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How to use tag_test method in Slash

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feature_extractor.py

Source:feature_extractor.py

1# coding=utf-82from itertools import chain3from pickle import dump4from nltk import bigrams5from nltk.classify.scikitlearn import SklearnClassifier6from nltk.collocations import BigramCollocationFinder7from nltk.metrics import BigramAssocMeasures8from nltk.probability import FreqDist, ConditionalFreqDist9from sklearn.linear_model import LogisticRegression10from sklearn.metrics import accuracy_score11from sklearn.naive_bayes import MultinomialNB, BernoulliNB12from sklearn.svm import SVC, LinearSVC, NuSVC13# Feature extraction function14def bag_of_words(words):15    """16    Use all words as features17    """18    return dict([(word, True) for word in words])19def bigram(words, score_function=BigramAssocMeasures.chi_sq, top_n=200):20    """21    Use bigrams as features (use chi square chose top 200 bigrams)22    """23    bigram_finder = BigramCollocationFinder.from_words(words)24    bigrams = bigram_finder.nbest(score_function, top_n)25    return bag_of_words(bigrams)26def bigram_words(words, score_function=BigramAssocMeasures.chi_sq, n=200):27    """28    Use words and bigrams as features (use chi square chose top 200 bigrams)29    """30    bigram_finder = BigramCollocationFinder.from_words(words)31    bigrams = bigram_finder.nbest(score_function, n)32    return bag_of_words(words + bigrams)33# è®¡ç®æ¯ä¸ªè¯çä¿¡æ¯çµï¼æåä¿¡æ¯éæå¤§çè¯ä½ä¸ºç¹å¾è¯34def calculate_word_scores(positive_data, negative_data):35    positive_words, negative_words = chain_words(positive_data, negative_data)36    return calculate_score(positive_words, negative_words)37def calculate_bigram_scores(positive_data, negative_data):38    positive_words, negative_words = chain_words(positive_data, negative_data)39    positive_bigram_finder = BigramCollocationFinder.from_words(positive_words)40    negative_bigram_finder = BigramCollocationFinder.from_words(negative_words)41    positive_bigrams = positive_bigram_finder.nbest(BigramAssocMeasures.chi_sq, 8000)42    negative_bigrams = negative_bigram_finder.nbest(BigramAssocMeasures.chi_sq, 8000)43    return calculate_score(negative_bigrams, positive_bigrams)44def calculate_word_bigram_scores(positive_data, negative_data):45    """46    Combine words and bigrams information scores47    """48    positive_words, negative_words = chain_words(positive_data, negative_data)49    positive_bigram_finder = BigramCollocationFinder.from_words(positive_words)50    negative_bigram_finder = BigramCollocationFinder.from_words(negative_words)51    positive_bigrams = positive_bigram_finder.nbest(BigramAssocMeasures.chi_sq, 5000)52    negative_bigrams = negative_bigram_finder.nbest(BigramAssocMeasures.chi_sq, 5000)53    return calculate_score(negative_words + negative_bigrams, positive_words + positive_bigrams)54def chain_words(positive_data, negative_data):55    positive_words = list(chain(*positive_data))56    negative_words = list(chain(*negative_data))57    return positive_words, negative_words58def calculate_score(positive_words, negative_words):59    # é¢çåå¸60    conditional_word_frequency_distribution, word_frequency_distribution = calculate_frequency(positive_words,61                                                                                               negative_words)62    positive_word_count = conditional_word_frequency_distribution['pos'].N()63    negative_word_count = conditional_word_frequency_distribution['neg'].N()64    total_word_count = positive_word_count + negative_word_count65    word_scores = {}66    for word, freq in word_frequency_distribution.iteritems():67        positive_score = BigramAssocMeasures.chi_sq(conditional_word_frequency_distribution['pos'][word],68                                                    (freq, positive_word_count), total_word_count)69        negative_score = BigramAssocMeasures.chi_sq(conditional_word_frequency_distribution['neg'][word],70                                                    (freq, negative_word_count), total_word_count)71        word_scores[word] = positive_score + negative_score72    return word_scores73def calculate_frequency(positive_words, negative_words):74    word_frequency_distribution = FreqDist()75    conditional_word_frequency_distribution = ConditionalFreqDist()76    for word in positive_words:77        word_frequency_distribution[word] += 178        conditional_word_frequency_distribution['pos'][word] += 179    for word in negative_words:80        word_frequency_distribution[word] += 181        conditional_word_frequency_distribution['neg'][word] += 182    return conditional_word_frequency_distribution, word_frequency_distribution83# 5 Second we should extact the most