made python unit tests work with new test server setup

tests/test_datasets/test_dataset_functions.py

@@ -90,13 +90,13 @@ def test_list_datasets(self):
         # data from the internet...
         datasets = openml.datasets.list_datasets()
         # 1087 as the number of datasets on openml.org
-        self.assertGreaterEqual(len(datasets), 1087)
+        self.assertGreaterEqual(len(datasets), 100)
         for did in datasets:
             self._check_dataset(datasets[did])
 
     def test_list_datasets_by_tag(self):
-        datasets = openml.datasets.list_datasets(tag='uci')
-        self.assertGreaterEqual(len(datasets), 5)
+        datasets = openml.datasets.list_datasets(tag='study_14')
+        self.assertGreaterEqual(len(datasets), 100)
         for did in datasets:
             self._check_dataset(datasets[did])
 
@@ -153,20 +153,20 @@ def test_get_dataset(self):
             openml.config.get_cache_directory(), "datasets", "1", "qualities.xml")))
 
     def test_get_dataset_with_string(self):
-        dataset = openml.datasets.get_dataset(373)
+        dataset = openml.datasets.get_dataset(101)
         self.assertRaises(PyOpenMLError, dataset._get_arff, 'arff')
         self.assertRaises(PyOpenMLError, dataset.get_data)
 
     def test_get_dataset_sparse(self):
-        dataset = openml.datasets.get_dataset(1571)
+        dataset = openml.datasets.get_dataset(102)
         X = dataset.get_data()
         self.assertIsInstance(X, scipy.sparse.csr_matrix)
 
     def test_download_rowid(self):
         # Smoke test which checks that the dataset has the row-id set correctly
-        did = 164
+        did = 44
         dataset = openml.datasets.get_dataset(did)
-        self.assertEqual(dataset.row_id_attribute, 'instance')
+        self.assertEqual(dataset.row_id_attribute, 'Counter')
 
     def test__get_dataset_description(self):
         description = _get_dataset_description(self.workdir, 2)

tests/test_flows/test_flow.py

@@ -104,7 +104,8 @@ def test_get_flow(self):
     def test_from_xml_to_xml(self):
         # Get the raw xml thing
         # TODO maybe get this via get_flow(), which would have to be refactored to allow getting only the xml dictionary
-        for flow_id in [1185, 1244, 1196, 1112, ]:
+        # TODO: no sklearn flows.
+        for flow_id in [3, 5, 7, 9, ]:
             flow_xml = _perform_api_call("flow/%d" % flow_id)
             flow_dict = xmltodict.parse(flow_xml)
 

tests/test_runs/test_run_functions.py

@@ -31,7 +31,7 @@ def _perform_run(self, task_id, num_instances, clf):
         return run
 
     def test_run_regression_on_classif_task(self):
-        task_id = 10107
+        task_id = 115
 
         clf = LinearRegression()
         task = openml.tasks.get_task(task_id)
@@ -43,7 +43,7 @@ def test_run_regression_on_classif_task(self):
 
     @mock.patch('openml.flows.sklearn_to_flow')
     def test_check_erronous_sklearn_flow_fails(self, sklearn_to_flow_mock):
-        task_id = 10107
+        task_id = 115
         task = openml.tasks.get_task(task_id)
 
         # Invalid parameter values
@@ -52,16 +52,16 @@ def test_check_erronous_sklearn_flow_fails(self, sklearn_to_flow_mock):
         self.assertRaisesRegexp(ValueError, "Penalty term must be positive; got \(C='abc'\)",
                                 openml.runs.run_task, task=task, model=clf)
 
-    def test_run_iris(self):
-        task_id = 10107
-        num_instances = 150
+    def test_run_diabetes(self):
+        task_id = 115
+        num_instances = 768
 
         clf = LogisticRegression()
         self._perform_run(task_id,num_instances, clf)
 
     def test_run_optimize_randomforest_iris(self):
-        task_id = 10107
-        num_instances = 150
+        task_id = 115
+        num_instances = 768
         num_folds = 10
         num_iterations = 5
 
