feat/Quantum art visualization

Dockerfile

@@ -22,16 +22,16 @@ RUN mkdir /root/mne_data
 RUN mkdir /home/mne_data
 
 ## Workaround for firestore
-RUN pip install protobuf==4.24.2
-RUN pip install google_cloud_firestore==2.11.1
+RUN pip install protobuf==4.24.3
+RUN pip install google_cloud_firestore==2.12.0
 ### Missing __init__ file in protobuf
-RUN touch /usr/local/lib/python3.8/site-packages/protobuf-4.24.2-py3.8-linux-x86_64.egg/google/__init__.py
+RUN touch /usr/local/lib/python3.8/site-packages/protobuf-4.24.3-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.11.1-py3.8.egg/google/cloud/firestore_v1/services/firestore/client.py'
-RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.8/site-packages/google_cloud_firestore-2.11.1-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.11.1-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.11.1-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.11.1-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.11.1-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.12.0-py3.8.egg/google/cloud/firestore_v1/services/firestore/client.py'
+RUN sed -i 's/from google.cloud.location import locations_pb2//g' '/usr/local/lib/python3.8/site-packages/google_cloud_firestore-2.12.0-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.12.0-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.12.0-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.12.0-py3.8.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/python3.8/site-packages/google_cloud_firestore-2.12.0-py3.8.egg/google/cloud/firestore_v1/services/firestore/async_client.py'
 
 ENTRYPOINT [ "python", "/examples/ERP/classify_P300_bi.py" ]

examples/toys_dataset/plot_quantum_art_vqc.py

@@ -0,0 +1,65 @@
+"""
+====================================================================
+Art visualization of Variational Quantum Classifier.
+====================================================================
+
+Display the variability of the weights inside the variational quantum
+classifier.
+
+"""
+
+# Author: Gregoire Cattan
+# License: BSD (3-clause)
+
+from pyriemann_qiskit.utils.hyper_params_factory import gen_two_local
+import matplotlib.pyplot as plt
+from pyriemann_qiskit.datasets import get_linearly_separable_dataset
+from pyriemann_qiskit.classification import QuanticVQC
+from pyriemann_qiskit.visualization import weights_spiral
+
+
+print(__doc__)
+
+###############################################################################
+# 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 :
+# - We initialize a VQC with different number of parameters and number of samples
+# - We train the VQC a couple of time and we store the fitted weights.
+# - We compute variability of the weight and display it in a fashion way.
+
+# Let's start by defining some plot area.
+fig, axes = plt.subplots(2, 2)
+fig.suptitle("VQC weights variability")
+
+# We will compute weight variability for different number of samples
+for i, n_samples in enumerate([2, 20]):
+    # ... and for differente number of parameters.
+    # (n_reps controls the number of parameters inside the circuit)
+    for j, n_reps in enumerate([1, 3]):
+        # instanciate VQC.
+        vqc = QuanticVQC(gen_var_form=gen_two_local(reps=n_reps))
+
+        # Get data. We will use a toy dataset here.
+        X, y = get_linearly_separable_dataset(n_samples=n_samples)
+
+        # Compute and display weight variability after training
+        axe = axes[i, j]
+        # ... This is all done in this method
+        # It displays a spiral. Each "branch of the spiral" is a parameter inside VQC.
+        # The larger is the branch, the higher is the parameter variability.
+        weights_spiral(axe, vqc, X, y, n_trainings=5)
+        n_params = vqc.parameter_count
+
+        # Just improve display of the graphics.
+        if j == 0:
+            axe.set_ylabel(f"n_samples: {n_samples}")
+        if i == 0:
+            axe.set_xlabel(f"n_params: {n_params}")
+        axe.xaxis.set_label_position("top")
+        axe.set_xticks(())
+        axe.set_yticks(())
+
+plt.tight_layout()
+plt.show()

pyriemann_qiskit/classification.py

@@ -529,6 +529,24 @@ def predict(self, X):
         labels = self._predict(X)
         return self._map_indices_to_classes(labels)
 
+    @property
+    def parameter_count(self):
+        """Returns the number of parameters inside the variational circuit.
+        This is determined by the `gen_var_form` attribute of this instance.
+
+        Returns
+        -------
+        n_params : int
+            The number of parameters in the variational circuit.
+            Returns 0 if the instance is not fit yet.
+        """
+
+        if hasattr(self, "_classifier"):
+            return len(self._classifier.ansatz.parameters)
+
+        self._log("Instance not initialized. Parameter count is 0.")
+        return 0
+
 
 class QuanticMDM(QuanticClassifierBase):
 

pyriemann_qiskit/datasets/utils.py

@@ -98,12 +98,19 @@ def get_mne_sample(n_trials=10, include_auditory=False):
     return X, y
 
 
-def get_qiskit_dataset():
+def get_qiskit_dataset(n_samples=30):
     """Return qiskit dataset.
 