informative words or bigrams based on the information score84def find_best_words(word_scores, number):85    best_vals = sorted(word_scores.iteritems(), key=lambda (w, s): s, reverse=True)[:number]86    best_words = set([w for w, s in best_vals])87    return best_words88# 6 Third we could use the most informative words and bigrams as machine learning features89# Use chi_sq to find most informative words of the review90def best_word_features(words, best_words):91    return dict([(word, True) for word in words if word in best_words])92# Use chi_sq to find most informative bigrams of the review93def best_bigram_features(words, best_words):94    return dict([(word, True) for word in bigrams(words) if word in best_words])95# Use chi_sq to find most informative words and bigrams of the review96def best_combined_features(words, best_words):97    best_word_feature = best_word_features(words, best_words)98    best_bigram_feature = best_bigram_features(words, best_words)99    combined_feature = dict(best_word_feature, **best_bigram_feature)100    return combined_feature101# 7 Transform review to features by setting labels to words in review102def set_positive_label(positive_reviews, feature_extraction_method, best_words):103    posFeatures = []104    for i in positive_reviews:105        posWords = [feature_extraction_method(i, best_words), 'pos']106        posFeatures.append(posWords)107    return posFeatures108def set_negative_label(negative_reviews, feature_extraction_method, best_words):109    negFeatures = []110    for i in negative_reviews:111        negWords = [feature_extraction_method(i, best_words), 'neg']112        negFeatures.append(negWords)113    return negFeatures114def clf_score(classifier, train_set, test, tag_test):115    classifier = SklearnClassifier(classifier)116    classifier.train(train_set)117    predict = classifier.classify_many(test)118    return accuracy_score(tag_test, predict)119def cal_classifier_accuracy(train_set, test, tag_test):120    classifierlist = []121    print 'åä¸ªåç±»å¨ååº¦ï¼'122    print 'BernoulliNB`s accuracy is %f' % clf_score(BernoulliNB(), train_set, test, tag_test)123    print 'MultinomiaNB`s accuracy is %f' % clf_score(MultinomialNB(), train_set, test, tag_test)124    print 'LogisticRegression`s accuracy is %f' % clf_score(LogisticRegression(), train_set, test, tag_test)125    print 'SVC`s accuracy is %f' % clf_score(SVC(gamma=0.001, C=100., kernel='linear'), train_set, test, tag_test)126    print 'LinearSVC`s accuracy is %f' % clf_score(LinearSVC(), train_set, test, tag_test)127    print 'NuSVC`s accuracy is %f' % clf_score(NuSVC(), train_set, test, tag_test)128    # print 'GaussianNB`s accuracy is %f' %clf_score(GaussianNB())129    classifierlist.append([BernoulliNB(), clf_score(BernoulliNB(), train_set, test, tag_test)])130    classifierlist.append([MultinomialNB(), clf_score(MultinomialNB(), train_set, test, tag_test)])131    classifierlist.append([LogisticRegression(), clf_score(LogisticRegression(), train_set, test, tag_test)])132    classifierlist.append([SVC(gamma=0.001, C=100., kernel='linear'),133                           clf_score(SVC(gamma=0.001, C=100., kernel='linear'), train_set, test, tag_test)])134    classifierlist.append([LinearSVC(), clf_score(LinearSVC(), train_set, test, tag_test)])135    classifierlist.append([NuSVC(), clf_score(NuSVC(), train_set, test, tag_test)])136    return classifierlist137def find_best_classifier(classifiers):138    max_val = 0139    for c in classifiers:140        if c[1] > max_val:141            max_val = c[1]142            best = c[0]143    return best144def store_classifier(object, train_set, path):145    object_classifier = SklearnClassifier(object)146    object_classifier.train(train_set)147    dump(object_classifier, open(path + '/classifier.pkl', 'w'))148# 4. å è½½åç±»å¨149# clf = pickle.load(open(path+'/classifier.pkl'))150# è®¡ç®æµè¯éçæ¦ç151# pred = clf.prob_classify_many(extract_features(test_review))152# ä¿å153def store_predict_result(path, pred):154    p_file = open(path + '/result/result.txt', 'w')155    for i in pred:156        p_file.write(str(i.prob('pos')) + ' ' + str(i.prob('neg')) + '\n')157    p_file.close()158    pred2 = []159    pos_count = 0160    neg_count = 0161    for i in pred:162        pred2.append([i.prob('pos'), i.prob('neg')])163        if i.prob('pos') > i.prob('neg'):164            pos_count += 1165        else:166            neg_count += 1167    print 'å¥½è¯å ï¼', '%f' % ((pos_count * 1.0) / ((pos_count + neg_count) * 1.0))168    print 'å·®è¯å ï¼', '%f' % ((neg_count * 1.0) / ((pos_count + neg_count) * 1.0))169def extract_features(dataset, best_words):170    feat = []171    for i in dataset:172        #         feat.append(best_word_features(i,best_words))173        feat.append(best_combined_features(i, best_words))...

