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How to use create_vocabulary method in localstack
Best Python code snippet using localstack_python
classify.py
Source:classify.py 
...18 bow = create_bow(vocab, directory+subdir+f)19 dataset.append({'label': label, 'bow': bow})20 return dataset21#Completed for you22def create_vocabulary(directory, cutoff):23 """ Create a vocabulary from the training directory24 return a sorted vocabulary list25 """26 top_level = os.listdir(directory)27 a = cutoff28 vocab = {}29 for d in top_level:30 subdir = d if d[-1] == '/' else d+'/'31 files = os.listdir(directory+subdir)32 for f in files:33 with open(directory+subdir+f,'r', encoding="utf-8") as doc:34 for word in doc:35 word = word.strip()36 if not word in vocab and len(word) > 0:37 vocab[word] = 138 elif len(word) > 0:39 vocab[word] += 140 return sorted([word for word in vocab if vocab[word] >= cutoff])41#The rest of the functions need modifications ------------------------------42#Needs modifications43def create_bow(vocab, filepath):44 """ Create a single dictionary for the data45 Note: label may be None46 """47 bow = {}48 # TODO: add your code here49 wordcount = 050 wordcountnone = 051 c = 052 for i in vocab:53 c+=154 with open(filepath, 'r', encoding="utf-8") as doc: ###############################################55 for word in doc:56 word = word.strip()57 if(c==1):58 if (word not in vocab):59 wordcountnone += 160 if(i == str(word)):61 wordcount += 162 #print(wordcount)63 if(wordcount > 0):64 bow[i] = wordcount65 wordcount = 066 if(wordcountnone != 0):67 bow[None] = wordcountnone68 return bow69#Needs modifications70def prior(training_data, label_list):71 """ return the prior probability of the label in the training set72 => frequency of DOCUMENTS73 """74 smooth = 1 # smoothing factor75 logprob = {}76 # TODO: add your code here77 numfile1 = 078 numfile2 = 079 for dic in training_data:80 if(dic["label"] == label_list[0]):81 numfile1 += 182 elif(dic["label"] == label_list[1]):83 numfile2 += 184 numtotal = numfile1 + numfile285 prob1 = (numfile1+smooth)/(numtotal+2)86 prob2 = (numfile2 + smooth) / (numtotal + 2)87 logprob[label_list[0]] = math.log(prob1)88 logprob[label_list[1]] = math.log(prob2)89 return logprob90#Needs modifications91def p_word_given_label(vocab, training_data, label):92 """ return the class conditional probability of label over all words, with smoothing """93 smooth = 1 # smoothing factor94 word_prob = {}95 # TODO: add your code here96 total_word = 097 word_prob[None] = 098 for dic in training_data:99 for index0, i0 in enumerate(dic['bow']):100 if (list(dic['bow'])[index0] in word_prob):101 continue;102 word_prob[list(dic['bow'])[index0]] = 0103 #word_prob[None] = 0104 if(dic["label"] == label):105 for index, i in enumerate(dic["bow"]):106 if(list(dic['bow'])[index] in vocab):107 if(list(dic['bow'])[index] in word_prob):108 word_prob[list(dic['bow'])[index]] += dic["bow"][i]109 else:110 word_prob[list(dic['bow'])[index]] = dic["bow"][i]111 else:112 if(None in word_prob):113 word_prob[None] += dic["bow"][i]114 else:115 word_prob[None] = 0116 total_word += dic["bow"][i]117 #word_prob [None] = 5118 for h in word_prob:119 word_prob[h] = math.log((word_prob[h] + smooth*1)) - math.log((total_word + smooth*(len(vocab) +1)))120 return word_prob121#Needs modifications122def train(training_directory, cutoff):123 """ return a dictionary formatted as follows:124 {125 'vocabulary': <the training set vocabulary>,126 'log prior': <the output of prior()>,127 'log p(w|y=2016)': <the output of p_word_given_label() for 2016>,128 'log p(w|y=2020)': <the output of p_word_given_label() for 2020>129 }130 """131 retval = {}132 label_list = os.listdir(training_directory)133 # TODO: add your code here134 vocal = create_vocabulary(training_directory, cutoff)135 training_data = load_training_data(vocal, training_directory)136 log_prior = prior(load_training_data(label_list, training_directory), label_list)137 label_word2020 = p_word_given_label(vocal,training_data, label_list[1])138 label_word2016 = p_word_given_label(vocal, training_data, label_list[0])139 retval['vocabulary'] = vocal140 retval['log prior'] = log_prior141 retval['log p(w|y=2016)'] = label_word2016142 retval['log p(w|y=2020)'] = label_word2020143 return retval144#Needs modifications145def classify(model, filepath):146 """ return a dictionary formatted as follows:147 {148 'predicted y': <'2016' or '2020'>,149 'log p(y=2016|x)': <log probability of 2016 label for the document>,150 'log p(y=2020|x)': <log probability of 2020 label for the document>151 }152 """153 retval = {}154 # TODO: add your code here155 vocab_x = model['vocabulary']156 prob_word_2016_total = 0157 prob_word_2020_total = 0158 bow_doc = create_bow(vocab_x,filepath)159 for i in bow_doc:160 prob_word_2016_total += model['log p(w|y=2016)'][i] * bow_doc[i]161 prob_word_2020_total += model['log p(w|y=2020)'][i] * bow_doc[i]162 label_2016 = model['log prior']['2016'] + prob_word_2016_total163 label_2020 = model['log prior']['2020'] + prob_word_2020_total164 if label_2016 > label_2020:165 label_x = '2016'166 else:167 label_x = '2020'168 retval['predicted y'] = label_x169 retval['log p(y=2016|x)'] = label_2016170 retval['log p(y=2020|x)'] = label_2020171 return retval172if __name__ == '__main__':173 vocab = {}174 #vocab = create_vocabulary('./EasyFiles/', 2)175 #print(vocab)176 #print(create_bow(vocab, './EasyFiles/2016/1.txt'))177 #vocab = create_vocabulary('./corpus/training/', 2)178 #training_data = load_training_data(vocab,'./corpus/training/')179 #print(training_data)180 #vocab = create_vocabulary('./corpus/training/', 2)181 #training_data = load_training_data(vocab, './corpus/training/')182 #print(prior(training_data, ['2020', '2016']))183 #vocab = create_vocabulary('./EasyFiles/', 1)184 #load_data = load_training_data(vocab, './EasyFiles/')185 #print(load_data)186 #for x in load_data:187 # for key in x["bow"]:188 # print(x['bow'][key])189 #vocab = create_vocabulary('./EasyFiles/', 1)190 #training_data = load_training_data(vocab, './EasyFiles/')191 #print(training_data)192 #print(p_word_given_label(vocab, training_data, '2016'))193 #print(train('./EasyFiles/', 2))194 #model = train('./corpus/test/', 2)195 #print(classify(model, './corpus/test/2016/0.txt'))196 #vocab = create_vocabulary('./EasyFiles/', 1)197 #print(create_bow(vocab, './EasyFiles/2016/1.txt'))198 #vocab = create_vocabulary('./EasyFiles/', 1)199 #print(load_training_data(vocab, './EasyFiles/'))200 #vocab = create_vocabulary('./corpus/training/', 2)201 #training_data = load_training_data(vocab, './corpus/training/')202 #print(prior(training_data, ['2020', '2016']))203 #vocab = create_vocabulary('./EasyFiles/', 1)204 #training_data = load_training_data(vocab, './EasyFiles/')205 #print(training_data)206 #print(p_word_given_label(vocab, training_data, '2016'))207 #print(p_word_given_label(vocab, training_data, '2020'))208 #print(train('./EasyFiles/', 2))209####210 #print(create_vocabulary('./EasyFiles/', 2))211 #vocab = create_vocabulary('./EasyFiles/', 2)212 #print(create_bow(vocab, './EasyFiles/2016/1.txt'))213 #vocab = create_vocabulary('./EasyFiles/', 1)214 #print(load_training_data(vocab, './EasyFiles/'))215 #vocab = create_vocabulary('./corpus/training/', 2)216 #training_data = load_training_data(vocab, './corpus/training/')217 #print(prior(training_data, ['2020', '2016']))218 #vocab = create_vocabulary('./EasyFiles/', 1)219 #training_data = load_training_data(vocab, './EasyFiles/')220 #print(p_word_given_label(vocab, training_data, '2016'))221 #vocab = create_vocabulary('./EasyFiles/', 2)222 #training_data = load_training_data(vocab, './EasyFiles/')223 #print(p_word_given_label(vocab, training_data, '2016'))224 #print(train('./EasyFiles/', 2))225 model = train('./corpus/training/', 2)...
