Add Excel tests

.flake8

@@ -0,0 +1,2 @@
+[flake8]
+exclude = .git,__init__.py

benchmark/Experiments.py

@@ -348,10 +348,12 @@ class GridSearch:
         platform,
         progress_bar=True,
         folds=5,
+        test=False,
     ):
         today = datetime.now()
-        self.time = today.strftime("%H:%M:%S")
+        self.test = test
-        self.date = today.strftime("%Y-%m-%d")
+        self.time = "12:00:00" if test else today.strftime("%H:%M:%S")
+        self.date = "2022-02-22" if test else today.strftime("%Y-%m-%d")
         self.output_file = os.path.join(
             Folders.results,
             Files.grid_output(
@@ -412,7 +414,7 @@ class GridSearch:
             return f"{duration/3600:.3f}h"
 
     def _store_result(self, name, grid, duration):
-        d_message = self._duration_message(duration)
+        d_message = "1s" if self.test else self._duration_message(duration)
         message = (
             f"v. {self.version}, Computed on {self.platform} on "
             f"{self.date} at {self.time} "

benchmark/Results.py

@@ -18,8 +18,8 @@ class BaseReport(abc.ABC):
             if not os.path.isfile(os.path.join(Folders.results, file_name)):
                 raise FileNotFoundError(f"{file_name} does not exists!")
             else:
-                file_name = os.path.join(Folders.results, file_name)
+                self.file_name = os.path.join(Folders.results, file_name)
-        with open(file_name) as f:
+        with open(self.file_name) as f:
             self.data = json.load(f)
         self.best_acc_file = best_file
         self.lines = self.data if best_file else self.data["results"]
@@ -205,7 +205,6 @@ class ReportBest(BaseReport):
         self.grid = grid
         file_name = os.path.join(Folders.results, name)
         super().__init__(file_name, best_file=True)
-        self.compare = False
         self.score_name = score
         self.model = model
 
@@ -246,11 +245,6 @@ class ReportBest(BaseReport):
 
     def footer(self, accuracy):
         self.header_line("*")
-        if self.compare:
-            for key, value in self._compare_totals.items():
-                self.header_line(
-                    f" {key} {self._status_meaning(key)} .....: {value:2d}"
-                )
         self.header_line(
             f" Scores compared to stree_default accuracy (liblinear-ovr) .: "
             f"{accuracy/BEST_ACCURACY_STREE:7.4f}"

benchmark/tests/Excel_test.py

@@ -0,0 +1,81 @@
+import os
+import csv
+import unittest
+from openpyxl import load_workbook
+from xlsxwriter import Workbook
+from ..Results import Excel
+from ..Utils import Folders
+
+
+class ExcelTest(unittest.TestCase):
+    def __init__(self, *args, **kwargs):
+        os.chdir(os.path.dirname(os.path.abspath(__file__)))
+        super().__init__(*args, **kwargs)
+
+    def tearDown(self) -> None:
+        files = [
+            "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.xlsx",
+            "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.xlsx",
+            "results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.xlsx",
+        ]
+        for file_name in files:
+            file_name = os.path.join(Folders.results, file_name)
+            if os.path.exists(file_name):
+                os.remove(file_name)
+        return super().tearDown()
+
+    def check_excel_sheet(self, sheet, file_name):
+        with open(file_name, "r") as f:
+            expected = csv.reader(f, delimiter=";")
+            for row, col, value in expected:
+                if value.isdigit():
+                    value = int(value)
+                else:
+                    try:
+                        value = float(value)
+                    except ValueError:
+                        pass
+                self.assertEqual(sheet.cell(int(row), int(col)).value, value)
+
+    def test_report_excel_compared(self):
+        file_name = (
+            "results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"
+        )
+        report = Excel(file_name, compare=True)
+        report.report()
+        file_output = report.get_file_name()
+        book = load_workbook(file_output)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "test_files/excel_compared.test")
+
+    def test_report_excel(self):
+        file_name = (
+            "results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"
+        )
+        report = Excel(file_name, compare=False)
+        report.report()
+        file_output = report.get_file_name()
+        book = load_workbook(file_output)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "test_files/excel.test")
+
+    def test_Excel_Add_sheet(self):
+        file_name = (
+            "results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"
+        )
+        excel_file_name = file_name.replace(".json", ".xlsx")
+        book = Workbook(excel_file_name)
+        excel = Excel(file_name=file_name, book=book)
+        excel.report()
+        report = Excel(
+            file_name="results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20"
+            "_0.json",
+            book=book,
+        )
+        report.report()
+        book.close()
+        book = load_workbook(excel_file_name)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "test_files/excel_add_STree.test")
+        sheet = book["ODTE"]
+        self.check_excel_sheet(sheet, "test_files/excel_add_ODTE.test")

