handy_evaluating.py

# Creates instance of extended version of BinaryClassificationMetrics
# using a DataFrame and its probability and label columns, as the output
# from the classifier
bcm = BinaryClassificationMetrics(predictions, scoreCol='probability', labelCol='Survived')

# We still can get the same metrics as the evaluator...
print("Area under ROC Curve: {:.4f}".format(bcm.areaUnderROC))
print("Area under PR Curve: {:.4f}".format(bcm.areaUnderPR))

# But now we can PLOT both ROC and PR curves!
fig, axs = plt.subplots(1, 2, figsize=(12, 4))
bcm.plot_roc_curve(ax=axs[0])
bcm.plot_pr_curve(ax=axs[1])

# We can also get all metrics (FPR, Recall and Precision) by threshold
bcm.getMetricsByThreshold().filter('fpr between 0.19 and 0.21').toPandas()

# And get the confusion matrix for any threshold we want
bcm.print_confusion_matrix(.415856)