Sklearn.metrics
has great functions for obtaining classification metrics, although something that I think is missing is a function to return the TP, FN, FP and FN counts given the predicted and actual label sequences. Or even from the confusion matrix.
I know it's possible to obtain the confusion matrix using sklearn
, but I need the actual TP, FN, FP and FN counts (for multilabel classification - more than 2 labels), and to obtain those counts for each of the classes.
So say, I have the confusion matrix below with 3 classes. Is there some package available to get the counts for each class from this? I was unable to find anything.
Scikit-learn can calculate and plot a multiclass confusion matrix, see this example from the documentation ( Demo on a Jupiter notebook ):
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names
# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
title='Confusion matrix, without normalization')
# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
title='Normalized confusion matrix')
plt.show()
Result (txt):
Confusion matrix, without normalization
[[13 0 0]
[ 0 10 6]
[ 0 0 9]]
Normalized confusion matrix
[[ 1. 0. 0. ]
[ 0. 0.62 0.38]
[ 0. 0. 1. ]]
Plot results:
See this code working on the link bellow:
DEMO ON A JUPYTER NOTEBOOK
I ended up implementing it myself, since I didn't find anything. Here is the code, case someone else looks for this in the future:
def counts_from_confusion(confusion):
"""
Obtain TP, FN FP, and TN for each class in the confusion matrix
"""
counts_list = []
# Iterate through classes and store the counts
for i in range(confusion.shape[0]):
tp = confusion[i, i]
fn_mask = np.zeros(confusion.shape)
fn_mask[i, :] = 1
fn_mask[i, i] = 0
fn = np.sum(np.multiply(confusion, fn_mask))
fp_mask = np.zeros(confusion.shape)
fp_mask[:, i] = 1
fp_mask[i, i] = 0
fp = np.sum(np.multiply(confusion, fp_mask))
tn_mask = 1 - (fn_mask + fp_mask)
tn_mask[i, i] = 0
tn = np.sum(np.multiply(confusion, tn_mask))
counts_list.append({'Class': i,
'TP': tp,
'FN': fn,
'FP': fp,
'TN': tn})
return counts_list
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