code: def test_metalearning(self):
      dataset_name_classification = "digits"
      initial_challengers_classification = {
          "ACC_METRIC": '--initial-challengers " '
          "-balancing:strategy 'weighting' "
          "-classifier:__choice__ 'proj_logit'",
          "AUC_METRIC": '--initial-challengers " '
          "-balancing:strategy 'weighting' "
          "-classifier:__choice__ 'liblinear_svc'",
          "BAC_METRIC": '--initial-challengers " '
          "-balancing:strategy 'weighting' "
          "-classifier:__choice__ 'proj_logit'",
          "F1_METRIC": '--initial-challengers " '
          "-balancing:strategy 'weighting' "
          "-classifier:__choice__ 'proj_logit'",
          "PAC_METRIC": '--initial-challengers " '
          "-balancing:strategy 'none' "
          "-classifier:__choice__ 'random_forest'",
      }
  
      dataset_name_regression = "diabetes"
      initial_challengers_regression = {
          "A_METRIC": '--initial-challengers " '
          "-imputation:strategy 'mean' "
          "-one_hot_encoding:minimum_fraction '0.01' "
          "-one_hot_encoding:use_minimum_fraction 'True' "
          "-preprocessor:__choice__ 'no_preprocessing' "
          "-regressor:__choice__ 'random_forest'",
          "R2_METRIC": '--initial-challengers " '
          "-imputation:strategy 'mean' "
          "-one_hot_encoding:minimum_fraction '0.01' "
          "-one_hot_encoding:use_minimum_fraction 'True' "
          "-preprocessor:__choice__ 'no_preprocessing' "
          "-regressor:__choice__ 'random_forest'",
      }
  
      for dataset_name, task, initial_challengers in [
          (
              dataset_name_regression,
              REGRESSION,
              initial_challengers_regression,
          ),
          (
              dataset_name_classification,
              MULTICLASS_CLASSIFICATION,
              initial_challengers_classification,
          ),
      ]:
          for metric in initial_challengers:
              configuration_space = get_configuration_space(
                  {"metric": metric, "task": task, "is_sparse": False},
                  include={
                      "feature_preprocessor": ["no_preprocessing"]
                  },
              )
  
              X_train, Y_train, X_test, Y_test = get_dataset(
                  dataset_name
              )
              categorical = {i: False for i in range(X_train.shape[1])}
  
              meta_features_label = _calculate_metafeatures(
                  X_train, Y_train, categorical, dataset_name, task
              )
              meta_features_encoded_label = (
                  _calculate_metafeatures_encoded(
                      X_train, Y_train, categorical, dataset_name, task
                  )
              )
  
              initial_configuration_strings_for_smac = (
                  suggest_via_metalearning(
                      meta_features_label,
                      meta_features_encoded_label,
                      configuration_space,
                      dataset_name,
                      metric,
                      task,
                      False,
                      1,
                      None,
                  )
              )
  
              print(metric)
              print(initial_configuration_strings_for_smac[0])
              self.assertTrue(
                  initial_configuration_strings_for_smac[0].startswith(
                      initial_challengers[metric]
                  )
              )
  
file_path: auto-sklearn/test/test_metalearning/test_metalearning.py
imports: from sklearn.datasets import load_breast_cancer
  from autosklearn.classification import AutoSklearnClassifier
  from autosklearn.constants import MULTICLASS_CLASSIFICATION, REGRESSION
  from autosklearn.metalearning.mismbo import suggest_via_metalearning
  from autosklearn.pipeline.util import get_dataset
  from autosklearn.smbo import _calculate_metafeatures, _calculate_metafeatures_encoded
  from autosklearn.util.pipeline import get_configuration_space
  import unittest
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.test_metalearning() without return types