[Scipy-svn] r5143 - trunk/scipy/stats/tests

[scipy-svn at scipy.org]

Author: josef
Date: 2008-11-18 00:19:37 -0600 (Tue, 18 Nov 2008)
New Revision: 5143

Added:
   trunk/scipy/stats/tests/test_fit.py
Log:
add fit test for record, test is renamed so nose doesn't run it

Added: trunk/scipy/stats/tests/test_fit.py
===================================================================
--- trunk/scipy/stats/tests/test_fit.py	2008-11-18 06:15:12 UTC (rev 5142)
+++ trunk/scipy/stats/tests/test_fit.py	2008-11-18 06:19:37 UTC (rev 5143)
@@ -0,0 +1,68 @@
+# NOTE: contains only one test, _est_cont_fit, that is renamed so that
+#       nose doesn't run it
+# I put this here for the record and for the case when someone wants to
+# verify the quality of fit
+# with current parameters: 
+
+
+import numpy.testing as npt
+import numpy as np
+
+from scipy import stats
+
+from test_continuous_basic import distcont
+
+# this is not a proper statistical test for convergence, but only
+# verifies that the estimate and true values don't differ by too much
+n_repl1 = 1000 # sample size for first run
+n_repl2 = 5000 # sample size for second run, if first run fails
+thresh_percent = 0.25 # percent of true parameters for fail cut-off
+thresh_min = 0.75  # minimum difference estimate - true to fail test
+
+#distcont = [['genextreme', (3.3184017469423535,)]]
+
+def test_cont_fit():
+    # this tests the closeness of the estimated parameters to the true
+    # parameters with fit method of continuous distributions
+    # Note: is slow, some distributions don't converge with sample size <= 10000
+
+    for distname, arg in distcont:
+        yield check_cont_fit, distname,arg
+
+
+def check_cont_fit(distname,arg):        
+    distfn = getattr(stats, distname)
+    rvs = distfn.rvs(size=n_repl1,*arg)
+    est = distfn.fit(rvs)  #,*arg) # start with default values
+
+    truearg = np.hstack([arg,[0.0,1.0]])
+    diff = est-truearg
+        
+    txt = ''
+    diffthreshold = np.max(np.vstack([truearg*thresh_percent,
+                    np.ones(distfn.numargs+2)*thresh_min]),0)
+    # threshold for location
+    diffthreshold[-2] = np.max([np.abs(rvs.mean())*thresh_percent,thresh_min])
+    
+    if np.any(np.isnan(est)):
+        raise AssertionError, 'nan returned in fit'
+    else:  
+        if np.any((np.abs(diff) - diffthreshold) > 0.0):
+##            txt = 'WARNING - diff too large with small sample'
+##            print 'parameter diff =', diff - diffthreshold, txt
+            rvs = np.concatenate([rvs,distfn.rvs(size=n_repl2-n_repl1,*arg)])
+            est = distfn.fit(rvs) #,*arg)
+            truearg = np.hstack([arg,[0.0,1.0]])
+            diff = est-truearg
+            if np.any((np.abs(diff) - diffthreshold) > 0.0):
+                txt  = 'parameter: %s\n' % str(truearg)
+                txt += 'estimated: %s\n' % str(est)
+                txt += 'diff     : %s\n' % str(diff)
+                raise AssertionError, 'fit not very good in %s\n' % distfn.name + txt
+                
+
+
+if __name__ == "__main__":
+    import nose
+    #nose.run(argv=['', __file__])
+    nose.runmodule(argv=[__file__,'-s'], exit=False)