DOC simplify plotting code in NMF LDA 20newsgroups example (scikit-learn#26727)

Author: mathurinm

examples/applications/plot_topics_extraction_with_nmf_lda.py

@@ -46,14 +46,13 @@ def plot_top_words(model, feature_names, n_top_words, title):
     fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)
     axes = axes.flatten()
     for topic_idx, topic in enumerate(model.components_):
-        top_features_ind = topic.argsort()[: -n_top_words - 1 : -1]
-        top_features = [feature_names[i] for i in top_features_ind]
+        top_features_ind = topic.argsort()[-n_top_words:]
+        top_features = feature_names[top_features_ind]
         weights = topic[top_features_ind]
 
         ax = axes[topic_idx]
         ax.barh(top_features, weights, height=0.7)
         ax.set_title(f"Topic {topic_idx +1}", fontdict={"fontsize": 30})
-        ax.invert_yaxis()
         ax.tick_params(axis="both", which="major", labelsize=20)
         for i in "top right left".split():
             ax.spines[i].set_visible(False)