[Biopython-dev] [Biopython (old issues only) - Bug #2693] LogisticRegression convergence criterion is too lenient

redmine at redmine.open-bio.org redmine at redmine.open-bio.org
Wed Nov 9 14:33:27 UTC 2016

Issue #2693 has been updated by Vincent Davis.

Description updated
Assignee changed from Biopython Dev Mailing List to Vincent Davis

Bug #2693: LogisticRegression convergence criterion is too lenient

* Author: Bruce Southey
* Status: New
* Priority: Normal
* Assignee: Vincent Davis
* Category: Main Distribution
* Target version: Not Applicable
* URL: 
In R and SAS, the example in the code and tutorial provides the following parameters:

Intercept =  18.9622
x1        =  -0.0714
x2        =   0.0444

By default, Bio/LogisticRegression.py defines the following parameters

The convergence threshold is too lenient so the iterations terminate before the expected values are obtained. Using more stringent criteria (CONVERGE_THRESHOLD = 0.000000001) permits convergence to the R/SAS values provided MAX_ITERATIONS is greater than 7761 with my system.

MAX_ITERATIONS and CONVERGE_THRESHOLD are fixed within Bio/LogisticRegression.py module but should be part of the API for the train function such as:
def train(xs, ys, update_fn=None, typecode=None, CONVERGE_THRESHOLD = 0.000000001, MAX_ITERATIONS=10000):

Note the algorithm used requires a large number of iterations and the train function does not display the degree of convergence attained when MAX_ITERATIONS is exceeded.

Jeffrey Whitaker provides Python code using an alternative algorithm: 

Furthermore, the update_fn should also pass the previous likelihood or difference is likelihood so the actual convergence can be seen. Really the update_fn should be more general than this and be able to display more information but the attached patches provides the previous llh (old_llik).
def show_progress(iteration, old_llh, loglikelihood):
    print "Iteration:", iteration, "Old", old_llh, "Log-likelihood function:", loglikelihood, "Diff:", (old_llh-loglikelihood)

model = LogisticRegression.train(xs, ys, update_fn=show_progress)

logreg.diff (2.34 KB)

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