redo grid_search arg validation and a couple test fixes (#409)

integration-tests/tests/test_random_forest_classifier.py

@@ -64,7 +64,7 @@ def test_random_forest_classifier(tc):
     predict_frame = model.predict(f)
     assert(set(predict_frame.column_names) == set(['Class', 'Dim_1', 'Dim_2','predicted_class']))
     assert(len(predict_frame.column_names) == 4)
-    metrics = model.test(f, 'Class')
+    metrics = model.test(f)
     assert(metrics.accuracy == 1.0)
     assert(metrics.f_measure == 1.0)
     assert(metrics.precision == 1.0)

integration-tests/tests/test_svm.py

@@ -59,7 +59,7 @@ def test_svm(tc):
     assert(set(predicted_frame.column_names) == set(['data', 'label', 'predicted_label']))
     assert(len(predicted_frame.column_names) == 3)
     assert(len(f.column_names) == 2)
-    metrics = model.test(predicted_frame, 'label')
+    metrics = model.test(predicted_frame)
     assert(metrics.accuracy == 1.0)
     assert(metrics.f_measure == 1.0)
     assert(metrics.precision == 1.0)

python/sparktk/models/_selection/grid_search.py

@@ -19,7 +19,7 @@
 from sparktk import TkContext
 from sparktk.frame.ops.classification_metrics_value import ClassificationMetricsValue
 from collections import namedtuple
-from sparktk.arguments import extract_call, validate_call
+from sparktk.arguments import extract_call, validate_call, require_type, affirm_type, value_error
 from sparktk.frame.frame import Frame
 from sparktk import arguments
 
@@ -156,11 +156,18 @@ def grid_search(train_frame, test_frame, train_descriptors, tc= TkContext.implic
 
     """
 
-    #validate input
+    # validate input
     TkContext.validate(tc)
-    if not isinstance(train_descriptors, list):
-        train_descriptors = [train_descriptors]
-    descriptors = [TrainDescriptor(x[0], x[1]) for x in train_descriptors if not isinstance(x, TrainDescriptor)]
+    descriptors = affirm_type.list_of_anything(train_descriptors, "train_descriptors")
+    for i in xrange(len(descriptors)):
+        item = descriptors[i]
+        if not isinstance(item, TrainDescriptor):
+            require_type(tuple, item, "item", "grid_search needs a list of items which are either of type TrainDescriptor or tuples of (model, train_kwargs)")
+            if len(item) != 2:
+                raise value_error("list requires tuples of len 2", item, "item in train_descriptors")
+            if not hasattr(item[0], 'train'):
+                raise value_error("first item in tuple needs to be a object with a 'train' function", item, "item in train_descriptors")
+            descriptors[i] = TrainDescriptor(item[0], item[1])
 
     arguments.require_type(Frame, train_frame, "frame")
     arguments.require_type(Frame, test_frame, "frame")
@@ -258,15 +265,14 @@ def _create_metric_sum(a, b):
 
 
 class TrainDescriptor(object):
-    """
-    Class that separates the model type and args from the input and handles the representation.
-    """
+    """Describes a train operation: a model type and the arguments for its train method"""
 
     def __init__(self, model_type, kwargs):
         """
-        Initializes the model_type and model's arguments
-        :param model_type: The name of the model
-        :param kwargs: The list of model parameters
+        Creates a TrainDescriptor
+
+        :param model_type: type object representing the model in question
+        :param kwargs: dict of key-value-pairs holding values for the train method's parameters
         """
         self.model_type = model_type
         self.kwargs = kwargs