How to smooth ROC curve?

Question

I draw the ROC curve for different classifiers with the following code, but in all plots (from different classifiers), the diagrams are triangular like the example below. How can I have a smoother plot?

def plot_roc_curve(fpr, tpr, classifier):
    plt.plot(fpr, tpr, color='orange', label='ROC')
    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title(str(classifier) + 'Receiver Operating Characteristic (ROC) Curve ')
    plt.legend(loc="lower right")
    plt.show()

from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_test, y_predicted)
plot_roc_curve(fpr, tpr, key)

What I get:

What I want:

The dataset: https://www.file.io/download/Aq7LT88NBVSh

y_test 
36     1
988    0
416    1
300    1
860    0
      ..
780    0
130    1
316    1
577    0
694    0

y_predict
[1 0 1 1 1 1 0 0 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1 0
 1 1 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 1 0 0 0 0 1 1 0 1 0 0 1 1 0
 1 1 0 1 1 0 0 0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 0 1 1
 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 1
 1 0 1 1 1 1 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 0 0 0 0 0 1 1 1 0 1
 1 0 0 0 0 1 0 0 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 0 0 1 0 0 1 0 1 0 1
 1 1 1]

Answer 1

You're using thresholded predictions to generate the ROC-curve. You should instead use the original confidence values, otherwise you will get only 1 intermediary point on the curve.

Here is some example data and the ROC-curves you would get.

y_test: [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...
y_predicted: [1, 0.405, 0.601, 0.579, 0.03, 0.98, 0.06, 0.242, 0.379, 0.09, ...
y_predicted_thresholded: [1, 0, 1, 1, 0, 1, 0, 0, 0, 0, ...