benchmark/tests/GridSearch_test.py

@@ -24,6 +24,7 @@ class GridSearchTest(unittest.TestCase):
             "progress_bar": False,
             "platform": "Test",
             "folds": 2,
+            "test": True,
         }
         return GridSearch(**params)
 
@@ -66,18 +67,15 @@ class GridSearchTest(unittest.TestCase):
                     "kernel": "liblinear",
                     "multiclass_strategy": "ovr",
                 },
-                "",
+                "v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s",
             ],
             "balloons": [
                 0.625,
                 {"C": 1.0, "kernel": "linear", "multiclass_strategy": "ovr"},
-                "",
+                "v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s",
             ],
         }
-        dt = Datasets()
+        self.assertSequenceEqual(data, expected)
-        for dataset in dt:
-            self.assertEqual(data[dataset][0], expected[dataset][0])
-            self.assertSequenceEqual(data[dataset][1], expected[dataset][1])
 
     def test_duration_message(self):
         expected = ["47.234s", "5.421m", "1.177h"]

benchmark/tests/Report_test.py

@@ -0,0 +1,99 @@
+import os
+import unittest
+from io import StringIO
+from unittest.mock import patch
+from ..Results import Report, BaseReport, ReportBest
+from ..Utils import Symbols
+
+
+class ReportTest(unittest.TestCase):
+    def __init__(self, *args, **kwargs):
+        os.chdir(os.path.dirname(os.path.abspath(__file__)))
+        super().__init__(*args, **kwargs)
+
+    def test_BaseReport(self):
+        with patch.multiple(BaseReport, __abstractmethods__=set()):
+            file_name = (
+                "results/results_accuracy_STree_iMac27_2021-09-30_11:"
+                "42:07_0.json"
+            )
+            a = BaseReport(file_name)
+            self.assertIsNone(a.header())
+            self.assertIsNone(a.print_line(None))
+            self.assertIsNone(a.footer(accuracy=1.0))
+
+    def test_report_with_folder(self):
+        report = Report(
+            file_name="results/results_accuracy_STree_iMac27_2021-09-30_11:"
+            "42:07_0.json"
+        )
+        with patch("sys.stdout", new=StringIO()) as fake_out:
+            report.report()
+            with open("test_files/report.test", "r") as f:
+                expected = f.read()
+            self.assertEqual(fake_out.getvalue(), expected)
+
+    def test_report_without_folder(self):
+        report = Report(
+            file_name="results_accuracy_STree_iMac27_2021-09-30_11:42:07_0"
+            ".json"
+        )
+        with patch("sys.stdout", new=StringIO()) as fake_out:
+            report.report()
+            with open("test_files/report.test", "r") as f:
+                expected = f.read()
+            self.assertEqual(fake_out.getvalue(), expected)
+
+    def test_report_compared(self):
+        report = Report(
+            file_name="results_accuracy_STree_iMac27_2021-09-30_11:42:07_0"
+            ".json",
+            compare=True,
+        )
+        with patch("sys.stdout", new=StringIO()) as fake_out:
+            report.report()
+            with open("test_files/report_compared.test", "r") as f:
+                expected = f.read()
+            self.assertEqual(fake_out.getvalue(), expected)
+
+    def test_compute_status(self):
+        file_name = "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"
+        report = Report(
+            file_name=file_name,
+            compare=True,
+        )
+        with patch("sys.stdout", new=StringIO()):
+            report.report()
+        res = report._compute_status("balloons", 0.99)
+        self.assertEqual(res, Symbols.better_best)
+        res = report._compute_status("balloons", 1.0)
+        self.assertEqual(res, Symbols.better_best)
+
+    def test_report_file_not_found(self):
+        with self.assertRaises(FileNotFoundError):
+            _ = Report("unknown_file")
+
+    def test_report_best(self):
+        report = ReportBest("accuracy", "STree", best=True, grid=False)
+        with patch("sys.stdout", new=StringIO()) as fake_out:
+            report.report()
+            with open("test_files/report_best.test", "r") as f:
+                expected = f.read()
+            self.assertEqual(fake_out.getvalue(), expected)
+
+    def test_report_grid(self):
+        report = ReportBest("accuracy", "STree", best=False, grid=True)
+        with patch("sys.stdout", new=StringIO()) as fake_out:
+            report.report()
+            with open("test_files/report_grid.test", "r") as f:
+                expected = f.read()
+            self.assertEqual(fake_out.getvalue(), expected)
+
+    def test_report_best_both(self):
+        report = ReportBest("accuracy", "STree", best=True, grid=True)
+        with patch("sys.stdout", new=StringIO()) as fake_out:
+            report.report()
+            with open("test_files/report_best.test", "r") as f:
+                expected = f.read()
+
+            self.assertEqual(fake_out.getvalue(), expected)

