add unittest for common

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tods/tests/common/run_tests.py

@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+
+import sys
+import unittest
+
+runner = unittest.TextTestRunner(verbosity=1)
+tests = unittest.TestLoader().discover('./')
+if not runner.run(tests).wasSuccessful():
+    sys.exit(1)
+
+#for each in ['data_processing', 'timeseries_processing', 'feature_analysis', 'detection_algorithm']:
+#    tests = unittest.TestLoader().discover(each)
+#    if not runner.run(tests).wasSuccessful():
+#        sys.exit(1)

tods/tests/common/test_fixed_split.py

@@ -9,14 +9,20 @@ from tods.common import FixedSplit
 
 
 class FixedSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
-    def test_produce_train_values(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
+
+    def _get_yahoo_dataset(self):
+        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..', '..', '..', 'datasets', 'anomaly','yahoo_sub_5','TRAIN','dataset_TRAIN', 'datasetDoc.json'))
 
         dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
 
+        return dataset
+
+    def test_produce_train_values(self):
+        dataset = self._get_yahoo_dataset()
+
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
         hyperparams_class = FixedSplit.FixedSplitDatasetSplitPrimitive.metadata.get_hyperparams()
@@ -44,20 +50,18 @@ class FixedSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
         for dataset in results:
             self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(results[0]['learningData'].shape[0], 147)
-        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(150) if i not in [9, 11, 13]])
+        self.assertEqual(results[0]['learningData'].shape[0], 1257)
+        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(1260) if i not in [9, 11, 13]])
 
     def test_produce_score_values(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = fixed_split.FixedSplitDatasetSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = FixedSplit.FixedSplitDatasetSplitPrimitive.metadata.get_hyperparams()
 
         hyperparams = hyperparams_class.defaults().replace({
             'primary_index_values': ['9', '11', '13'],
@@ -67,7 +71,7 @@ class FixedSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
         # a pickle because runtime populates this primitive as a list from a split file.
         self.assertEqual(hyperparams.values_to_json_structure(), {'primary_index_values': ['9', '11', '13'], 'row_indices': [], 'delete_recursive': False})
 
-        primitive = fixed_split.FixedSplitDatasetSplitPrimitive(hyperparams=hyperparams)
+        primitive = FixedSplit.FixedSplitDatasetSplitPrimitive(hyperparams=hyperparams)
 
         primitive.set_training_data(dataset=dataset)
         primitive.fit()
@@ -83,18 +87,15 @@ class FixedSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
         self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(150) if i in [9, 11, 13]])
 
     def test_produce_train_indices(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        hyperparams_class = FixedSplit.FixedSplitDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        hyperparams_class = fixed_split.FixedSplitDatasetSplitPrimitive.metadata.get_hyperparams()
-
-        primitive = fixed_split.FixedSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = FixedSplit.FixedSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'row_indices': [9, 11, 13],
         }))
 
@@ -111,22 +112,20 @@ class FixedSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
         for dataset in results:
             self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(results[0]['learningData'].shape[0], 147)
-        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(150) if i not in [9, 11, 13]])
+        self.assertEqual(results[0]['learningData'].shape[0], 1257)
+        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(1260) if i not in [9, 11, 13]])
 
     def test_produce_score_indices(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = fixed_split.FixedSplitDatasetSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = FixedSplit.FixedSplitDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        primitive = fixed_split.FixedSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = FixedSplit.FixedSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'row_indices': [9, 11, 13],
         }))
 

tods/tests/common/test_kfold_split.py

@@ -5,26 +5,31 @@ import unittest
 from d3m import container
 from d3m.metadata import base as metadata_base
 
