class_docstr: An 'Auto Regressive Integrated (Random) Forest'
class_docstr_tok: ["An","'Auto","Regressive","Integrated","(","Random",")","Forest","'"]
code: def extend_partial(self, obs_losses, num_steps, config=None):
      # TODO: add variance predictions
      if config is None:
          config = []
  
      d_losses = self.apply_differencing(obs_losses)
  
      for t in range(num_steps):
          x = np.hstack([d_losses[-self.order :], config])
          y = self.rfr.predict([x])
          d_losses = np.hstack([d_losses, y])
  
      prediction = self.invert_differencing(
          obs_losses, d_losses[-num_steps:]
      )
  
      return prediction
  
file_path: HpBandSter/hpbandster/optimizers/learning_curve_models/arif.py
imports: import sys
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from hpbandster.learning_curve_models.base import LCModel
  from IPython import embed
label_desc: Use image node_id 5 for calling the ARIF obj's underlying member method code with example usage: obj.extend_partial(obs_losses, num_steps, config) and returns: prediction