# Python sklearn - average classification reports

Using Python's sklearn module,

 from sklearn.metrics import classification_report
y1_predict = [0, 1, 1, 0]
y1_dev = [0, 1, 1, 0]
report_1 = classification_report(y1_dev, y1_predict)
y2_predict = [1, 0, 1, 0]
y2_dev = [1, 1, 0, 0]
report_2 = classification_report(y2_dev, y2_predict)


Is there a way to combine (maybe just an average) report_1 and report_2 I'm looking for an implementation like:

 report_average = average(report_1,report_2)


Or does this have to be done manually? I was hoping that printing the report_average would have average values between the two reports.

Here's a MWE of the accepted answer:

    from sklearn.metrics import classification_report
import pandas as pd
import numpy as np
from functools import reduce
def report_average(*args):
report_list = list()
for report in args:
splited = [' '.join(x.split()) for x in report.split('\n\n')]
header = [x for x in splited.split(' ')]
data = np.array(splited.split(' ')).reshape(-1, len(header) + 1)
data = np.delete(data, 0, 1).astype(float)
avg_total = np.array([x for x in splited.split(' ')][3:]).astype(float).reshape(-1, len(header))
report_list.append(df)
res = reduce(lambda x, y: x.add(y, fill_value=0), report_list) / len(report_list)
return res.rename(index={res.index[-1]: 'avg / total'})

y1_predict = [0, 1, 1, 0]
y1_dev = [0, 1, 1, 0]
report_1 = classification_report(y1_dev, y1_predict)
y2_predict = [1, 0, 1, 0]
y2_dev = [1, 1, 0, 0]
report_2 = classification_report(y2_dev, y2_predict)

report_ave = report_average(report_1,report_2)

print(report_ave)


Which yields

             precision  recall  f1-score  support
0                 0.75    0.75      0.75      2.0
1                 0.75    0.75      0.75      2.0
avg / total       0.75    0.75      0.75      4.0

• I would have made a comment but I do not have enough reputation. If you set output_dict to True for classification_report() my function will take a list of dics and the label names and returns a pandas DataFrame with each cell containing a string "mean +- std" over all the dics you passed. def report_average(class_reports=None, label_names=None): sum_up = np.zeros((len(class_reports),len(label_names)+3,4)) for i, report in enumerate(class_reports): j = 0 for class_name, results in report.items(): h = 0 for metric, value in results.items(): sum_up[i,j,h] = value h += 1 j += 1 report_mean = np.m – Hannes Feb 22 '19 at 16:35

It maybe a little bit complicated, since I convert the reports to pandas.DataFrame for calculation. But I think it's worth it, because it works well with two or more report as well. Try below:

import pandas as pd
import numpy as np
from functools import reduce
def report_average(*args):
report_list = list()
for report in args:
splited = [' '.join(x.split()) for x in report.split('\n\n')]
header = [x for x in splited.split(' ')]
data = np.array(splited.split(' ')).reshape(-1, len(header) + 1)
data = np.delete(data, 0, 1).astype(float)
avg_total = np.array([x for x in splited.split(' ')][3:]).astype(float).reshape(-1, len(header))
report_list.append(df)
res = reduce(lambda x, y: x.add(y, fill_value=0), report_list) / len(report_list)
return res.rename(index={res.index[-1]: 'avg / total'})


output:

report_average  = report_average(report_1, report_2)
print(report_average)
precision  recall  f1-score  support
0                 0.75    0.75      0.75      2.0
1                 0.75    0.75      0.75      2.0
avg / total       0.75    0.75      0.75      4.0

report_3 = report_2
report_average  = report_average(report_1, report_2,report_3)
print(report_average)
precision    recall  f1-score  support
0             0.666667  0.666667  0.666667      2.0
1             0.666667  0.666667  0.666667      2.0
avg / total   0.666667  0.666667  0.666667      4.0

• Looks a bit long, but seems to work ok, thanks! – Charles May 9 '18 at 23:21

Just another way to do this when the reports (as_dict) are passed as list. This will return the result as a dictionary.

def report_average(reports):
mean_dict = dict()
for label in reports.keys():
dictionary = dict()

if label in 'accuracy':
mean_dict[label] = sum(d[label] for d in reports) / len(reports)
continue

for key in reports[label].keys():
dictionary[key] = sum(d[label][key] for d in reports) / len(reports)
mean_dict[label] = dictionary

return mean_dict