bin_ratings#

bin_ratings(ratings=None, nbins=4, verbose=True, ignore_invalid=False)[source]#

Convert from continuous to discrete ratings.

Resample if quantiles are equal at high or low end to ensure proper assignment of binned confidence

Parameters:
ratingslist | np.ndarray

Ratings on a continuous scale.

nbinsint

The number of discrete ratings to resample. Default set to 4.

verbosebool

If True, warnings will be returned.

ignore_invalidbool

If False (default), an error will be raised in case of impossible discretisation of the confidence ratings. This is mostly due to identical values, and SDT values should not be extracted from the data. If True, the discretisation will process anyway. This option can be useful for plotting.

Returns:
discreteRatingsnp.ndarray

New rating array only containing integers between 1 and nbins.

outdict

Dictionary containing logs of the discretisation process:

  • 'confbins': list or 1d array-like - If the ratings were

    reampled, a list containing the new ratings and the new low or hg threshold, appened before or after the rating, respectively. Else, only returns the ratings.

  • 'rebin': boolean - If True, the ratings were resampled due to

    larger numbers of highs or low ratings.

  • 'binCount' : int - Number of bins

Warning

This function will automatically control for bias in high or low confidence ratings. If the first two or the last two quantiles have identical values, low or high confidence trials are excluded (respectively), and the function is run again on the remaining data.

Raises:
ValueError:

If the confidence ratings contains a lot of identical values and ignore_invalid is False.

Examples

>>> import numpy as np
>>> from cpm.utils.metad import bin_ratings
>>> ratings = np.array([
...     96, 98, 95, 90, 32, 58, 77,  6, 78, 78, 62, 60, 38, 12,
...     63, 18, 15, 13, 49, 26,  2, 38, 60, 23, 25, 39, 22, 33,
...     32, 27, 40, 13, 35, 16, 35, 73, 50,  3, 40, 0, 34, 47,
...     52,  0,  0,  0, 25,  1, 16, 37, 59, 20, 25, 23, 45, 22,
...     28, 62, 61, 69, 20, 75, 10, 18, 61, 27, 63, 22, 54, 30,
...     36, 66, 14,  2, 53, 58, 88, 23, 77, 54])
>>> bin_ratings(ratings)
(array([4, 4, 4, 4, 2, 3, 4, 1, 4, 4, 4, 4, 3, 1, 4, 1, 1, 1, 3, 2, 1, 3,
    4, 2, 2, 3, 2, 2, 2, 2, 3, 1, 3, 1, 3, 4, 3, 1, 3, 1, 2, 3, 3, 1,
    1, 1, 2, 1, 1, 3, 3, 2, 2, 2, 3, 2, 2, 4, 4, 4, 2, 4, 1, 1, 4, 2,
    4, 2, 3, 2, 3, 4, 1, 1, 3, 3, 4, 2, 4, 3]),
{'confBins': array([ 0., 20., 35., 60., 98.]), 'rebin': 0, 'binCount': 21})
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