code: def test_plot_classification_continuous():
      data = fetch_openml("MiceProtein")
      df = data_df_from_bunch(data)
      # only univariate plots
      figures = plot_classification_continuous(
          df, target_col="target", plot_pairwise=False
      )
      assert len(figures) == 1
      # top 10 axes
      assert len(figures[0].get_axes()) == 10
      # six is the minimum number of features for histograms
      # (last column is target)
      figures = plot_classification_continuous(
          df.iloc[:, -7:], target_col="target", plot_pairwise=False
      )
      assert len(figures) == 1
      assert len(figures[0].get_axes()) == 6
  
      # for 5 features, do full pairplot
      figures = plot_classification_continuous(
          df.iloc[:, -6:], target_col="target", plot_pairwise=False
      )
      assert len(figures) == 1
      assert figures[0].size == 5 * 5
  
      # also do pairwise plots
      figures = plot_classification_continuous(
          df, target_col="target", random_state=42
      )
      # univariate, pairwise, pca, lda
      assert len(figures) == 4
      # univariate
      axes = figures[0].get_axes()
      assert len(axes) == 10
      # known result
      assert axes[0].get_xlabel() == "SOD1_N"
      # bar plot never has ylabel
      assert axes[0].get_ylabel() == ""
      # pairwise
      axes = figures[1].ravel()
      assert len(axes) == 4
      # known result
      assert axes[0].get_xlabel() == "SOD1_N"
      assert axes[0].get_ylabel() == "S6_N"
  
      # PCA
      axes = figures[2].get_axes()
      assert len(axes) == 4
      # known result
      assert axes[0].get_xlabel() == "PCA 1"
      assert axes[0].get_ylabel() == "PCA 5"
  
      # LDA
      axes = figures[3].get_axes()
      assert len(axes) == 4
      # known result
      assert axes[0].get_xlabel() == "LDA 0"
      assert axes[0].get_ylabel() == "LDA 1"
  
imports: import pytest
  import numpy
  import pandas
  import matplotlib.pyplot
  import warnings
  import itertools
  from sklearn.datasets import make_regression, make_blobs, load_digits, fetch_openml, load_diabetes
  from sklearn.preprocessing import KBinsDiscretizer
  from dabl.preprocessing import clean, detect_types, guess_ordinal
  from dabl.plot.supervised import plot, plot_classification_categorical, plot_classification_continuous, plot_regression_categorical, plot_regression_continuous
  from dabl.utils import data_df_from_bunch
  from dabl import set_config
label_desc: Use image node_id 14 for calling a global function with example usage: test_plot_classification_continuous() without return types