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Fix some excel issues

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This commit is contained in:
Ricardo Montañana Gómez
2023-05-21 22:22:15 +02:00
parent 9041c412d5
commit 83bd321dd6
15 changed files with 145 additions and 105 deletions
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2
benchmark/Results.py
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@@ -6,7 +6,7 @@ from types import SimpleNamespace
import xlsxwriter import xlsxwriter
from .Datasets import Datasets from .Datasets import Datasets
from .ResultsBase import BaseReport, StubReport from .ResultsBase import BaseReport, StubReport, get_input
from .ResultsFiles import Excel from .ResultsFiles import Excel
from .Utils import NO_RESULTS, Files, Folders, TextColor from .Utils import NO_RESULTS, Files, Folders, TextColor

4
benchmark/tests/Excel_test.py
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@@ -3,7 +3,7 @@ from openpyxl import load_workbook
from xlsxwriter import Workbook from xlsxwriter import Workbook
from .TestBase import TestBase from .TestBase import TestBase
from ..ResultsFiles import Excel from ..ResultsFiles import Excel
from ..Utils import Folders from ..Utils import Folders, Files
class ExcelTest(TestBase): class ExcelTest(TestBase):
@@ -36,7 +36,7 @@ class ExcelTest(TestBase):
def test_Excel_Add_sheet(self): def test_Excel_Add_sheet(self):
file_name = "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json" file_name = "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"
excel_file_name = file_name.replace(".json", ".xlsx") excel_file_name = file_name.replace(Files.report_ext, ".xlsx")
book = Workbook(os.path.join(Folders.excel, excel_file_name)) book = Workbook(os.path.join(Folders.excel, excel_file_name))
excel = Excel(file_name=file_name, book=book) excel = Excel(file_name=file_name, book=book)
excel.report() excel.report()

1
benchmark/tests/TestBase.py
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@@ -31,6 +31,7 @@ class TestBase(unittest.TestCase):
os.remove(file_name) os.remove(file_name)
def generate_excel_sheet(self, sheet, file_name): def generate_excel_sheet(self, sheet, file_name):
file_name += self.ext
with open(os.path.join(self.test_files, file_name), "w") as f: with open(os.path.join(self.test_files, file_name), "w") as f:
for row in range(1, sheet.max_row + 1): for row in range(1, sheet.max_row + 1):
for col in range(1, sheet.max_column + 1): for col in range(1, sheet.max_column + 1):

1
benchmark/tests/excel/.gitignore vendored Normal file
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@@ -0,0 +1 @@
#

12
benchmark/tests/scripts/Be_List_test.py
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@@ -39,12 +39,14 @@ class BeListTest(TestBase):
stdout, stderr = self.execute_script("be_list", ["-m", "STree"]) stdout, stderr = self.execute_script("be_list", ["-m", "STree"])
self.assertEqual(stderr.getvalue(), "") self.assertEqual(stderr.getvalue(), "")
self.check_output_file(stdout, "be_list_report_excel") self.check_output_file(stdout, "be_list_report_excel")
book = load_workbook(os.path.join(Folders.excel, Files.be_list_excel)) book = load_workbook(os.path.join(Folders.excel, Files.be_list_excel))
sheet = book["STree"] sheet = book["STree"]
self.check_excel_sheet(sheet, "excel") self.check_excel_sheet(sheet, "excel")
@patch( @patch(
"benchmark.Results.get_input", side_effect=iter(["e 2", "e 1", "q"]) "benchmark.Results.get_input",
side_effect=iter(["e 2", "e 1", "q"]),
) )
def test_be_list_report_excel_twice(self, input_data): def test_be_list_report_excel_twice(self, input_data):
stdout, stderr = self.execute_script("be_list", ["-m", "STree"]) stdout, stderr = self.execute_script("be_list", ["-m", "STree"])
@@ -65,9 +67,10 @@ class BeListTest(TestBase):
self.assertEqual(stdout.getvalue(), f"{NO_RESULTS}\n") self.assertEqual(stdout.getvalue(), f"{NO_RESULTS}\n")
@patch( @patch(
"benchmark.Results.get_input", side_effect=iter(["d 0", "y", "", "q"]) "benchmark.Results.get_input",
side_effect=iter(["d 0", "y", "", "q"]),
) )
# @patch("benchmark.Results.get_input", side_effect=iter(["q"])) # @patch("benchmark.ResultsBase.get_input", side_effect=iter(["q"]))
def test_be_list_delete(self, input_data): def test_be_list_delete(self, input_data):
def copy_files(source_folder, target_folder, file_name): def copy_files(source_folder, target_folder, file_name):
source = os.path.join(source_folder, file_name) source = os.path.join(source_folder, file_name)
@@ -91,7 +94,8 @@ class BeListTest(TestBase):
self.fail("test_be_list_delete() should not raise exception") self.fail("test_be_list_delete() should not raise exception")
@patch( @patch(
"benchmark.Results.get_input", side_effect=iter(["h 0", "y", "", "q"]) "benchmark.Results.get_input",
side_effect=iter(["h 0", "y", "", "q"]),
) )
def test_be_list_hide(self, input_data): def test_be_list_hide(self, input_data):
def swap_files(source_folder, target_folder, file_name): def swap_files(source_folder, target_folder, file_name):

