Skip to content
GitLab
Projects
Groups
Snippets
/
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
Menu
Open sidebar
bob
bob.measure
Commits
91b44bd9
Commit
91b44bd9
authored
Mar 20, 2018
by
Theophile GENTILHOMME
Browse files
Fix doc and add guide description
parent
880d989b
Pipeline
#17771
failed with stage
in 20 minutes and 43 seconds
Changes
3
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
bob/measure/utils.py
View file @
91b44bd9
...
...
@@ -9,24 +9,20 @@ def confidence_for_indicator_variable(x, n, alpha=0.05):
The Clopper-Pearson interval method is used for estimating the confidence
intervals.
More info on confidence intervals
---------------------------------
https://en.wikipedia.org/wiki/Confidence_interval
https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Clopper-Pearson_interval
Parameters
----------
x : int
The number of successes.
n : int
The number of trials.
alpha : float, optional
alpha :
:obj:`
float
`
, optional
The 1-confidence value that you want. For example, alpha should be 0.05
to obtain 95% confidence intervals.
Returns
-------
(float, float) Returns a tuple of (lower_bound, upper_bound) which
(:obj:`float`, :obj:`float`)
a tuple of (lower_bound, upper_bound) which
shows the limit of your success rate: lower_bound < x/n < upper_bound
'''
lower_bound
=
scipy
.
stats
.
beta
.
ppf
(
alpha
/
2.0
,
x
,
n
-
x
+
1
)
...
...
doc/guide.rst
View file @
91b44bd9
...
...
@@ -205,6 +205,25 @@ Both functions require that at least one probe item exists, which has no accordi
>>> dir = bob.measure.detection_identification_rate(rr_scores, threshold = 0, rank=1)
>>> far = bob.measure.false_alarm_rate(rr_scores, threshold = 0)
Confidence interval
-------------------
A confidence interval for parameter `x` consists of a lower
estimate `L`, and an upper estimate `U`, such that the probability of the true value being
within the interval estimate is equal to `\alpha`. For example,
a 95% confidence interval (i.e. `\alpha = 0.95`) for a parameter `x` is given by `[L, U]` such that
`Prob(x∈[L,U]) = 95%`. The smaller the test size, the wider the confidence
interval will be, and the greater `alpha`, the smaller the confidence interval
will be.
`The Clopper-Pearson interval`_, a common method for calculating
confidence intervals, is function of the number of success, the number of trials
and confidence
value `\alpha` is used as :py:func:`bob.measure.utils.confidence_for_indicator_variable`.
It is based on the cumulative probabilities of the binomial distribution. This
method is quite conservative, meaning that the true coverage rate of a 95%
Clopper–Pearson interval may be well above 95%.
Plotting
--------
...
...
@@ -436,4 +455,4 @@ that best suits you.
.. _`The Expected Performance Curve`: http://publications.idiap.ch/downloads/reports/2005/bengio_2005_icml.pdf
.. _`The DET curve in assessment of detection task performance`: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.4489&rep=rep1&type=pdf
.. _
openbr
: http://
openbiometrics.org
.. _
`The Clopper-Pearson interval`
: http
s
://
en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Clopper-Pearson_interval
doc/py_api.rst
View file @
91b44bd9
...
...
@@ -64,6 +64,13 @@ Generic
bob.measure.rmse
bob.measure.get_config
Confidence interval
-------------------
.. autosummary::
bob.measure.utils.confidence_for_indicator_variable
Calibration
-----------
...
...
Write
Preview
Supports
Markdown
0%
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment