3_h2o_gbm.py 文件源码

def run_all_gbm(csvfile = saving_fp, 
                space = [hp.quniform('ntrees', 200, 750, 1), hp.quniform('max_depth', 5, 15, 1), hp.uniform('learn_rate', 0.03, 0.35)]):
  # Search space is a stochastic argument-sampling program:
  start_save(csvfile = csvfile)
  trials = Trials()
  best = fmin(objective,
      space = space,
      algo=tpe.suggest,
      max_evals=evals,
      trials=trials)
  print best
  # from hyperopt import space_eval
  # print space_eval(space, best)
  # trials.trials # list of dictionaries representing everything about the search
  # trials.results # list of dictionaries returned by 'objective' during the search
  print trials.losses() # list of losses (float for each 'ok' trial)
  # trials.statuses() # list of status strings
  with open('output/gbmbest.pkl', 'w') as output:
    pickle.dump(best, output, -1)
  with open('output/gbmtrials.pkl', 'w') as output:
    pickle.dump(trials, output, -1)

# with open('output/gbmtrials.pkl', 'rb') as input:
#     trials = pickle.load(input)
# with open('output/gbmbest.pkl', 'rb') as input:
#   best = pickle.load(input)