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examples/undocumented/python_modular/classifier_custom_kernel_modular.py
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61 changes: 32 additions & 29 deletions
61
examples/undocumented/python_modular/classifier_libsvm_minimal_modular.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,29 +1,32 @@ | ||
| from numpy import * | ||
| from numpy.random import randn | ||
| from shogun.Features import * | ||
| from shogun.Classifier import * | ||
| from shogun.Kernel import * | ||
|
|
||
| num=1000 | ||
| dist=1 | ||
| width=2.1 | ||
| C=1 | ||
|
|
||
| traindata_real=concatenate((randn(2,num)-dist, randn(2,num)+dist), axis=1) | ||
| testdata_real=concatenate((randn(2,num)-dist, randn(2,num)+dist), axis=1); | ||
|
|
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| trainlab=concatenate((-ones(num), ones(num))); | ||
| testlab=concatenate((-ones(num), ones(num))); | ||
|
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| feats_train=RealFeatures(traindata_real); | ||
| feats_test=RealFeatures(testdata_real); | ||
| kernel=GaussianKernel(feats_train, feats_train, width); | ||
|
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| labels=BinaryLabels(trainlab); | ||
| svm=LibSVM(C, kernel, labels); | ||
| svm.train(); | ||
|
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| kernel.init(feats_train, feats_test); | ||
| out=svm.apply().get_labels(); | ||
| testerr=mean(sign(out)!=testlab) | ||
| print(testerr) | ||
| from numpy import mean, sign | ||
|
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| from tools.load import LoadMatrix | ||
| lm=LoadMatrix() | ||
|
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| traindat = lm.load_numbers('../data/fm_train_real.dat') | ||
| testdat = lm.load_numbers('../data/fm_test_real.dat') | ||
| label_traindat = lm.load_labels('../data/label_train_twoclass.dat') | ||
|
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| parameter_list = [[traindat,testdat,label_traindat,2.1,1]] | ||
|
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| def classifier_libsvm_minimal_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1): | ||
| from shogun.Features import RealFeatures, BinaryLabels | ||
| from shogun.Classifier import LibSVM | ||
| from shogun.Kernel import GaussianKernel | ||
|
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||
| feats_train=RealFeatures(fm_train_real); | ||
| feats_test=RealFeatures(fm_test_real); | ||
| kernel=GaussianKernel(feats_train, feats_train, width); | ||
|
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| labels=BinaryLabels(label_train_twoclass); | ||
| svm=LibSVM(C, kernel, labels); | ||
| svm.train(); | ||
|
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| kernel.init(feats_train, feats_test); | ||
| out=svm.apply().get_labels(); | ||
| testerr=mean(sign(out)!=label_train_twoclass) | ||
| print(testerr) | ||
|
|
||
| if __name__=='__main__': | ||
| print('LibSVM Minimal') | ||
| classifier_libsvm_minimal_modular(*parameter_list[0]) |
147 changes: 77 additions & 70 deletions
147
examples/undocumented/python_modular/classifier_multiclass_ecoc.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,79 +1,86 @@ | ||
| import classifier_multiclass_shared | ||
| import re | ||
| import time | ||
| from tools.multiclass_shared import prepare_data | ||
|
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| # run with toy data | ||
| [traindat, label_traindat, testdat, label_testdat] = classifier_multiclass_shared.prepare_data() | ||
| [traindat, label_traindat, testdat, label_testdat] = prepare_data() | ||
| # run with opt-digits if available | ||
| #[traindat, label_traindat, testdat, label_testdat] = classifier_multiclass_shared.prepare_data(False) | ||
| #[traindat, label_traindat, testdat, label_testdat] = prepare_data(False) | ||
|
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| parameter_list = [[traindat,testdat,label_traindat,label_testdat,2.1,1,1e-5]] | ||
|
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| import shogun.Classifier as Classifier | ||
| from shogun.Classifier import ECOCStrategy | ||
| from shogun.Features import RealFeatures, MulticlassLabels | ||
| from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine | ||
| from shogun.Evaluation import MulticlassAccuracy | ||
| def classifier_multiclass_ecoc (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,lawidth=2.1,C=1,epsilon=1e-5): | ||
|
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| def nonabstract_class(name): | ||
| try: | ||
| getattr(Classifier, name)() | ||
| except TypeError: | ||
| return False | ||
| return True | ||
| import shogun.Classifier as Classifier | ||
| from shogun.Classifier import ECOCStrategy, LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine | ||
| from shogun.Evaluation import MulticlassAccuracy | ||
| from shogun.Features import RealFeatures, MulticlassLabels | ||
|
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||
| import re | ||
| encoders = [x for x in dir(Classifier) | ||
| if re.match(r'ECOC.+Encoder', x) and nonabstract_class(x)] | ||
| decoders = [x for x in dir(Classifier) | ||
| if re.match(r'ECOC.+Decoder', x) and nonabstract_class(x)] | ||
|
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| fea_train = RealFeatures(traindat) | ||
| fea_test = RealFeatures(testdat) | ||
| gnd_train = MulticlassLabels(label_traindat) | ||
| if label_testdat is None: | ||
| gnd_test = None | ||
| else: | ||
