pre-commit.ci formatting (#144)

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^(.github/|.devcontainer/|doc/_static/|pyriemann_qiskit/_version.py|setup.py)

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* pre-commit-config.yaml

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remove isort

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

---------

Co-authored-by: Gregoire Cattan <gregoire.cattan@ibm.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/testing.yml

@@ -41,4 +41,3 @@ jobs:
         FIREBASE_CERTIFICATE: ${{ secrets.FIREBASE_CERTIFICATE }}
       run: |
         pytest
-

.pre-commit-config.yaml

@@ -25,23 +25,17 @@ repos:
     rev: 23.3.0
     hooks:
       - id: black
-        exclude: ^(.github/|.devcontainer/|doc/_static/|_version.py|setup.py)
-
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        exclude: ^(.github/|.devcontainer/|doc/_static/|_version.py|setup.py)
+        exclude: ^(.github/|.devcontainer/|doc/_static/|doc/conf.py|pyriemann_qiskit/_version.py|setup.py)
 
   - repo: https://github.com/pycqa/flake8
     rev: 6.0.0
     hooks:
       - id: flake8
-        exclude: ^(.github/|.devcontainer/|doc/_static/|_version.py|setup.py)
+        exclude: ^(.github/|.devcontainer/|doc/_static/|doc/conf.py|pyriemann_qiskit/_version.py|setup.py)
 
   - repo: https://github.com/pre-commit/mirrors-prettier
     rev: v3.0.0-alpha.6
     hooks:
       - id: prettier
         args: [--print-width=90, --prose-wrap=always]
-        exclude: ^(.github/|.devcontainer/|doc/_static/|_version.py|setup.py)
+        exclude: ^(.github/|.devcontainer/|doc/_static/|doc/conf.py|pyriemann_qiskit/_version.py|setup.py)

doc/api.rst

@@ -137,4 +137,3 @@ Quantum Provider
     get_provider
     get_devices
     get_simulator
-

doc/installing.rst

@@ -3,7 +3,7 @@
 Installing pyRiemann-qiskit
 ===========================
 
-There is no yet stable version of pyRiemann-qiskit. 
+There is no yet stable version of pyRiemann-qiskit.
 
 Therefore, it is recommanded to clone the source code on `github <https://github.com/pyRiemann/pyRiemann-qiskit>`__ and install directly the package from source.
 

doc/introduction.rst

@@ -19,7 +19,7 @@ Qiskit implements a quantum version of support vector
 and varitional quantum classifier (VQC) [4]_. These classifiers likely offer
 an advantage over classical SVM in situations where the classification task
 is complex. Task complexity is raised by the encoding of the data into a
-quantum state, the number of available data and the quality of the data. An initial 
+quantum state, the number of available data and the quality of the data. An initial
 study is available in [5]_, and it can be downloaded from `here
 <https://github.com/pyRiemann/pyRiemann-qiskit/blob/main/doc/Presentations/QuantumERPClassification.pdf>`_.
 Although there is no study on this topic at the time of writting,
@@ -44,8 +44,8 @@ Quantum drawbacks
 
 - Time complexity
 
-    A higher number of trials or dimension increases time to completion of the quantum algorithm, especially when running on a local machine. This is why the number of trials is limited in the examples we provided. However, you should avoid such practices in your own analysis. 
-    
+    A higher number of trials or dimension increases time to completion of the quantum algorithm, especially when running on a local machine. This is why the number of trials is limited in the examples we provided. However, you should avoid such practices in your own analysis.
+
     Although these aspects are less important in a remote backend, it may happen that the quantum algorithm is queued depending on the number of concurrent users.
 
     For all these aspects, the use of pyRiemann-qiskit should be limited to offline analysis only.

examples/ERP/classify_P300_bi.py

@@ -33,8 +33,9 @@
 from moabb.datasets import bi2012
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import P300
-from pyriemann_qiskit.classification import \
-    QuantumClassifierWithDefaultRiemannianPipeline
+from pyriemann_qiskit.classification import (
+    QuantumClassifierWithDefaultRiemannianPipeline,
+)
 from sklearn.decomposition import PCA
 
 print(__doc__)
@@ -84,7 +85,7 @@
     # On a real Quantum computer (n_components = qubits)
     dim_red=PCA(n_components=5),
     # params={'q_account_token': '<IBM Quantum TOKEN>'}
-    )
+)
 