-from common_primitives import kfold_split
+from tods.common import KFoldSplit
 
 
 class KFoldDatasetSplitPrimitiveTestCase(unittest.TestCase):
-    def test_produce_train(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'database_dataset_1', 'datasetDoc.json'))
+    def _get_yahoo_dataset(self):
+        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..', '..', '..', 'datasets', 'anomaly','yahoo_sub_5','TRAIN','dataset_TRAIN', 'datasetDoc.json'))
 
         dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
 
+        return dataset
+
+    def test_produce_train(self):
+        dataset = self._get_yahoo_dataset()
+
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
-        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = kfold_split.KFoldDatasetSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = KFoldSplit.KFoldDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        primitive = kfold_split.KFoldDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = KFoldSplit.KFoldDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'number_of_folds': 10,
             'shuffle': True,
-            'delete_recursive': True,
+            'delete_recursive': False,
         }))
 
         primitive.set_training_data(dataset=dataset)
@@ -33,68 +38,45 @@ class KFoldDatasetSplitPrimitiveTestCase(unittest.TestCase):
         # To test that pickling works.
         pickle.dumps(primitive)
 
-        results = primitive.produce(inputs=container.List([0, 1], generate_metadata=True)).value
+        results = primitive.produce(inputs=container.List([0], generate_metadata=True)).value
 
-        self.assertEqual(len(results), 2)
+        self.assertEqual(len(results), 1)
 
         for dataset in results:
-            self.assertEqual(len(dataset), 4)
-
-        self.assertEqual(results[0]['codes'].shape[0], 3)
-        self.assertEqual(results[1]['codes'].shape[0], 3)
-
-        self.assertEqual(set(results[0]['codes'].iloc[:, 0]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 40)
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 1]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 2]), {'aaa', 'bbb', 'ccc', 'ddd', 'eee'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 3]), {'1990', '2000', '2010'})
+            self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(set(results[1]['codes'].iloc[:, 0]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(len(results[1]['learningData'].iloc[:, 0]), 40)
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 1]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 2]), {'aaa', 'bbb', 'ccc', 'ddd', 'eee'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 3]), {'1990', '2000', '2010'})
+        self.assertEqual(results[0]['learningData'].shape[0], 1134)
 
     def test_produce_score(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'database_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
-        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = kfold_split.KFoldDatasetSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = KFoldSplit.KFoldDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        primitive = kfold_split.KFoldDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = KFoldSplit.KFoldDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'number_of_folds': 10,
             'shuffle': True,
-            'delete_recursive': True,
+            'delete_recursive': False,
         }))
 
         primitive.set_training_data(dataset=dataset)
         primitive.fit()
 
-        results = primitive.produce_score_data(inputs=container.List([0, 1], generate_metadata=True)).value
+        results = primitive.produce_score_data(inputs=container.List([0], generate_metadata=True)).value
 
-        self.assertEqual(len(results), 2)
+        self.assertEqual(len(results), 1)
 
         for dataset in results:
-            self.assertEqual(len(dataset), 4)
-
-        self.assertEqual(set(results[0]['codes'].iloc[:, 0]), {'AAA', 'BBB'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 0]), {'5', '11', '28', '31', '38'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 1]), {'AAA', 'BBB'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 2]), {'aaa', 'bbb', 'ddd', 'eee'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 3]), {'1990', '2000'})
+            self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(set(results[1]['codes'].iloc[:, 0]), {'BBB', 'CCC'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 0]), {'12', '26', '29', '32', '39'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 1]), {'BBB', 'CCC'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 2]), {'bbb', 'ccc', 'ddd', 'eee'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 3]), {'1990', '2000', '2010'})
+        self.assertEqual(results[0]['learningData'].shape[0], 126)
 
 
 if __name__ == '__main__':
     unittest.main()
+
+

tods/tests/common/test_kfold_timeseries_split.py

@@ -5,24 +5,31 @@ import unittest
 from d3m import container
 from d3m.metadata import base as metadata_base
 
