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Ricardo Montañana Gómez
2022-04-22 21:48:44 +02:00
parent 668f6463b3
commit 63f7d1b3aa
13 changed files with 2843 additions and 734 deletions
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14
.coveragerc Normal file
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@@ -0,0 +1,14 @@
[run]
branch = True
source = benchmark
[report]
exclude_lines =
if self.debug:
pragma: no cover
raise NotImplementedError
if __name__ == .__main__.:
ignore_errors = True
omit =
benchmark/__init__.py
benchmark/_version.py

8
Makefile
View File

@@ -7,6 +7,14 @@ lint: ## Lint and static-check
flake8 benchmark
mypy benchmark
test: ## Run tests
python -m unittest -v benchmark.tests
coverage: ## Run tests with coverage
coverage erase
coverage run -m unittest -v benchmark.tests
coverage report -m
audit: ## Audit pip
pip-audit

15
benchmark/Utils.py
View File

@@ -87,28 +87,29 @@ class Files:
return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
@staticmethod
def open(name):
def open(name, test=False):
if os.path.isfile(name):
command = (
Files.cmd_open_macos
if Files.is_exe(Files.cmd_open_macos)
else Files.cmd_open_linux
)
subprocess.run([command, name])
return (
subprocess.run([command, name])
if not test
else [command, name]
)
return None
def get_all_results(self, hidden) -> list[str]:
first_path = "."
first_try = os.path.join(
first_path, Folders.hidden_results if hidden else Folders.results
)
second_path = ".."
second_try = os.path.join(second_path, first_try)
if os.path.isdir(first_try):
files_list = os.listdir(first_try)
elif os.path.isdir(second_try):
files_list = os.listdir(second_try)
else:
raise ValueError(f"{first_try} or {second_try} does not exist")
raise ValueError(f"{first_try} does not exist")
result = []
prefix, suffix = self.results_suffixes()
for result_file in files_list:

375
benchmark/best_results_STree-x.json
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@@ -1,375 +0,0 @@
{
"balance-scale": [
0,
{
"C": 10000.0,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"balloons": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"breast-cancer-wisc-diag": [
0,
{
"C": 0.2,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"breast-cancer-wisc-prog": [
0,
{
"C": 0.2,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"breast-cancer-wisc": [
0,
{ "kernel": "rbf", "multiclass_strategy": "ovr" },
""
],
"breast-cancer": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"cardiotocography-10clases": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"cardiotocography-3clases": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"conn-bench-sonar-mines-rocks": [
0,
{ "kernel": "rbf", "multiclass_strategy": "ovr" },
""
],
"cylinder-bands": [0, { "kernel": "rbf", "multiclass_strategy": "ovr" }, ""],
"dermatology": [
0,
{
"C": 55,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"echocardiogram": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "poly",
"max_features": "auto",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"fertility": [
0,
{
"C": 0.05,
"max_features": "auto",
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"haberman-survival": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"heart-hungarian": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"hepatitis": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"ilpd-indian-liver": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"ionosphere": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"iris": [0, { "kernel": "liblinear", "multiclass_strategy": "ovr" }, ""],
"led-display": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"libras": [
0,
{
"C": 0.08,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"low-res-spect": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"lymphography": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"mammographic": [0, { "kernel": "rbf", "multiclass_strategy": "ovr" }, ""],
"molec-biol-promoter": [
0,
{ "kernel": "rbf", "multiclass_strategy": "ovr" },
""
],
"musk-1": [
0,
{
"C": 0.05,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"oocytes_merluccius_nucleus_4d": [
0,
{ "C": 8.25, "gamma": 0.1, "kernel": "poly", "multiclass_strategy": "ovr" },
""
],