@@ -80,8 +80,8 @@ def test_run_optimize_randomforest_iris(self):
         self.assertEqual(len(run.trace_content), num_iterations * num_folds)
 
     def test_run_optimize_bagging_iris(self):
-        task_id = 10107
-        num_instances = 150
+        task_id = 115
+        num_instances = 768
         num_folds = 10
         num_iterations = 9 # (num values for C times gamma)
 
@@ -94,8 +94,8 @@ def test_run_optimize_bagging_iris(self):
         self.assertEqual(len(run.trace_content), num_iterations * num_folds)
 
     def test_run_pipeline(self):
-        task_id = 10107
-        num_instances = 150
+        task_id = 115
+        num_instances = 768
         num_folds = 10
         num_iterations = 9  # (num values for C times gamma)
 
@@ -107,8 +107,11 @@ def test_run_pipeline(self):
         self.assertEqual(run.trace_content, None)
 
     def test__run_task_get_arffcontent(self):
-        task = openml.tasks.get_task(1939)
+        task = openml.tasks.get_task(7)
         class_labels = task.class_labels
+        num_instances = 3196
+        num_folds = 10
+        num_repeats = 1
 
         clf = SGDClassifier(loss='hinge', random_state=1)
         self.assertRaisesRegexp(AttributeError,
@@ -125,20 +128,24 @@ def test__run_task_get_arffcontent(self):
         self.assertIsInstance(arff_tracecontent, type(None))
 
         # 10 times 10 fold CV of 150 samples
-        self.assertEqual(len(arff_datacontent), 1500)
+        self.assertEqual(len(arff_datacontent), num_instances * num_repeats)
         for arff_line in arff_datacontent:
-            self.assertEqual(len(arff_line), 8)
+            print(arff_line)
+            # check number columns
+            self.assertEqual(len(arff_line), 7)
+            # check repeat
             self.assertGreaterEqual(arff_line[0], 0)
-            self.assertLessEqual(arff_line[0], 9)
+            self.assertLessEqual(arff_line[0], num_repeats - 1)
+            # check fold
             self.assertGreaterEqual(arff_line[1], 0)
-            self.assertLessEqual(arff_line[1], 9)
+            self.assertLessEqual(arff_line[1], num_folds - 1)
+            # check row id
             self.assertGreaterEqual(arff_line[2], 0)
-            self.assertLessEqual(arff_line[2], 149)
-            self.assertAlmostEqual(sum(arff_line[3:6]), 1.0)
-            self.assertIn(arff_line[6], ['Iris-setosa', 'Iris-versicolor',
-                                         'Iris-virginica'])
-            self.assertIn(arff_line[7], ['Iris-setosa', 'Iris-versicolor',
-                                         'Iris-virginica'])
+            self.assertLessEqual(arff_line[2], num_instances - 1)
+            # check confidences
+            self.assertAlmostEqual(sum(arff_line[3:5]), 1.0)
+            self.assertIn(arff_line[5], ['won', 'nowin'])
+            self.assertIn(arff_line[6], ['won', 'nowin'])
 
     def test_get_run(self):
         # this run is not available on test
@@ -163,12 +170,16 @@ def _check_run(self, run):
         self.assertEqual(len(run), 5)
 
     def test_get_runs_list(self):
+        # TODO: comes from live, no such lists on test
+        openml.config.server = self.production_server
         runs = openml.runs.list_runs(id=[2])
         self.assertEqual(len(runs), 1)
         for rid in runs:
             self._check_run(runs[rid])
 
     def test_get_runs_list_by_task(self):
+        # TODO: comes from live, no such lists on test
+        openml.config.server = self.production_server
         task_ids = [20]
         runs = openml.runs.list_runs(task=task_ids)
         self.assertGreaterEqual(len(runs), 590)
@@ -185,6 +196,8 @@ def test_get_runs_list_by_task(self):
             self._check_run(runs[rid])
 
     def test_get_runs_list_by_uploader(self):
+        # TODO: comes from live, no such lists on test
+        openml.config.server = self.production_server
         # 29 is Dominik Kirchhoff - Joaquin and Jan have too many runs right now
         uploader_ids = [29]
 
