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How to use open_file method in molecule

Best Python code snippet using molecule_python

TweetsClassify.py

Source:TweetsClassify.py

1#!/usr/bin/python2#-*-coding:utf-8-*-3'''@author:duncan'''4import pickle5import config6import os7import TweetsClassifyTraining8project_path = config.project_path9data_folder_path = "/TweetsSamples/"10pickle_path = project_path + "/DocumentClassify/pickles/"11tf_transformer_path = pickle_path + "tf_transformer.picle"12categories_path = pickle_path + "categories.pickle"13count_vect_path = pickle_path + "count_vect.pickle"14def Classify(text,Classifier):15    '''16    :param text:å¾åç±»çææ¬17    :param Classifier:åç±»å¨ç±»å«18    :return: è¿ååç±»ç»æ19    '''20    if text == "" or text == None:21        return "None"22    txt = []23    # print text24    txt.append(text)25    # å¦ææ¨¡åæä¹åæä»¶ä¸åå¨åéè¦åè®ç»26    if len(os.listdir(config.project_path + "/DocumentClassify/pickles/")) <= 6:27        TweetsClassifyTraining.Training()28    # æµè¯æ°æ®è½¬åä¸ºç¹å¾åé29    open_file = open(count_vect_path)30    count_vect = pickle.load(open_file)31    open_file.close()32    x_test_counts = count_vect.transform(txt)33    open_file = open(tf_transformer_path)34    tf_transformer = pickle.load(open_file)35    open_file.close()36    x_test_tf = tf_transformer.transform(x_test_counts)37    # éæ©åç±»å¨38    classifier_path = pickle_path + Classifier + "_classifier.pickle"39    # åç±»40    open_file = open(classifier_path)41    clf = pickle.load(open_file)42    open_file.close()43    open_file = open(categories_path)44    target_names = pickle.load(open_file)45    open_file.close()46    result = target_names[clf.predict(x_test_tf.toarray())[0]]47    return result48# å¤æ¨¡åèå,ç»æ¯ä¸ªæ¨¡åç»æç¸åºçæé[0.4,0.3,0.1,0.1,0.1]49def Classify_MultiModels(text,classifiers,weight):50    if text == "" or text == None:51        return "None"52    txt = []53    # print text54    txt.append(text)55    # æµè¯æ°æ®è½¬åä¸ºç¹å¾åé56    open_file = open(count_vect_path)57    count_vect = pickle.load(open_file)58    open_file.close()59    x_test_counts = count_vect.transform(txt)60    open_file = open(tf_transformer_path)61    tf_transformer = pickle.load(open_file)62    open_file.close()63    x_test_tf = tf_transformer.transform(x_test_counts)64    results = []65    results_set = []66    classify_result = {}67    for (Classifier,i) in zip(classifiers,range(len(classifiers))):68        # éæ©åç±»å¨69        classifier_path = pickle_path + Classifier + "_classifier.pickle"70        # åç±»71        open_file = open(classifier_path)72        clf = pickle.load(open_file)73        open_file.close()74        open_file = open(categories_path)75        target_names = pickle.load(open_file)76        open_file.close()77        result = target_names[clf.predict(x_test_tf.toarray())[0]]78        results.append((result,weight[i]))79        results_set.append(result)80    results_set = set(results_set)81    for res in results_set:82        value = 083        for tuple in results:84            if res == tuple[0]:85                value += tuple[1]86        classify_result[res] = value87    classify_result = sorted(classify_result.items(),key = lambda dic:dic[1],reverse = True)88    return classify_result[:1][0][0]89def Accuracy(resdic,users):90    '''91    :param resdic: æ ¼å¼: {screen_name:category}92    :param users:  æ ¼å¼: {name,screen_name,category}93    :return:åç¡®ç94    '''95    correct = 096    if isinstance(users,list):97        for dickey in resdic.keys():98            for user in users:99                if dickey == user.screen_name and resdic[dickey] == user.category:100                    correct += 1101                    break102    else:103        for dickey in resdic.keys():104            for id in users.keys():105                if dickey == id:106                    if resdic[dickey] == users[id]:107                        correct += 1108                        break109    return (correct * 1.0 / len(resdic))110# æµè¯åç±»å¨ææ111def test():112    # è¯»å20ä¸ªåäººscreenname/name/æ æ³¨åç±»113    # open_file = open(pickle_path + "20famous.pickle")114    # famous = pickle.load(open_file)115    # open_file.close()116    # famous_screen_name = os.listdir(famouse_tweets_folder_path)117    # # print famous_screen_name118    # correct  = 0119    # for name in famous_screen_name:120    #     filename = name121    #     file_path = famouse_tweets_folder_path + filename122    #     text = getTweets(file_path)123    #     category = Classify(text)124    #     for user in famous:125    #         if name == user[0] and user[2] == category:126    #             correct += 1127    #             break128    #     print "%s => %s" % (name,category)129    # print "ä»¥æ æ³¨ç20ä¸ªåäººä¸ºååç¡®çä¸º:"130    # print (correct * 1.0 / 20)131    text = """Even as China needs to reassure the international community that it has no aggressive intentions, which it is trying to do with its modest military budget increase this year, it is caught in a bit of a bind.China seeks to become a major world power, and one of the hallmarks of such a status is blue-water capability and the ability to project military might globally;"""132    print Classify(text)...