tests.py

Source:tests.py

2from django.test import TestCase3from django.template import Context, Template4from django.template import TemplateSyntaxError5class LatexifyTests(TestCase):6    def tag_test(self, template, context, output):7        t = Template("{}{}".format('{% load latexify %}', template))8        c = Context(context)9        self.assertEqual(t.render(c), output)10    def test__normal_text(self):11        template = '{% latexify test_text %}'12        context = {'test_text': 'hello world'}13        output = '<span class="django-latexify text">hello world</span>'14        self.tag_test(template, context, output)15    def test__parse_math_is_true_and_no_math_in_text(self):16        template = '{% latexify test_text parse_math=True %}'17        context = {'test_text': 'hello world'}18        output = '<span class="django-latexify text">hello world</span>'19        self.tag_test(template, context, output)20    def test__parse_math_is_true_and_inline_math_in_text(self):21        template = '{% latexify test_text parse_math=True %}'22        context = {'test_text': '\$f(x)=\sqrt(2)\$'}23        output = '<span class="django-latexify text">' \24                 '<span class="django-latexify math inline">' \25                 'f(x)=\\sqrt(2)</span>' \26                 '</span>'27        self.tag_test(template, context, output)28    def test__parse_math_is_true_and_block_math_in_text(self):29        template = '{% latexify test_text parse_math=True %}'30        context = {'test_text': '\$$f(x)=\sqrt(2)\$$'}31        output = '<span class="django-latexify text">' \32                 '<span class="django-latexify math block">' \33                 'f(x)=\\sqrt(2)</span>' \34                 '</span>'35        self.tag_test(template, context, output)36    def test__parse_math_is_true_and_block_and_inline_math_in_text(self):37        template = '{% latexify test_text parse_math=True %}'38        context = {'test_text': '\$$f(x)=\sqrt(4)\$$ is \$f(x)=2\$'}39        output = '<span class="django-latexify text">' \40                 '<span class="django-latexify math block">' \41                 'f(x)=\\sqrt(4)</span> is ' \42                 '<span class="django-latexify math inline">f(x)=2</span>' \43                 '</span>'44        self.tag_test(template, context, output)45    def test__parse_math_is_true_and_inline_and_block_math_in_text(self):46        template = '{% latexify test_text parse_math=True %}'47        context = {'test_text': '\$f(x)=\sqrt(4)\$ is \$$f(x)=2\$$'}48        output = '<span class="django-latexify text">' \49                 '<span class="django-latexify math inline">' \50                 'f(x)=\\sqrt(4)</span> is ' \51                 '<span class="django-latexify math block">f(x)=2</span>' \52                 '</span>'53        self.tag_test(template, context, output)54    def test__parse_math_is_true_not_closed_inline_math_tag_in_text(self):55        template = '{% latexify test_text parse_math=True %}'56        context = {'test_text': '\$f(x)=\sqrt(4)'}57        output = '<span class="django-latexify text">\\$f(x)=\\sqrt(4)</span>'58        self.tag_test(template, context, output)59    def test__parse_math_is_true_not_closed_block_math_tag_in_text(self):60        template = '{% latexify test_text parse_math=True %}'61        context = {'test_text': '\$$f(x)=\sqrt(4)'}62        output = '<span class="django-latexify text">' \63                 '\\$$f(x)=\\sqrt(4)' \64                 '</span>'65        self.tag_test(template, context, output)66    def test__parse_math_is_true_unmatching_math_tags_in_text(self):67        template = '{% latexify test_text parse_math=True %}'68        context = {'test_text': '\$$f(x)=\sqrt(4)\$'}69        output = '<span class="django-latexify text">' \70                 '\\$$f(x)=\\sqrt(4)\\$' \71                 '</span>'72        self.tag_test(template, context, output)73    def test__parse_math_is_true_unmatching_math_tags_reversed(self):74        