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test fix: IOptTuner excluded
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@VadimsAhmers
VadimsAhmers committed Dec 28, 2024
1 parent c8ed3de commit 6a49878
Showing 1 changed file with 7 additions and 21 deletions.
28 changes: 7 additions & 21 deletions test/integration/pipelines/tuning/test_pipeline_tuning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
from hyperopt.pyll.stochastic import sample as hp_sample

from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline, \
ts_ets_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 \
Expand Down Expand Up @@ -128,7 +128,7 @@ def get_class_pipelines():


def get_ts_forecasting_pipelines():
pipelines = [ts_ets_pipeline(), ts_complex_ridge_smoothing_pipeline()]
pipelines = [ts_polyfit_ridge_pipeline(2), ts_complex_ridge_smoothing_pipeline()]
return pipelines


Expand Down Expand Up @@ -169,7 +169,7 @@ def get_not_default_search_space():
'lgbmreg': {
'learning_rate': {
'hyperopt-dist': hp.loguniform,
'sampling-scope': [0.05, 0.1],
'sampling-scope': [0.03, 0.1],
'type': 'continuous'},
'colsample_bytree': {
'hyperopt-dist': hp.uniform,
Expand Down Expand Up @@ -271,7 +271,6 @@ def run_node_tuner(train_data,


@pytest.mark.parametrize('data_fixture', ['classification_dataset'])
@pytest.mark.skip('Memory error')
def test_custom_params_setter(data_fixture, request):
data = request.getfixturevalue(data_fixture)
pipeline = get_complex_class_pipeline()
Expand All @@ -291,8 +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.skip('Memory error')
@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)
Expand All @@ -313,7 +311,6 @@ def test_pipeline_tuner_correct(data_fixture, pipelines, loss_functions, request


@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, IOptTuner, OptunaTuner])
@pytest.mark.skip('Memory error')
def test_pipeline_tuner_with_no_parameters_to_tune(classification_dataset, tuner):
pipeline = get_pipeline_with_no_params_to_tune()
pipeline_tuner, tuned_pipeline = run_pipeline_tuner(tuner=tuner,
Expand All @@ -327,8 +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.skip('Memory error')
@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
Expand All @@ -352,8 +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.skip('Memory error')
@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)
Expand All @@ -376,7 +371,6 @@ def test_pipeline_tuner_with_custom_search_space(data_fixture, pipelines, loss_f
('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.skip('Memory error')
def test_certain_node_tuning_correct(data_fixture, pipelines, loss_functions, request):
""" Test SequentialTuner for particular node based on hyperopt library """
data = request.getfixturevalue(data_fixture)
Expand All @@ -400,7 +394,6 @@ def test_certain_node_tuning_correct(data_fixture, pipelines, loss_functions, re
('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.skip('Memory error')
def test_certain_node_tuner_with_custom_search_space(data_fixture, pipelines, loss_functions, request):
""" Test SequentialTuner for particular node with different search spaces """
data = request.getfixturevalue(data_fixture)
Expand All @@ -418,7 +411,6 @@ def test_certain_node_tuner_with_custom_search_space(data_fixture, pipelines, lo

@pytest.mark.parametrize('n_steps', [100, 133, 217, 300])
@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, IOptTuner, OptunaTuner])
@pytest.mark.skip('Memory error')
def test_ts_pipeline_with_stats_model(n_steps, tuner):
""" Tests tuners for time series forecasting task with AR model """
train_data, test_data = get_ts_data(n_steps=n_steps, forecast_length=5)
Expand All @@ -438,7 +430,6 @@ def test_ts_pipeline_with_stats_model(n_steps, tuner):


@pytest.mark.parametrize('data_fixture', ['tiny_classification_dataset'])
@pytest.mark.skip('Memory error')
def test_early_stop_in_tuning(data_fixture, request):
data = request.getfixturevalue(data_fixture)
train_data, test_data = train_test_data_setup(data=data)
Expand Down Expand Up @@ -470,7 +461,6 @@ def test_early_stop_in_tuning(data_fixture, request):
assert time() - start_node_tuner < 1


@pytest.mark.skip('Memory error')
def test_search_space_correctness_after_customization():
default_search_space = PipelineSearchSpace()

Expand Down Expand Up @@ -499,7 +489,6 @@ def test_search_space_correctness_after_customization():
assert default_params['0 || gbr | max_depth'] != custom_with_replace_params['0 || gbr | max_depth']


@pytest.mark.skip('Memory error')
def test_search_space_get_operation_parameter_range():
default_search_space = PipelineSearchSpace()
gbr_operations = ['loss', 'learning_rate', 'max_depth', 'min_samples_split',
Expand All @@ -523,7 +512,6 @@ def test_search_space_get_operation_parameter_range():
assert custom_with_replace_operations == ['max_depth']


@pytest.mark.skip('Memory error')
def test_complex_search_space():
space = PipelineSearchSpace()
for i in range(20):
Expand All @@ -535,7 +523,6 @@ def test_complex_search_space():

# TODO: (YamLyubov) add IOptTuner when it will support nested parameters.
@pytest.mark.parametrize('tuner', [SimultaneousTuner, SequentialTuner, OptunaTuner])
@pytest.mark.skip('Memory error')
def test_complex_search_space_tuning_correct(tuner):
""" Tests Tuners for time series forecasting task with GLM model that has a complex glm search space"""
train_data, test_data = get_ts_data(n_steps=700, forecast_length=20)
Expand All @@ -559,8 +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.skip('Memory error')
@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)
Expand Down

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