Fix params setting (#1121)

* Fix params setting

* Minor

* PEP 8

* Correct test

* Add comment

fedot/core/pipelines/node.py

@@ -15,7 +15,7 @@
 from fedot.core.operations.operation import Operation
 from fedot.core.operations.operation_parameters import OperationParameters
 from fedot.core.repository.operation_types_repository import OperationTypesRepository
-from fedot.core.utils import DEFAULT_PARAMS_STUB
+from fedot.core.utils import DEFAULT_PARAMS_STUB, NESTED_PARAMS_LABEL
 
 
 @register_serializable
@@ -57,9 +57,6 @@ def __init__(self, operation_type: Optional[Union[str, Operation]] = None,
             # Define operation, based on content dictionary
             operation = self._process_content_init(passed_content)
             params = passed_content.get('params', {})
-            # The check for "default_params" is needed for backward compatibility.
-            if params == DEFAULT_PARAMS_STUB:
-                params = {}
             self.metadata = passed_content.get('metadata', NodeMetadata())
         else:
             # There is no content for node
@@ -72,9 +69,10 @@ def __init__(self, operation_type: Optional[Union[str, Operation]] = None,
         self.fit_time_in_seconds = 0
         self.inference_time_in_seconds = 0
 
-        self._parameters = OperationParameters.from_operation_type(operation.operation_type, **params)
-        super().__init__(content={'name': operation,
-                                  'params': self._parameters.to_dict()}, nodes_from=nodes_from)
+        super().__init__(content={'name': operation}, nodes_from=nodes_from)
+        # use parameters.setter to process input parameters correctly
+        self.parameters = params
+
         self.log = default_log(self)
         self._fitted_operation = None
         self.rating = None
@@ -325,18 +323,18 @@ def parameters(self) -> dict:
 
     @parameters.setter
     def parameters(self, params: dict):
-        """Sets custom parameters of the node
+        """Sets custom parameters of the node or set default
 
         Args:
             params: new parameters to be placed instead of existing
         """
-        if params:
+        if params is not None:
             # The check for "default_params" is needed for backward compatibility.
             if params == DEFAULT_PARAMS_STUB:
                 params = {}
-            # take nested composer params if they appeared
-            if 'nested_space' in params:
-                params = params['nested_space']
+            # take nested params if they appeared (mostly used for tuning)
+            if NESTED_PARAMS_LABEL in params:
+                params = params[NESTED_PARAMS_LABEL]
             self._parameters = OperationParameters.from_operation_type(self.operation.operation_type, **params)
             self.content['params'] = self._parameters.to_dict()
 

fedot/core/pipelines/tuning/search_space.py

@@ -4,6 +4,8 @@
 from golem.core.tuning.search_space import SearchSpace, OperationParametersMapping
 from hyperopt import hp
 
+from fedot.core.utils import NESTED_PARAMS_LABEL
+
 
 class PipelineSearchSpace(SearchSpace):
     """
@@ -325,7 +327,7 @@ def get_parameters_dict(self):
                     'type': 'continuous'}
             },
             'glm': {
-                'nested_space': {
+                NESTED_PARAMS_LABEL: {
                     'hyperopt-dist': hp.choice,
                     'sampling-scope': [[
                         {

fedot/core/utils.py

@@ -11,6 +11,7 @@
 from sklearn.model_selection import train_test_split
 
 DEFAULT_PARAMS_STUB = 'default_params'
+NESTED_PARAMS_LABEL = 'nested_space'
 
 
 def fedot_project_root() -> Path:

test/integration/pipelines/tuning/test_pipeline_tuning.py

@@ -22,7 +22,7 @@
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.repository.quality_metrics_repository import RegressionMetricsEnum, ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
-from fedot.core.utils import fedot_project_root
+from fedot.core.utils import fedot_project_root, NESTED_PARAMS_LABEL
 from test.unit.tasks.test_forecasting import get_ts_data
 
 seed(1)
@@ -489,23 +489,27 @@ def test_complex_search_space():
     space = PipelineSearchSpace()
     for i in range(20):
         operation_parameters = space.parameters_per_operation.get("glm")
-        new_value = hp_sample(operation_parameters["nested_space"])
+        new_value = hp_sample(operation_parameters[NESTED_PARAMS_LABEL])
         for params in new_value['sampling-scope'][0]:
             assert params['link'] in GLMImplementation.family_distribution[params['family']]['available_links']
 
 
 @pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, IOptTuner])
 def test_complex_search_space_tuning_correct(tuner):
     """ Tests SimultaneousTuner for time series forecasting task with GLM model that has a complex glm search space"""
-    train_data, test_data = get_ts_data(n_steps=200, forecast_length=5)
+    train_data, test_data = get_ts_data(n_steps=700, forecast_length=20)
 
     glm_pipeline = Pipeline(PipelineNode('glm'))
-    glm_custom_params = glm_pipeline.nodes[0].parameters
+    initial_parameters = glm_pipeline.nodes[0].parameters
     tuner = TunerBuilder(train_data.task) \
         .with_tuner(tuner) \
         .with_metric(RegressionMetricsEnum.MSE) \
         .with_iterations(100) \
         .build(train_data)
     tuned_glm_pipeline = tuner.tune(glm_pipeline)
-    new_custom_params = tuned_glm_pipeline.nodes[0].parameters
-    assert glm_custom_params == new_custom_params
+    found_parameters = tuned_glm_pipeline.nodes[0].parameters
+    if tuner.init_metric == tuner.obtained_metric:
+        # TODO: (YamLyubov) Remove the check when IOptTuner will be able to tune categorical parameters.
+        assert initial_parameters == found_parameters
+    else:
+        assert initial_parameters != found_parameters

test/unit/pipelines/test_node.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-from golem.core.dag.graph_utils import ordered_subnodes_hierarchy
 from golem.core.dag.linked_graph_node import LinkedGraphNode
 from sklearn.datasets import load_iris
 from sklearn.linear_model import LogisticRegression
@@ -12,6 +11,7 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.repository.dataset_types import DataTypesEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
+from fedot.core.utils import DEFAULT_PARAMS_STUB, NESTED_PARAMS_LABEL
 
 
 @pytest.fixture()
@@ -108,3 +108,18 @@ def test_node_return_correct_operation_info():
 
     correct_tags = ["simple", "imputation"]
     assert all(correct_tag in operation_tags for correct_tag in correct_tags)
+
+
+@pytest.mark.parametrize('params, expected_params', [(DEFAULT_PARAMS_STUB, {"family": "gaussian", "link": "identity"}),
+                                                     ({}, {"family": "gaussian", "link": "identity"}),
+                                                     ({NESTED_PARAMS_LABEL: {'family': 'gaussian', 'link': 'log'}},
+                                                      {'family': 'gaussian', 'link': 'log'}),
+                                                     ({'family': 'inverse_gaussian', 'link': 'inverse_power'},
+                                                      {'family': 'inverse_gaussian', 'link': 'inverse_power'})])
+def test_init_and_set_pipeline_node_with_params(params, expected_params):
+    node = PipelineNode(content={'name': 'glm', 'params': params})
+    assert node.parameters == expected_params
+
+    node = PipelineNode('glm')
+    node.parameters = params
+    assert node.parameters == expected_params