mirror of
https://github.com/Doctorado-ML/benchmark.git
synced 2025-08-15 23:45:54 +00:00
100% coverage in Datasets and BestResults
This commit is contained in:
@@ -42,15 +42,11 @@ class DatasetsTanveer:
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def load(self, name):
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file_name = os.path.join(self.folder(), self.dataset_names(name))
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try:
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data = pd.read_csv(
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file_name,
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sep="\t",
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index_col=0,
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)
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except FileNotFoundError:
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print(f"Couldn't open data file {file_name}")
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exit(1)
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data = pd.read_csv(
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file_name,
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sep="\t",
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index_col=0,
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)
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X = data.drop("clase", axis=1).to_numpy()
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y = data["clase"].to_numpy()
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return X, y
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@@ -67,14 +63,10 @@ class DatasetsSurcov:
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def load(self, name):
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file_name = os.path.join(self.folder(), self.dataset_names(name))
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try:
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data = pd.read_csv(
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file_name,
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index_col=0,
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)
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except FileNotFoundError:
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print(f"Couldn't open data file {file_name}")
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exit(1)
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data = pd.read_csv(
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file_name,
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index_col=0,
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)
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data.dropna(axis=0, how="any", inplace=True)
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self.columns = data.columns
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X = data.drop("class", axis=1).to_numpy()
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@@ -92,12 +84,8 @@ class Datasets:
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self.dataset = class_name()
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if dataset_name is None:
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file_name = os.path.join(self.dataset.folder(), Files.index)
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try:
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with open(file_name) as f:
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self.data_sets = f.read().splitlines()
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except FileNotFoundError:
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print(f"Couldn't open index file {file_name}")
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exit(1)
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with open(file_name) as f:
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self.data_sets = f.read().splitlines()
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else:
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self.data_sets = [dataset_name]
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@@ -109,10 +97,11 @@ class Datasets:
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class BestResults:
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def __init__(self, score, model, datasets):
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def __init__(self, score, model, datasets, quiet=False):
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self.score_name = score
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self.datasets = datasets
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self.model = model
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self.quiet = quiet
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self.data = {}
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def _get_file_name(self):
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@@ -154,7 +143,9 @@ class BestResults:
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score=self.score_name, model=self.model
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)
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all_files = sorted(list(os.walk(Folders.results)))
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for root, _, files in tqdm(all_files, desc="files"):
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for root, _, files in tqdm(
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all_files, desc="files", disable=self.quiet
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):
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for name in files:
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if name.startswith(init_suffix) and name.endswith(end_suffix):
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file_name = os.path.join(root, name)
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@@ -164,7 +155,7 @@ class BestResults:
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# Build best results json file
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output = {}
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datasets = Datasets()
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for name in tqdm(list(datasets), desc="datasets"):
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for name in tqdm(list(datasets), desc="datasets", disable=self.quiet):
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output[name] = (
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results[name]["score"],
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results[name]["hyperparameters"],
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7
benchmark/tests/.env.dist
Normal file
7
benchmark/tests/.env.dist
Normal file
@@ -0,0 +1,7 @@
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score=accuracy
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platform=iMac27
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n_folds=5
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model=ODTE
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stratified=0
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# Source of data Tanveer/Surcov
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source_data=Tanveer
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7
benchmark/tests/.env.surcov
Normal file
7
benchmark/tests/.env.surcov
Normal file
@@ -0,0 +1,7 @@
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score=accuracy
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platform=iMac27
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n_folds=5
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model=ODTE
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stratified=0
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# Source of data Tanveer/Surcov
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source_data=Surcov
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72
benchmark/tests/BestResults_test.py
Normal file
72
benchmark/tests/BestResults_test.py
Normal file
@@ -0,0 +1,72 @@
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import os
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import unittest
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from ..Models import Models
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from ..Experiments import BestResults, Datasets
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class BestResultTest(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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os.chdir(os.path.dirname(os.path.abspath(__file__)))
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super().__init__(*args, **kwargs)
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def tearDown(self) -> None:
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return super().tearDown()
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def test_load(self):
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expected = {
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"balance-scale": [
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0.98,
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{"splitter": "iwss", "max_features": "auto"},
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"results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json",
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],
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"balloons": [
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0.86,
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{
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"C": 7,
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"gamma": 0.1,
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"kernel": "rbf",
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"max_iter": 10000.0,
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"multiclass_strategy": "ovr",
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},
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"results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
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],
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}
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dt = Datasets()
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model = "STree"
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best = BestResults(
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score="accuracy", model=model, datasets=dt, quiet=True
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)
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best.build()
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self.assertSequenceEqual(best.load({}), expected)
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def test_load_error(self):
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dt = Datasets()
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model = "STree"
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best = BestResults(
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score="accuracy", model=model, datasets=dt, quiet=True
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)
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file_name = best._get_file_name()
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os.rename(file_name, file_name + ".bak")
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try:
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best.load({})
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except ValueError:
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pass
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else:
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self.fail("BestResults.load() should raise ValueError")
