class_docstr: The main class for FEDOT AutoML API.
  
  Alternatively, may be initialized using the class :class:`~fedot.api.builder.FedotBuilder`,
  where all the optional AutoML parameters are documented and separated by meaning.
  
  Args:
      problem: name of the modelling problem to solve.
          .. details:: Possible options:
  
              - ``classification`` -> for classification task
              - ``regression`` -> for regression task
              - ``ts_forecasting`` -> for time series forecasting task
  
      timeout: time for model design (in minutes): ``None`` or ``-1`` means infinite time.
      task_params: additional parameters of the task.
      seed: value for a fixed random seed.
      logging_level: logging levels are the same as in
          `built-in logging library <https://docs.python.org/3/library/logging.html>`_.
  
          .. details:: Possible options:
  
              - ``50`` -> critical
              - ``40`` -> error
              - ``30`` -> warning
              - ``20`` -> info
              - ``10`` -> debug
              - ``0`` -> nonset
  
      safe_mode: if set ``True`` it will cut large datasets to prevent memory overflow and use label encoder
          instead of OneHot encoder if summary cardinality of categorical features is high.
          Default value is ``False``.
  
      n_jobs: num of ``n_jobs`` for parallelization (set to ``-1`` to use all cpu's). Defaults to ``-1``.
  
      composer_tuner_params: Additional optional parameters. See their documentation at the methods of
          :class:`~fedot.api.builder.FedotBuilder`.
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code: def plot_pareto(self):
      metric_names = [str(metric) for metric in self.metrics]
      # archive_history stores archives of the best models.
      # Each archive is sorted from the best to the worst model,
      # so the best_candidates is sorted too.
      best_candidates = self.history.archive_history[-1]
      visualise_pareto(
          front=best_candidates,
          objectives_names=metric_names,
          show=True,
      )
  
imports: import logging
  from copy import deepcopy
  from typing import Any, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from golem.core.dag.graph_utils import graph_structure
  from golem.core.log import Log, default_log
  from golem.core.optimisers.opt_history_objects.opt_history import OptHistory
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from golem.utilities.data_structures import ensure_wrapped_in_sequence
  from golem.visualisation.opt_viz_extra import visualise_pareto
  from fedot.api.api_utils.api_composer import ApiComposer
  from fedot.api.api_utils.api_data import ApiDataProcessor
  from fedot.api.api_utils.data_definition import FeaturesType, TargetType
  from fedot.api.api_utils.input_analyser import InputAnalyser
  from fedot.api.api_utils.params import ApiParams
  from fedot.api.api_utils.predefined_model import PredefinedModel
  from fedot.core.constants import DEFAULT_API_TIMEOUT_MINUTES, DEFAULT_TUNING_ITERATIONS_NUMBER
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.data.visualisation import plot_biplot, plot_forecast, plot_roc_auc
  from fedot.core.optimisers.objective import PipelineObjectiveEvaluate
  from fedot.core.optimisers.objective.metrics_objective import MetricsObjective
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.ts_wrappers import convert_forecast_to_output, out_of_sample_ts_forecast
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.metrics_repository import MetricCallable
  from fedot.core.repository.tasks import TaskParams, TaskTypesEnum
  from fedot.core.utils import set_random_seed
  from fedot.explainability.explainer_template import Explainer
  from fedot.explainability.explainers import explain_pipeline
  from fedot.preprocessing.base_preprocessing import BasePreprocessor
  from fedot.remote.remote_evaluator import RemoteEvaluator
  from fedot.utilities.define_metric_by_task import MetricByTask
  from fedot.utilities.memory import MemoryAnalytics
  from fedot.utilities.project_import_export import export_project_to_zip, import_project_from_zip
label_desc: Use image node_id 9 for calling the Fedot obj's underlying member method code with example usage: obj.plot_pareto() without return types