11
benchmark/tests/scripts/Be_Report_test.py
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@@ -13,7 +13,6 @@ class BeReportTest(TestBase):
def tearDown(self) -> None: def tearDown(self) -> None:
files = [ files = [
"results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.sql",
"results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.xlsx", "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.xlsx",
] ]
self.remove_files(files, Folders.results) self.remove_files(files, Folders.results)
@@ -21,6 +20,10 @@ class BeReportTest(TestBase):
[Files.datasets_report_excel], [Files.datasets_report_excel],
os.path.join(os.getcwd(), Folders.excel), os.path.join(os.getcwd(), Folders.excel),
) )
files = [
"results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.sql",
]
self.remove_files(files, Folders.sql)
return super().tearDown() return super().tearDown()
def test_be_report(self): def test_be_report(self):
@@ -37,7 +40,7 @@ class BeReportTest(TestBase):
self.assertEqual(stderr.getvalue(), "") self.assertEqual(stderr.getvalue(), "")
self.assertEqual(stdout.getvalue(), "unknown does not exists!\n") self.assertEqual(stdout.getvalue(), "unknown does not exists!\n")
def test_be_report_compare(self): def test_be_report_compared(self):
file_name = "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json" file_name = "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"
stdout, stderr = self.execute_script( stdout, stderr = self.execute_script(
"be_report", ["file", file_name, "-c"] "be_report", ["file", file_name, "-c"]
@@ -149,7 +152,7 @@ class BeReportTest(TestBase):
["file", file_name, "-x"], ["file", file_name, "-x"],
) )
file_name = os.path.join( file_name = os.path.join(
Folders.excel, file_name.replace(".json", ".xlsx") Folders.excel, file_name.replace(Files.report_ext, ".xlsx")
) )
book = load_workbook(file_name) book = load_workbook(file_name)
sheet = book["STree"] sheet = book["STree"]
@@ -164,7 +167,7 @@ class BeReportTest(TestBase):
["file", file_name, "-q"], ["file", file_name, "-q"],
) )
file_name = os.path.join( file_name = os.path.join(
Folders.results, file_name.replace(".json", ".sql") Folders.sql, file_name.replace(Files.report_ext, ".sql")
) )
self.check_file_file(file_name, "sql") self.check_file_file(file_name, "sql")
self.assertEqual(stderr.getvalue(), "") self.assertEqual(stderr.getvalue(), "")

1
benchmark/tests/sql/.gitignore vendored Normal file
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@@ -0,0 +1 @@
#

3
benchmark/tests/test_files/be_list_report.test
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@@ -14,7 +14,8 @@
Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
============================== ====== ===== === ======= ======= ======= =============== ================= =============== ============================== ====== ===== === ======= ======= ======= =============== ================= ===============
balance-scale 625 4 3 18.78 9.88 5.90 0.970000±0.0020 0.233304±0.0481 {'max_features': 'auto', 'splitter': 'mutual'} balance-scale 625 4 3 18.78 9.88 5.90 0.970000±0.0020 0.233304±0.0481 {'max_features': 'auto', 'splitter': 'mutual'}
balloons 16 4 2 4.72 2.86 2.78 0.556667±0.2941 0.021352±0.0058 {'max_features': 'auto', 'splitter': 'mutual'} balloons 16 4 2 4.72 2.86 2.78 0.556667±0.2941✗ 0.021352±0.0058 {'max_features': 'auto', 'splitter': 'mutual'}
************************************************************************************************************************* *************************************************************************************************************************
* ✗ Less than or equal to ZeroR...: 1 *
* accuracy compared to STree_default (liblinear-ovr) .: 0.0379 * * accuracy compared to STree_default (liblinear-ovr) .: 0.0379 *
************************************************************************************************************************* *************************************************************************************************************************

