Support property attributes in repr of BaseMixin (#469)

* replace self.__dict__[arg] with getattr(self, arg)

* update changelog

* add tests

* fix

* fix

---------

Co-authored-by: Egor Baturin <[email protected]>

CHANGELOG.md

@@ -29,7 +29,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 
 ### Changed
-- 
+- Add support of property attributes in `__repr__` and `to_dict` of `BaseMixin` ([#469](https://github.com/etna-team/etna/pull/469))
 - 
 - 
 - 

etna/core/mixins.py

@@ -32,11 +32,11 @@ def __repr__(self):
             if param.kind == param.VAR_POSITIONAL:
                 continue
             elif param.kind == param.VAR_KEYWORD:
-                for arg_, value in self.__dict__[arg].items():
+                for arg_, value in getattr(self, arg).items():
                     args_str_representation += f"{arg_} = {repr(value)}, "
             else:
                 try:
-                    value = self.__dict__[arg]
+                    value = getattr(self, arg)
                 except KeyError as e:
                     value = None
                     warnings.warn(f"You haven't set all parameters inside class __init__ method: {e}")
@@ -90,7 +90,7 @@ def to_dict(self):
         init_parameters = self._get_init_parameters()
         params = {}
         for arg in init_parameters.keys():
-            value = self.__dict__[arg]
+            value = getattr(self, arg)
             if value is None:
                 continue
             params[arg] = BaseMixin._parse_value(value=value)

pyproject.toml

@@ -209,7 +209,6 @@ filterwarnings = [
     "ignore: Given top_k=.* is less than n_segments=.*. Algo will filter data without Gale-Shapley run.",
     "ignore: This model doesn't work with exogenous features",
     "ignore: Some of external objects in input parameters could be not",
-    "ignore: You haven't set all parameters inside class __init__ method.* 'is_freezed'",
     # external warnings
     "ignore: Attribute 'logging_metrics' is an instance of `nn.Module` and is already",
     "ignore: Attribute 'loss' is an instance of `nn.Module` and is already",

tests/test_core/conftest.py

@@ -0,0 +1,11 @@
+from etna.core.mixins import BaseMixin
+
+
+class BaseDummy(BaseMixin):
+    def __init__(self, a: int = 1, b: int = 2):
+        self.a = a
+        self._b = b
+
+    @property
+    def b(self):
+        return self._b

tests/test_core/test_repr.py

@@ -0,0 +1,6 @@
+from tests.test_core.conftest import BaseDummy
+
+
+def test_repr_public_property_private_attribute():
+    dummy = BaseDummy(a=1, b=2)
+    assert repr(dummy) == "BaseDummy(a = 1, b = 2, )"

tests/test_core/test_set_params.py

@@ -4,6 +4,7 @@
 from etna.models import CatBoostMultiSegmentModel
 from etna.pipeline import Pipeline
 from etna.transforms import AddConstTransform
+from tests.test_core.conftest import BaseDummy
 
 
 def test_base_mixin_set_params_changes_params_estimator():
@@ -130,3 +131,11 @@ def test_update_nested_structure(nested_structure, keys, value, expected_result)
 def test_update_nested_structure_fail(nested_structure, keys, value):
     with pytest.raises(ValueError, match=f"Structure to update is .* with type .*"):
         _ = BaseMixin._update_nested_structure(nested_structure, keys, value)
+
+
+def test_set_params_public_property_private_attribute():
+    dummy = BaseDummy(a=1, b=2)
+    dummy = dummy.set_params(**{"a": 10, "b": 20})
+    expected_dict = {"a": 10, "b": 20, "_target_": "tests.test_core.conftest.BaseDummy"}
+    obtained_dict = dummy.to_dict()
+    assert obtained_dict == expected_dict

tests/test_core/test_to_dict.py

@@ -29,6 +29,7 @@
 from etna.transforms import LogTransform
 from etna.transforms.decomposition.change_points_based import RupturesChangePointsModel
 from etna.transforms.decomposition.change_points_based import SklearnRegressionPerIntervalModel
+from tests.test_core.conftest import BaseDummy
 
 
 def ensemble_samples():
@@ -205,3 +206,8 @@ def __init__(self, a: _Dummy):
 def test_warnings():
     with pytest.warns(Warning, match="Some of external objects in input parameters could be not written in dict"):
         _ = _InvalidParsing(_Dummy()).to_dict()
+
+
+def test_to_dict_public_property_private_attribute():
+    dummy = BaseDummy(a=1, b=2)
+    assert dummy.to_dict() == {"a": 1, "b": 2, "_target_": "tests.test_core.conftest.BaseDummy"}