1027 remove separation on primary secondary pipeline nodes

cases/credit_scoring/credit_scoring_problem_multiobj.py

@@ -11,7 +11,7 @@
 from fedot.core.optimisers.gp_comp.operators.inheritance import GeneticSchemeTypesEnum
 from fedot.core.optimisers.gp_comp.operators.selection import SelectionTypesEnum
 from fedot.core.optimisers.gp_comp.pipeline_composer_requirements import PipelineComposerRequirements
-from fedot.core.pipelines.node import PrimaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.sequential import SequentialTuner
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
@@ -94,7 +94,7 @@ def run_credit_scoring_problem(train_file_path, test_file_path,
             .build(dataset_to_compose)
         nodes = pipeline_evo_composed.nodes
         for node_index, node in enumerate(nodes):
-            if isinstance(node, PrimaryNode):
+            if isinstance(node, PipelineNode) and node.is_primary:
                 pipeline_evo_composed = tuner.tune_node(pipeline_evo_composed, node_index)
 
         pipeline_evo_composed.fit(input_data=dataset_to_compose)

cases/river_levels_prediction/river_level_case_composer.py

@@ -10,7 +10,7 @@
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.optimisers.gp_comp.gp_params import GPGraphOptimizerParameters
 from fedot.core.optimisers.gp_comp.pipeline_composer_requirements import PipelineComposerRequirements
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.pipelines.tuning.unified import PipelineTuner
@@ -156,10 +156,10 @@ def run_river_composer_experiment(file_path, init_pipeline, file_to_save,
 
 if __name__ == '__main__':
     # Define pipeline to start composing with it
-    node_encoder = PrimaryNode('one_hot_encoding')
-    node_rans = SecondaryNode('ransac_lin_reg', nodes_from=[node_encoder])
-    node_scaling = SecondaryNode('scaling', nodes_from=[node_rans])
-    node_final = SecondaryNode('linear', nodes_from=[node_scaling])
+    node_encoder = PipelineNode('one_hot_encoding')
+    node_rans = PipelineNode('ransac_lin_reg', nodes_from=[node_encoder])
+    node_scaling = PipelineNode('scaling', nodes_from=[node_rans])
+    node_final = PipelineNode('linear', nodes_from=[node_scaling])
 
     init_pipeline = Pipeline(node_final)
 

cases/river_levels_prediction/river_level_case_manual.py

@@ -7,7 +7,7 @@
 
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.pipelines.tuning.unified import PipelineTuner
@@ -85,11 +85,11 @@ def run_river_experiment(file_path, pipeline, iterations=20, tuner=None,
 
 
 if __name__ == '__main__':
-    node_encoder = PrimaryNode('one_hot_encoding')
-    node_scaling = SecondaryNode('scaling', nodes_from=[node_encoder])
-    node_ridge = SecondaryNode('ridge', nodes_from=[node_scaling])
-    node_lasso = SecondaryNode('lasso', nodes_from=[node_scaling])
-    node_final = SecondaryNode('rfr', nodes_from=[node_ridge, node_lasso])
+    node_encoder = PipelineNode('one_hot_encoding')
+    node_scaling = PipelineNode('scaling', nodes_from=[node_encoder])
+    node_ridge = PipelineNode('ridge', nodes_from=[node_scaling])
+    node_lasso = PipelineNode('lasso', nodes_from=[node_scaling])
+    node_final = PipelineNode('rfr', nodes_from=[node_ridge, node_lasso])
 
     init_pipeline = Pipeline(node_final)
 

cases/spam_detection.py

@@ -5,14 +5,14 @@
 from cases.dataset_preparation import unpack_archived_data
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 
 
 def execute_pipeline_for_text_problem(train_data, test_data):
-    node_text_clean = PrimaryNode('text_clean')
-    node_tfidf = SecondaryNode('tfidf', nodes_from=[node_text_clean])
-    model_node = SecondaryNode('multinb', nodes_from=[node_tfidf])
+    node_text_clean = PipelineNode('text_clean')
+    node_tfidf = PipelineNode('tfidf', nodes_from=[node_text_clean])
+    model_node = PipelineNode('multinb', nodes_from=[node_tfidf])
     pipeline = Pipeline(model_node)
     pipeline.fit(train_data)
 

cases/time_series_gapfilling_case.py

@@ -5,7 +5,7 @@
 import pandas as pd
 from sklearn.metrics import mean_absolute_error, mean_squared_error, median_absolute_error
 
