Pipeline save&load path refactoring

examples/advanced/automl/h2o_example.py

@@ -2,7 +2,6 @@
 from sklearn.metrics import roc_auc_score as roc_auc, mean_squared_error, mean_absolute_error
 
 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 PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
@@ -36,11 +35,10 @@ def pipeline_h2o_ts(window_size: int = 20):
 
 def export_h2o(pipeline, pipeline_path, test_data):
     # Export it
-    pipeline.save(path=pipeline_path)
+    pipeline.save(path=pipeline_path, create_subdir=False)
 
     # Import pipeline
-    json_path_load = create_correct_path(pipeline_path)
-    new_pipeline = Pipeline.from_serialized(json_path_load)
+    new_pipeline = Pipeline().load(pipeline_path)
 
     results = new_pipeline.predict(input_data=test_data, output_mode="full_probs")
     prediction_after_export = results.predict[:, 0]

examples/advanced/automl/tpot_example.py

@@ -1,7 +1,6 @@
 import numpy as np
 
 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 PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
@@ -49,11 +48,10 @@ def tpot_classification_pipeline_evaluation():
     print(f'Before export {prediction_before_export[:4]}')
 
     # Export it
-    pipeline.save(path=pipeline_path)
+    pipeline.save(path=pipeline_path, create_subdir=False)
 
     # Import pipeline
-    json_path_load = create_correct_path(pipeline_path)
-    new_pipeline = Pipeline.from_serialized(json_path_load)
+    new_pipeline = Pipeline().load(pipeline_path)
 
     predicted_output_after_export = new_pipeline.predict(test_data, output_mode="full_probs")
     prediction_after_export = predicted_output_after_export.predict[:, 0]
@@ -80,11 +78,10 @@ def tpot_regression_pipeline_evaluation():
     print(f'Before export {results.predict[:4]}')
 
     # Export it
-    pipeline.save(path=pipeline_path)
+    pipeline.save(path=pipeline_path, create_subdir=False)
 
     # Import pipeline
-    json_path_load = create_correct_path(pipeline_path)
-    new_pipeline = Pipeline.from_serialized(json_path_load)
+    new_pipeline = Pipeline().load(pipeline_path)
 
     predicted_output_after_export = new_pipeline.predict(test_data)
     prediction_after_export = predicted_output_after_export.predict[:4]
@@ -106,11 +103,10 @@ def tpot_ts_pipeline_evaluation():
     print(f'Before export {test_pred.predict[:4]}')
 
     # Export it
-    pipeline.save(path=pipeline_path)
+    pipeline.save(path=pipeline_path, create_subdir=False)
 
     # Import pipeline
-    json_path_load = create_correct_path(pipeline_path)
-    new_pipeline = Pipeline.from_serialized(json_path_load)
+    new_pipeline = Pipeline().load(pipeline_path)
 
     predicted_output_after_export = new_pipeline.predict(test_data)
     prediction_after_export = predicted_output_after_export.predict[:4]

examples/advanced/sensitivity_analysis/pipeline_export_with_sa.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 
 from examples.advanced.sensitivity_analysis.dataset_access import get_scoring_data
@@ -9,21 +7,6 @@
 from fedot.sensitivity.nodes_sensitivity import NodesAnalysis
 
 
-def create_correct_path(path: str, dirname_flag: bool = False):
-    """
-    Create path with time which was created during the testing process.
-    """
-
-    for dirname in next(os.walk(os.path.curdir))[1]:
-        if dirname.endswith(path):
-            if dirname_flag:
-                return dirname
-            else:
-                file = os.path.join(dirname, path + '.json')
-                return file
-    return None
-
-
 def run_import_export_example(pipeline_path):
     # Prepare data to train the model
     train_data, test_data = get_scoring_data()
@@ -41,11 +24,10 @@ def run_import_export_example(pipeline_path):
                               NodeReplaceOperationAnalyze]).analyze()
 