template = '{% latexify test_text parse_math=True %}'75        context = {'test_text': '\$f(x)=\sqrt(4)\$$'}76        output = '<span class="django-latexify text">' \77                 '\\$f(x)=\\sqrt(4)\\$$' \78                 '</span>'79        self.tag_test(template, context, output)80    def test__parse_math_is_true_inline_tag_inside_block_tag(self):81        template = '{% latexify test_text parse_math=True %}'82        context = {'test_text': '\$$g(x)=\$\sqrt(9)\$\$$'}83        output = '<span class="django-latexify text">' \84                 '\\$$g(x)=\\$\\sqrt(9)\\$\\$$' \85                 '</span>'86        self.tag_test(template, context, output)87    def test__parse_math_is_true_block_tag_inside_inline_tag(self):88        template = '{% latexify test_text parse_math=True %}'89        context = {'test_text': '\$y(x)=\$$\sqrt(16)\$$\$'}90        out = '<span class="django-latexify text">' \91              '\\$y(x)=\\$$\\sqrt(16)\\$$\\$' \92              '</span>'93        self.tag_test(template, context, out)94    def test__parse_math_is_true_normal_text_then_block_tag(self):95        template = '{% latexify test_text parse_math=True %}'96        context = {'test_text':97                   'The following math eq: \$$y(x)=\sqrt(16)\$$ is complete'}98        output = '<span class="django-latexify text">' \99                 'The following math eq: ' \100                 '<span class="django-latexify math block">' \101                 'y(x)=\\sqrt(16)</span>' \102                 ' is complete</span>'103        self.tag_test(template, context, output)104    def test__parse_math_is_true_normal_text_then_inline_tag(self):105        template = '{% latexify test_text parse_math=True %}'106        context = {'test_text':107                   'The following math eq: \$y(x)=\sqrt(16)\$ is complete'}108        output = '<span class="django-latexify text">' \109                 'The following math eq: ' \110                 '<span class="django-latexify math inline">' \111                 'y(x)=\\sqrt(16)</span>' \112                 ' is complete</span>'113        self.tag_test(template, context, output)114    def test__both_inline_and_block_args_are_true(self):115        template = '{% latexify test_text math_inline=True math_block=True %}'116        context = {'test_text':117                   'The following math eq: \$y(x)=\sqrt(16)\$ is complete'}118        output = '<span class="django-latexify math inline">' \119                 'The following math eq: ' \120                 '\\$y(x)=\\sqrt(16)\\$ is complete' \121                 '</span>'122        self.tag_test(template, context, output)123    def test__math_block_set_true(self):124        template = '{% latexify test_text math_block=True %}'125        context = {'test_text':126                   'The following math eq: \$$y(x)=\sqrt(16)\$$ is complete'}127        output = '<span class="django-latexify math block">' \128                 'The following math eq: ' \129                 '\\$$y(x)=\\sqrt(16)\\$$ ' \130                 'is complete</span>'131        self.tag_test(template, context, output)132    def test__latex_css_is_specified(self):133        template = '{% latexify test_text latex_css="my_css_class" %}'134        context = {'test_text':135                   'Hello world'}136        output = '<span class="django-latexify my_css_class">' \137                 'Hello world' \138                 '</span>'139        self.tag_test(template, context, output)140    def test__value_from_setting(self):141        template = '{% value_from_settings "LATEX_MATH_INLINE_CSS_CLASS" %}'142        context = {}143        output = 'django-latexify math inline'144        self.tag_test(template, context, output)145    def test__value_from_setting_no_arg(self):146        with self.assertRaises(TemplateSyntaxError):147            template = '{% value_from_settings %}'148            context = {}149            output = ''...

