Confusion matrix raws are mismatched

Question

I've created a confusion matrix that works all right but its raws don't seem to be connected with the labels as should be.

I have some list of strings which is splitted into train and test sections:

 train + test:

 positive: 16 + 4 = 20

 negprivate:  53 + 14 = 67

 negstratified: 893 + 224 = 1117

The Confusion matrix is built on the test data:

 [[  0  14   0]

 [  3 220   1]

 [  0   4   0]]

Here is the code:

my_tags = ['negprivate', 'negstratified', 'positive']



def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):

    logging.info('plot_confusion_matrix')

    plt.imshow(cm, interpolation='nearest', cmap=cmap)

    plt.title(title)

    plt.colorbar()

    tick_marks = np.arange(len(my_tags))

    target_names = my_tags

    plt.xticks(tick_marks, target_names, rotation=45)

    plt.yticks(tick_marks, target_names)

    plt.tight_layout()

    plt.ylabel('True label')

    plt.xlabel('Predicted label') 

    plt.show()



def evaluate_prediction(target, predictions, taglist, title="Confusion matrix"):

    logging.info('Evaluate prediction')

    print('accuracy %s' % accuracy_score(target, predictions))

    cm = confusion_matrix(target, predictions)

    print('confusion matrix\n %s' % cm)

    print('(row=expected, col=predicted)')

    print 'rows: \n %s \n %s \n %s ' % (taglist[0], taglist[1], taglist[2])



    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    plot_confusion_matrix(cm_normalized, title + ' Normalized')

...

test_targets, test_regressors = zip(

    *[(doc.tags[0], doc2vec_model.infer_vector(doc.words, steps=20)) for doc in alltest]) 

logreg = linear_model.LogisticRegression(n_jobs=1, C=1e5)

logreg = logreg.fit(train_regressors, train_targets)

evaluate_prediction(test_targets, logreg.predict(test_regressors), my_tags, title=str(doc2vec_model))

But the point is that I actually have to look at the numbers in the resulting matrix and to change the order of my_tags so that they could be in accordance with each other. And as far as I understand this should be made in some automatic way.
In which, I wonder?

Answer 1

It's always best to have integer class labels, everything seems to run a bit smoother. You can get these using LabelEncoder, i.e.

from sklearn import preprocessing
my_tags = ['negprivate', 'negstratified', 'positive']
le = preprocessing.LabelEncoder()
new_tags = le.fit_transform(my_tags)

So now you will have [0 1 2] as your new tags. When you do your plotting, you want your labels to be intuitive, so you can use inverse_transform to get your labels, i.e.

le.inverse_transform(0)

Outputs:

'negprivate'

Confusion matrix raws are mismatched

Sign up