     # Export it
-    pipeline.save(path=pipeline_path)
+    pipeline.save(path=pipeline_path, create_subdir=False)
 
     # Import pipeline
-    json_path_load = create_correct_path(pipeline_path)
-    new_pipeline = Pipeline.from_serialized(json_path_load)
+    new_pipeline = Pipeline().load(pipeline_path)
 
     predicted_output_after_export = new_pipeline.predict(test_data)
     prediction_after_export = np.array(predicted_output_after_export.predict)

examples/simple/pipeline_import_export.py

@@ -1,5 +1,4 @@
 import json
-import os
 
 import numpy as np
 
@@ -12,22 +11,6 @@
 from fedot.core.utils import fedot_project_root
 
 
-def create_correct_path(path: str, dirname_flag: bool = False):
-    """
-    Create path with time which was created during the testing process.
-    """
-    # TODO: this function is used in many places, but now is not really needed
-    last_el = None
-    for dirname in next(os.walk(os.path.curdir))[1]:
-        if dirname.endswith(path):
-            if dirname_flag:
-                last_el = dirname
-            else:
-                file = os.path.join(dirname, path + '.json')
-                last_el = file
-    return last_el
-
-
 def run_import_export_example(pipeline_path, pipeline):
     features_options = {'informative': 1, 'bias': 0.0}
     samples_amount = 100

test/unit/models/test_atomized_model.py

@@ -11,7 +11,7 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.utils import fedot_project_root
-from test.unit.utilities.test_pipeline_import_export import create_correct_path, create_func_delete_files
+from test.unit.utilities.test_pipeline_import_export import create_func_delete_files
 
 
 @pytest.fixture(scope='session', autouse=True)
@@ -109,31 +109,29 @@ def create_input_data():
 def test_save_load_atomized_pipeline_correctly():
     pipeline = create_pipeline_with_several_nested_atomized_model()
 
-    json_actual, _ = pipeline.save('test_save_load_atomized_pipeline_correctly', create_subdir=False)
+    path = 'test_save_load_atomized_pipeline_correctly'
+    json_actual, _ = pipeline.save(path, create_subdir=False)
 
-    json_path_load = create_correct_path('test_save_load_atomized_pipeline_correctly')
-
-    with open(json_path_load, 'r') as json_file:
+    with open(os.path.join(path, path + '.json'), 'r') as json_file:
         json_expected = json.load(json_file)
 
-    pipeline_loaded = Pipeline.from_serialized(json_path_load)
+    pipeline_loaded = Pipeline().load(path)
 
     assert pipeline.length == pipeline_loaded.length
     assert json_actual == json.dumps(json_expected, indent=4)
 
 
 def test_save_load_fitted_atomized_pipeline_correctly():
     train_data, test_data = create_input_data()
+    path = 'test_save_load_fitted_atomized_pipeline_correctly'
 
     pipeline = create_pipeline_with_several_nested_atomized_model()
 
     pipeline.fit(train_data)
     before_save_predicted = pipeline.predict(test_data)
-    json_actual, _ = pipeline.save('test_save_load_fitted_atomized_pipeline_correctly', create_subdir=False)
-
-    json_path_load = create_correct_path('test_save_load_fitted_atomized_pipeline_correctly')
+    json_actual, _ = pipeline.save(path, create_subdir=False)
 
-    pipeline_loaded = Pipeline.from_serialized(json_path_load)
+    pipeline_loaded = Pipeline().load(path)
     json_expected, _ = pipeline_loaded.save('test_save_load_fitted_atomized_pipeline_correctly_loaded',
                                             create_subdir=False)
 

test/unit/models/test_custom_model_introduction.py

@@ -8,7 +8,6 @@
 import pandas as pd
 
 from examples.advanced.time_series_forecasting.custom_model_tuning import get_fitting_custom_pipeline
-from examples.simple.pipeline_import_export import create_correct_path
 from fedot.api.main import Fedot
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
@@ -194,15 +193,14 @@ def test_save_pipeline_with_custom():
     pipeline = get_centered_pipeline()
     pipeline.fit_from_scratch(train_input)
 
-    pipeline.save(path='test_pipeline', create_subdir=False)
-    json_path_load = create_correct_path('test_pipeline')
-    new_pipeline = Pipeline.from_serialized(json_path_load)
+    path = 'test_pipeline'
+    pipeline.save(path=path, create_subdir=False)
+    new_pipeline = Pipeline().load(path)
     predicted_output_after_export = new_pipeline.predict(predict_input)
     prediction_after_export = np.array(predicted_output_after_export.predict)
 
     # recursive deleting
-    dir_ = os.path.dirname(json_path_load)
-    shutil.rmtree(dir_)
+    shutil.rmtree(path)
 
     assert prediction_after_export is not None