code: def test_weighting_effect(self):
      data = sklearn.datasets.make_classification(
          n_samples=200,
          n_features=10,
          n_redundant=2,
          n_informative=2,
          n_repeated=2,
          n_clusters_per_class=2,
          weights=[0.8, 0.2],
          random_state=1,
      )
  
      for name, clf, acc_no_weighting, acc_weighting, places in [
          ("adaboost", AdaboostClassifier, 0.810, 0.735, 3),
          ("decision_tree", DecisionTree, 0.780, 0.643, 3),
          ("extra_trees", ExtraTreesClassifier, 0.78, 0.8, 3),
          ("random_forest", RandomForest, 0.75, 0.789, 3),
          ("libsvm_svc", LibSVM_SVC, 0.769, 0.72, 3),
          ("liblinear_svc", LibLinear_SVC, 0.762, 0.735, 3),
          ("passive_aggressive", PassiveAggressive, 0.16, 0.222, 3),
          ("sgd", SGD, 0.818, 0.567, 2),
          (
              "gradient_boosting",
              GradientBoostingClassifier,
              0.666,
              0.682,
              2,
          ),
      ]:
          for strategy, acc in [
              ("none", acc_no_weighting),
              ("weighting", acc_weighting),
          ]:
              # Fit
              data_ = copy.copy(data)
              X_train = data_[0][:100]
              Y_train = data_[1][:100]
              X_test = data_[0][100:]
              Y_test = data_[1][100:]
  
              model_args = {
                  "random_state": 1,
                  "include": {
                      "classifier": [name],
                      "feature_preprocessor": ["no_preprocessing"],
                  },
              }
  
              classifier = SimpleClassificationPipeline(**model_args)
              cs = classifier.get_hyperparameter_search_space()
              default = cs.get_default_configuration()
              default._values["balancing:strategy"] = strategy
  
              classifier = SimpleClassificationPipeline(
                  config=default, **model_args
              )
              classifier.fit(X_train, Y_train)
  
              predictions1 = classifier.predict(X_test)
              self.assertAlmostEqual(
                  sklearn.metrics.f1_score(predictions1, Y_test),
                  acc,
                  places=places,
                  msg=(name, strategy),
              )
  
              # fit_transformer and fit_estimator
              data_ = copy.copy(data)
              X_train = data_[0][:100]
              Y_train = data_[1][:100]
              X_test = data_[0][100:]
              Y_test = data_[1][100:]
  
              classifier = SimpleClassificationPipeline(
                  config=default, **model_args
              )
              Xt, fit_params = classifier.fit_transformer(
                  X_train, Y_train
              )
              classifier.fit_estimator(Xt, Y_train, **fit_params)
  
              predictions2 = classifier.predict(X_test)
              np.testing.assert_allclose(
                  predictions1,
                  predictions2,
                  err_msg=f"name = {name}, strategy = {strategy}",
              )
              self.assertAlmostEqual(
                  sklearn.metrics.f1_score(predictions2, Y_test),
                  acc,
                  places=places,
                  msg=(name, strategy),
              )
  
      for name, pre, acc_no_weighting, acc_weighting in [
          (
              "extra_trees_preproc_for_classification",
              ExtraTreesPreprocessorClassification,
              0.810,
              0.590,
          ),
          (
              "liblinear_svc_preprocessor",
              LibLinear_Preprocessor,
              0.837,
              0.562,
          ),
      ]:
          for strategy, acc in [
              ("none", acc_no_weighting),
              ("weighting", acc_weighting),
          ]:
              data_ = copy.copy(data)
              X_train = data_[0][:100]
              Y_train = data_[1][:100]
              X_test = data_[0][100:]
              Y_test = data_[1][100:]
  
              include = {
                  "classifier": ["sgd"],
                  "feature_preprocessor": [name],
              }
  
              classifier = SimpleClassificationPipeline(
                  random_state=1, include=include
              )
              cs = classifier.get_hyperparameter_search_space()
              default = cs.get_default_configuration()
              default._values["balancing:strategy"] = strategy
              classifier.set_hyperparameters(default)
              predictor = classifier.fit(X_train, Y_train)
              predictions = predictor.predict(X_test)
              self.assertAlmostEqual(
                  sklearn.metrics.f1_score(predictions, Y_test),
                  acc,
                  places=3,
                  msg=(name, strategy),
              )
  
              # fit_transformer and fit_estimator
              data_ = copy.copy(data)
              X_train = data_[0][:100]
              Y_train = data_[1][:100]
              X_test = data_[0][100:]
              Y_test = data_[1][100:]
  
              default._values["balancing:strategy"] = strategy
              classifier = SimpleClassificationPipeline(
                  config=default, random_state=1, include=include
              )
              Xt, fit_params = classifier.fit_transformer(
                  X_train, Y_train
              )
              classifier.fit_estimator(Xt, Y_train, **fit_params)
              predictions = classifier.predict(X_test)
              self.assertAlmostEqual(
                  sklearn.metrics.f1_score(predictions, Y_test),
                  acc,
                  places=3,
              )
  
imports: import copy
  import numpy
  import sklearn.datasets
  import sklearn.metrics
  from autosklearn.pipeline.classification import SimpleClassificationPipeline
  from autosklearn.pipeline.components.classification.adaboost import AdaboostClassifier
  from autosklearn.pipeline.components.classification.decision_tree import DecisionTree
  from autosklearn.pipeline.components.classification.extra_trees import ExtraTreesClassifier
  from autosklearn.pipeline.components.classification.gradient_boosting import GradientBoostingClassifier
  from autosklearn.pipeline.components.classification.liblinear_svc import LibLinear_SVC
  from autosklearn.pipeline.components.classification.libsvm_svc import LibSVM_SVC
  from autosklearn.pipeline.components.classification.passive_aggressive import PassiveAggressive
  from autosklearn.pipeline.components.classification.random_forest import RandomForest
  from autosklearn.pipeline.components.classification.sgd import SGD
  from autosklearn.pipeline.components.data_preprocessing.balancing.balancing import Balancing
  from autosklearn.pipeline.components.feature_preprocessing.extra_trees_preproc_for_classification import ExtraTreesPreprocessorClassification
  from autosklearn.pipeline.components.feature_preprocessing.liblinear_svc_preprocessor import LibLinear_Preprocessor
  import unittest
label_desc: Use image node_id 4 for calling the BalancingComponentTest obj's underlying member method code with example usage: obj.test_weighting_effect() without return types