"oocytes_merluccius_states_2f": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"oocytes_trisopterus_nucleus_2f": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"oocytes_trisopterus_states_5b": [
0,
{
"C": 0.11,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"parkinsons": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"pima": [0, { "kernel": "liblinear", "multiclass_strategy": "ovr" }, ""],
"pittsburg-bridges-MATERIAL": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"pittsburg-bridges-REL-L": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"pittsburg-bridges-SPAN": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"pittsburg-bridges-T-OR-D": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"planning": [
0,
{
"C": 7,
"gamma": 10.0,
"kernel": "rbf",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"post-operative": [
0,
{
"C": 55,
"degree": 5,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"seeds": [
0,
{
"C": 10000.0,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"statlog-australian-credit": [
0,
{
"C": 0.05,
"max_features": "auto",
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"statlog-german-credit": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"statlog-heart": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"statlog-image": [
0,
{
"C": 7,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"statlog-vehicle": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"synthetic-control": [
0,
{
"C": 0.55,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"tic-tac-toe": [
0,
{
"C": 0.2,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000.0,
"multiclass_strategy": "ovr"
},
""
],
"vertebral-column-2clases": [
0,
{ "kernel": "liblinear", "multiclass_strategy": "ovr" },
""
],
"wine": [
0,
{
"C": 0.55,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
],
"zoo": [
0,
{
"C": 0.1,
"max_iter": 10000.0,
"kernel": "liblinear",
"multiclass_strategy": "ovr"
},
""
]
}

351
benchmark/best_results_SVC-x.json
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@@ -1,351 +0,0 @@
{
"balance-scale": [
0,
{
"C": 10000.0,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0
},
""
],
"balloons": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0
},
""
],
"breast-cancer-wisc-diag": [
0,
{
"C": 0.2,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"breast-cancer-wisc-prog": [
0,
{
"C": 0.2,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"breast-cancer-wisc": [0, { "kernel": "rbf" }, ""],
"breast-cancer": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"cardiotocography-10clases": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"cardiotocography-3clases": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"conn-bench-sonar-mines-rocks": [0, { "kernel": "rbf" }, ""],
"cylinder-bands": [0, { "kernel": "rbf" }, ""],
"dermatology": [
0,
{
"C": 55,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"echocardiogram": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000.0
},
""
],
"fertility": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"haberman-survival": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"heart-hungarian": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"hepatitis": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0
},
""
],
"ilpd-indian-liver": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"ionosphere": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0
},
""
],
"iris": [0, { "kernel": "linear", "decision_function_shape": "ovr" }, ""],
"led-display": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"libras": [
0,
{
"C": 0.08,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"low-res-spect": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"lymphography": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"mammographic": [0, { "kernel": "rbf" }, ""],
"molec-biol-promoter": [0, { "kernel": "rbf" }, ""],
"musk-1": [
0,
{
"C": 0.05,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000.0
},
""
],
"oocytes_merluccius_nucleus_4d": [
0,
{ "C": 8.25, "gamma": 0.1, "kernel": "poly" },
""
],
"oocytes_merluccius_states_2f": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"oocytes_trisopterus_nucleus_2f": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"oocytes_trisopterus_states_5b": [
0,
