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How to use get_labels method in autotest
Best Python code snippet using autotest_python
test_labels.py
Source:test_labels.py 
...8 sentence[1].add_label("sentiment", "positive")9 sentence[2].add_label("pos", "proper noun")10 sentence[0].add_label("pos", "pronoun")11 # check if there are three POS labels with correct text and values12 labels: List[Label] = sentence.get_labels("pos")13 assert 3 == len(labels)14 assert "I" == labels[0].data_point.text15 assert "pronoun" == labels[0].value16 assert "love" == labels[1].data_point.text17 assert "verb" == labels[1].value18 assert "Berlin" == labels[2].data_point.text19 assert "proper noun" == labels[2].value20 # check if there are is one SENTIMENT label with correct text and values21 labels: List[Label] = sentence.get_labels("sentiment")22 assert 1 == len(labels)23 assert "love" == labels[0].data_point.text24 assert "positive" == labels[0].value25 # check if all tokens are correctly labeled26 assert 3 == len(sentence)27 assert "I" == sentence[0].text28 assert "love" == sentence[1].text29 assert "Berlin" == sentence[2].text30 assert 1 == len(sentence[0].get_labels("pos"))31 assert 1 == len(sentence[1].get_labels("pos"))32 assert 2 == len(sentence[1].labels)33 assert 1 == len(sentence[2].get_labels("pos"))34 assert "verb" == sentence[1].get_label("pos").value35 assert "positive" == sentence[1].get_label("sentiment").value36 # remove the pos label from the last word37 sentence[2].remove_labels("pos")38 # there should be 2 POS labels left39 labels: List[Label] = sentence.get_labels("pos")40 assert 2 == len(labels)41 assert 1 == len(sentence[0].get_labels("pos"))42 assert 1 == len(sentence[1].get_labels("pos"))43 assert 2 == len(sentence[1].labels)44 assert 0 == len(sentence[2].get_labels("pos"))45 # now remove all pos tags46 sentence.remove_labels("pos")47 print(sentence[0].get_labels("pos"))48 assert 0 == len(sentence.get_labels("pos"))49 assert 1 == len(sentence.get_labels("sentiment"))50 assert 1 == len(sentence.labels)51 assert 0 == len(sentence[0].get_labels("pos"))52 assert 0 == len(sentence[1].get_labels("pos"))53 assert 0 == len(sentence[2].get_labels("pos"))54def test_span_tags():55 # set 3 labels for 2 spans (HU is tagged twice)56 sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")57 sentence[0:4].add_label("ner", "Organization")58 sentence[0:4].add_label("ner", "University")59 sentence[7:8].add_label("ner", "City")60 # check if there are three labels with correct text and values61 labels: List[Label] = sentence.get_labels("ner")62 assert 3 == len(labels)63 assert "Humboldt Universität zu Berlin" == labels[0].data_point.text64 assert "Organization" == labels[0].value65 assert "Humboldt Universität zu Berlin" == labels[1].data_point.text66 assert "University" == labels[1].value67 assert "Berlin" == labels[2].data_point.text68 assert "City" == labels[2].value69 # check if there are two spans with correct text and values70 spans: List[Span] = sentence.get_spans("ner")71 assert 2 == len(spans)72 assert "Humboldt Universität zu Berlin" == spans[0].text73 assert 2 == len(spans[0].get_labels("ner"))74 assert "Berlin" == spans[1].text75 assert "City" == spans[1].get_label("ner").value76 # now delete the NER tags of "Humboldt-Universität zu Berlin"77 sentence[0:4].remove_labels("ner")78 # should be only one NER label left79 labels: List[Label] = sentence.get_labels("ner")80 assert 1 == len(labels)81 assert "Berlin" == labels[0].data_point.text82 assert "City" == labels[0].value83 # and only one NER span84 spans: List[Span] = sentence.get_spans("ner")85 assert 1 == len(spans)86 assert "Berlin" == spans[0].text87 assert "City" == spans[0].get_label("ner").value88def test_different_span_tags():89 # set 3 labels for 2 spans (HU is tagged twice with different tags)90 sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")91 sentence[0:4].add_label("ner", "Organization")92 sentence[0:4].add_label("orgtype", "University")93 sentence[7:8].add_label("ner", "City")94 # check if there are three labels with correct text and values95 labels: List[Label] = sentence.get_labels("ner")96 assert 2 == len(labels)97 assert "Humboldt