 # Here we provide a pipeline for comparison:
 
@@ -97,7 +98,7 @@
         nfilter=2,
         classes=[labels_dict["Target"]],
         estimator="lwf",
-        xdawn_estimator="scm"
+        xdawn_estimator="scm",
     ),
     TangentSpace(),
     PCA(n_components=10),
@@ -107,16 +108,13 @@
 print("Total pipelines to evaluate: ", len(pipelines))
 
 evaluation = WithinSessionEvaluation(
-    paradigm=paradigm,
-    datasets=datasets,
-    suffix="examples",
-    overwrite=overwrite
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
 )
 
 results = evaluation.process(pipelines)
 
 print("Averaging the session performance:")
-print(results.groupby('pipeline').mean('score')[['score', 'time']])
+print(results.groupby("pipeline").mean("score")[["score", "time"]])
 
 ##############################################################################
 # Plot Results
@@ -136,11 +134,7 @@
     zorder=1,
     palette="Set1",
 )
-sns.pointplot(data=results,
-              y="score",
-              x="pipeline",
-              ax=ax, zorder=1,
-              palette="Set1")
+sns.pointplot(data=results, y="score", x="pipeline", ax=ax, zorder=1, palette="Set1")
 
 ax.set_ylabel("ROC AUC")
 ax.set_ylim(0.3, 1)

examples/ERP/compare_dim_red.py

@@ -20,8 +20,9 @@
 # License: BSD (3-clause)
 
 from pyriemann_qiskit.datasets import get_mne_sample
-from pyriemann_qiskit.classification import \
-    QuantumClassifierWithDefaultRiemannianPipeline
+from pyriemann_qiskit.classification import (
+    QuantumClassifierWithDefaultRiemannianPipeline,
+)
 from pyriemann_qiskit.utils.filtering import NaiveDimRed
 from sklearn.decomposition import PCA
 from sklearn.model_selection import StratifiedKFold, GridSearchCV
@@ -41,7 +42,7 @@
     # This defines how we entangle the data into a quantum state
     # the more complex is the kernel, the more outcomes we can expect from
     # a quantum vs classical classifier.
-    "feature_entanglement": ['linear'],  # ['linear', 'sca', 'full'],
+    "feature_entanglement": ["linear"],  # ['linear', 'sca', 'full'],
     # This parameter change the depth of the circuit when entangling data.
     # There is a trade-off between accuracy and noise when the depth of the
     # circuit increases.
@@ -55,7 +56,7 @@
     # especially when using a simulated quantum machine.
     # A quantum simulator is also limited to only 24qbits
     # (and so is the size of the feature).
-    "dim_red": [PCA(n_components=10), NaiveDimRed()]
+    "dim_red": [PCA(n_components=10), NaiveDimRed()],
 }
 
 pipe = QuantumClassifierWithDefaultRiemannianPipeline()
@@ -64,14 +65,20 @@
 def customize(custom_params: dict):
     new_params = {}
     for key in default_params:
-        new_params[key] = custom_params[key] if key in custom_params \
-            else default_params[key]
+        new_params[key] = (
+            custom_params[key] if key in custom_params else default_params[key]
+        )
     return new_params
 
 
 def search(params: dict):
-    grid = GridSearchCV(pipe, params, scoring='balanced_accuracy',
-                        n_jobs=-1, cv=StratifiedKFold(n_splits=2))
+    grid = GridSearchCV(
+        pipe,
+        params,
+        scoring="balanced_accuracy",
+        n_jobs=-1,
+        cv=StratifiedKFold(n_splits=2),
+    )
     grid.fit(X, y)
     return grid.best_params_
 
@@ -84,13 +91,8 @@ def analyze_multiple(l_params: list[dict]):
     print(best_params)
 
 
-SVC = {
-    "shots": [None]
-}
+SVC = {"shots": [None]}
 QSVC = {}
-VQC = {
-    "spsa_trials": [40],
-    "two_local_reps": [2]
-}
+VQC = {"spsa_trials": [40], "two_local_reps": [2]}
 
 analyze_multiple([SVC, QSVC, VQC])

examples/ERP/firebase_moabb.py

@@ -25,11 +25,11 @@
 from pyriemann.estimation import XdawnCovariances
 from pyriemann.tangentspace import TangentSpace
 from pyriemann_qiskit.utils import (
-        generate_caches,
-        filter_subjects_by_incomplete_results,
-        add_moabb_dataframe_results_to_caches,
-        convert_caches_to_dataframes
-    )
+    generate_caches,
+    filter_subjects_by_incomplete_results,
+    add_moabb_dataframe_results_to_caches,
+    convert_caches_to_dataframes,
+)
 from sklearn.pipeline import make_pipeline
 from matplotlib import pyplot as plt
 import warnings
@@ -39,8 +39,9 @@
 from moabb.datasets import bi2012
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import P300
-from pyriemann_qiskit.classification import \
-    QuantumClassifierWithDefaultRiemannianPipeline
+from pyriemann_qiskit.classification import (
+    QuantumClassifierWithDefaultRiemannianPipeline,
+)
 from sklearn.decomposition import PCA
 
 print(__doc__)
@@ -92,7 +93,7 @@
     # On a real Quantum computer (n_components = qubits)
     dim_red=PCA(n_components=5),
     # params={'q_account_token': '<IBM Quantum TOKEN>'}
-    )
+)
 
