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Branch to repeating bugs - [raifhack_bug](https://github.com/nccr-itmo/FEDOT/tree/raifhack_bug)
Input is [table dataset](https://russianhackers.notion.site/c666cd6d3d8c44adb79d73f69d3ee81a?v=2b1e857b…
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When I tried to import the train test data setup it through the error, before i have installed all the requirements but I don't know why am I getting thi error
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After the [last ](https://github.com/nccr-itmo/FEDOT/pull/416) merge related with API refactoring i found some issues with design.
- Naming in api_utils module
![image](https://user-images.githubu…
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Sometimes the test didn't work. I wrote a special test to reproduce the exception https://github.com/nccr-itmo/FEDOT/blob/617cc8dcd1e101ef36d1ab75703c758b1c7f9978/test/unit/validation/test_table_cv.py…
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The current problems are:
- composing history is too large, the a lot of time is requred to load it
- composing history is saveing as pickled class (that can be changed)
Is should be resovled dur…
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I am trying to play with example https://github.com/ITMO-NSS-team/fedot_electro_ts_case/blob/main/case/traffic_example/simple_automl.ipynb
After execution of
model = Fedot(problem='ts_forecastin…
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If send for composer params:
```
'history_folder': '/some/path'
```
overwise save history to default path _**~/home/user/Fedot/composing_history**_
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ValueError: RANSAC could not find a valid consensus set. All `max_trials` iterations were skipped because each randomly chosen sub-sample failed the passing criteria. See estimator attributes for diag…
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```
def test_unshaffled_data():
target_column = 'species'
df_el, y = load_iris(return_X_y=True, as_frame=True)
df_el[target_column] = LabelEncoder().fit_transform(y)
features, t…
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I'm testing the AutoML approach with `Fedot` on a dataset with 11 rows and 66 columns (never mind the many more columns than rows in this case). The default parameters for the strategy (`ransac_non_li…