Remove tests that moved to golem (#1049)

* Del tests that fully moved to GOLEM

* WIP transfer tests

* Fixes after rebase

* Remove some tests moved to golem

* Remove some tests moved to golem

* Remove some tests moved to golem

* Remove some tests moved to golem

* Fixes after rebase

* Remove PrimaryNode and SecondaryNode

* Fix test_complex_search_space_tuning_correct

examples/advanced/fedot_profiler_example.py → examples/advanced/profiler_example.py

@@ -17,8 +17,9 @@
     path = os.path.join(os.path.expanduser("~"), 'memory_profiler')
     full_path_train, full_path_test = get_scoring_data()
     arguments = {'train_file_path': full_path_train, 'test_file_path': full_path_test}
+    print(path)
     MemoryProfiler(run_credit_scoring_problem, kwargs=arguments, path=path,
-                   roots=[run_credit_scoring_problem], max_depth=8)
+                   roots=[run_credit_scoring_problem], max_depth=8, visualization=True)
 
     # EXAMPLE of TimeProfiler.
 

test/integration/automl/test_automl.py

@@ -5,8 +5,7 @@
     h2o_regression_pipeline_evaluation, h2o_ts_pipeline_evaluation
 from examples.advanced.automl.pipeline_from_automl import run_pipeline_from_automl
 from examples.advanced.automl.tpot_vs_fedot import run_tpot_vs_fedot_example
-from fedot.core.repository.operation_types_repository import OperationTypesRepository, get_operations_for_task
-from fedot.core.repository.tasks import TaskTypesEnum, Task
+from fedot.core.repository.operation_types_repository import OperationTypesRepository
 from fedot.core.utils import fedot_project_root
 
 

test/integration/preprocessing/test_pipeline_preprocessing.py

@@ -4,7 +4,7 @@
 import pytest
 
 from fedot.core.data.data import InputData
-from fedot.core.pipelines.node import PrimaryNode
+from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.preprocessing.dummy_preprocessing import DummyPreprocessor
 from fedot.preprocessing.preprocessing import DataPreprocessor
@@ -16,7 +16,7 @@ def test_pipeline_has_dummy_preprocessor_with_disabled_preprocessing():
     """
     Tests pipeline with disabled input data preprocessing has dummy preprocessor
     """
-    pipeline = Pipeline(PrimaryNode('ridge'), use_input_preprocessing=False)
+    pipeline = Pipeline(PipelineNode('ridge'), use_input_preprocessing=False)
     assert type(pipeline.preprocessor) is DummyPreprocessor
 
 
@@ -42,7 +42,7 @@ def test_disabled_pipeline_data_preprocessing(case: Tuple[Callable[[], InputData
     data_getter, is_fit_stage = case
     input_data = data_getter()
 
-    pipeline = Pipeline(PrimaryNode('ridge'), use_input_preprocessing=False)
+    pipeline = Pipeline(PipelineNode('ridge'), use_input_preprocessing=False)
     if is_fit_stage:
         preprocessed_data = pipeline._preprocess(input_data, is_fit_stage=is_fit_stage)
     else:
@@ -94,11 +94,11 @@ def test_data_preprocessor_performs_obligatory_data_preprocessing_only_once():
 def test_data_preprocessor_performs_optional_data_preprocessing_only_once():
     input_data = data_with_only_categorical_features()
     preprocessor = DataPreprocessor()
-    pipeline = Pipeline(PrimaryNode('ridge'))
+    pipeline = Pipeline(PipelineNode('ridge'))
 
     preprocessed_data = preprocessor.optional_prepare_for_fit(pipeline, input_data)
 
