How to plot scikit learn classification report?

Question

Is it possible to plot with matplotlib scikit-learn classification report?. Let\'s assume I print the classification report like this:

print \'\\n*Classifica         


        

Answer 1

I just wrote a function plot_classification_report() for this purpose. Hope it helps. This function takes out put of classification_report function as an argument and plot the scores. Here is the function.

def plot_classification_report(cr, title='Classification report ', with_avg_total=False, cmap=plt.cm.Blues):

    lines = cr.split('\n')

    classes = []
    plotMat = []
    for line in lines[2 : (len(lines) - 3)]:
        #print(line)
        t = line.split()
        # print(t)
        classes.append(t[0])
        v = [float(x) for x in t[1: len(t) - 1]]
        print(v)
        plotMat.append(v)

    if with_avg_total:
        aveTotal = lines[len(lines) - 1].split()
        classes.append('avg/total')
        vAveTotal = [float(x) for x in t[1:len(aveTotal) - 1]]
        plotMat.append(vAveTotal)


    plt.imshow(plotMat, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    x_tick_marks = np.arange(3)
    y_tick_marks = np.arange(len(classes))
    plt.xticks(x_tick_marks, ['precision', 'recall', 'f1-score'], rotation=45)
    plt.yticks(y_tick_marks, classes)
    plt.tight_layout()
    plt.ylabel('Classes')
    plt.xlabel('Measures')

For the example classification_report provided by you. Here are the code and output.

sampleClassificationReport = """             precision    recall  f1-score   support

          1       0.62      1.00      0.76        66
          2       0.93      0.93      0.93        40
          3       0.59      0.97      0.73        67
          4       0.47      0.92      0.62       272
          5       1.00      0.16      0.28       413

avg / total       0.77      0.57      0.49       858"""


plot_classification_report(sampleClassificationReport)

Here is how to use it with sklearn classification_report output:

from sklearn.metrics import classification_report
classificationReport = classification_report(y_true, y_pred, target_names=target_names)

plot_classification_report(classificationReport)

With this function, you can also add the "avg / total" result to the plot. To use it just add an argument with_avg_total like this:

plot_classification_report(classificationReport, with_avg_total=True)