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hotfix: go for FeaturesType instead of InputData in a pipeline tuning #1311
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All PEP8 errors has been fixed, thanks ❤️ Comment last updated at |
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #1311 +/- ##
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Coverage 79.93% 79.94%
=======================================
Files 146 146
Lines 10097 10102 +5
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+ Hits 8071 8076 +5
Misses 2026 2026 ☔ View full report in Codecov by Sentry. |
fedot/api/main.py
Outdated
if input_data is None: | ||
input_data = self.train_data | ||
else: | ||
input_data = self.data_processor.define_data(features=input_data, target=self.target, is_predict=False) |
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Если подаются новые данные для тюнинга, то у них скорее всего будет новый и target
. Поэтому в tune
нужно по аналогии и target: TargetType = 'target'
добавить
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@Lopa10ko Я мб еще учел если у нас input_data is None
. то target
должен быть также self.target
. Или при вызове define_data
он таким же и будет?
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кажется, если input_data is None
, то пост-тюнинг запускают на уже обработанных данных (на self.train_data
), просто на этот раз дольше, например
а возможность запустить с указанными данными нужна в случае, если тюним на данных, которые не видели раньше (тогда и указываем FeaturesType
+ TargetType
)
This is a 🙋 feature or enhancement.
Summary
tune
function so that theinput_data
param can be of typeFeaturesType
. This allows passing one of the following data formats:str, PathLike, numpy array, pandas DataFrame, FEDOT-native InputData, FEDOT-native MultiModalData, dictionary, or tuple
add unit tests with different data formats(covered withtest/unit/api/test_main_api
andtest/integration/pipelines/tuning/test_pipeline_tuning
)Context
related to discussion in #1298