add target kwargs for multi model

pandas_ml_utils/model/models.py

@@ -397,30 +397,21 @@ def __init__(self,
                  loss_alpha: float = 0.5,
                  target_kwargs: Dict[str, Dict[str, Any]] = None):
         assert isinstance(model_provider.features_and_labels.labels, (TargetLabelEncoder, Dict))
-        super().__init__(model_provider.features_and_labels, # FIXME {target: self.features_and_labels.labels(kwargs) for target, kwargs in target_kwargs.items()} if target_kwargs else model_provider.features_and_labels
-                         summary_provider,
-                         **model_provider.kwargs)
+        super().__init__(
+            # if we have a target args and a target encoder then we need generate multiple targets with different kwargs
+            {self.features_and_labels.with_labels(self.features_and_labels.labels.with_kwargs(kwargs)) for target, kwargs in target_kwargs.items()} \
+                if target_kwargs and isinstance(model_provider.features_and_labels.labels, TargetLabelEncoder) else model_provider.features_and_labels,
+            summary_provider,
+            **model_provider.kwargs)
 
         if isinstance(model_provider, MultiModel):
             raise ValueError("Nesting Multi Models is not supported, you might use a flat structure of all your models")
 
+        self.models = {target: model_provider() for target in self.features_and_labels.labels.keys()}
         self.model_provider = model_provider
         self.target_kwargs = target_kwargs
         self.loss_alpha = loss_alpha
 
-        if target_kwargs:
-            self.models = {target: model_provider(**kwargs) for target, kwargs in target_kwargs.items()}
-
-            # FIXME we need to fix current models labels -> wrap all encoders/labels into a dict
-            #  this is a bit ugly as this will only work with label encoders what if we dont use encoders?
-            kwargs = {}
-            target = self.features_and_labels.with_labels(self.features_and_labels.labels.with_kwargs(kwargs))
-
-            {target: self.features_and_labels.labels.with_kwargs(kwargs) for target, kwargs in target_kwargs.items()}
-
-        else:
-            self.models = {target: model_provider() for target in self.features_and_labels.labels.keys()}
-
     def fit(self, x, y, x_val, y_val, df_index_train, df_index_test) -> float:
         losses = []
         pos = 0