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Issue #2693 has been updated by Vincent Davis.
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<p>This seems worth checking into more. I will create a github issue.</p>
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<h1><a href="https://redmine.open-bio.org/issues/2693#change-15334">Bug #2693: LogisticRegression convergence criterion is too lenient</a></h1>
<ul><li>Author: Bruce Southey</li>
<li>Status: New</li>
<li>Priority: Normal</li>
<li>Assignee: Biopython Dev Mailing List</li>
<li>Category: Main Distribution</li>
<li>Target version: Not Applicable</li>
<li>URL: </li></ul>
<p>In R and SAS, the example in the code and tutorial provides the following parameters:</p>
<p>Intercept = 18.9622<br />x1 = -0.0714<br />x2 = 0.0444</p>
<p>By default, Bio/LogisticRegression.py defines the following parameters<br /> MAX_ITERATIONS = 500<br /> CONVERGE_THRESHOLD = 0.01</p>
<p>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.</p>
<p>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:<br />def train(xs, ys, update_fn=None, typecode=None, CONVERGE_THRESHOLD = 0.000000001, MAX_ITERATIONS=10000):</p>
<p>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.</p>
<p>Jeffrey Whitaker provides Python code using an alternative algorithm: <br /><a class="external" href="http://www.cdc.noaa.gov/people/jeffrey.s.whitaker/python/logistic_regression.py">http://www.cdc.noaa.gov/people/jeffrey.s.whitaker/python/logistic_regression.py</a></p>
<p>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).<br />def show_progress(iteration, old_llh, loglikelihood):<br /> print "Iteration:", iteration, "Old", old_llh, "Log-likelihood function:", loglikelihood, "Diff:", (old_llh-loglikelihood)</p>
<p>model = LogisticRegression.train(xs, ys, update_fn=show_progress)</p>
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<a href="https://redmine.open-bio.org/attachments/download/1134/logreg.diff">logreg.diff</a>
(2.34 KB)<br />
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