{
"C": 0.11,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"parkinsons": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"pima": [0, { "kernel": "linear", "decision_function_shape": "ovr" }, ""],
"pittsburg-bridges-MATERIAL": [
0,
{
"C": 7,
"gamma": 0.1,
"kernel": "rbf",
"max_iter": 10000.0
},
""
],
"pittsburg-bridges-REL-L": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"pittsburg-bridges-SPAN": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"pittsburg-bridges-T-OR-D": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"planning": [
0,
{
"C": 7,
"gamma": 10.0,
"kernel": "rbf",
"max_iter": 10000.0
},
""
],
"post-operative": [
0,
{
"C": 55,
"degree": 5,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000.0
},
""
],
"seeds": [
0,
{
"C": 10000.0,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"statlog-australian-credit": [
0,
{
"C": 0.05,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"statlog-german-credit": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"statlog-heart": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"statlog-image": [
0,
{
"C": 7,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"statlog-vehicle": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"synthetic-control": [
0,
{
"C": 0.55,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"tic-tac-toe": [
0,
{
"C": 0.2,
"gamma": 0.1,
"kernel": "poly",
"max_iter": 10000.0
},
""
],
"vertebral-column-2clases": [
0,
{ "kernel": "linear", "decision_function_shape": "ovr" },
""
],
"wine": [
0,
{
"C": 0.55,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
],
"zoo": [
0,
{
"C": 0.1,
"max_iter": 10000.0,
"kernel": "linear",
"decision_function_shape": "ovr"
},
""
]
}

1
benchmark/paper_hyperparams.txt
View File

@@ -1 +0,0 @@
{"balance-scale": {"C": 10000.0, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}, "balloons": {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}, "breast-cancer-wisc-diag": {"C": 0.2, "max_iter": 10000.0, "kernel": "liblinear"}, "breast-cancer-wisc-prog": {"C": 0.2, "max_iter": 10000.0, "kernel": "liblinear"}, "breast-cancer-wisc": {"kernel": "rbf"}, "breast-cancer": {"kernel": "liblinear"}, "cardiotocography-10clases": {"kernel": "liblinear"}, "cardiotocography-3clases": {"kernel": "liblinear"}, "conn-bench-sonar-mines-rocks": {"kernel": "rbf"}, "cylinder-bands": {"kernel": "rbf"}, "dermatology": {"C": 55, "max_iter": 10000.0, "kernel": "liblinear"}, "echocardiogram": {"C": 7, "gamma": 0.1, "kernel": "poly", "max_features": "auto", "max_iter": 10000.0}, "fertility": {"C": 0.05, "max_features": "auto", "max_iter": 10000.0, "kernel": "liblinear"}, "haberman-survival": {"kernel": "liblinear"}, "heart-hungarian": {"C": 0.05, "max_iter": 10000.0, "kernel": "liblinear"}, "hepatitis": {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}, "ilpd-indian-liver": {"kernel": "liblinear"}, "ionosphere": {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}, "iris": {"kernel": "liblinear"}, "led-display": {"kernel": "liblinear"}, "libras": {"C": 0.08, "max_iter": 10000.0, "kernel": "liblinear"}, "low-res-spect": {"C": 0.05, "max_iter": 10000.0, "kernel": "liblinear"}, "lymphography": {"C": 0.05, "max_iter": 10000.0, "kernel": "liblinear"}, "mammographic": {"kernel": "rbf"}, "molec-biol-promoter": {"kernel": "rbf"}, "musk-1": {"C": 0.05, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}, "oocytes_merluccius_nucleus_4d": {"C": 8.25, "gamma": 0.1, "kernel": "poly"}, "oocytes_merluccius_states_2f": {"kernel": "liblinear"}, "oocytes_trisopterus_nucleus_2f": {"kernel": "liblinear"}, "oocytes_trisopterus_states_5b": {"C": 0.11, "max_iter": 10000.0, "kernel": "liblinear"}, "parkinsons": {"kernel": "liblinear"}, "pima": {"kernel": "liblinear"}, "pittsburg-bridges-MATERIAL": {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0}, "pittsburg-bridges-REL-L": {"kernel": "liblinear"}, "pittsburg-bridges-SPAN": {"C": 0.05, "max_iter": 10000.0, "kernel": "liblinear"}, "pittsburg-bridges-T-OR-D": {"kernel": "liblinear"}, "planning": {"C": 7, "gamma": 10.0, "kernel": "rbf", "max_iter": 10000.0}, "post-operative": {"C": 55, "degree": 5, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}, "seeds": {"C": 10000.0, "max_iter": 