code: def partial_plot_test():
      # import the iris dataset:
      # this dataset is used to classify the type of iris plant
      # the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Iris
      iris = h2o.import_file(
          pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
  
      # convert response column to a factor
      iris["class"] = iris["class"].asfactor()
      iris["random_cat"] = iris["class"]
  
      # set the predictor names and the response column name
      predictors = iris.col_names[:-1]
      response = "class"
  
      # split into train and validation
      train, valid = iris.split_frame(ratios=[0.8], seed=1234)
  
      # build model
      model = H2OGeneralizedLinearEstimator(family="multinomial")
      model.train(
          x=predictors,
          y=response,
          training_frame=train,
          validation_frame=valid,
      )
  
      # h2o multinomial PDP
  
      # numeric columns
      # one class target
      cols = ["petal_len"]
      targets = ["Iris-setosa"]
      pdp_petal_len_se = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=False,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_se)
  
      pdp_petal_len_se_std = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=True,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_se_std)
  
      # two clasess target
      targets = ["Iris-setosa", "Iris-virginica"]
      pdp_petal_len_se_vi = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=False,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_se_vi)
  
      pdp_petal_len_se_vi_std = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=True,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_se_vi_std)
  
      # three classes target
      targets = ["Iris-setosa", "Iris-virginica", "Iris-versicolor"]
      pdp_petal_len_se_vi_ve_std = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=True,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_se_vi_ve_std)
  
      # two columns and three classes target
      cols = ["sepal_len", "petal_len"]
      pdp_petal_len_sepal_len_se_vi_ve_std = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=True,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_sepal_len_se_vi_ve_std)
  
      # three columns and three classes target
      cols = ["sepal_len", "petal_len", "sepal_wid"]
      pdp_petal_len_sepal_len_sepal_wid_se_vi_ve = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=False,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_sepal_len_sepal_wid_se_vi_ve)
  
      pdp_petal_len_sepal_len_sepal_wid_se_vi_ve_std = (
          model.partial_plot(
              frame=iris,
              cols=cols,
              targets=targets,
              plot_stddev=True,
              plot=True,
              server=True,
          )
      )
      print(pdp_petal_len_sepal_len_sepal_wid_se_vi_ve_std)
  
      # categorical column - nonsense column, just for testing
      cols = ["random_cat"]
      targets = ["Iris-setosa"]
      pdp_petal_len_cat = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=False,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_cat)
  
      targets = ["Iris-setosa", "Iris-versicolor"]
      pdp_petal_len_cat_std = model.partial_plot(
          frame=iris,
          cols=cols,
          targets=targets,
          plot_stddev=True,
          plot=True,
          server=True,
      )
      print(pdp_petal_len_cat_std)
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: partial_plot_test() without return types