ts_lr = make_pipeline(Covariances(estimator='oas'),
TangentSpace(metric='riemann'), LR(C=1.0))
results = evaluation.process({'csp+lda': csp_lda, 'ts+lr': ts_lr})
print(results.head())
##############################################################################
# Electrode selection
# -------------------
#
# It is possible to select the electrodes that are shared by all datasets
# using the `find_intersecting_channels` function. Datasets that have 0
# overlap with others are discarded. It returns the set of common channels,
# as well as the list of datasets with valid channels.
electrodes, datasets = find_intersecting_channels(datasets)
evaluation = WithinSessionEvaluation(paradigm=paradigm,
datasets=datasets,
overwrite=True)
results = evaluation.process({'csp+lda': csp_lda, 'ts+lr': ts_lr})
print(results.head())
##############################################################################
# Plot results
# ------------
#
# Compare the obtained results with the two pipelines, CSP+LDA and logistic
# regression computed in the tangent space of the covariance matrices.
fig = moabb_plt.paired_plot(results, 'csp+lda', 'ts+lr')
plt.show()
# ---------------- # # Here we plot the results using some of the convenience methods within the # toolkit. The score_plot visualizes all the data with one score per subject # for every dataset and pipeline. fig = moabb_plt.score_plot(results) plt.show() ############################################################################### # For a comparison of two algorithms, there is the paired_plot, which plots # performance in one versus the performance in the other over all chosen # datasets. Note that there is only one score per subject, regardless of the # number of sessions. fig = moabb_plt.paired_plot(results, "CSP+LDA", "RG+LDA") plt.show() ############################################################################### # Statistical testing and further plots # ---------------------------------------- # # If the statistical significance of results is of interest, the method # compute_dataset_statistics allows one to show a meta-analysis style plot as # well. For an overview of how all algorithms perform in comparison with each # other, the method find_significant_differences and the summary_plot are # possible. stats = compute_dataset_statistics(results) P, T = find_significant_differences(stats)
LogisticRegression(penalty="l1", solver="liblinear"),
)
sk_eval = CrossSessionEvaluation(
paradigm=paradigm,
datasets=datasets,
suffix="examples",
overwrite=True,
)
sk_res = sk_eval.process(sk_ppl)
###############################################################################
# Combining results
# -----------------
#
# Even if the results have been obtained by different evaluation processes, it
# possible to combine the resulting dataframes to analyze and plot the results.
all_res = pd.concat([mne_res, adv_res, sk_res])
# We could compare the Euclidean and Riemannian performance using a `paired_plot`
paired_plot(all_res, "XDAWN LR", "RG LR")
# All the results could be compared and statistical analysis could highlight the
# differences between pipelines.
stats = compute_dataset_statistics(all_res)
P, T = find_significant_differences(stats)
summary_plot(P, T)
plt.show()
# ---------------- # # Here we plot the results using some of the convenience methods within the # toolkit. The score_plot visualizes all the data with one score per subject # for every dataset and pipeline. fig = moabb_plt.score_plot(results) plt.show() ############################################################################### # For a comparison of two algorithms, there is the paired_plot, which plots # performance in one versus the performance in the other over all chosen # datasets. Note that there is only one score per subject, regardless of the # number of sessions. fig = moabb_plt.paired_plot(results, 'CSP + LDA', 'RG + LDA') plt.show() ############################################################################### # Statistical testing and further plots # ---------------------------------------- # # If the statistical significance of results is of interest, the method # compute_dataset_statistics allows one to show a meta-analysis style plot as # well. For an overview of how all algorithms perform in comparison with each # other, the method find_significant_differences and the summary_plot are # possible. stats = compute_dataset_statistics(results) P, T = find_significant_differences(stats)
ts_lr = make_pipeline(Covariances(estimator="oas"),
TangentSpace(metric="riemann"), LR(C=1.0))
results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
print(results.head())
##############################################################################
# Electrode selection
# -------------------
#
# It is possible to select the electrodes that are shared by all datasets
# using the `find_intersecting_channels` function. Datasets that have 0
# overlap with others are discarded. It returns the set of common channels,
# as well as the list of datasets with valid channels.
electrodes, datasets = find_intersecting_channels(datasets)
evaluation = WithinSessionEvaluation(paradigm=paradigm,
datasets=datasets,
overwrite=True)
results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
print(results.head())
##############################################################################
# Plot results
# ------------
#
# Compare the obtained results with the two pipelines, CSP+LDA and logistic
# regression computed in the tangent space of the covariance matrices.
fig = moabb_plt.paired_plot(results, "csp+lda", "ts+lr")
plt.show()