add_set_params_and_corresponding_tests

CHANGELOG.md

@@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## Unreleased
 ### Added
 
--
+- `set_params` method to change parameters of ETNA objects [#1025](https://github.com/tinkoff-ai/etna/issues/1025)
 -
 ### Changed
 

etna/core/mixins.py

@@ -89,6 +89,45 @@ def to_dict(self):
         params["_target_"] = BaseMixin._get_target_from_class(self)
         return params
 
+    def set_params(self, **params):
+        """Set the parameters of this estimator.
+
+        The method works on simple estimators as well as on nested objects
+        (such as :class:`~etna.pipeline.Pipeline`). The latter have
+        parameters of the form ``<component>.<parameter>`` so that it's
+        possible to update each component of a nested object.
+
+        Parameters
+        ----------
+        **params : dict
+            Estimator parameters.
+        Returns
+        -------
+        self : estimator instance
+            Estimator instance.
+        """
+        for parameter, parameter_value in params.items():
+            # split specification into list of nested params, "model.depth" -> ["model", "depth"]
+            param_nested = parameter.split(".")
+            # all nested params except the last specify path to the estimator whose attribute will be set
+            # in the following cycle, we find this estimator
+            estimator = self
+            nesting_correct = True
+            for param_current_nesting_level in param_nested[:-1]:
+                try:
+                    estimator = estimator.__getattribute__(param_current_nesting_level)
+                except AttributeError:
+                    nesting_correct = False
+                    break
+            if nesting_correct:
+                try:
+                    # if there is no such attribute, the first row will throw AttributeError
+                    estimator.__getattribute__(param_nested[-1])
+                    estimator.__setattr__(param_nested[-1], parameter_value)
+                except AttributeError:
+                    pass
+        return self
+
 
 class StringEnumWithRepr(str, Enum):
     """Base class for str enums, that has alternative __repr__ method."""

tests/test_core/test_set_params.py

@@ -0,0 +1,82 @@
+from etna.models import CatBoostMultiSegmentModel
+from etna.pipeline import Pipeline
+from etna.transforms import AddConstTransform
+
+
+def test_base_mixin_set_params_changes_params_estimator():
+    catboost_model = CatBoostMultiSegmentModel(iterations=1000, depth=10)
+    catboost_model.set_params(**{"learning_rate": 1e-3, "depth": 8})
+    expected_dict = {
+        "iterations": 1000,
+        "depth": 8,
+        "learning_rate": 1e-3,
+        "logging_level": "Silent",
+        "kwargs": {},
+        "_target_": "etna.models.catboost.CatBoostMultiSegmentModel",
+    }
+    obtained_dict = catboost_model.to_dict()
+    assert obtained_dict == expected_dict
+
+
+def test_base_mixin_set_params_changes_params_pipeline():
+    pipeline = Pipeline(model=CatBoostMultiSegmentModel(iterations=1000, depth=10), transforms=(), horizon=5)
+    pipeline.set_params(**{"model.learning_rate": 1e-3, "model.depth": 8, "transforms": AddConstTransform("column", 1)})
+    expected_dict = {
+        "_target_": "etna.pipeline.pipeline.Pipeline",
+        "horizon": 5,
+        "model": {
+            "_target_": "etna.models.catboost.CatBoostMultiSegmentModel",
+            "depth": 8,
+            "iterations": 1000,
+            "kwargs": {},
+            "learning_rate": 0.001,
+            "logging_level": "Silent",
+        },
+        "transforms": {
+            "_target_": "etna.transforms.math.add_constant.AddConstTransform",
+            "in_column": "column",
+            "inplace": True,
+            "value": 1,
+        },
+    }
+    obtained_dict = pipeline.to_dict()
+    assert obtained_dict == expected_dict
+
+
+def test_base_mixin_set_params_with_nonexistent_attributes_estimator():
+    catboost_model = CatBoostMultiSegmentModel(iterations=1000, depth=10)
+    catboost_model.set_params(**{"incorrect_attribute_1": 1e-3, "incorrect_attribute_2": 8})
+    expected_dict = {
+        "iterations": 1000,
+        "depth": 10,
+        "logging_level": "Silent",
+        "kwargs": {},
+        "_target_": "etna.models.catboost.CatBoostMultiSegmentModel",
+    }
+    obtained_dict = catboost_model.to_dict()
+    assert obtained_dict == expected_dict
+
+
+def test_base_mixin_set_params_with_nonexistent_attributes_pipeline():
+    pipeline = Pipeline(model=CatBoostMultiSegmentModel(iterations=1000, depth=10), transforms=(), horizon=5)
+    pipeline.set_params(
+        **{
+            "incorrect_estimator": "value",
+            "model.incorrect_attribute": "value",
+            "model.incorrect_nesting.depth": "value",
+        }
+    )
+    expected_dict = {
+        "_target_": "etna.pipeline.pipeline.Pipeline",
+        "horizon": 5,
+        "model": {
+            "_target_": "etna.models.catboost.CatBoostMultiSegmentModel",
+            "depth": 10,
+            "iterations": 1000,
+            "kwargs": {},
+            "logging_level": "Silent",
+        },
+        "transforms": (),
+    }
+    obtained_dict = pipeline.to_dict()
+    assert obtained_dict == expected_dict