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.utils import fedot_project_root
 from fedot.utilities.ts_gapfilling import ModelGapFiller
@@ -72,24 +72,24 @@ def get_composite_pipeline():
     :return: Pipeline object
     """
 
-    node_1 = PrimaryNode('lagged')
+    node_1 = PipelineNode('lagged')
     node_1.parameters = {'window_size': 150}
-    node_2 = PrimaryNode('lagged')
+    node_2 = PipelineNode('lagged')
     node_2.parameters = {'window_size': 100}
-    node_linear_1 = SecondaryNode('linear', nodes_from=[node_1])
-    node_linear_2 = SecondaryNode('linear', nodes_from=[node_2])
+    node_linear_1 = PipelineNode('linear', nodes_from=[node_1])
+    node_linear_2 = PipelineNode('linear', nodes_from=[node_2])
 
-    node_final = SecondaryNode('ridge', nodes_from=[node_linear_1,
+    node_final = PipelineNode('ridge', nodes_from=[node_linear_1,
                                                     node_linear_2])
     pipeline = Pipeline(node_final)
     return pipeline
 
 
 def get_simple_pipeline():
     """ Function returns simple pipeline """
-    node_lagged = PrimaryNode('lagged')
+    node_lagged = PipelineNode('lagged')
     node_lagged.parameters = {'window_size': 150}
-    node_ridge = SecondaryNode('ridge', nodes_from=[node_lagged])
+    node_ridge = PipelineNode('ridge', nodes_from=[node_lagged])
     ridge_pipeline = Pipeline(node_ridge)
     return ridge_pipeline
 

docs/source/advanced/data_preprocessing.rst

@@ -101,7 +101,7 @@ For example, converting column to numerical if the number of unique values is gr
 .. code:: python
 
     # pipeline for which to set params
-    pipeline = Pipeline(PrimaryNode('dt'))
+    pipeline = Pipeline(PipelineNode('dt'))
     pipeline = correct_preprocessing_params(pipeline, numerical_min_uniques=5)
 
 After this preprocessing with this pipeline will be performed according to the specified conditions.

docs/source/advanced/pipeline_import_export.rst

@@ -34,21 +34,21 @@ this way your pipeline and trained models will be saved in a folder in the follo
 
     from cases.data.data_utils import get_scoring_case_data_paths
     from fedot.core.pipelines.pipeline import Pipeline
-    from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+    from fedot.core.pipelines.node import PipelineNode
     from fedot.core.data.data import InputData
 
     train_file_path, test_file_path = get_scoring_case_data_paths()
     train_data = InputData.from_csv(train_file_path)
 
     pipeline = Pipeline()
-    node_logit = PrimaryNode('logit')
+    node_logit = PipelineNode('logit')
 
-    node_lda = PrimaryNode('lda')
+    node_lda = PipelineNode('lda')
     node_lda.parameters = {'n_components': 1}
 
-    node_xgboost = PrimaryNode('xgboost')
+    node_xgboost = PipelineNode('xgboost')
 
-    node_knn_second = SecondaryNode('knn')
+    node_knn_second = PipelineNode('knn')
     node_knn_second.parameters = {'n_neighbors': 5}
     node_knn_second.nodes_from = [node_logit, node_lda, node_xgboost]
 

docs/source/introduction/tutorial/composing_pipelines/manual_creation.rst

@@ -38,9 +38,9 @@ Manual way
 
 .. code:: python
 
-   node_first = PrimaryNode('logit')
-   node_second = PrimaryNode('xgboost')
-   node_final = SecondaryNode('knn', nodes_from=[node_first, node_second])
+   node_first = PipelineNode('logit')
+   node_second = PipelineNode('xgboost')
+   node_final = PipelineNode('knn', nodes_from=[node_first, node_second])
    pipeline = Pipeline(node_final)
 
