Complete be_benchmark tests

benchmark/Results.py

@@ -1,3 +1,4 @@
+from multiprocessing.sharedctypes import Value
 import os
 from operator import itemgetter
 import math
@@ -641,6 +642,8 @@ class Benchmark:
         summary = Summary()
         summary.acquire(given_score=self._score)
         self._models = summary.get_models()
+        if self._models == []:
+            raise ValueError(NO_RESULTS)
         for model in self._models:
             best = summary.best_result(
                 criterion="model", value=model, score=self._score

benchmark/scripts/be_benchmark.py

@@ -19,6 +19,6 @@ def main(args_test=None):
         benchmark.exreport()
         if args.excel:
             benchmark.excel()
-            Files.open(benchmark.get_excel_file_name())
+            Files.open(benchmark.get_excel_file_name(), test=args.quiet)
         if args.tex_output:
             print(f"File {benchmark.get_tex_file()} generated")

benchmark/tests/Benchmark_test.py

@@ -3,19 +3,19 @@ from io import StringIO
 from unittest.mock import patch
 from openpyxl import load_workbook
 from .TestBase import TestBase
-from ..Utils import Folders, Files
+from ..Utils import Folders, Files, NO_RESULTS
 from ..Results import Benchmark
 
 
 class BenchmarkTest(TestBase):
-    def tearDown(self):
+    def tearDown(self) -> None:
         benchmark = Benchmark("accuracy", visualize=False)
         files = []
         for score in ["accuracy", "unknown"]:
             files.append(Files.exreport(score))
             files.append(Files.exreport_output(score))
             files.append(Files.exreport_err(score))
-        files.append(Files.exreport_excel("accuracy"))
+        files.append(Files.exreport_excel(score))
         files.append(Files.exreport_pdf)
         files.append(Files.tex_output("accuracy"))
         self.remove_files(files, Folders.exreport)
@@ -65,20 +65,20 @@ class BenchmarkTest(TestBase):
         self.assertFalse(os.path.exists(Folders.report))
 
     def test_exreport_error(self):
-        benchmark = Benchmark("unknown", visualize=False)
+        benchmark = Benchmark("accuracy", visualize=False)
         benchmark.compile_results()
         benchmark.save_results()
+        # Make Rscript exreport fail
+        benchmark._score = "unknown"
         with patch(self.output, new=StringIO()) as stdout:
             benchmark.exreport()
         self.check_output_file(stdout, "exreport_error")
 
     def test_exreport_no_data(self):
         benchmark = Benchmark("f1-weighted", visualize=False)
-        benchmark.compile_results()
+        with self.assertRaises(ValueError) as msg:
-        benchmark.save_results()
+            benchmark.compile_results()
-        with patch(self.output, new=StringIO()) as stdout:
+        self.assertEqual(str(msg.exception), NO_RESULTS)
-            benchmark.exreport()
-        self.check_output_file(stdout, "exreport_error")
 
     def test_tex_output(self):
         benchmark = Benchmark("accuracy", visualize=False)

benchmark/tests/scripts/Be_Benchmark_test.py

@@ -1,36 +1,66 @@
 import os
-import json
+from openpyxl import load_workbook
-from ...Utils import Folders, Files
+from ...Utils import NO_RESULTS, Folders, Files
 from ..TestBase import TestBase
 
 
 class BeBenchmarkTest(TestBase):
     def setUp(self):
         self.prepare_scripts_env()
+        self.score = "accuracy"
 
     def tearDown(self) -> None:
         files = []
-        for score in ["accuracy", "unknown"]:
+        for score in [self.score, "unknown"]:
             files.append(Files.exreport(score))
             files.append(Files.exreport_output(score))
             files.append(Files.exreport_err(score))
-
+        files.append(Files.exreport_excel(self.score))
-        files.append(Files.exreport_excel("accuracy"))
         files.append(Files.exreport_pdf)
-        files.append(Files.tex_output("accuracy"))
+        files.append(Files.tex_output(self.score))
         self.remove_files(files, Folders.exreport)
         self.remove_files(files, ".")
         return super().tearDown()
 