40
benchmark/tests/test_files/excel.test
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@@ -3,12 +3,12 @@
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"
3;5;" 624.25 s" 3;5;" 624.25 s"
3;7;" " 3;7;"Platform"
3;8;"Platform"
3;9;"iMac27" 3;9;"iMac27"
3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]" 3;11;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
4;5;" 0.17 h" 4;5;" 0.17 h"
4;10;"Stratified: False" 4;11;"Stratified: False"
4;13;"Discretized: False"
6;1;"Dataset" 6;1;"Dataset"
6;2;"Samples" 6;2;"Samples"
6;3;"Features" 6;3;"Features"
@@ -17,10 +17,11 @@
6;6;"Leaves" 6;6;"Leaves"
6;7;"Depth" 6;7;"Depth"
6;8;"Score" 6;8;"Score"
6;9;"Score Std." 6;9;"Stat"
6;10;"Time" 6;10;"Score Std."
6;11;"Time Std." 6;11;"Time"
6;12;"Hyperparameters" 6;12;"Time Std."
6;13;"Hyperparameters"
7;1;"balance-scale" 7;1;"balance-scale"
7;2;"625" 7;2;"625"
7;3;"4" 7;3;"4"
@@ -29,10 +30,11 @@
7;6;"4" 7;6;"4"
7;7;"3" 7;7;"3"
7;8;"0.97056" 7;8;"0.97056"
7;9;"0.0150468069702512" 7;9;" "
7;10;"0.01404867172241211" 7;10;"0.0150468069702512"
7;11;"0.002026269126958884" 7;11;"0.01404867172241211"
7;12;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}" 7;12;"0.002026269126958884"
7;13;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
8;1;"balloons" 8;1;"balloons"
8;2;"16" 8;2;"16"
8;3;"4" 8;3;"4"
@@ -41,8 +43,12 @@
8;6;"2" 8;6;"2"
8;7;"2" 8;7;"2"
8;8;"0.86" 8;8;"0.86"
8;9;"0.2850146195080759" 8;9;"➶"
8;10;"0.0008541679382324218" 8;10;"0.2850146195080759"
8;11;"3.629469326417878e-05" 8;11;"0.0008541679382324218"
8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}" 8;12;"3.629469326417878e-05"
10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454" 8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
11;2;"➶"
11;3;"1"
11;4;"Better than ZeroR + 10.0%"
13;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454"

40
benchmark/tests/test_files/excel2.test
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@@ -3,12 +3,12 @@
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"
3;5;"3,395.01 s" 3;5;"3,395.01 s"
3;7;" " 3;7;"Platform"
3;8;"Platform"
3;9;"iMac27" 3;9;"iMac27"
3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]" 3;11;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
4;5;" 0.94 h" 4;5;" 0.94 h"
4;10;"Stratified: False" 4;11;"Stratified: False"
4;13;"Discretized: False"
6;1;"Dataset" 6;1;"Dataset"
6;2;"Samples" 6;2;"Samples"
6;3;"Features" 6;3;"Features"
@@ -17,10 +17,11 @@
6;6;"Leaves" 6;6;"Leaves"
6;7;"Depth" 6;7;"Depth"
6;8;"Score" 6;8;"Score"
6;9;"Score Std." 6;9;"Stat"
6;10;"Time" 6;10;"Score Std."
6;11;"Time Std." 6;11;"Time"
6;12;"Hyperparameters" 6;12;"Time Std."
6;13;"Hyperparameters"
7;1;"balance-scale" 7;1;"balance-scale"
7;2;"625" 7;2;"625"
7;3;"4" 7;3;"4"
@@ -29,10 +30,11 @@
7;6;"5.9" 7;6;"5.9"
7;7;"5.9" 7;7;"5.9"
7;8;"0.98" 7;8;"0.98"
7;9;"0.001" 7;9;" "
7;10;"0.2852065515518188" 7;10;"0.001"
7;11;"0.06031593282605064" 7;11;"0.2852065515518188"
7;12;"{'splitter': 'best', 'max_features': 'auto'}" 7;12;"0.06031593282605064"
7;13;"{'splitter': 'best', 'max_features': 'auto'}"
8;1;"balloons" 8;1;"balloons"
8;2;"16" 8;2;"16"
8;3;"4" 8;3;"4"
@@ -41,8 +43,12 @@
8;6;"2.56" 8;6;"2.56"
8;7;"2.56" 8;7;"2.56"
8;8;"0.695" 8;8;"0.695"
8;9;"0.2756860130252853" 8;9;"➶"
8;10;"0.02120100021362305" 8;10;"0.2756860130252853"
8;11;"0.003526023309468471" 8;11;"0.02120100021362305"
8;12;"{'splitter': 'best', 'max_features': 'auto'}" 8;12;"0.003526023309468471"
10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0416" 8;13;"{'splitter': 'best', 'max_features': 'auto'}"
11;2;"➶"
11;3;"1"
11;4;"Better than ZeroR + 10.0%"
13;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0416"

