ADD unit test to ensure example listing (#421)

* ADD unit test to ensure example listing

* Update test_study_examples.py

Author: mfeurer

tests/test_study/test_study_examples.py

@@ -0,0 +1,53 @@
+from openml.testing import TestBase
+
+
+class TestStudyFunctions(TestBase):
+    _multiprocess_can_split_ = True
+    """Test the example code of Bischl et al. (2018)"""
+
+    def test_Figure1a(self):
+        """Test listing in Figure 1a on a single task and the old OpenML100 study.
+        
+        The original listing is pasted into the comment below because it the 
+        actual unit test differs a bit, as for example it does not run for all tasks, 
+        but only a single one.
+
+        import openml
+        import sklearn.tree, sklearn.preprocessing
+        benchmark_suite = openml.study.get_study('OpenML-CC18','tasks') # obtain the benchmark suite
+        clf = sklearn.pipeline.Pipeline(steps=[('imputer',sklearn.preprocessing.Imputer()),  ('estimator',sklearn.tree.DecisionTreeClassifier())]) # build a sklearn classifier
+        for task_id in benchmark_suite.tasks:                          # iterate over all tasks
+            task = openml.tasks.get_task(task_id)                        # download the OpenML task
+            X, y = task.get_X_and_y()                                    # get the data (not used in this example)
+            openml.config.apikey = 'FILL_IN_OPENML_API_KEY'              # set the OpenML Api Key
+            run = openml.runs.run_model_on_task(task,clf)                # run classifier on splits (requires API key)
+            score = run.get_metric_fn(sklearn.metrics.accuracy_score) # print accuracy score
+            print('Data set: %s; Accuracy: %0.2f' % (task.get_dataset().name,score.mean()))
+            run.publish()                                                # publish the experiment on OpenML (optional)
+            print('URL for run: %s/run/%d' %(openml.config.server,run.run_id))
+        """
+        import openml
+        import sklearn.tree, sklearn.preprocessing
+        benchmark_suite = openml.study.get_study(
+            'OpenML100', 'tasks'
+        )  # obtain the benchmark suite
+        clf = sklearn.pipeline.Pipeline(
+            steps=[
+                ('imputer', sklearn.preprocessing.Imputer()),
+                ('estimator', sklearn.tree.DecisionTreeClassifier())
+            ]
+        )  # build a sklearn classifier
+        for task_id in benchmark_suite.tasks[:1]:  # iterate over all tasks
+            task = openml.tasks.get_task(task_id)  # download the OpenML task
+            X, y = task.get_X_and_y()  # get the data (not used in this example)
+            openml.config.apikey = openml.config.apikey  # set the OpenML Api Key
+            run = openml.runs.run_model_on_task(
+                task, clf,
+            )  # run classifier on splits (requires API key)
+            score = run.get_metric_fn(
+                sklearn.metrics.accuracy_score
+            )  # print accuracy score
+            print('Data set: %s; Accuracy: %0.2f' % (
+            task.get_dataset().name, score.mean()))
+            run.publish()  # publish the experiment on OpenML (optional)
+            print('URL for run: %s/run/%d' % (openml.config.server, run.run_id))