10000.0, "kernel": "liblinear"}, "statlog-australian-credit": {"C": 0.05, "max_features": "auto", "max_iter": 10000.0, "kernel": "liblinear"}, "statlog-german-credit": {"kernel": "liblinear"}, "statlog-heart": {"kernel": "liblinear"}, "statlog-image": {"C": 7, "max_iter": 10000.0, "kernel": "liblinear"}, "statlog-vehicle": {"kernel": "liblinear"}, "synthetic-control": {"C": 0.55, "max_iter": 10000.0, "kernel": "liblinear"}, "tic-tac-toe": {"C": 0.2, "gamma": 0.1, "kernel": "poly", "max_iter": 10000.0}, "vertebral-column-2clases": {"kernel": "liblinear"}, "wine": {"C": 0.55, "max_iter": 10000.0, "kernel": "liblinear"}, "zoo": {"C": 0.1, "max_iter": 10000.0, "kernel": "liblinear"}}

233
benchmark/tests/Util_test.py Normal file
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@@ -0,0 +1,233 @@
import os
import sys
import unittest
import argparse
from ..Utils import Folders, Files, Symbols, TextColor, EnvData, EnvDefault
class UtilTest(unittest.TestCase):
def __init__(self, *args, **kwargs):
self._random_state = 1
self._kernels = ["liblinear", "linear", "rbf", "poly", "sigmoid"]
super().__init__(*args, **kwargs)
def test_Folders(self):
self.assertEqual("results", Folders.results)
self.assertEqual("hidden_results", Folders.hidden_results)
self.assertEqual("exreport", Folders.exreport)
self.assertEqual(
os.path.join(Folders.exreport, "exreport_output"), Folders.report
)
self.assertTrue(
Folders.src().endswith("benchmark/benchmark"), "Folders.src()"
)
def test_Files_attributes(self):
self.assertEqual(Files.index, "all.txt", "Files.index")
self.assertEqual(Files.report_ext, ".json", "Files.report_ext")
self.assertEqual(
Files.cmd_open_macos, "/usr/bin/open", "Files.cmd_open_macos"
)
self.assertEqual(
Files.cmd_open_linux, "/usr/bin/xdg-open", "Files.cmd_open_linux"
)
self.assertEqual(
Files.exreport_pdf, "Rplots.pdf", "Files.exreport_pdf"
)
self.assertEqual(Files.benchmark_r, "benchmark.r", "Files.benchmark_r")
self.assertEqual(Files.dot_env, ".env", "Files.dot_env")
def test_Files_methods(self):
items = [
"score_test",
"model-test",
"platform-test",
"date-test",
"time-test",
"stratified-test",
]
tests = [
(Files.exreport_output("score_test"), "exreport_score-test.txt"),
(Files.exreport_err("score_test"), "exreport_err_score-test.txt"),
(Files.exreport_excel("score_test"), "exreport_score-test.xlsx"),
(Files.exreport("score_test"), "exreport_score-test.csv"),
(Files.tex_output("score_test"), "exreport_score-test.tex"),
(
Files.best_results("score_test", "model_test"),
"best_results_score-test_model_test.json",
),
(
Files.results(*items),
"results_score-test_model-test_platform-test_date-test_time-"
"test_stratified-test.json",
),
(
Files.grid_input("score_test", "model_test"),
"grid_input_score-test_model_test.json",
),
(
Files.grid_output("score_test", "model_test"),
"grid_output_score-test_model_test.json",
),
(
Files.grid("input", "score_test", "model_test"),
"grid_input_score-test_model_test.json",
),
(
Files.grid("output", "score_test", "model_test"),
"grid_output_score-test_model_test.json",
),
]
for computed, expected in tests:
self.assertEqual(computed, expected)
file_name = (
"results_score-test_model-test_platform-test_date-test_time-"
"test_stratified-test.json"
)
# split_file_name
expected = items
expected[0] = "score-test"
self.assertSequenceEqual(Files().split_file_name(file_name), expected)
# result_suffixes
self.assertSequenceEqual(
Files.results_suffixes(), ["results_", Files.report_ext]
)
self.assertSequenceEqual(
Files.results_suffixes("score-test"),
["results_score-test_", Files.report_ext],
)
self.assertSequenceEqual(
Files.results_suffixes("score-test", "model-test"),
["results_score-test_model-test_", Files.report_ext],
)
# is_exe
self.assertTrue(Files.is_exe(sys.executable))
def test_Files_open(self):
self.assertIsNone(Files.open("xxx.xxx"))
cmd = (
Files.cmd_open_macos
if sys.platform == "darwin"
else Files.cmd_open_linux
)
self.assertSequenceEqual(
Files.open(__file__, test=True), [cmd, __file__]
)
def test_Files_get_results(self):
os.chdir(os.path.dirname(os.path.abspath(__file__)))
self.assertSequenceEqual(
Files().get_all_results(hidden=False),
[
"results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json",
"results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0."
