fix/quantum-art-example

doc/api.rst

@@ -132,8 +132,8 @@ Datasets
     :toctree: generated/
 
     get_mne_sample
-    get_linearly_separable_dataset
-    get_qiskit_dataset
+    generate_linearly_separable_dataset
+    generate_qiskit_dataset
     get_feature_dimension
     MockDataset
 

examples/toys_dataset/plot_classifier_comparison.py

@@ -19,7 +19,10 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.datasets import make_moons, make_circles
 from sklearn.svm import SVC
-from pyriemann_qiskit.datasets import get_linearly_separable_dataset, get_qiskit_dataset
+from pyriemann_qiskit.datasets import (
+    generate_linearly_separable_dataset,
+    generate_qiskit_dataset,
+)
 from pyriemann_qiskit.classification import QuanticSVM
 
 # uncomment to run comparison with QuanticVQC (disabled for CI/CD)
@@ -51,8 +54,8 @@
 datasets = [
     make_moons(noise=0.3, random_state=0),
     make_circles(noise=0.2, factor=0.5, random_state=1),
-    get_linearly_separable_dataset(),
-    get_qiskit_dataset(),
+    generate_linearly_separable_dataset(),
+    generate_qiskit_dataset(),
 ]
 
 figure = plt.figure(figsize=(15, 9))

examples/toys_dataset/plot_quantum_art_vqc.py

@@ -13,7 +13,7 @@
 
 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.datasets import generate_linearly_separable_dataset
 from pyriemann_qiskit.classification import QuanticVQC
 from pyriemann_qiskit.visualization import weights_spiral
 
@@ -25,9 +25,9 @@
 # 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.
+# - 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.
 
 # Let's start by defining some plot area.
 fig, axes = plt.subplots(2, 2)
@@ -42,7 +42,7 @@
         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)
+        X, y = generate_linearly_separable_dataset(n_samples=n_samples)
 
         # Compute and display weight variability after training
         axe = axes[i, j]

pyriemann_qiskit/datasets/__init__.py

@@ -1,16 +1,16 @@
 from .utils import (
     get_mne_sample,
-    get_linearly_separable_dataset,
-    get_qiskit_dataset,
+    generate_linearly_separable_dataset,
+    generate_qiskit_dataset,
     get_feature_dimension,
     MockDataset,
 )
 
 
 __all__ = [
     "get_mne_sample",
-    "get_linearly_separable_dataset",
-    "get_qiskit_dataset",
+    "generate_linearly_separable_dataset",
+    "generate_qiskit_dataset",
     "get_feature_dimension",
     "MockDataset",
 ]

pyriemann_qiskit/datasets/utils.py

@@ -98,14 +98,15 @@ def get_mne_sample(n_trials=10, include_auditory=False):
     return X, y
 
 
-def get_qiskit_dataset(n_samples=30):
+def generate_qiskit_dataset(n_samples=30):
     """Return qiskit dataset.
 
     Notes
     -----
     .. versionadded:: 0.0.1
     .. versionchanged:: 0.1.0
         Added `n_samples` parameter.
+        Rename from `get_qiskit_dataset` to `generate_qiskit_dataset`.
 
     Parameters
     ----------
@@ -132,14 +133,16 @@ def get_qiskit_dataset(n_samples=30):
     return (X, y)
 
 
-def get_linearly_separable_dataset(n_samples=100):
+def generate_linearly_separable_dataset(n_samples=100):
     """Return a linearly separable dataset.
 
     Notes
     -----
     .. versionadded:: 0.0.1
     .. versionchanged:: 0.1.0
         Added `n_samples` parameter.
+        Rename from `get_linearly_separable_dataset` to
+        `generate_linearly_separable_dataset`.
 
     Parameters
     ----------