diff --git a/python/varspark/core.py b/python/varspark/core.py
index ae8ee9ae..8d85ebd4 100644
--- a/python/varspark/core.py
+++ b/python/varspark/core.py
@@ -1,5 +1,4 @@
 import sys
-from random import randint
 
 from pyspark import SparkConf
 from pyspark.sql import SQLContext
@@ -7,7 +6,7 @@
 
 from varspark import java
 from varspark.etc import find_jar
-
+from varspark.featuresource import FeatureSource
 
 class VarsparkContext(object):
     """The main entry point for VariantSpark functionality.
@@ -78,85 +77,3 @@ def stop(self):
 
 # Deprecated
 VariantsContext = VarsparkContext
-
-
-class FeatureSource(object):
-
-    def __init__(self, _jvm, _vs_api, _jsql, sql, _jfs):
-        self._jfs = _jfs
-        self._jvm = _jvm
-        self._vs_api = _vs_api
-        self._jsql = _jsql
-        self.sql = sql
-
-    @params(self=object, label_source=object, n_trees=Nullable(int), mtry_fraction=Nullable(float),
-            oob=Nullable(bool), seed=Nullable(int), batch_size=Nullable(int),
-            var_ordinal_levels=Nullable(int), max_depth=int, min_node_size=int)
-    def importance_analysis(self, label_source, n_trees=1000, mtry_fraction=None,
-                            oob=True, seed=None, batch_size=100, var_ordinal_levels=3,
-                            max_depth=java.MAX_INT, min_node_size=1):
-        """Builds random forest classifier.
-
-        :param label_source: The ingested label source
-        :param int n_trees: The number of trees to build in the forest.
-        :param float mtry_fraction: The fraction of variables to try at each split.
-        :param bool oob: Should OOB error be calculated.
-        :param int seed: Random seed to use.
-        :param int batch_size: The number of trees to build in one batch.
-        :param int var_ordinal_levels:
-
-        :return: Importance analysis model.
-        :rtype: :py:class:`ImportanceAnalysis`
-        """
-        vs_algo = self._jvm.au.csiro.variantspark.algo
-        jrf_params = vs_algo.RandomForestParams(bool(oob),
-                                                java.jfloat_or(
-                                                    mtry_fraction),
-                                                True, java.NAN, True,
-                                                java.jlong_or(seed,
-                                                              randint(
-                                                                  java.MIN_LONG,
-                                                                  java.MAX_LONG)),
-                                                max_depth,
-                                                min_node_size, False,
-                                                0)
-        jia = self._vs_api.ImportanceAnalysis(self._jsql, self._jfs, label_source,
-                                              jrf_params, n_trees, batch_size, var_ordinal_levels)
-        return ImportanceAnalysis(jia, self.sql)
-
-
-class ImportanceAnalysis(object):
-    """ Model for random forest based importance analysis
-    """
-
-    def __init__(self, _jia, sql):
-        self._jia = _jia
-        self.sql = sql
-
-    @params(self=object, limit=Nullable(int))
-    def important_variables(self, limit=10):
-        """ Gets the top limit important variables as a list of tuples (name, importance) where:
-            - name: string - variable name
-            - importance: double - gini importance
-        """
-        jimpvarmap = self._jia.importantVariablesJavaMap(limit)
-        return sorted(jimpvarmap.items(), key=lambda x: x[1], reverse=True)
-
-    def oob_error(self):
-        """ OOB (Out of Bag) error estimate for the model
-
-        :rtype: float
-        """
-        return self._jia.oobError()
-
-    def variable_importance(self):
-        """ Returns a DataFrame with the gini importance of variables.
-
-        The DataFrame has two columns:
-        - variable: string - variable name
-        - importance: double - gini importance
-        """
-        jdf = self._jia.variableImportance()
-        jdf.count()
-        jdf.createTempView("df")
-        return self.sql.table("df")