.2,2 +118,7.7,2.6,6.9,2.3,2 +119,6.0,2.2,5.0,1.5,2 +120,6.9,3.2,5.7,2.3,2 +121,5.6,2.8,4.9,2.0,2 +122,7.7,2.8,6.7,2.0,2 +123,6.3,2.7,4.9,1.8,2 +124,6.7,3.3,5.7,2.1,2 +125,7.2,3.2,6.0,1.8,2 +126,6.2,2.8,4.8,1.8,2 +127,6.1,3.0,4.9,1.8,2 +128,6.4,2.8,5.6,2.1,2 +129,7.2,3.0,5.8,1.6,2 +130,7.4,2.8,6.1,1.9,2 +131,7.9,3.8,6.4,2.0,2 +132,6.4,2.8,5.6,2.2,2 +133,6.3,2.8,5.1,1.5,2 +134,6.1,2.6,5.6,1.4,2 +135,7.7,3.0,6.1,2.3,2 +136,6.3,3.4,5.6,2.4,2 +137,6.4,3.1,5.5,1.8,2 +138,6.0,3.0,4.8,1.8,2 +139,6.9,3.1,5.4,2.1,2 +140,6.7,3.1,5.6,2.4,2 +141,6.9,3.1,5.1,2.3,2 +142,5.8,2.7,5.1,1.9,2 +143,6.8,3.2,5.9,2.3,2 +144,6.7,3.3,5.7,2.5,2 +145,6.7,3.0,5.2,2.3,2 +146,6.3,2.5,5.0,1.9,2 +147,6.5,3.0,5.2,2.0,2 +148,6.2,3.4,5.4,2.3,2 +149,5.9,3.0,5.1,1.8,2 diff --git a/benchmark/tests/datasets/wine.csv b/benchmark/tests/datasets/wine.csv new file mode 100644 index 0000000..c80b363 --- /dev/null +++ b/benchmark/tests/datasets/wine.csv @@ -0,0 +1,179 @@ +,alcohol,malic_acid,ash,alcalinity_of_ash,magnesium,total_phenols,flavanoids,nonflavanoid_phenols,proanthocyanins,color_intensity,hue,od280/od315_of_diluted_wines,proline,class +0,14.23,1.71,2.43,15.6,127.0,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065.0,0 +1,13.2,1.78,2.14,11.2,100.0,2.65,2.76,0.26,1.28,4.38,1.05,3.4,1050.0,0 +2,13.16,2.36,2.67,18.6,101.0,2.8,3.24,0.3,2.81,5.68,1.03,3.17,1185.0,0 +3,14.37,1.95,2.5,16.8,113.0,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480.0,0 +4,13.24,2.59,2.87,21.0,118.0,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735.0,0 +5,14.2,1.76,2.45,15.2,112.0,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450.0,0 +6,14.39,1.87,2.45,14.6,96.0,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290.0,0 +7,14.06,2.15,2.61,17.6,121.0,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295.0,0 +8,14.83,1.64,2.17,14.0,97.0,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045.0,0 +9,13.86,1.35,2.27,16.0,98.0,2.98,3.15,0.22,1.85,7.22,1.01,3.55,1045.0,0 +10,14.1,2.16,2.3,18.0,105.0,2.95,3.32,0.22,2.38,5.75,1.25,3.17,1510.0,0 +11,14.12,1.48,2.32,16.8,95.0,2.2,2.43,0.26,1.57,5.0,1.17,2.82,1280.0,0 +12,13.75,1.73,2.41,16.0,89.0,2.6,2.76,0.29,1.81,5.6,1.15,2.9,1320.0,0 +13,14.75,1.73,2.39,11.4,91.0,3.1,3.69,0.43,2.81,5.4,1.25,2.73,1150.0,0 +14,14.38,1.87,2.38,12.0,102.0,3.3,3.64,0.29,2.96,7.5,1.2,3.0,1547.0,0 +15,13.63,1.81,2.7,17.2,112.0,2.85,2.91,0.3,1.46,7.3,1.28,2.88,1310.0,0 +16,14.3,1.92,2.72,20.0,120.0,2.8,3.14,0.33,1.97,6.2,1.07,2.65,1280.0,0 +17,13.83,1.57,2.62,20.0,115.0,2.95,3.4,0.4,1.72,6.6,1.13,2.57,1130.0,0 +18,14.19,1.59,2.48,16.5,108.0,3.3,3.93,0.32,1.86,8.7,1.23,2.82,1680.0,0 +19,13.64,3.1,2.56,15.2,116.0,2.7,3.03,0.17,1.66,5.1,0.96,3.36,845.0,0 +20,14.06,1.63,2.28,16.0,126.0,3.0,3.17,0.24,2.1,5.65,1.09,3.71,780.0,0 +21,12.93,3.8,2.65,18.6,102.0,2.41,2.41,0.25,1.98,4.5,1.03,3.52,770.0,0 +22,13.71,1.86,2.36,16.6,101.0,2.61,2.88,0.27,1.69,3.8,1.11,4.0,1035.0,0 +23,12.85,1.6,2.52,17.8,95.0,2.48,2.37,0.26,1.46,3.93,1.09,3.63,1015.0,0 +24,13.5,1.81,2.61,20.0,96.0,2.53,2.61,0.28,1.66,3.52,1.12,3.82,845.0,0 +25,13.05,2.05,3.22,25.0,124.0,2.63,2.68,0.47,1.92,3.58,1.13,3.2,830.0,0 +26,13.39,1.77,2.62,16.1,93.0,2.85,2.94,0.34,1.45,4.8,0.92,3.22,1195.0,0 +27,13.3,1.72,2.14,17.0,94.0,2.4,2.19,0.27,1.35,3.95,1.02,2.77,1285.0,0 +28,13.87,1.9,2.8,19.4,107.0,2.95,2.97,0.37,1.76,4.5,1.25,3.4,915.0,0 +29,14.02,1.68,2.21,16.0,96.0,2.65,2.33,0.26,1.98,4.7,1.04,3.59,1035.0,0 +30,13.73,1.5,2.7,22.5,101.0,3.0,3.25,0.29,2.38,5.7,1.19,2.71,1285.0,0 +31,13.58,1.66,2.36,19.1,106.0,2.86,3.19,0.22,1.95,6.9,1.09,2.88,1515.0,0 +32,13.68,1.83,2.36,17.2,104.0,2.42,2.69,0.42,1.97,3.84,1.23,2.87,990.0,0 +33,13.76,1.53,2.7,19.5,132.0,2.95,2.74,0.5,1.35,5.4,1.25,3.0,1235.0,0 +34,13.51,1.8,2.65,19.0,110.0,2.35,2.53,0.29,1.54,4.2,1.1,2.87,1095.0,0 +35,13.48,1.81,2.41,20.5,100.0,2.7,2.98,0.26,1.86,5.1,1.04,3.47,920.0,0 +36,13.28,1.64,2.84,15.5,110.0,2.6,2.68,0.34,1.36,4.6,1.09,2.78,880.0,0 +37,13.05,1.65,2.55,18.0,98.0,2.45,2.43,0.29,1.44,4.25,1.12,2.51,1105.0,0 +38,13.07,1.5,2.1,15.5,98.0,2.4,2.64,0.28,1.37,3.7,1.18,2.69,1020.0,0 +39,14.22,3.99,2.51,13.2,128.0,3.0,3.04,0.2,2.08,5.1,0.89,3.53,760.0,0 +40,13.56,1.71,2.31,16.2,117.0,3.15,3.29,0.34,2.34,6.13,0.95,3.38,795.0,0 +41,13.41,3.84,2.12,18.8,90.0,2.45,2.68,0.27,1.48,4.28,0.91,3.0,1035.0,0 +42,13.88,1.89,2.59,15.0,101.0,3.25,3.56,0.17,1.7,5.43,0.88,3.56,1095.0,0 +43,13.24,3.98,2.29,17.5,103.0,2.64,2.63,0.32,1.66,4.36,0.82,3.0,680.0,0 +44,13.05,1.77,2.1,17.0,107.0,3.0,3.0,0.28,2.03,5.04,0.88,3.35,885.0,0 +45,14.21,4.04,2.44,18.9,111.0,2.85,2.65,0.3,1.25,5.24,0.87,3.33,1080.0,0 +46,14.38,3.59,2.28,16.0,102.0,3.25,3.17,0.27,2.19,4.9,1.04,3.44,1065.0,0 +47,13.9,1.68,2.12,16.0,101.0,3.1,3.39,0.21,2.14,6.1,0.91,3.33,985.0,0 +48,14.1,2.02,2.4,18.8,103.0,2.75,2.92,0.32,2.38,6.2,1.07,2.75,1060.0,0 +49,13.94,1.73,2.27,17.4,108.0,2.88,3.54,0.32,2.08,8.9,1.12,3.1,1260.0,0 +50,13.05,1.73,2.04,12.4,92.0,2.72,3.27,0.17,2.91,7.2,1.12,2.91,1150.0,0 +51,13.83,1.65,2.6,17.2,94.0,2.45,2.99,0.22,2.29,5.6,1.24,3.37,1265.0,0 +52,13.82,1.75,2.42,14.0,111.0,3.88,3.74,0.32,1.87,7.05,1.01,3.26,1190.0,0 +53,13.77,1.9,2.68,17.1,115.0,3.0,2.79,0.39,1.68,6.3,1.13,2.93,1375.0,0 +54,13.74,1.67,2.25,16.4,118.0,2.6,2.9,0.21,1.62,5.85,0.92,3.2,1060.0,0 +55,13.56,1.73,2.46,20.5,116.0,2.96,2.78,0.2,2.45,6.25,0.98,3.03,1120.0,0 +56,14.22,1.7,2.3,16.3,118.0,3.2,3.0,0.26,2.03,6.38,0.94,3.31,970.0,0 +57,13.29,1.97,2.68,16.8,102.0,3.0,3.23,0.31,1.66,6.0,1.07,2.84,1270.0,0 +58,13.72,1.43,2.5,16.7,108.0,3.4,3.67,0.19,2.04,6.8,0.89,2.87,1285.0,0 +59,12.37,0.94,1.36,10.6,88.0,1.98,0.57,0.28,0.42,1.95,1.05,1.82,520.0,1 +60,12.33,1.1,2.28,16.0,101.0,2.05,1.09,0.63,0.41,3.27,1.25,1.67,680.0,1 +61,12.64,1.36,2.02,16.8,100.0,2.02,1.41,0.53,0.62,5.75,0.98,1.59,450.0,1 +62,13.67,1.25,1.92,18.0,94.0,2.1,1.79,0.32,0.73,3.8,1.23,2.46,630.0,1 +63,12.37,1.13,2.16,19.0,87.0,3.5,3.1,0.19,1.87,4.45,1.22,2.87,420.0,1 +64,12.17,1.45,2.53,19.0,104.0,1.89,1.75,0.45,1.03,2.95,1.45,2.23,355.0,1 +65,12.37,1.21,2.56,18.1,98.0,2.42,2.65,0.37,2.08,4.6,1.19,2.3,678.0,1 +66,13.11,1.01,1.7,15.0,78.0,2.98,3.18,0.26,2.28,5.3,1.12,3.18,502.0,1 +67,12.37,1.17,1.92,19.6,78.0,2.11,2.0,0.27,1.04,4.68,1.12,3.48,510.0,1 +68,13.34,0.94,2.36,17.0,110.0,2.53,1.3,0.55,0.42,3.17,1.02,1.93,750.0,1 +69,12.21,1.19,1.75,16.8,151.0,1.85,1.28,0.14,2.5,2.85,1.28,3.07,718.0,1 +70,12.29,1.61,2.21,20.4,103.0,1.1,1.02,0.37,1.46,3.05,0.906,1.82,870.0,1 +71,13.86,1.51,2.67,25.0,86.0,2.95,2.86,0.21,1.87,3.38,1.36,3.16,410.0,1 +72,13.49,1.66,2.24,24.0,87.0,1.88,1.84,0.27,1.03,3.74,0.98,2.78,472.0,1 +73,12.99,1.67,2.6,30.0,139.0,3.3,2.89,0.21,1.96,3.35,1.31,3.5,985.0,1 +74,11.96,1.09,2.3,21.0,101.0,3.38,2.14,0.13,1.65,3.21,0.99,3.13,886.0,1 +75,11.66,1.88,1.92,16.0,97.0,1.61,1.57,0.34,1.15,3.8,1.23,2.14,428.0,1 +76,13.03,0.9,1.71,16.0,86.0,1.95,2.03,0.24,1.46,4.6,1.19,2.48,392.0,1 +77,11.84,2.89,2.23,18.0,112.0,1.72,1.32,0.43,0.95,2.65,0.96,2.52,500.0,1 +78,12.33,0.99,1.95,14.8,136.0,1.9,1.85,0.35,2.76,3.4,1.06,2.31,750.0,1 +79,12.7,3.87,2.4,23.0,101.0,2.83,2.55,0.43,1.95,2.57,1.19,3.13,463.0,1 +80,12.0,0.92,2.0,19.0,86.0,2.42,2.26,0.3,1.43,2.5,1.38,3.12,278.0,1 +81,12.72,1.81,2.2,18.8,86.0,2.2,2.53,0.26,1.77,3.9,1.16,3.14,714.0,1 +82,12.08,1.13,2.51,24.0,78.0,2.0,1.58,0.4,1.4,2.2,1.31,2.72,630.0,1 +83,13.05,3.86,2.32,22.5,85.0,1.65,1.59,0.61,1.62,4.8,0.84,2.01,515.0,1 +84,11.84,0.89,2.58,18.0,94.0,2.2,2.21,0.22,2.35,3.05,0.79,3.08,520.0,1 +85,12.67,0.98,2.24,18.0,99.0,2.2,1.94,0.3,1.46,2.62,1.23,3.16,450.0,1 +86,12.16,1.61,2.31,22.8,90.0,1.78,1.69,0.43,1.56,2.45,1.33,2.26,495.0,1 +87,11.65,1.67,2.62,26.0,88.0,1.92,1.61,0.4,1.34,2.6,1.36,3.21,562.0,1 +88,11.64,2.06,2.46,21.6,84.0,1.95,1.69,0.48,1.35,2.8,1.0,2.75,680.0,1 +89,12.08,1.33,2.3,23.6,70.0,2.2,1.59,0.42,1.38,1.74,1.07,3.21,625.0,1 +90,12.08,1.83,2.32,18.5,81.0,1.6,1.5,0.52,1.64,2.4,1.08,2.27,480.0,1 +91,12.0,1.51,2.42,22.0,86.0,1.45,1.25,0.5,1.63,3.6,1.05,2.65,450.0,1 +92,12.69,1.53,2.26,20.7,80.0,1.38,1.46,0.58,1.62,3.05,0.96,2.06,495.0,1 +93,12.29,2.83,2.22,18.0,88.0,2.45,2.25,0.25,1.99,2.15,1.15,3.3,290.0,1 +94,11.62,1.99,2.28,18.0,98.0,3.02,2.26,0.17,1.35,3.25,1.16,2.96,345.0,1 +95,12.47,1.52,2.2,19.0,162.0,2.5,2.27,0.32,3.28,2.6,1.16,2.63,937.0,1 +96,11.81,2.12,2.74,21.5,134.0,1.6,0.99,0.14,1.56,2.5,0.95,2.26,625.0,1 +97,12.29,1.41,1.98,16.0,85.0,2.55,2.5,0.29,1.77,2.9,1.23,2.74,428.0,1 +98,12.37,1.07,2.1,18.5,88.0,3.52,3.75,0.24,1.95,4.5,1.04,2.77,660.0,1 +99,12.29,3.17,2.21,18.0,88.0,2.85,2.99,0.45,2.81,2.3,1.42,2.83,406.0,1 +100,12.08,2.08,1.7,17.5,97.0,2.23,2.17,0.26,1.4,3.3,1.27,2.96,710.0,1 +101,12.6,1.34,1.9,18.5,88.0,1.45,1.36,0.29,1.35,2.45,1.04,2.77,562.0,1 +102,12.34,2.45,2.46,21.0,98.0,2.56,2.11,0.34,1.31,2.8,0.8,3.38,438.0,1 +103,11.82,1.72,1.88,19.5,86.0,2.5,1.64,0.37,1.42,2.06,0.94,2.44,415.0,1 +104,12.51,1.73,1.98,20.5,85.0,2.2,1.92,0.32,1.48,2.94,1.04,3.57,672.0,1 +105,12.42,2.55,2.27,22.0,90.0,1.68,1.84,0.66,1.42,2.7,0.86,3.3,315.0,1 +106,12.25,1.73,2.12,19.0,80.0,1.65,2.03,0.37,1.63,3.4,1.0,3.17,510.0,1 +107,12.72,1.75,2.28,22.5,84.0,1.38,1.76,0.48,1.63,3.3,0.88,2.42,488.0,1 +108,12.22,1.29,1.94,19.0,92.0,2.36,2.04,0.39,2.08,2.7,0.86,3.02,312.0,1 +109,11.61,1.35,2.7,20.0,94.0,2.74,2.92,0.29,2.49,2.65,0.96,3.26,680.0,1 +110,11.46,3.74,1.82,19.5,107.0,3.18,2.58,0.24,3.58,2.9,0.75,2.81,562.0,1 +111,12.52,2.43,2.17,21.0,88.0,2.55,2.27,0.26,1.22,2.0,0.9,2.78,325.0,1 +112,11.76,2.68,2.92,20.0,103.0,1.75,2.03,0.6,1.05,3.8,1.23,2.5,607.0,1 +113,11.41,0.74,2.5,21.0,88.0,2.48,2.01,0.42,1.44,3.08,1.1,2.31,434.0,1 +114,12.08,1.39,2.5,22.5,84.0,2.56,2.29,0.43,1.04,2.9,0.93,3.19,385.0,1 +115,11.03,1.51,2.2,21.5,85.0,2.46,2.17,0.52,2.01,1.9,1.71,2.87,407.0,1 +116,11.82,1.47,1.99,20.8,86.0,1.98,1.6,0.3,1.53,1.95,0.95,3.33,495.0,1 +117,12.42,1.61,2.19,22.5,108.0,2.0,2.09,0.34,1.61,2.06,1.06,2.96,345.0,1 +118,12.77,3.43,1.98,16.0,80.0,1.63,1.25,0.43,0.83,3.4,0.7,2.12,372.0,1 +119,12.0,3.43,2.0,19.0,87.0,2.0,1.64,0.37,1.87,1.28,0.93,3.05,564.0,1 +120,11.45,2.4,2.42,20.0,96.0,2.9,2.79,0.32,1.83,3.25,0.8,3.39,625.0,1 +121,11.56,2.05,3.23,28.5,119.0,3.18,5.08,0.47,1.87,6.0,0.93,3.69,465.0,1 +122,12.42,4.43,2.73,26.5,102.0,2.2,2.13,0.43,1.71,2.08,0.92,3.12,365.0,1 +123,13.05,5.8,2.13,21.5,86.0,2.62,2.65,0.3,2.01,2.6,0.73,3.1,380.0,1 +124,11.87,4.31,2.39,21.0,82.0,2.86,3.03,0.21,2.91,2.8,0.75,3.64,380.0,1 +125,12.07,2.16,2.17,21.0,85.0,2.6,2.65,0.37,1.35,2.76,0.86,3.28,378.0,1 +126,12.43,1.53,2.29,21.5,86.0,2.74,3.15,0.39,1.77,3.94,0.69,2.84,352.0,1 +127,11.79,2.13,2.78,28.5,92.0,2.13,2.24,0.58,1.76,3.0,0.97,2.44,466.0,1 +128,12.37,1.63,2.3,24.5,88.0,2.22,2.45,0.4,1.9,2.12,0.89,2.78,342.0,1 +129,12.04,4.3,2.38,22.0,80.0,2.1,1.75,0.42,1.35,2.6,0.79,2.57,580.0,1 +130,12.86,1.35,2.32,18.0,122.0,1.51,1.25,0.21,0.94,4.1,0.76,1.29,630.0,2 +131,12.88,2.99,2.4,20.0,104.0,1.3,1.22,0.24,0.83,5.4,0.74,1.42,530.0,2 +132,12.81,2.31,2.4,24.0,98.0,1.15,1.09,0.27,0.83,5.7,0.66,1.36,560.0,2 +133,12.7,3.55,2.36,21.5,106.0,1.7,1.2,0.17,0.84,5.0,0.78,1.29,600.0,2 +134,12.51,1.24,2.25,17.5,85.0,2.0,0.58,0.6,1.25,5.45,0.75,1.51,650.0,2 +135,12.6,2.46,2.2,18.5,94.0,1.62,0.66,0.63,0.94,7.1,0.73,1.58,695.0,2 +136,12.25,4.72,2.54,21.0,89.0,1.38,0.47,0.53,0.8,3.85,0.75,1.27,720.0,2 +137,12.53,5.51,2.64,25.0,96.0,1.79,0.6,0.63,1.1,5.0,0.82,1.69,515.0,2 +138,13.49,3.59,2.19,19.5,88.0,1.62,0.48,0.58,0.88,5.7,0.81,1.82,580.0,2 +139,12.84,2.96,2.61,24.0,101.0,2.32,0.6,0.53,0.81,4.92,0.89,2.15,590.0,2 +140,12.93,2.81,2.7,21.0,96.0,1.54,0.5,0.53,0.75,4.6,0.77,2.31,600.0,2 +141,13.36,2.56,2.35,20.0,89.0,1.4,0.5,0.37,0.64,5.6,0.7,2.47,780.0,2 +142,13.52,3.17,2.72,23.5,97.0,1.55,0.52,0.5,0.55,4.35,0.89,2.06,520.0,2 +143,13.62,4.95,2.35,20.0,92.0,2.0,0.8,0.47,1.02,4.4,0.91,2.05,550.0,2 +144,12.25,3.88,2.2,18.5,112.0,1.38,0.78,0.29,1.14,8.21,0.65,2.0,855.0,2 +145,13.16,3.57,2.15,21.0,102.0,1.5,0.55,0.43,1.3,4.0,0.6,1.68,830.0,2 +146,13.88,5.04,2.23,20.0,80.0,0.98,0.34,0.4,0.68,4.9,0.58,1.33,415.0,2 +147,12.87,4.61,2.48,21.5,86.0,1.7,0.65,0.47,0.86,7.65,0.54,1.86,625.0,2 +148,13.32,3.24,2.38,21.5,92.0,1.93,0.76,0.45,1.25,8.42,0.55,1.62,650.0,2 +149,13.08,3.9,2.36,21.5,113.0,1.41,1.39,0.34,1.14,9.4,0.57,1.33,550.0,2 +150,13.5,3.12,2.62,24.0,123.0,1.4,1.57,0.22,1.25,8.6,0.59,1.3,500.0,2 +151,12.79,2.67,2.48,22.0,112.0,1.48,1.36,0.24,1.26,10.8,0.48,1.47,480.0,2 +152,13.11,1.9,2.75,25.5,116.0,2.2,1.28,0.26,1.56,7.1,0.61,1.33,425.0,2 +153,13.23,3.3,2.28,18.5,98.0,1.8,0.83,0.61,1.87,10.52,0.56,1.51,675.0,2 +154,12.58,1.29,2.1,20.0,103.0,1.48,0.58,0.53,1.4,7.6,0.58,1.55,640.0,2 +155,13.17,5.19,2.32,22.0,93.0,1.74,0.63,0.61,1.55,7.9,0.6,1.48,725.0,2 +156,13.84,4.12,2.38,19.5,89.0,1.8,0.83,0.48,1.56,9.01,0.57,1.64,480.0,2 +157,12.45,3.03,2.64,27.0,97.0,1.9,0.58,0.63,1.14,7.5,0.67,1.73,880.0,2 +158,14.34,1.68,2.7,25.0,98.0,2.8,1.31,0.53,2.7,13.0,0.57,1.96,660.0,2 +159,13.48,1.67,2.64,22.5,89.0,2.6,1.1,0.52,2.29,11.75,0.57,1.78,620.0,2 +160,12.36,3.83,2.38,21.0,88.0,2.3,0.92,0.5,1.04,7.65,0.56,1.58,520.0,2 +161,13.69,3.26,2.54,20.0,107.0,1.83,0.56,0.5,0.8,5.88,0.96,1.82,680.0,2 +162,12.85,3.27,2.58,22.0,106.0,1.65,0.6,0.6,0.96,5.58,0.87,2.11,570.0,2 +163,12.96,3.45,2.35,18.5,106.0,1.39,0.7,0.4,0.94,5.28,0.68,1.75,675.0,2 +164,13.78,2.76,2.3,22.0,90.0,1.35,0.68,0.41,1.03,9.58,0.7,1.68,615.0,2 +165,13.73,4.36,2.26,22.5,88.0,1.28,0.47,0.52,1.15,6.62,0.78,1.75,520.0,2 +166,13.45,3.7,2.6,23.0,111.0,1.7,0.92,0.43,1.46,10.68,0.85,1.56,695.0,2 +167,12.82,3.37,2.3,19.5,88.0,1.48,0.66,0.4,0.97,10.26,0.72,1.75,685.0,2 +168,13.58,2.58,2.69,24.5,105.0,1.55,0.84,0.39,1.54,8.66,0.74,1.8,750.0,2 +169,13.4,4.6,2.86,25.0,112.0,1.98,0.96,0.27,1.11,8.5,0.67,1.92,630.0,2 +170,12.2,3.03,2.32,19.0,96.0,1.25,0.49,0.4,0.73,5.5,0.66,1.83,510.0,2 +171,12.77,2.39,2.28,19.5,86.0,1.39,0.51,0.48,0.64,9.899999,0.57,1.63,470.0,2 +172,14.16,2.51,2.48,20.0,91.0,1.68,0.7,0.44,1.24,9.7,0.62,1.71,660.0,2 +173,13.71,5.65,2.45,20.5,95.0,1.68,0.61,0.52,1.06,7.7,0.64,1.74,740.0,2 +174,13.4,3.91,2.48,23.0,102.0,1.8,0.75,0.43,1.41,7.3,0.7,1.56,750.0,2 +175,13.27,4.28,2.26,20.0,120.0,1.59,0.69,0.43,1.35,10.2,0.59,1.56,835.0,2 +176,13.17,2.59,2.37,20.0,120.0,1.65,0.68,0.53,1.46,9.3,0.6,1.62,840.0,2 +177,14.13,4.1,2.74,24.5,96.0,2.05,0.76,0.56,1.35,9.2,0.61,1.6,560.0,2 diff --git a/benchmark/tests/results/a.json b/benchmark/tests/results/a.json deleted file mode 100644 index e69de29..0000000 diff --git a/benchmark/tests/results/b.json b/benchmark/tests/results/b.json deleted file mode 100644 index e69de29..0000000 diff --git a/benchmark/tests/results/best_results_accuracy_STree.json b/benchmark/tests/results/best_results_accuracy_STree.json new file mode 100644 index 0000000..ece4a7a --- /dev/null +++ b/benchmark/tests/results/best_results_accuracy_STree.json @@ -0,0 +1 @@ +{"balance-scale": [0.98, {"splitter": "iwss", "max_features": "auto"}, "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"], "balloons": [0.86, {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0, "multiclass_strategy": "ovr"}, "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"]} \ No newline at end of file diff --git a/benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json new file mode 100644 index 0000000..6704808 --- /dev/null +++ b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json @@ -0,0 +1,55 @@ +{ + "score_name": "accuracy", + "model": "STree", + "stratified": false, + "folds": 5, + "date": "2021-09-30", + "time": "11:42:07", + "duration": 624.2505249977112, + "seeds": [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1], + "platform": "iMac27", + "results": [ + { + "dataset": "balance-scale", + "samples": 625, + "features": 4, + "classes": 3, + "hyperparameters": { + "C": 10000.0, + "gamma": 0.1, + "kernel": "rbf", + "max_iter": 10000.0, + "multiclass_strategy": "ovr" + }, + "nodes": 7.0, + "leaves": 4.0, + "depth": 3.0, + "score": 0.97056, + "score_std": 0.015046806970251203, + "time": 0.01404867172241211, + "time_std": 0.002026269126958884 + }, + { + "dataset": "balloons", + "samples": 16, + "features": 4, + "classes": 2, + "hyperparameters": { + "C": 7, + "gamma": 0.1, + "kernel": "rbf", + "max_iter": 10000.0, + "multiclass_strategy": "ovr" + }, + "nodes": 3.0, + "leaves": 2.0, + "depth": 2.0, + "score": 0.86, + "score_std": 0.28501461950807594, + "time": 0.0008541679382324218, + "time_std": 3.629469326417878e-5 + } + ], + "title": "With gridsearched hyperparameters", + "version": "1.2.3" +} diff --git a/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json index dc5a36b..bdf4003 100644 --- a/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json +++ b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json @@ -1,859 +1,49 @@ { - "score_name": "accuracy", - "model": "STree", - "stratified": false, - "folds": 5, - "date": "2021-10-27", - "time": "09:40:40", - "duration": 3395.009148836136, - "seeds": [ - 57, - 31, - 1714, - 17, - 23, - 79, - 83, - 97, - 7, - 1 - ], - "platform": "iMac27", - "results": [ - { - "dataset": "balance-scale", - "samples": 625, - "features": 4, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 11.08, - "leaves": 5.9, - "depth": 5.9, - "score": NaN, - "score_std": NaN, - "time": 0.28520655155181884, - "time_std": 0.06031593282605064 - }, - { - "dataset": "balloons", - "samples": 16, - "features": 4, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 4.12, - "leaves": 2.56, - "depth": 2.56, - "score": 0.695, - "score_std": 0.2756860130252853, - "time": 0.021201000213623047, - "time_std": 0.003526023309468471 - }, - { - "dataset": "breast-cancer-wisc-diag", - "samples": 569, - "features": 30, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 4.5, - "leaves": 2.74, - "depth": 2.8, - "score": NaN, - "score_std": NaN, - "time": 0.8052136468887329, - "time_std": 0.07564554278016206 - }, - { - "dataset": "breast-cancer-wisc-prog", - "samples": 198, - "features": 33, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.4, - "leaves": 1.2, - "depth": 1.2, - "score": 0.7626538461538462, - "score_std": 0.06885699313039004, - "time": 0.12720062732696533, - "time_std": 0.04950349592657325 - }, - { - "dataset": "breast-cancer-wisc", - "samples": 699, - "features": 9, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 3.8, - "leaves": 2.4, - "depth": 2.38, - "score": 0.9466382322713258, - "score_std": 0.016639565009802557, - "time": 0.28473299503326416, - "time_std": 0.03698680751837435 - }, - { - "dataset": "breast-cancer", - "samples": 286, - "features": 9, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.7028009679370839, - "score_std": 0.04595046555906242, - "time": 0.036680173873901364, - "time_std": 0.0007553549684553433 - }, - { - "dataset": "cardiotocography-10clases", - "samples": 2126, - "features": 21, - "classes": 10, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 8.16, - "leaves": 4.12, - "depth": 8.88, - "score": NaN, - "score_std": NaN, - "time": 7.2233285808563235, - "time_std": 2.3604767394664794 - }, - { - "dataset": "cardiotocography-3clases", - "samples": 2126, - "features": 21, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 7.5, - "leaves": 4.22, - "depth": 4.04, - "score": NaN, - "score_std": NaN, - "time": 10.057809262275695, - "time_std": 1.1201468189930344 - }, - { - "dataset": "conn-bench-sonar-mines-rocks", - "samples": 208, - "features": 60, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 9.18, - "leaves": 5.02, - "depth": 4.34, - "score": NaN, - "score_std": NaN, - "time": 1.0514076519012452, - "time_std": 0.24663376756212574 - }, - { - "dataset": "cylinder-bands", - "samples": 512, - "features": 35, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 2.34, - "leaves": 1.66, - "depth": 1.66, - "score": NaN, - "score_std": NaN, - "time": 0.498666844367981, - "time_std": 0.24064363337021621 - }, - { - "dataset": "dermatology", - "samples": 366, - "features": 34, - "classes": 6, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 4.12, - "leaves": 2.2, - "depth": 7.02, - "score": NaN, - "score_std": NaN, - "time": 1.1228968811035156, - "time_std": 0.29292156787589296 - }, - { - "dataset": "echocardiogram", - "samples": 131, - "features": 10, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 3.46, - "leaves": 2.2, - "depth": 2.26, - "score": NaN, - "score_std": NaN, - "time": 0.07180672168731689, - "time_std": 0.04348555603761243 - }, - { - "dataset": "fertility", - "samples": 100, - "features": 9, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.88, - "score_std": 0.0547722557505166, - "time": 0.028572516441345217, - "time_std": 0.004158940793946356 - }, - { - "dataset": "haberman-survival", - "samples": 306, - "features": 3, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 3.44, - "leaves": 2.16, - "depth": 2.28, - "score": NaN, - "score_std": NaN, - "time": 0.0562580680847168, - "time_std": 0.02979371654044955 - }, - { - "dataset": "heart-hungarian", - "samples": 294, - "features": 12, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 4.78, - "leaves": 2.86, - "depth": 2.78, - "score": NaN, - "score_std": NaN, - "time": 0.14676546573638916, - "time_std": 0.09107633071497274 - }, - { - "dataset": "hepatitis", - "samples": 155, - "features": 19, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.7935483870967742, - "score_std": 0.07126039365927266, - "time": 0.05298082828521729, - "time_std": 0.003874758115245114 - }, - { - "dataset": "ilpd-indian-liver", - "samples": 583, - "features": 9, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.7135661656351313, - "score_std": 0.038048725185040336, - "time": 0.16761460781097412, - "time_std": 0.0038467797660095785 - }, - { - "dataset": "ionosphere", - "samples": 351, - "features": 33, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 3.72, - "leaves": 2.36, - "depth": 2.34, - "score": 0.7544265593561369, - "score_std": 0.04933029218981169, - "time": 0.44574220180511476, - "time_std": 0.11355314876610266 - }, - { - "dataset": "iris", - "samples": 150, - "features": 4, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 5.04, - "leaves": 3.02, - "depth": 3.02, - "score": 0.95, - "score_std": 0.03415650255319865, - "time": 0.05279052257537842, - "time_std": 0.004794317991174971 - }, - { - "dataset": "led-display", - "samples": 1000, - "features": 7, - "classes": 10, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 18.86, - "leaves": 9.64, - "depth": 7.0, - "score": NaN, - "score_std": NaN, - "time": 2.398168988227844, - "time_std": 0.9011693293327879 - }, - { - "dataset": "libras", - "samples": 360, - "features": 90, - "classes": 15, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 2.88, - "leaves": 1.46, - "depth": 9.78, - "score": NaN, - "score_std": NaN, - "time": 5.12455846786499, - "time_std": 2.3835694032560912 - }, - { - "dataset": "low-res-spect", - "samples": 531, - "features": 100, - "classes": 9, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 15.3, - "leaves": 7.9, - "depth": 7.68, - "score": NaN, - "score_std": NaN, - "time": 5.045088052749634, - "time_std": 1.3873869849574738 - }, - { - "dataset": "lymphography", - "samples": 148, - "features": 18, - "classes": 4, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 2.86, - "leaves": 1.92, - "depth": 1.98, - "score": NaN, - "score_std": NaN, - "time": 0.13686522483825683, - "time_std": 0.05176593741708166 - }, - { - "dataset": "mammographic", - "samples": 961, - "features": 5, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 3.0, - "leaves": 2.0, - "depth": 2.0, - "score": 0.8213433721934368, - "score_std": 0.023399701915177804, - "time": 0.6547147846221923, - "time_std": 0.01715971877325126 - }, - { - "dataset": "molec-biol-promoter", - "samples": 106, - "features": 57, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 9.12, - "leaves": 4.98, - "depth": 4.2, - "score": NaN, - "score_std": NaN, - "time": 0.7287868213653564, - "time_std": 0.17832306735655218 - }, - { - "dataset": "musk-1", - "samples": 476, - "features": 166, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 6.64, - "leaves": 3.8, - "depth": 3.52, - "score": NaN, - "score_std": NaN, - "time": 3.558695454597473, - "time_std": 1.3190187943298837 - }, - { - "dataset": "oocytes_merluccius_nucleus_4d", - "samples": 1022, - "features": 41, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.6702563366810138, - "score_std": 0.024253557618839905, - "time": 1.9674934434890747, - "time_std": 0.06688110747728285 - }, - { - "dataset": "oocytes_merluccius_states_2f", - "samples": 1022, - "features": 25, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 9.5, - "leaves": 5.18, - "depth": 4.52, - "score": NaN, - "score_std": NaN, - "time": 3.2290832376480103, - "time_std": 0.6823102916067391 - }, - { - "dataset": "oocytes_trisopterus_nucleus_2f", - "samples": 912, - "features": 25, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 8.02, - "leaves": 4.48, - "depth": 3.88, - "score": NaN, - "score_std": NaN, - "time": 2.1974784898757935, - "time_std": 0.49544544299207494 - }, - { - "dataset": "oocytes_trisopterus_states_5b", - "samples": 912, - "features": 32, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 4.8, - "leaves": 2.88, - "depth": 3.0, - "score": NaN, - "score_std": NaN, - "time": 2.3718439626693724, - "time_std": 0.3733733951135386 - }, - { - "dataset": "parkinsons", - "samples": 195, - "features": 22, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 4.94, - "leaves": 2.96, - "depth": 3.0, - "score": NaN, - "score_std": NaN, - "time": 0.21737953186035155, - "time_std": 0.023372055483572327 - }, - { - "dataset": "pima", - "samples": 768, - "features": 8, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 4.1, - "leaves": 2.5, - "depth": 3.02, - "score": NaN, - "score_std": NaN, - "time": 0.5491303777694703, - "time_std": 0.11633868088180814 - }, - { - "dataset": "pittsburg-bridges-MATERIAL", - "samples": 106, - "features": 7, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 2.96, - "leaves": 1.98, - "depth": 1.98, - "score": 0.7452813852813851, - "score_std": 0.08866160199698558, - "time": 0.05663308143615722, - "time_std": 0.007940314386137024 - }, - { - "dataset": "pittsburg-bridges-REL-L", - "samples": 103, - "features": 7, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.66, - "leaves": 1.28, - "depth": 1.64, - "score": NaN, - "score_std": NaN, - "time": 0.044896450042724606, - "time_std": 0.028028274876593307 - }, - { - "dataset": "pittsburg-bridges-SPAN", - "samples": 92, - "features": 7, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 3.16, - "leaves": 1.82, - "depth": 3.6, - "score": NaN, - "score_std": NaN, - "time": 0.09178715705871582, - "time_std": 0.035767686272824714 - }, - { - "dataset": "pittsburg-bridges-T-OR-D", - "samples": 102, - "features": 7, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.8628095238095238, - "score_std": 0.0747571882042698, - "time": 0.024580354690551757, - "time_std": 0.002032839785047058 - }, - { - "dataset": "planning", - "samples": 182, - "features": 12, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.7143693693693695, - "score_std": 0.0715459100205182, - "time": 0.04235292434692383, - "time_std": 0.0020579522623622084 - }, - { - "dataset": "post-operative", - "samples": 90, - "features": 8, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 2.72, - "leaves": 1.78, - "depth": 2.18, - "score": NaN, - "score_std": NaN, - "time": 0.1600242519378662, - "time_std": 0.056587742131730484 - }, - { - "dataset": "seeds", - "samples": 210, - "features": 7, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 9.04, - "leaves": 5.02, - "depth": 4.2, - "score": 0.8995238095238095, - "score_std": 0.04862975023285386, - "time": 0.1732833480834961, - "time_std": 0.022076642064504184 - }, - { - "dataset": "statlog-australian-credit", - "samples": 690, - "features": 14, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.6782608695652174, - "score_std": 0.03904983647915211, - "time": 0.2839461183547974, - "time_std": 0.004584262988941458 - }, - { - "dataset": "statlog-german-credit", - "samples": 1000, - "features": 24, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.7000000000000002, - "score_std": 0.028017851452243787, - "time": 0.84711181640625, - "time_std": 0.0059129439587605696 - }, - { - "dataset": "statlog-heart", - "samples": 270, - "features": 13, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 5.3, - "leaves": 3.1, - "depth": 3.44, - "score": NaN, - "score_std": NaN, - "time": 0.18118916511535643, - "time_std": 0.034632531864398554 - }, - { - "dataset": "statlog-image", - "samples": 2310, - "features": 18, - "classes": 7, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 10.34, - "leaves": 5.52, - "depth": 6.08, - "score": NaN, - "score_std": NaN, - "time": 8.48775242805481, - "time_std": 0.9260743696103542 - }, - { - "dataset": "statlog-vehicle", - "samples": 846, - "features": 18, - "classes": 4, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 15.26, - "leaves": 7.98, - "depth": 6.62, - "score": NaN, - "score_std": NaN, - "time": 1.8453552770614623, - "time_std": 0.3317876287778824 - }, - { - "dataset": "synthetic-control", - "samples": 600, - "features": 60, - "classes": 6, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 20.08, - "leaves": 10.42, - "depth": 6.46, - "score": NaN, - "score_std": NaN, - "time": 3.9311794376373292, - "time_std": 0.5379200359100783 - }, - { - "dataset": "tic-tac-toe", - "samples": 958, - "features": 9, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.6534505890052357, - "score_std": 0.028021260679892277, - "time": 0.2912741708755493, - "time_std": 0.003530730041693393 - }, - { - "dataset": "vertebral-column-2clases", - "samples": 310, - "features": 6, - "classes": 2, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 3.72, - "leaves": 2.36, - "depth": 2.34, - "score": 0.8412903225806452, - "score_std": 0.045202745949944154, - "time": 0.16892774105072023, - "time_std": 0.023473559642938596 - }, - { - "dataset": "wine", - "samples": 178, - "features": 13, - "classes": 3, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 7.62, - "leaves": 4.3, - "depth": 3.62, - "score": NaN, - "score_std": NaN, - "time": 0.16751108169555665, - "time_std": 0.02962241075170574 - }, - { - "dataset": "zoo", - "samples": 101, - "features": 16, - "classes": 7, - "hyperparameters": { - "splitter": "iwss", - "max_features": "auto" - }, - "nodes": 9.62, - "leaves": 5.16, - "depth": 6.58, - "score": NaN, - "score_std": NaN, - "time": 0.29728739261627196, - "time_std": 0.05456727302178703 - } - ], - "title": "default", - "version": "1.2.3" -} \ No newline at end of file + "score_name": "accuracy", + "model": "STree", + "stratified": false, + "folds": 5, + "date": "2021-10-27", + "time": "09:40:40", + "duration": 3395.009148836136, + "seeds": [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1], + "platform": "iMac27", + "results": [ + { + "dataset": "balance-scale", + "samples": 625, + "features": 4, + "classes": 3, + "hyperparameters": { + "splitter": "iwss", + "max_features": "auto" + }, + "nodes": 11.08, + "leaves": 5.9, + "depth": 5.9, + "score": 0.98, + "score_std": 0.001, + "time": 0.28520655155181884, + "time_std": 0.06031593282605064 + }, + { + "dataset": "balloons", + "samples": 16, + "features": 4, + "classes": 2, + "hyperparameters": { + "splitter": "iwss", + "max_features": "auto" + }, + "nodes": 4.12, + "leaves": 2.56, + "depth": 2.56, + "score": 0.695, + "score_std": 0.2756860130252853, + "time": 0.021201000213623047, + "time_std": 0.003526023309468471 + } + ], + "title": "default A", + "version": "1.2.3" +} diff --git a/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json b/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json index 362c276..1ca939e 100644 --- a/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json +++ b/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json @@ -1,859 +1,49 @@ { - "score_name": "accuracy", - "model": "STree", - "stratified": false, - "folds": 5, - "date": "2021-11-01", - "time": "19:17:07", - "duration": 4115.042420864105, - "seeds": [ - 57, - 31, - 1714, - 17, - 23, - 79, - 83, - 97, - 7, - 1 - ], - "platform": "macbook-pro", - "results": [ - { - "dataset": "balance-scale", - "samples": 625, - "features": 4, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 18.78, - "leaves": 9.88, - "depth": 5.9, - "score": NaN, - "score_std": NaN, - "time": 0.23330417156219482, - "time_std": 0.048087665954193885 - }, - { - "dataset": "balloons", - "samples": 16, - "features": 4, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 4.72, - "leaves": 2.86, - "depth": 2.78, - "score": 0.5566666666666668, - "score_std": 0.2941277122460771, - "time": 0.021352062225341795, - "time_std": 0.005808742398555902 - }, - { - "dataset": "breast-cancer-wisc-diag", - "samples": 569, - "features": 30, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.34, - "leaves": 3.66, - "depth": 3.5, - "score": NaN, - "score_std": NaN, - "time": 0.401257061958313, - "time_std": 0.07412488954035189 - }, - { - "dataset": "breast-cancer-wisc-prog", - "samples": 198, - "features": 33, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.72, - "leaves": 1.36, - "depth": 1.36, - "score": 0.7621794871794871, - "score_std": 0.06710004600274146, - "time": 0.11651344776153565, - "time_std": 0.06591102690356337 - }, - { - "dataset": "breast-cancer-wisc", - "samples": 699, - "features": 9, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 5.8, - "leaves": 3.4, - "depth": 3.36, - "score": 0.9592250770811923, - "score_std": 0.014554348848704999, - "time": 0.1478545618057251, - "time_std": 0.020419480773263374 - }, - { - "dataset": "breast-cancer", - "samples": 286, - "features": 9, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.16, - "leaves": 3.56, - "depth": 3.42, - "score": NaN, - "score_std": NaN, - "time": 0.11039722442626954, - "time_std": 0.06210483736075941 - }, - { - "dataset": "cardiotocography-10clases", - "samples": 2126, - "features": 21, - "classes": 10, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 41.52, - "leaves": 21.14, - "depth": 8.8, - "score": NaN, - "score_std": NaN, - "time": 3.9766879796981813, - "time_std": 0.9151663540578105 - }, - { - "dataset": "cardiotocography-3clases", - "samples": 2126, - "features": 21, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 20.88, - "leaves": 10.9, - "depth": 6.0, - "score": NaN, - "score_std": NaN, - "time": 1.657118821144104, - "time_std": 0.32172103166558413 - }, - { - "dataset": "conn-bench-sonar-mines-rocks", - "samples": 208, - "features": 60, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 13.18, - "leaves": 7.08, - "depth": 4.66, - "score": NaN, - "score_std": NaN, - "time": 1.3676620960235595, - "time_std": 0.5325323156595473 - }, - { - "dataset": "cylinder-bands", - "samples": 512, - "features": 35, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 4.52, - "leaves": 2.76, - "depth": 2.68, - "score": 0.6638568437083572, - "score_std": 0.03712163130225706, - "time": 0.37873063564300535, - "time_std": 0.183016784550629 - }, - { - "dataset": "dermatology", - "samples": 366, - "features": 34, - "classes": 6, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 18.4, - "leaves": 9.7, - "depth": 6.74, - "score": 0.9587338022954462, - "score_std": 0.024233083712969238, - "time": 1.5716090679168702, - "time_std": 0.5530620641812005 - }, - { - "dataset": "echocardiogram", - "samples": 131, - "features": 10, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 