 The following pages provide examples of manually constructed pipelines for different tasks:

examples/advanced/additional_learning.py

@@ -5,7 +5,7 @@
 
 from fedot.api.main import Fedot
 from fedot.core.operations.atomized_model import AtomizedModel
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from fedot.core.utils import fedot_project_root
@@ -34,7 +34,7 @@ def run_additional_learning_example():
 
     prev_model.unfit()
     atomized_model = Pipeline(
-        SecondaryNode(operation_type=AtomizedModel(prev_model), nodes_from=[PrimaryNode('scaling')]))
+        PipelineNode(operation_type=AtomizedModel(prev_model), nodes_from=[PipelineNode('scaling')]))
     non_atomized_model = deepcopy(prev_model)
 
     train_data = train_data.head(5000)

examples/advanced/automl/h2o_example.py

@@ -4,7 +4,7 @@
 from examples.advanced.time_series_forecasting.composing_pipelines import visualise
 from examples.simple.pipeline_import_export import create_correct_path
 from fedot.core.data.data_split import train_test_data_setup
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.repository.operation_types_repository import OperationTypesRepository
 from test.unit.tasks.test_classification import get_iris_data
@@ -13,21 +13,21 @@
 
 
 def pipeline_h2o_class() -> Pipeline:
-    node = PrimaryNode('h2o_class')
+    node = PipelineNode('h2o_class')
     pipeline = Pipeline(node)
     return pipeline
 
 
 def pipeline_h2o_regr() -> Pipeline:
-    node = PrimaryNode('h2o_regr')
+    node = PipelineNode('h2o_regr')
     pipeline = Pipeline(node)
     return pipeline
 
 
 def pipeline_h2o_ts(window_size: int = 20):
-    node_lagged = PrimaryNode('lagged')
+    node_lagged = PipelineNode('lagged')
     node_lagged.parameters = {'window_size': window_size}
-    node_root = SecondaryNode('h2o_regr', nodes_from=[node_lagged])
+    node_root = PipelineNode('h2o_regr', nodes_from=[node_lagged])
 
     pipeline = Pipeline(node_root)
 

examples/advanced/automl/pipeline_from_automl.py

@@ -5,7 +5,7 @@
 
 from cases.data.data_utils import get_scoring_case_data_paths
 from fedot.core.data.data import InputData
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 
 
@@ -26,13 +26,13 @@ def run_pipeline_from_automl(train_file_path: str, test_file_path: str,
 
         testing_target = test_data.target
 
-        node_scaling = PrimaryNode('scaling')
-        node_tpot = PrimaryNode('tpot_class')
+        node_scaling = PipelineNode('scaling')
+        node_tpot = PipelineNode('tpot_class')
 
         node_tpot.parameters = {'timeout': max_run_time.seconds}
 
-        node_lda = SecondaryNode('lda', nodes_from=[node_scaling])
-        node_rf = SecondaryNode('rf', nodes_from=[node_tpot, node_lda])
+        node_lda = PipelineNode('lda', nodes_from=[node_scaling])
+        node_rf = PipelineNode('rf', nodes_from=[node_tpot, node_lda])
         pipeline = Pipeline(node_rf)
 
         pipeline.fit(train_data)

examples/advanced/automl/tpot_example.py

@@ -3,7 +3,7 @@
 from examples.advanced.time_series_forecasting.composing_pipelines import visualise
 from examples.simple.pipeline_import_export import create_correct_path
 from fedot.core.data.data_split import train_test_data_setup
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from sklearn.metrics import roc_auc_score as roc_auc, mean_squared_error, mean_absolute_error
 
