Implementation of NCH (Nearest Convex Hull) classifier (#253)

* Initial version of NearestConvexHull. * Added script for testing. * First version that runs. * Improved code. * Added support for parallel processing. It gives an error: AttributeError: Pipeline has none of the following attributes: decision_function. * renamed * New version that uses a new class that implements a NCH classifier. * small update * Updated to newest code - the new version of the distance function. Added an example that runs on a small number of test samples, so that we can get results quicker. * [pre-commit.ci] auto fixes from pre-commit.com hooks * reinforce constraint on weights * - remove constraints on weights - limite size of training set - change to slsqp optimizer * [pre-commit.ci] auto fixes from pre-commit.com hooks * Added n_max_hull parameter. MOABB support tested. * [pre-commit.ci] auto fixes from pre-commit.com hooks * added multiple hulls. * [pre-commit.ci] auto fixes from pre-commit.com hooks * Code cleanups. Added second parameter that specifies the number of hulls. * [pre-commit.ci] auto fixes from pre-commit.com hooks * Improved code. Added support for transform(). Added a new pipeline [NCH+LDA] * [pre-commit.ci] auto fixes from pre-commit.com hooks * updated default parameters * General improvements. Improvements requested by GC. * [pre-commit.ci] auto fixes from pre-commit.com hooks * removed commented code * Small adjustments. * Better class separation. * [pre-commit.ci] auto fixes from pre-commit.com hooks * Added support for n_samples_per_hull = -1 which takes all the samples for a class. * [pre-commit.ci] auto fixes from pre-commit.com hooks * Update pyriemann_qiskit/classification.py Set of SPD matrices. Co-authored-by: Quentin Barthélemy <[email protected]> * Update pyriemann_qiskit/classification.py Added new lines to before Parameters Co-authored-by: Quentin Barthélemy <[email protected]> * Update pyriemann_qiskit/classification.py [y == c, :, :] => [y == c] Co-authored-by: Quentin Barthélemy <[email protected]> * Update pyriemann_qiskit/classification.py NearestConvexHull text change Co-authored-by: Quentin Barthélemy <[email protected]> * Improvements proposed by Quentin. * [pre-commit.ci] auto fixes from pre-commit.com hooks * Added comment for the optimizer. * [pre-commit.ci] auto fixes from pre-commit.com hooks * Added some comments in classification. Changes about the global optimizer so, that it is more evident that a global one is used. * [pre-commit.ci] auto fixes from pre-commit.com hooks * Implemented min hull. Added support for both "min-hull" and "random-hull" using the constructor parameter "hull-type". * [pre-commit.ci] auto fixes from pre-commit.com hooks * Reverted to previous version as requested by Gregoire. * fix lint issues * [pre-commit.ci] auto fixes from pre-commit.com hooks --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: gcattan <[email protected]> Co-authored-by: Gregoire Cattan <[email protected]> Co-authored-by: Quentin Barthélemy <[email protected]>
pyRiemann · Mar 18, 2024 · a0241ce · a0241ce
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diff --git a/examples/ERP/classify_P300_nch.py b/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 sklearn.pipeline import make_pipeline
+from matplotlib import pyplot as plt
+import warnings
+import seaborn as sns
+from moabb import set_log_level
+from moabb.datasets import bi2013a
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import P300
+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+RANDOM_HULL"] = 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_per_class=1,
+        n_samples_per_hull=3,
+        n_jobs=12,
+        hull_type="random-hull",
+        quantum=False,
+    ),
+)
+
+pipelines["NCH+MIN_HULL"] = 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_per_class=1,
+        n_samples_per_hull=3,
+        n_jobs=12,
+        hull_type="min-hull",
+        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()