-    other_pipeline = Pipeline(PrimaryNode('dt'))
+    other_pipeline = Pipeline(PipelineNode('dt'))
     preprocessed_data_same = preprocessor.optional_prepare_for_fit(other_pipeline, preprocessed_data)
 
     assert id(preprocessed_data) == id(preprocessed_data_same)

test/unit/adapter/test_adapt_pipeline.py

@@ -1,7 +1,10 @@
 from copy import deepcopy
 from random import choice
 
+import numpy as np
 import pytest
+
+from golem.core.dag.graph_node import GraphNode
 from golem.core.dag.graph_verifier import GraphVerifier
 from golem.core.dag.verification_rules import DEFAULT_DAG_RULES
 from golem.core.optimisers.graph import OptNode
@@ -10,6 +13,8 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
+from test.unit.dag.test_graph_utils import find_first
+from test.unit.tasks.test_regression import get_synthetic_regression_data
 
 
 def get_pipelines():
@@ -41,6 +46,80 @@ def get_pipelines():
             skip_connection_structure]
 
 
+def generate_so_complex_pipeline():
+    node_imp = PipelineNode('simple_imputation')
+    node_lagged = PipelineNode('lagged', nodes_from=[node_imp])
+    node_ridge = PipelineNode('ridge', nodes_from=[node_lagged])
+    node_decompose = PipelineNode('decompose', nodes_from=[node_lagged, node_ridge])
+    node_pca = PipelineNode('pca', nodes_from=[node_decompose])
+    node_final = PipelineNode('ridge', nodes_from=[node_ridge, node_pca])
+    pipeline = Pipeline(node_final)
+    return pipeline
+
+
+def pipeline_with_custom_parameters(alpha_value):
+    node_scaling = PipelineNode('scaling')
+    node_norm = PipelineNode('normalization')
+    node_dtreg = PipelineNode('dtreg', nodes_from=[node_scaling])
+    node_lasso = PipelineNode('lasso', nodes_from=[node_norm])
+    node_final = PipelineNode('ridge', nodes_from=[node_dtreg, node_lasso])
+    node_final.parameters = {'alpha': alpha_value}
+    pipeline = Pipeline(node_final)
+
+    return pipeline
+
+
+def test_pipeline_adapters_params_correct():
+    """ Checking the correct conversion of hyperparameters in nodes when nodes
+    are passing through adapter
+    """
+    init_alpha = 12.1
+    pipeline = pipeline_with_custom_parameters(init_alpha)
+
+    # Convert into OptGraph object
+    adapter = PipelineAdapter()
+    opt_graph = adapter.adapt(pipeline)
+    # Get Pipeline object back
+    restored_pipeline = adapter.restore(opt_graph)
+    # Get hyperparameter value after pipeline restoration
+    restored_alpha = restored_pipeline.root_node.parameters['alpha']
+    assert np.isclose(init_alpha, restored_alpha)
+
+
+def test_preds_before_and_after_convert_equal():
+    """ Check if the pipeline predictions change before and after conversion
+    through the adapter
+    """
+    init_alpha = 12.1
+    pipeline = pipeline_with_custom_parameters(init_alpha)
+
+    # Generate data
+    input_data = get_synthetic_regression_data(n_samples=10, n_features=2,
+                                               random_state=2021)
+    # Init fit
+    pipeline.fit(input_data)
+    init_preds = pipeline.predict(input_data)
+
+    # Convert into OptGraph object
+    adapter = PipelineAdapter()
+    opt_graph = adapter.adapt(pipeline)
+    restored_pipeline = adapter.restore(opt_graph)
+
+    # Restored pipeline fit
+    restored_pipeline.fit(input_data)
+    restored_preds = restored_pipeline.predict(input_data)
+
+    assert np.array_equal(init_preds.predict, restored_preds.predict)
+
+
+def test_no_opt_or_graph_nodes_after_adapt_so_complex_graph():
+    adapter = PipelineAdapter()
+    pipeline = generate_so_complex_pipeline()
+    adapter.adapt(pipeline)
+
+    assert not find_first(pipeline, lambda n: type(n) in (GraphNode, OptNode))
+
+
 @pytest.mark.parametrize('pipeline', get_pipelines())
 def test_pipelines_adapt_properly(pipeline):
     adapter = PipelineAdapter()