Merge pull request #9 from shaddyab/master

fixing Indentation in base.py

pylift/methods/base.py

@@ -70,7 +70,7 @@ class BaseProxyMethod:
         randomized search scoring function. If a list of scoring_methods is
         passed, a dictionary can also be passed here, where the keys are the
         scoring_method strings and the values are the scoring cutoff for those
-        specific methods.  
+        specific methods.
     sklearn_model : scikit-learn regressor
         Model used for grid searching and fitting.
 
@@ -264,7 +264,7 @@ def __init__(self, df, transform_func, untransform_func, col_treatment='Treatmen
                 'estimator': self.sklearn_model(),
                 **default_params
             }
-        self.bayes_search_params = {
+            self.bayes_search_params = {
                 'estimator': self.sklearn_model(),
                 **default_params
             }
@@ -366,15 +366,15 @@ def grid_search(self, **kwargs):
     def bayes_search(self, **kwargs):
         """
         Grid search using skopt.BayesSearchCV
-        
+
         Any parameters typically associated with BayesSearchCV (see
-        Scikit-Optimize documentation) can be passed as keyword arguments to 
+        Scikit-Optimize documentation) can be passed as keyword arguments to
         this function.
-        
+
          The final dictionary used for the grid search is saved to
         `self.bayes_search_params`. This is updated with any parameters that
         are passed.
-        
+
         Examples
         --------
         # Passing kwargs.
@@ -384,7 +384,7 @@ def bayes_search(self, **kwargs):
         self.bayes_search_ = BayesSearchCV(**self.bayes_search_params)
         self.bayes_search_.fit(self.x_train, self.transformed_y_train)
         return self.bayes_search_
-        
+
     def fit(self, productionize=False, **kwargs):
         """A fit wrapper around any sklearn Regressor.
 
@@ -594,4 +594,3 @@ def plot(self, plot_type='cgains', ax=None, n_bins=None, show_noise_fits=False,
             ax.plot([x[0], x[-1]], [bs_means[0], bs_means[-1]], '--', color=[0.6,0.6,0.6])
 
         return ax
-