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100% coverage in Datasets and BestResults

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This commit is contained in:
Ricardo Montañana Gómez
2022-04-23 13:05:28 +02:00
parent 7bbfb4b68e
commit e30ea34c58
20 changed files with 1309 additions and 1748 deletions
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43
benchmark/Experiments.py
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@@ -42,15 +42,11 @@ class DatasetsTanveer:
def load(self, name): def load(self, name):
file_name = os.path.join(self.folder(), self.dataset_names(name)) file_name = os.path.join(self.folder(), self.dataset_names(name))
try: data = pd.read_csv(
data = pd.read_csv( file_name,
file_name, sep="\t",
sep="\t", index_col=0,
index_col=0, )
)
except FileNotFoundError:
print(f"Couldn't open data file {file_name}")
exit(1)
X = data.drop("clase", axis=1).to_numpy() X = data.drop("clase", axis=1).to_numpy()
y = data["clase"].to_numpy() y = data["clase"].to_numpy()
return X, y return X, y
@@ -67,14 +63,10 @@ class DatasetsSurcov:
def load(self, name): def load(self, name):
file_name = os.path.join(self.folder(), self.dataset_names(name)) file_name = os.path.join(self.folder(), self.dataset_names(name))
try: data = pd.read_csv(
data = pd.read_csv( file_name,
file_name, index_col=0,
index_col=0, )
)
except FileNotFoundError:
print(f"Couldn't open data file {file_name}")
exit(1)
data.dropna(axis=0, how="any", inplace=True) data.dropna(axis=0, how="any", inplace=True)
self.columns = data.columns self.columns = data.columns
X = data.drop("class", axis=1).to_numpy() X = data.drop("class", axis=1).to_numpy()
@@ -92,12 +84,8 @@ class Datasets:
self.dataset = class_name() self.dataset = class_name()
if dataset_name is None: if dataset_name is None:
file_name = os.path.join(self.dataset.folder(), Files.index) file_name = os.path.join(self.dataset.folder(), Files.index)
try: with open(file_name) as f:
with open(file_name) as f: self.data_sets = f.read().splitlines()
self.data_sets = f.read().splitlines()
except FileNotFoundError:
print(f"Couldn't open index file {file_name}")
exit(1)
else: else:
self.data_sets = [dataset_name] self.data_sets = [dataset_name]
@@ -109,10 +97,11 @@ class Datasets:
class BestResults: class BestResults:
def __init__(self, score, model, datasets): def __init__(self, score, model, datasets, quiet=False):
self.score_name = score self.score_name = score
self.datasets = datasets self.datasets = datasets
self.model = model self.model = model
self.quiet = quiet
self.data = {} self.data = {}
def _get_file_name(self): def _get_file_name(self):
@@ -154,7 +143,9 @@ class BestResults:
score=self.score_name, model=self.model score=self.score_name, model=self.model
) )
all_files = sorted(list(os.walk(Folders.results))) 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: for name in files:
if name.startswith(init_suffix) and name.endswith(end_suffix): if name.startswith(init_suffix) and name.endswith(end_suffix):
file_name = os.path.join(root, name) file_name = os.path.join(root, name)
@@ -164,7 +155,7 @@ class BestResults:
# Build best results json file # Build best results json file
output = {} output = {}
datasets = Datasets() datasets = Datasets()
for name in tqdm(list(datasets), desc="datasets"): for name in tqdm(list(datasets), desc="datasets", disable=self.quiet):
output[name] = ( output[name] = (
results[name]["score"], results[name]["score"],
results[name]["hyperparameters"], results[name]["hyperparameters"],

7
benchmark/tests/.env.dist Normal file
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@@ -0,0 +1,7 @@
score=accuracy
platform=iMac27
n_folds=5
model=ODTE
stratified=0
# Source of data Tanveer/Surcov
source_data=Tanveer

7
benchmark/tests/.env.surcov Normal file
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@@ -0,0 +1,7 @@
score=accuracy
platform=iMac27
n_folds=5
model=ODTE
stratified=0
# Source of data Tanveer/Surcov
source_data=Surcov

