Fix tests

benchmark/Datasets.py

@@ -28,6 +28,12 @@ class DatasetsArff:
     def folder():
         return "datasets"
 
+    @staticmethod
+    def get_range_features(X, c_features):
+        if c_features.strip() == "all":
+            return list(range(X.shape[1]))
+        return json.loads(c_features)
+
     def load(self, name, class_name):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = arff.loadarff(file_name)
@@ -51,6 +57,10 @@ class DatasetsTanveer:
     def folder():
         return "data"
 
+    @staticmethod
+    def get_range_features(X, name):
+        return []
+
     def load(self, name, *args):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = pd.read_csv(
@@ -76,6 +86,10 @@ class DatasetsSurcov:
     def folder():
         return "datasets"
 
+    @staticmethod
+    def get_range_features(X, name):
+        return []
+
     def load(self, name, *args):
         file_name = os.path.join(self.folder(), self.dataset_names(name))
         data = pd.read_csv(
@@ -179,16 +193,13 @@ class Datasets:
         }
 
     def load(self, name, dataframe=False):
-        def get_range_features(X, name):
-            c_features = self.continuous_features[name]
-            if c_features.strip() == "all":
-                return list(range(X.shape[1]))
-            return json.loads(c_features)
 
         try:
             class_name = self.class_names[self.data_sets.index(name)]
             X, y = self.dataset.load(name, class_name)
-            self.continuous_features_dataset = get_range_features(X, name)
+            self.continuous_features_dataset = self.dataset.get_range_features(
+                X, self.continuous_features[name]
+            )
             if self.discretize:
                 X = self.discretize_dataset(X, y)
                 self.build_states(name, X)

benchmark/tests/.env

@@ -7,5 +7,4 @@ stratified=0
 source_data=Tanveer
 seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
 discretize=0
-ignore_nan=0
+ignore_nan=0
-

benchmark/tests/.env.dist

@@ -7,4 +7,4 @@ stratified=0
 source_data=Tanveer
 seeds=[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
 discretize=0
-ignore_nan=0
+ignore_nan=0

benchmark/tests/Dataset_test.py

@@ -1,4 +1,3 @@
-import shutil
 from .TestBase import TestBase
 from ..Experiments import Randomized
 from ..Datasets import Datasets
@@ -17,10 +16,6 @@ class DatasetTest(TestBase):
         self.set_env(".env.dist")
         return super().tearDown()
 
-    @staticmethod
-    def set_env(env):
-        shutil.copy(env, ".env")
-
     def test_Randomized(self):
         expected = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
         self.assertSequenceEqual(Randomized.seeds(), expected)

benchmark/tests/Experiment_test.py

@@ -8,10 +8,10 @@ class ExperimentTest(TestBase):
     def setUp(self):
         self.exp = self.build_exp()
 
-    def build_exp(self, hyperparams=False, grid=False):
+    def build_exp(self, hyperparams=False, grid=False, model="STree"):
         params = {
             "score_name": "accuracy",
-            "model_name": "STree",
+            "model_name": model,
             "stratified": "0",
             "datasets": Datasets(),
             "hyperparams_dict": "{}",
@@ -21,6 +21,7 @@ class ExperimentTest(TestBase):
             "title": "Test",
             "progress_bar": False,
             "folds": 2,
+            "ignore_nan": False,
         }
         return Experiment(**params)
 
@@ -31,6 +32,7 @@ class ExperimentTest(TestBase):
             ],
             ".",
         )
+        self.set_env(".env.dist")
         return super().tearDown()
 
     def test_build_hyperparams_file(self):
@@ -89,7 +91,7 @@ class ExperimentTest(TestBase):
     def test_exception_n_fold_crossval(self):
         self.exp.do_experiment()
         with self.assertRaises(ValueError):
-            self.exp._n_fold_crossval([], [], {})
+            self.exp._n_fold_crossval("", [], [], {})
 