Universität zu Berlin" == labels[0].data_point.text98 assert "Organization" == labels[0].value99 assert "Berlin" == labels[1].data_point.text100 assert "City" == labels[1].value101 # check if there are two spans with correct text and values102 spans: List[Span] = sentence.get_spans("ner")103 assert 2 == len(spans)104 assert "Humboldt Universität zu Berlin" == spans[0].text105 assert "Organization" == spans[0].get_label("ner").value106 assert "University" == spans[0].get_label("orgtype").value107 assert 1 == len(spans[0].get_labels("ner"))108 assert "Berlin" == spans[1].text109 assert "City" == spans[1].get_label("ner").value110 # now delete the NER tags of "Humboldt-Universität zu Berlin"111 sentence[0:4].remove_labels("ner")112 # should be only one NER label left113 labels: List[Label] = sentence.get_labels("ner")114 assert 1 == len(labels)115 assert "Berlin" == labels[0].data_point.text116 assert "City" == labels[0].value117 # and only one NER span118 spans: List[Span] = sentence.get_spans("ner")119 assert 1 == len(spans)120 assert "Berlin" == spans[0].text121 assert "City" == spans[0].get_label("ner").value122 # but there is also one orgtype span and label123 labels: List[Label] = sentence.get_labels("orgtype")124 assert 1 == len(labels)125 assert "Humboldt Universität zu Berlin" == labels[0].data_point.text126 assert "University" == labels[0].value127 # and only one NER span128 spans: List[Span] = sentence.get_spans("orgtype")129 assert 1 == len(spans)130 assert "Humboldt Universität zu Berlin" == spans[0].text131 assert "University" == spans[0].get_label("orgtype").value132 # let's add the NER tag back133 sentence[0:4].add_label("ner", "Organization")134 # check if there are three labels with correct text and values135 labels: List[Label] = sentence.get_labels("ner")136 print(labels)137 assert 2 == len(labels)138 assert "Humboldt Universität zu Berlin" == labels[0].data_point.text139 assert "Organization" == labels[0].value140 assert "Berlin" == labels[1].data_point.text141 assert "City" == labels[1].value142 # check if there are two spans with correct text and values143 spans: List[Span] = sentence.get_spans("ner")144 assert 2 == len(spans)145 assert "Humboldt Universität zu Berlin" == spans[0].text146 assert "Organization" == spans[0].get_label("ner").value147 assert "University" == spans[0].get_label("orgtype").value148 assert 1 == len(spans[0].get_labels("ner"))149 assert "Berlin" == spans[1].text150 assert "City" == spans[1].get_label("ner").value151 # now remove all NER tags152 sentence.remove_labels("ner")153 assert 0 == len(sentence.get_labels("ner"))154 assert 0 == len(sentence.get_spans("ner"))155 assert 1 == len(sentence.get_spans("orgtype"))156 assert 1 == len(sentence.get_labels("orgtype"))157 assert 1 == len(sentence.labels)158 assert 0 == len(sentence[0:4].get_labels("ner"))159 assert 1 == len(sentence[0:4].get_labels("orgtype"))160def test_relation_tags():161 # set 3 labels for 2 spans (HU is tagged twice with different tags)162 sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")163 # create two relation label164 Relation(sentence[0:4], sentence[7:8]).add_label("rel", "located in")165 Relation(sentence[0:2], sentence[3:4]).add_label("rel", "university of")166 Relation(sentence[0:2], sentence[3:4]).add_label("syntactic", "apposition")167 # there should be two relation labels168 labels: List[Label] = sentence.get_labels("rel")169 assert 2 == len(labels)170 assert "located in" == labels[0].value171 assert "university of" == labels[1].value172 # there should be one syntactic labels173 labels: List[Label] = sentence.get_labels("syntactic")174 assert 1 == len(labels)175 # there should be two relations, one with two and one with one label176 relations: List[Relation] = sentence.get_relations("rel")177 assert 2 == len(relations)178 assert 1 == len(relations[0].labels)179 assert 2 == len(relations[1].labels)180def test_sentence_labels():181 # example sentence182 sentence = Sentence("I love Berlin")183 sentence.add_label("sentiment", "positive")184 sentence.add_label("topic", "travelling")185 assert 2 == len(sentence.labels)186 assert 1 == len(sentence.get_labels("sentiment"))187 assert 1 == len(sentence.get_labels("topic"))188 # add another topic label189 sentence.add_label("topic", "travelling")190 assert 3 == len(sentence.labels)191 assert 1 == len(sentence.get_labels("sentiment"))192 assert 2 == len(sentence.get_labels("topic"))193 sentence.remove_labels("topic")194 assert 1 == len(sentence.labels)195 assert 1 == len(sentence.get_labels("sentiment"))196 assert 0 == len(sentence.get_labels("topic"))197def test_mixed_labels():198 # example sentence199 sentence = Sentence("I love New York")200 # has sentiment value201 sentence.add_label("sentiment", "positive")202 # has 4 part of speech tags203 sentence[1].add_label("pos", "verb")204 sentence[2].add_label("pos", "proper noun")205 sentence[3].add_label("pos", "proper noun")206 sentence[0].add_label("pos", "pronoun")207 # has 1 NER tag208 sentence[2:4].add_label("ner", "City")209 # should be in total 6 labels210 assert 6 == len(sentence.labels)211 assert 4 == len(sentence.get_labels("pos"))212 assert 1 == len(sentence.get_labels("sentiment"))213 assert 1 == len(sentence.get_labels("ner"))214def test_data_point_equality():215 # example sentence216 sentence = Sentence("George Washington went to Washington .")217 # add two NER labels218 sentence[0:2].add_label("span_ner", "PER")219 sentence[0:2].add_label("span_other", "Politician")220 sentence[4].add_label("ner", "LOC")221 sentence[4].add_label("other", "Village")222 # get the four labels223 ner_label = sentence.get_label("ner")224 other_label = sentence.get_label("other")225 span_ner_label = sentence.get_label("span_ner")226 span_other_label = sentence.get_label("span_other")227 # check that only two of the respective data points are equal...
pspeech_features.py
Source:pspeech_features.py
...41from python_speech_features import ssc42import scipy.io.wavfile as wav43import os 44# get labels for later 45def get_labels(vector, label, label2):46 sample_list=list()47 for i in range(len(vector)):48 sample_list.append(label+str(i+1)+'_'+label2)49 return sample_list50def pspeech_featurize(file):51 # convert if .mp3 to .wav or it will fail 52 convert=False 53 if file[-4:]=='.mp3':54 convert=True 55 os.system('ffmpeg -i %s %s'%(file, file[0:-4]+'.wav'))56 file = file[0:-4] +'.wav'57 (rate,sig) = wav.read(file)58 mfcc_feat = mfcc(sig,rate)59 fbank_feat = logfbank(sig,rate)60 ssc_feat=ssc(sig, rate)61 one_=np.mean(mfcc_feat, axis=0)62 one=get_labels(one_, 'mfcc_', 'means')63 two_=np.std(mfcc_feat, axis=0)64 two=get_labels(one_, 'mfcc_', 'stds')65 three_=np.amax(mfcc_feat, axis=0)66 three=get_labels(one_, 'mfcc_', 'max')67 four_=np.amin(mfcc_feat, axis=0)68 four=get_labels(one_, 'mfcc_', 'min')69 five_=np.median(mfcc_feat, axis=0)70 five=get_labels(one_, 'mfcc_', 'medians')71 six_=np.mean(fbank_feat, axis=0)72 six=get_labels(six_, 'fbank_', 'means')73 seven_=np.mean(fbank_feat, axis=0)74 seven=get_labels(six_, 'fbank_', 'stds')75 eight_=np.mean(fbank_feat, axis=0)76 eight=get_labels(six_, 'fbank_', 'max')77 nine_=np.mean(fbank_feat, axis=0)78 nine=get_labels(six_, 'fbank_', 'min')79 ten_=np.mean(fbank_feat, axis=0)80 ten=get_labels(six_, 'fbank_', 'medians')81 eleven_=np.mean(ssc_feat, axis=0)82 eleven=get_labels(eleven_, 'spectral_centroid_', 'means')83 twelve_=np.mean(ssc_feat, axis=0)84 twelve=get_labels(eleven_, 'spectral_centroid_', 'stds')85 thirteen_=np.mean(ssc_feat, axis=0)86 thirteen=get_labels(eleven_, 'spectral_centroid_', 'max')87 fourteen_=np.mean(ssc_feat, axis=0)88 fourteen=get_labels(eleven_, 'spectral_centroid_', 'min')89 fifteen_=np.mean(ssc_feat, axis=0)90 fifteen=get_labels(eleven_, 'spectral_centroid_', 'medians')91 labels=one+two+three+four+five+six+seven+eight+nine+ten+eleven+twelve+thirteen+fourteen+fifteen92 features=np.append(one_,two_)93 features=np.append(features, three_)94 features=np.append(features, four_)95 features=np.append(features, five_)96 features=np.append(features, six_)97 features=np.append(features, seven_)98 features=np.append(features, eight_)99 features=np.append(features, nine_)100 features=np.append(features, ten_)101 features=np.append(features, eleven_)102 features=np.append(features, twelve_)103 features=np.append(features, thirteen_)104 features=np.append(features, fourteen_)...
