add label to EvaluateResult #13
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keigohtr
reviewed
Feb 7, 2019
python/sklearn-digits/app.py
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| @@ -97,8 +97,9 @@ def evaluate(self, file_path: str) -> Tuple[EvaluateResult, List[EvaluateResultD | |||
| label_predict.append(result.label) | |||
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| accuracy = accuracy_score(label_gold, label_predict) | |||
| p_r_f = precision_recall_fscore_support(label_gold, label_predict) | |||
| res = EvaluateResult(num, accuracy, p_r_f[0].tolist(), p_r_f[1].tolist(), p_r_f[2].tolist(), {}) | |||
| uniq_labels = sorted(list(set(label_gold))) | |||
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Does it work? I think the label order must not be sorted since we need to correspond them to the output.
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That's right. I removed sorted , so please check it again.
keigohtr
reviewed
Feb 8, 2019
python/sklearn-digits/app.py
Outdated
| @@ -97,8 +97,9 @@ def evaluate(self, file_path: str) -> Tuple[EvaluateResult, List[EvaluateResultD | |||
| label_predict.append(result.label) | |||
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| accuracy = accuracy_score(label_gold, label_predict) | |||
| p_r_f = precision_recall_fscore_support(label_gold, label_predict) | |||
| res = EvaluateResult(num, accuracy, p_r_f[0].tolist(), p_r_f[1].tolist(), p_r_f[2].tolist(), {}) | |||
| uniq_labels = list(set(label_gold)) | |||
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In this sample, we need to determine the labels by hand to fit the gold label.
Example
labels = ["0","1","2","3","4","5","6","7","8","9"]
keigohtr
approved these changes
Feb 8, 2019
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What is this PR for?
added label to
EvaluationResultto identify which precision, recall and fvalue correspond to which labels.This PR includes
What type of PR is it?
Feature/Bugfix/....
What is the issue?
N/A
How should this be tested?