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How to use get_vocabulary_filter method in localstack

Best Python code snippet using localstack_python

client.pyi

Source:client.pyi

...278        Provides information about the specified custom vocabulary.279        [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.24.58/reference/services/transcribe.html#TranscribeService.Client.get_vocabulary)280        [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_transcribe/client.html#get_vocabulary)281        """282    def get_vocabulary_filter(283        self, *, VocabularyFilterName: str284    ) -> GetVocabularyFilterResponseTypeDef:285        """286        Provides information about the specified custom vocabulary filter.287        [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.24.58/reference/services/transcribe.html#TranscribeService.Client.get_vocabulary_filter)288        [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_transcribe/client.html#get_vocabulary_filter)289        """290    def list_call_analytics_categories(291        self, *, NextToken: str = None, MaxResults: int = None292    ) -> ListCallAnalyticsCategoriesResponseTypeDef:293        """294        Provides a list of Call Analytics categories, including all rules that make up295        each category.296        [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.24.58/reference/services/transcribe.html#TranscribeService.Client.list_call_analytics_categories)...

app.py

Source:app.py

...66            )67            msg = "Upload Done ! "68            # Removing extension from name to transcribe69            # try:70            #     customizable_filter = transcribe.get_vocabulary_filter(71            #         VocabularyFilterName=str(name) + '-vocabularyfilter'72            #     )73            # except:74            #     customizable_filter = transcribe.create_vocabulary_filter(75            #         VocabularyFilterName=str(name) + '-vocabularyfilter',76            #         LanguageCode='en-US',77            #         Words=[78            #             customvocabulary,79            #         ],80            #     )81            # if customizable_filter:82            #     try:83            #         transcribe_response = transcribe.start_transcription_job(84            #             TranscriptionJobName=str(name) + '-transcribe',...

simpson.py

Source:simpson.py

...28                if token not in stoplist:29                    voca.add(token)30    print "Size of vocabulary: " + str(len(voca))31    return voca32def get_vocabulary_filter(filename):33    #Union tokens from all abstracts to generate vocabulary34    #Remove stopword list in nltk from vocabulary35    #Remove all common words36    count = 037    voca2counter = {}38    stoplist = set(stopwords.words('english'))39    with open(filename) as csvfile:40        csvreader = csv.reader(csvfile, delimiter=',')41        for row in csvreader:42            count += 143            if count == 1:44                continue45            #abstract = row[6].lower().decode('utf-8')46            abstract = row[6].lower()47            #year = row[8] 48            tokens = abstract.split()49            for token in tokens:50                #token = stemmer.stem(token)51                if token not in stoplist:52                    if token in voca2counter:53                        voca2counter[token] += 154                    else:55                        voca2counter[token] = 156    threshold = len(voca2counter)/10057    voca = set()58    for token in voca2counter:59        if voca2counter[token] < threshold:60            voca.add(token)61    print "Size of vocabulary: " + str(len(voca))62    return voca63def get_vocabulary_lda(term_file):64    print "Building vocabulary using lda words..."65    tokens = []66    with open(term_file) as lines:67        for line in lines:68            token = line.strip("\n")69            tokens.append(token)70    print "Number of all lda terms: " + str(len(tokens))71    voca = set(tokens)72    return voca73           74def compute_simpson_index(filename, voca):75    #print "Size of vocabulary: " + str(len(voca))76    #Counting vocabulary77    year2counter = {}78    year2N = {}79    count = 080    with open(filename) as csvfile:81        csvreader = csv.reader(csvfile, delimiter=',')82        N = 0 #Number of occurences of all tokens83        for row in csvreader:84            count += 185            if count == 1:86                continue87            abstract = row[6].lower()88            #abstract = row[6].lower().decode('utf-8')89            year = row[8] 90            if year in year2counter:91                counter = year2counter[year]92            else:93                counter = {}94                year2counter[year] = counter95                year2N[year] = 096            tokens = abstract.split()97            for token in tokens:98                #token = stemmer.stem(token)99                if token in voca:100                #if token:101                    year2N[year] += 1102                    if token in counter:103                        counter[token] += 1104                    else:105                        counter[token] = 1106    #Compute the Simpson index107    year2index = []108    N = 0109    for year in year2counter:110            #print "Year: \t" + str(year)111            if not year.isdigit():112                continue113            counter = year2counter[year]114            #print "Number of voca words: \t" + str(len(counter))115            N = year2N[year]116            #print "Number of occurences of voca words: \t" + str(N)117            #X = N*(N-1)118            X = N*N119            sum = 0120            for token in counter:121                #print counter[token]122                #sum += (counter[token] * (counter[token] - 1))/float(X)123                sum += (counter[token] * (counter[token] ))/float(X)124            year2index.append([int(year), sum])125    year2index = sorted(year2index, key=lambda item: item[0]) 126    return year2index127def get_vocabulary_dict(filename):128    voca = set()129    with open(filename) as file:130        csvreader = csv.reader(file)131        for row in csvreader:132            term = row[1]133            voca.add(term)134    return voca135def plot(year2index, filename):136    OX = []137    OY = []138    for item in year2index:139        OX.append(item[0]) #year140        OY.append(item[1]) #index141    fig = plt.figure()142    ax = plt.subplot(111)143    width = 0.8144    ind = np.arange(len(OY))145    ax.bar(ind, OY, width=width)146    ax.set_xticks(ind + width/2)147    ax.set_xticklabels(OX, rotation=90)148    plt.savefig(filename)149def main(argv):150    filename = "mrs.csv"151    voca_all = get_vocabulary_all(filename)152    #voca = get_vocabulary_filter(filename)153    voca_lda = get_vocabulary_lda("lda-bd92346b-100-c1fbb8bd/05000/term-index.txt")154    voca_dict = get_vocabulary_dict("materialsdictionary_v1.csv")155    year2index_all = compute_simpson_index(filename, voca_all)156    year2index_lda = compute_simpson_index(filename, voca_lda)157    year2index_dict = compute_simpson_index(filename, voca_dict)158    plot(year2index_all, "figure_all.pdf")159    plot(year2index_lda, "figure_lda.pdf")160    plot(year2index_dict, "figure_dictionary.pdf")161if __name__=="__main__":...

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