"json",
],
)
self.assertSequenceEqual(
Files().get_all_results(hidden=True),
["results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json"],
)
def test_Symbols(self):
self.assertEqual(Symbols.check_mark, "\N{heavy check mark}")
self.assertEqual(
Symbols.exclamation, "\N{heavy exclamation mark symbol}"
)
self.assertEqual(Symbols.black_star, "\N{black star}")
self.assertEqual(Symbols.equal_best, Symbols.check_mark)
self.assertEqual(Symbols.better_best, Symbols.black_star)
def test_EnvData(self):
os.chdir(os.path.dirname(os.path.abspath(__file__)))
expected = {
"score": "accuracy",
"platform": "iMac27",
"n_folds": "5",
"model": "ODTE",
"stratified": "0",
"source_data": "Tanveer",
}
computed = EnvData().load()
self.assertDictEqual(computed, expected)
def test_EnvDefault(self):
ap = argparse.ArgumentParser()
ap.add_argument(
"-s",
"--score",
action=EnvDefault,
envvar="score",
type=str,
required=True,
help="score name {accuracy, f1_macro, ...}",
)
ap.add_argument(
"-P",
"--platform",
action=EnvDefault,
envvar="platform",
type=str,
required=True,
help="Platform where the test is run",
)
ap.add_argument(
"-m",
"--model",
type=str,
required=True,
help="model name",
)
ap.add_argument(
"-n",
"--n_folds",
action=EnvDefault,
envvar="n_folds",
type=int,
required=True,
help="number of folds",
)
ap.add_argument(
"-t",
"--stratified",
action=EnvDefault,
envvar="stratified",
type=str,
required=True,
help="Stratified",
)
# ap.add_argument(
# "--title",
# type=str,
# required=True,
# )
# args = ap.parse_args([
# "--title",
# "test",
# ])
# args = ap.parse_known_args(namespace=unittest)
# computed = args.__dict__
# for key, value in expected.items():
# self.assertEqual(computed[key], value)
# print(computed)
def test_TextColor(self):
self.assertEqual(TextColor.BLUE, "\033[94m")
self.assertEqual(TextColor.CYAN, "\033[96m")
self.assertEqual(TextColor.GREEN, "\033[92m")
self.assertEqual(TextColor.MAGENTA, "\033[95m")
self.assertEqual(TextColor.YELLOW, "\033[93m")
self.assertEqual(TextColor.RED, "\033[91m")
self.assertEqual(TextColor.HEADER, TextColor.MAGENTA)
self.assertEqual(TextColor.LINE1, TextColor.BLUE)
self.assertEqual(TextColor.LINE2, TextColor.CYAN)
self.assertEqual(TextColor.SUCCESS, TextColor.GREEN)
self.assertEqual(TextColor.WARNING, TextColor.YELLOW)
self.assertEqual(TextColor.FAIL, TextColor.RED)
self.assertEqual(TextColor.ENDC, "\033[0m")
self.assertEqual(TextColor.BOLD, "\033[1m")
self.assertEqual(TextColor.UNDERLINE, "\033[4m")

3
benchmark/tests/__init__.py Normal file
View File

@@ -0,0 +1,3 @@
from .Util_test import UtilTest
all = ["UtilTest"]

859
benchmark/tests/hidden_results/results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json Normal file
View File

@@ -0,0 +1,859 @@
{
"score_name": "accuracy",
"model": "STree",
"stratified": false,
"folds": 5,
"date": "2021-11-01",
"time": "23:55:16",
"duration": 354.0638499259949,
"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": "random",
"max_features": "auto"
},
"nodes": 29.36,
"leaves": 15.18,
"depth": 7.04,
"score": 0.8446400000000002,
"score_std": 0.03509402228300427,
"time": 0.12386286735534668,
"time_std": 0.018833498089019858
},
{
"dataset": "balloons",
"samples": 16,
"features": 4,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 5.52,
"leaves": 3.26,
"depth": 3.14,
"score": 0.7833333333333331,
"score_std": 0.2713136766016618,
"time": 0.007491922378540039,
"time_std": 0.0017495650509012713
},
{
"dataset": "breast-cancer-wisc-diag",