benchmark/tests/SQL_test.py

@@ -0,0 +1,81 @@
+import os
+import csv
+import unittest
+from openpyxl import load_workbook
+from xlsxwriter import Workbook
+from ..Results import Excel
+from ..Utils import Folders
+
+
+class SQLTest(unittest.TestCase):
+    def __init__(self, *args, **kwargs):
+        os.chdir(os.path.dirname(os.path.abspath(__file__)))
+        super().__init__(*args, **kwargs)
+
+    def tearDown(self) -> None:
+        files = [
+            "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.xlsx",
+            "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.xlsx",
+            "results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.sql",
+        ]
+        for file_name in files:
+            file_name = os.path.join(Folders.results, file_name)
+            if os.path.exists(file_name):
+                os.remove(file_name)
+        return super().tearDown()
+
+    def check_excel_sheet(self, sheet, file_name):
+        with open(file_name, "r") as f:
+            expected = csv.reader(f, delimiter=";")
+            for row, col, value in expected:
+                if value.isdigit():
+                    value = int(value)
+                else:
+                    try:
+                        value = float(value)
+                    except ValueError:
+                        pass
+                self.assertEqual(sheet.cell(int(row), int(col)).value, value)
+
+    def test_report_excel_compared(self):
+        file_name = (
+            "results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"
+        )
+        report = Excel(file_name, compare=True)
+        report.report()
+        file_output = report.get_file_name()
+        book = load_workbook(file_output)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "test_files/excel_compared.test")
+
+    def test_report_excel(self):
+        file_name = (
+            "results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"
+        )
+        report = Excel(file_name, compare=False)
+        report.report()
+        file_output = report.get_file_name()
+        book = load_workbook(file_output)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "test_files/excel.test")
+
+    def test_Excel_Add_sheet(self):
+        file_name = (
+            "results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"
+        )
+        excel_file_name = file_name.replace(".json", ".xlsx")
+        book = Workbook(excel_file_name)
+        excel = Excel(file_name=file_name, book=book)
+        excel.report()
+        report = Excel(
+            file_name="results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20"
+            "_0.json",
+            book=book,
+        )
+        report.report()
+        book.close()
+        book = load_workbook(excel_file_name)
+        sheet = book["STree"]
+        self.check_excel_sheet(sheet, "test_files/excel_add_STree.test")
+        sheet = book["ODTE"]
+        self.check_excel_sheet(sheet, "test_files/excel_add_ODTE.test")

benchmark/tests/Util_test.py

@@ -121,6 +121,7 @@ class UtilTest(unittest.TestCase):
                 "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
                 "results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0."
                 "json",
+                "results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json",
             ],
         )
         self.assertCountEqual(