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finally:
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os.rename(file_name + ".bak", file_name)
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def test_fill(self):
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dt = Datasets()
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model = "STree"
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best = BestResults(
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score="accuracy", model=model, datasets=dt, quiet=True
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)
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self.assertSequenceEqual(
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best.fill({"test": "test"}, {"balloons": []}),
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{"balloons": [], "balance-scale": (0.0, {"test": "test"}, "")},
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)
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self.assertSequenceEqual(
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best.fill({}),
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{"balance-scale": (0.0, {}, ""), "balloons": (0.0, {}, "")},
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)
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71
benchmark/tests/Dataset_test.py
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71
benchmark/tests/Dataset_test.py
Normal file
@@ -0,0 +1,71 @@
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import os
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import shutil
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import unittest
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from ..Experiments import Randomized, Datasets
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class DatasetTest(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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os.chdir(os.path.dirname(os.path.abspath(__file__)))
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self.datasets_values = {
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"balance-scale": (625, 4, 3),
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"balloons": (16, 4, 2),
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"iris": (150, 4, 3),
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"wine": (178, 13, 3),
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}
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super().__init__(*args, **kwargs)
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def tearDown(self) -> None:
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self.set_env(".env.dist")
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return super().tearDown()
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@staticmethod
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def set_env(env):
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shutil.copy(env, ".env")
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def test_Randomized(self):
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expected = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
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self.assertSequenceEqual(Randomized.seeds, expected)
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def test_Datasets_iterator(self):
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test = {
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".env.dist": ["balance-scale", "balloons"],
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".env.surcov": ["iris", "wine"],
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}
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for key, value in test.items():
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self.set_env(key)
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dt = Datasets()
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computed = []
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for dataset in dt:
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computed.append(dataset)
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X, y = dt.load(dataset)
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m, n = X.shape
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c = max(y) + 1
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# Check dataset integrity
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self.assertSequenceEqual(
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(m, n, c), self.datasets_values[dataset]
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)
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self.assertSequenceEqual(computed, value)
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self.set_env(".env.dist")
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def test_Datasets_subset(self):
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test = {
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".env.dist": "balloons",
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".env.surcov": "wine",
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}
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for key, value in test.items():
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self.set_env(key)
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dt = Datasets(value)
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computed = []
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for dataset in dt:
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computed.append(dataset)
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X, y = dt.load(dataset)
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m, n = X.shape
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c = max(y) + 1
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# Check dataset integrity
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self.assertSequenceEqual(
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(m, n, c), self.datasets_values[dataset]
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)
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self.assertSequenceEqual(computed, [value])
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self.set_env(".env.dist")
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@@ -16,9 +16,6 @@ from ..Models import Models
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class ModelTest(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def test_Models(self):
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test = {
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"STree": Stree,
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@@ -114,15 +114,16 @@ class UtilTest(unittest.TestCase):
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def test_Files_get_results(self):
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os.chdir(os.path.dirname(os.path.abspath(__file__)))
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self.assertSequenceEqual(
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self.assertCountEqual(
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Files().get_all_results(hidden=False),
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[
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"results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json",
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"results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
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"results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0."
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"json",
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],
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)
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self.assertSequenceEqual(
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self.assertCountEqual(
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Files().get_all_results(hidden=True),
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["results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json"],
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)
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@@ -1,4 +1,6 @@
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from .Util_test import UtilTest
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from .Models_test import ModelTest
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from .Dataset_test import DatasetTest
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from .BestResults_test import BestResultTest
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all = ["UtilTest", "ModelTest"]
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all = ["UtilTest", "ModelTest", "DatasetTest", "BestResultTest"]
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2
benchmark/tests/data/all.txt
Normal file
2
benchmark/tests/data/all.txt
Normal file
@@ -0,0 +1,2 @@
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balance-scale
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balloons
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626
benchmark/tests/data/balance-scale_R.dat
Executable file
626
benchmark/tests/data/balance-scale_R.dat
Executable file
@@ -0,0 +1,626 @@
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f1 f2 f3 f4 clase
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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
|
||||
17
benchmark/tests/data/balloons_R.dat
Executable file
17
benchmark/tests/data/balloons_R.dat
Executable file
@@ -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
|
||||
2
benchmark/tests/datasets/all.txt
Normal file
2
benchmark/tests/datasets/all.txt
Normal file
@@ -0,0 +1,2 @@
|
||||
iris
|
||||
wine
|
||||
151
benchmark/tests/datasets/iris.csv
Normal file
151
benchmark/tests/datasets/iris.csv
Normal file
@@ -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
|
||||
|
179
benchmark/tests/datasets/wine.csv
Normal file
179
benchmark/tests/datasets/wine.csv
Normal file
@@ -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
|
||||
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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
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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
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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
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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
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||||
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
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||||
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
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||||
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
|
||||
|
1
benchmark/tests/results/best_results_accuracy_STree.json
Normal file
1
benchmark/tests/results/best_results_accuracy_STree.json
Normal file
@@ -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"]}
|
||||
@@ -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"
|
||||
}
|
||||
@@ -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,
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||||
"depth": 5.9,
|
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{
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||||
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{
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{
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],
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||||
"title": "default B",
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"version": "1.2.3"
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||||
}
|
||||
|
||||
Reference in New Issue
Block a user