"samples": 569,
"features": 30,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 6.52,
"leaves": 3.76,
"depth": 3.36,
"score": 0.9506148113646948,
"score_std": 0.016760290481343566,
"time": 0.033224782943725585,
"time_std": 0.007172363785211332
},
{
"dataset": "breast-cancer-wisc-prog",
"samples": 198,
"features": 33,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 1.36,
"leaves": 1.18,
"depth": 1.18,
"score": 0.7631923076923076,
"score_std": 0.06880246070894211,
"time": 0.0075318336486816405,
"time_std": 0.003073462711967005
},
{
"dataset": "breast-cancer-wisc",
"samples": 699,
"features": 9,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 7.0,
"leaves": 4.0,
"depth": 3.84,
"score": 0.9599434737923948,
"score_std": 0.01787003608568562,
"time": 0.030069727897644043,
"time_std": 0.004751972467423199
},
{
"dataset": "breast-cancer",
"samples": 286,
"features": 9,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 13.48,
"leaves": 7.24,
"depth": 5.02,
"score": 0.712238354506957,
"score_std": 0.049745781147657896,
"time": 0.03615377426147461,
"time_std": 0.012487842118072367
},
{
"dataset": "cardiotocography-10clases",
"samples": 2126,
"features": 21,
"classes": 10,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 223.08,
"leaves": 112.04,
"depth": 23.42,
"score": 0.7907325048329191,
"score_std": 0.02381315649896072,
"time": 1.980188593864441,
"time_std": 0.23106894626887964
},
{
"dataset": "cardiotocography-3clases",
"samples": 2126,
"features": 21,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 48.8,
"leaves": 24.9,
"depth": 8.9,
"score": 0.8832561170947253,
"score_std": 0.02049585132750282,
"time": 0.5831333923339844,
"time_std": 0.060750236860767005
},
{
"dataset": "conn-bench-sonar-mines-rocks",
"samples": 208,
"features": 60,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 15.64,
"leaves": 8.32,
"depth": 5.34,
"score": 0.7565853658536585,
"score_std": 0.05994250747027105,
"time": 0.038976125717163086,
"time_std": 0.010003533602517408
},
{
"dataset": "cylinder-bands",
"samples": 512,
"features": 35,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 8.88,
"leaves": 4.94,
"depth": 4.1,
"score": 0.6660156101275463,
"score_std": 0.034842449045547084,
"time": 0.06690076828002929,
"time_std": 0.022855829892385368
},
{
"dataset": "dermatology",
"samples": 366,
"features": 34,
"classes": 6,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 27.92,
"leaves": 14.46,
"depth": 8.72,
"score": 0.9480747871158832,
"score_std": 0.03361950799023345,
"time": 0.0804334306716919,
"time_std": 0.015477111291214336
},
{
"dataset": "echocardiogram",
"samples": 131,
"features": 10,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 4.2,
"leaves": 2.6,
"depth": 2.38,
"score": 0.848974358974359,
"score_std": 0.06695913759706462,
"time": 0.011046781539916992,
"time_std": 0.004861198277349794
},
{
"dataset": "fertility",
"samples": 100,
"features": 9,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 1.0,
"leaves": 1.0,
"depth": 1.0,
"score": 0.88,
"score_std": 0.0547722557505166,
"time": 0.003735198974609375,
"time_std": 0.00012074325822180572
},
{
"dataset": "haberman-survival",
"samples": 306,
"features": 3,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 4.68,
"leaves": 2.84,
"depth": 2.38,
"score": 0.7261819143310418,
"score_std": 0.046974913251216534,
"time": 0.013467421531677246,
"time_std": 0.007957865466574262
},
{
"dataset": "heart-hungarian",