sax.py

Source:sax.py

...7        self.__cur_attrs = {}8        self.__file_ast = VerilogFileAST()9        self.__cur_ast = None10        self.scanner = None11    def __tag_test(self, *args, back=False):12        if back:13            slice_pos = len(self.__tag_stack) - len(args)14            return self.__tag_stack[slice_pos:] == list(args)15        else:16            return self.__tag_stack[:len(args)] == list(args)17    def __attr_test(self, name, value=None):18        name = self.__prefix + name19        if not value:20            return self.__cur_attrs.get(name)21        else:22            return self.__cur_attrs[name] == value23    def start_element(self, name: str, attrs: dict):24        tag_test = self.__tag_test25        attr_test = self.__attr_test26        if name.startswith(self.__prefix):27            self.__tag_stack.append(name[len(self.__prefix):])28            self.__cur_attrs = attrs29            if tag_test('design', 'adHocConnections', 'adHocConnection'):30                if tag_test('externalPortReference', back=True):31                    port_name = attr_test('portRef')32                    if self.__file_ast.ports[port_name].direction == 'in':33                        self.__cur_ast.reference = port_name34                    else:   # out & inout35                        assign = VerilogAssignAST()36                        assign.name = port_name37                        assign.reference = self.__cur_ast.name38                        self.__file_ast.assigns.append(assign)39                if tag_test('internalPortReference', back=True):40                    con_ast = VerilogIOConnectionAST()41                    con_ast.port_name = attr_test('portRef')42                    con_ast.wire_name = self.__cur_ast.name43                    mod_ref = attr_test('componentRef')44                    mod_ast = self.__file_ast.modules[mod_ref]45                    mod_ast.connections.append(con_ast)46                    bus = self.scanner.get_bus(mod_ast.module_name, con_ast.port_name)47                    if self.__cur_ast.bus != bus:48                        if self.__cur_ast.bus:49                            print('warning: bus mismatch in wire "%s, %s" '50                                  'connected to port "%s, %s" of module '51                                  '"%s"' % \52                                  (self.__cur_ast.name, str(self.__cur_ast.bus),53                                  con_ast.port_name, str(bus), mod_ast.module_name))54                        self.__cur_ast.bus = bus55    def end_element(self, name: str):56        tag_test = self.__tag_test57        if name.startswith(self.__prefix):58            if tag_test('component', 'model', 'ports', 'port', back=True):59                self.__file_ast.ports[self.__cur_ast.name] = self.__cur_ast60                self.__cur_ast = None61            elif tag_test('design', 'componentInstances', 'componentInstance', back=True):62                self.__file_ast.modules[self.__cur_ast.instance_name] = self.__cur_ast63                self.__cur_ast = None64            elif tag_test('design', 'adHocConnections', 'adHocConnection', back=True):65                self.__file_ast.wires.append(self.__cur_ast)66                self.__cur_ast = None67            self.__tag_stack.pop()68    def char_data(self, text: str):69        tag_test = self.__tag_test70        attr_test = self.__attr_test71        try:72            if tag_test('component', 'name'):73                self.__file_ast.module_name = text74            elif tag_test('component', 'model', 'ports', 'port'):75                self.__cur_ast = self.__cur_ast or VerilogPortAST()76                if tag_test('name', back=True):77                    self.__cur_ast.name = text78                elif tag_test('wire', 'direction', back=True):79                    self.__cur_ast.direction = text80                elif tag_test('wire', 'vector', 'left', back=True):81                    self.__cur_ast.bus[0] = int(text)82                elif tag_test('wire', 'vector', 'right', back=True):83                    self.__cur_ast.bus[1] = int(text)84            elif tag_test('design', 'componentInstances', 'componentInstance'):85                self.__cur_ast = self.__cur_ast or VerilogModuleAST()86                if tag_test('instanceName', back=True):87                    self.__cur_ast.instance_name = text88                elif tag_test('configurableElementValues',89                        'configurableElementValue', back=True):90                    if attr_test('referenceId', 'bd:referenceName'):91                        # TODO: an inappropriate implementation92                        if not self.__cur_ast.module_name:93                            self.__cur_ast.module_name = text94                        else:95                            self.__cur_ast.module_name += text96            elif tag_test('design', 'adHocConnections', 'adHocConnection'):97                self.__cur_ast = self.__cur_ast or VerilogWireAST()98                if tag_test('name', back=True):99                    # TODO: an inappropriate implementation100                    if not self.__cur_ast.name:101                        self.__cur_ast.name = text.lower()102                    else:103                        self.__cur_ast.name += text.lower()104        except IndexError:105            pass106    107    def generate(self, bd_name):108        content = self.__file_ast.generate()...

