title: Partial dependence plot (PDPbox) error on lightGBM model: "wrong number of items passed 3, placement implies "
body: <p>I have a snippet of code like this</p>
  
  <pre><code>import lightgbm as lgb
  from pdpbox import pdp, get_dataset, info_plots
  import seaborn as sns
  from sklearn.model_selection import train_test_split
  
  #load some data
  df = sns.load_dataset("iris")
  
  X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.80)
  lgd_train = lgb.Dataset(X_train, label=y_train)
  params={ "objective": "multiclass",
              "num_class": 3,}
  clf = lgb.train(params, d_train)
  #plot partial dependence
  pdp_dist = pdp.pdp_isolate(
              model=clf, dataset=X_train, model_features=X_train.columns, feature='petal_width'
          )
  pdp.pdp_plot(pdp_dist, 'petal_width')
  </code></pre>
  
  <p>This probably has something to do with predict output is probably for the 3 iris class, but I don't know how to fix it. </p>
  
  <p>If I use the other lightGBM syntax of lgb.LGBMClassifier().fit(X_train, y_train), then it keeps return an error </p>
  
  <blockquote>
    <p>[LightGBM] [Fatal] Do not support special JSON characters in feature name. </p>
  </blockquote>
  
  <p>despite my data has absolutely no special characters. Any way to get around it? Thanks1</p>