-from common_primitives import kfold_split_timeseries
+from tods.common import KFoldSplitTimeseries
 
 
 class KFoldTimeSeriesSplitPrimitiveTestCase(unittest.TestCase):
-    def test_produce_train_timeseries_1(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'timeseries_dataset_1', 'datasetDoc.json'))
+
+    def _get_yahoo_dataset(self):
+        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..', '..', '..', 'datasets', 'anomaly','yahoo_sub_5','TRAIN','dataset_TRAIN', 'datasetDoc.json'))
 
         dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
 
+        return dataset
+
+    def test_produce_train_timeseries_1(self):
+        dataset = self._get_yahoo_dataset()
+
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
-        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 1), 'https://metadata.datadrivendiscovery.org/types/Time')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
 
         folds = 5
-        primitive = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'number_of_folds': folds,
             'number_of_window_folds': 1,
         }))
@@ -33,35 +40,29 @@ class KFoldTimeSeriesSplitPrimitiveTestCase(unittest.TestCase):
         # To test that pickling works.
         pickle.dumps(primitive)
 
-        results = primitive.produce(inputs=container.List([0, 1], generate_metadata=True)).value
+        results = primitive.produce(inputs=container.List([0], generate_metadata=True)).value
 
-        self.assertEqual(len(results), 2)
+        self.assertEqual(len(results), 1)
 
         for dataset in results:
              self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 8)
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 3]), {'2013-11-05', '2013-11-06', '2013-11-07', '2013-11-08', '2013-11-11',
-                '2013-11-12', '2013-11-13', '2013-11-14'})
-
-        self.assertEqual(len(results[1]['learningData'].iloc[:, 0]), 8)
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 3]), {'2013-11-13', '2013-11-14', '2013-11-15', '2013-11-18', '2013-11-19',
-                '2013-11-20', '2013-11-21', '2013-11-22'})
+        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 210)
+        #TODO: correct the semantic type and validate unix timestamp
 
     def test_produce_score_timeseries_1(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'timeseries_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
-        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 1), 'https://metadata.datadrivendiscovery.org/types/Time')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
 
         folds = 5
-        primitive = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'number_of_folds': folds,
             'number_of_window_folds': 1,
         }))
@@ -69,38 +70,31 @@ class KFoldTimeSeriesSplitPrimitiveTestCase(unittest.TestCase):
         primitive.set_training_data(dataset=dataset)
         primitive.fit()
 
-        results = primitive.produce_score_data(inputs=container.List([0, 1], generate_metadata=True)).value
+        results = primitive.produce_score_data(inputs=container.List([0], generate_metadata=True)).value
 
-        self.assertEqual(len(results), 2)
+        self.assertEqual(len(results), 1)
 
         for dataset in results:
              self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 6)
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 3]), {'2013-11-15', '2013-11-18', '2013-11-19',
-                '2013-11-20', '2013-11-21', '2013-11-22'})
-
-        self.assertEqual(len(results[1]['learningData'].iloc[:, 0]), 6)
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 3]), {'2013-11-25', '2013-11-26', '2013-11-27',
-                '2013-11-29', '2013-12-02', '2013-12-03'})
+        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 210)
 
     def test_produce_train(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'database_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
-        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 1), 'https://metadata.datadrivendiscovery.org/types/Time')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
         # We fake that the dataset is time-series.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 3), 'https://metadata.datadrivendiscovery.org/types/Time')
+        #dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 1), 'https://metadata.datadrivendiscovery.org/types/Time')
 
-        hyperparams_class = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
 
         folds = 5
-        primitive = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'number_of_folds': folds,
             'number_of_window_folds': 1,
         }))
@@ -111,45 +105,30 @@ class KFoldTimeSeriesSplitPrimitiveTestCase(unittest.TestCase):
         # To test that pickling works.
         pickle.dumps(primitive)
 
-        results = primitive.produce(inputs=container.List([0, 1], generate_metadata=True)).value
+        results = primitive.produce(inputs=container.List([0], generate_metadata=True)).value
 
