class_docstr: In-memory results.
docstr: Save the predictions of trained estimators.
  
  Parameters
  ----------
  strategy_name : string
      Name of fitted strategy
  dataset_name: string
      Name of dataset on which the strategy is fitted
  y_true : numpy array
      array with true labels
  y_pred : numpy array
      array of predicted labels
  y_proba : numpy array
      array of probabilities associated with the predicted values
  index : numpy array
      dataset indices of the y_true data points
  fit_estimator_start_time : pandas timestamp (default=None)
      timestamp when fitting the estimator began
  fit_estimator_end_time : pandas timestamp (default=None)
      timestamp when fitting the estimator ended
  predict_estimator_begin_time : pandas timestamp (default=None)
      timestamp when the estimator began making predictions
  predict_estimator_end_time : pandas timestamp (default=None)
      timestamp when the estimator finished making predictions
docstr_tok: ["Save","the","predictions","of","trained","estimators",".","Parameters","--","--","--","--","--","strategy_name",":","string","Name","of","fitted","strategy","dataset_name",":","string","Name","of","dataset","on","which","the","strategy","is","fitted","y_true",":","numpy","array","array","with","true","labels","y_pred",":","numpy","array","array","of","predicted","labels","y_proba",":","numpy","array","array","of","probabilities","associated","with","the","predicted","values","index",":","numpy","array","dataset","indices","of","the","y_true","data","points","fit_estimator_start_time",":","pandas","timestamp","(","default=None",")","timestamp","when","fitting","the","estimator","began","fit_estimator_end_time",":","pandas","timestamp","(","default=None",")","timestamp","when","fitting","the","estimator","ended","predict_estimator_begin_time",":","pandas","timestamp","(","default=None",")","timestamp","when","the","estimator","began","making","predictions","predict_estimator_end_time",":","pandas","timestamp","(","default=None",")","timestamp","when","the","estimator","finished","making","predictions"]
code: def save_predictions(
      self,
      strategy_name,
      dataset_name,
      y_true,
      y_pred,
      y_proba,
      index,
      cv_fold,
      train_or_test,
      fit_estimator_start_time=None,
      fit_estimator_end_time=None,
      predict_estimator_start_time=None,
      predict_estimator_end_time=None,
  ):
      """Save the predictions of trained estimators.
  
      Parameters
      ----------
      strategy_name : string
          Name of fitted strategy
      dataset_name: string
          Name of dataset on which the strategy is fitted
      y_true : numpy array
          array with true labels
      y_pred : numpy array
          array of predicted labels
      y_proba : numpy array
          array of probabilities associated with the predicted values
      index : numpy array
          dataset indices of the y_true data points
      fit_estimator_start_time : pandas timestamp (default=None)
          timestamp when fitting the estimator began
      fit_estimator_end_time : pandas timestamp (default=None)
          timestamp when fitting the estimator ended
      predict_estimator_begin_time : pandas timestamp (default=None)
          timestamp when the estimator began making predictions
      predict_estimator_end_time : pandas timestamp (default=None)
          timestamp when the estimator finished making predictions
      """
      key = self._generate_key(
          strategy_name, dataset_name, cv_fold, train_or_test
      )
      index = np.asarray(index)
      y_true = np.asarray(y_true)
      y_pred = np.asarray(y_pred)
      y_proba = np.asarray(y_proba)
      self.results[key] = _PredictionsWrapper(
          strategy_name,
          dataset_name,
          index,
          y_true,
          y_pred,
          fit_estimator_start_time,
          fit_estimator_end_time,
          predict_estimator_start_time,
          predict_estimator_end_time,
          y_proba,
      )
      self._append_key(strategy_name, dataset_name)
  
imports: import os
  import numpy
  import pandas
  from joblib import load
  from sktime.benchmarking.base import BaseResults, HDDBaseResults, _PredictionsWrapper
label_desc: Use image node_id 2 for calling the RAMResults obj's underlying member method code with example usage: obj.save_predictions(strategy_name, dataset_name, y_true, y_pred, y_proba, index, cv_fold, train_or_test, fit_estimator_start_time, fit_estimator_end_time, predict_estimator_start_time, predict_estimator_end_time) without return types