3.04, - "leaves": 2.02, - "depth": 2.02, - "score": 0.855156695156695, - "score_std": 0.06266151037590971, - "time": 0.05919990062713623, - "time_std": 0.011073717584111756 - }, - { - "dataset": "fertility", - "samples": 100, - "features": 9, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.88, - "score_std": 0.0547722557505166, - "time": 0.02746262550354004, - "time_std": 0.01040171957861759 - }, - { - "dataset": "haberman-survival", - "samples": 306, - "features": 3, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.92, - "leaves": 1.36, - "depth": 1.84, - "score": NaN, - "score_std": NaN, - "time": 0.021888227462768556, - "time_std": 0.013721911772333317 - }, - { - "dataset": "heart-hungarian", - "samples": 294, - "features": 12, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.36, - "leaves": 3.68, - "depth": 3.4, - "score": 0.8037463471654003, - "score_std": 0.048507217236332716, - "time": 0.1568096923828125, - "time_std": 0.04548054341259107 - }, - { - "dataset": "hepatitis", - "samples": 155, - "features": 19, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 4.26, - "leaves": 2.62, - "depth": 2.38, - "score": NaN, - "score_std": NaN, - "time": 0.13556980609893798, - "time_std": 0.09738847551268014 - }, - { - "dataset": "ilpd-indian-liver", - "samples": 583, - "features": 9, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.7135661656351313, - "score_std": 0.038048725185040336, - "time": 0.04697585105895996, - "time_std": 0.009319869024571067 - }, - { - "dataset": "ionosphere", - "samples": 351, - "features": 33, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.62, - "leaves": 3.8, - "depth": 3.68, - "score": NaN, - "score_std": NaN, - "time": 0.43261568069458006, - "time_std": 0.1203589287143651 - }, - { - "dataset": "iris", - "samples": 150, - "features": 4, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 5.0, - "leaves": 3.0, - "depth": 3.0, - "score": 0.9553333333333331, - "score_std": 0.0295221197673127, - "time": 0.05880905151367188, - "time_std": 0.030683507003767284 - }, - { - "dataset": "led-display", - "samples": 1000, - "features": 7, - "classes": 10, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 24.68, - "leaves": 12.82, - "depth": 6.04, - "score": NaN, - "score_std": NaN, - "time": 1.0960806465148927, - "time_std": 0.2569562117525986 - }, - { - "dataset": "libras", - "samples": 360, - "features": 90, - "classes": 15, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 15.94, - "leaves": 8.06, - "depth": 9.98, - "score": NaN, - "score_std": NaN, - "time": 7.918476514816284, - "time_std": 4.523357567107953 - }, - { - "dataset": "low-res-spect", - "samples": 531, - "features": 100, - "classes": 9, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 21.02, - "leaves": 10.82, - "depth": 7.5, - "score": NaN, - "score_std": NaN, - "time": 5.516749286651612, - "time_std": 1.5967287706922784 - }, - { - "dataset": "lymphography", - "samples": 148, - "features": 18, - "classes": 4, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 4.24, - "leaves": 2.62, - "depth": 2.42, - "score": 0.6430574712643677, - "score_std": 0.11622985095663692, - "time": 0.15373097419738768, - "time_std": 0.09630802209142511 - }, - { - "dataset": "mammographic", - "samples": 961, - "features": 5, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 3.08, - "leaves": 2.04, - "depth": 2.04, - "score": 0.8172760146804835, - "score_std": 0.02227227188271779, - "time": 0.08565653800964355, - "time_std": 0.010440249149778561 - }, - { - "dataset": "molec-biol-promoter", - "samples": 106, - "features": 57, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 11.18, - "leaves": 6.08, - "depth": 4.2, - "score": NaN, - "score_std": NaN, - "time": 0.8062765884399414, - "time_std": 0.3043906987511426 - }, - { - "dataset": "musk-1", - "samples": 476, - "features": 166, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 15.04, - "leaves": 8.02, - "depth": 5.06, - "score": 0.739747807017544, - "score_std": 0.049023720603262086, - "time": 8.218964619636536, - "time_std": 24.22936251192802 - }, - { - "dataset": "oocytes_merluccius_nucleus_4d", - "samples": 1022, - "features": 41, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.8, - "leaves": 1.4, - "depth": 1.4, - "score": 0.6747513151602104, - "score_std": 0.02805948085218652, - "time": 0.3347061347961426, - "time_std": 0.14471256643972377 - }, - { - "dataset": "oocytes_merluccius_states_2f", - "samples": 1022, - "features": 25, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 10.92, - "leaves": 5.92, - "depth": 4.6, - "score": NaN, - "score_std": NaN, - "time": 0.8252322387695312, - "time_std": 0.1689867212720567 - }, - { - "dataset": "oocytes_trisopterus_nucleus_2f", - "samples": 912, - "features": 25, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 9.78, - "leaves": 5.36, - "depth": 4.58, - "score": NaN, - "score_std": NaN, - "time": 0.6476831912994385, - "time_std": 0.2510785700135029 - }, - { - "dataset": "oocytes_trisopterus_states_5b", - "samples": 912, - "features": 32, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.88, - "leaves": 3.92, - "depth": 3.96, - "score": NaN, - "score_std": NaN, - "time": 0.6995281982421875, - "time_std": 0.20416980252110092 - }, - { - "dataset": "parkinsons", - "samples": 195, - "features": 22, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.04, - "leaves": 3.52, - "depth": 3.4, - "score": 0.8656410256410255, - "score_std": 0.04715718536440063, - "time": 0.2024482822418213, - "time_std": 0.041679247929405305 - }, - { - "dataset": "pima", - "samples": 768, - "features": 8, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 3.36, - "leaves": 2.18, - "depth": 2.18, - "score": 0.7555699855699856, - "score_std": 0.026071249124277357, - "time": 0.11018041133880616, - "time_std": 0.015981550148259464 - }, - { - "dataset": "pittsburg-bridges-MATERIAL", - "samples": 