Update lkauto.py

Tests at least run, but a lot of deprecation warning + some tests fail

Author: Max

lkauto/lkauto.py

@@ -3,26 +3,28 @@
 
 from ConfigSpace import ConfigurationSpace
 
-from lkauto.utils.get_model_from_cs import get_model_from_cs
-from lkauto.optimization_strategies.bayesian_optimization import bayesian_optimization
-from lkauto.optimization_strategies.random_search import random_search
-from lkauto.utils.filer import Filer
-from lkauto.ensemble.ensemble_builder import build_ensemble
-from lkauto.preprocessing.preprocessing import preprocess_data
-from lkauto.utils.logging import get_logger
-
-from lenskit.metrics.predict import rmse
-from lenskit.metrics.topn import ndcg
-from lenskit.algorithms import Predictor
-from lenskit import Recommender
+from utils.get_model_from_cs import get_model_from_cs
+from optimization_strategies.bayesian_optimization import bayesian_optimization
+from optimization_strategies.random_search import random_search
+from utils.filer import Filer
+from ensemble.ensemble_builder import build_ensemble
+from preprocessing.preprocessing import preprocess_data
+from utils.logging import get_logger
+
+from lenskit.metrics import RMSE
+from lenskit.metrics import NDCG
+from lenskit.pipeline import Component
+
+# from lenskit.algorithms import Predictor
+# from lenskit import Recommender
 
 from typing import Tuple
 
 
 def get_best_prediction_model(train: pd.DataFrame,
                               validation: pd.DataFrame = None,
                               cs: ConfigurationSpace = None,
-                              optimization_metric=rmse,
+                              optimization_metric=RMSE,
                               optimization_strategie: str = 'bayesian',
                               time_limit_in_sec: int = 2700,
                               num_evaluations: int = 500,
@@ -42,7 +44,7 @@ def get_best_prediction_model(train: pd.DataFrame,
                               timestamp_col: str = 'timestamp',
                               include_timestamp: bool = True,
                               log_level: str = 'INFO',
-                              filer: Filer = None) -> Tuple[Predictor, dict]:
+                              filer: Filer = None) -> Tuple[Component, dict]:
     """
         returns the best Predictor found in the defined search time
 
@@ -211,7 +213,7 @@ def get_best_prediction_model(train: pd.DataFrame,
     else:
         # build model from best model configuration found by SMAC
         model = get_model_from_cs(incumbent, feedback='explicit')
-        incumbent = incumbent.get_dictionary()
+        incumbent = dict(incumbent)
         logger.info('--Best Model--')
         logger.info(incumbent)
 
@@ -224,7 +226,7 @@ def get_best_prediction_model(train: pd.DataFrame,
 def get_best_recommender_model(train: pd.DataFrame,
                                validation: pd.DataFrame = None,
                                cs: ConfigurationSpace = None,
-                               optimization_metric=ndcg,
+                               optimization_metric=NDCG,
                                optimization_strategie: str = 'bayesian',
                                time_limit_in_sec: int = 2700,
                                num_evaluations: int = 500,
@@ -244,7 +246,7 @@ def get_best_recommender_model(train: pd.DataFrame,
                                timestamp_col: str = 'timestamp',
                                include_timestamp: bool = True,
                                log_level: str = 'INFO',
-                               filer: Filer = None) -> Tuple[Recommender, dict]:
+                               filer: Filer = None) -> Tuple[Component, dict]:
     """
         returns the best Recommender found in the defined search time