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1 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
2 # vi: set ft=python sts=4 ts=4 sw=4 et:
3 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
4 #
5 # See COPYING file distributed along with the PyMVPA package for the
6 # copyright and license terms.
7 #
8 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
9 """Unit tests for PyMVPA basic Classifiers"""
10
11 from mvpa.support.copy import deepcopy
12 from mvpa.base import externals
13
14 from mvpa.datasets import Dataset
15 from mvpa.mappers.mask import MaskMapper
16 from mvpa.datasets.splitters import NFoldSplitter, OddEvenSplitter
17
18 from mvpa.misc.exceptions import UnknownStateError
19
20 from mvpa.clfs.base import DegenerateInputError, FailedToTrainError
21 from mvpa.clfs.meta import CombinedClassifier, \
22 BinaryClassifier, MulticlassClassifier, \
23 SplitClassifier, MappedClassifier, FeatureSelectionClassifier, \
24 TreeClassifier
25 from mvpa.clfs.transerror import TransferError
26 from mvpa.algorithms.cvtranserror import CrossValidatedTransferError
27
28 from tests_warehouse import *
29 from tests_warehouse_clfs import *
30
31 # What exceptions to allow while testing degenerate cases.
32 # If it pukes -- it is ok -- user will notice that something
33 # is wrong
34 _degenerate_allowed_exceptions = [DegenerateInputError, FailedToTrainError]
35 if externals.exists('rpy'):
36 import rpy
37 _degenerate_allowed_exceptions += [rpy.RPyRException]
41
43 self.clf_sign = SameSignClassifier()
44 self.clf_less1 = Less1Classifier()
45
46 # simple binary dataset
47 self.data_bin_1 = Dataset(
48 samples=[[0,0],[-10,-1],[1,0.1],[1,-1],[-1,1]],
49 labels=[1, 1, 1, -1, -1], # labels
50 chunks=[0, 1, 2, 2, 3]) # chunks
51
53 clf = SameSignClassifier(enable_states=['training_confusion'])
54 clf.train(self.data_bin_1)
55 self.failUnlessRaises(UnknownStateError, clf.states.__getattribute__,
56 "predictions")
57 """Should have no predictions after training. Predictions
58 state should be explicitely disabled"""
59
60 if not _all_states_enabled:
61 self.failUnlessRaises(UnknownStateError, clf.states.__getattribute__,
62 "trained_dataset")
63
64 self.failUnlessEqual(clf.training_confusion.percentCorrect,
65 100,
66 msg="Dummy clf should train perfectly")
67 self.failUnlessEqual(clf.predict(self.data_bin_1.samples),
68 list(self.data_bin_1.labels))
69
70 self.failUnlessEqual(len(clf.predictions), self.data_bin_1.nsamples,
71 msg="Trained classifier stores predictions by default")
72
73 clf = SameSignClassifier(enable_states=['trained_dataset'])
74 clf.train(self.data_bin_1)
75 self.failUnless((clf.trained_dataset.samples ==
76 self.data_bin_1.samples).all())
77 self.failUnless((clf.trained_dataset.labels ==
78 self.data_bin_1.labels).all())
79
80
82 # XXXXXXX
83 # silly test if we get the same result with boosted as with a single one
84 bclf = CombinedClassifier(clfs=[self.clf_sign.clone(),
85 self.clf_sign.clone()])
86
87 self.failUnlessEqual(list(bclf.predict(self.data_bin_1.samples)),
88 list(self.data_bin_1.labels),
89 msg="Boosted classifier should work")
90 self.failUnlessEqual(bclf.predict(self.data_bin_1.samples),
91 self.clf_sign.predict(self.data_bin_1.samples),
