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error_calculator.py
134 lines (101 loc) · 5.48 KB
/
error_calculator.py
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# coding: utf-8
"""
Computes and prints the overall classification error and precision, recall, F-score over punctuations.
"""
from __future__ import print_function
from numpy import nan
import data
import sys
from io import open
MAPPING = {}#{"!EXCLAMATIONMARK": ".PERIOD", "?QUESTIONMARK": ".PERIOD", ":COLON": ".PERIOD", ";SEMICOLON": ".PERIOD"} # Can be used to estimate 2-class performance for example
def compute_error(target_paths, predicted_paths):
counter = 0
total_correct = 0
correct = 0.
substitutions = 0.
deletions = 0.
insertions = 0.
true_positives = {}
false_positives = {}
false_negatives = {}
for target_path, predicted_path in zip(target_paths, predicted_paths):
target_punctuation = " "
predicted_punctuation = " "
t_i = 0
p_i = 0
with open(target_path, 'r', encoding='utf-8') as target, open(predicted_path, 'r', encoding='utf-8') as predicted:
target_stream = target.read().split()
predicted_stream = predicted.read().split()
while True:
if data.PUNCTUATION_MAPPING.get(target_stream[t_i], target_stream[t_i]) in data.PUNCTUATION_VOCABULARY:
while data.PUNCTUATION_MAPPING.get(target_stream[t_i], target_stream[t_i]) in data.PUNCTUATION_VOCABULARY: # skip multiple consecutive punctuations
target_punctuation = data.PUNCTUATION_MAPPING.get(target_stream[t_i], target_stream[t_i])
target_punctuation = MAPPING.get(target_punctuation, target_punctuation)
t_i += 1
else:
target_punctuation = " "
if predicted_stream[p_i] in data.PUNCTUATION_VOCABULARY:
predicted_punctuation = MAPPING.get(predicted_stream[p_i], predicted_stream[p_i])
p_i += 1
else:
predicted_punctuation = " "
is_correct = target_punctuation == predicted_punctuation
counter += 1
total_correct += is_correct
if predicted_punctuation == " " and target_punctuation != " ":
deletions += 1
elif predicted_punctuation != " " and target_punctuation == " ":
insertions += 1
elif predicted_punctuation != " " and target_punctuation != " " and predicted_punctuation == target_punctuation:
correct += 1
elif predicted_punctuation != " " and target_punctuation != " " and predicted_punctuation != target_punctuation:
substitutions += 1
true_positives[target_punctuation] = true_positives.get(target_punctuation, 0.) + float(is_correct)
false_positives[predicted_punctuation] = false_positives.get(predicted_punctuation, 0.) + float(not is_correct)
false_negatives[target_punctuation] = false_negatives.get(target_punctuation, 0.) + float(not is_correct)
assert target_stream[t_i] == predicted_stream[p_i] or predicted_stream[p_i] == "<unk>", \
("File: %s \n" + \
"Error: %s (%s) != %s (%s) \n" + \
"Target context: %s \n" + \
"Predicted context: %s") % \
(target_path,
target_stream[t_i], t_i, predicted_stream[p_i], p_i,
" ".join(target_stream[t_i-2:t_i+2]),
" ".join(predicted_stream[p_i-2:p_i+2]))
t_i += 1
p_i += 1
if t_i >= len(target_stream)-1 and p_i >= len(predicted_stream)-1:
break
overall_tp = 0.0
overall_fp = 0.0
overall_fn = 0.0
print("-"*46)
print("{:<16} {:<9} {:<9} {:<9}".format('PUNCTUATION','PRECISION','RECALL','F-SCORE'))
for p in data.PUNCTUATION_VOCABULARY:
if p == data.SPACE:
continue
overall_tp += true_positives.get(p,0.)
overall_fp += false_positives.get(p,0.)
overall_fn += false_negatives.get(p,0.)
punctuation = p
precision = (true_positives.get(p,0.) / (true_positives.get(p,0.) + false_positives[p])) if p in false_positives else nan
recall = (true_positives.get(p,0.) / (true_positives.get(p,0.) + false_negatives[p])) if p in false_negatives else nan
f_score = (2. * precision * recall / (precision + recall)) if (precision + recall) > 0 else nan
print(u"{:<16} {:<9} {:<9} {:<9}".format(punctuation, round(precision,3)*100, round(recall,3)*100, round(f_score,3)*100).encode('utf-8'))
print("-"*46)
pre = overall_tp/(overall_tp+overall_fp) if overall_fp else nan
rec = overall_tp/(overall_tp+overall_fn) if overall_fn else nan
f1 = (2.*pre*rec)/(pre+rec) if (pre + rec) else nan
print("{:<16} {:<9} {:<9} {:<9}".format("Overall", round(pre,3)*100, round(rec,3)*100, round(f1,3)*100))
print("Err: %s%%" % round((100.0 - float(total_correct) / float(counter-1) * 100.0), 2))
print("SER: %s%%" % round((substitutions + deletions + insertions) / (correct + substitutions + deletions) * 100, 1))
if __name__ == "__main__":
if len(sys.argv) > 1:
target_path = sys.argv[1]
else:
sys.exit("Ground truth file path argument missing")
if len(sys.argv) > 2:
predicted_path = sys.argv[2]
else:
sys.exit("Model predictions file path argument missing")
compute_error([target_path], [predicted_path])