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 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

User · Answer

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

User · Answer

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  plot roc curve m xs y   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

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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

User · Answer

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

User · Answer

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

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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      FP 0     TP 0     for i in range len score            if  score i   gt  thresh               if y i     1                  TP   TP   1             if y i     0                  FP   FP   1     fpr append FP float N       tpr append TP float P    plt scatter fpr  tpr  plt show

User · Answer

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  - gt  np ndarray      return array2d   array2d sum axis 1  keepdims True   class names     quot Cat quot    quot Dog quot    quot Pig quot   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  metriculous compare classifiers      ground truth ground truth      model predictions  perfect model  noisy model  random model       model names   quot Perfect Model quot    quot Noisy Model quot    quot Random Model quot        class names class names      one vs all figures True    This line is important to include ROC curves in the output   save html  quot model comparison html quot   display    The ROC curves in the output   The plots are zoomable and draggable  and you get further details when hovering with your mouse over the plot

User · Answer

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  amp  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

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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    Model  clf   LogisticRegression penalty  l2   C 0 1  clf fit X train  y train  y pred   clf predict X test    Accuracy  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   plt legend loc 4  plt show

User · Answer

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   plt show      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

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You can also follow the offical documentation form scikit   https   scikit-learn org stable auto examples model selection plot roc html sphx-glr-auto-examples-model-selection-plot-roc-py

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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  gt  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  gt  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

[python] How to plot ROC curve in Python

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