update __eq__ function for parameters to use it to cover test cases

neps/search_spaces/hyperparameters/categorical.py

@@ -46,7 +46,12 @@ def id(self):
     def __eq__(self, other):
         if not isinstance(other, self.__class__):
             return False
-        return self.choices == other.choices and self.value == other.value
+        return (self.choices == other.choices
+                and self.value == other.value
+                and self.is_fidelity == other.is_fidelity
+                and self.default == other.default
+                and self.default_confidence_score == other.default_confidence_score
+                )
 
     def __repr__(self):
         return f"<Categorical, choices: {self.choices}, value: {self.value}>"

neps/search_spaces/hyperparameters/constant.py

@@ -15,7 +15,8 @@ def __init__(self, value: Union[float, int, str], is_fidelity: bool = False):
     def __eq__(self, other):
         if not isinstance(other, self.__class__):
             return False
-        return self.value == other.value
+        return (self.value == other.value
+                and self.is_fidelity == other.is_fidelity)
 
     def __repr__(self):
         return f"<Constant, value: {self.id}>"

neps/search_spaces/hyperparameters/float.py

@@ -51,7 +51,10 @@ def __eq__(self, other):
             self.lower == other.lower
             and self.upper == other.upper
             and self.log == other.log
+            and self.is_fidelity == other.is_fidelity
             and self.value == other.value
+            and self.default == other.default
+            and self.default_confidence_score == other.default_confidence_score
         )
 
     def __repr__(self):

tests/test_yaml_search_space/test_search_space.py

@@ -18,45 +18,20 @@ def test_correct_yaml_file(path):
         """Test the function with a correctly formatted YAML file."""
         pipeline_space = pipeline_space_from_yaml(path)
         assert isinstance(pipeline_space, dict)
-        assert isinstance(pipeline_space["param_float1"], FloatParameter)
-        assert pipeline_space["param_float1"].lower == 0.00001
-        assert pipeline_space["param_float1"].upper == 0.1
-        assert pipeline_space["param_float1"].log is True
-        assert pipeline_space["param_float1"].is_fidelity is False
-        assert pipeline_space["param_float1"].default is None
-        assert pipeline_space["param_float1"].default_confidence_score == 0.5
-        assert isinstance(pipeline_space["param_int1"], IntegerParameter)
-        assert pipeline_space["param_int1"].lower == -3
-        assert pipeline_space["param_int1"].upper == 30
-        assert pipeline_space["param_int1"].log is False
-        assert pipeline_space["param_int1"].is_fidelity is True
-        assert pipeline_space["param_int1"].default is None
-        assert pipeline_space["param_int1"].default_confidence_score == 0.5
-        assert isinstance(pipeline_space["param_int2"], IntegerParameter)
-        assert pipeline_space["param_int2"].lower == 100
-        assert pipeline_space["param_int2"].upper == 30000
-        assert pipeline_space["param_int2"].log is True
-        assert pipeline_space["param_int2"].is_fidelity is False
-        assert pipeline_space["param_int2"].default is None
-        assert pipeline_space["param_int2"].default_confidence_score == 0.5
-        assert isinstance(pipeline_space["param_float2"], FloatParameter)
-        assert pipeline_space["param_float2"].lower == 3.3e-5
-        assert pipeline_space["param_float2"].upper == 0.15
-        assert pipeline_space["param_float2"].log is False
-        assert pipeline_space["param_float2"].is_fidelity is False
-        assert pipeline_space["param_float2"].default is None
-        assert pipeline_space["param_float2"].default_confidence_score == 0.5
-        assert isinstance(pipeline_space["param_cat"], CategoricalParameter)
-        assert pipeline_space["param_cat"].choices == [2, "sgd", 10e-3]
-        assert pipeline_space["param_cat"].is_fidelity is False
-        assert pipeline_space["param_cat"].default is None
-        assert pipeline_space["param_cat"].default_confidence_score == 2
-        assert isinstance(pipeline_space["param_const1"], ConstantParameter)
-        assert pipeline_space["param_const1"].value == 0.5
-        assert pipeline_space["param_const1"].is_fidelity is False
-        assert isinstance(pipeline_space["param_const2"], ConstantParameter)
-        assert pipeline_space["param_const2"].value == 1e3
-        assert pipeline_space["param_const2"].is_fidelity is True
+        float1 = FloatParameter(0.00001, 0.1, True, False)
+        assert float1.__eq__(pipeline_space["param_float1"]) is True
+        int1 = IntegerParameter(-3, 30, False, True)
+        assert int1.__eq__(pipeline_space["param_int1"]) is True
+        int2 = IntegerParameter(100, 30000, True, False)
+        assert int2.__eq__(pipeline_space["param_int2"]) is True
+        float2 = FloatParameter(3.3e-5, 0.15, False, False)
+        assert float2.__eq__(pipeline_space["param_float2"]) is True
+        cat1 = CategoricalParameter([2, "sgd", 10e-3], False)
+        assert cat1.__eq__(pipeline_space["param_cat"]) is True
+        const1 = ConstantParameter(0.5, False)
+        assert const1.__eq__(pipeline_space["param_const1"]) is True
+        const2 = ConstantParameter(1e3, True)
+        assert const2.__eq__(pipeline_space["param_const2"]) is True
 
