Example with Motor Imagery (#177)

* push example with alexMI

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

* Update Dockerfile

* Update classify_alexmi_with_quantum_pipeline.py

---------

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

examples/MI/classify_alexmi_with_quantum_pipeline.py

@@ -0,0 +1,122 @@
+"""
+====================================================================
+Classification of MI datasets from MOABB using MDM
+and quantum-enhanced MDM
+====================================================================
+
+This example demonstrates how to use quantum pipeline on a MI dataset.
+
+pip install moabb==0.5.0
+
+"""
+# Author: Gregoire Cattan
+# Modified from ERP/classify_P300_bi_quantum_mdm.py
+# License: BSD (3-clause)
+
+from matplotlib import pyplot as plt
+import warnings
+import seaborn as sns
+from moabb import set_log_level
+from moabb.datasets import AlexMI
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import MotorImagery
+
+# inject convex distance and mean to pyriemann (if not done already)
+from pyriemann_qiskit.utils import distance, mean  # noqa
+from pyriemann_qiskit.pipelines import (
+    QuantumMDMWithRiemannianPipeline,
+)
+
+from sklearn.pipeline import make_pipeline
+from pyriemann.estimation import ERPCovariances
+from pyriemann.classification import MDM
+
+print(__doc__)
+
+##############################################################################
+# getting rid of the warnings about the future
+warnings.simplefilter(action="ignore", category=FutureWarning)
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+
+warnings.filterwarnings("ignore")
+
+set_log_level("info")
+
+##############################################################################
+# Initialization
+# ----------------
+#
+# 1) Create paradigm
+# 2) Load datasets
+
+paradigm = MotorImagery(events=["feet", "right_hand"], n_classes=2)
+
+datasets = [AlexMI()]
+
+# reduce the number of subjects
+n_subjects = 2
+title = "Datasets: "
+for dataset in datasets:
+    title = title + " " + dataset.code
+    dataset.subject_list = dataset.subject_list[0:n_subjects]
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+
+pipelines = {}
+
+# Will run QAOA under the hood
+pipelines["mean=logeuclid/distance=convex"] = QuantumMDMWithRiemannianPipeline(
+    convex_metric="distance", quantum=True
+)
+
+# Classical baseline for evaluation
+pipelines["R-MDM"] = make_pipeline(ERPCovariances(estimator="lwf"), MDM())
+
+##############################################################################
+# Run evaluation
+# ----------------
+#
+# Compare the pipelines using a within session evaluation.
+
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm,
+    datasets=datasets,
+    overwrite=True,
+)
+
+results = evaluation.process(pipelines)
+
+print("Averaging the session performance:")
+print(results.groupby("pipeline").mean("score")[["score", "time"]])
+
+
+# ##############################################################################
+# # Plot Results
+# # ----------------
+# #
+# # Here we plot the results to compare two pipelines
+
+fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])
+
+sns.stripplot(
+    data=results,
+    y="score",
+    x="pipeline",
+    ax=ax,
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sns.pointplot(data=results, y="score", x="pipeline", ax=ax, palette="Set1").set(
+    title=title
+)
+
+ax.set_ylabel("ROC AUC")
+ax.set_ylim(0.3, 1)
+
+plt.show()