cleaning code

EvoMSA/back_prop.py

@@ -163,10 +163,14 @@ def _combine_optimizer_kwargs(self):
         optimizer_defaults.update(self.optimizer_kwargs)        
         return optimizer_defaults
 
+    def initial_parameters(self, D, y=None):
+        """Compute the initial parameters"""
+        super(BoWBP, self).fit(D, y=y)
+
     def fit(self, D: List[Union[dict, list]], 
             y: Union[np.ndarray, None]=None) -> 'BoWBP':
         D, y = self.set_validation_set(D, y=y)
-        super(BoWBP, self).fit(D, y=y)
+        self.initial_parameters(D, y=y)
         optimizer_kwargs = self._combine_optimizer_kwargs()
         texts = self._transform(D)
         labels = self.dependent_variable(D, y=y)
@@ -195,12 +199,11 @@ class DenseBoWBP(DenseBoW, BoWBP):
     """
 
     def __init__(self, emoji: bool=True,
-                 dataset: bool=True, keyword: bool=True,
+                 dataset: bool=False, keyword: bool=True,
                  estimator_kwargs=dict(dual='auto', class_weight='balanced'),
-                 validation_set=0, **kwargs):
+                 **kwargs):
         super(DenseBoWBP, self).__init__(emoji=emoji, dataset=dataset,
                                          keyword=keyword,
-                                         validation_set=validation_set,
                                          estimator_kwargs=estimator_kwargs,
                                          **kwargs)
 
@@ -238,15 +241,6 @@ def parameters(self, value):
                         self.text_representations):
             m.intercept = float(x)
 
-    def set_validation_set(self, D: List[Union[dict, list]], 
-                           y: Union[np.ndarray, None]=None):
-        """Procedure to create the validation set"""
-
-        n_dim = len(self.text_representations)
-        if n_dim >= len(D) and self.validation_set == 0:
-            self.validation_set = None
-        return super(DenseBoWBP, self).set_validation_set(D=D, y=y)
-
     def __sklearn_clone__(self):
         ins = super(DenseBoWBP, self).__sklearn_clone__()
         _ = [clone(m) for m in self.text_representations]