Created
October 4, 2020 17:30
-
-
Save abhishek-shrm/3eafb0f021befb104f8c94e7c1f142f0 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
def evaluate_micro_average(actual_keys,predicted_keys): | |
# Combining actual keywords | |
ground_truth=[] | |
for i in actual_keys: | |
ground_truth.extend(i) | |
# Combining extracted keywords | |
extracted_keywords=[] | |
for i in predicted_keys: | |
extracted_keywords.extend(i) | |
# Number of extracted keywords | |
num_extract=len(extracted_keywords) | |
# Number of keywords in ground truth | |
num_actual=len(ground_truth) | |
# Number of correctly extracted keywords | |
num_correct=0 | |
for i,j in zip(actual_keys, predicted_keys): | |
num_correct+=len(set(i).intersection(set(j))) | |
# If no correct keywords were extracted | |
if num_correct==0: | |
return [0,0,0] | |
# Precision | |
precision=num_correct/num_extract | |
# Recall | |
recall=num_correct/num_actual | |
# F-measure | |
Fmeasure=(2*precision*recall)/(precision+recall) | |
return precision*100,recall*100,Fmeasure*100 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment