plot_results.py

# #### plot curves to see results
# %matplotlib notebook
# import matplotlib.pyplot as plt

# #### precision-recall curve
# closest_zero = np.argmin(np.abs(thresholds))
# closest_zero_p = precision[closest_zero]
# closest_zero_r = recall[closest_zero]

# plt.figure()
# plt.xlim([0.0, 1.01])
# plt.ylim([0.0, 1.01])
# plt.plot(precision, recall, label='Precision-Recall Curve')
# plt.plot(closest_zero_p, closest_zero_r, 'o', markersize = 12, fillstyle = 'none', c='r', mew=3)
# plt.xlabel('Precision', fontsize=16)
# plt.ylabel('Recall', fontsize=16)
# plt.axes().set_aspect('equal')
# plt.show()

# #### roc curve
# plt.figure()
# plt.xlim([-0.01, 1.00])
# plt.ylim([-0.01, 1.01])
# plt.plot(fpr_lr, tpr_lr, lw=3, label='LogRegr ROC curve (area = {:0.2f})'.format(roc_auc_lr))
# plt.xlabel('False Positive Rate', fontsize=16)
# plt.ylabel('True Positive Rate', fontsize=16)
# plt.title('ROC curve (1-of-10 digits classifier)', fontsize=16)
# plt.legend(loc='lower right', fontsize=13)
# plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')
# plt.axes().set_aspect('equal')
# plt.show()

# #### for plotting decision tree
# from adspy_shared_utilities import plot_decision_tree
# plot_decision_tree(clf, X_train.columns, ['0','1'])
# # X_train.columns
# # y_train