benchmark/tests/__init__.py

@@ -4,6 +4,9 @@ from .Dataset_test import DatasetTest
 from .BestResults_test import BestResultTest
 from .Experiment_test import ExperimentTest
 from .GridSearch_test import GridSearchTest
+from .Report_test import ReportTest
+from .Excel_test import ExcelTest
+from .SQL_test import SQLTest
 
 all = [
     "UtilTest",
@@ -12,4 +15,7 @@ all = [
     "BestResultTest",
     "ExperimentTest",
     "GridSearchTest",
+    "ReportTest",
+    "ExcelTest",
+    "SQLTest",
 ]

benchmark/tests/results/grid_output_accuracy_STree.json

@@ -6,7 +6,7 @@
             "kernel": "liblinear",
             "multiclass_strategy": "ovr"
         },
-        "v. 1.2.4, Computed on Test on 2022-04-24 at 00:18:17 took 0.261s"
+        "v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s"
     ],
     "balloons": [
         0.625,
@@ -15,6 +15,6 @@
             "kernel": "linear",
             "multiclass_strategy": "ovr"
         },
-        "v. 1.2.4, Computed on Test on 2022-04-24 at 00:18:17 took 0.478s"
+        "v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s"
     ]
 }

benchmark/tests/results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json

@@ -0,0 +1,57 @@
+{
+  "score_name": "accuracy",
+  "title": "Gridsearched hyperparams v022.1b random_init",
+  "model": "ODTE",
+  "version": "0.3.2",
+  "stratified": false,
+  "folds": 5,
+  "date": "2022-04-20",
+  "time": "10:52:20",
+  "duration": 22591.471411943436,
+  "seeds": [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1],
+  "platform": "Galgo",
+  "results": [
+    {
+      "dataset": "balance-scale",
+      "samples": 625,
+      "features": 4,
+      "classes": 3,
+      "hyperparameters": {
+        "base_estimator__C": 57,
+        "base_estimator__gamma": 0.1,
+        "base_estimator__kernel": "rbf",
+        "base_estimator__multiclass_strategy": "ovr",
+        "n_estimators": 100,
+        "n_jobs": -1
+      },
+      "nodes": 7.361199999999999,
+      "leaves": 4.180599999999999,
+      "depth": 3.536,
+      "score": 0.96352,
+      "score_std": 0.024949741481626608,
+      "time": 0.31663217544555666,
+      "time_std": 0.19918813895255585
+    },
+    {
+      "dataset": "balloons",
+      "samples": 16,
+      "features": 4,
+      "classes": 2,
+      "hyperparameters": {
+        "base_estimator__C": 5,
+        "base_estimator__gamma": 0.14,
+        "base_estimator__kernel": "rbf",
+        "base_estimator__multiclass_strategy": "ovr",
+        "n_estimators": 100,
+        "n_jobs": -1
+      },
+      "nodes": 2.9951999999999996,
+      "leaves": 1.9975999999999998,
+      "depth": 1.9975999999999998,
+      "score": 0.785,
+      "score_std": 0.2461311755051675,
+      "time": 0.11560620784759522,
+      "time_std": 0.012784241828599895
+    }
+  ]
+}

benchmark/tests/test_files/excel.test

@@ -0,0 +1,48 @@
+1;1;" Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
+2;1;" With gridsearched hyperparameters"
+3;1;" Score is accuracy"
+3;2;" Execution time"
+3;5;" 624.25 s"
+3;7;" "
+3;8;"Platform"
+3;9;"iMac27"
+3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+4;5;"  0.17 h"
+4;10;"Stratified: False"
+6;1;"Dataset"
+6;2;"Samples"
+6;3;"Features"
+6;4;"Classes"
+6;5;"Nodes"
+6;6;"Leaves"
+6;7;"Depth"
+6;8;"Score"
+6;9;"Score Std."
+6;10;"Time"
+6;11;"Time Std."
+6;12;"Hyperparameters"
+7;1;"balance-scale"
+7;2;"625"
+7;3;"4"
+7;4;"3"
+7;5;"7"
+7;6;"4"
+7;7;"3"
+7;8;"0.97056"
+7;9;"0.0150468069702512"
+7;10;"0.01404867172241211"
+7;11;"0.002026269126958884"
+7;12;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;1;"balloons"
+8;2;"16"
+8;3;"4"
+8;4;"2"
+8;5;"3"
+8;6;"2"
+8;7;"2"
+8;8;"0.86"
+8;9;"0.2850146195080759"
+8;10;"0.0008541679382324218"
+8;11;"3.629469326417878e-05"
+8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0454"

