How to Draw Roc Curve Using Value of Confusion Matrix

How to Plot the ROC Curve in rStudios from the given values?

Using only base-R, you could write the following code:

## your data
df <- read.table(header = TRUE, text = "
Cut_off TP FP TN FN
0.1 100 50 500 450
0.2 150 100 450 400
0.3 250 150 400 300
0.4 300 200 350 250
0.5 350 250 300 200
0.6 350 300 250 200
0.7 400 350 200 150
0.8 400 400 150 150
0.9 450 450 100 100
1.0 500 500 50 50")

## calculate False Positive ratio
df$FPR <- df$FP/(df$FP + df$TN)
## calculte True Positive Ratio
df$TPR <- df$TP/(df$TP + df$FN)

## df is now: 
   Cut_off  TP  FP  TN  FN        FPR       TPR
      0.1 100  50 500 450 0.09090909 0.1818182
      0.2 150 100 450 400 0.18181818 0.2727273
      0.3 250 150 400 300 0.27272727 0.4545455
      0.4 300 200 350 250 0.36363636 0.5454545
      0.5 350 250 300 200 0.45454545 0.6363636
      0.6 350 300 250 200 0.54545455 0.6363636
      0.7 400 350 200 150 0.63636364 0.7272727
      0.8 400 400 150 150 0.72727273 0.7272727
      0.9 450 450 100 100 0.81818182 0.8181818
      1.0 500 500  50  50 0.90909091 0.9090909

## plot the ROC with base plot
plot(df$FPR, df$TPR, type = "b", 
     xlim = c(0,1), ylim = c(0,1), 
     main = 'ROC Curve',
     xlab = "False Positive Rate (1 - Specificity)",
     ylab = "True Positive Rate (Sensitivity)",
     col = "blue")
abline(a = 0, b = 1, lty=2, col = "grey") ### pure chance line

yielding the following plot:

if you want to mark the cut-off points with a label you need the following line after the line with abline(...

text(df$FPR, df$TPR+.05, df$Cut_off, col = "blue", cex = .7)

yielding this plot:

Calculate ROC curve, classification report and confusion matrix for multilabel classification problem

From v0.21 onwards, scikit-learn includes a multilabel confusion matrix; adapting the example from the docs for 5 classes:

import numpy as np
from sklearn.metrics import multilabel_confusion_matrix
y_true = np.array([[1, 0, 1, 0, 0],
                   [0, 1, 0, 1, 1],
                   [1, 1, 1, 0, 1]])
y_pred = np.array([[1, 0, 0, 0, 1],
                   [0, 1, 1, 1, 0],
                   [1, 1, 1, 0, 0]])

multilabel_confusion_matrix(y_true, y_pred)
# result:
array([[[1, 0],
        [0, 2]],

       [[1, 0],
        [0, 2]],

       [[0, 1],
        [1, 1]],

       [[2, 0],
        [0, 1]],

       [[0, 1],
        [2, 0]]])

The usual classification_report also works fine:

from sklearn.metrics import classification_report
print(classification_report(y_true, y_pred))
# result
              precision    recall  f1-score   support

           0       1.00      1.00      1.00         2
           1       1.00      1.00      1.00         2
           2       0.50      0.50      0.50         2
           3       1.00      1.00      1.00         1
           4       0.00      0.00      0.00         2

   micro avg       0.75      0.67      0.71         9
   macro avg       0.70      0.70      0.70         9
weighted avg       0.67      0.67      0.67         9
 samples avg       0.72      0.64      0.67         9

Regarding ROC, you can take some ideas from the Plot ROC curves for the multilabel problem example in the docs (not quite sure the concept itself is very useful though).

Confusion matrix and classification report require hard class predictions (as in the example); ROC requires the predictions as probabilities.

To convert your probabilistic predictions to hard classes, you need a threshold. Now, usually (and implicitly), this threshold is taken to be 0.5, i.e. predict 1 if y_pred > 0.5, else predict 0. Nevertheless, this is not necessarily the case always, and it depends on the particular problem. Once you have set such a threshold, you can easily convert your probabilistic predictions to hard classes with a list comprehension; here is a simple example:

import numpy as np

y_prob = np.array([[0.9, 0.05, 0.12, 0.23, 0.78],
                   [0.11, 0.81, 0.51, 0.63, 0.34],
                   [0.68, 0.89, 0.76, 0.43, 0.27]])

thresh = 0.5

y_pred = np.array([[1 if i > thresh else 0 for i in j] for j in y_prob])

y_pred
# result:
array([[1, 0, 0, 0, 1],
       [0, 1, 1, 1, 0],
       [1, 1, 1, 0, 0]])

Manually ROC curve doen´t match with sklearn.metrics

FPR is not 1-precision. The former is FP/(FP+TN), the latter is FP/(FP+TP).

Correcting the recall_fpr function to have

    False_Positive_rate = round(cm[1, 0] / (cm[1, 0] + cm[1, 1]), 3) #FP /(FP + TN)

gives the correct ROC curve:

Confusion matrix, threshold and ROC curve in statsmodel LogIt

Well, I think it's because your data is imbalanced. You have a label=1 to label=0 ratio of 0.83%. You can try the LogisticRegression object from the sklearn package. There you have the option to specify class_weight='balanced'. I am not sure if statsmodels also supports this. Alternatively, you could resample you data to fix the imbalance problem. For that, I highly recommend using the package ìmblearn, which is an extension of scitkit-learn and straight-forward to implement.

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