92 msg="Boosted classifier should have the same as regular")
93
94
96 bclf = CombinedClassifier(clfs=[self.clf_sign.clone(),
97 self.clf_sign.clone()],
98 enable_states=['feature_ids'])
99
100 # check states enabling propagation
101 self.failUnlessEqual(self.clf_sign.states.isEnabled('feature_ids'),
102 _all_states_enabled)
103 self.failUnlessEqual(bclf.clfs[0].states.isEnabled('feature_ids'), True)
104
105 bclf2 = CombinedClassifier(clfs=[self.clf_sign.clone(),
106 self.clf_sign.clone()],
107 propagate_states=False,
108 enable_states=['feature_ids'])
109
110 self.failUnlessEqual(self.clf_sign.states.isEnabled('feature_ids'),
111 _all_states_enabled)
112 self.failUnlessEqual(bclf2.clfs[0].states.isEnabled('feature_ids'),
113 _all_states_enabled)
114
115
116
118 ds = Dataset(samples=[ [0,0], [0,1], [1,100], [-1,0], [-1,-3], [ 0,-10] ],
119 labels=[ 'sp', 'sp', 'sp', 'dn', 'sn', 'dp'])
120 testdata = [ [0,0], [10,10], [-10, -1], [0.1, -0.1], [-0.2, 0.2] ]
121 # labels [s]ame/[d]ifferent (sign), and [p]ositive/[n]egative first element
122
123 clf = SameSignClassifier()
124 # lets create classifier to descriminate only between same/different,
125 # which is a primary task of SameSignClassifier
126 bclf1 = BinaryClassifier(clf=clf,
127 poslabels=['sp', 'sn'],
128 neglabels=['dp', 'dn'])
129
130 orig_labels = ds.labels[:]
131 bclf1.train(ds)
132
133 self.failUnless(bclf1.predict(testdata) ==
134 [['sp', 'sn'], ['sp', 'sn'], ['sp', 'sn'],
135 ['dn', 'dp'], ['dn', 'dp']])
136
137 self.failUnless((ds.labels == orig_labels).all(),
138 msg="BinaryClassifier should not alter labels")
139
140
141 @sweepargs(clf=clfswh['binary'])
143 """Simple test if classifiers can generalize ok on simple data
144 """
145 te = CrossValidatedTransferError(TransferError(clf), NFoldSplitter())
146 cve = te(datasets['uni2medium'])
147 if cfg.getboolean('tests', 'labile', default='yes'):
148 self.failUnless(cve < 0.25,
149 msg="Got transfer error %g" % (cve))
150
151
152 @sweepargs(clf=clfswh[:] + regrswh[:])
154 """Basic testing of the clf summary
155 """
156 summary1 = clf.summary()
157 self.failUnless('not yet trained' in summary1)
158 clf.train(datasets['uni2small'])
159 summary = clf.summary()
160 # It should get bigger ;)
161 self.failUnless(len(summary) > len(summary1))
162 self.failUnless(not 'not yet trained' in summary)
163
164
165 @sweepargs(clf=clfswh[:] + regrswh[:])
167 """Test how clf handles degenerate cases
168 """
169 # Whenever we have only 1 feature with only 0s in it
170 ds1 = datasets['uni2small'][:, [0]]
171 # XXX this very line breaks LARS in many other unittests --
172 # very interesting effect. but screw it -- for now it will be
173 # this way
174 ds1.samples[:] = 0.0 # all 0s
175
176 #ds2 = datasets['uni2small'][[0], :]
177 #ds2.samples[:] = 0.0 # all 0s
178
179 clf.states._changeTemporarily(
180 enable_states=['values', 'training_confusion'])
181
182 # Good pukes are good ;-)
183 # TODO XXX add
184 # - ", ds2):" to test degenerate ds with 1 sample
185 # - ds1 but without 0s -- just 1 feature... feature selections
186 # might lead to 'surprises' due to magic in combiners etc
187 for ds in (ds1, ):
188 try:
189 clf.train(ds) # should not crash or stall
190 # could we still get those?
