Fix test issues

benchmark/Experiments.py

@@ -156,7 +156,7 @@ class BestResults:
                     self._process_datafile(results, data, name)
                     found = True
         if not found:
-            raise ValueError(f"No results found")
+            raise ValueError("** No results found **")
         # Build best results json file
         output = {}
         datasets = Datasets()

benchmark/Results.py

@@ -521,6 +521,34 @@ class Excel(BaseReport):
             self.book.close()
 
 
+class ReportDatasets:
+    @staticmethod
+    def report():
+        data_sets = Datasets()
+        color_line = TextColor.LINE1
+        print(color_line, end="")
+        print(f"{'Dataset':30s} Samp. Feat. Cls Balance")
+        print("=" * 30 + " ===== ===== === " + "=" * 40)
+        for dataset in data_sets:
+            X, y = data_sets.load(dataset)
+            color_line = (
+                TextColor.LINE2
+                if color_line == TextColor.LINE1
+                else TextColor.LINE1
+            )
+            values, counts = np.unique(y, return_counts=True)
+            comp = ""
+            sep = ""
+            for count in counts:
+                comp += f"{sep}{count/sum(counts)*100:5.2f}%"
+                sep = "/ "
+            print(color_line, end="")
+            print(
+                f"{dataset:30s} {X.shape[0]:5,d} {X.shape[1]:5,d} "
+                f"{len(np.unique(y)):3d} {comp:40s}"
+            )
+
+
 class SQL(BaseReport):
     table_name = "results"
 
@@ -1112,8 +1140,7 @@ class Summary:
             score, model, input_data, sort_key, number
         )
         if data == []:
-            print("*No results found*")
+            raise ValueError("** No results found **")
-            exit(1)
         max_file = max(len(x["file"]) for x in data)
         max_title = max(len(x["title"]) for x in data)
         if self.hidden:
@@ -1285,11 +1312,14 @@ class Summary:
         return best_results
 
     def show_top(self, score="accuracy", n=10):
+        try:
             self.list_results(
                 score=score,
                 input_data=self.best_results(score=score, n=n),
                 sort_key="metric",
             )
+        except ValueError as e:
+            print(e)
 
 
 class PairCheck:

benchmark/Utils.py

@@ -9,6 +9,7 @@ class Folders:
     hidden_results = "hidden_results"
     exreport = "exreport"
     report = os.path.join(exreport, "exreport_output")
+    img = "img"
 
     @staticmethod
     def src():

benchmark/scripts/be_build_best.py

@@ -9,14 +9,15 @@ from benchmark.Arguments import Arguments
 
 def main():
     arguments = Arguments()
-    arguments.xset("score").xset("report").xset("model")
+    arguments.xset("score", mandatory=True).xset("report")
+    arguments.xset("model", mandatory=True)
     args = arguments.parse()
     datasets = Datasets()
     best = BestResults(args.score, args.model, datasets)
     try:
         best.build()
-    except ValueError:
+    except ValueError as e:
-        print("** No results found **")
+        print(e)
     else:
         if args.report:
             report = ReportBest(args.score, args.model, best=True, grid=False)

benchmark/scripts/be_grid.py

@@ -8,7 +8,7 @@ from benchmark.Arguments import Arguments
 
 def main():
     arguments = Arguments()
-    arguments.xset("score").xset("platform").xset("model")
+    arguments.xset("score").xset("platform").xset("model", mandatory=True)
     arguments.xset("quiet").xset("stratified").xset("dataset").xset("n_folds")
     args = arguments.parse()
     if not args.quiet:

benchmark/scripts/be_list.py

@@ -15,12 +15,16 @@ def main():
     args = arguments.parse()
     data = Summary(hidden=args.hidden)
     data.acquire()
+    try:
         data.list_results(
             score=args.score,
             model=args.model,
             sort_key=args.key,
             number=args.number,
         )
+    except ValueError as e:
+        print(e)
+    else:
         if args.nan:
             results_nan = []
             results = data.get_results_criteria(

benchmark/scripts/be_main.py

@@ -10,10 +10,10 @@ from benchmark.Arguments import Arguments
 
 def main():
     arguments = Arguments()
-    arguments.xset("stratified").xset("score").xset("model").xset("dataset")
+    arguments.xset("stratified").xset("score").xset("model", mandatory=True)
     arguments.xset("n_folds").xset("platform").xset("quiet").xset("title")
     arguments.xset("hyperparameters").xset("paramfile").xset("report")
-    arguments.xset("grid_paramfile")
+    arguments.xset("grid_paramfile").xset("dataset")
     args = arguments.parse()
     report = args.report or args.dataset is not None
     if args.grid_paramfile:

benchmark/scripts/be_print_strees.py

@@ -62,7 +62,6 @@ def add_color(source):
 