106, - "features": 7, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 4.4, - "leaves": 2.66, - "depth": 2.86, - "score": NaN, - "score_std": NaN, - "time": 0.08267138481140136, - "time_std": 0.04320844494910074 - }, - { - "dataset": "pittsburg-bridges-REL-L", - "samples": 103, - "features": 7, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.12, - "leaves": 3.54, - "depth": 3.32, - "score": NaN, - "score_std": NaN, - "time": 0.10082945346832276, - "time_std": 0.030223867202597298 - }, - { - "dataset": "pittsburg-bridges-SPAN", - "samples": 92, - "features": 7, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 8.14, - "leaves": 4.54, - "depth": 3.94, - "score": NaN, - "score_std": NaN, - "time": 0.1462726402282715, - "time_std": 0.051240780130172595 - }, - { - "dataset": "pittsburg-bridges-T-OR-D", - "samples": 102, - "features": 7, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.8628095238095238, - "score_std": 0.0747571882042698, - "time": 0.021972088813781737, - "time_std": 0.003819453019423127 - }, - { - "dataset": "planning", - "samples": 182, - "features": 12, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.7143693693693695, - "score_std": 0.0715459100205182, - "time": 0.04498013973236084, - "time_std": 0.010887584800643972 - }, - { - "dataset": "post-operative", - "samples": 90, - "features": 8, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.24, - "leaves": 1.1, - "depth": 1.2, - "score": NaN, - "score_std": NaN, - "time": 0.030997161865234376, - "time_std": 0.010812193782303116 - }, - { - "dataset": "seeds", - "samples": 210, - "features": 7, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 8.16, - "leaves": 4.58, - "depth": 4.12, - "score": 0.8895238095238095, - "score_std": 0.05254519704431894, - "time": 0.14443633556365967, - "time_std": 0.027390718772962643 - }, - { - "dataset": "statlog-australian-credit", - "samples": 690, - "features": 14, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 1.0, - "leaves": 1.0, - "depth": 1.0, - "score": 0.6782608695652174, - "score_std": 0.03904983647915211, - "time": 0.0670243501663208, - "time_std": 0.0032695152984500934 - }, - { - "dataset": "statlog-german-credit", - "samples": 1000, - "features": 24, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 3.76, - "leaves": 2.38, - "depth": 2.3, - "score": 0.7114, - "score_std": 0.032787802610117066, - "time": 0.31878210067749024, - "time_std": 0.15745286923647758 - }, - { - "dataset": "statlog-heart", - "samples": 270, - "features": 13, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 5.72, - "leaves": 3.36, - "depth": 3.18, - "score": 0.7770370370370372, - "score_std": 0.047279921176986504, - "time": 0.16321456909179688, - "time_std": 0.05491986712932649 - }, - { - "dataset": "statlog-image", - "samples": 2310, - "features": 18, - "classes": 7, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 33.72, - "leaves": 17.26, - "depth": 8.9, - "score": NaN, - "score_std": NaN, - "time": 2.2605089950561523, - "time_std": 0.5281135673254995 - }, - { - "dataset": "statlog-vehicle", - "samples": 846, - "features": 18, - "classes": 4, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 29.76, - "leaves": 15.3, - "depth": 7.76, - "score": NaN, - "score_std": NaN, - "time": 1.4117946910858155, - "time_std": 0.32902609386000614 - }, - { - "dataset": "synthetic-control", - "samples": 600, - "features": 60, - "classes": 6, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 23.36, - "leaves": 12.18, - "depth": 5.9, - "score": 0.9494999999999999, - "score_std": 0.0215, - "time": 2.877303485870361, - "time_std": 3.3802458181271033 - }, - { - "dataset": "tic-tac-toe", - "samples": 958, - "features": 9, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 13.5, - "leaves": 7.24, - "depth": 5.18, - "score": NaN, - "score_std": NaN, - "time": 0.29811314105987546, - "time_std": 0.16349860688880868 - }, - { - "dataset": "vertebral-column-2clases", - "samples": 310, - "features": 6, - "classes": 2, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 6.52, - "leaves": 3.76, - "depth": 3.64, - "score": 0.8254838709677418, - "score_std": 0.04899510476938654, - "time": 0.0902666187286377, - "time_std": 0.020145062050195707 - }, - { - "dataset": "wine", - "samples": 178, - "features": 13, - "classes": 3, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 7.02, - "leaves": 4.0, - "depth": 3.34, - "score": NaN, - "score_std": NaN, - "time": 0.14963967800140382, - "time_std": 0.029198707180122286 - }, - { - "dataset": "zoo", - "samples": 101, - "features": 16, - "classes": 7, - "hyperparameters": { - "max_features": "auto", - "splitter": "mutual" - }, - "nodes": 11.52, - "leaves": 6.2, - "depth": 6.42, - "score": NaN, - "score_std": NaN, - "time": 36.339180612564085, - "time_std": 251.6950015788668 - } - ], - "title": "default", - "version": "1.2.3" -} \ No newline at end of file + "score_name": "accuracy", + "model": "STree", + "stratified": false, + "folds": 5, + "date": "2021-11-01", + "time": "19:17:07", + "duration": 4115.042420864105, + "seeds": [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1], + "platform": "macbook-pro", + "results": [ + { + "dataset": "balance-scale", + "samples": 625, + "features": 4, + "classes": 3, + "hyperparameters": { + "max_features": "auto", + "splitter": "mutual" + }, + "nodes": 18.78, + "leaves": 9.88, + "depth": 5.9, + "score": 0.97, + "score_std": 0.002, + "time": 0.23330417156219482, + "time_std": 0.048087665954193885 + }, + { + "dataset": "balloons", + "samples": 16, + "features": 4, + "classes": 2, + "hyperparameters": { + "max_features": "auto", + "splitter": "mutual" + }, + "nodes": 4.72, + "leaves": 2.86, + "depth": 2.78, + "score": 0.5566666666666668, + "score_std": 0.2941277122460771, + "time": 0.021352062225341795, + "time_std": 0.005808742398555902 + } + ], + "title": "default B", + "version": "1.2.3" +}