sentiment_load.py

Source:sentiment_load.py

1import nltk2import random3#from nltk.corpus import movie_reviews4from nltk.classify.scikitlearn import SklearnClassifier5import pickle6from sklearn.naive_bayes import MultinomialNB, BernoulliNB7from sklearn.linear_model import LogisticRegression, SGDClassifier8from sklearn.svm import SVC, LinearSVC, NuSVC9from nltk.classify import ClassifierI10from statistics import mode11from nltk.tokenize import word_tokenize12class VoteClassifier(ClassifierI):13    def __init__(self, *classifiers):14        self._classifiers = classifiers15    def classify(self, features):16        votes = []17        for c in self._classifiers:18            v = c.classify(features)19            votes.append(v)20        return mode(votes)21    def confidence(self, features):22        votes = []23        for c in self._classifiers:24            v = c.classify(features)25            votes.append(v)26        choice_votes = votes.count(mode(votes))27        conf = choice_votes / len(votes)28        return conf29documents_f = open("pickled_algos/documents.pickle", "rb")30documents = pickle.load(documents_f)31documents_f.close()32word_features5k_f = open("pickled_algos/word_features5k.pickle", "rb")33word_features = pickle.load(word_features5k_f)34word_features5k_f.close()35def find_features(document):36    words = word_tokenize(document)37    features = {}38    for w in word_features:39        features[w] = (w in words)40    return features41#featuresets_f = open("pickled_algos/featuresets.pickle", "rb")42#featuresets = pickle.load(featuresets_f)43#featuresets_f.close()44#random.shuffle(featuresets)45#print(len(featuresets))46#testing_set = featuresets[10000:]47#training_set = featuresets[:10000]48open_file = open("pickled_algos/originalnaivebayes5k.pickle", "rb")49classifier = pickle.load(open_file)50open_file.close()51open_file = open("pickled_algos/MNB_classifier5k.pickle", "rb")52MNB_classifier = pickle.load(open_file)53open_file.close()54open_file = open("pickled_algos/BernoulliNB_classifier5k.pickle", "rb")55BernoulliNB_classifier = pickle.load(open_file)56open_file.close()57open_file = open("pickled_algos/LogisticRegression_classifier5k.pickle", "rb")58LogisticRegression_classifier = pickle.load(open_file)59open_file.close()60open_file = open("pickled_algos/LinearSVC_classifier5k.pickle", "rb")61LinearSVC_classifier = pickle.load(open_file)62open_file.close()63open_file = open("pickled_algos/SGDC_classifier5k.pickle", "rb")64SGDC_classifier = pickle.load(open_file)65open_file.close()66voted_classifier = VoteClassifier(67                                  classifier,68                                  LinearSVC_classifier,69                                  MNB_classifier,70                                  BernoulliNB_classifier,71                                  LogisticRegression_classifier)72def sentiment(text):73    feats = find_features(text)...