191 summary = clf.summary()
192 cm = clf.states.training_confusion
193 # If succeeded to train/predict (due to
194 # training_confusion) without error -- results better be
195 # at "chance"
196 continue
197 if 'ACC' in cm.stats:
198 self.failUnlessEqual(cm.stats['ACC'], 0.5)
199 else:
200 self.failUnless(N.isnan(cm.stats['CCe']))
201 except tuple(_degenerate_allowed_exceptions):
202 pass
203 clf.states._resetEnabledTemporarily()
204
205
206 # TODO: validate for regressions as well!!!
208 ds = self.data_bin_1
209 clf = SplitClassifier(clf=SameSignClassifier(),
210 splitter=NFoldSplitter(1),
211 enable_states=['confusion', 'training_confusion',
212 'feature_ids'])
213 clf.train(ds) # train the beast
214 error = clf.confusion.error
215 tr_error = clf.training_confusion.error
216
217 clf2 = clf.clone()
218 cv = CrossValidatedTransferError(
219 TransferError(clf2),
220 NFoldSplitter(),
221 enable_states=['confusion', 'training_confusion'])
222 cverror = cv(ds)
223 tr_cverror = cv.training_confusion.error
224
225 self.failUnlessEqual(error, cverror,
226 msg="We should get the same error using split classifier as"
227 " using CrossValidatedTransferError. Got %s and %s"
228 % (error, cverror))
229
230 self.failUnlessEqual(tr_error, tr_cverror,
231 msg="We should get the same training error using split classifier as"
232 " using CrossValidatedTransferError. Got %s and %s"
233 % (tr_error, tr_cverror))
234
235 self.failUnlessEqual(clf.confusion.percentCorrect,
236 100,
237 msg="Dummy clf should train perfectly")
238 self.failUnlessEqual(len(clf.confusion.sets),
239 len(ds.uniquechunks),
240 msg="Should have 1 confusion per each split")
241 self.failUnlessEqual(len(clf.clfs), len(ds.uniquechunks),
242 msg="Should have number of classifiers equal # of epochs")
243 self.failUnlessEqual(clf.predict(ds.samples), list(ds.labels),
244 msg="Should classify correctly")
245
246 # feature_ids must be list of lists, and since it is not
247 # feature-selecting classifier used - we expect all features
248 # to be utilized
249 # NOT ANYMORE -- for BoostedClassifier we have now union of all
250 # used features across slave classifiers. That makes
251 # semantics clear. If you need to get deeper -- use upcoming
252 # harvesting facility ;-)
253 # self.failUnlessEqual(len(clf.feature_ids), len(ds.uniquechunks))
254 # self.failUnless(N.array([len(ids)==ds.nfeatures
255 # for ids in clf.feature_ids]).all())
256
257 # Just check if we get it at all ;-)
258 summary = clf.summary()
259
260
261 @sweepargs(clf_=clfswh['binary', '!meta'])
263 clf2 = clf_.clone()
264 ds = datasets['uni2medium']#self.data_bin_1
265 clf = SplitClassifier(clf=clf_, #SameSignClassifier(),
266 splitter=NFoldSplitter(1),
267 enable_states=['confusion', 'feature_ids'])
268 clf.train(ds) # train the beast
269 error = clf.confusion.error
270
271 cv = CrossValidatedTransferError(
272 TransferError(clf2),
273 NFoldSplitter(),
274 enable_states=['confusion', 'training_confusion'])
275 cverror = cv(ds)
276
277 self.failUnless(abs(error-cverror)<0.01,
278 msg="We should get the same error using split classifier as"
279 " using CrossValidatedTransferError. Got %s and %s"
280 % (error, cverror))
281
282 if cfg.getboolean('tests', 'labile', default='yes'):
283 self.failUnless(error < 0.25,
284 msg="clf should generalize more or less fine. "
285 "Got error %s" % error)
286 self.failUnlessEqual(len(clf.confusion.sets), len(ds.uniquechunks),
287 msg="Should have 1 confusion per each split")
288 self.failUnlessEqual(len(clf.clfs), len(ds.uniquechunks),
289 msg="Should have number of classifiers equal # of epochs")
290 #self.failUnlessEqual(clf.predict(ds.samples), list(ds.labels),
291 # msg="Should classify correctly")
292
293
294
296 """Basic testing of harvesting based on SplitClassifier
297 """
298 ds = self.data_bin_1
299 clf = SplitClassifier(clf=SameSignClassifier(),
300 splitter=NFoldSplitter(1),
