[MRG+1] Chassifier chain example fix (scikit-learn#9408)

Author: Adam Kleczewski

examples/multioutput/plot_classifier_chain_yeast.py

@@ -5,12 +5,12 @@
 Example of using classifier chain on a multilabel dataset.
 
 For this example we will use the `yeast
-<http://mldata.org/repository/data/viewslug/yeast>`_ dataset which
-contains 2417 datapoints each with 103 features and 14 possible labels. Each
-datapoint has at least one label. As a baseline we first train a logistic
-regression classifier for each of the 14 labels. To evaluate the performance
-of these classifiers we predict on a held-out test set and calculate the
-:ref:`User Guide <jaccard_similarity_score>`.
+<http://mldata.org/repository/data/viewslug/yeast>`_ dataset which contains
+2417 datapoints each with 103 features and 14 possible labels. Each
+data point has at least one label. As a baseline we first train a logistic
+regression classifier for each of the 14 labels. To evaluate the performance of
+these classifiers we predict on a held-out test set and calculate the
+:ref:`jaccard similarity score <jaccard_similarity_score>`.
 
 Next we create 10 classifier chains. Each classifier chain contains a
 logistic regression model for each of the 14 labels. The models in each
@@ -79,7 +79,7 @@
 model_scores = [ovr_jaccard_score] + chain_jaccard_scores
 model_scores.append(ensemble_jaccard_score)
 
-model_names = ('Independent Models',
+model_names = ('Independent',
                'Chain 1',
                'Chain 2',
                'Chain 3',
@@ -90,21 +90,22 @@
                'Chain 8',
                'Chain 9',
                'Chain 10',
-               'Ensemble Average')
+               'Ensemble')
 
-y_pos = np.arange(len(model_names))
-y_pos[1:] += 1
-y_pos[-1] += 1
+x_pos = np.arange(len(model_names))
 
 # Plot the Jaccard similarity scores for the independent model, each of the
 # chains, and the ensemble (note that the vertical axis on this plot does
 # not begin at 0).
 
-fig = plt.figure(figsize=(7, 4))
-plt.title('Classifier Chain Ensemble')
-plt.xticks(y_pos, model_names, rotation='vertical')
-plt.ylabel('Jaccard Similarity Score')
-plt.ylim([min(model_scores) * .9, max(model_scores) * 1.1])
+fig, ax = plt.subplots(figsize=(7, 4))
+ax.grid(True)
+ax.set_title('Classifier Chain Ensemble Performance Comparison')
+ax.set_xticks(x_pos)
+ax.set_xticklabels(model_names, rotation='vertical')
+ax.set_ylabel('Jaccard Similarity Score')
+ax.set_ylim([min(model_scores) * .9, max(model_scores) * 1.1])
 colors = ['r'] + ['b'] * len(chain_jaccard_scores) + ['g']
-plt.bar(y_pos, model_scores, align='center', alpha=0.5, color=colors)
+ax.bar(x_pos, model_scores, alpha=0.5, color=colors)
+plt.tight_layout()
 plt.show()