40
benchmark/tests/test_files/excel_add_ODTE.test
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@@ -3,12 +3,12 @@
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"
3;5;"22,591.47 s" 3;5;"22,591.47 s"
3;7;" " 3;7;"Platform"
3;8;"Platform"
3;9;"Galgo" 3;9;"Galgo"
3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]" 3;11;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
4;5;" 6.28 h" 4;5;" 6.28 h"
4;10;"Stratified: False" 4;11;"Stratified: False"
4;13;"Discretized: False"
6;1;"Dataset" 6;1;"Dataset"
6;2;"Samples" 6;2;"Samples"
6;3;"Features" 6;3;"Features"
@@ -17,10 +17,11 @@
6;6;"Leaves" 6;6;"Leaves"
6;7;"Depth" 6;7;"Depth"
6;8;"Score" 6;8;"Score"
6;9;"Score Std." 6;9;"Stat"
6;10;"Time" 6;10;"Score Std."
6;11;"Time Std." 6;11;"Time"
6;12;"Hyperparameters" 6;12;"Time Std."
6;13;"Hyperparameters"
7;1;"balance-scale" 7;1;"balance-scale"
7;2;"625" 7;2;"625"
7;3;"4" 7;3;"4"
@@ -29,10 +30,11 @@
7;6;"4.180599999999999" 7;6;"4.180599999999999"
7;7;"3.536" 7;7;"3.536"
7;8;"0.96352" 7;8;"0.96352"
7;9;"0.02494974148162661" 7;9;" "
7;10;"0.3166321754455567" 7;10;"0.02494974148162661"
7;11;"0.1991881389525559" 7;11;"0.3166321754455567"
7;12;"{'base_estimator__C': 57, 'base_estimator__gamma': 0.1, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1}" 7;12;"0.1991881389525559"
7;13;"{'base_estimator__C': 57, 'base_estimator__gamma': 0.1, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1}"
8;1;"balloons" 8;1;"balloons"
8;2;"16" 8;2;"16"
8;3;"4" 8;3;"4"
@@ -41,8 +43,12 @@
8;6;"1.9976" 8;6;"1.9976"
8;7;"1.9976" 8;7;"1.9976"
8;8;"0.785" 8;8;"0.785"
8;9;"0.2461311755051675" 8;9;"➶"
8;10;"0.1156062078475952" 8;10;"0.2461311755051675"
8;11;"0.0127842418285999" 8;11;"0.1156062078475952"
8;12;"{'base_estimator__C': 5, 'base_estimator__gamma': 0.14, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1}" 8;12;"0.0127842418285999"
10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0434" 8;13;"{'base_estimator__C': 5, 'base_estimator__gamma': 0.14, 'base_estimator__kernel': 'rbf', 'base_estimator__multiclass_strategy': 'ovr', 'n_estimators': 100, 'n_jobs': -1}"
11;2;"➶"
11;3;"1"
11;4;"Better than ZeroR + 10.0%"
13;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0434"

38
benchmark/tests/test_files/excel_add_STree.test
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@@ -3,12 +3,12 @@
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"
3;5;"3,395.01 s" 3;5;"3,395.01 s"
3;7;" " 3;7;"Platform"
3;8;"Platform"
3;9;"iMac27" 3;9;"iMac27"
3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]" 3;11;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
4;5;" 0.94 h" 4;5;" 0.94 h"
4;10;"Stratified: False" 4;11;"Stratified: False"
4;13;"Discretized: False"
6;1;"Dataset" 6;1;"Dataset"
6;2;"Samples" 6;2;"Samples"
6;3;"Features" 6;3;"Features"
@@ -17,9 +17,11 @@
6;6;"Leaves" 6;6;"Leaves"
6;7;"Depth" 6;7;"Depth"
6;8;"Score" 6;8;"Score"
6;9;"Score Std." 6;9;"Stat"
6;10;"Time" 6;10;"Score Std."
6;11;"Time Std." 6;11;"Time"
6;12;"Time Std."
6;13;"Hyperparameters"
7;1;"balance-scale" 7;1;"balance-scale"
7;2;"625" 7;2;"625"
7;3;"4" 7;3;"4"
@@ -28,9 +30,11 @@
7;6;"5.9" 7;6;"5.9"
7;7;"5.9" 7;7;"5.9"
7;8;"0.98" 7;8;"0.98"
7;9;"0.001" 7;9;" "
7;10;"0.2852065515518188" 7;10;"0.001"
7;11;"0.06031593282605064" 7;11;"0.2852065515518188"
7;12;"0.06031593282605064"
7;13;"{'splitter': 'best', 'max_features': 'auto'}"
8;1;"balloons" 8;1;"balloons"
8;2;"16" 8;2;"16"
8;3;"4" 8;3;"4"
@@ -39,8 +43,12 @@
8;6;"2.56" 8;6;"2.56"
8;7;"2.56" 8;7;"2.56"
8;8;"0.695" 8;8;"0.695"
8;9;"0.2756860130252853" 8;9;"➶"
8;10;"0.02120100021362305" 8;10;"0.2756860130252853"
8;11;"0.003526023309468471" 8;11;"0.02120100021362305"
8;12;"{'splitter': 'best', 'max_features': 'auto'}" 8;12;"0.003526023309468471"
10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0416" 8;13;"{'splitter': 'best', 'max_features': 'auto'}"
11;2;"➶"
11;3;"1"
11;4;"Better than ZeroR + 10.0%"
13;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0416"

