pyRiemann-qiskit v0.1.0

Dockerfile

@@ -13,6 +13,8 @@ RUN apt-get -y install --fix-missing git-core
 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==0.25.1
 RUN python setup.py develop
 RUN pip install .[docs]
 RUN pip install .[tests]

README.md

@@ -103,6 +103,16 @@ package on your local machine using the `setup.py` script:
 python setup.py develop
 ```
 
+Or directly pip:
+
+```
+pip install .
+```
+
+Note that the steps above need to be re-executed in your local environment after any
+changes inside your local copy of the `pyriemann_qiskit` folder, including pulling from
+remote.
+
 To check the installation, open a python shell and type:
 
 ```
@@ -163,24 +173,24 @@ They are pushed to the docker registry on each release.
 
 ## Contributor Guidelines
 
-Everyone is welcomed to contribute to this repository. There are two types of
+Everyone is welcome to contribute to this repository. There are two types of
 contributions:
 
 - [Raise an issue](https://github.com/pyRiemann/pyRiemann-qiskit/issues/new) on the
   repository. Issues can be either a bug report or an enhancement proposal. Note that it
-  is necessary to register on GitHub before. There is no special template which is
-  expected but, if you raise a defect please provide as much details as possible.
+  is necessary to register on GitHub before. There is no special template that is expected
+  but, if you raise a defect please provide as many details as possible.
 
 - [Raise a pull request](https://github.com/pyRiemann/pyRiemann-qiskit/compare). Fork the
   repository and work on your own branch. Then raise a pull request with your branch
   against master. As much as possible, we ask you to:
   - avoid merging master into your branch. Always prefer git rebase.
-  - always provide full documentation of public method.
+  - always provide full documentation of public methods.
 
 Code contribution (pull request) can be either on core functionalities, documentation or
 automation.
 
-- The core functionalies are based on `Python`,
+- The core functionalities are based on `Python`,
   [pyRiemann](https://github.com/pyRiemann/pyRiemann),
   [Qiskit ML](https://github.com/Qiskit/qiskit-machine-learning) and follow the best
   practice from [scikit-learn](https://scikit-learn.org/stable/index.html). We use
@@ -209,9 +219,9 @@ automation.
   pytest tests/test_classification.py
   ```
 
-  Workflows are automatically triggered when you push a commit. However, the worflow for
+  Workflows are automatically triggered when you push a commit. However, the workflow for
   example execution is only triggered when you modify one of the examples or the
-  documentation as the execution take a lot of time. You can enable
+  documentation as the execution takes a lot of time. You can enable
   [Github Actions](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/enabling-features-for-your-repository/managing-github-actions-settings-for-a-repository)
   in your fork to see the result of the CI pipeline. Results are also indicated at the end
   of your pull request when raised. However note, that workflows in the pull request need

doc/index.rst

@@ -29,7 +29,7 @@ pyRiemann-qiskit: Qiskit wrapper for pyRiemann
       <div class="row">
        <a href="auto_examples/ERP/plot_classify_EEG_quantum_svm.html">
          <div class="col-md-3 thumbnail">
-           <img src="_images/sphx_glr_plot_classify_EEG_quantum_thumb.png">
+           <img src="_images/sphx_glr_plot_classify_EEG_quantum_svm_thumb.png">
          </div>
        </a>
        <a href="auto_examples/ERP/plot_classify_P300_bi.html">

examples/ERP/README.txt

@@ -1,4 +1,4 @@
 Classification of ERP
 ---------------------
 
-Event related potential classification with quantum algorithm and Riemannian geometry.
+Event related potential classification with quantum algorithms and Riemannian geometry.

examples/ERP/classify_P300_bi_quantum_mdm.py

@@ -3,7 +3,7 @@
 Classification of P300 datasets from MOABB using Quantum MDM
 ====================================================================
 
-The mean and the distance in MDM algorithm are formualted as
+The mean and the distance in MDM algorithm are formulated as
 optimization problems. These optimization problems are translated
 to Qiskit using Docplex and additional glue code. These optimizations
 are enabled when we use convex mean or convex distance. This is set
@@ -13,6 +13,7 @@
 
 If you want to use GPU, you need to use qiskit-aer-gpu that will replace
 qiskit-aer. It is only available on Linux.
+
 pip install qiskit-aer-gpu
 
 pip install moabb==0.5.0

examples/MI/README.txt

@@ -0,0 +1,4 @@
+Classification of MI
+---------------------
+
+Motor imagery classification with quantum algorithms and Riemannian geometry.

examples/other_datasets/README.txt

@@ -0,0 +1,4 @@
+Classification on other datasets
+--------------------------------
+
+Contains examples with other datasets, like the titanic dataset.

examples/toys_dataset/plot_quantum_art_vqc.py

@@ -24,7 +24,8 @@
 # In this example we will display weights variability of the parameter inside
 # the variational quantum circuit which is used by VQC.
 #
-# The idea is simple :
+# The idea is simple:
+#
 # - We initialize a VQC with different number of parameters and number of samples.
 # - We train the VQC a couple of times and we store the fitted weights.
 # - We compute variability of the weight and display it in a fashionable way.

pyriemann_qiskit/_version.py

@@ -1 +1 @@
-__version__ = '0.0.4'
+__version__ = '0.1.0'

pyriemann_qiskit/classification.py

@@ -244,7 +244,7 @@ class QuanticSVM(QuanticClassifierBase):
     -----
     .. versionadded:: 0.0.1
     .. versionchanged:: 0.0.2
-       Qiskit's Pegasos implementation [4, 5]_.
+       Qiskit's Pegasos implementation [4]_, [5]_.
     .. versionchanged:: 0.1.0
        Fix: copy estimator not keeping base class parameters.
 
@@ -408,14 +408,14 @@ class QuanticVQC(QuanticClassifierBase):
     ----------
     optimizer : Optimizer (default:SPSA)
         The classical optimizer to use.
-        See [3] for details.
+        See [3]_ for details.
     gen_var_form : Callable[int, QuantumCircuit | VariationalForm] \
                    (default: Callable[int, TwoLocal])
         Function generating a variational form instance.
     quantum : bool (default: True)
         - If true will run on local or remote backend
-        (depending on q_account_token value).
-        - If false, will perform classical computing instead
+          (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,
         the classification task will be running on a IBM quantum backend.
@@ -552,11 +552,10 @@ class QuanticMDM(QuanticClassifierBase):
 
     """Quantum-enhanced MDM
 
-    # 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 at the moment, but any type of metric
-    # can be used for centroid estimation.
+    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
+    at the moment, but any type of metric can be used for centroid estimation.
 
     Notes
     -----
@@ -577,7 +576,7 @@ class QuanticMDM(QuanticClassifierBase):
     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).
+          (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,

pyriemann_qiskit/utils/hyper_params_factory.py

@@ -139,7 +139,7 @@ def gen_two_local(reps=3, rotation_blocks=["ry", "rz"], entanglement_blocks="cz"
 
 def get_spsa(max_trials=40, c=(None, None, None, None, 4.0)):
     """Return an instance of SPSA.
-    SPSA [1, 2]_ is an algorithmic method for optimizing systems
+    SPSA [1]_, [2]_ is an algorithmic method for optimizing systems
     with multiple unknown parameters.
     For more details, see [3]_ and [4]_.