     Notes
     -----
     .. versionadded:: 0.0.1
+    .. versionchanged:: 0.1.0
+        Added `n_samples` parameter.
+
+    Parameters
+    ----------
+    n_samples : int (default: 30)
+        Number of trials to return.
 
     Returns
     -------
@@ -117,20 +124,27 @@ def get_qiskit_dataset():
 
     feature_dim = 2
     X, _, _, _ = ad_hoc_data(
-        training_size=30, test_size=0, n=feature_dim, gap=0.3, plot_data=False
+        training_size=n_samples, test_size=0, n=feature_dim, gap=0.3, plot_data=False
     )
 
-    y = np.concatenate(([0] * 30, [1] * 30))
+    y = np.concatenate(([0] * n_samples, [1] * n_samples))
 
     return (X, y)
 
 
-def get_linearly_separable_dataset():
+def get_linearly_separable_dataset(n_samples=100):
     """Return a linearly separable dataset.
 
     Notes
     -----
     .. versionadded:: 0.0.1
+    .. versionchanged:: 0.1.0
+        Added `n_samples` parameter.
+
+    Parameters
+    ----------
+    n_samples : int (default: 100)
+        Number of trials to return.
 
     Returns
     -------
@@ -142,6 +156,7 @@ def get_linearly_separable_dataset():
 
     """
     X, y = make_classification(
+        n_samples=n_samples,
         n_features=2,
         n_redundant=0,
         n_informative=2,

pyriemann_qiskit/visualization/__init__.py

@@ -0,0 +1,3 @@
+from .art import weights_spiral
+
+__all__ = ["weights_spiral"]

pyriemann_qiskit/visualization/art.py

@@ -0,0 +1,74 @@
+import pandas as pd
+import numpy as np
+
+
+def weights_spiral(axe, vqc, X, y, n_trainings=5):
+    """Artistic representation of vqc training.
+
+    Display a spiral. Each "branch" of the spiral corresponds to a parameter inside VQC.
+    When the branch is "large" it means that the weight of the parameter varies a lot
+    between different trainings.
+
+    Notes
+    -----
+    .. versionadded:: 0.1.0
+
+    Parameters
+    ----------
+    axe: Axe
+        Pointer to the matplotlib plot or subplot.
+    vqc: QuanticVQC
+        The instance of VQC to evaluate.
+    X: ndarray, shape (n_samples, n_features)
+        Input vector, where `n_samples` is the number of samples and
+        `n_features` is the number of features.
+    y: ndarray, shape (n_samples,)
+        Predicted target vector relative to X.
+    n_trainings: int (default: 5)
+        Number of trainings to run, in order to evaluate the variability of the
+        parameters' weights.
+
+    Returns
+    -------
+    X: ndarray, shape (n_samples, n_features)
+        Input vector, where `n_samples` is the number of samples and
+        `n_features` is the number of features.
+    y: ndarray, shape (n_samples,)
+        Predicted target vector relative to X.
+
+    """
+
+    weights = []
+
+    for i in range(5):
+        vqc.fit(X, y)
+        train_weights = vqc._classifier.weights
+        weights.append(train_weights)
+
+    df = pd.DataFrame(weights)
+
+    theta = np.arange(0, 8 * np.pi, 0.1)
+    a = 1
+    b = 0.2
+
+    n_params = len(df.columns)
+
+    max_var = df.var().max()
+
+    # https://matplotlib.org/3.1.1/gallery/misc/fill_spiral.html
+    for i in range(n_params):
+        dt = 2 * np.pi / n_params * i
+        x = a * np.cos(theta + dt) * np.exp(b * theta)
+        y = a * np.sin(theta + dt) * np.exp(b * theta)
+
+        var = df[i].var()
+
+        dt = dt + (var / max_var) * np.pi / 4.0
+
+        x2 = a * np.cos(theta + dt) * np.exp(b * theta)
+        y2 = a * np.sin(theta + dt) * np.exp(b * theta)
+
+        xf = np.concatenate((x, x2[::-1]))
+        yf = np.concatenate((y, y2[::-1]))
+
+        axe.fill(xf, yf)