From b37b67054b04d306eec4e19a23c2f8c154b2e47e Mon Sep 17 00:00:00 2001 From: Andrey Getmanov <91334765+andreygetmanov@users.noreply.github.com> Date: Fri, 19 Aug 2022 18:14:36 +0300 Subject: [PATCH] Hotfix of #827 test_auto_preset_converted_correctly fails (#828) * Hotfix of #827 test_auto_preset_converted_correctly fails when the PC performance is quite high --- test/unit/api/test_presets.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/test/unit/api/test_presets.py b/test/unit/api/test_presets.py index 71ba9240a8..6fa7c10207 100644 --- a/test/unit/api/test_presets.py +++ b/test/unit/api/test_presets.py @@ -89,11 +89,13 @@ def test_presets_inserting_in_params_correct(): def test_auto_preset_converted_correctly(): """ Checks that the proposed method of automatic preset detection correctly converts a preset """ + tiny_timeout_value = 0.005 + large_pop_size = 500 data = data_with_binary_features_and_categorical_target() simple_init_assumption = Pipeline(PrimaryNode('logit')) - fedot_model = Fedot(problem='classification', preset='auto', timeout=0.01, - initial_assumption=simple_init_assumption, pop_size=500) + fedot_model = Fedot(problem='classification', preset='auto', timeout=tiny_timeout_value, + initial_assumption=simple_init_assumption, pop_size=large_pop_size) # API must return initial assumption without composing and tuning (due to population size is too large) fedot_model.fit(data) assert fedot_model.api_composer.preset_name == FAST_TRAIN_PRESET_NAME