301 enable_states=['confusion', 'training_confusion',
302 'feature_ids'],
303 harvest_attribs=['clf.feature_ids',
304 'clf.training_time'],
305 descr="DESCR")
306 clf.train(ds) # train the beast
307 # Number of harvested items should be equal to number of chunks
308 self.failUnlessEqual(len(clf.harvested['clf.feature_ids']),
309 len(ds.uniquechunks))
310 # if we can blame multiple inheritance and ClassWithCollections.__init__
311 self.failUnlessEqual(clf.descr, "DESCR")
312
313
315 samples = N.array([ [0,0,-1], [1,0,1], [-1,-1, 1], [-1,0,1], [1, -1, 1] ])
316 testdata3 = Dataset(samples=samples, labels=1)
317 res110 = [1, 1, 1, -1, -1]
318 res101 = [-1, 1, -1, -1, 1]
319 res011 = [-1, 1, -1, 1, -1]
320
321 clf110 = MappedClassifier(clf=self.clf_sign, mapper=MaskMapper(N.array([1,1,0])))
322 clf101 = MappedClassifier(clf=self.clf_sign, mapper=MaskMapper(N.array([1,0,1])))
323 clf011 = MappedClassifier(clf=self.clf_sign, mapper=MaskMapper(N.array([0,1,1])))
324
325 self.failUnlessEqual(clf110.predict(samples), res110)
326 self.failUnlessEqual(clf101.predict(samples), res101)
327 self.failUnlessEqual(clf011.predict(samples), res011)
328
329
331 from test_rfe import SillySensitivityAnalyzer
332 from mvpa.featsel.base import \
333 SensitivityBasedFeatureSelection
334 from mvpa.featsel.helpers import \
335 FixedNElementTailSelector
336
337 # should give lowest weight to the feature with lowest index
338 sens_ana = SillySensitivityAnalyzer()
339 # should give lowest weight to the feature with highest index
340 sens_ana_rev = SillySensitivityAnalyzer(mult=-1)
341
342 # corresponding feature selections
343 feat_sel = SensitivityBasedFeatureSelection(sens_ana,
344 FixedNElementTailSelector(1, mode='discard'))
345
346 feat_sel_rev = SensitivityBasedFeatureSelection(sens_ana_rev,
347 FixedNElementTailSelector(1))
348
349 samples = N.array([ [0,0,-1], [1,0,1], [-1,-1, 1], [-1,0,1], [1, -1, 1] ])
350
351 testdata3 = Dataset(samples=samples, labels=1)
352 # dummy train data so proper mapper gets created
353 traindata = Dataset(samples=N.array([ [0, 0,-1], [1,0,1] ]), labels=[1,2])
354
355 # targets
356 res110 = [1, 1, 1, -1, -1]
357 res011 = [-1, 1, -1, 1, -1]
358
359 # first classifier -- 0th feature should be discarded
360 clf011 = FeatureSelectionClassifier(self.clf_sign, feat_sel,
361 enable_states=['feature_ids'])
362
363 self.clf_sign.states._changeTemporarily(enable_states=['values'])
364 clf011.train(traindata)
365
366 self.failUnlessEqual(clf011.predict(testdata3.samples), res011)
367 # just silly test if we get values assigned in the 'ProxyClassifier'
368 self.failUnless(len(clf011.values) == len(res110),
369 msg="We need to pass values into ProxyClassifier")
370 self.clf_sign.states._resetEnabledTemporarily()
371
372 self.failUnlessEqual(len(clf011.feature_ids), 2)
373 "Feature selection classifier had to be trained on 2 features"
374
375 # first classifier -- last feature should be discarded
376 clf011 = FeatureSelectionClassifier(self.clf_sign, feat_sel_rev)
377 clf011.train(traindata)
378 self.failUnlessEqual(clf011.predict(testdata3.samples), res110)
379
381 from test_rfe import SillySensitivityAnalyzer
382 from mvpa.featsel.base import \
383 SensitivityBasedFeatureSelection
384 from mvpa.featsel.helpers import \
385 FixedNElementTailSelector
386 if sample_clf_reg is None:
387 # none regression was found, so nothing to test
388 return
389 # should give lowest weight to the feature with lowest index
390 sens_ana = SillySensitivityAnalyzer()
391
392 # corresponding feature selections
393 feat_sel = SensitivityBasedFeatureSelection(sens_ana,
394 FixedNElementTailSelector(1, mode='discard'))
395
396 # now test with regression-based classifier. The problem is
397 # that it is determining predictions twice from values and
398 # then setting the values from the results, which the second
399 # time is set to predictions. The final outcome is that the
400 # values are actually predictions...
