update compute
Browse files- charmatch.py +25 -20
charmatch.py
CHANGED
@@ -87,30 +87,35 @@ class charmatch(evaluate.Metric):
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# TODO: Download external resources if needed
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pass
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def _compute(self,
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elif len(deduped) == 2:
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if expected == output:
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return 1.0
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else:
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distance_to_expected = lev(output, expected)
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print(f'dl(s,g): {expected_corrections}\ndl(s,h): {distance_to_input}\ndl(h,g): {distance_to_expected}')
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return {
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"fscore":
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}
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# TODO: Download external resources if needed
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pass
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def _compute(self, inputs, expected, outputs):
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def get_score(input, expected, output):
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print(input, expected, output)
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deduped = {input, expected, output}
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if len(deduped) == 1:
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return 1.0
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elif len(deduped) == 2:
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if expected == output:
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return 1.0
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else:
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return 0.0
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else:
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expected_corrections = lev(input, expected)
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distance_to_input = lev(input, output)
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distance_to_expected = lev(output, expected)
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print(f'dl(s,g): {expected_corrections}\ndl(s,h): {distance_to_input}\ndl(h,g): {distance_to_expected}')
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true_positives = min(expected_corrections, max(0, (expected_corrections + distance_to_input - distance_to_expected))) / 2
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print(f'T: {true_positives}')
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precision = true_positives / distance_to_input
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recall = true_positives / expected_corrections
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f_05 = (1 + 0.5**2) * (precision * recall) / (0.5**2 * precision + recall)
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print(f'P: {precision}\nR: {recall}')
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return f_05
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avg = sum([get_score(*row) for row in zip(inputs, expected, outputs)]) / len(inputs) * 100
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return {
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"fscore": avg
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}
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