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 rate as well as the false positive rate; however, I am unable to figure out how to plot these correctly using matplotlib and calculate the AUC value. How could I do that?

Here are two ways you may try, assuming your model is an sklearn predictor:

import sklearn.metrics as metrics
# calculate the fpr and tpr for all thresholds of the classification
probs = model.predict_proba(X_test)
preds = probs[:,1]
fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
roc_auc = metrics.auc(fpr, tpr)

# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label="AUC = %0.2f" % roc_auc)
plt.legend(loc="lower right")
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')

# method II: ggplot
from ggplot import *
df = pd.DataFrame(dict(fpr = fpr, tpr = tpr))
ggplot(df, aes(x = 'fpr', y = 'tpr')) + geom_line() + geom_abline(linetype="dashed")

or try

ggplot(df, aes(x = 'fpr', ymin = 0, ymax = 'tpr')) + geom_line(aes(y = 'tpr')) + geom_area(alpha = 0.2) + ggtitle("ROC Curve w/ AUC = %s" % str(roc_auc)) 

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)

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.

ROC Curves

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

AUC curve For Binary Classification using matplotlib

from sklearn import svm, datasets
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
import matplotlib.pyplot as plt

Load Breast Cancer Dataset

breast_cancer = load_breast_cancer()

X = breast_cancer.data
y = breast_cancer.target

Split the Dataset

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=44)


clf = LogisticRegression(penalty='l2', C=0.1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)


print("Accuracy", metrics.accuracy_score(y_test, y_pred))

AUC Curve

y_pred_proba = clf.predict_proba(X_test)[::,1]
fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)
auc = metrics.roc_auc_score(y_test, y_pred_proba)
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))

AUC Curve

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

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

Here is python code for computing the ROC curve (as a scatter plot):

import matplotlib.pyplot as plt
import numpy as np

score = np.array([0.9, 0.8, 0.7, 0.6, 0.55, 0.54, 0.53, 0.52, 0.51, 0.505, 0.4, 0.39, 0.38, 0.37, 0.36, 0.35, 0.34, 0.33, 0.30, 0.1])
y = np.array([1,1,0, 1, 1, 1, 0, 0, 1, 0, 1,0, 1, 0, 0, 0, 1 , 0, 1, 0])

# false positive rate
fpr = []
# true positive rate
tpr = []
# Iterate thresholds from 0.0, 0.01, ... 1.0
thresholds = np.arange(0.0, 1.01, .01)

# get number of positive and negative examples in the dataset
P = sum(y)
N = len(y) - P

# iterate through all thresholds and determine fraction of true positives
# and false positives found at this threshold
for thresh in thresholds:
    for i in range(len(score)):
        if (score[i] > thresh):
            if y[i] == 1:
                TP = TP + 1
            if y[i] == 0:
                FP = FP + 1

plt.scatter(fpr, tpr)

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

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

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

Result :
ROC Curve

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

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:

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])

There is a library called metriculous that will do that for you:

$ pip install metriculous

Let’s first mock some data, this would usually come from the test dataset and the model(s):

import numpy as np

def normalize(array2d: np.ndarray) -> np.ndarray:
    return array2d / array2d.sum(axis=1, keepdims=True)

class_names = ["Cat", "Dog", "Pig"]
num_classes = len(class_names)
num_samples = 500

# Mock ground truth
ground_truth = np.random.choice(range(num_classes), size=num_samples, p=[0.5, 0.4, 0.1])

# Mock model predictions
perfect_model = np.eye(num_classes)[ground_truth]
noisy_model = normalize(
    perfect_model + 2 * np.random.random((num_samples, num_classes))
random_model = normalize(np.random.random((num_samples, num_classes)))

Now we can use metriculous to generate a table with various metrics and diagrams, including ROC curves:

import metriculous

    model_predictions=[perfect_model, noisy_model, random_model],
    model_names=["Perfect Model", "Noisy Model", "Random Model"],
    one_vs_all_figures=True, # This line is important to include ROC curves in the output

The ROC curves in the output:
metriculous ROC curves

The plots are zoomable and draggable, and you get further details when hovering with your mouse over the plot:

metriculous ROC curve

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! 🙂


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)

    return best_threshold

A sample roc graph produced by this code

When you need the probabilities as well… The following gets the AUC value and plots it all in one shot.

from sklearn.metrics import plot_roc_curve


When you have the probabilities… you can’t get the auc value and plots in one shot. Do the following:

from sklearn.metrics import roc_curve

fpr,tpr,_ = roc_curve(y,y_probas)
plt.plot(fpr,tpr, label="AUC = " + str(round(roc_auc_score(y,m.oob_decision_function_[:,1]), 2)))
plt.legend(loc="lower right")

A new open-source I help maintain have many ways to test model performance. to see ROC curve you can do:

from deepchecks.checks import RocReport
from deepchecks import Dataset

RocReport().run(Dataset(df, label="target"), model)

And the result looks like this:
enter image description here
A more elaborate example of RocReport can be found here

In my code, I have X_train and y_train and classes are 0 and 1. The clf.predict_proba() method computes probabilities for both classes for every data point. I compare the probability of class1 with different values of threshold.

probability = clf.predict_proba(X_train) 

def plot_roc(y_train, probability):
  threshold_values = np.linspace(0,1,100)       #Threshold values range from 0 to 1
  FPR_list = []
  TPR_list = []

  for threshold in threshold_values:            #For every value of threshold
    y_pred = []                                 #Classify every data point in the test set

#prob is an array consisting of 2 values - Probability of datapoint in Class0 and Class1.
    for prob in probability:
      if ((prob[1])<threshold):                 #Prob of class1 (positive class) 
      elif ((prob[1])>=threshold): y_pred.append(1)

#Plot Confusion Matrix and Obtain values of TP, FP, TN, FN
    c_m = confusion_matrix(y, y_pred)           
    TN = c_m[0][0]                                                          
    FP = c_m[0][1]
    FN = c_m[1][0]      
    TP = c_m[1][1]                                                      

    FPR = FP/(FP + TN)                          #Obtain False Positive Rate                                          
    TPR = TP/(TP + FN)                          #Obtain True Positive Rate                                      


  fig = plt.figure()
  plt.plot(FPR_list, TPR_list)