code: def test_dirty_float_target_regression():
      import matplotlib
  
      matplotlib.use("agg")
      titanic_data = load_titanic()
      data = pd.DataFrame({"one": np.repeat(np.arange(50), 2)})
      dirty = make_dirty_float()
      data["target"] = dirty
      with pytest.warns(
          UserWarning,
          match="Discarding dirty_float targets that "
          "cannot be converted to float.",
      ):
          clean(data, target_col="target")
      with pytest.warns(
          UserWarning,
          match="Discarding dirty_float targets that "
          "cannot be converted to float.",
      ):
          plot(data, target_col="target")
  
      # check if works for non dirty_float targets
      plot(titanic_data, target_col="survived")
  
imports: import os
  import pytest
  import random
  import string
  import warnings
  import numpy
  import pandas
  from sklearn.datasets import load_iris, load_digits
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LogisticRegression
  from dabl.preprocessing import USELESS_TYPES, detect_types, EasyPreprocessor, DirtyFloatCleaner, clean, _FLOAT_REGEX, _float_matching, detect_type_series
  from dabl.utils import data_df_from_bunch
  from dabl.datasets import load_titanic
  from dabl import plot
label_desc: Use image node_id 25 for calling a global function with example usage: test_dirty_float_target_regression() without return types