++
Browse files- src/display/utils.py +8 -8
- src/leaderboard/read_evals.py +21 -21
src/display/utils.py
CHANGED
@@ -20,17 +20,17 @@ class ColumnContent:
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# Init
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auto_eval_column_dict = [
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["eval_name", ColumnContent, ColumnContent("Eval Name", "str", True)],
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-
["result_name", ColumnContent, ColumnContent("Result Name", "str",
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["date", ColumnContent, ColumnContent("Submission Date", "str", True)],
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["miou", ColumnContent, ColumnContent("mIoU ⬆️", "number", True)],
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["accuracy", ColumnContent, ColumnContent("Accuracy ⬆️", "number", False)],
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["precision_score", ColumnContent, ColumnContent("Precision ⬆️", "number", False)],
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["recall", ColumnContent, ColumnContent("Recall ⬆️", "number", False)],
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["f1", ColumnContent, ColumnContent("F1 ⬆️", "number", False)],
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["producer_accuracy", ColumnContent, ColumnContent("Producer Accuracy", "list", False)],
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["user_accuracy", ColumnContent, ColumnContent("User Accuracy", "list", False)],
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["confusion_matrix", ColumnContent, ColumnContent("Confusion Matrix", "matrix", False)],
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["num_classes", ColumnContent, ColumnContent("Number of classes", "number", False)],
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]
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# We use make dataclass to dynamically fill the scores from Tasks
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# Init
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auto_eval_column_dict = [
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["eval_name", ColumnContent, ColumnContent("Eval Name", "str", True)],
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+
["result_name", ColumnContent, ColumnContent("Result Name", "str", False)],
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["date", ColumnContent, ColumnContent("Submission Date", "str", True)],
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["miou", ColumnContent, ColumnContent("mIoU ⬆️", "number", True)],
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["accuracy", ColumnContent, ColumnContent("Accuracy ⬆️", "number", False)],
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+
# ["precision_score", ColumnContent, ColumnContent("Precision ⬆️", "number", False)],
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# ["recall", ColumnContent, ColumnContent("Recall ⬆️", "number", False)],
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# ["f1", ColumnContent, ColumnContent("F1 ⬆️", "number", False)],
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# ["producer_accuracy", ColumnContent, ColumnContent("Producer Accuracy", "list", False)],
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# ["user_accuracy", ColumnContent, ColumnContent("User Accuracy", "list", False)],
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# ["confusion_matrix", ColumnContent, ColumnContent("Confusion Matrix", "matrix", False)],
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# ["num_classes", ColumnContent, ColumnContent("Number of classes", "number", False)],
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]
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# We use make dataclass to dynamically fill the scores from Tasks
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src/leaderboard/read_evals.py
CHANGED
@@ -10,13 +10,13 @@ class EvalResult:
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result_name: str
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date: str
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accuracy: float
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precision_score: float
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recall: float
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f1: float
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miou: float
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producer_accuracy: List[float]
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user_accuracy: List[float]
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-
confusion_matrix: List[List[float]]
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@classmethod
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def init_from_json_file(cls, json_filepath):
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@@ -39,14 +39,14 @@ class EvalResult:
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eval_name=result_key,
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result_name=result_name,
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date=data.get("submitted_time", ""),
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accuracy=data.get("accuracy"),
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precision_score=data.get("precision"),
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recall=data.get("recall"),
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f1=data.get("f1"),
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miou=data.get("miou"),
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producer_accuracy=data.get("producer_accuracy", []),
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user_accuracy=data.get("user_accuracy", []),
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confusion_matrix=data.get("confusion_matrix", []),
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)
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@@ -58,14 +58,14 @@ class EvalResult:
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AutoEvalColumn.result_name.name: self.result_name,
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AutoEvalColumn.date.name: self.date,
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AutoEvalColumn.accuracy.name: self.accuracy,
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AutoEvalColumn.precision_score.name: self.precision_score,
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AutoEvalColumn.recall.name: self.recall,
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AutoEvalColumn.f1.name: self.f1,
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AutoEvalColumn.producer_accuracy.name: self.producer_accuracy,
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AutoEvalColumn.user_accuracy.name: self.user_accuracy,
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AutoEvalColumn.confusion_matrix.name: self.confusion_matrix,
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AutoEvalColumn.miou.name: self.miou,
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AutoEvalColumn.num_classes.name: len(self.
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}
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return data_dict
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result_name: str
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date: str
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accuracy: float
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# precision_score: float
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# recall: float
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# f1: float
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miou: float
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# producer_accuracy: List[float]
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# user_accuracy: List[float]
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# confusion_matrix: List[List[float]]
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@classmethod
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def init_from_json_file(cls, json_filepath):
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eval_name=result_key,
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result_name=result_name,
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date=data.get("submitted_time", ""),
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accuracy=data.get("accuracy", 0)*100,
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# precision_score=data.get("precision")*100,
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# recall=data.get("recall")*100,
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# f1=data.get("f1")*100,
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miou=data.get("miou", 0)*100,
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# producer_accuracy=data.get("producer_accuracy", []),
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# user_accuracy=data.get("user_accuracy", []),
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# confusion_matrix=data.get("confusion_matrix", []),
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)
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AutoEvalColumn.result_name.name: self.result_name,
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AutoEvalColumn.date.name: self.date,
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AutoEvalColumn.accuracy.name: self.accuracy,
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# AutoEvalColumn.precision_score.name: self.precision_score,
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# AutoEvalColumn.recall.name: self.recall,
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# AutoEvalColumn.f1.name: self.f1,
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# AutoEvalColumn.producer_accuracy.name: self.producer_accuracy,
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# AutoEvalColumn.user_accuracy.name: self.user_accuracy,
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# AutoEvalColumn.confusion_matrix.name: self.confusion_matrix,
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AutoEvalColumn.miou.name: self.miou,
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# AutoEvalColumn.num_classes.name: len(self.num_classes) if self.num_classes else 0,
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}
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return data_dict
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