Update the driver for 0.8 release

tpot/driver.py

@@ -228,10 +228,10 @@ def _get_arg_parser():
             'f1_micro, '
             'f1_samples, '
             'f1_weighted, '
-            'log_loss, '
-            'mean_absolute_error, '
-            'mean_squared_error, '
-            'median_absolute_error, '
+            'neg_log_loss, '
+            'neg_mean_absolute_error, '
+            'neg_mean_squared_error, '
+            'neg_median_absolute_error, '
             'precision, '
             'precision_macro, '
             'precision_micro, '
@@ -254,7 +254,7 @@ def _get_arg_parser():
         default=5,
         type=int,
         help=(
-            'Number of folds to evaluate each pipeline over in k-fold '
+            'Number of folds to evaluate each pipeline over in stratified k-fold '
             'cross-validation during the TPOT optimization process.'
         )
     )
@@ -266,8 +266,8 @@ def _get_arg_parser():
         default=1.0,
         type=float,
         help=(
-            'Subsample ratio of the training instance. Setting it to 0.5 means that TPOT'
-            'randomly collects half of training samples for pipeline optimization process.'
+            'Subsample ratio of the training instance. Setting it to 0.5 means that TPOT '
+            'use a random subsample of half of training data for the pipeline optimization process.'
         )
     )
 
@@ -327,12 +327,13 @@ def _get_arg_parser():
         '-config',
         action='store',
         dest='CONFIG_FILE',
-        default='',
+        default=None,
         type=str,
         help=(
             'Configuration file for customizing the operators and parameters '
-            'that TPOT uses in the optimization process. Must be a python '
-            'module containing a dict export named "tpot_config".'
+            'that TPOT uses in the optimization process. Must be a Python '
+            'module containing a dict export named "tpot_config" or the name of '
+            'built-in configuration.'
         )
     )
 
@@ -370,14 +371,14 @@ def _get_arg_parser():
 
 def _print_args(args):
     print('\nTPOT settings:')
-    for arg, arg_val in args.__dict__.items():
+    for arg, arg_val in sorted(args.__dict__.items()):
         if arg == 'DISABLE_UPDATE_CHECK':
             continue
         elif arg == 'SCORING_FN' and arg_val is None:
             if args.TPOT_MODE == 'classification':
                 arg_val = 'accuracy'
             else:
-                arg_val = 'mean_squared_error'
+                arg_val = 'neg_mean_squared_error'
         elif arg == 'OFFSPRING_SIZE' and arg_val is None:
             arg_val = args.__dict__['POPULATION_SIZE']
         else:
@@ -419,7 +420,7 @@ def main(args):
     training_features, testing_features, training_target, testing_target = \
         train_test_split(features, input_data[args.TARGET_NAME], random_state=args.RANDOM_STATE)
     tpot_type = TPOTClassifier if args.TPOT_MODE == 'classification' else TPOTRegressor
-    tpot = tpot_type(
+    tpot_obj = tpot_type(
         generations=args.GENERATIONS,
         population_size=args.POPULATION_SIZE,
         offspring_size=args.OFFSPRING_SIZE,
@@ -437,29 +438,27 @@ def main(args):
         disable_update_check=args.DISABLE_UPDATE_CHECK
     )
 
-    print('')
-
-    tpot.fit(training_features, training_target)
+    tpot_obj.fit(training_features, training_target)
 
-    if args.VERBOSITY in [1, 2] and tpot._optimized_pipeline:
-        training_score = max([x.wvalues[1] for x in tpot._pareto_front.keys])
+    if args.VERBOSITY in [1, 2] and tpot_obj._optimized_pipeline:
+        training_score = max([x.wvalues[1] for x in tpot_obj._pareto_front.keys])
         print('\nTraining score: {}'.format(abs(training_score)))
-        print('Holdout score: {}'.format(tpot.score(testing_features, testing_target)))
+        print('Holdout score: {}'.format(tpot_obj.score(testing_features, testing_target)))
 
-    elif args.VERBOSITY >= 3 and tpot._pareto_front:
+    elif args.VERBOSITY >= 3 and tpot_obj._pareto_front:
         print('Final Pareto front testing scores:')
-        pipelines = zip(tpot._pareto_front.items, reversed(tpot._pareto_front.keys))
+        pipelines = zip(tpot_obj._pareto_front.items, reversed(tpot_obj._pareto_front.keys))
         for pipeline, pipeline_scores in pipelines:
-            tpot._fitted_pipeline = tpot._pareto_front_fitted_pipelines[str(pipeline)]
+            tpot_obj._fitted_pipeline = tpot_obj.pareto_front_fitted_pipelines_[str(pipeline)]
             print('{TRAIN_SCORE}\t{TEST_SCORE}\t{PIPELINE}'.format(
                     TRAIN_SCORE=int(abs(pipeline_scores.wvalues[0])),
-                    TEST_SCORE=tpot.score(testing_features, testing_target),
+                    TEST_SCORE=tpot_obj.score(testing_features, testing_target),
                     PIPELINE=pipeline
                 )
             )
 
     if args.OUTPUT_FILE != '':
-        tpot.export(args.OUTPUT_FILE)
+        tpot_obj.export(args.OUTPUT_FILE)
 
 
 if __name__ == '__main__':