benchmark/tests/test_files/excel_add_ODTE.test

@@ -0,0 +1,48 @@
+1;1;" Report ODTE ver. 0.3.2 with 5 Folds cross validation and 10 random seeds. 2022-04-20 10:52:20"
+2;1;" Gridsearched hyperparams v022.1b random_init"
+3;1;" Score is accuracy"
+3;2;" Execution time"
+3;5;"22,591.47 s"
+3;7;" "
+3;8;"Platform"
+3;9;"Galgo"
+3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+4;5;"  6.28 h"
+4;10;"Stratified: False"
+6;1;"Dataset"
+6;2;"Samples"
+6;3;"Features"
+6;4;"Classes"
+6;5;"Nodes"
+6;6;"Leaves"
+6;7;"Depth"
+6;8;"Score"
+6;9;"Score Std."
+6;10;"Time"
+6;11;"Time Std."
+6;12;"Hyperparameters"
+7;1;"balance-scale"
+7;2;"625"
+7;3;"4"
+7;4;"3"
+7;5;"7.361199999999999"
+7;6;"4.180599999999999"
+7;7;"3.536"
+7;8;"0.96352"
+7;9;"0.02494974148162661"
+7;10;"0.3166321754455567"
+7;11;"0.1991881389525559"
+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}"
+8;1;"balloons"
+8;2;"16"
+8;3;"4"
+8;4;"2"
+8;5;"2.9952"
+8;6;"1.9976"
+8;7;"1.9976"
+8;8;"0.785"
+8;9;"0.2461311755051675"
+8;10;"0.1156062078475952"
+8;11;"0.0127842418285999"
+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}"
+10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0434"

benchmark/tests/test_files/excel_add_STree.test

@@ -0,0 +1,46 @@
+1;1;" Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-10-27 09:40:40"
+2;1;" default A"
+3;1;" Score is accuracy"
+3;2;" Execution time"
+3;5;"3,395.01 s"
+3;7;" "
+3;8;"Platform"
+3;9;"iMac27"
+3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+4;5;"  0.94 h"
+4;10;"Stratified: False"
+6;1;"Dataset"
+6;2;"Samples"
+6;3;"Features"
+6;4;"Classes"
+6;5;"Nodes"
+6;6;"Leaves"
+6;7;"Depth"
+6;8;"Score"
+6;9;"Score Std."
+6;10;"Time"
+6;11;"Time Std."
+7;1;"balance-scale"
+7;2;"625"
+7;3;"4"
+7;4;"3"
+7;5;"11.08"
+7;6;"5.9"
+7;7;"5.9"
+7;8;"0.98"
+7;9;"0.001"
+7;10;"0.2852065515518188"
+7;11;"0.06031593282605064"
+8;1;"balloons"
+8;2;"16"
+8;3;"4"
+8;4;"2"
+8;5;"4.12"
+8;6;"2.56"
+8;7;"2.56"
+8;8;"0.695"
+8;9;"0.2756860130252853"
+8;10;"0.02120100021362305"
+8;11;"0.003526023309468471"
+8;12;"{'splitter': 'iwss', 'max_features': 'auto'}"
+10;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0416"