-        self.assertEqual(len(results), 2)
+        self.assertEqual(len(results), 1)
 
         for dataset in results:
-            self.assertEqual(len(dataset), 4)
+            self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(results[0]['codes'].shape[0], 3)
-        self.assertEqual(results[1]['codes'].shape[0], 3)
-
-        self.assertEqual(set(results[0]['codes'].iloc[:, 0]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 9)
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 1]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 2]), {'bbb', 'ccc', 'ddd'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 3]), {'1990'})
-
-        self.assertEqual(set(results[1]['codes'].iloc[:, 0]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(len(results[1]['learningData'].iloc[:, 0]), 9)
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 1]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 2]), {'aaa', 'bbb', 'ddd', 'eee'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 3]), {'1990', '2000'})
+        self.assertEqual(results[0]['learningData'].shape[0], 210)
+        #TODO: correct the semantic type and validate unix timestamp
 
     def test_produce_score(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'database_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
-        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 1), 'https://metadata.datadrivendiscovery.org/types/Time')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        # We fake that the dataset is time-series.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 3), 'https://metadata.datadrivendiscovery.org/types/Time')
 
-        hyperparams_class = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
 
         folds = 5
-        primitive = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'number_of_folds': folds,
             'number_of_window_folds': 1,
         }))
@@ -162,37 +141,24 @@ class KFoldTimeSeriesSplitPrimitiveTestCase(unittest.TestCase):
         self.assertEqual(len(results), 2)
 
         for dataset in results:
-            self.assertEqual(len(dataset), 4)
-
-        self.assertEqual(results[0]['codes'].shape[0], 3)
-        self.assertEqual(results[1]['codes'].shape[0], 3)
+            self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(set(results[0]['codes'].iloc[:, 0]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 0]), {'2', '3', '32', '33', '37', '38', '39'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 1]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 2]), {'aaa', 'ddd', 'eee'})
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 3]), {'1990', '2000'})
+        self.assertEqual(results[0]['learningData'].shape[0], 210)
 
-        self.assertEqual(set(results[1]['codes'].iloc[:, 0]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 0]), {'22', '23', '24', '31', '40', '41', '42'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 1]), {'AAA', 'BBB', 'CCC'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 2]), {'ccc', 'ddd', 'eee'})
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 3]), {'2000'})
 
     def test_unsorted_datetimes_timeseries_4(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'timeseries_dataset_4', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
-        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 1), 'https://metadata.datadrivendiscovery.org/types/Time')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive.metadata.get_hyperparams()
 
         folds = 5
-        primitive = kfold_split_timeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = KFoldSplitTimeseries.KFoldTimeSeriesSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'number_of_folds': folds,
             'number_of_window_folds': 1,
         }))
@@ -203,20 +169,16 @@ class KFoldTimeSeriesSplitPrimitiveTestCase(unittest.TestCase):
         # To test that pickling works.
         pickle.dumps(primitive)
 
-        results = primitive.produce(inputs=container.List([0, 1], generate_metadata=True)).value
+        results = primitive.produce(inputs=container.List([0], generate_metadata=True)).value
 
-        self.assertEqual(len(results), 2)
+        self.assertEqual(len(results), 1)
 
         for dataset in results:
              self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 8)
-        self.assertEqual(set(results[0]['learningData'].iloc[:, 3]), {'2013-11-05', '2013-11-06', '2013-11-07', '2013-11-08', '2013-11-11',
-                '2013-11-12', '2013-11-13', '2013-11-14'})
+        self.assertEqual(len(results[0]['learningData'].iloc[:, 0]), 210)
 
-        self.assertEqual(len(results[1]['learningData'].iloc[:, 0]), 8)
-        self.assertEqual(set(results[1]['learningData'].iloc[:, 3]), {'2013-11-13', '2013-11-14', '2013-11-15', '2013-11-18', '2013-11-19',
-                '2013-11-20', '2013-11-21', '2013-11-22'})
+        #TODO: correct the semantic type and validate unix timestamp
 
 
 if __name__ == '__main__':

tods/tests/common/test_no_split.py

@@ -5,23 +5,29 @@ import unittest
 from d3m import container
 from d3m.metadata import base as metadata_base
 
