yasa.EpochByEpochAgreement.summary

EpochByEpochAgreement.summary(by_stage=False, **kwargs)

Return group-level agreement scores.

Parameters:

by_stagebool

If False (default), summary will include agreement scores derived from average-based metrics. If True, returned summary DataFrame will include agreement scores for each sleep stage, derived from one-vs-rest metrics.

**kwargskey, value pairs

Additional keyword arguments are passed to pandas.DataFrame.groupby.agg. This can be used to customize the descriptive statistics returned.

Returns:

summarypandas.DataFrame

A pandas.DataFrame summarizing agreement scores across the entire dataset with descriptive statistics. Each row is an agreement metric and each column is a descriptive statistic (e.g., mean, standard deviation).

Examples

Call get_agreement (or get_agreement_bystage if by_stage=True) before calling summary:

>>> import yasa
>>> ref_hyps = [yasa.simulate_hypnogram(tib=600, scorer="Human", seed=i) for i in range(5)]
>>> obs_hyps = [h.simulate_similar(scorer="YASA", seed=i) for i, h in enumerate(ref_hyps)]
>>> ebe = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
>>> _ = ebe.get_agreement()
>>> ebe.summary()
                     mad      mean       std       min    median       max
metric
accuracy      0.053000  0.285833  0.062686  0.214167  0.305833  0.350833
balanced_acc  0.041301  0.241583  0.058478  0.168548  0.238700  0.326814
kappa         0.054327  0.044791  0.073235 -0.057258  0.064022  0.140052
mcc           0.054725  0.045146  0.073966 -0.058031  0.064533  0.141520
precision     0.062393  0.282627  0.073269  0.202928  0.306433  0.349311
recall        0.053000  0.285833  0.062686  0.214167  0.305833  0.350833
f1            0.058863  0.279704  0.068751  0.205014  0.305590  0.345510

To control the descriptive statistics included as columns:

>>> ebe.summary(func=["count", "mean", "sem"])
              count      mean       sem
metric
accuracy        5.0  0.285833  0.028034
balanced_acc    5.0  0.241583  0.026152
kappa           5.0  0.044791  0.032752
mcc             5.0  0.045146  0.033078
precision       5.0  0.282627  0.032767
recall          5.0  0.285833  0.028034
f1              5.0  0.279704  0.030747