 # Here we provide a pipeline for comparison:
 
@@ -105,7 +106,7 @@
         nfilter=2,
         classes=[labels_dict["Target"]],
         estimator="lwf",
-        xdawn_estimator="scm"
+        xdawn_estimator="scm",
     ),
     TangentSpace(),
     PCA(n_components=10),
@@ -122,28 +123,24 @@
 filter_subjects_by_incomplete_results(caches, copy_datasets, pipelines)
 
 print("Total pipelines to evaluate: ", len(pipelines))
-print("Subjects to evaluate",
-      sum([len(dataset.subject_list) for dataset in copy_datasets]))
+print(
+    "Subjects to evaluate",
+    sum([len(dataset.subject_list) for dataset in copy_datasets]),
+)
 evaluation = WithinSessionEvaluation(
-    paradigm=paradigm,
-    datasets=copy_datasets,
-    suffix="examples",
-    overwrite=overwrite
+    paradigm=paradigm, datasets=copy_datasets, suffix="examples", overwrite=overwrite
 )
 
 try:
     results = evaluation.process(pipelines)
-    add_moabb_dataframe_results_to_caches(results,
-                                          copy_datasets,
-                                          pipelines,
-                                          caches)
+    add_moabb_dataframe_results_to_caches(results, copy_datasets, pipelines, caches)
 except ValueError:
     print("No subjects left to evaluate.")
 
 df = convert_caches_to_dataframes(caches, datasets, pipelines)
 
 print("Averaging the session performance:")
-print(df.groupby('pipeline').mean('score')[['score', 'time']])
+print(df.groupby("pipeline").mean("score")[["score", "time"]])
 
 ##############################################################################
 # Plot Results
@@ -164,11 +161,7 @@
     palette="Set1",
 )
 
-sns.pointplot(data=df,
-              y="score",
-              x="pipeline",
-              ax=ax,
-              palette="Set1")
+sns.pointplot(data=df, y="score", x="pipeline", ax=ax, palette="Set1")
 
 ax.set_ylabel("ROC AUC")
 ax.set_ylim(0.3, 1)

examples/ERP/plot_classify_EEG_quantum.py

@@ -20,8 +20,11 @@
 from pyriemann_qiskit.datasets import get_mne_sample
 from sklearn.pipeline import make_pipeline
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import (confusion_matrix, ConfusionMatrixDisplay,
-                             balanced_accuracy_score)
+from sklearn.metrics import (
+    confusion_matrix,
+    ConfusionMatrixDisplay,
+    balanced_accuracy_score,
+)
 from matplotlib import pyplot as plt
 
 
@@ -31,8 +34,7 @@
 X, y = get_mne_sample(n_trials=-1)
 
 # ...skipping the KFold validation parts (for the purpose of the test only)
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.3, random_state=1)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
 
 ###############################################################################
 # Decoding in tangent space with a quantum classifier
@@ -53,13 +55,12 @@
 
 
 # https://stackoverflow.com/questions/61825227/plotting-multiple-confusion-matrix-side-by-side
-f, axes = plt.subplots(1, 2, sharey='row')
+f, axes = plt.subplots(1, 2, sharey="row")
 
 disp = None
 
 # Results will be computed for QuanticSVM versus SKLearnSVM for comparison
 for quantum in [True, False]:
-
     qsvm = QuanticSVM(verbose=True, quantum=quantum)
     clf = make_pipeline(sf, tg, dim_red, qsvm)
     clf.fit(X_train, y_train)
@@ -77,12 +78,12 @@
     disp.plot(ax=axe, xticks_rotation=45)
     disp.ax_.set_title(title)
     disp.im_.colorbar.remove()
-    disp.ax_.set_xlabel('')
+    disp.ax_.set_xlabel("")
     if not quantum:
-        disp.ax_.set_ylabel('')
+        disp.ax_.set_ylabel("")
 
 if disp:
-    f.text(0.4, 0.1, 'Predicted label', ha='left')
+    f.text(0.4, 0.1, "Predicted label", ha="left")
     plt.subplots_adjust(wspace=0.40, hspace=0.1)
     f.colorbar(disp.im_, ax=axes)
     plt.show()