[EHN] Improve pipeline test coverage

moabb/datasets/braininvaders.py

@@ -284,7 +284,7 @@ def _bi_data_path(  # noqa: C901
                 zip_ref = z.ZipFile(path_zip, "r")
                 zip_ref.extractall(path_folder)
                 os.makedirs(osp.join(directory, f"Session{i + 1}"))
-                shutil.copy_tree(path_zip.strip(".zip"), directory)
+                shutil.copytree(path_zip.strip(".zip"), directory)
                 shutil.rmtree(path_zip.strip(".zip"))
 
         # filter the data regarding the experimental conditions

moabb/tests/test_benchmark.py

@@ -23,28 +23,30 @@ def test_benchmark_strdataset(self):
             pipelines=str(self.pp_dir),
             evaluations=["WithinSession"],
             include_datasets=[
-                "FakeDataset-imagery-10-2--60-60--120-120--lefthand-righthand--c3-cz-c4",
-                "FakeDataset-p300-10-2--60-60--120-120--target-nontarget--c3-cz-c4",
-                "FakeDataset-ssvep-10-2--60-60--120-120--13-15--c3-cz-c4",
-                "FakeDataset-cvep-10-2--60-60--120-120--10-00--c3-cz-c4",
+                "FakeDataset-imagery-2-2--60-60--120-120--lefthand-righthand--c3-cz-c4",
+                "FakeDataset-p300-2-2--60-60--120-120--target-nontarget--c3-cz-c4",
+                "FakeDataset-ssvep-2-2--60-60--120-120--13-15--c3-cz-c4",
+                "FakeDataset-cvep-2-2--60-60--120-120--10-00--c3-cz-c4",
             ],
             overwrite=True,
         )
-        assert len(res) == 80
+        assert len(res) == 24
 
     def test_benchmark_objdataset(self):
         res = benchmark(
             pipelines=str(self.pp_dir),
             evaluations=["WithinSession"],
             include_datasets=[
-                FakeDataset(["left_hand", "right_hand"], paradigm="imagery"),
-                FakeDataset(["Target", "NonTarget"], paradigm="p300"),
-                FakeDataset(["13", "15"], paradigm="ssvep"),
-                FakeDataset(["1.0", "0.0"], paradigm="cvep"),
+                FakeDataset(
+                    ["left_hand", "right_hand"], paradigm="imagery", n_subjects=2
+                ),
+                FakeDataset(["Target", "NonTarget"], paradigm="p300", n_subjects=2),
+                FakeDataset(["13", "15"], paradigm="ssvep", n_subjects=2),
+                FakeDataset(["1.0", "0.0"], paradigm="cvep", n_subjects=2),
             ],
             overwrite=True,
         )
-        assert len(res) == 80
+        assert len(res) == 24
 
     def test_nodataset(self):
         with pytest.raises(ValueError):
@@ -61,7 +63,7 @@ def test_selectparadigm(self):
             paradigms=["FakeImageryParadigm"],
             overwrite=True,
         )
-        assert len(res) == 40
+        assert len(res) == 16
 
     def test_include_exclude(self):
         with pytest.raises(AttributeError):

moabb/tests/test_pipelines/CSP_SVM_grid.yml

@@ -0,0 +1,31 @@
+name: CSP + SVM Grid
+
+paradigms:
+  - FakeImageryParadigm
+
+pipeline:
+  - name: Covariances
+    from: pyriemann.estimation
+    parameters:
+      estimator: oas
+
+  - name: CSP
+    from: pyriemann.spatialfilters
+    parameters:
+      nfilter: 6
+
+  - name: SVC
+    from: sklearn.svm
+    parameters:
+      kernel: "linear"
+
+param_grid:
+  csp__nfilter:
+    - 2
+    - 4
+    - 6
+  svc__C:
+    - 1
+  svc__kernel:
+    - "rbf"
+    - "linear"

moabb/tests/test_pipelines/WTRCSP.py

@@ -0,0 +1,11 @@
+from pyriemann.estimation import Covariances
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.pipeline import make_pipeline
+
+from moabb.pipelines.csp import TRCSP
+
+
+pipe = make_pipeline(Covariances("scm"), TRCSP(nfilter=6), LinearDiscriminantAnalysis())
+
+# this is what will be loaded
+PIPELINE = {"name": "TRCSP + LDA", "paradigms": ["FakeImageryParadigm"], "pipeline": pipe}

moabb/tests/test_pipelines/XdawnCov_MDM.yml

@@ -0,0 +1,14 @@
+name: XdawnCovariances + MDM
+
+paradigms:
+  - FakeP300Paradigm
+
+pipeline:
+  - name: XdawnCovariances
+    from: pyriemann.estimation
+    parameters:
+      estimator: oas
+      xdawn_estimator: scm
+
+  - name: MDM
+    from: pyriemann.classification