-from common_primitives import no_split
+from tods.common import NoSplit
 
 
 class NoSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
-    def test_produce_train(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
+    def _get_yahoo_dataset(self):
+        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..', '..', '..', 'datasets', 'anomaly','yahoo_sub_5','TRAIN','dataset_TRAIN', 'datasetDoc.json'))
 
         dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
 
+        return dataset
+
+    def test_produce_train(self):
+        dataset = self._get_yahoo_dataset()
+
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = no_split.NoSplitDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        primitive = no_split.NoSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults())
+        hyperparams_class = NoSplit.NoSplitDatasetSplitPrimitive.metadata.get_hyperparams()
+
+        primitive = NoSplit.NoSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults())
 
         primitive.set_training_data(dataset=dataset)
         primitive.fit()
@@ -36,22 +42,20 @@ class NoSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
         for dataset in results:
             self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(results[0]['learningData'].shape[0], 150)
-        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(150)])
+        self.assertEqual(results[0]['learningData'].shape[0], 1260)
+        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(1260)])
 
     def test_produce_score(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = no_split.NoSplitDatasetSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = NoSplit.NoSplitDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        primitive = no_split.NoSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults())
+        primitive = NoSplit.NoSplitDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults())
 
         primitive.set_training_data(dataset=dataset)
         primitive.fit()
@@ -63,8 +67,8 @@ class NoSplitDatasetSplitPrimitiveTestCase(unittest.TestCase):
         for dataset in results:
             self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(results[0]['learningData'].shape[0], 150)
-        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(150)])
+        self.assertEqual(results[0]['learningData'].shape[0], 1260)
+        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [str(i) for i in range(1260)])
 
 
 if __name__ == '__main__':

tods/tests/common/test_redact_columns.py

@@ -4,18 +4,18 @@ import unittest
 from d3m import container, utils
 from d3m.metadata import base as metadata_base
 
-from common_primitives import redact_columns
-
+from tods.common import RedactColumns
 
 class RedactColumnsPrimitiveTestCase(unittest.TestCase):
+
     def _get_datasets(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
+        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..', '..', '..', 'datasets', 'anomaly','yahoo_sub_5','TRAIN','dataset_TRAIN', 'datasetDoc.json'))
 
         dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
         datasets = container.List([dataset], {
@@ -37,9 +37,9 @@ class RedactColumnsPrimitiveTestCase(unittest.TestCase):
     def test_basic(self):
         dataset_doc_path, datasets = self._get_datasets()
 
-        hyperparams_class = redact_columns.RedactColumnsPrimitive.metadata.get_hyperparams()
+        hyperparams_class = RedactColumns.RedactColumnsPrimitive.metadata.get_hyperparams()
 
-        primitive = redact_columns.RedactColumnsPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = RedactColumns.RedactColumnsPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'semantic_types': ('https://metadata.datadrivendiscovery.org/types/TrueTarget',),
             'add_semantic_types': ('https://metadata.datadrivendiscovery.org/types/RedactedTarget', 'https://metadata.datadrivendiscovery.org/types/MissingData'),
         }))
@@ -50,15 +50,15 @@ class RedactColumnsPrimitiveTestCase(unittest.TestCase):
         redacted_dataset = redacted_datasets[0]
 
         self.assertIsInstance(redacted_dataset, container.Dataset)
-        self.assertEqual(redacted_dataset['learningData']['species'].values.tolist(), [''] * 150)
 
-        self._test_metadata(redacted_datasets.metadata, dataset_doc_path, True)
-        self._test_metadata(redacted_dataset.metadata, dataset_doc_path, False)
+        # TODO: check metadata of yahoo dataset
+        #self._test_metadata(redacted_datasets.metadata, dataset_doc_path, True)
+        #self._test_metadata(redacted_dataset.metadata, dataset_doc_path, False)
 
     def _test_metadata(self, metadata, dataset_doc_path, is_list):
         top_metadata = {
             'structural_type': 'd3m.container.dataset.Dataset',
-            'id': 'iris_dataset_1',
+            'id': 'yahoo_sub_5_dataset_TRAIN',
             'version': '4.0.0',
             'name': 'Iris Dataset',
             'location_uris': [

tods/tests/common/test_train_score_split.py

@@ -5,23 +5,33 @@ import unittest
 from d3m import container
 from d3m.metadata import base as metadata_base
 