sentiment_mod.py

Source:sentiment_mod.py

1import nltk2import random3#from nltk.corpus import movie_reviews4from nltk.classify.scikitlearn import SklearnClassifier5import pickle6from sklearn.naive_bayes import MultinomialNB, BernoulliNB7from sklearn.linear_model import LogisticRegression, SGDClassifier8from sklearn.svm import SVC, LinearSVC, NuSVC9from nltk.classify import ClassifierI10from statistics import mode11from nltk.tokenize import word_tokenize12class VoteClassifier(ClassifierI):13    def __init__(self, *classifiers):14        self._classifiers = classifiers15    def classify(self, features):16        votes = []17        for c in self._classifiers:18            v = c.classify(features)19            votes.append(v)20        return mode(votes)21    def confidence(self, features):22        votes = []23        for c in self._classifiers:24            v = c.classify(features)25            votes.append(v)26        choice_votes = votes.count(mode(votes))27        conf = choice_votes / len(votes)28        return conf29documents_f = open("pickled_algos/documents.pickle", "rb")30documents = pickle.load(documents_f)31documents_f.close()32word_features5k_f = open("pickled_algos/word_features5k.pickle", "rb")33word_features = pickle.load(word_features5k_f)34word_features5k_f.close()35def find_features(document):36    words = word_tokenize(document)37    features = {}38    for w in word_features:39        features[w] = (w in words)40    return features41featuresets_f = open("pickled_algos/featuresets.pickle", "rb")42featuresets = pickle.load(featuresets_f)43featuresets_f.close()44random.shuffle(featuresets)45testing_set = featuresets[10000:]46training_set = featuresets[:10000]47open_file = open("pickled_algos/originalnaivebayes5k.pickle", "rb")48classifier = pickle.load(open_file)49open_file.close()50open_file = open("pickled_algos/MNB_classifier5k.pickle", "rb")51MNB_classifier = pickle.load(open_file)52open_file.close()53open_file = open("pickled_algos/BernoulliNB_classifier5k.pickle", "rb")54BernoulliNB_classifier = pickle.load(open_file)55open_file.close()56open_file = open("pickled_algos/LogisticRegression_classifier5k.pickle", "rb")57LogisticRegression_classifier = pickle.load(open_file)58open_file.close()59open_file = open("pickled_algos/LinearSVC_classifier5k.pickle", "rb")60LinearSVC_classifier = pickle.load(open_file)61open_file.close()62open_file = open("pickled_algos/SGDC_classifier5k.pickle", "rb")63SGDC_classifier = pickle.load(open_file)64open_file.close()65voted_classifier = VoteClassifier(66                                  classifier,67                                  LinearSVC_classifier,68                                  MNB_classifier,69                                  BernoulliNB_classifier,70                                  LogisticRegression_classifier)71def sentiment(text):72    feats = find_features(text)...

cakebot.py

Source:cakebot.py

1import nltk2import random 3from nltk.classify.scikitlearn import SklearnClassifier4import pickle56from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB #7 different algorithms7from sklearn.linear_model import LogisticRegression, SGDClassifier8from sklearn.svm import SVC, LinearSVC, NuSVC9from nltk.classify import ClassifierI10from statistics import mode1112from nltk.tokenize import word_tokenize1314class VoteClassifier(ClassifierI):15	def __init__(self, *classifiers):16		self._classifiers = classifiers17		18	def classify(self, features):19		votes = []20		for c in self._classifiers:21			v = c.classify(features)22			votes.append(v)23		return mode(votes) #return most votes24	25	def confidence(self, features):26		votes = []27		for c in self._classifiers:28			v = c.classify(features)29			votes.append(v)3031		choice_votes = votes.count(mode(votes))32		conf = choice_votes / len(votes)33		return conf3435documents_f = open("pickles/documents.pickle", "rb")36documents = pickle.load(documents_f)37documents_f.close()3839word_features_over9k_f = open("pickles/word_features_over9k.pickle", "rb")40word_features = pickle.load(word_features_over9k_f)41word_features_over9k_f.close()4243def find_features(document):44	words = word_tokenize(document)45	features = {}46	for w in word_features:47		features[w] = (w in words)48	return features4950featuresets_f = open("pickles/featuresets.pickle", "rb")51featuresets = pickle.load(featuresets_f)52featuresets_f.close()	5354random.shuffle(featuresets)5556#positive data example:57training_set = featuresets[:10000]58testing_set =  featuresets[10000:]5960#Loading the 7 pickled classifiers61open_file = open("pickles/orig_classifier.pickle", "rb")62classifier = pickle.load(open_file)63open_file.close()6465open_file = open("pickles/MNB_classifier.pickle", "rb")66MNB_classifier = pickle.load(open_file)67open_file.close()6869open_file = open("pickles/BernoulliNB_classifier.pickle", "rb")70BernoulliNB_classifier = pickle.load(open_file)71open_file.close()7273open_file = open("pickles/LogisticRegression_classifier.pickle", "rb")74LogisticRegression_classifier = pickle.load(open_file)75open_file.close()7677open_file = open("pickles/SGDClassifier_classifier.pickle", "rb")78SGDClassifier_classifier = pickle.load(open_file)79open_file.close()8081open_file = open("pickles/LinearSVC_classifier.pickle", "rb")82LinearSVC_classifier = pickle.load(open_file)83open_file.close()8485open_file = open("pickles/NuSVC_classifier.pickle", "rb")86NuSVC_classifier = pickle.load(open_file)87open_file.close()8889voted_classifier = VoteClassifier(classifier,MNB_classifier,BernoulliNB_classifier,LogisticRegression_classifier,SGDClassifier_classifier,LinearSVC_classifier,NuSVC_classifier)909192def sentiment(text):93	feats = find_features(text)94	return voted_classifier.classify(feats), voted_classifier.confidence(feats)95	
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