401 dat = Dataset(samples=N.random.randn(4,10),labels=[-1,-1,1,1])
402 clf_reg = FeatureSelectionClassifier(sample_clf_reg, feat_sel)
403 clf_reg.train(dat)
404 res = clf_reg.predict(dat.samples)
405 self.failIf((N.array(clf_reg.values)-clf_reg.predictions).sum()==0,
406 msg="Values were set to the predictions in %s." %
407 sample_clf_reg)
408
409
411 """Basic tests for TreeClassifier
412 """
413 ds = datasets['uni4small']
414 clfs = clfswh['binary'] # pool of classifiers
415 # Lets permute so each time we try some different combination
416 # of the classifiers
417 clfs = [clfs[i] for i in N.random.permutation(len(clfs))]
418 # Test conflicting definition
419 tclf = TreeClassifier(clfs[0], {
420 'L0+2' : (('L0', 'L2'), clfs[1]),
421 'L2+3' : ((2, 3), clfs[2])})
422 self.failUnlessRaises(ValueError, tclf.train, ds)
423 """Should raise exception since label 2 is in both"""
424
425 # Test insufficient definition
426 tclf = TreeClassifier(clfs[0], {
427 'L0+5' : (('L0', 'L5'), clfs[1]),
428 'L2+3' : ((2, 3), clfs[2])})
429 self.failUnlessRaises(ValueError, tclf.train, ds)
430 """Should raise exception since no group for L1"""
431
432 # proper definition now
433 tclf = TreeClassifier(clfs[0], {
434 'L0+1' : (('L0', 1), clfs[1]),
435 'L2+3' : ((2, 3), clfs[2])})
436
437 # Lets test train/test cycle using CVTE
438 cv = CrossValidatedTransferError(
439 TransferError(tclf),
440 OddEvenSplitter(),
441 enable_states=['confusion', 'training_confusion'])
442 cverror = cv(ds)
443 try:
444 rtclf = repr(tclf)
445 except:
446 self.fail(msg="Could not obtain repr for TreeClassifier")
447
448 # Test accessibility of .clfs
449 self.failUnless(tclf.clfs['L0+1'] is clfs[1])
450 self.failUnless(tclf.clfs['L2+3'] is clfs[2])
451
452 cvtrc = cv.training_confusion
453 cvtc = cv.confusion
454 if cfg.getboolean('tests', 'labile', default='yes'):
455 # just a dummy check to make sure everything is working
456 self.failUnless(cvtrc != cvtc)
457 self.failUnless(cverror < 0.3)
458
459 # TODO: whenever implemented
460 tclf = TreeClassifier(clfs[0], {
461 'L0' : (('L0',), clfs[1]),
462 'L1+2+3' : ((1, 2, 3), clfs[2])})
463 # TEST ME
464
465
466 @sweepargs(clf=clfswh[:])
468 if isinstance(clf, MulticlassClassifier):
469 # TODO: handle those values correctly
470 return
471 ds = datasets['uni2small']
472 clf.states._changeTemporarily(enable_states = ['values'])
473 cv = CrossValidatedTransferError(
474 TransferError(clf),
475 OddEvenSplitter(),
476 enable_states=['confusion', 'training_confusion'])
477 cverror = cv(ds)
478 #print clf.descr, clf.values[0]
479 # basic test either we get 1 set of values per each sample
480 self.failUnlessEqual(len(clf.values), ds.nsamples/2)
481
482 clf.states._resetEnabledTemporarily()
483
484 @sweepargs(clf=clfswh['linear', 'svm', 'libsvm', '!meta'])
486 oldC = None
487 # XXX somewhat ugly way to force non-dataspecific C value.
