updated best metric retrieval API for min and max metric use-case

Author: mukeshmk

sklearn/fml/constants.py

@@ -3,7 +3,8 @@ class URI:
 
     _METRIC = '/metric'
     _RETRIEVE = '/retrieve'
-    _BEST = '/best'
+    _MAX = '/max'
+    _MIN = '/min'
     _ALL = '/all'
 
     def post_metric(self):
@@ -12,5 +13,8 @@ def post_metric(self):
     def retrieve_all(self):
         return self._SERVER + self._METRIC + self._RETRIEVE + self._ALL
 
-    def retrieve_best(self):
-        return self._SERVER + self._METRIC + self._RETRIEVE + self._BEST
+    def retrieve_best_min(self):
+        return self._SERVER + self._METRIC + self._RETRIEVE + self._MIN
+
+    def retrieve_best_max(self):
+        return self._SERVER + self._METRIC + self._RETRIEVE + self._MAX

sklearn/fml/fml_client.py

@@ -50,16 +50,22 @@ def retrieve_all_metrics(self, dataset):
 
         return self._post_msg(URI().retrieve_all(), data)
 
-    def retrieve_best_metric(self, dataset):
+    def retrieve_best_metric(self, dataset, min=True):
         """
         Function to retrieve metric that best matches the dataset_hash
+        @min = True fetch the minimum value of the metric
+        @min = False fetches the maximum value of the metric
         """
         dataset_hash = FMLHash().hashValAndReturnString(dataset)
 
         data = {}
         data['dataset_hash'] = dataset_hash
 
-        return self._post_msg(URI().retrieve_best(), data)
+        if min:
+            return self._post_msg(URI().retrieve_best_min(), data)
+        else:
+            return self._post_msg(URI().retrieve_best_max(), data)
+
 
     def _test_publish(self, model=linear_model.LinearRegression(), metric_name='RMSE', metric_value='0', dataset='asdfasdfasdfd'):
         """