-    def test_be_benchmark(self):
+    def test_be_benchmark_complete(self):
         stdout, stderr = self.execute_script(
-            "be_benchmark", ["-s", "accuracy", "-q", "1", "-t", "1", "-x", "1"]
+            "be_benchmark", ["-s", self.score, "-q", "1", "-t", "1", "-x", "1"]
         )
         self.assertEqual(stderr.getvalue(), "")
         # Check output
-        self.check_output_file(stdout, "exreport_report")
+        self.check_output_file(stdout, "be_benchmark_complete")
         # Check csv file
-        file_name = os.path.join(Folders.exreport, Files.exreport("accuracy"))
+        file_name = os.path.join(Folders.exreport, Files.exreport(self.score))
         self.check_file_file(file_name, "exreport_csv")
         # Check tex file
+        file_name = os.path.join(
+            Folders.exreport, Files.tex_output(self.score)
+        )
+        self.assertTrue(os.path.exists(file_name))
+        self.check_file_file(file_name, "exreport_tex")
         # Check excel file
+        file_name = os.path.join(
+            Folders.exreport, Files.exreport_excel(self.score)
+        )
+        book = load_workbook(file_name)
+        for sheet_name in book.sheetnames:
+            sheet = book[sheet_name]
+            self.check_excel_sheet(sheet, f"exreport_excel_{sheet_name}")
+
+    def test_be_benchmark_single(self):
+        stdout, stderr = self.execute_script(
+            "be_benchmark", ["-s", self.score, "-q", "1"]
+        )
+        self.assertEqual(stderr.getvalue(), "")
+        # Check output
+        self.check_output_file(stdout, "be_benchmark")
+        # Check csv file
+        file_name = os.path.join(Folders.exreport, Files.exreport(self.score))
+        self.check_file_file(file_name, "exreport_csv")
+
+    def test_be_benchmark_no_data(self):
+        stdout, stderr = self.execute_script(
+            "be_benchmark", ["-s", "f1-weighted"]
+        )
+        self.assertEqual(stderr.getvalue(), "")
+        self.assertEqual(stdout.getvalue(), f"{NO_RESULTS}\n")

benchmark/tests/test_files/be_benchmark.test

@@ -0,0 +1,32 @@
+Dataset                            ODTE      RandomForest      STree     
+============================== ============= ============= ============= 
+balance-scale                  0.96352±0.025 0.83616±0.026 0.97056±0.015 
+balloons                       0.78500±0.246 0.62500±0.250 0.86000±0.285 
+
+Model                          File Name                                                                   Score
+============================== =========================================================================== ========
+             ODTE              results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json               0.04341
+         RandomForest          results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json      0.03627
+            STree              results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json             0.04544
+****************************************************************************************************
+Benchmark Ok
+****************************************************************************************************
+---------------------------------------------------------------------
+Friedman test, objetive maximize output variable accuracy. Obtained p-value: 1.3534e-01
+Chi squared with 2 degrees of freedom statistic: 4.0000
+Test accepted: p-value: 1.3534e-01 >= 0.0500
+---------------------------------------------------------------------
+Control post hoc test for output accuracy
+Adjust method: Holm
+
+Control method: STree
+p-values:
+           ODTE	0.3173
+   RandomForest	0.0910
+---------------------------------------------------------------------
+$testMultiple
+               classifier     pvalue rank win tie loss
+STree               STree         NA    1  NA  NA   NA
+ODTE                 ODTE 0.31731051    2   2   0    0
+RandomForest RandomForest 0.09100053    3   2   0    0
+

benchmark/tests/test_files/be_benchmark_complete.test

@@ -0,0 +1,33 @@
+Dataset                            ODTE      RandomForest      STree     
+============================== ============= ============= ============= 
+balance-scale                  0.96352±0.025 0.83616±0.026 0.97056±0.015 
+balloons                       0.78500±0.246 0.62500±0.250 0.86000±0.285 
+
+Model                          File Name                                                                   Score
+============================== =========================================================================== ========
+             ODTE              results/results_accuracy_ODTE_Galgo_2022-04-20_10:52:20_0.json               0.04341
+         RandomForest          results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json      0.03627
+            STree              results/results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json             0.04544
+****************************************************************************************************
+Benchmark Ok
+****************************************************************************************************
+---------------------------------------------------------------------
+Friedman test, objetive maximize output variable accuracy. Obtained p-value: 1.3534e-01
+Chi squared with 2 degrees of freedom statistic: 4.0000
+Test accepted: p-value: 1.3534e-01 >= 0.0500
+---------------------------------------------------------------------
+Control post hoc test for output accuracy
+Adjust method: Holm
+
+Control method: STree
+p-values:
+           ODTE	0.3173
+   RandomForest	0.0910
+---------------------------------------------------------------------
+$testMultiple
+               classifier     pvalue rank win tie loss
+STree               STree         NA    1  NA  NA   NA
+ODTE                 ODTE 0.31731051    2   2   0    0
+RandomForest RandomForest 0.09100053    3   2   0    0
+
+File exreport/exreport_accuracy.tex generated

benchmark/tests/test_files/exreport_error.test

@@ -1,7 +1,9 @@
 ****************************************************************************************************
 Error computing benchmark
 ****************************************************************************************************
-Error in dim(ordered) <- ns : 
+Error in file(file, "rt") : no se puede abrir la conexión
-  dims [producto 1] no coincide con la longitud del objeto [0]
+Calls: read.csv -> read.table -> file
-Calls: testMultipleControl -> .doFriedmanTest -> <Anonymous> -> cast
+Además: Warning message:
+In file(file, "rt") :
+  no fue posible abrir el archivo 'exreport/exreport_unknown.csv': No such file or directory
 Ejecución interrumpida