"samples": 294,
"features": 12,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 11.28,
"leaves": 6.14,
"depth": 4.88,
"score": 0.7941905318527178,
"score_std": 0.06102540298960804,
"time": 0.03379280090332031,
"time_std": 0.009598072383003038
},
{
"dataset": "hepatitis",
"samples": 155,
"features": 19,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 7.56,
"leaves": 4.28,
"depth": 3.84,
"score": 0.8045161290322581,
"score_std": 0.07395754892276983,
"time": 0.01713006019592285,
"time_std": 0.00564776741550471
},
{
"dataset": "ilpd-indian-liver",
"samples": 583,
"features": 9,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 1.0,
"leaves": 1.0,
"depth": 1.0,
"score": 0.7135661656351313,
"score_std": 0.038048725185040336,
"time": 0.023022899627685545,
"time_std": 0.0008606962034225632
},
{
"dataset": "ionosphere",
"samples": 351,
"features": 33,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 10.44,
"leaves": 5.72,
"depth": 5.34,
"score": 0.8877102615694165,
"score_std": 0.032858364863469956,
"time": 0.047363419532775876,
"time_std": 0.007994140238689355
},
{
"dataset": "iris",
"samples": 150,
"features": 4,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 5.8,
"leaves": 3.4,
"depth": 3.4,
"score": 0.9353333333333333,
"score_std": 0.04077580982232807,
"time": 0.01657679557800293,
"time_std": 0.003917464306726484
},
{
"dataset": "led-display",
"samples": 1000,
"features": 7,
"classes": 10,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 50.32,
"leaves": 25.66,
"depth": 7.88,
"score": 0.6885999999999999,
"score_std": 0.03541242719724248,
"time": 0.4535458850860596,
"time_std": 0.05757012198652795
},
{
"dataset": "libras",
"samples": 360,
"features": 90,
"classes": 15,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 110.0,
"leaves": 55.5,
"depth": 12.9,
"score": 0.7258333333333331,
"score_std": 0.05818831179566588,
"time": 0.39428802967071536,
"time_std": 0.045030901693777124
},
{
"dataset": "low-res-spect",
"samples": 531,
"features": 100,
"classes": 9,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 41.12,
"leaves": 21.06,
"depth": 8.28,
"score": 0.8785364133309824,
"score_std": 0.034171008792067296,
"time": 0.1645474100112915,
"time_std": 0.02733202300300527
},
{
"dataset": "lymphography",
"samples": 148,
"features": 18,
"classes": 4,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 14.04,
"leaves": 7.52,
"depth": 5.3,
"score": 0.7275862068965517,
"score_std": 0.10033949505916255,
"time": 0.03291699886322021,
"time_std": 0.011587270459432045
},
{
"dataset": "mammographic",
"samples": 961,
"features": 5,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 3.12,
"leaves": 2.06,
"depth": 2.06,
"score": 0.8165479274611398,
"score_std": 0.023830462165504267,
"time": 0.0689641523361206,
"time_std": 0.004527857287873337
},
{
"dataset": "molec-biol-promoter",
"samples": 106,
"features": 57,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 14.68,
"leaves": 7.84,
"depth": 4.98,
"score": 0.7574458874458873,
"score_std": 0.09667201719120466,
"time": 0.025699548721313477,
"time_std": 0.004682522726130544
},
{
"dataset": "musk-1",
"samples": 476,
"features": 166,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 20.0,
"leaves": 10.5,
"depth": 6.3,
"score": 0.7693464912280702,
"score_std": 0.04900109333834644,
"time": 0.10321691989898682,
"time_std": 0.018834104491724592
},
{
"dataset": "oocytes_merluccius_nucleus_4d",
"samples": 1022,
