Merge branch 'main' into update-readme

.readthedocs.yaml

@@ -18,3 +18,6 @@ sphinx:
 python:
   install:
     - requirements: doc/requirements.txt
+    # Install our python package before building the docs
+    - method: pip
+      path: .

Dockerfile

@@ -14,7 +14,7 @@ RUN apt-get -y install build-essential
 
 RUN pip install urllib3==1.26.12
 RUN pip install "numpy<1.24"
-RUN pip install qiskit-terra==0.25.2.1
+RUN pip install qiskit-terra==0.45.0
 RUN python setup.py develop
 RUN pip install .[docs]
 RUN pip install .[tests]
@@ -24,10 +24,10 @@ RUN mkdir /root/mne_data
 RUN mkdir /home/mne_data
 
 ## Workaround for firestore
-RUN pip install protobuf==4.24.4
+RUN pip install protobuf==4.25.0
 RUN pip install google_cloud_firestore==2.13.1
 ### Missing __init__ file in protobuf
-RUN touch /usr/local/lib/python3.9/site-packages/protobuf-4.24.4-py3.9.egg/google/__init__.py
+RUN touch /usr/local/lib/python3.9/site-packages/protobuf-4.25.0-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-2.13.1-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-2.13.1-py3.9.egg/google/cloud/firestore_v1/services/firestore/transports/base.py'

doc/requirements.txt

@@ -1,20 +1,20 @@
-numpy<1.24
+numpy<1.27
 sphinx-gallery
 sphinx-bootstrap_theme
 numpydoc
 cython
 mne[data]>=0.24
-seaborn
+seaborn>=0.12.1
 scikit-learn
 joblib
 pandas
-cvxpy==1.3.2
+cvxpy==1.4.1
 qiskit_machine_learning==0.6.1
-qiskit-ibm-provider==0.7.0
+qiskit-ibm-provider==0.7.2
 qiskit-optimization==0.5.0
-qiskit-aer==0.12.2
+qiskit-aer==0.13.0
 scipy==1.11.3
-moabb>=0.4.6
+moabb>=1.0.0
 pyriemann==0.5
 docplex>=2.21.207
 firebase_admin==6.2.0

ecosystem.json

@@ -0,0 +1,4 @@
+{
+  "dependencies_files": ["requirements.txt"],
+  "extra_dependencies": ["pytest", "seaborn", "flake8", "mne", "pooch"]
+}

pyriemann_qiskit/classification.py

@@ -4,32 +4,34 @@
 in several modes quantum/classical and simulated/real
 quantum computer.
 """
-import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.svm import SVC
-from qiskit_ibm_provider import IBMProvider, least_busy
-from qiskit.utils import QuantumInstance, algorithm_globals
-from qiskit.utils.quantum_instance import logger
-from qiskit_machine_learning.algorithms import QSVC, VQC, PegasosQSVC
-from qiskit_machine_learning.kernels.quantum_kernel import QuantumKernel
 from datetime import datetime
 import logging
-from .utils.hyper_params_factory import gen_zz_feature_map, gen_two_local, get_spsa
-from .utils import get_provider, get_devices, get_simulator
+import numpy as np
+
 from pyriemann.classification import MDM
 from pyriemann_qiskit.datasets import get_feature_dimension
 from pyriemann_qiskit.utils import (
     ClassicalOptimizer,
     NaiveQAOAOptimizer,
     set_global_optimizer,
 )
+from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit.utils.quantum_instance import logger
+from qiskit_ibm_provider import IBMProvider, least_busy
+from qiskit_machine_learning.algorithms import QSVC, VQC, PegasosQSVC
+from qiskit_machine_learning.kernels.quantum_kernel import QuantumKernel
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.svm import SVC
+
+from .utils.hyper_params_factory import gen_zz_feature_map, gen_two_local, get_spsa
+from .utils import get_provider, get_devices, get_simulator
 