     def test_do_experiment(self):
         self.exp.do_experiment()
@@ -131,3 +133,27 @@ class ExperimentTest(TestBase):
         ):
             for key, value in expected_result.items():
                 self.assertEqual(computed_result[key], value)
+
+    def test_build_fit_parameters(self):
+        self.set_env(".env.arff")
+        expected = {
+            "state_names": {
+                "sepallength": [0, 1, 2],
+                "sepalwidth": [0, 1, 3, 4],
+                "petallength": [0, 1, 2, 3],
+                "petalwidth": [0, 1, 2, 3],
+            },
+            "features": [
+                "sepallength",
+                "sepalwidth",
+                "petallength",
+                "petalwidth",
+            ],
+        }
+        exp = self.build_exp(model="TAN")
+        X, y = exp.datasets.load("iris")
+        computed = exp._build_fit_params("iris")
+        for key, value in expected["state_names"].items():
+            self.assertEqual(computed["state_names"][key], value)
+        for feature in expected["features"]:
+            self.assertIn(feature, computed["features"])

benchmark/tests/TestBase.py

@@ -4,6 +4,7 @@ import pathlib
 import sys
 import csv
 import unittest
+import shutil
 from importlib import import_module
 from io import StringIO
 from unittest.mock import patch
@@ -19,6 +20,10 @@ class TestBase(unittest.TestCase):
         self.stree_version = "1.2.4"
         super().__init__(*args, **kwargs)
 
+    @staticmethod
+    def set_env(env):
+        shutil.copy(env, ".env")
+
     def remove_files(self, files, folder):
         for file_name in files:
             file_name = os.path.join(folder, file_name)

benchmark/tests/Util_test.py

@@ -180,6 +180,7 @@ class UtilTest(TestBase):
             "source_data": "Tanveer",
             "seeds": "[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]",
             "discretize": "0",
+            "ignore_nan": "0",
         }
         computed = EnvData().load()
         self.assertDictEqual(computed, expected)
@@ -191,8 +192,16 @@ class UtilTest(TestBase):
             "n_folds": 5,
             "model": "STree",
             "stratified": "0",
+            "ignore_nan": "0",
         }
         ap = argparse.ArgumentParser()
+        ap.add_argument(
+            "--ignore-nan",
+            action=EnvDefault,
+            envvar="ignore_nan",
+            required=True,
+            help="Ignore nan results",
+        )
         ap.add_argument(
             "-s",
             "--score",

benchmark/tests/datasets/all.txt

@@ -1,2 +1,2 @@
-iris,class
+iris,class,all
-wine,class
+wine,class,[0, 1]

benchmark/tests/test_files/exreport_excel_Datasets.test

@@ -1,25 +1,28 @@
-1;1;"Datasets used in benchmark ver. 0.2.0"
+1;1;"Datasets used in benchmark ver. 0.4.0"
 2;1;" Default score accuracy"
 2;2;"Cross validation"
-2;5;"5 Folds"
+2;6;"5 Folds"
 3;2;"Stratified"
-3;5;"False"
+3;6;"False"
 4;2;"Discretized"
-4;5;"False"
+4;6;"False"
 5;2;"Seeds"
-5;5;"[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
+5;6;"[57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]"
 6;1;"Dataset"
 6;2;"Samples"
 6;3;"Features"
-6;4;"Classes"
+6;4;"Continuous"
-6;5;"Balance"
+6;5;"Classes"
+6;6;"Balance"
 7;1;"balance-scale"
 7;2;"625"
 7;3;"4"
-7;4;"3"
+7;4;"0"
-7;5;" 7.84%/ 46.08%/ 46.08%"
+7;5;"3"
+7;6;" 7.84%/ 46.08%/ 46.08%"
 8;1;"balloons"
 8;2;"16"
 8;3;"4"
-8;4;"2"
+8;4;"0"
-8;5;"56.25%/ 43.75%"
+8;5;"2"
+8;6;"56.25%/ 43.75%"

benchmark/tests/test_files/report_datasets.test

@@ -1,6 +1,6 @@
 Datasets used in benchmark ver. 0.2.0
 
-Dataset                        Sampl. Feat. Cls Balance
+Dataset                        Sampl. Feat. Cont Cls Balance
-============================== ====== ===== === ============================================================
+============================== ====== ===== ==== === ============================================================
-balance-scale                     625     4   3  7.84%/ 46.08%/ 46.08%                  
+balance-scale                     625     4    0   3  7.84%/ 46.08%/ 46.08%                  
-balloons                           16     4   2 56.25%/ 43.75%                          
+balloons                           16     4    0   2 56.25%/ 43.75%