"features": 41,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 5.44,
"leaves": 3.22,
"depth": 3.0,
"score": 0.7268938307030129,
"score_std": 0.04810611872256133,
"time": 0.15883247375488282,
"time_std": 0.05975218912975299
},
{
"dataset": "oocytes_merluccius_states_2f",
"samples": 1022,
"features": 25,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 19.36,
"leaves": 10.18,
"depth": 6.18,
"score": 0.8972649450023912,
"score_std": 0.01928052605378522,
"time": 0.15902264595031737,
"time_std": 0.028622129774997354
},
{
"dataset": "oocytes_trisopterus_nucleus_2f",
"samples": 912,
"features": 25,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 8.4,
"leaves": 4.7,
"depth": 3.72,
"score": 0.7191827298384676,
"score_std": 0.03845659744173081,
"time": 0.14449056148529052,
"time_std": 0.02824995721322464
},
{
"dataset": "oocytes_trisopterus_states_5b",
"samples": 912,
"features": 32,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 21.56,
"leaves": 11.28,
"depth": 7.36,
"score": 0.8612970635921455,
"score_std": 0.02785125896060016,
"time": 0.17455099105834962,
"time_std": 0.034860340324268
},
{
"dataset": "parkinsons",
"samples": 195,
"features": 22,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 5.32,
"leaves": 3.16,
"depth": 3.08,
"score": 0.8630769230769231,
"score_std": 0.050665524745773904,
"time": 0.01626851558685303,
"time_std": 0.003433163961593399
},
{
"dataset": "pima",
"samples": 768,
"features": 8,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 3.12,
"leaves": 2.06,
"depth": 2.06,
"score": 0.7415151515151515,
"score_std": 0.039306614599336764,
"time": 0.06483261585235596,
"time_std": 0.009690010766938022
},
{
"dataset": "pittsburg-bridges-MATERIAL",
"samples": 106,
"features": 7,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 9.44,
"leaves": 5.22,
"depth": 4.36,
"score": 0.8242424242424242,
"score_std": 0.08411757810289604,
"time": 0.02251164436340332,
"time_std": 0.008085472626911226
},
{
"dataset": "pittsburg-bridges-REL-L",
"samples": 103,
"features": 7,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 17.36,
"leaves": 9.18,
"depth": 5.8,
"score": 0.6614285714285714,
"score_std": 0.09774556261712276,
"time": 0.04106622695922851,
"time_std": 0.010728542484034934
},
{
"dataset": "pittsburg-bridges-SPAN",
"samples": 92,
"features": 7,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 13.92,
"leaves": 7.46,
"depth": 5.42,
"score": 0.6186549707602339,
"score_std": 0.09087329802667218,
"time": 0.03503801822662354,
"time_std": 0.00814708102230533
},
{
"dataset": "pittsburg-bridges-T-OR-D",
"samples": 102,
"features": 7,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 1.08,
"leaves": 1.04,
"depth": 1.04,
"score": 0.8628095238095238,
"score_std": 0.0747571882042698,
"time": 0.003855118751525879,
"time_std": 0.0009679068824871095
},
{
"dataset": "planning",
"samples": 182,
"features": 12,
"classes": 2,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 1.0,
"leaves": 1.0,
"depth": 1.0,
"score": 0.7143693693693695,
"score_std": 0.0715459100205182,
"time": 0.005747194290161133,
"time_std": 0.0001757650822726821
},
{
"dataset": "post-operative",
"samples": 90,
"features": 8,
"classes": 3,
"hyperparameters": {
"splitter": "random",
"max_features": "auto"
},
"nodes": 3.08,
"leaves": 2.04,
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benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json Normal file
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