-from common_primitives import train_score_split
+from tods.common import TrainScoreSplit 
 
 
 class TrainScoreDatasetSplitPrimitiveTestCase(unittest.TestCase):
-    def test_produce_train(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
+    def _get_yahoo_dataset(self):
+        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..', '..', '..', 'datasets', 'anomaly','yahoo_sub_5','TRAIN','dataset_TRAIN', 'datasetDoc.json'))
 
         dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
 
+        return dataset
+
+    def test_produce_train(self):
+        dataset = self._get_yahoo_dataset()
+
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 2), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 3), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 6), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = train_score_split.TrainScoreDatasetSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = TrainScoreSplit.TrainScoreDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        primitive = train_score_split.TrainScoreDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = TrainScoreSplit.TrainScoreDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'shuffle': True,
         }))
 
@@ -38,51 +48,42 @@ class TrainScoreDatasetSplitPrimitiveTestCase(unittest.TestCase):
         for dataset in results:
             self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(results[0]['learningData'].shape[0], 112)
-        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [
-            '0', '1', '2', '3', '4', '5', '6', '9', '10', '11', '12', '13', '14', '15', '17', '19', '20',
-            '21', '23', '25', '28', '29', '30', '31', '32', '34', '35', '36', '38', '39', '41', '42', '43',
-            '46', '47', '48', '49', '50', '52', '53', '55', '56', '57', '58', '60', '61', '64', '65', '67',
-            '68', '69', '70', '72', '74', '75', '77', '79', '80', '81', '82', '85', '87', '88', '89', '91',
-            '92', '94', '95', '96', '98', '99', '101', '102', '103', '104', '105', '106', '108', '109', '110',
-            '111', '112', '113', '115', '116', '117', '118', '119', '120', '122', '123', '124', '125', '128',
-            '129', '130', '131', '133', '135', '136', '138', '139', '140', '141', '142', '143', '144', '145',
-            '146', '147', '148', '149',
-        ])
-
-        self.assertEqual(results.metadata.query((0, 'learningData'))['dimension']['length'], 112)
+        self.assertEqual(results[0]['learningData'].shape[0], 945)
 
-        column_names = ['d3mIndex', 'sepalLength', 'sepalWidth', 'petalLength', 'petalWidth', 'species']
-        for i in range(6):
-            self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, i))['name'], column_names[i])
+        column_names = ['d3mIndex', 'timestamp', 'value_0', 'value_1', 'value_2', 'value_3', 'value_4','ground_truth']
+        for i in range(8):
+            self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, i))['name'],
+                             column_names[i])
 
         self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, 0))['semantic_types'], (
-            "http://schema.org/Integer",  "https://metadata.datadrivendiscovery.org/types/PrimaryKey"
+            'http://schema.org/Integer', 'https://metadata.datadrivendiscovery.org/types/PrimaryKey', 'https://metadata.datadrivendiscovery.org/types/Index'
         ))
-        for i in range(1, 5):
-            self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, i))['semantic_types'], (
-                'http://schema.org/Float', 'https://metadata.datadrivendiscovery.org/types/Attribute'
-            ))
-        self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, 5))['semantic_types'],(
-            'https://metadata.datadrivendiscovery.org/types/CategoricalData',
+        for i in range(2, 6):
+            self.assertEqual(
+                results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, i))['semantic_types'], ('http://schema.org/Float',)
+            )
+        self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, 7))['semantic_types'],(
+            'http://schema.org/Integer',
             'https://metadata.datadrivendiscovery.org/types/SuggestedTarget',
-            'https://metadata.datadrivendiscovery.org/types/Target',
-            'https://metadata.datadrivendiscovery.org/types/TrueTarget'
+            'https://metadata.datadrivendiscovery.org/types/TrueTarget',
         ))
 
     def test_produce_score(self):
-        dataset_doc_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tests', 'data', 'datasets', 'iris_dataset_1', 'datasetDoc.json'))
-
-        dataset = container.Dataset.load('file://{dataset_doc_path}'.format(dataset_doc_path=dataset_doc_path))
+        dataset = self._get_yahoo_dataset()
 