benchmark/tests/test_files/excel_compared.test

@@ -0,0 +1,52 @@
+1;1;" Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07"
+2;1;" With gridsearched hyperparameters"
+3;1;" Score is accuracy"
+3;2;" Execution time"
+3;5;" 624.25 s"
+3;8;"Platform"
+3;9;"iMac27"
+3;10;"Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+4;10;"Stratified: False"
+6;1;"Dataset"
+6;2;"Samples"
+6;3;"Features"
+6;4;"Classes"
+6;5;"Nodes"
+6;6;"Leaves"
+6;7;"Depth"
+6;8;"Score"
+6;9;"Stat"
+6;10;"Score Std."
+6;11;"Time"
+6;12;"Time Std."
+6;13;"Hyperparameters"
+7;1;"balance-scale"
+7;2;625
+7;3;4
+7;4;3
+7;5;7
+7;6;4
+7;7;3
+7;8;0.97056
+7;9;" "
+7;10;0.0150468069702512
+7;11;0.01404867172241211
+7;12;0.002026269126958884
+7;13;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+8;1;"balloons"
+8;2;16
+8;3;4
+8;4;2
+8;5;3
+8;6;2
+8;7;2
+8;8;0.86
+8;9;"✔"
+8;10;0.2850146195080759
+8;11;0.0008541679382324218
+8;12;3.629469326417878e-05
+8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
+11;2;"✔"
+11;3;1
+11;4;"Equal to best"
+13;1;"** Accuracy compared to stree_default (liblinear-ovr) .:  0.0454"

benchmark/tests/test_files/report.test

@@ -0,0 +1,15 @@
+***********************************************************************************************************************
+* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07                      *
+* With gridsearched hyperparameters                                                                                   *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
+* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                             *
+* Score is accuracy                                                                                                   *
+***********************************************************************************************************************
+
+Dataset                        Samp  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': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, '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': 10000.0, 'multiclass_strategy': 'ovr'} 
+***********************************************************************************************************************
+* Accuracy compared to stree_default (liblinear-ovr) .:  0.0454                                                       *
+***********************************************************************************************************************

benchmark/tests/test_files/report_best.test

@@ -0,0 +1,11 @@
+******************************************************************************************************************************************************************
+* Report Best accuracy Scores with STree in any platform                                                                                                         *
+******************************************************************************************************************************************************************
+
+Dataset                        Score    File/Message                                                                 Hyperparameters                               
+============================== ======== ============================================================================ ============================================= 
+balance-scale                  0.980000 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json                     {'splitter': 'iwss', 'max_features': 'auto'} 
+balloons                       0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                     {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'} 
+******************************************************************************************************************************************************************
+* Scores compared to stree_default accuracy (liblinear-ovr) .:  0.0457                                                                                           *
+******************************************************************************************************************************************************************

benchmark/tests/test_files/report_compared.test

@@ -0,0 +1,16 @@
+***********************************************************************************************************************
+* Report STree ver. 1.2.3 with 5 Folds cross validation and 10 random seeds. 2021-09-30 11:42:07                      *
+* With gridsearched hyperparameters                                                                                   *
+* Random seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Stratified: False                                            *
+* Execution took  624.25 seconds,   0.17 hours, on iMac27                                                             *
+* Score is accuracy                                                                                                   *
+***********************************************************************************************************************
+
+Dataset                        Samp  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': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, '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': 10000.0, 'multiclass_strategy': 'ovr'} 
+***********************************************************************************************************************
+* ✔ Equal to best .....:  1                                                                                           *
+* Accuracy compared to stree_default (liblinear-ovr) .:  0.0454                                                       *
+***********************************************************************************************************************

benchmark/tests/test_files/report_grid.test

@@ -0,0 +1,11 @@
+******************************************************************************************************************************************************************
+* Report Grid accuracy Scores with STree in any platform                                                                                                         *
+******************************************************************************************************************************************************************
+
+Dataset                        Score    File/Message                                                                 Hyperparameters                               
+============================== ======== ============================================================================ ============================================= 
+balance-scale                  0.919995 v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s                 {'C': 1.0, 'kernel': 'liblinear', 'multiclass_strategy': 'ovr'} 
+balloons                       0.625000 v. 1.2.4, Computed on Test on 2022-02-22 at 12:00:00 took 1s                 {'C': 1.0, 'kernel': 'linear', 'multiclass_strategy': 'ovr'} 
+******************************************************************************************************************************************************************
+* Scores compared to stree_default accuracy (liblinear-ovr) .:  0.0384                                                                                           *
+******************************************************************************************************************************************************************

requirements.txt

@@ -3,4 +3,5 @@ scikit-learn
 odte
 mufs
 xlsxwriter
+openpyxl
 tqdm