code: def test_dense_missing():
      xs = np.array([[0, 1], [0, 2], [1, 2], [1, 0], [0.1, 0.1]] * 10)
      ys = np.array([0, 0, 3, 2, 0.2] * 10)
      # set too high n_estimators to check empty trees too
      reg = XGBRegressor(n_estimators=100, max_depth=2, missing=0)
      reg.fit(xs, ys)
      res = explain_prediction(reg, np.array([2, 0]))
      check_targets_scores(res)
      for expl in format_as_all(res, reg, show_feature_values=True):
          assert "x0" in expl
          assert "x1" in expl
          assert "Missing" in expl
      flt_res = explain_prediction(
          reg,
          np.array([2, 0]),
          feature_filter=lambda _, v: not np.isnan(v),
      )
      for expl in format_as_all(flt_res, reg, show_feature_values=True):
          assert "x1" not in expl
          assert "Missing" not in expl
  
imports: from __future__ import absolute_import
  import pytest
  import numpy
  import scipy.sparse
  from sklearn.feature_extraction.text import CountVectorizer
  import xgboost
  from xgboost import XGBClassifier, XGBRegressor
  from sklearn.pipeline import FeatureUnion
  from sklearn.preprocessing import FunctionTransformer
  from eli5.xgboost import _parse_tree_dump, _xgb_n_targets, _missing_values_set_to_nan, _parent_value, _parse_dump_line, _check_booster_args
  from eli5.explain import explain_prediction, explain_weights
  from eli5.formatters.text import format_as_text
  from eli5.formatters import fields
  from .utils import format_as_all, get_all_features, check_targets_scores
  from .test_sklearn_explain_weights import test_explain_tree_classifier, test_explain_random_forest_and_tree_feature_filter, test_feature_importances_no_remaining, assert_tree_classifier_explained
  from .test_sklearn_explain_prediction import assert_linear_regression_explained, assert_trained_linear_regression_explained, assert_explain_prediction_single_target, test_explain_clf_binary_iris, test_explain_prediction_pandas
label_desc: Use image node_id 12 for calling a global function with example usage: test_dense_missing() without return types