 
 def print_stree(clf, dataset, X, y, color, quiet):
-    output_folder = "img"
     samples, features = X.shape
     classes = max(y) + 1
     accuracy = clf.score(X, y)
@@ -72,7 +71,7 @@ def print_stree(clf, dataset, X, y, color, quiet):
     if color:
         dot_source = add_color(dot_source)
     grp = Source(dot_source)
-    file_name = os.path.join(output_folder, f"stree_{dataset}")
+    file_name = os.path.join(Folders.img, f"stree_{dataset}")
     grp.render(format="png", filename=f"{file_name}")
     os.remove(f"{file_name}")
     print(f"File {file_name}.png generated")

benchmark/scripts/be_report.py

@@ -1,11 +1,6 @@
 #!/usr/bin/env python
-import numpy as np
+from benchmark.Results import Report, Excel, SQL, ReportBest, ReportDatasets
-from benchmark.Experiments import Datasets
+from benchmark.Utils import Files
-from benchmark.Results import Report, Excel, SQL, ReportBest
-from benchmark.Utils import (
-    Files,
-    TextColor,
-)
 from benchmark.Arguments import Arguments
 
 
@@ -15,32 +10,6 @@ If no argument is set, displays the datasets and its characteristics
 """
 
 
-def default_report():
-    sets = Datasets()
-    color_line = TextColor.LINE1
-    print(color_line, end="")
-    print(f"{'Dataset':30s} Samp. Feat Cls Balance")
-    print("=" * 30 + " ===== ==== === " + "=" * 40)
-    for line in sets:
-        X, y = sets.load(line)
-        color_line = (
-            TextColor.LINE2
-            if color_line == TextColor.LINE1
-            else TextColor.LINE1
-        )
-        values, counts = np.unique(y, return_counts=True)
-        comp = ""
-        sep = ""
-        for value, count in zip(values, counts):
-            comp += f"{sep}{count/sum(counts)*100:5.2f}%"
-            sep = "/ "
-        print(color_line, end="")
-        print(
-            f"{line:30s} {X.shape[0]:5,d} {X.shape[1]:4d} "
-            f"{len(np.unique(y)):3d} {comp:40s}"
-        )
-
-
 def main():
     arguments = Arguments()
     arguments.xset("file").xset("excel").xset("sql").xset("compare")
@@ -48,11 +17,10 @@ def main():
         "score"
     )
     args = arguments.parse()
-
     if args.grid:
         args.best = False
     if args.file is None and args.best is None:
-        default_report()
+        ReportDatasets.report()
     else:
         if args.best is not None or args.grid is not None:
             report = ReportBest(args.score, args.model, args.best, args.grid)

benchmark/tests/GridSearch_test.py

@@ -37,7 +37,8 @@ class GridSearchTest(TestBase):
             ],
             ".",
         )
-        _ = self.build_exp()
+        grid = self.build_exp()
+        grid._init_data()
         # check the output file is initialized
         with open(file_name) as f:
             data = json.load(f)

benchmark/tests/test_files/summary_show_results.test

@@ -1,12 +1,12 @@
-*********************************************************************************
+*********************************************************************************
-*                                                                               *
+*                                                                               *
-*                       With gridsearched hyperparameters                       *
+*                       With gridsearched hyperparameters                       *
-*                                                                               *
+*                                                                               *
-* Model: STree           Ver. 1.2.3      Score: accuracy   Metric:  0.0454434   *
+* Model: STree           Ver. 1.2.3      Score: accuracy   Metric:  0.0454434   *
-*                                                                               *
+*                                                                               *
-* Date : 2021-09-30      Time: 11:42:07           Time Spent:    624.25 secs.   *
+* Date : 2021-09-30      Time: 11:42:07           Time Spent:    624.25 secs.   *
-* Seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Platform: iMac27              *
+* Seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Platform: iMac27              *
-* Stratified: False                                                             *
+* Stratified: False                                                             *
-* results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
+* results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
-*                                                                               *
+*                                                                               *
-*********************************************************************************
+*********************************************************************************

benchmark/tests/test_files/summary_show_results_title.test

@@ -1,14 +1,14 @@
-*********************************************************************************
+*********************************************************************************
-*                                   **Title**                                   *
+*                                   **Title**                                   *
 *-------------------------------------------------------------------------------*
-*                                                                               *
+*                                                                               *
-*                       With gridsearched hyperparameters                       *
+*                       With gridsearched hyperparameters                       *
-*                                                                               *
+*                                                                               *
-* Model: STree           Ver. 1.2.3      Score: accuracy   Metric:  0.0454434   *
+* Model: STree           Ver. 1.2.3      Score: accuracy   Metric:  0.0454434   *
-*                                                                               *
+*                                                                               *
-* Date : 2021-09-30      Time: 11:42:07           Time Spent:    624.25 secs.   *
+* Date : 2021-09-30      Time: 11:42:07           Time Spent:    624.25 secs.   *
-* Seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Platform: iMac27              *
+* Seeds: [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1] Platform: iMac27              *
-* Stratified: False                                                             *
+* Stratified: False                                                             *
-* results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
+* results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json                      *
-*                                                                               *
+*                                                                               *
-*********************************************************************************
+*********************************************************************************