@@ -14,23 +14,23 @@
 
 
 def pipeline_tpot_class() -> Pipeline:
-    node = PrimaryNode('tpot_class')
+    node = PipelineNode('tpot_class')
     pipeline = Pipeline(node)
 
     return pipeline
 
 
 def pipeline_tpot_regr() -> Pipeline:
-    node = PrimaryNode('tpot_regr')
+    node = PipelineNode('tpot_regr')
     pipeline = Pipeline(node)
 
     return pipeline
 
 
 def pipeline_tpot_ts(window_size: int = 20):
-    node_lagged = PrimaryNode('lagged')
+    node_lagged = PipelineNode('lagged')
     node_lagged.parameters = {'window_size': window_size}
-    node_root = SecondaryNode('tpot_regr', nodes_from=[node_lagged])
+    node_root = PipelineNode('tpot_regr', nodes_from=[node_lagged])
 
     pipeline = Pipeline(node_root)
 

examples/advanced/automl/tpot_vs_fedot.py

@@ -6,7 +6,7 @@
 from tpot.export_utils import set_param_recursive
 
 from fedot.core.pipelines.pipeline import Pipeline
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.data.data import InputData
 
 
@@ -35,9 +35,9 @@ def run_tpot_vs_fedot_example(train_file_path: str, test_file_path: str):
 
     print(f'ROC AUC for TPOT: {roc_auc_value}')
 
-    node_scaling = PrimaryNode('scaling')
-    node_bernb = SecondaryNode('bernb', nodes_from=[node_scaling])
-    node_rf = SecondaryNode('rf', nodes_from=[node_bernb, node_scaling])
+    node_scaling = PipelineNode('scaling')
+    node_bernb = PipelineNode('bernb', nodes_from=[node_scaling])
+    node_rf = PipelineNode('rf', nodes_from=[node_bernb, node_scaling])
     pipeline = Pipeline(node_rf)
 
     pipeline.fit(train_data)

examples/advanced/decompose/classification_refinement_example.py

@@ -4,7 +4,7 @@
 
 from cases.credit_scoring.credit_scoring_problem import get_scoring_data, calculate_validation_metric
 from fedot.core.data.data import InputData
-from fedot.core.pipelines.node import PrimaryNode, SecondaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
 from fedot.core.pipelines.tuning.unified import PipelineTuner
@@ -17,22 +17,22 @@
 
 def get_refinement_pipeline():
     """ Create 3-level pipeline with class_decompose node """
-    node_scaling = PrimaryNode('scaling')
-    node_logit = SecondaryNode('logit', nodes_from=[node_scaling])
-    node_decompose = SecondaryNode('class_decompose', nodes_from=[node_logit, node_scaling])
-    node_rfr = SecondaryNode('rfr', nodes_from=[node_decompose])
-    node_rf = SecondaryNode('rf', nodes_from=[node_rfr, node_logit])
+    node_scaling = PipelineNode('scaling')
+    node_logit = PipelineNode('logit', nodes_from=[node_scaling])
+    node_decompose = PipelineNode('class_decompose', nodes_from=[node_logit, node_scaling])
+    node_rfr = PipelineNode('rfr', nodes_from=[node_decompose])
+    node_rf = PipelineNode('rf', nodes_from=[node_rfr, node_logit])
 
     pipeline = Pipeline(node_rf)
     return pipeline
 
 
 def get_non_refinement_pipeline():
     """ Create 3-level pipeline without class_decompose node """
-    node_scaling = PrimaryNode('scaling')
-    node_rf = SecondaryNode('rf', nodes_from=[node_scaling])
-    node_logit = SecondaryNode('logit', nodes_from=[node_scaling])
-    node_rf = SecondaryNode('rf', nodes_from=[node_logit, node_rf])
+    node_scaling = PipelineNode('scaling')
+    node_rf = PipelineNode('rf', nodes_from=[node_scaling])
+    node_logit = PipelineNode('logit', nodes_from=[node_scaling])
+    node_rf = PipelineNode('rf', nodes_from=[node_logit, node_rf])
     pipeline = Pipeline(node_rf)
     return pipeline