488 # Otherwise multiclass libsvm builtin and our MultiClass would differ
489 # in results
490 if clf.params.isKnown('C') and clf.C<0:
491 oldC = clf.C
492 clf.C = 1.0 # reset C to be 1
493
494 svm, svm2 = clf, clf.clone()
495 svm2.states.enable(['training_confusion'])
496
497 mclf = MulticlassClassifier(clf=svm,
498 enable_states=['training_confusion'])
499
500 svm2.train(datasets['uni2small_train'])
501 mclf.train(datasets['uni2small_train'])
502 s1 = str(mclf.training_confusion)
503 s2 = str(svm2.training_confusion)
504 self.failUnlessEqual(s1, s2,
505 msg="Multiclass clf should provide same results as built-in "
506 "libsvm's %s. Got %s and %s" % (svm2, s1, s2))
507
508 svm2.untrain()
509
510 self.failUnless(svm2.trained == False,
511 msg="Un-Trained SVM should be untrained")
512
513 self.failUnless(N.array([x.trained for x in mclf.clfs]).all(),
514 msg="Trained Boosted classifier should have all primary classifiers trained")
515 self.failUnless(mclf.trained,
516 msg="Trained Boosted classifier should be marked as trained")
517
518 mclf.untrain()
519
520 self.failUnless(not mclf.trained,
521 msg="UnTrained Boosted classifier should not be trained")
522 self.failUnless(not N.array([x.trained for x in mclf.clfs]).any(),
523 msg="UnTrained Boosted classifier should have no primary classifiers trained")
524
525 if oldC is not None:
526 clf.C = oldC
527
528 # XXX meta should also work but TODO
529 @sweepargs(clf=clfswh['svm', '!meta'])
531 knows_probabilities = 'probabilities' in clf.states.names and clf.params.probability
532 enable_states = ['values']
533 if knows_probabilities: enable_states += ['probabilities']
534
535 clf.states._changeTemporarily(enable_states = enable_states)
536 for traindata, testdata in [
537 (datasets['uni2small_train'], datasets['uni2small_test']) ]:
538 clf.train(traindata)
539 predicts = clf.predict(testdata.samples)
540 # values should be different from predictions for SVMs we have
541 self.failUnless( (predicts != clf.values).any() )
542
543 if knows_probabilities and clf.states.isSet('probabilities'):
544 # XXX test more thoroughly what we are getting here ;-)
545 self.failUnlessEqual( len(clf.probabilities), len(testdata.samples) )
546 clf.states._resetEnabledTemporarily()
547
548
549 @sweepargs(clf=clfswh['retrainable'])
551 # we need a copy since will tune its internals later on
552 clf = clf.clone()
553 clf.states._changeTemporarily(enable_states = ['values'],
554 # ensure that it does do predictions
555 # while training
556 disable_states=['training_confusion'])
557 clf_re = clf.clone()
558 # TODO: .retrainable must have a callback to call smth like
559 # _setRetrainable
560 clf_re._setRetrainable(True)
561
562 # need to have high snr so we don't 'cope' with problematic
563 # datasets since otherwise unittests would fail.
