will fix

proglearn/deciders.py

@@ -34,12 +34,16 @@ def fit(
         transformer_id_to_voters,
         classes=None,
     ):
-        if self.classes == None and len(y) == 0:
-            raise ValueError("Classification Decider classes undefined with no class labels fed to fit")
+        try:
+            self.classes = list(self.classes)
+        except TypeError:
+            if len(y) == 0:
+                raise ValueError("Classification Decider classes undefined with no class labels fed to fit")
+            else:
+                self.classes = np.unique(y)
         self.classes = self.classes if self.classes != None else np.unique(y)
         self.transformer_id_to_transformers = transformer_id_to_transformers
         self.transformer_id_to_voters = transformer_id_to_voters
-
         return self
 
     def predict_proba(self, X, transformer_ids=None):

proglearn/forest.py

@@ -6,6 +6,7 @@
 from .transformers import TreeClassificationTransformer
 from .voters import TreeClassificationVoter
 from .deciders import SimpleAverage
+import numpy as np
 
 class LifelongClassificationForest:
     def __init__(self, n_estimators=100, tree_construction_proportion=0.67, finite_sample_correction=False):
@@ -26,7 +27,7 @@ def add_task(
             X,
             y,
             task_id=task_id,
-            transformer_voter_decider_split=[self.tree_construction_proportion, 1-tree_construction_proportion, 0],
+            transformer_voter_decider_split=[self.tree_construction_proportion, 1-self.tree_construction_proportion, 0],
             num_transformers=self.n_estimators,
             decider_kwargs = {"classes" : np.unique(y)}
         )
@@ -60,7 +61,7 @@ def fit(self, X, y):
             n_estimators=self.n_estimators,
             finite_sample_correction=self.finite_sample_correction,
         )
-        self.lf.add_task(X, y, task_id=0, decider_kwargs = {"classes" : np.unique(y)})
+        self.lf.add_task(X, y, task_id=0)
         return self
 
     def predict(self, X):