72
benchmark/tests/BestResults_test.py Normal file
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@@ -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, {}, "")},
)

71
benchmark/tests/Dataset_test.py Normal file
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@@ -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")

3
benchmark/tests/Models_test.py
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@@ -16,9 +16,6 @@ from ..Models import Models
class ModelTest(unittest.TestCase): class ModelTest(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def test_Models(self): def test_Models(self):
test = { test = {
"STree": Stree, "STree": Stree,

5
benchmark/tests/Util_test.py
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@@ -114,15 +114,16 @@ class UtilTest(unittest.TestCase):
def test_Files_get_results(self): def test_Files_get_results(self):
os.chdir(os.path.dirname(os.path.abspath(__file__))) os.chdir(os.path.dirname(os.path.abspath(__file__)))
self.assertSequenceEqual( self.assertCountEqual(
Files().get_all_results(hidden=False), Files().get_all_results(hidden=False),
[ [
"results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json", "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." "results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0."
"json", "json",
], ],
) )
self.assertSequenceEqual( self.assertCountEqual(
Files().get_all_results(hidden=True), Files().get_all_results(hidden=True),
["results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json"], ["results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json"],
) )

4
benchmark/tests/__init__.py
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@@ -1,4 +1,6 @@
from .Util_test import UtilTest from .Util_test import UtilTest
from .Models_test import ModelTest from .Models_test import ModelTest
from .Dataset_test import DatasetTest
from .BestResults_test import BestResultTest
all = ["UtilTest", "ModelTest"] all = ["UtilTest", "ModelTest", "DatasetTest", "BestResultTest"]

2
benchmark/tests/data/all.txt Normal file
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@@ -0,0 +1,2 @@
balance-scale
balloons

626
benchmark/tests/data/balance-scale_R.dat Executable file
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@@ -0,0 +1,626 @@
f1 f2 f3 f4 clase
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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
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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
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@@ -0,0 +1,2 @@
iris
wine