564 dsargs = {'perlabel':50, 'nlabels':2, 'nfeatures':5, 'nchunks':1,
565 'nonbogus_features':[2,4], 'snr': 5.0}
566
567 ## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
568 # NB datasets will be changed by the end of testing, so if
569 # are to change to use generic datasets - make sure to copy
570 # them here
571 dstrain = deepcopy(datasets['uni2large_train'])
572 dstest = deepcopy(datasets['uni2large_test'])
573
574 clf.untrain()
575 clf_re.untrain()
576 trerr, trerr_re = TransferError(clf), \
577 TransferError(clf_re, disable_states=['training_confusion'])
578
579 # Just check for correctness of retraining
580 err_1 = trerr(dstest, dstrain)
581 self.failUnless(err_1<0.3,
582 msg="We should test here on easy dataset. Got error of %s" % err_1)
583 values_1 = clf.values[:]
584 # some times retraining gets into deeper optimization ;-)
585 eps = 0.05
586 corrcoef_eps = 0.85 # just to get no failures... usually > 0.95
587
588
589 def batch_test(retrain=True, retest=True, closer=True):
590 err = trerr(dstest, dstrain)
591 err_re = trerr_re(dstest, dstrain)
592 corr = N.corrcoef(clf.values, clf_re.values)[0,1]
593 corr_old = N.corrcoef(values_1, clf_re.values)[0,1]
594 if __debug__:
595 debug('TEST', "Retraining stats: errors %g %g corr %g "
596 "with old error %g corr %g" %
597 (err, err_re, corr, err_1, corr_old))
598 self.failUnless(clf_re.states.retrained == retrain,
599 ("Must fully train",
600 "Must retrain instead of full training")[retrain])
601 self.failUnless(clf_re.states.repredicted == retest,
602 ("Must fully test",
603 "Must retest instead of full testing")[retest])
604 self.failUnless(corr > corrcoef_eps,
605 msg="Result must be close to the one without retraining."
606 " Got corrcoef=%s" % (corr))
607 if closer:
608 self.failUnless(corr >= corr_old,
609 msg="Result must be closer to current without retraining"
610 " than to old one. Got corrcoef=%s" % (corr_old))
611
612 # Check sequential retraining/retesting
613 for i in xrange(3):
614 flag = bool(i!=0)
615 # ok - on 1st call we should train/test, then retrain/retest
616 # and we can't compare for closinest to old result since
617 # we are working on the same data/classifier
618 batch_test(retrain=flag, retest=flag, closer=False)
619
620 # should retrain nicely if we change a parameter
621 if 'C' in clf.params.names:
622 clf.params.C *= 0.1
623 clf_re.params.C *= 0.1
624 batch_test()
625 elif 'sigma_noise' in clf.params.names:
626 clf.params.sigma_noise *= 100
627 clf_re.params.sigma_noise *= 100
628 batch_test()
629 else:
630 raise RuntimeError, \
631 'Please implement testing while changing some of the ' \
632 'params for clf %s' % clf
633
634 # should retrain nicely if we change kernel parameter
635 if hasattr(clf, 'kernel_params') and len(clf.kernel_params.names):
636 clf.kernel_params.gamma = 0.1
637 clf_re.kernel_params.gamma = 0.1
638 # retest is false since kernel got recomputed thus
639 # can't expect to use the same kernel
640 batch_test(retest=not('gamma' in clf.kernel_params.names))
641
642 # should retrain nicely if we change labels
643 oldlabels = dstrain.labels[:]
644 dstrain.permuteLabels(status=True, assure_permute=True)
645 self.failUnless((oldlabels != dstrain.labels).any(),
646 msg="We should succeed at permutting -- now got the same labels")
647 batch_test()
648
649 # Change labels in testing
650 oldlabels = dstest.labels[:]
651 dstest.permuteLabels(status=True, assure_permute=True)
652 self.failUnless((oldlabels != dstest.labels).any(),
653 msg="We should succeed at permutting -- now got the same labels")
