How to use open_file method in molecule

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TweetsClassify.py

Source:TweetsClassify.py Github

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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:待分类的文本17 :param Classifier:分类器类别18 :return: 返回分类结果19 '''20 if text == "" or text == None:21 return "None"22 txt = []23 # print text24 txt.append(text)25 # 如果模型持久化文件不存在则需要先训练26 if len(os.listdir(config.project_path + "/DocumentClassify/pickles/")) <= 6:27 TweetsClassifyTraining.Training()28 # 测试数据转化为特征向量29 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 # 选择分类器38 classifier_path = pickle_path + Classifier + "_classifier.pickle"39 # 分类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# 多模型融合,给每个模型结果相应的权重[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 # 测试数据转化为特征向量56 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 # 选择分类器69 classifier_path = pickle_path + Classifier + "_classifier.pickle"70 # 分类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: 格式: {screen_name:category}92 :param users: 格式: {name,screen_name,category}93 :return:准确率94 '''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# 测试分类器效果111def test():112 # 读取20个名人screenname/name/标注分类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 "以标注的20个名人为准准确率为:"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)...

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sentiment_load.py

Source:sentiment_load.py Github

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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)...

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sentiment_mod.py

Source:sentiment_mod.py Github

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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)...

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cakebot.py

Source:cakebot.py Github

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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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