linting

Author: frederikhoengaard

python/src/lazylearn/lazylearn.py

@@ -13,7 +13,7 @@ def __init__(self, random_state=None):
         self.dataset = None
         self.task = None
         self.models = None
-        self.leaderboard = None
+        self._leaderboard = None
         self.random_state = random_state
         self.target = None
 
@@ -56,7 +56,9 @@ def run_autopilot(self):
             target=self.target,
             random_state=self.random_state,
         )
-        self.leaderboard = sorted([model for model in sb.models], key=lambda x: x.score)
+        self._leaderboard = sorted(
+            [model for model in sb.models], key=lambda x: x.score
+        )
 
-    def get_leaderboard(self):
-        return [(item.name, item.score) for item in self.leaderboard]
+    def leaderboard(self):
+        return [(item.name, item.score) for item in self._leaderboard]

python/src/lazylearn/regression/models/randomforest/randomforest.py

@@ -32,7 +32,9 @@ def fit(self):
 
         self.pipeline.add(OrdinalConverter(cat_vars=cat_vars))
 
-        self.pipeline.add(RandomForestRegressorStep(random_state=self.random_state))
+        self.pipeline.add(
+            RandomForestRegressorStep(random_state=self.random_state)
+        )  # noqa
 
         self.pipeline.fit()
 

python/src/lazylearn/regression/models/xgboost/xgb.py

@@ -1,7 +1,9 @@
 from models.models import Dataset
 from pipeline.pipeline import RegressionPipeline
 from preprocessing.encoding.encoders import OrdinalConverter
-from regression.models.xgboost.xgb_regressor_steps.regressor_step import XGBRegressorStep
+from regression.models.xgboost.xgb_regressor_steps.regressor_step import (
+    XGBRegressorStep,
+)
 
 
 class XGBRegressionRunner:

python/src/lazylearn/regression/models/xgboost/xgb_regressor_steps/regressor_step.py

@@ -4,7 +4,9 @@
 
 class XGBRegressorStep(PipelineStep):
     def __init__(self, random_state=None):
-        self.regressor = XGBRegressor(n_estimators=1000, random_state=random_state)
+        self.regressor = XGBRegressor(
+            n_estimators=1000, random_state=random_state
+        )  # noqa
 
     def fit(self, pipeline: RegressionPipeline):
         pipeline.feature_list = [

python/src/lazylearn/strategies/strategy_builder.py

@@ -1,5 +1,7 @@
 from models.models import Model
-from regression.models.randomforest.randomforest import RandomForestRegressionRunner
+from regression.models.randomforest.randomforest import (  # noqa
+    RandomForestRegressionRunner,
+)
 from regression.models.xgboost.xgb import XGBRegressionRunner
 from sklearn.metrics import mean_absolute_error
 
@@ -17,13 +19,12 @@ def __init__(self, task, dataset, target, random_state=None):
         self.start()
 
     def build(self):
-        # TODO: Make sure pipelines are not overwriting data for each other
         if self.task == "regression":
             self.strategies.append(
                 XGBRegressionRunner(
                     target=self.target,
                     dataset=self.dataset,
-                    random_state=self.random_state  # noqa
+                    random_state=self.random_state,  # noqa
                 )
             )
             self.strategies.append(
@@ -42,7 +43,7 @@ def start(self):
             strategy.fit()
 
             # get holdout scores
-            strategy.predict(self.dataset.partitions["test"])
+            strategy.predict(self.dataset.partitions["test"].copy())
             strategy.pipeline.holdout_score = mean_absolute_error(
                 self.dataset.partitions["test"][self.target],
                 strategy.pipeline.tmp_pred,