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How to write a confusion matrix

Asked 2010-01-27T16:27:19.777
104

I wrote a confusion matrix calculation code in Python:

def conf_mat(prob_arr, input_arr):
    # confusion matrix
    conf_arr = [[0, 0], [0, 0]]

    for i in range(len(prob_arr)):
        if int(input_arr[i]) == 1:
            if float(prob_arr[i]) < 0.5:
                conf_arr[0][1] = conf_arr[0][1] + 1
            else:
                conf_arr[0][0] = conf_arr[0][0] + 1
        elif int(input_arr[i]) == 2:
            if float(prob_arr[i]) >= 0.5:
                conf_arr[1][0] = conf_arr[1][0] +1
            else:
                conf_arr[1][1] = conf_arr[1][1] +1

    accuracy = float(conf_arr[0][0] + conf_arr[1][1])/(len(input_arr))

prob_arr is an array that my classification code returned and a sample array is like this:

 [1.0, 1.0, 1.0, 0.41592955657342651, 1.0, 0.0053405015805891975, 4.5321494433440449e-299, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.70943426182688163, 1.0, 1.0, 1.0, 1.0]

input_arr is the original class labels for a dataset and it is like this:

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

What my code is trying to do is: I get prob_arr and input_arr and for each class (1 and 2) I check if they are misclassified or not.

But my code only works for two classes. If I run this code for a multiple classed data, it doesn't work. How can I make this for multiple classes?

For example, for a data set with three classes, it should return: [[21, 7, 3], [3, 38, 6],[5, 4, 19]].

python scikit-learn confusion-matrix
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2 Answers

4

This function creates confusion matrices for any number of classes. 

def create_conf_matrix(expected, predicted, n_classes):
    m = [[0] * n_classes for i in range(n_classes)]
    for pred, exp in zip(predicted, expected):
        m[pred][exp] += 1
    return m

def calc_accuracy(conf_matrix):
    t = sum(sum(l) for l in conf_matrix)
    return sum(conf_matrix[i][i] for i in range(len(conf_matrix))) / t

In contrast to your function above, you have to extract the predicted classes before calling the function, based on your classification results, i.e. sth. like

[1 if p < .5 else 2 for p in classifications]
answered 2010-01-27T16:55:38.160
2

You can make your code more concise and (sometimes) to run faster using numpy. For example, in two-classes case your function can be rewritten as (see mply.acc()):

def accuracy(actual, predicted):
    """accuracy = (tp + tn) / ts

    , where:    

        ts - Total Samples
        tp - True Positives
        tn - True Negatives
    """
    return (actual == predicted).sum() / float(len(actual))

, where:

actual    = (numpy.array(input_arr) == 2)
predicted = (numpy.array(prob_arr) < 0.5)
answered 2010-01-27T18:18:04.913

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