convlab2.nlu.svm package

Subpackages

• convlab2.nlu.svm.camrest package
  • Submodules
  • convlab2.nlu.svm.camrest.evaluate module
  • convlab2.nlu.svm.camrest.nlu module
  • convlab2.nlu.svm.camrest.preprocess module
  • Module contents
• convlab2.nlu.svm.multiwoz package
  • Submodules
  • convlab2.nlu.svm.multiwoz.evaluate module
  • convlab2.nlu.svm.multiwoz.nlu module
  • convlab2.nlu.svm.multiwoz.preprocess module
  • Module contents

Submodules

convlab2.nlu.svm.Classifier module

class convlab2.nlu.svm.Classifier.SGD(config)

Bases: object

load(params)

params()

predict(X)

train(X, y)

class convlab2.nlu.svm.Classifier.SVM(config)

Bases: object

load(params)

params()

pickC(X, y)

predict(X)

train(X, y)

class convlab2.nlu.svm.Classifier.classifier(config)

Bases: object

cacheFeature(dw, config=None)

createDictionary()

decode()

decodeToFile(dw, output_fname, config=None)

decode_sent(sentinfo, output_fname, config=None)

export(models_fname, dictionary_fname, config_fname)

extractFeatures(dw, log_input_key='batch')

extractFeatures2(sentinfo, log_input_key='batch')

load(fname)

save(save_fname)

train(dw, config=None)

convlab2.nlu.svm.Classifier.toSparse(baseX, X, dictionary)

convlab2.nlu.svm.Classifier.trainSVMwrapper(X, y)

convlab2.nlu.svm.Features module

class convlab2.nlu.svm.Features.cnNgram(words, logp, delta=0)

Bases: object

logscore()

score()

string_repn()

word_list()

convlab2.nlu.svm.Features.cn_ngram_merge(ngrams)

convlab2.nlu.svm.Features.cn_ngram_prune(ngrams, n)

convlab2.nlu.svm.Features.cn_ngram_replaced(ngram, searchwords, replacement)

class convlab2.nlu.svm.Features.cnet(config)

Bases: object

calculate(log_turn, log_input_key='batch')

tuple_calculate(this_tuple, log_turn, log_input_key='batch')

convlab2.nlu.svm.Features.get_cnngrams(cnet, max_ngrams, max_length)

convlab2.nlu.svm.Features.get_ngrams(sentence, max_length, skip_ngrams=False, add_tags=True)

class convlab2.nlu.svm.Features.lastSys(config)

Bases: object

calculate(log_turn, log_input_key='batch')

tuple_calculate(this_tuple, log_turn, log_input_key='batch')

class convlab2.nlu.svm.Features.nbest(config)

Bases: object

calculate(log_turn, log_input_key='batch')

calculate_sent(log_turn, log_input_key='batch')

tuple_calculate(this_tuple, log_turn, log_input_key='batch')

class convlab2.nlu.svm.Features.nbestLengths(config)

Bases: object

calculate(log_turn, log_input_key='batch')

tuple_calculate(this_tuple, log_turn, log_input_key='batch')

class convlab2.nlu.svm.Features.nbestScores(config)

Bases: object

calculate(log_turn, log_input_key='batch')

tuple_calculate(this_tuple, log_turn, log_input_key='batch')

class convlab2.nlu.svm.Features.valueIdentifying(config)

Bases: object

calculate(log_turn, log_input_key='batch')

tuple_calculate(this_tuple, log_turn, log_input_key='batch')

convlab2.nlu.svm.Tuples module

convlab2.nlu.svm.Tuples.actual_value(value)

class convlab2.nlu.svm.Tuples.genericValue(slot, value=None)

Bases: object

convlab2.nlu.svm.Tuples.generic_to_specific(tup)

convlab2.nlu.svm.Tuples.is_generic(value)

convlab2.nlu.svm.Tuples.makes_valid_act(tuples)

convlab2.nlu.svm.Tuples.tuple_to_act(t)

class convlab2.nlu.svm.Tuples.tuples(config)

Bases: object

activeTuples(log_turn)

activeTuples_sent(log_turn)

distributionToNbest(tuple_distribution)

uactsToTuples(uacts)

convlab2.nlu.svm.dataset_walker module

class convlab2.nlu.svm.dataset_walker.Call(applog_filename, labels_filename)

Bases: object

class convlab2.nlu.svm.dataset_walker.dataset_walker(dataListFile, labels=False, dataroot=None)

Bases: object

convlab2.nlu.svm.sutils module

convlab2.nlu.svm.sutils.import_class(cl)

convlab2.nlu.svm.sutils.powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)

convlab2.nlu.svm.sutils.svm_to_libsvm(model, labels=None)

convlab2.nlu.svm.train module

convlab2.nlu.svm.train.train(config)

convlab2.nlu.svm.train.usage()

Module contents