[Bug]: fit time is incorrect

[DRMPN]

Expected Behavior

Current Behavior

Green is real time.
Purple or red is calculated(estimated) time.

8 hours run

5 minutes run

Possible Solution

Multiplication instead of addition in time estimator I guess.

Steps to Reproduce

Data can be obtained from: https://www.kaggle.com/competitions/playground-series-s4e9/data
Simple notebooks to reproduce the bug:

• 8 hours notebook:

import pandas as pd
import numpy as np
from fedot.api.main import Fedot

train = pd.read_csv("C:/Users/nnikitin-user/Desktop/automl-september/playground-series-s4e9/train.csv")
test = pd.read_csv("C:/Users/nnikitin-user/Desktop/automl-september/playground-series-s4e9/test.csv")
sub = pd.read_csv("C:/Users/nnikitin-user/Desktop/automl-september/playground-series-s4e9/sample_submission.csv")

train.drop(columns=["id"], inplace=True)
test.drop(columns=["id"], inplace=True)

auto_model = Fedot(
    problem="regression",
    metric=["rmse"],
    preset="best_quality",
    with_tuning=True,
    timeout=480,
    cv_folds=10,
    seed=42,
    n_jobs=1,
    logging_level=10,
    use_pipelines_cache=False,
    use_auto_preprocessing=False,
)

auto_model.fit(features=train, target="price")

auto_model.current_pipeline.save(
    path="C:/Users/nnikitin-user/Desktop/automl-september/run_8hours/saved_pipelines",
    create_subdir=True,
    is_datetime_in_path=True,
)

prediction = auto_model.predict(features=test)

sub["price"] = prediction.ravel()
sub.to_csv("submission.csv", index=False)

• 30 mins notebook:

import pandas as pd
import numpy as np
from fedot.api.main import Fedot
from fedot.core.pipelines.pipeline_builder import PipelineBuilder

train = pd.read_csv("C:/Users/nnikitin-user/Desktop/automl-september/playground-series-s4e9/train.csv")
test = pd.read_csv("C:/Users/nnikitin-user/Desktop/automl-september/playground-series-s4e9/test.csv")
sub = pd.read_csv("C:/Users/nnikitin-user/Desktop/automl-september/playground-series-s4e9/sample_submission.csv")

train.drop(columns=["id"], inplace=True)
test.drop(columns=["id"], inplace=True)

auto_model = Fedot(
    problem="regression",
    metric=["rmse"],
    preset="best_quality",
    with_tuning=True,
    timeout=5,
    cv_folds=10,
    seed=42,
    n_jobs=1,
    logging_level=10,
    initial_assumption=PipelineBuilder().add_node("lgbmreg").build(),
    use_pipelines_cache=False,
    use_auto_preprocessing=False,
)

auto_model.fit(features=train, target="price")

auto_model.current_pipeline.save(
    path="C:/Users/nnikitin-user/Desktop/automl-september/run_lgbm/saved_pipelines",
    create_subdir=True,
    is_datetime_in_path=True,
)

prediction = auto_model.predict(features=test)

sub["price"] = prediction
sub.to_csv("submission.csv", index=False)

Context [OPTIONAL]

Participating in a Kaggle competition https://www.kaggle.com/competitions/playground-series-s4e9

[aPovidlo]

@DRMPN А какие значения в n_jobs стояли?

[DRMPN]

@DRMPN А какие значения в n_jobs стояли?

n_jobs = 1