diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 0000000..a709d91
--- /dev/null
+++ b/.coveragerc
@@ -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
\ No newline at end of file
diff --git a/Makefile b/Makefile
index 82e2928..29f3cf9 100644
--- a/Makefile
+++ b/Makefile
@@ -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
 
diff --git a/benchmark/Utils.py b/benchmark/Utils.py
index 8ca939d..576810b 100644
--- a/benchmark/Utils.py
+++ b/benchmark/Utils.py
@@ -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:
diff --git a/benchmark/best_results_STree-x.json b/benchmark/best_results_STree-x.json
deleted file mode 100644
index dabb462..0000000
--- a/benchmark/best_results_STree-x.json
+++ /dev/null
@@ -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"
-    },
-    ""
-  ]
-}
diff --git a/benchmark/best_results_SVC-x.json b/benchmark/best_results_SVC-x.json
deleted file mode 100644
index 736af49..0000000
--- a/benchmark/best_results_SVC-x.json
+++ /dev/null
@@ -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"
-    },
-    ""
-  ]
-}
diff --git a/benchmark/paper_hyperparams.txt b/benchmark/paper_hyperparams.txt
deleted file mode 100644
index d698ba7..0000000
--- a/benchmark/paper_hyperparams.txt
+++ /dev/null
@@ -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"}}
\ No newline at end of file
diff --git a/benchmark/tests/Util_test.py b/benchmark/tests/Util_test.py
new file mode 100644
index 0000000..8b0f62a
--- /dev/null
+++ b/benchmark/tests/Util_test.py
@@ -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")
diff --git a/benchmark/tests/__init__.py b/benchmark/tests/__init__.py
new file mode 100644
index 0000000..8523cc1
--- /dev/null
+++ b/benchmark/tests/__init__.py
@@ -0,0 +1,3 @@
+from .Util_test import UtilTest
+
+all = ["UtilTest"]
diff --git a/benchmark/tests/hidden_results/results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json b/benchmark/tests/hidden_results/results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json
new file mode 100644
index 0000000..2bcba45
--- /dev/null
+++ b/benchmark/tests/hidden_results/results_accuracy_STree_iMac27_2021-11-01_23:55:16_0.json
@@ -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",
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diff --git a/benchmark/tests/results/a.json b/benchmark/tests/results/a.json
new file mode 100644
index 0000000..e69de29
diff --git a/benchmark/tests/results/b.json b/benchmark/tests/results/b.json
new file mode 100644
index 0000000..e69de29
diff --git a/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json
new file mode 100644
index 0000000..dc5a36b
--- /dev/null
+++ b/benchmark/tests/results/results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json
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diff --git a/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json b/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json
new file mode 100644
index 0000000..362c276
--- /dev/null
+++ b/benchmark/tests/results/results_accuracy_STree_macbook-pro_2021-11-01_19:17:07_0.json
@@ -0,0 +1,859 @@
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\ No newline at end of file