 logger.level = logging.INFO
 
 
 class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
 
-    """Quantum classification.
+    """Quantum classifier
 
     This class implements a scikit-learn wrapper around Qiskit library [1]_.
     It provides a mean to run classification tasks on a local and
@@ -49,12 +51,12 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
           (depending on q_account_token value),
         - If false, will perform classical computing instead.
     q_account_token : string (default:None)
-        If quantum==True and q_account_token provided,
+        If `quantum` is True and `q_account_token` provided,
         the classification task will be running on a IBM quantum backend.
         If `load_account` is provided, the classifier will use the previous
         token saved with `IBMProvider.save_account()`.
     verbose : bool (default:True)
-        If true will output all intermediate results and logs.
+        If true, will output all intermediate results and logs.
     shots : int (default:1024)
         Number of repetitions of each circuit, for sampling.
     gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
@@ -76,6 +78,7 @@ class QuanticClassifierBase(BaseEstimator, ClassifierMixin):
     --------
     QuanticSVM
     QuanticVQC
+    QuanticMDM
 
     References
     ----------
@@ -234,9 +237,10 @@ def _predict(self, X):
 
 class QuanticSVM(QuanticClassifierBase):
 
-    """Quantum-enhanced SVM classification.
+    """Quantum-enhanced SVM classifier
 
-    This class implements SVC [1]_ on a quantum machine [2]_.
+    This class implements a support-vector machine (SVM) classifier [1]_,
+    called SVC, on a quantum machine [2]_.
     Note that if `quantum` parameter is set to `False`
     then a classical SVC will be perfomed instead.
 
@@ -270,12 +274,12 @@ class QuanticSVM(QuanticClassifierBase):
           (depending on q_account_token value),
         - If false, will perform classical computing instead.
     q_account_token : string (default:None)
-        If quantum==True and q_account_token provided,
+        If `quantum` is True and `q_account_token` provided,
         the classification task will be running on a IBM quantum backend.
         If `load_account` is provided, the classifier will use the previous
         token saved with `IBMProvider.save_account()`.
     verbose : bool (default:True)
-        If true will output all intermediate results and logs.
+        If true, will output all intermediate results and logs.
     shots : int (default:1024)
         Number of repetitions of each circuit, for sampling.
     gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
@@ -401,8 +405,9 @@ def predict(self, X):
 
 class QuanticVQC(QuanticClassifierBase):
 
-    """Variational Quantum Classifier
+    """Variational quantum classifier
 
+    This class implements a variational quantum classifier (VQC).
     Note that there is no classical version of this algorithm.
     This will always run on a quantum computer (simulated or not).
 
@@ -419,12 +424,12 @@ class QuanticVQC(QuanticClassifierBase):
           (depending on q_account_token value).
         - If false, will perform classical computing instead.
     q_account_token : string (default:None)
-        If quantum==True and q_account_token provided,
+        If `quantum` is True and `q_account_token` provided,
         the classification task will be running on a IBM quantum backend.
         If `load_account` is provided, the classifier will use the previous
         token saved with `IBMProvider.save_account()`.
     verbose : bool (default:True)
-        If true will output all intermediate results and logs
+        If true, will output all intermediate results and logs
     shots : int (default:1024)
         Number of repetitions of each circuit, for sampling
     gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
@@ -552,11 +557,11 @@ def parameter_count(self):
 
 class QuanticMDM(QuanticClassifierBase):
 
-    """Quantum-enhanced MDM
+    """Quantum-enhanced MDM classifier
 
-    This class is a convex implementation of the MDM [1]_,
-    that can runs with quantum optimization.
-    Only log-euclidian distance between trial and class prototypes is supported
+    This class is a convex implementation of the Minimum Distance to Mean (MDM)
+    [1]_, which can run with quantum optimization.
+    Only log-Euclidean distance between trial and class prototypes is supported
     at the moment, but any type of metric can be used for centroid estimation.
 
     Notes
@@ -577,16 +582,17 @@ class QuanticMDM(QuanticClassifierBase):
         distance in order to keep the good sensitivity for the classification.
     quantum : bool (default: True)
         Only applies if `metric` contains a convex distance or mean.
+
         - If true will run on local or remote backend
           (depending on q_account_token value),
         - If false, will perform classical computing instead.
     q_account_token : string (default:None)
-        If quantum==True and q_account_token provided,
+        If `quantum` is True and `q_account_token` provided,
         the classification task will be running on a IBM quantum backend.
         If `load_account` is provided, the classifier will use the previous
         token saved with `IBMProvider.save_account()`.
     verbose : bool (default:True)
-        If true will output all intermediate results and logs.
+        If true, will output all intermediate results and logs.
     shots : int (default:1024)
         Number of repetitions of each circuit, for sampling.
     gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \

pyriemann_qiskit/datasets/utils.py

@@ -14,16 +14,16 @@
 
 
 def get_mne_sample(n_trials=10, include_auditory=False):
-    """Return sample data from the mne dataset.
+    """Return sample data from the MNE dataset.
 
-    ```
+    ``
     In this experiment, checkerboard patterns were presented to the subject
     into the left and right visual field, interspersed by tones to the
     left or right ear. The interval between the stimuli was 750 ms.
     Occasionally a smiley face was presented at the center of the visual field.
     The subject was asked to press a key with the right index finger
     as soon as possible after the appearance of the face.
-    ``` [1]_
+    `` [1]_
 
     The samples returned by this method are epochs of duration 1s.
     Only visual left and right trials are selected.
@@ -35,8 +35,7 @@ def get_mne_sample(n_trials=10, include_auditory=False):
         Number of trials to return.
         If -1, then all trials are returned.
     include_auditory : boolean (default:False)
-        If True, it returns also the auditory stimulation
-        in the MNE dataset.
+        If True, it returns also the auditory stimulation in the MNE dataset.
 
     Returns
     -------
@@ -236,19 +235,12 @@ class MockDataset:
     Attributes
     ----------
     code_ : str
-        The code of the dataset.
-        The code of the dataset is also the string representation
+        The code of the dataset, which is also the string representation
         of the dataset.
-    data_ : Dict
-        A dictionnary representing the dataset. Ex:
-        ```
-        {
-            subject1: (samples1, labels1),
-            subject2: (samples2, labels2),
-            ...
-        }
-        ```
-    subjects_ : List[int]
+    data_ : dict
+        A dictionnary representing the dataset, e.g.:
+        ``{subject1: (samples1, labels1), subject2: (samples2, labels2), ...}``
+    subjects_ : list[int]
         The subjects of the dataset.
 
     Notes

pyriemann_qiskit/pipelines.py

@@ -49,7 +49,7 @@ def _log(self, trace):
             print("[" + self.code + "] ", trace)
 
     def fit(self, X, y):
-        """Train the riemann quantum classifier.
+        """Train the Riemannian quantum classifier.
 
         Parameters
         ----------
@@ -70,6 +70,7 @@ def fit(self, X, y):
 
     def score(self, X, y):
         """Return the accuracy.
+
         You might want to use a different metric by using sklearn
         cross_val_score
 
@@ -139,7 +140,7 @@ def transform(self, X):
 
 class QuantumClassifierWithDefaultRiemannianPipeline(BasePipeline):
 
-    """Default pipeline with Riemann Geometry and a quantum classifier.
+    """Default pipeline with Riemannian geometry and a quantum classifier.
 
     Projects the data into the tangent space of the Riemannian manifold
     and applies quantum classification.
@@ -310,12 +311,12 @@ class QuantumMDMWithRiemannianPipeline(BasePipeline):
           (depending on q_account_token value),
         - If false, will perform classical computing instead.
     q_account_token : string (default:None)
-        If quantum==True and q_account_token provided,
+        If `quantum` is True and `q_account_token` provided,
         the classification task will be running on a IBM quantum backend.
         If `load_account` is provided, the classifier will use the previous
         token saved with `IBMProvider.save_account()`.
     verbose : bool (default:True)
-        If true will output all intermediate results and logs.
+        If true, will output all intermediate results and logs.
     shots : int (default:1024)
         Number of repetitions of each circuit, for sampling.
     gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \
@@ -405,12 +406,12 @@ class QuantumMDMVotingClassifier(BasePipeline):
           (depending on q_account_token value),
         - If false, will perform classical computing instead.
     q_account_token : string (default:None)
-        If quantum==True and q_account_token provided,
+        If `quantum` is True and `q_account_token` provided,
         the classification task will be running on a IBM quantum backend.
         If `load_account` is provided, the classifier will use the previous
         token saved with `IBMProvider.save_account()`.
     verbose : bool (default:True)
-        If true will output all intermediate results and logs.
+        If true, will output all intermediate results and logs.
     shots : int (default:1024)
         Number of repetitions of each circuit, for sampling.
     gen_feature_map : Callable[int, QuantumCircuit | FeatureMap] \