adding test_pipeline to see flow and ease debugging

test_pipeline.py

@@ -0,0 +1,31 @@
+from datasets.simulated import Dataset
+from objective import Objective
+from solvers.python_gd import Solver
+
+# In general the test requires having installed the dependencies before
+# For the original template having benchopt installed is sufficient
+def test_pipeline():
+    # We add manually attributes of dataset, objective, solver for the test
+
+    dataset = Dataset()
+    dataset.n_samples = 1000
+    dataset.n_features = 500
+    dataset.random_state = 27
+    dataset_params = dataset.get_data()
+    objective = Objective()
+    objective.whiten_y = True
+    objective.set_data(**dataset_params)
+    objective_params = objective.get_objective()
+
+    solver = Solver()
+    solver.scale_step = 1.
+    solver.set_objective(**objective_params)
+    solver.run(n_iter=10)
+    results = solver.get_result()
+
+    metrics = objective.compute(results)
+    assert 'value' in metrics.keys()
+    print('Pipeline functional')
+
+if __name__ == '__main__':
+    test_pipeline()