| gnd_test = MulticlassLabels(label_testdat) | ||
|
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| base_classifier = LibLinear(L2R_L2LOSS_SVC) | ||
| base_classifier.set_bias_enabled(True) | ||
|
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| print('Testing with %d encoders and %d decoders' % (len(encoders), len(decoders))) | ||
| print('-' * 70) | ||
| format_str = '%%15s + %%-10s %%-10%s %%-10%s %%-10%s' | ||
| print((format_str % ('s', 's', 's')) % ('encoder', 'decoder', 'codelen', 'time', 'accuracy')) | ||
|
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| def run_ecoc(ier, idr): | ||
| encoder = getattr(Classifier, encoders[ier])() | ||
| decoder = getattr(Classifier, decoders[idr])() | ||
|
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| # whether encoder is data dependent | ||
| if hasattr(encoder, 'set_labels'): | ||
| encoder.set_labels(gnd_train) | ||
| encoder.set_features(fea_train) | ||
|
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| strategy = ECOCStrategy(encoder, decoder) | ||
| classifier = LinearMulticlassMachine(strategy, fea_train, base_classifier, gnd_train) | ||
| classifier.train() | ||
| label_pred = classifier.apply(fea_test) | ||
| if gnd_test is not None: | ||
| evaluator = MulticlassAccuracy() | ||
| acc = evaluator.evaluate(label_pred, gnd_test) | ||
| else: | ||
| acc = None | ||
|
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| return (classifier.get_num_machines(), acc) | ||
| def nonabstract_class(name): | ||
| try: | ||
| getattr(Classifier, name)() | ||
| except TypeError: | ||
| return False | ||
| return True | ||
|
|
||
| encoders = [x for x in dir(Classifier) | ||
| if re.match(r'ECOC.+Encoder', x) and nonabstract_class(x)] | ||
| decoders = [x for x in dir(Classifier) | ||
| if re.match(r'ECOC.+Decoder', x) and nonabstract_class(x)] | ||
|
|
||
| import time | ||
| for ier in range(len(encoders)): | ||
| for idr in range(len(decoders)): | ||
| t_begin = time.clock() | ||
| (codelen, acc) = run_ecoc(ier, idr) | ||
| if acc is None: | ||
| acc_fmt = 's' | ||
| acc = 'N/A' | ||
| else: | ||
| acc_fmt = '.4f' | ||
|
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| t_elapse = time.clock() - t_begin | ||
| print((format_str % ('d', '.3f', acc_fmt)) % | ||
| (encoders[ier][4:-7], decoders[idr][4:-7], codelen, t_elapse, acc)) | ||
| fea_train = RealFeatures(fm_train_real) | ||
| fea_test = RealFeatures(fm_test_real) | ||
| gnd_train = MulticlassLabels(label_train_multiclass) | ||
| if label_test_multiclass is None: | ||
| gnd_test = None | ||
| else: | ||
| gnd_test = MulticlassLabels(label_test_multiclass) | ||
|
|
||
| base_classifier = LibLinear(L2R_L2LOSS_SVC) | ||
| base_classifier.set_bias_enabled(True) | ||
|
|
||
| print('Testing with %d encoders and %d decoders' % (len(encoders), len(decoders))) | ||
| print('-' * 70) | ||
| format_str = '%%15s + %%-10s %%-10%s %%-10%s %%-10%s' | ||
| print((format_str % ('s', 's', 's')) % ('encoder', 'decoder', 'codelen', 'time', 'accuracy')) | ||
|
|
||
| def run_ecoc(ier, idr): | ||
| encoder = getattr(Classifier, encoders[ier])() | ||
| decoder = getattr(Classifier, decoders[idr])() | ||
|
|
||
| # whether encoder is data dependent | ||
| if hasattr(encoder, 'set_labels'): | ||
| encoder.set_labels(gnd_train) | ||
| encoder.set_features(fea_train) | ||
|
|
||
| strategy = ECOCStrategy(encoder, decoder) | ||
| classifier = LinearMulticlassMachine(strategy, fea_train, base_classifier, gnd_train) | ||
| classifier.train() | ||
| label_pred = classifier.apply(fea_test) | ||
| if gnd_test is not None: | ||
| evaluator = MulticlassAccuracy() | ||
| acc = evaluator.evaluate(label_pred, gnd_test) | ||
| else: | ||
| acc = None | ||
|
|
||
| return (classifier.get_num_machines(), acc) | ||
|
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||
|
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| for ier in range(len(encoders)): | ||
| for idr in range(len(decoders)): | ||
| t_begin = time.clock() | ||
| (codelen, acc) = run_ecoc(ier, idr) | ||
| if acc is None: | ||
| acc_fmt = 's' | ||
| acc = 'N/A' | ||
| else: | ||
| acc_fmt = '.4f' | ||
|
|
||
| t_elapse = time.clock() - t_begin | ||
| print((format_str % ('d', '.3f', acc_fmt)) % | ||
| (encoders[ier][4:-7], decoders[idr][4:-7], codelen, t_elapse, acc)) | ||
|
|
||
| if __name__=='__main__': | ||
| print('MulticlassECOC') | ||
| classifier_multiclass_ecoc(*parameter_list[0]) | ||
|
|
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4 changes: 2 additions & 2 deletions
4
examples/undocumented/python_modular/classifier_multiclass_relaxedtree.py
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4 changes: 2 additions & 2 deletions
4
examples/undocumented/python_modular/classifier_multiclass_shareboost.py
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23 changes: 0 additions & 23 deletions
23
examples/undocumented/python_modular/classifier_multiclass_shared.py
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4 changes: 2 additions & 2 deletions
4
examples/undocumented/python_modular/classifier_multiclassliblinear_modular.py
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4 changes: 2 additions & 2 deletions
4
examples/undocumented/python_modular/classifier_multiclasslibsvm_modular.py
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4 changes: 2 additions & 2 deletions
4
examples/undocumented/python_modular/classifier_multiclasslinearmachine_modular.py
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