Implementation of NCH (Nearest Convex Hull) classifier

examples/ERP/classify_P300_nch.py

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+"""
+====================================================================
+Classification of P300 datasets from MOABB using NCH
+====================================================================
+
+Demonstrates classification with QunatumNCH.
+Evaluation is done using MOABB.
+
+If parameter "shots" is None then a classical SVM is used similar to the one
+in scikit learn.
+If "shots" is not None and IBM Qunatum token is provided with "q_account_token"
+then a real Quantum computer will be used.
+You also need to adjust the "n_components" in the PCA procedure to the number
+of qubits supported by the real quantum computer you are going to use.
+A list of real quantum  computers is available in your IBM quantum account.
+
+"""
+# Author: Anton Andreev
+# Modified from plot_classify_EEG_tangentspace.py of pyRiemann
+# License: BSD (3-clause)
+
+from pyriemann.estimation import XdawnCovariances
+from pyriemann.tangentspace import TangentSpace
+from sklearn.pipeline import make_pipeline
+from matplotlib import pyplot as plt
+import warnings
+import seaborn as sns
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from moabb import set_log_level
+from moabb.datasets import bi2012, BNCI2014009, bi2013a
+from moabb.evaluations import WithinSessionEvaluation, CrossSubjectEvaluation
+from moabb.paradigms import P300
+from pyriemann_qiskit.pipelines import (
+    QuantumClassifierWithDefaultRiemannianPipeline,
+)
+from sklearn.decomposition import PCA
+from pyriemann_qiskit.classification import QuanticNCH
+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")
+
+##############################################################################
+# Create Pipelines
+# ----------------
+#
+# Pipelines must be a dict of sklearn pipeline transformer.
+
+##############################################################################
+# We have to do this because the classes are called 'Target' and 'NonTarget'
+# but the evaluation function uses a LabelEncoder, transforming them
+# to 0 and 1
+labels_dict = {"Target": 1, "NonTarget": 0}
+
+paradigm = P300(resample=128)
+
+datasets = [bi2013a()]  # MOABB provides several other P300 datasets
+
+# reduce the number of subjects, the Quantum pipeline takes a lot of time
+# if executed on the entire dataset
+n_subjects = 1
+for dataset in datasets:
+    dataset.subject_list = dataset.subject_list[0:n_subjects]
+
+overwrite = True  # set to True if we want to overwrite cached results
+
+pipelines = {}
+
+pipelines["NCH"] = make_pipeline(
+    # applies XDawn and calculates the covariance matrix, output it matrices
+    XdawnCovariances(
+        nfilter=3,
+        classes=[labels_dict["Target"]],
+        estimator="lwf",
+        xdawn_estimator="scm",
+    ),
+    QuanticNCH(n_hulls=3, n_samples_per_hull=15, n_jobs=12, quantum=False),
+)
+
+# this is a non quantum pipeline
+pipelines["XD+MDM"] = make_pipeline(
+    XdawnCovariances(
+        nfilter=3,
+        classes=[labels_dict["Target"]],
+        estimator="lwf",
+        xdawn_estimator="scm",
+    ),
+    MDM(),
+)
+
+print("Total pipelines to evaluate: ", len(pipelines))
+
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=datasets, suffix="examples", overwrite=overwrite
+)
+
+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 the 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")
+
+ax.set_ylabel("ROC AUC")
+ax.set_ylim(0.3, 1)
+
+plt.show()