class_docstr: Learning Curve
class_docstr_tok: ["Learning","Curve"]
docstr: Add information of learning curve to report.
  
  Args:
      history(:dict of dict): a dict of training log dict.
          key: epoch index
          value: learning epoch information
                  Each dict has two keys:
                      - params: a dict of params on current epochs (Optional)
                      - val_scores: a dict of which key is the metric name
                                  and value is metric value
      best_idx(str):
          - the best epoch based on benchmark metric, corresponding the `history` dict key
      benchmark_metric(:str): the metric used for benchmarking during learning
      benchmark_threshold(:float, Optional): the benchmarking threshold to accept the training
  
      training_params(:dict): a dict of which key is training parameter name
                                  and value is training parameter value
      notes(:str): text to explain the block
docstr_tok: ["Add","information","of","learning","curve","to","report",".","Args",":","history","(",":","dict","of","dict",")",":","a","dict","of","training","log","dict",".","key",":","epoch","index","value",":","learning","epoch","information","Each","dict","has","two","keys",":","-","params",":","a","dict","of","params","on","current","epochs","(","Optional",")","-","val_scores",":","a","dict","of","which","key","is","the","metric","name","and","value","is","metric","value","best_idx","(","str",")",":","-","the","best","epoch","based","on","benchmark","metric",",","corresponding","the","`","history","`","dict","key","benchmark_metric","(",":","str",")",":","the","metric","used","for","benchmarking","during","learning","benchmark_threshold","(",":","float",",","Optional",")",":","the","benchmarking","threshold","to","accept","the","training","training_params","(",":","dict",")",":","a","dict","of","which","key","is","training","parameter","name","and","value","is","training","parameter","value","notes","(",":","str",")",":","text","to","explain","the","block"]
code: def __init__(
      self,
      history: dict,
      best_idx: str,
      benchmark_metric=None,
      benchmark_threshold=None,
      training_params=None,
      notes=None,
  ) -> None:
      """
      Add information of learning curve to report.
  
      Args:
          history(:dict of dict): a dict of training log dict.
              key: epoch index
              value: learning epoch information
                      Each dict has two keys:
                          - params: a dict of params on current epochs (Optional)
                          - val_scores: a dict of which key is the metric name
                                      and value is metric value
          best_idx(str):
              - the best epoch based on benchmark metric, corresponding the `history` dict key
          benchmark_metric(:str): the metric used for benchmarking during learning
          benchmark_threshold(:float, Optional): the benchmarking threshold to accept the training
  
          training_params(:dict): a dict of which key is training parameter name
                                      and value is training parameter value
          notes(:str): text to explain the block
      """
      super(LearningCurve, self).__init__(
          history,
          best_idx,
          benchmark_metric,
          benchmark_threshold,
          training_params,
          notes,
      )
      self._history = history
      self._best_idx = best_idx
      self._benchmark_metric = benchmark_metric
      self._benchmark_threshold = benchmark_threshold
      self._training_params = training_params
      self._notes = notes
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from xai.formatter.contents.base import Content
  from xai.formatter.writer.base import Writer
label_desc: Use image node_id 1 to create a new LearningCurve object from inherited base classes: Content with example: obj = LearningCurve(history, best_idx, benchmark_metric, benchmark_threshold, training_params, notes)