improving API

examples/ensembling.py

@@ -53,9 +53,4 @@
 
 # Now we can requests the scores
 status = engine.request_status()
-scores = status['scores']
-print('\nScores:', scores)
-
-# We can also get the score of the best id
-best_id = status['best_id']
-print('\nBest score:', scores[best_id])
+print('\nScores:', status['scores'])

examples/getting_started.py

@@ -53,18 +53,14 @@
 sleep(5)
 status = engine.request_status()
 
-# Let's print the scores of the engine
-print('\nScores:', status['scores'])
+# Let's print the score of the engine
+print('\nScore:', status['score'])
 
-# Okay, let's wait 5 more seconds, shut it down and check if it's running every
-# second. The print inside the loop may not be called.
+# Okay, let's wait 5 more seconds and shut it down.
 print('\nWaiting 5 seconds...')
 sleep(5)
 engine.interrupt()
-while engine.is_running():
-    print('\nEngine still running')
-    sleep(1)
 
-# The engine's status is still available. The score may have improved
+# The engine's status is still available and the score may have improved
 status = engine.request_status()
-print('\nScores:', status['scores'])
+print('\nScore:', status['score'])

examples/lightgbm_wrapper.py

@@ -101,4 +101,4 @@ def predict_proba(self, X):
 engine.interrupt()
 
 status = engine.request_status()
-print('\nScores:', status['scores'])
+print('\nScore:', status['score'])

examples/on_improvement.py

@@ -23,9 +23,7 @@
 # Simply printing the best score on improvement. This function must receive a
 # dictionary, which is the return of the request_status method.
 def on_improvement(status):
-    best_id = status['best_id']
-    scores = status['scores']
-    print('Best score:', scores[best_id])
+    print('Score:', status['score'])
 
 # Instantiating the engine
 engine = Engine(config, on_improvement=on_improvement)

examples/parameters_rules.py

@@ -70,4 +70,4 @@ def logistic_regression_parameters_rules(parameters):
 engine.interrupt()
 
 status = engine.request_status()
-print('\nScores', status['scores'])
+print('\nScore:', status['score'])

miraiml/__init__.py

@@ -11,7 +11,7 @@
 >>> from miraiml import SearchSpace, Config, Engine
 """
 
-__version__ = '3.1.6.5'
+__version__ = '4.1.6.5'
 
 from .main import SearchSpace, Config, Engine
 

miraiml/main.py

@@ -514,19 +514,17 @@ def request_status(self):
             If no score has been computed yet, returns ``None``. The available
             keys for the dictionary are:
 
-            * ``'best_id'``: The best id of the engine
+            * ``'score'``: The score of the best id
 
             * ``'scores'``: A dictionary containing the score of each id
 
-            * ``'train_predictions'``: A dataframe containing the predictions of each id for the training dataset
-
-            * ``'test_predictions'``: A dataframe containing the predictions of each id for the testing dataset
+            * ``'predictions'``: A ``pandas.Series`` object containing the\
+                predictions of the best id for the testing dataset
         """
         if self.best_id is None:
             return None
         return dict(
-            best_id = self.best_id,
+            score = self.scores[self.best_id],
             scores = self.scores.copy(),
-            train_predictions = self.train_predictions_df.copy(),
-            test_predictions = self.test_predictions_df.copy()
+            predictions = self.test_predictions_df[self.best_id].copy()
         )