654 batch_test()
655
656 # should re-train if we change data
657 # reuse trained SVM and its 'final' optimization point
658 if not clf.__class__.__name__ in ['GPR']: # on GPR everything depends on the data ;-)
659 oldsamples = dstrain.samples.copy()
660 dstrain.samples[:] += dstrain.samples*0.05
661 self.failUnless((oldsamples != dstrain.samples).any())
662 batch_test(retest=False)
663 clf.states._resetEnabledTemporarily()
664
665 # test retrain()
666 # TODO XXX -- check validity
667 clf_re.retrain(dstrain); self.failUnless(clf_re.states.retrained)
668 clf_re.retrain(dstrain, labels=True); self.failUnless(clf_re.states.retrained)
669 clf_re.retrain(dstrain, traindataset=True); self.failUnless(clf_re.states.retrained)
670
671 # test repredict()
672 clf_re.repredict(dstest.samples);
673 self.failUnless(clf_re.states.repredicted)
674 self.failUnlessRaises(RuntimeError, clf_re.repredict,
675 dstest.samples, labels=True,
676 msg="for now retesting with anything changed makes no sense")
677 clf_re._setRetrainable(False)
678
679
681 """Test all classifiers for conformant behavior
682 """
683 for clf_, traindata in \
684 [(clfswh['binary'], datasets['dumb2']),
685 (clfswh['multiclass'], datasets['dumb'])]:
686 traindata_copy = deepcopy(traindata) # full copy of dataset
687 for clf in clf_:
688 clf.train(traindata)
689 self.failUnless(
690 (traindata.samples == traindata_copy.samples).all(),
691 "Training of a classifier shouldn't change original dataset")
692
693 # TODO: enforce uniform return from predict??
694 #predicted = clf.predict(traindata.samples)
695 #self.failUnless(isinstance(predicted, N.ndarray))
696
697 # Just simple test that all of them are syntaxed correctly
698 self.failUnless(str(clf) != "")
699 self.failUnless(repr(clf) != "")
700
701 # TODO: unify str and repr for all classifiers
702
703 # XXX TODO: should work on smlr, knn, ridgereg, lars as well! but now
704 # they fail to train
705 # GNB -- cannot train since 1 sample isn't sufficient to assess variance
706 @sweepargs(clf=clfswh['!smlr', '!knn', '!gnb', '!lars', '!meta', '!ridge'])
708 """To check if known/present Classifiers are working properly
709 with samples being first dimension. Started to worry about
710 possible problems while looking at sg where samples are 2nd
711 dimension
712 """
713 # specially crafted dataset -- if dimensions are flipped over
714 # the same storage, problem becomes unseparable. Like in this case
715 # incorrect order of dimensions lead to equal samples [0, 1, 0]
716 traindatas = [
717 Dataset(samples=N.array([ [0, 0, 1.0],
718 [1, 0, 0] ]), labels=[-1, 1]),
719 Dataset(samples=N.array([ [0, 0.0],
720 [1, 1] ]), labels=[-1, 1])]
721
722 clf.states._changeTemporarily(enable_states = ['training_confusion'])
723 for traindata in traindatas:
724 clf.train(traindata)
725 self.failUnlessEqual(clf.training_confusion.percentCorrect, 100.0,
726 "Classifier %s must have 100%% correct learning on %s. Has %f" %
727 (`clf`, traindata.samples, clf.training_confusion.percentCorrect))
728
729 # and we must be able to predict every original sample thus
730 for i in xrange(traindata.nsamples):
731 sample = traindata.samples[i,:]
732 predicted = clf.predict([sample])
733 self.failUnlessEqual([predicted], traindata.labels[i],
734 "We must be able to predict sample %s using " % sample +
735 "classifier %s" % `clf`)
736 clf.states._resetEnabledTemporarily()
737
739 return unittest.makeSuite(ClassifiersTests)
740
741
742 if __name__ == '__main__':
743 import runner
744
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