50
benchmark/tests/test_files/excel_compared.test
View File

@@ -3,10 +3,12 @@
3;1;" Score is accuracy" 3;1;" Score is accuracy"
3;2;" Execution time" 3;2;" Execution time"
3;5;" 624.25 s" 3;5;" 624.25 s"
3;8;"Platform" 3;7;"Platform"
3;9;"iMac27" 3;9;"iMac27"
3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]" 3;11;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
4;10;"Stratified: False" 4;5;" 0.17 h"
4;11;"Stratified: False"
4;13;"Discretized: False"
6;1;"Dataset" 6;1;"Dataset"
6;2;"Samples" 6;2;"Samples"
6;3;"Features" 6;3;"Features"
@@ -21,32 +23,32 @@
6;12;"Time Std." 6;12;"Time Std."
6;13;"Hyperparameters" 6;13;"Hyperparameters"
7;1;"balance-scale" 7;1;"balance-scale"
7;2;625 7;2;"625"
7;3;4 7;3;"4"
7;4;3 7;4;"3"
7;5;7 7;5;"7"
7;6;4 7;6;"4"
7;7;3 7;7;"3"
7;8;0.97056 7;8;"0.97056"
7;9;" " 7;9;" "
7;10;0.0150468069702512 7;10;"0.0150468069702512"
7;11;0.01404867172241211 7;11;"0.01404867172241211"
7;12;0.002026269126958884 7;12;"0.002026269126958884"
7;13;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}" 7;13;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
8;1;"balloons" 8;1;"balloons"
8;2;16 8;2;"16"
8;3;4 8;3;"4"
8;4;2 8;4;"2"
8;5;3 8;5;"3"
8;6;2 8;6;"2"
8;7;2 8;7;"2"
8;8;0.86 8;8;"0.86"
8;9;"✔" 8;9;"✔"
8;10;0.2850146195080759 8;10;"0.2850146195080759"
8;11;0.0008541679382324218 8;11;"0.0008541679382324218"
8;12;3.629469326417878e-05 8;12;"3.629469326417878e-05"
8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}" 8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
11;2;"✔" 11;2;"✔"
11;3;1 11;3;"1"
11;4;"Equal to best" 11;4;"Equal to best"
13;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454" 13;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454"

3
benchmark/tests/test_files/report.test
View File

@@ -9,7 +9,8 @@
Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
============================== ====== ===== === ======= ======= ======= =============== ================= =============== ============================== ====== ===== === ======= ======= ======= =============== ================= ===============
balance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} balance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
balloons 16 4 2 3.00 2.00 2.00 0.860000±0.2850 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} balloons 16 4 2 3.00 2.00 2.00 0.860000±0.2850➶ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
************************************************************************************************************************* *************************************************************************************************************************
* ➶ Better than ZeroR + 10.0%.....: 1 *
* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 * * accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
************************************************************************************************************************* *************************************************************************************************************************

2
benchmark/tests/test_files/report_compared.test
View File

@@ -11,6 +11,6 @@ Dataset Sampl. Feat. Cls Nodes Leaves Depth Score
balance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} balance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
balloons 16 4 2 3.00 2.00 2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'} balloons 16 4 2 3.00 2.00 2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
************************************************************************************************************************* *************************************************************************************************************************
* ✔ Equal to best .....: 1 * * ✔ Equal to best.................: 1 *
* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 * * accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
************************************************************************************************************************* *************************************************************************************************************************