151
benchmark/tests/datasets/iris.csv Normal file
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@@ -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
1 sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) class
2 0 5.1 3.5 1.4 0.2 0
3 1 4.9 3.0 1.4 0.2 0
4 2 4.7 3.2 1.3 0.2 0
5 3 4.6 3.1 1.5 0.2 0
6 4 5.0 3.6 1.4 0.2 0
7 5 5.4 3.9 1.7 0.4 0
8 6 4.6 3.4 1.4 0.3 0
9 7 5.0 3.4 1.5 0.2 0
10 8 4.4 2.9 1.4 0.2 0
11 9 4.9 3.1 1.5 0.1 0
12 10 5.4 3.7 1.5 0.2 0
13 11 4.8 3.4 1.6 0.2 0
14 12 4.8 3.0 1.4 0.1 0
15 13 4.3 3.0 1.1 0.1 0
16 14 5.8 4.0 1.2 0.2 0
17 15 5.7 4.4 1.5 0.4 0
18 16 5.4 3.9 1.3 0.4 0
19 17 5.1 3.5 1.4 0.3 0
20 18 5.7 3.8 1.7 0.3 0
21 19 5.1 3.8 1.5 0.3 0
22 20 5.4 3.4 1.7 0.2 0
23 21 5.1 3.7 1.5 0.4 0
24 22 4.6 3.6 1.0 0.2 0
25 23 5.1 3.3 1.7 0.5 0
26 24 4.8 3.4 1.9 0.2 0
27 25 5.0 3.0 1.6 0.2 0
28 26 5.0 3.4 1.6 0.4 0
29 27 5.2 3.5 1.5 0.2 0
30 28 5.2 3.4 1.4 0.2 0
31 29 4.7 3.2 1.6 0.2 0
32 30 4.8 3.1 1.6 0.2 0
33 31 5.4 3.4 1.5 0.4 0
34 32 5.2 4.1 1.5 0.1 0
35 33 5.5 4.2 1.4 0.2 0
36 34 4.9 3.1 1.5 0.2 0
37 35 5.0 3.2 1.2 0.2 0
38 36 5.5 3.5 1.3 0.2 0
39 37 4.9 3.6 1.4 0.1 0
40 38 4.4 3.0 1.3 0.2 0
41 39 5.1 3.4 1.5 0.2 0
42 40 5.0 3.5 1.3 0.3 0
43 41 4.5 2.3 1.3 0.3 0
44 42 4.4 3.2 1.3 0.2 0
45 43 5.0 3.5 1.6 0.6 0
46 44 5.1 3.8 1.9 0.4 0
47 45 4.8 3.0 1.4 0.3 0
48 46 5.1 3.8 1.6 0.2 0
49 47 4.6 3.2 1.4 0.2 0
50 48 5.3 3.7 1.5 0.2 0
51 49 5.0 3.3 1.4 0.2 0
52 50 7.0 3.2 4.7 1.4 1
53 51 6.4 3.2 4.5 1.5 1
54 52 6.9 3.1 4.9 1.5 1
55 53 5.5 2.3 4.0 1.3 1
56 54 6.5 2.8 4.6 1.5 1
57 55 5.7 2.8 4.5 1.3 1
58 56 6.3 3.3 4.7 1.6 1
59 57 4.9 2.4 3.3 1.0 1
60 58 6.6 2.9 4.6 1.3 1
61 59 5.2 2.7 3.9 1.4 1
62 60 5.0 2.0 3.5 1.0 1
63 61 5.9 3.0 4.2 1.5 1
64 62 6.0 2.2 4.0 1.0 1
65 63 6.1 2.9 4.7 1.4 1
66 64 5.6 2.9 3.6 1.3 1
67 65 6.7 3.1 4.4 1.4 1
68 66 5.6 3.0 4.5 1.5 1
69 67 5.8 2.7 4.1 1.0 1
70 68 6.2 2.2 4.5 1.5 1
71 69 5.6 2.5 3.9 1.1 1
72 70 5.9 3.2 4.8 1.8 1
73 71 6.1 2.8 4.0 1.3 1
74 72 6.3 2.5 4.9 1.5 1
75 73 6.1 2.8 4.7 1.2 1
76 74 6.4 2.9 4.3 1.3 1
77 75 6.6 3.0 4.4 1.4 1
78 76 6.8 2.8 4.8 1.4 1
79 77 6.7 3.0 5.0 1.7 1
80 78 6.0 2.9 4.5 1.5 1
81 79 5.7 2.6 3.5 1.0 1
82 80 5.5 2.4 3.8 1.1 1
83 81 5.5 2.4 3.7 1.0 1
84 82 5.8 2.7 3.9 1.2 1
85 83 6.0 2.7 5.1 1.6 1
86 84 5.4 3.0 4.5 1.5 1
87 85 6.0 3.4 4.5 1.6 1
88 86 6.7 3.1 4.7 1.5 1
89 87 6.3 2.3 4.4 1.3 1
90 88 5.6 3.0 4.1 1.3 1
91 89 5.5 2.5 4.0 1.3 1
92 90 5.5 2.6 4.4 1.2 1
93 91 6.1 3.0 4.6 1.4 1
94 92 5.8 2.6 4.0 1.2 1
95 93 5.0 2.3 3.3 1.0 1
96 94 5.6 2.7 4.2 1.3 1
97 95 5.7 3.0 4.2 1.2 1
98 96 5.7 2.9 4.2 1.3 1
99 97 6.2 2.9 4.3 1.3 1
100 98 5.1 2.5 3.0 1.1 1
101 99 5.7 2.8 4.1 1.3 1
102 100 6.3 3.3 6.0 2.5 2
103 101 5.8 2.7 5.1 1.9 2
104 102 7.1 3.0 5.9 2.1 2
105 103 6.3 2.9 5.6 1.8 2
106 104 6.5 3.0 5.8 2.2 2
107 105 7.6 3.0 6.6 2.1 2
108 106 4.9 2.5 4.5 1.7 2
109 107 7.3 2.9 6.3 1.8 2
110 108 6.7 2.5 5.8 1.8 2
111 109 7.2 3.6 6.1 2.5 2
112 110 6.5 3.2 5.1 2.0 2
113 111 6.4 2.7 5.3 1.9 2
114 112 6.8 3.0 5.5 2.1 2
115 113 5.7 2.5 5.0 2.0 2
116 114 5.8 2.8 5.1 2.4 2
117 115 6.4 3.2 5.3 2.3 2
118 116 6.5 3.0 5.5 1.8 2
119 117 7.7 3.8 6.7 2.2 2
120 118 7.7 2.6 6.9 2.3 2
121 119 6.0 2.2 5.0 1.5 2
122 120 6.9 3.2 5.7 2.3 2
123 121 5.6 2.8 4.9 2.0 2
124 122 7.7 2.8 6.7 2.0 2
125 123 6.3 2.7 4.9 1.8 2
126 124 6.7 3.3 5.7 2.1 2
127 125 7.2 3.2 6.0 1.8 2
128 126 6.2 2.8 4.8 1.8 2
129 127 6.1 3.0 4.9 1.8 2
130 128 6.4 2.8 5.6 2.1 2
131 129 7.2 3.0 5.8 1.6 2
132 130 7.4 2.8 6.1 1.9 2
133 131 7.9 3.8 6.4 2.0 2
134 132 6.4 2.8 5.6 2.2 2
135 133 6.3 2.8 5.1 1.5 2
136 134 6.1 2.6 5.6 1.4 2
137 135 7.7 3.0 6.1 2.3 2
138 136 6.3 3.4 5.6 2.4 2
139 137 6.4 3.1 5.5 1.8 2
140 138 6.0 3.0 4.8 1.8 2
141 139 6.9 3.1 5.4 2.1 2
142 140 6.7 3.1 5.6 2.4 2
143 141 6.9 3.1 5.1 2.3 2
144 142 5.8 2.7 5.1 1.9 2
145 143 6.8 3.2 5.9 2.3 2
146 144 6.7 3.3 5.7 2.5 2
147 145 6.7 3.0 5.2 2.3 2
148 146 6.3 2.5 5.0 1.9 2
149 147 6.5 3.0 5.2 2.0 2
150 148 6.2 3.4 5.4 2.3 2
151 149 5.9 3.0 5.1 1.8 2

