Update test for higher python versions

tests/test_comparisons/test_sklearn_classification.py

@@ -11,6 +11,12 @@
 from permetrics import ClassificationMetric
 
 
+def is_close_enough(x1, x2, eps=1e-5):
+    if abs(x1 - x2) <= eps:
+        return True
+    return False
+
+
 @pytest.fixture(scope="module")  # scope: Call only 1 time at the beginning
 def data():
     y_true1 = np.array([0, 1, 0, 0, 1, 0, 0, 1, 1, 0])
@@ -44,64 +50,56 @@ def test_AS(data):
     (y_true1, y_pred1), (y_true2, y_pred2), (y_true3, y_pred3), cm1, cm2, cm3 = data
     res11 = cm1.PS(average="micro")
     res12 = accuracy_score(y_true1, y_pred1)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
     # res21 = cm2.PS(average="micro")
     # res22 = accuracy_score(y_true2, y_pred2)      # ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets
-    # assert res21 == res22
+    # assert is_close_enough(res21, res22)
 
     # res31 = cm3.PS(average="micro")
     # res32 = accuracy_score(y_true3, y_pred3)      # ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets
-    # assert res31 == res32
-
-    # avg_paras = [None, "macro", "micro", "weighted"]
-    # outs = (dict, float, float, float)
-    #
-    # for idx, avg in enumerate(avg_paras):
-    #     for cm in data:
-    #         res = cm.PS(average=avg)
-    #         assert isinstance(res, outs[idx])
+    # assert is_close_enough(res31, res32)
 
 
 def test_F1S(data):
     (y_true1, y_pred1), (y_true2, y_pred2), (y_true3, y_pred3), cm1, cm2, cm3 = data
     res11 = cm1.F1S(average="micro")
     res12 = f1_score(y_true1, y_pred1, average="micro")
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
     res11 = cm1.F1S(average="macro")
     res12 = f1_score(y_true1, y_pred1, average="macro")
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_FBS(data):
     (y_true1, y_pred1), (y_true2, y_pred2), (y_true3, y_pred3), cm1, cm2, cm3 = data
     res11 = cm1.FBS(average="micro", beta=1.5)
     res12 = fbeta_score(y_true1, y_pred1, average="micro", beta=1.5)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
     res11 = cm1.FBS(average="macro", beta=2.0)
     res12 = fbeta_score(y_true1, y_pred1, average="macro", beta=2.0)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_PS(data):
     (y_true1, y_pred1), (y_true2, y_pred2), (y_true3, y_pred3), cm1, cm2, cm3 = data
     res11 = cm1.PS(average="micro")
     res12 = precision_score(y_true1, y_pred1, average="micro")
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
     res11 = cm1.PS(average="macro")
     res12 = precision_score(y_true1, y_pred1, average="macro")
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_RS(data):
     (y_true1, y_pred1), (y_true2, y_pred2), (y_true3, y_pred3), cm1, cm2, cm3 = data
     res11 = cm1.RS(average="micro")
     res12 = recall_score(y_true1, y_pred1, average="micro")
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
     res11 = cm1.RS(average="macro")
     res12 = recall_score(y_true1, y_pred1, average="macro")
-    assert res11 == res12
+    assert is_close_enough(res11, res12)

tests/test_comparisons/test_sklearn_regression.py

@@ -12,6 +12,12 @@
 from permetrics import RegressionMetric
 
 
+def is_close_enough(x1, x2, eps=1e-5):
+    if abs(x1 - x2) <= eps:
+        return True
+    return False
+
+
 @pytest.fixture(scope="module")  # scope: Call only 1 time at the beginning
 def data():
     y_true = np.array([3, -0.5, 2, 7, 5, 3, 4, -3, 10])
@@ -24,46 +30,46 @@ def test_EVS(data):
     y_true, y_pred, rm = data
     res11 = rm.EVS()
     res12 = explained_variance_score(y_true, y_pred)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_ME(data):
     y_true, y_pred, rm = data
     res11 = rm.ME()
     res12 = max_error(y_true, y_pred)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_MAE(data):
     y_true, y_pred, rm = data
     res11 = rm.MAE()
     res12 = mean_absolute_error(y_true, y_pred)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_MSE(data):
     y_true, y_pred, rm = data
     res11 = rm.MSE()
     res12 = mean_squared_error(y_true, y_pred)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_MedAE(data):
     y_true, y_pred, rm = data
     res11 = rm.MedAE()
     res12 = median_absolute_error(y_true, y_pred)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_R2(data):
     y_true, y_pred, rm = data
     res11 = rm.R2()
     res12 = r2_score(y_true, y_pred)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)
 
 
 def test_MAPE(data):
     y_true, y_pred, rm = data
     res11 = rm.MAPE()
     res12 = mean_absolute_percentage_error(y_true, y_pred)
-    assert res11 == res12
+    assert is_close_enough(res11, res12)