test.py
Source:test.py
1'''2HW4 is to be written in a file called classify.py with the following interface:3create_vocabulary(training_directory: str, cutoff: int)4create_bow(vocab: dict, filepath: str)5load_training_data(vocab: list, directory: str)6prior(training_data: list, label_list: list)7p_word_given_label(vocab: list, training_data: list, label: str)8train(training_directory: str, cutoff: int)9classify(model: dict, filepath: str)10'''11__author__ = 'cs540-testers'12__credits__ = ['Saurabh Kulkarni', 'Alex Moon', 'Stephen Jasina',13 'Harrison Clark']14version = 'V1.1.2'15from classify import train, create_bow, load_training_data, prior, \16 p_word_given_label, classify, create_vocabulary17import unittest18class TestClassify(unittest.TestCase):19 def compare_dicts(self, a, b):20 '''Compares two dicts that map strings to other (non-container) data'''21 # Check that all elements of a are in b22 for k in a:23 self.assertIn(k, b)24 if isinstance(a[k], float):25 self.assertAlmostEqual(a[k], b[k])26 elif isinstance(a[k], dict):27 self.compare_dicts(a[k], b[k])28 else:29 self.assertEqual(a[k], b[k])30 # Check if b has unexpected extra entries31 for k in b:32 self.assertIn(k, a)33 # create_vocabulary(training_directory: str, cutoff: int)34 # returns a list35 def test_create_vocabulary(self):36 vocab = create_vocabulary('./EasyFiles/', 1)37 expected_vocab = [',', '.', '19', '2020', 'a', 'cat', 'chases', 'dog',38 'february', 'hello', 'is', 'it', 'world']39 self.assertEqual(vocab, expected_vocab)40 vocab = create_vocabulary('./EasyFiles/', 2)41 expected_vocab = ['.', 'a']42 self.assertEqual(vocab, expected_vocab)43 # create_bow(vocab: dict, filepath: str)44 # returns a dict45 def test_create_bow(self):46 vocab = create_vocabulary('./EasyFiles/', 1)47 bow = create_bow(vocab, './EasyFiles/2016/1.txt')48 expected_bow = {'a': 2, 'dog': 1, 'chases': 1, 'cat': 1, '.': 1}49 self.assertEqual(bow, expected_bow)50 bow = create_bow(vocab, './EasyFiles/2020/2.txt')51 expected_bow = {'it': 1, 'is': 1, 'february': 1, '19': 1, ',': 1,52 '2020': 1, '.': 1}53 self.assertEqual(bow, expected_bow)54 vocab = create_vocabulary('./EasyFiles/', 2)55 bow = create_bow(vocab, './EasyFiles/2016/1.txt')56 expected_bow = {'a': 2, None: 3, '.': 1}57 self.assertEqual(bow, expected_bow)58 # load_training_data(vocab: list, directory: str)59 # returns a list of dicts60 def test_load_training_data(self):61 vocab = create_vocabulary('./EasyFiles/', 1)62 training_data = load_training_data(vocab, './EasyFiles/')63 expected_training_data = [64 {65 'label': '2020',66 'bow': {'it': 1, 'is': 1, 'february': 1, '19': 1, ',': 1,67 '2020': 1, '.': 1}68 },69 {70 'label': '2016',71 'bow': {'hello': 1, 'world': 1}72 },73 {74 'label': '2016',75 'bow': {'a': 2, 'dog': 1, 'chases': 1, 'cat': 1, '.': 1}76 }77 ]78 self.assertCountEqual(training_data, expected_training_data)79 # prior(training_data: list, label_list: list)80 # returns a dict mapping labels to floats81 # assertAlmostEqual(a, b) can be handy here82 def test_prior(self):83 vocab = create_vocabulary('./corpus/training/', 2)84 training_data = load_training_data(vocab, './corpus/training/')85 log_probabilities = prior(training_data, ['2020', '2016'])86 expected_log_probabilities = {'2020': -0.32171182103809226,87 '2016': -1.2906462863976689}88 self.compare_dicts(log_probabilities, expected_log_probabilities)89 # p_word_given_label(vocab: list, training_data: