How to plot ROC curve in Python

Question

I am trying to plot a ROC curve to evaluate the accuracy of a prediction model I developed in Python using logistic regression packages. I have computed the true positive ra

Answer 1

Based on multiple comments from stackoverflow, scikit-learn documentation and some other, I made a python package to plot ROC curve (and other metric) in a really simple way.

To install package : pip install plot-metric (more info at the end of post)

To plot a ROC Curve (example come from the documentation) :

Binary classification

Let's load a simple dataset and make a train & test set :

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(n_samples=1000, n_classes=2, weights=[1,1], random_state=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=2)

Train a classifier and predict test set :

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=50, random_state=23)
model = clf.fit(X_train, y_train)

# Use predict_proba to predict probability of the class
y_pred = clf.predict_proba(X_test)[:,1]

You can now use plot_metric to plot ROC Curve :

from plot_metric.functions import BinaryClassification
# Visualisation with plot_metric
bc = BinaryClassification(y_test, y_pred, labels=["Class 1", "Class 2"])

# Figures
plt.figure(figsize=(5,5))
bc.plot_roc_curve()
plt.show()

Result :

You can find more example of on the github and documentation of the package:

• Github : https://github.com/yohann84L/plot_metric
• Documentation : https://plot-metric.readthedocs.io/en/latest/

Answer 2

It is not at all clear what the problem is here, but if you have an array true_positive_rate and an array false_positive_rate, then plotting the ROC curve and getting the AUC is as simple as:

import matplotlib.pyplot as plt
import numpy as np

x = # false_positive_rate
y = # true_positive_rate 

# This is the ROC curve
plt.plot(x,y)
plt.show() 

# This is the AUC
auc = np.trapz(y,x)

Answer 3

The previous answers assume that you indeed calculated TP/Sens yourself. It's a bad idea to do this manually, it's easy to make mistakes with the calculations, rather use a library function for all of this.

the plot_roc function in scikit_lean does exactly what you need: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

The essential part of the code is:

  for i in range(n_classes):
      fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
      roc_auc[i] = auc(fpr[i], tpr[i])

Answer 4

This is the simplest way to plot an ROC curve, given a set of ground truth labels and predicted probabilities. Best part is, it plots the ROC curve for ALL classes, so you get multiple neat-looking curves as well

import scikitplot as skplt
import matplotlib.pyplot as plt

y_true = # ground truth labels
y_probas = # predicted probabilities generated by sklearn classifier
skplt.metrics.plot_roc_curve(y_true, y_probas)
plt.show()

Here's a sample curve generated by plot_roc_curve. I used the sample digits dataset from scikit-learn so there are 10 classes. Notice that one ROC curve is plotted for each class.

Disclaimer: Note that this uses the scikit-plot library, which I built.

Answer 5

from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt

y_true = # true labels
y_probas = # predicted results
fpr, tpr, thresholds = metrics.roc_curve(y_true, y_probas, pos_label=0)

# Print ROC curve
plt.plot(fpr,tpr)
plt.show() 

# Print AUC
auc = np.trapz(tpr,fpr)
print('AUC:', auc)

Answer 6

I have made a simple function included in a package for the ROC curve. I just started practicing machine learning so please also let me know if this code has any problem!

Have a look at the github readme file for more details! :)

https://github.com/bc123456/ROC

from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

def plot_ROC(y_train_true, y_train_prob, y_test_true, y_test_prob):
    '''
    a funciton to plot the ROC curve for train labels and test labels.
    Use the best threshold found in train set to classify items in test set.
    '''
    fpr_train, tpr_train, thresholds_train = roc_curve(y_train_true, y_train_prob, pos_label =True)
    sum_sensitivity_specificity_train = tpr_train + (1-fpr_train)
    best_threshold_id_train = np.argmax(sum_sensitivity_specificity_train)
    best_threshold = thresholds_train[best_threshold_id_train]
    best_fpr_train = fpr_train[best_threshold_id_train]
    best_tpr_train = tpr_train[best_threshold_id_train]
    y_train = y_train_prob > best_threshold

    cm_train = confusion_matrix(y_train_true, y_train)
    acc_train = accuracy_score(y_train_true, y_train)
    auc_train = roc_auc_score(y_train_true, y_train)

    print 'Train Accuracy: %s ' %acc_train
    print 'Train AUC: %s ' %auc_train
    print 'Train Confusion Matrix:'
    print cm_train

    fig = plt.figure(figsize=(10,5))
    ax = fig.add_subplot(121)
    curve1 = ax.plot(fpr_train, tpr_train)
    curve2 = ax.plot([0, 1], [0, 1], color='navy', linestyle='--')
    dot = ax.plot(best_fpr_train, best_tpr_train, marker='o', color='black')
    ax.text(best_fpr_train, best_tpr_train, s = '(%.3f,%.3f)' %(best_fpr_train, best_tpr_train))
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.0])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC curve (Train), AUC = %.4f'%auc_train)

    fpr_test, tpr_test, thresholds_test = roc_curve(y_test_true, y_test_prob, pos_label =True)

    y_test = y_test_prob > best_threshold

    cm_test = confusion_matrix(y_test_true, y_test)
    acc_test = accuracy_score(y_test_true, y_test)
    auc_test = roc_auc_score(y_test_true, y_test)

    print 'Test Accuracy: %s ' %acc_test
    print 'Test AUC: %s ' %auc_test
    print 'Test Confusion Matrix:'
    print cm_test

    tpr_score = float(cm_test[1][1])/(cm_test[1][1] + cm_test[1][0])
    fpr_score = float(cm_test[0][1])/(cm_test[0][0]+ cm_test[0][1])

    ax2 = fig.add_subplot(122)
    curve1 = ax2.plot(fpr_test, tpr_test)
    curve2 = ax2.plot([0, 1], [0, 1], color='navy', linestyle='--')
    dot = ax2.plot(fpr_score, tpr_score, marker='o', color='black')
    ax2.text(fpr_score, tpr_score, s = '(%.3f,%.3f)' %(fpr_score, tpr_score))
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.0])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC curve (Test), AUC = %.4f'%auc_test)
    plt.savefig('ROC', dpi = 500)
    plt.show()

    return best_threshold

A sample roc graph produced by this code