update dispatcher

hypernets/core/trial.py

@@ -409,7 +409,7 @@ def to_df(self, include_params=False):
 
  return df
 
- def plot_best_trials(self, index=True, figsize=(5, 5), loc='upper left', bbox_to_anchor=None, xlim=None, ylim=None):
+ def plot_best_trials(self, index=True, figsize=(5, 5), loc=None, bbox_to_anchor=None, xlim=None, ylim=None):
  try:
  from matplotlib import pyplot as plt
  except Exception:

hypernets/dispatchers/in_process_dispatcher.py

@@ -25,19 +25,20 @@ def dispatch(self, hyper_model, X, y, X_eval, y_eval, X_test, cv, num_folds, max
  trial_no = 1
  retry_counter = 0
 
- importances = None
+ space_options = {}
  if hyper_model.searcher.kind() == const.SEARCHER_MOO:
  if 'feature_usage' in [_.name for _ in hyper_model.searcher.objectives]:
  tb = get_tool_box(X, y)
  preprocessor = tb.general_preprocessor(X)
  estimator = tb.general_estimator(X, y, task=hyper_model.task)
  estimator.fit(preprocessor.fit_transform(X, y), y)
  importances = list(zip(estimator.feature_name_, estimator.feature_importances_))
+ space_options['importances'] = importances
 
  while trial_no <= max_trials:
  gc.collect()
  try:
- space_options = dict(importances=importances)
+
  space_sample = hyper_model.searcher.sample(space_options=space_options)
  if hyper_model.history.is_existed(space_sample):
  if retry_counter >= retry_limit: