Int test fix (#1354)

* test fix: IOptTuner excluded due to data leak

---------

Co-authored-by: Вадим Ахмеров <vadimspring@mail.ru>

test/integration/composer/test_composer.py

@@ -26,7 +26,7 @@
 from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, ComplexityMetricsEnum
 from fedot.core.repository.operation_types_repository import OperationTypesRepository, get_operations_for_task
 from fedot.core.repository.tasks import Task, TaskTypesEnum
-from fedot.core.utils import fedot_project_root, set_random_seed
+from fedot.core.utils import fedot_project_root
 from test.unit.pipelines.test_pipeline_comparison import pipeline_first, pipeline_second
 
 
@@ -164,7 +164,6 @@ def test_parameter_free_composer_build_pipeline_correct(data_fixture, request):
 
 @pytest.mark.parametrize('data_fixture', ['file_data_setup'])
 def test_multi_objective_composer(data_fixture, request):
-    set_random_seed(42)
 
     data = request.getfixturevalue(data_fixture)
     dataset_to_compose = data

test/integration/pipelines/tuning/test_pipeline_tuning.py

@@ -2,9 +2,6 @@
 from time import time
 
 import pytest
-
-from fedot.core.pipelines.pipeline_builder import PipelineBuilder
-from fedot.core.repository.dataset_types import DataTypesEnum
 from golem.core.tuning.hyperopt_tuner import get_node_parameters_for_hyperopt
 from golem.core.tuning.iopt_tuner import IOptTuner
 from golem.core.tuning.optuna_tuner import OptunaTuner
@@ -15,15 +12,17 @@
 from hyperopt.pyll.stochastic import sample as hp_sample
 
 from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline, \
-     ts_polyfit_ridge_pipeline
+    ts_polyfit_ridge_pipeline
 from fedot.core.data.data import InputData
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import \
     GLMImplementation
 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.pipelines.tuning.search_space import PipelineSearchSpace
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
+from fedot.core.repository.dataset_types import DataTypesEnum
 from fedot.core.repository.metrics_repository import RegressionMetricsEnum, ClassificationMetricsEnum
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 from fedot.core.utils import fedot_project_root, NESTED_PARAMS_LABEL
@@ -291,7 +290,7 @@ def test_custom_params_setter(data_fixture, request):
                           ('multi_classification_dataset', get_class_pipelines(), get_class_losses()),
                           ('ts_forecasting_dataset', get_ts_forecasting_pipelines(), get_regr_losses()),
                           ('multimodal_dataset', get_multimodal_pipelines(), get_class_losses())])
-@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, IOptTuner, OptunaTuner])
+@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, OptunaTuner])
 def test_pipeline_tuner_correct(data_fixture, pipelines, loss_functions, request, tuner):
     """ Test all tuners for pipeline """
     data = request.getfixturevalue(data_fixture)
@@ -325,7 +324,7 @@ def test_pipeline_tuner_with_no_parameters_to_tune(classification_dataset, tuner
     assert not tuned_pipeline.is_fitted
 
 
-@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, IOptTuner, OptunaTuner])
+@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, OptunaTuner])
 def test_pipeline_tuner_with_initial_params(classification_dataset, tuner):
     """ Test all tuners for pipeline with initial parameters """
     # a model
@@ -349,7 +348,7 @@ def test_pipeline_tuner_with_initial_params(classification_dataset, tuner):
                           ('multi_classification_dataset', get_class_pipelines(), get_class_losses()),
                           ('ts_forecasting_dataset', get_ts_forecasting_pipelines(), get_regr_losses()),
                           ('multimodal_dataset', get_multimodal_pipelines(), get_class_losses())])
-@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, IOptTuner, OptunaTuner])
+@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, OptunaTuner])
 def test_pipeline_tuner_with_custom_search_space(data_fixture, pipelines, loss_functions, request, tuner):
     """ Test tuners with different search spaces """
     data = request.getfixturevalue(data_fixture)
@@ -547,7 +546,7 @@ def test_complex_search_space_tuning_correct(tuner):
                           ('multi_classification_dataset', get_class_pipelines(), get_class_losses()),
                           ('ts_forecasting_dataset', get_ts_forecasting_pipelines(), get_regr_losses()),
                           ('multimodal_dataset', get_multimodal_pipelines(), get_class_losses())])
-@pytest.mark.parametrize('tuner', [OptunaTuner, IOptTuner])
+@pytest.mark.parametrize('tuner', [OptunaTuner])
 def test_multiobj_tuning(data_fixture, pipelines, loss_functions, request, tuner):
     """ Test multi objective tuning is correct """
     data = request.getfixturevalue(data_fixture)

test/integration/pipelines/tuning/test_tuner_builder.py

@@ -41,7 +41,6 @@ def test_tuner_builder_with_default_params():
     assert isinstance(tuner.search_space, PipelineSearchSpace)
     assert tuner.iterations == DEFAULT_TUNING_ITERATIONS_NUMBER
     assert tuner.algo == tpe.suggest
-    assert tuner.max_seconds == 300
 
 
 @pytest.mark.parametrize('tuner_class', [SimultaneousTuner, SequentialTuner, IOptTuner])

test/integration/real_applications/test_examples.py

@@ -84,7 +84,7 @@ def test_api_classification_example():
 
 
 def test_api_ts_forecasting_example():
-    for _ in range(100):
+    for _ in range(10):
         forecast = run_ts_forecasting_example(dataset='salaries', timeout=2, with_tuning=False)
         assert forecast is not None