SentimentAnalysisBaseOnMachine.py

Source:SentimentAnalysisBaseOnMachine.py

1#coding=utf-82import feature3import pickle4import nltk5import sklearn6from nltk.classify.scikitlearn import SklearnClassifier7from sklearn.svm import SVC, LinearSVC, NuSVC8from sklearn.naive_bayes import MultinomialNB, BernoulliNB9from sklearn.linear_model import LogisticRegression10from sklearn.metrics import accuracy_score11def pos_features(feature_extraction_method, pos, best_words):12    posFeatures = []13    for i in pos:14        posWords = [feature_extraction_method(i, best_words),'pos'] #ä¸ºç§¯æææ¬èµäº"pos"15        posFeatures.append(posWords)16    return posFeatures17def test_pos_features(feature_extraction_method, pos, best_words):18    posFeatures = []19    for i in pos:20        posWords = [feature_extraction_method(i, best_words)] #ä¸ºç§¯æææ¬èµäº"pos"21        posFeatures.append(posWords)22    return posFeatures23def neg_features(feature_extraction_method, neg, best_words):24    negFeatures = []25    for j in neg:26        negWords = [feature_extraction_method(j, best_words),'neg'] #ä¸ºæ¶æææ¬èµäº"neg"27        negFeatures.append(negWords)28    return negFeatures29def test_neg_features(feature_extraction_method, neg, best_words):30    negFeatures = []31    for j in neg:32        negWords = [feature_extraction_method(j, best_words)] #ä¸ºæ¶æææ¬èµäº"neg"33        negFeatures.append(negWords)34    return negFeatures35def score(classifier, trainSet, test, tag_test):36    classifier = SklearnClassifier(classifier) #å¨nltk ä¸ä½¿ç¨scikit-learn çæ¥å£37    classifier.train(trainSet) #è®ç»åç±»å¨38    pred = classifier.classify_many(test) #å¯¹å¼åæµè¯éçæ°æ®è¿è¡åç±»ï¼ç»åºé¢æµçæ ç¾39    return accuracy_score(tag_test, pred) #å¯¹æ¯åç±»é¢æµç»æåäººå·¥æ æ³¨çæ£ç¡®ç»æï¼ç»åºåç±»å¨åç¡®åº¦40'''41file_name_pos = 'source/pos.pkl'.decode('utf-8')42file_name_neg = 'source/neg.pkl'.decode('utf-8')43pos_review = pickle.load(open(file_name_pos,'r'))44neg_review = pickle.load(open(file_name_neg,'r'))45pos = pos_review46neg = neg_review47word_scores = feature.create_word_bigram_scores() #ä½¿ç¨è¯ååè¯æéä½ä¸ºç¹å¾48best_words = feature.find_best_words(word_scores, 1500) #ç¹å¾ç»´åº¦150049posFeatures = pos_features(feature.best_word_features, pos, best_words)50negFeatures = neg_features(feature.best_word_features, neg, best_words)51trainSet = posFeatures[:800] + negFeatures[:800] #ä½¿ç¨äºæ´å¤æ°æ®52testSet = posFeatures[800:900] + negFeatures[800:900]53test, tag_test = zip(*testSet)54print final_score(BernoulliNB())55'''56dimension = ['500','1000','1500','2000','2500','3000']57file_name_pos = 'source/pos.pkl'.decode('utf-8')58file_name_neg = 'source/neg.pkl'.decode('utf-8')59pos_review = pickle.load(open(file_name_pos,'r'))60neg_review = pickle.load(open(file_name_neg,'r'))61pos = pos_review62neg = neg_review63for d in dimension:64    word_scores = feature.create_word_bigram_scores()65    best_words = feature.find_best_words(word_scores, int(d))66    posFeatures = pos_features(feature.best_word_features, pos, best_words)67    negFeatures = neg_features(feature.best_word_features, neg, best_words)68    trainSet = posFeatures[:800]+negFeatures[:800]69    testSet = posFeatures[800:900]+negFeatures[800:900]70    test, tag_test = zip(*testSet)71    #print 'Feature number %f' %d72    print 'BernoulliNB`s accuracy is %f' %score(BernoulliNB(), trainSet, test, tag_test)73    print 'MultinomiaNB`s accuracy is %f' %score(MultinomialNB(), trainSet, test, tag_test)74    print 'LogisticRegression`s accuracy is %f' %score(LogisticRegression(), trainSet, test, tag_test)75    print 'SVC`s accuracy is %f' %score(SVC(), trainSet, test, tag_test)76    print 'LinearSVC`s accuracy is %f' %score(LinearSVC(), trainSet, test, tag_test)77    print 'NuSVC`s accuracy is %f' %score(NuSVC(), trainSet, test, tag_test)...

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