(forest)
| 18 | std[indices[f]]) |
| 19 | |
| 20 | def graph_importance(forest): |
| 21 | importances = forest.feature_importances_ |
| 22 | std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0) |
| 23 | indices = np.argsort(importances)[::-1] |
| 24 | |
| 25 | fig, ax = plt.subplots(1,1) |
| 26 | plt.title("Feature importances") |
| 27 | xlabels = [token_features[int(i)] for i in indices] |
| 28 | plt.bar(range(X_training.shape[1]), importances[indices], |
| 29 | color="r", yerr=std[indices], align="center") |
| 30 | plt.xticks(range(X_training.shape[1]), xlabels, rotation=15) |
| 31 | plt.xlim([-1, X_training.shape[1]]) |
| 32 | plt.ylim([0, 1]) |
| 33 | |
| 34 | for tick in ax.xaxis.get_major_ticks(): |
| 35 | tick.tick1line.set_markersize(0) |
| 36 | tick.tick2line.set_markersize(0) |
| 37 | tick.label1.set_horizontalalignment('right') |
| 38 | |
| 39 | plt.show() |
| 40 | |
| 41 | data = np.loadtxt("samples/stringtemplate4/style.csv", delimiter=",", skiprows=1) |
| 42 |
no test coverage detected