class_docstr: Fit and predict one or more estimators on one or more datasets.
class_docstr_tok: ["Fit","and","predict","one","or","more","estimators","on","one","or","more","datasets","."]
docstr: Predict strategy on one dataset.
docstr_tok: ["Predict","strategy","on","one","dataset","."]
code: def _predict_proba_one(strategy, task, data, y_true, y_pred):
      """Predict strategy on one dataset."""
      # TODO always try to get probabilistic predictions first, compute
      #  deterministic predictions using
      #  argmax to avoid rerunning predictions, only if no predict_proba
      #  is available, run predict
  
      # if the task is classification and the strategies supports
      # probabilistic predictions,
      # get probabilistic predictions
      if isinstance(task, TSCTask) and hasattr(
          strategy, "predict_proba"
      ):
          return strategy.predict_proba(data)
  
          # otherwise, return deterministic predictions in expected format
          # else:
          #     n_class_true = len(np.unique(y_true))
          #     n_class_pred = len(np.unique(y_pred))
          #     n_classes = np.maximum(n_class_pred, n_class_true)
          #     n_predictions = len(y_pred)
          #     y_proba = (n_predictions, n_classes)
          #     y_proba = np.zeros(y_proba)
          #     y_proba[:, np.array(y_pred, dtype=int)] = 1
  
      else:
          return None
  
file_path: sktime/sktime/benchmarking/orchestration.py
imports: import logging
  import pandas
  from sklearn.base import clone
  from sktime.benchmarking.tasks import TSCTask, TSRTask
label_desc: Use image node_id 6 for calling the Orchestrator obj's underlying member method code with example usage: obj._predict_proba_one(strategy, task, data, y_true, y_pred) and returns: strategy, None