list, label: str)90 # returns a dict mapping words to floats91 # assertAlmostEqual(a, b) can be handy here92 def test_p_word_given_label_2020(self):93 vocab = create_vocabulary('./EasyFiles/', 1)94 training_data = load_training_data(vocab, './EasyFiles/')95 log_probabilities = p_word_given_label(vocab, training_data, '2020')96 expected_log_probabilities = {',': -2.3513752571634776,97 '.': -2.3513752571634776, '19': -2.3513752571634776,98 '2020': -2.3513752571634776, 'a': -3.044522437723423,99 'cat': -3.044522437723423, 'chases': -3.044522437723423,100 'dog': -3.044522437723423, 'february': -2.3513752571634776,101 'hello': -3.044522437723423, 'is': -2.3513752571634776,102 'it': -2.3513752571634776, 'world': -3.044522437723423,103 None: -3.044522437723423}104 self.compare_dicts(log_probabilities, expected_log_probabilities)105 vocab = create_vocabulary('./EasyFiles/', 2)106 training_data = load_training_data(vocab, './EasyFiles/')107 log_probabilities = p_word_given_label(vocab, training_data, '2020')108 expected_log_probabilities = {'.': -1.6094379124341005,109 'a': -2.302585092994046, None: -0.35667494393873267}110 self.compare_dicts(log_probabilities, expected_log_probabilities)111 def test_p_word_given_label_2016(self):112 vocab = create_vocabulary('./EasyFiles/', 1)113 training_data = load_training_data(vocab, './EasyFiles/')114 log_probabilities = p_word_given_label(vocab, training_data, '2016')115 expected_log_probabilities = {',': -3.091042453358316,116 '.': -2.3978952727983707, '19': -3.091042453358316,117 '2020': -3.091042453358316, 'a': -1.9924301646902063,118 'cat': -2.3978952727983707, 'chases': -2.3978952727983707,119 'dog': -2.3978952727983707, 'february': -3.091042453358316,120 'hello': -2.3978952727983707, 'is': -3.091042453358316,121 'it': -3.091042453358316, 'world': -2.3978952727983707,122 None: -3.091042453358316}123 self.compare_dicts(log_probabilities, expected_log_probabilities)124 vocab = create_vocabulary('./EasyFiles/', 2)125 training_data = load_training_data(vocab, './EasyFiles/')126 log_probabilities = p_word_given_label(vocab, training_data, '2016')127 expected_log_probabilities = {'.': -1.7047480922384253,128 'a': -1.2992829841302609, None: -0.6061358035703157}129 self.compare_dicts(log_probabilities, expected_log_probabilities)130 # train(training_directory: str, cutoff: int)131 # returns a dict132 def test_train(self):133 model = train('./EasyFiles/', 2)134 expected_model = {135 'vocabulary': ['.', 'a'],136 'log prior': {137 '2020': -0.916290731874155,138 '2016': -0.5108256237659905...
flow_configuration.py
Source:flow_configuration.py
...33 return 'Prepared images'34def create_vocab(**kwargs):35 voc = Vocabulary(imgs_path=kwargs['imgdir'], vocab_size = 800, feature_type='daisy')36 all_features = voc.feature_extraction()37 voc.create_vocabulary(all_features)38 voc.save_vocabulary()39 return 'Created vocabulary'40def create_hists(**kwargs):41 ph = PatternHistograms(path=kwargs['imgdir'], feature_type='daisy')42 hists = ph.create_histogram()43 ph.save_histograms(hists)44 return 'Created histograms'45def create_hists(**kwargs):46 ph = PatternHistograms(path=kwargs['imgdir'], feature_type='daisy')47 hists = ph.create_histogram()48 ph.save_histograms(hists)49 return 'Created histograms'50def compute_similarity(**kwargs):51 image_similarity = ImageSimilarity(path=sys.argv[1])...
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