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* Create light_benchmark.py * push skeleton of light_benchmark.py * [pre-commit.ci] auto fixes from pre-commit.com hooks * use pip to install dependencies * use paradigm to get data * fix bug with loop+dictionnary * fix label encoding * run benchmarl on main too * syntax error * missing `with` * little clean up * fix dockerfile --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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| name: Light Benchmark | ||
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| on: | ||
| push: | ||
| paths: | ||
| - 'pyriemann_qiskit/**' | ||
| - 'examples/**' | ||
| - '.github/workflows/light_benchmark.yml' | ||
| pull_request: | ||
| paths: | ||
| - 'pyriemann_qiskit/**' | ||
| - 'examples/**' | ||
| - '.github/workflows/light_benchmark.yml' | ||
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| 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 |
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| """ | ||
| ==================================================================== | ||
| 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) | ||
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| 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 | ||
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| print(__doc__) | ||
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| ############################################################################## | ||
| # getting rid of the warnings about the future | ||
| warnings.simplefilter(action="ignore", category=FutureWarning) | ||
| warnings.simplefilter(action="ignore", category=RuntimeWarning) | ||
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| warnings.filterwarnings("ignore") | ||
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| set_log_level("info") | ||
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| ############################################################################## | ||
| # Create Pipelines | ||
| # ---------------- | ||
| # | ||
| # Pipelines must be a dict of sklearn pipeline transformer. | ||
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| ############################################################################## | ||
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| paradigm = P300(resample=128) | ||
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| dataset = bi2012() # MOABB provides several other P300 datasets | ||
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| X, y, _ = paradigm.get_data(dataset, subjects=[1]) | ||
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| y = LabelEncoder().fit_transform(y) | ||
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| X_train, X_test, y_train, y_test = train_test_split( | ||
| X, y, test_size=0.33, random_state=42 | ||
| ) | ||
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| pipelines = {} | ||
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| pipelines["RG+QuantumSVM"] = QuantumClassifierWithDefaultRiemannianPipeline( | ||
| shots=512, | ||
| nfilter=2, | ||
| dim_red=PCA(n_components=5), | ||
| ) | ||
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| pipelines["RG+LDA"] = make_pipeline( | ||
| XdawnCovariances( | ||
| nfilter=2, | ||
| estimator="lwf", | ||
| xdawn_estimator="scm", | ||
| ), | ||
| TangentSpace(), | ||
| PCA(n_components=10), | ||
| LDA(solver="lsqr", shrinkage="auto"), | ||
| ) | ||
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| scores = {} | ||
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| 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 | ||
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| print("Scores: ", scores) |