@@ -204,6 +217,8 @@ def test_get_runs_list_by_uploader(self):
             self._check_run(runs[rid])
 
     def test_get_runs_list_by_flow(self):
+        # TODO: comes from live, no such lists on test
+        openml.config.server = self.production_server
         flow_ids = [1154]
         runs = openml.runs.list_runs(flow=flow_ids)
         self.assertGreaterEqual(len(runs), 1)
@@ -220,6 +235,8 @@ def test_get_runs_list_by_flow(self):
             self._check_run(runs[rid])
 
     def test_get_runs_pagination(self):
+        # TODO: comes from live, no such lists on test
+        openml.config.server = self.production_server
         uploader_ids = [1]
         size = 10
         max = 100
@@ -230,9 +247,11 @@ def test_get_runs_pagination(self):
                 self.assertIn(runs[rid]["uploader"], uploader_ids)
 
     def test_get_runs_list_by_filters(self):
+        # TODO: comes from live, no such lists on test
+        openml.config.server = self.production_server
         ids = [505212, 6100]
         tasks = [2974, 339]
-        uploaders_1 = [1, 17]
+        uploaders_1 = [1, 2]
         uploaders_2 = [29, 274]
         flows = [74, 1718]
 
@@ -253,6 +272,8 @@ def test_get_runs_list_by_filters(self):
         runs = openml.runs.list_runs(id=ids, task=tasks, uploader=uploaders_1)
 
     def test_get_runs_list_by_tag(self):
+        # TODO: comes from live, no such lists on test
+        openml.config.server = self.production_server
         runs = openml.runs.list_runs(tag='curves')
         self.assertGreaterEqual(len(runs), 1)
 

tests/test_tasks/test_task.py

@@ -14,10 +14,6 @@
 
 class OpenMLTaskTest(TestBase):
 
-    def test_get_clustering_task(self):
-        self.assertRaisesRegexp(KeyError, 'oml:target_feature',
-                                openml.tasks.get_task, 10128)
-
     @mock.patch('openml.datasets.get_dataset', autospec=True)
     def test_get_dataset(self, patch):
         patch.return_value = mock.MagicMock()
@@ -40,11 +36,11 @@ def test_get_X_and_Y(self):
         self.assertEqual(Y.dtype, int)
 
         # Regression task
-        task = openml.tasks.get_task(2280)
+        task = openml.tasks.get_task(631)
         X, Y = task.get_X_and_y()
-        self.assertEqual((8192, 8), X.shape)
+        self.assertEqual((52, 2), X.shape)
         self.assertIsInstance(X, np.ndarray)
-        self.assertEqual((8192,), Y.shape)
+        self.assertEqual((52,), Y.shape)
         self.assertIsInstance(Y, np.ndarray)
         self.assertEqual(Y.dtype, float)
 

tests/test_tasks/test_task_functions.py

@@ -59,15 +59,15 @@ def test_list_tasks_by_type(self):
             self._check_task(tasks[tid])
 
     def test_list_tasks_by_tag(self):
-        num_basic_tasks = 54 # number is flexible, check server if fails
-        tasks = openml.tasks.list_tasks(tag='basic')
+        num_basic_tasks = 100 # number is flexible, check server if fails
+        tasks = openml.tasks.list_tasks(tag='study_14')
         self.assertGreaterEqual(len(tasks), num_basic_tasks)
         for tid in tasks:
             self._check_task(tasks[tid])
 
     def test_list_tasks(self):
         tasks = openml.tasks.list_tasks()
-        self.assertGreaterEqual(len(tasks), 2000)
+        self.assertGreaterEqual(len(tasks), 900)
         for tid in tasks:
             self._check_task(tasks[tid])
 
@@ -83,7 +83,7 @@ def test_list_tasks_paginate(self):
     def test_list_tasks_per_type_paginate(self):
         size = 10
         max = 100
-        task_types = 5
+        task_types = 4
         for j in range(1,task_types):
             for i in range(0, max, size):
                 tasks = openml.tasks.list_tasks(task_type_id=j, offset=i, size=size)