Create light_benchmark.py

.github/workflows/light_benchmark.yml

@@ -0,0 +1,39 @@
+name: Light Benchmark
+
+on:
+  push:
+    paths:
+    - 'pyriemann_qiskit/**'
+    - 'examples/**'
+    - '.github/workflows/light_benchmark.yml'
+  pull_request:
+    paths:
+    - 'pyriemann_qiskit/**'
+    - 'examples/**'
+    - '.github/workflows/light_benchmark.yml'
+
+jobs:
+  light_benchmark:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Cache dependencies
+        uses: actions/cache@v3
+        with:
+          path: ~/.cache/pip
+          key: light_benchmark.yml
+      - name: Install dependencies
+        run: |
+          pip install .[docs]
+      - name: Run benchmark script (PR)
+        run: |
+          python benchmarks/light_benchmark.py
+      - uses: actions/checkout@v4
+        with:
+          ref: 'main'
+      - name: Install dependencies
+        run: |
+          pip install .[docs]
+      - name: Run benchmark script (main)
+        run: |
+          python benchmarks/light_benchmark.py

Dockerfile

@@ -25,15 +25,15 @@ RUN mkdir /home/mne_data
 
 ## Workaround for firestore
 RUN pip install protobuf==4.25.1
-RUN pip install google_cloud_firestore==2.14.0rc1
+RUN pip install google_cloud_firestore==2.14.0
 ### Missing __init__ file in protobuf
 RUN touch /usr/local/lib/python3.9/site-packages/protobuf-4.25.1-py3.9-linux-x86_64.egg/google/__init__.py
 ## google.cloud.location is never used in these files, and is missing in path.
-RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0rc1-py3.9.egg/google/cloud/firestore_v1/services/firestore/client.py'
-RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0rc1-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/base.py'
-RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0rc1-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/grpc.py'
-RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0rc1-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/grpc_asyncio.py'
-RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0rc1-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/rest.py'
-RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0rc1-py3.9.egg/google/cloud/firestore_v1/services/firestore/async_client.py'
+RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/client.py'
+RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/base.py'
+RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/grpc.py'
+RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/grpc_asyncio.py'
+RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/rest.py'
+RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.9/site-packages/google_cloud_firestore-2.14.0-py3.9.egg/google/cloud/firestore_v1/services/firestore/async_client.py'
 
 ENTRYPOINT [ "python", "/examples/ERP/classify_P300_bi.py" ]

benchmarks/light_benchmark.py

@@ -0,0 +1,89 @@
+"""
+====================================================================
+Light Benchmark
+====================================================================
+
+This benchmark is a non-regression performance test, intended
+to run on Ci with each PRs.
+
+"""
+# Author: Gregoire Cattan
+# Modified from plot_classify_P300_bi.py of pyRiemann
+# License: BSD (3-clause)
+
+from pyriemann.estimation import XdawnCovariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.pipeline import make_pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import balanced_accuracy_score
+from sklearn.preprocessing import LabelEncoder
+import warnings
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from moabb import set_log_level
+from moabb.datasets import bi2012
+from moabb.paradigms import P300
+from pyriemann_qiskit.pipelines import (
+    QuantumClassifierWithDefaultRiemannianPipeline,
+)
+from sklearn.decomposition import PCA
+
+print(__doc__)
+
+##############################################################################
+# getting rid of the warnings about the future
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+
+warnings.filterwarnings("ignore")
+
+set_log_level("info")
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+
+##############################################################################
+
+paradigm = P300(resample=128)
+
+dataset = bi2012()  # MOABB provides several other P300 datasets
+
+X, y, _ = paradigm.get_data(dataset, subjects=[1])
+
+y = LabelEncoder().fit_transform(y)
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.33, random_state=42
+)
+
+pipelines = {}
+
+pipelines["RG+QuantumSVM"] = QuantumClassifierWithDefaultRiemannianPipeline(
+    shots=512,
+    nfilter=2,
+    dim_red=PCA(n_components=5),
+)
+
+pipelines["RG+LDA"] = make_pipeline(
+    XdawnCovariances(
+        nfilter=2,
+        estimator="lwf",
+        xdawn_estimator="scm",
+    ),
+    TangentSpace(),
+    PCA(n_components=10),
+    LDA(solver="lsqr", shrinkage="auto"),
+)
+
+scores = {}
+
+for key, pipeline in pipelines.items():
+    pipeline.fit(X_train, y_train)
+    y_pred = pipeline.predict(X_test)
+    score = balanced_accuracy_score(y_test, y_pred)
+    scores[key] = score
+
+
+print("Scores: ", scores)