         # We set semantic types like runtime would.
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Target')
-        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 0), 'https://metadata.datadrivendiscovery.org/types/Index')
+        dataset.metadata = dataset.metadata.add_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/TrueTarget')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 2), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 3), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 4), 'https://metadata.datadrivendiscovery.org/types/Attribute')
         dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 5), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 6), 'https://metadata.datadrivendiscovery.org/types/Attribute')
+        dataset.metadata = dataset.metadata.remove_semantic_type(('learningData', metadata_base.ALL_ELEMENTS, 7), 'https://metadata.datadrivendiscovery.org/types/Attribute')
 
-        hyperparams_class = train_score_split.TrainScoreDatasetSplitPrimitive.metadata.get_hyperparams()
+        hyperparams_class = TrainScoreSplit.TrainScoreDatasetSplitPrimitive.metadata.get_hyperparams()
 
-        primitive = train_score_split.TrainScoreDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
+        primitive = TrainScoreSplit.TrainScoreDatasetSplitPrimitive(hyperparams=hyperparams_class.defaults().replace({
             'shuffle': True,
         }))
 
@@ -96,33 +97,28 @@ class TrainScoreDatasetSplitPrimitiveTestCase(unittest.TestCase):
         for dataset in results:
             self.assertEqual(len(dataset), 1)
 
-        self.assertEqual(results[0]['learningData'].shape[0], 38)
-        self.assertEqual(list(results[0]['learningData'].iloc[:, 0]), [
-            '7', '8', '16', '18', '22', '24', '26', '27', '33', '37', '40', '44', '45', '51', '54',
-            '59', '62', '63', '66', '71', '73', '76', '78', '83', '84', '86', '90', '93', '97', '100',
-            '107', '114', '121', '126', '127', '132', '134', '137',
-        ])
+        self.assertEqual(results[0]['learningData'].shape[0], 315)
+        #TODO check data type
 
-        self.assertEqual(results.metadata.query((0, 'learningData'))['dimension']['length'], 38)
+        self.assertEqual(results.metadata.query((0, 'learningData'))['dimension']['length'], 315)
 
-        column_names = ['d3mIndex', 'sepalLength', 'sepalWidth', 'petalLength', 'petalWidth', 'species']
-        for i in range(6):
+        column_names = ['d3mIndex', 'timestamp', 'value_0', 'value_1', 'value_2', 'value_3', 'value_4','ground_truth']
+        for i in range(8):
             self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, i))['name'],
                              column_names[i])
 
         self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, 0))['semantic_types'], (
-            "http://schema.org/Integer", "https://metadata.datadrivendiscovery.org/types/PrimaryKey"
+            'http://schema.org/Integer', 'https://metadata.datadrivendiscovery.org/types/PrimaryKey', 'https://metadata.datadrivendiscovery.org/types/Index'
         ))
-        for i in range(1, 5):
+        for i in range(2, 6):
             self.assertEqual(
-                results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, i))['semantic_types'], (
-                    'http://schema.org/Float', 'https://metadata.datadrivendiscovery.org/types/Attribute'
-                ))
-        self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, 5))['semantic_types'], (
-            'https://metadata.datadrivendiscovery.org/types/CategoricalData',
+                results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, i))['semantic_types'], ('http://schema.org/Float',)
+            )
+        print(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, 7))['semantic_types'])
+        self.assertEqual(results.metadata.query((0, 'learningData', metadata_base.ALL_ELEMENTS, 7))['semantic_types'], (
+            'http://schema.org/Integer',
             'https://metadata.datadrivendiscovery.org/types/SuggestedTarget',
-            'https://metadata.datadrivendiscovery.org/types/Target',
-            'https://metadata.datadrivendiscovery.org/types/TrueTarget'
+            'https://metadata.datadrivendiscovery.org/types/TrueTarget',
         ))