yasa.EpochByEpochAgreement.get_confusion_matrix#

EpochByEpochAgreement.get_confusion_matrix(sleep_id=None, agg_func=None, **kwargs)[source]#

Return a ref_hyp / obs_hyp confusion matrix from either a single session or all sessions concatenated together.

Parameters:
sleep_idNone or a valid sleep ID

If None (default), cross-tabulation is derived from the entire group dataset. If a valid sleep ID, cross-tabulation is derived using only the reference and observed scored hypnograms from that sleep session.

agg_funcNone or str

If None (default), group results returns a DataFrame complete with all individual session results. If not None, group results returns a DataFrame aggregated across sessions where agg_func is passed as func parameter in pandas.DataFrame.groupby.agg. For example, set agg_func="sum" to get a single confusion matrix across all epochs that does not take session into account.

**kwargskey, value pairs

Additional keyword arguments are passed to sklearn.metrics.confusion_matrix.

Returns:
conf_matrpandas.DataFrame

A confusion matrix with stages from the reference scorer as indices and stages from the observed scorer as columns.

Examples

>>> import yasa
>>> ref_hyps = [yasa.simulate_hypnogram(tib=90, scorer="Rater1", seed=i) for i in range(3)]
>>> obs_hyps = [h.simulate_similar(scorer="Rater2", seed=i) for i, h in enumerate(ref_hyps)]
>>> ebe = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
>>> ebe.get_confusion_matrix(sleep_id=2)
Rater2  WAKE  N1  N2  N3  REM
Rater1
WAKE       1   2  23   0    0
N1         0   9  13   0    0
N2         0   6  71   0    0
N3         0  13  42   0    0
REM        0   0   0   0    0

>>> ebe.get_confusion_matrix()
Rater2           WAKE  N1  N2  N3  REM
sleep_id Rater1
1        WAKE      30   0   3   0   35
         N1         3   2   7   0    0
         N2        21  12   7   0    4
         N3         0   0   0   0    0
         REM        2   8  29   0   17
2        WAKE       1   2  23   0    0
         N1         0   9  13   0    0
         N2         0   6  71   0    0
         N3         0  13  42   0    0
         REM        0   0   0   0    0
3        WAKE      16   0   7  19   19
         N1         0   7   2   0    5
         N2         0  10  12   7    5
         N3         0   0  16  11    0
         REM        0  15  11  18    0

>>> ebe.get_confusion_matrix(agg_func="sum")
Rater2  WAKE  N1  N2  N3  REM
Rater1
WAKE      47   2  33  19   54
N1         3  18  22   0    5
N2        21  28  90   7    9
N3         0  13  58  11    0
REM        2  23  40  18   17
On this page