179
benchmark/tests/datasets/wine.csv Normal file
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,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
1 alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue od280/od315_of_diluted_wines proline class
2 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
3 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
4 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
5 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
6 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
7 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
8 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
9 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
10 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
11 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
12 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
13 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
14 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
15 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
16 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
17 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
18 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
19 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
20 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
21 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
22 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
23 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
24 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
25 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
26 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
27 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
28 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
29 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
30 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
31 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
32 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
33 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
34 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
35 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
36 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
37 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
38 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
39 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
40 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
41 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
42 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
43 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
44 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
45 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
46 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
47 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
48 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
49 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
50 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
51 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
52 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
53 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
54 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
55 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
56 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
57 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
58 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
59 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
60 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
61 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
62 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
63 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
64 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
65 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
66 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
67 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
68 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
69 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
70 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
71 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
72 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
73 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
74 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
75 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
76 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
77 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
78 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
79 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
80 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
81 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
82 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
83 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
84 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
85 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
86 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
87 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
88 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
89 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
90 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
91 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
92 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
93 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
94 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
95 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
96 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
97 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
98 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
99 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
100 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
101 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
102 100 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
103 101 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
104 102 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
105 103 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
106 104 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
107 105 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
108 106 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
109 107 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
110 108 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
111 109 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
112 110 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
113 111 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
114 112 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
115 113 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
116 114 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
117 115 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
118 116 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
119 117 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
120 118 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
121 119 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
122 120 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
123 121 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
124 122 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
125 123 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
126 124 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
127 125 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
128 126 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
129 127 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
130 128 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
131 129 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
132 130 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
133 131 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
134 132 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
135 133 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
136 134 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
137 135 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
138 136 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
139 137 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
140 138 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
141 139 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
142 140 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
143 141 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
144 142 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
145 143 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
146 144 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
147 145 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
148 146 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
149 147 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
150 148 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
151 149 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
152 150 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
153 151 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
154 152 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
155 153 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
156 154 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
157 155 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
158 156 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
159 157 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
160 158 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
161 159 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
162 160 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
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benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json
View File

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