Resolve "Experiment pipeline"

Closes #12 (closed)

code/compute_hyperparameters.py

@@ -24,7 +24,7 @@ I had to install skopt from this repository
 https://github.com/darenr/scikit-optimize that handles
 the issue described here https://github.com/scikit-optimize/scikit-optimize/issues/762.
 """
-from skopt.space import Categorical, Integer, Real
+from skopt.space import Categorical, Integer
 
 
 def clean_numpy_int_dict(dictionary):
@@ -77,7 +77,6 @@ def compute_best_params_over_seeds(seeds, dataset_name, param_space, args):
 
     # Move k best_parameters to a list of dict
     all_best_params = [opt_result['_best_parameters'] for opt_result in opt_results]
-    print(all_best_params)
 
     """
     list of hyperparam dicts -> list of hyperparam list
@@ -114,7 +113,7 @@ def compute_best_params_over_seeds(seeds, dataset_name, param_space, args):
             break
 
     return {
-        '_scorer': opt_results[0]['_best_parameters'],
+        '_scorer': opt_results[0]['_scorer'],
         '_best_score_train': np.mean([opt_result['_best_score_train'] for opt_result in opt_results]),
         '_best_score_test': np.mean([opt_result['_best_score_test'] for opt_result in opt_results]),
         '_best_parameters': best_params,
@@ -153,7 +152,7 @@ if __name__ == "__main__":
         logger.warning('seeds and random_seed_number parameters are both specified. Seeds will be used.')    
 
     # Seeds are either provided as parameters or generated at random
-    if args.use_variable_seed_number:
+    if not args.use_variable_seed_number:
         seeds = args.seeds if args.seeds is not None \
             else [random.randint(begin_random_seed_range, end_random_seed_range) \
             for i in range(args.random_seed_number)]
@@ -174,6 +173,6 @@ if __name__ == "__main__":
                 for i in range(DatasetLoader.dataset_seed_numbers[dataset_name])]
 
         dict_results = compute_best_params_over_seeds(seeds, dataset_name,
-            DICT_PARAM_SPACE, args)        
+            DICT_PARAM_SPACE, args)
 
         save_obj_to_json(os.path.join(dataset_dir, 'params.json'), dict_results)