test changes

src/py/crankshaft/crankshaft/analysis_data_provider.py

@@ -73,12 +73,12 @@ class AnalysisDataProvider(object):
     def get_segmentation_model_data(self, params):
         """
            fetch data for Segmentation
-           params = {"subquery": query,
-                     "target": variable,
-                     "features": feature_columns}
+        params = {"subquery": query,
+                  "target": variable,
+                  "features": feature_columns}
         """
         columns = ', '.join(['array_agg("{col}") As "{col}"'.format(col=col)
-                            for col in params['feature_columns']])
+                             for col in params['features']])
         query = '''
                 SELECT
                   array_agg("{target}") As target,

src/py/crankshaft/crankshaft/segmentation/segmentation.py

@@ -69,7 +69,7 @@ class Segmentation(object):
 
         params = {"subquery": target_query,
                   "id_col": id_col,
-                  "feature_columns": features}
+                  "feature_columns": feature_columns}
 
         target, features, target_mean, \
             feature_means = self.clean_data(variable, feature_columns, query)
@@ -101,6 +101,9 @@ class Segmentation(object):
 
         results = []
         cursors = self.data_provider.get_segmentation_predict_data(params)
+        # cursors = [{'': ,
+        #             '': }]
+        #
         while True:
             rows = cursors.fetch(batch_size)
             if not rows:
@@ -127,6 +130,14 @@ class Segmentation(object):
 
         data = self.data_provider.get_segmentation_model_data(params)
 
+        '''
+        data: [{'target': [2.9, 4.9, 4, 5, 6]},
+        {'feature1': [1,2,3,4]}, {'feature2' : [2,3,4,5]}
+        ]
+        '''
+
+        [{target: [dsdfs]}]
+
         # extract target data from plpy object
         target = np.array(data[0]['target'])
 

src/py/crankshaft/test/test_segmentation.py

@@ -4,6 +4,21 @@ from helper import plpy, fixture_file
 from crankshaft.segmentation import Segmentation
 import json
 
+class RawDataProvider(AnalysisDataProvider):
+    def __init__(self, raw_data1, raw_data2, raw_data3):
+        self.raw_data1 = raw_data1
+        self.raw_data2 = raw_data2
+        self.raw_data3 = raw_data3
+
+    def get_segmentation_data(self, params):
+        return self.raw_data1
+
+    def get_segmentation_predict_data(self, params):
+        return self.raw_data2
+
+    def get_segmentation_model_data(self, params):
+        return self.raw_data3
+
 class SegmentationTest(unittest.TestCase):
     """Testing class for Moran's I functions"""
 
@@ -36,19 +51,23 @@ class SegmentationTest(unittest.TestCase):
 
 
         ids =  [{'cartodb_ids': range(len(test_data))}]
-        rows =  [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
-
-        plpy._define_result('select \* from  \(select \* from training\) a  limit 1',rows)
-        plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
-        plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
-        plpy._define_result('.*select \* from test.*' ,test_data)
-
-        model_parameters =  {'n_estimators': 1200,
-                             'max_depth': 3,
-                             'subsample' : 0.5,
-                             'learning_rate': 0.01,
-                             'min_samples_leaf': 1}
-        seg = Segmentation()
+        rows =  [{'x1': 0, 'x2': 0, 'x3': 0, 'y': 0, 'cartodb_id': 0}]
+
+        plpy._define_result('select \* from  \(select \* from training\) a  limit 1', rows)
+        plpy._define_result('.*from \(select \* from training\) as a', training_data)
+        plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a', ids)
+        plpy._define_result('.*select \* from test.*', test_data)
+
+        model_parameters = {'n_estimators': 1200,
+                            'max_depth': 3,
+                            'subsample' : 0.5,
+                            'learning_rate': 0.01,
+                            'min_samples_leaf': 1}
+        data = [{'target': [],
+                 'x1': [],
+                 'x2': [],
+                 'x3': []}]
+        seg = Segmentation(RawDataProvider(test, train, predict))
         '''
         self, query, variable, feature_columns,
                                        target_query, model_params,