     test_correct_yaml_file(BASE_PATH + "correct_config.yaml")
     test_correct_yaml_file(
@@ -71,28 +46,14 @@ def test_correct_including_priors_yaml_file():
         BASE_PATH + "correct_config_including_priors.yml"
     )
     assert isinstance(pipeline_space, dict)
-    assert isinstance(pipeline_space["learning_rate"], FloatParameter)
-    assert pipeline_space["learning_rate"].lower == 0.00001
-    assert pipeline_space["learning_rate"].upper == 0.1
-    assert pipeline_space["learning_rate"].log is True
-    assert pipeline_space["learning_rate"].is_fidelity is False
-    assert pipeline_space["learning_rate"].default == 3.3e-2
-    assert pipeline_space["learning_rate"].default_confidence_score == 0.125
-    assert isinstance(pipeline_space["num_epochs"], IntegerParameter)
-    assert pipeline_space["num_epochs"].lower == 3
-    assert pipeline_space["num_epochs"].upper == 30
-    assert pipeline_space["num_epochs"].log is False
-    assert pipeline_space["num_epochs"].is_fidelity is True
-    assert pipeline_space["num_epochs"].default == 10
-    assert pipeline_space["num_epochs"].default_confidence_score == 0.5
-    assert isinstance(pipeline_space["optimizer"], CategoricalParameter)
-    assert pipeline_space["optimizer"].choices == ["adam", 90e-3, "rmsprop"]
-    assert pipeline_space["optimizer"].is_fidelity is False
-    assert pipeline_space["optimizer"].default == 90e-3
-    assert pipeline_space["optimizer"].default_confidence_score == 4
-    assert isinstance(pipeline_space["dropout_rate"], ConstantParameter)
-    assert pipeline_space["dropout_rate"].value == 1e3
-    assert pipeline_space["dropout_rate"].default == 1e3
+    float1 = FloatParameter(0.00001, 0.1, True, False, 3.3e-2, "high")
+    assert float1.__eq__(pipeline_space["learning_rate"]) is True
+    int1 = IntegerParameter(3, 30, False, True, 10)
+    assert int1.__eq__(pipeline_space["num_epochs"]) is True
+    cat1 = CategoricalParameter(["adam", 90e-3, "rmsprop"], False, 90e-3, "medium")
+    assert cat1.__eq__(pipeline_space["optimizer"]) is True
+    const1 = ConstantParameter(1e3, True)
+    assert const1.__eq__(pipeline_space["dropout_rate"]) is True
 
 
 @pytest.mark.neps_api