parse_predictions.py
Source:parse_predictions.py
...16 import run_regression17 predicted_labels = []18 if task == "MRPC":19 #ids = MrpcProcessor().get_test_examples(os.environ['GLUE_DIR'] + "/MRPC")20 labels = run_classifier.MrpcProcessor().get_labels()21 if task == "RTE":22 labels = run_classifier.RTEProcessor().get_labels()23 if task == "QNLI":24 labels = run_classifier.QNLIProcessor().get_labels()25 if task == "QNLIV2":26 labels = run_classifier.QNLIProcessor().get_labels()27 if task == "MNLI":28 labels = run_classifier.MnliProcessor().get_labels()29 if task == "SST2":30 labels = run_classifier.SST2Processor().get_labels()31 if task == "CoLA":32 labels = run_classifier.ColaProcessor().get_labels()33 if task == "QQP":34 labels = run_classifier.QQPProcessor().get_labels()35 if task == "diagnostic":36 labels = run_classifier.DiagnosticProcessor().get_labels()37 with codecs.open(input_path, "r", "utf8") as f_in:38 for line in f_in.readlines():39 predictions = np.array(line.split("\t"), dtype=np.float32)40 if task != "STSB":41 predicted_index = np.argmax(predictions)42 predicted_labels.append(labels[predicted_index])43 else:44 predicted_labels.append(predictions[0])45 f_in.close()46 with codecs.open(output_path, "w", "utf8") as f_out:47 f_out.write("index\tprediction\n")48 for i, prediction in enumerate(predicted_labels):49 f_out.write(str(i) + "\t" + str(prediction) + "\n")50 f_out.close()51def write_fake_predictions(output_path, task="MRPC"):52 """53 :param input_path:54 :param output_path:55 :param task:56 :return:57 >>> write_fake_predictions("/work/anlausch/replant/bert/predictions/base_32_5e-05_3.0/copy_for_submission/fakes/STS-B.tsv", task="STSB")58 """59 if task != "STSB":60 import run_classifier61 else:62 import run_regression63 if task == "MNLI":64 test_examples = run_classifier.MnliProcessor().get_test_examples(os.environ['GLUE_DIR'] + "/" + task, False)65 labels = run_classifier.MnliProcessor().get_labels()66 elif task == "QQP":67 test_examples = run_classifier.QQPProcessor().get_test_examples(os.environ['GLUE_DIR'] + "/" + task)68 labels = run_classifier.QQPProcessor().get_labels()69 elif task == "WNLI":70 test_examples = run_classifier.WNLIProcessor().get_test_examples(os.environ['GLUE_DIR'] + "/" + task)71 labels = run_classifier.WNLIProcessor().get_labels()72 elif task == "CoLA":73 test_examples = run_classifier.ColaProcessor().get_test_examples(os.environ['GLUE_DIR'] + "/" + task)74 labels = run_classifier.ColaProcessor().get_labels()75 elif task == "STSB":76 test_examples = run_regression.STSBProcessor().get_test_examples(os.environ['GLUE_DIR'] + "/" + task)77 elif task == "diagnostic":78 test_examples = run_classifier.DiagnosticProcessor().get_test_examples(os.environ['GLUE_DIR'] + "/" + task)79 labels = run_classifier.DiagnosticProcessor().get_labels()80 with codecs.open(output_path, "w", "utf8") as f_out:81 f_out.write("index\tprediction\n")82 if task != "STSB":83 for i, data in enumerate(test_examples):84 f_out.write(str(i) + "\t" + str(labels[0]) + "\n")85 else:86 for i, data in enumerate(test_examples):87 f_out.write(str(i) + "\t" + str(2.5) + "\n")88 f_out.close()89def main():90 parser = argparse.ArgumentParser(description="Running prediction parser")91 parser.add_argument("--task", type=str, default=None,92 help="Input path in case train and dev are in a single file", required=True)93 parser.add_argument("--input_path", type=str, default="/work/anlausch/replant/bert/predictions/wn_binary_32_5e-05_3.0/test_results.tsv",...
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