code: def test_explain_prediction_feature_union_sparse(
      newsgroups_train_binary,
  ):
      # FeatureUnion with sparce features and text highlighting
      docs, ys, target_names = newsgroups_train_binary
      vec = FeatureUnion(
          [
              ("word", CountVectorizer(stop_words="english")),
              ("char", CountVectorizer(ngram_range=(3, 3))),
          ]
      )
      clf = XGBClassifier(n_estimators=100, max_depth=2, missing=0)
      xs = vec.fit_transform(docs)
      clf.fit(xs, ys)
      res = explain_prediction(
          clf,
          "computer graphics in space: a sign of atheism",
          vec=vec,
          target_names=target_names,
      )
      format_as_all(res, clf)
      check_targets_scores(res)
      weights = res.targets[0].feature_weights
      pos_features = get_all_features(weights.pos)
      assert "word__graphics" in pos_features
      assert res.targets[0].weighted_spans
  
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 17 for calling a global function with example usage: test_explain_prediction_feature_union_sparse(newsgroups_train_binary) without return types