使用神经网络-垃圾邮件检测-LSTM或者CNN（一维卷积）效果都不错【代码有问题，pass】
from sklearn.feature_extraction.text import CountVectorizer import os from sklearn.naive_bayes import GaussianNB from sklearn.model_selection import train_test_split from sklearn import metrics import matplotlib.pyplot as plt import numpy as np from sklearn import svm from sklearn.feature_extraction.text import TfidfTransformer import tensorflow as tf import tflearn from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_1d, global_max_pool from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.merge_ops import merge from tflearn.layers.estimator import regression from tflearn.data_utils import to_categorical, pad_sequences from sklearn.neural_network import MLPClassifier from tflearn.layers.normalization import local_response_normalization from tensorflow.contrib import learn max_features=500 max_document_length=1024 def load_one_file(filename): x="" with open(filename) as f: for line in f: line=line.strip('\n') line = line.strip('\r') x+=line return x def load_files_from_dir(rootdir): x=[] list = os.listdir(rootdir) for i in range(0, len(list)): path = os.path.join(rootdir, list[i]) if os.path.isfile(path): v=load_one_file(path) x.append(v) return x def load_all_files(): ham=[] spam=[] for i in range(1,5): path="../data/mail/enron%d/ham/" % i print "Load %s" % path ham+=load_files_from_dir(path) path="../data/mail/enron%d/spam/" % i print "Load %s" % path spam+=load_files_from_dir(path) return ham,spam def get_features_by_wordbag(): ham, spam=load_all_files() x=ham+spam y=[0]*len(ham)+[1]*len(spam) vectorizer = CountVectorizer( decode_error='ignore', strip_accents='ascii', max_features=max_features, stop_words='english', max_df=1.0, min_df=1 ) print vectorizer x=vectorizer.fit_transform(x) x=x.toarray() return x,y def show_diffrent_max_features(): global max_features a=[] b=[] for i in range(1000,20000,2000): max_features=i print "max_features=%d" % i x, y = get_features_by_wordbag() x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=0) gnb = GaussianNB() gnb.fit(x_train, y_train) y_pred = gnb.predict(x_test) score=metrics.accuracy_score(y_test, y_pred) a.append(max_features) b.append(score) plt.plot(a, b, 'r') plt.xlabel("max_features") plt.ylabel("metrics.accuracy_score") plt.title("metrics.accuracy_score VS max_features") plt.legend() plt.show() def do_nb_wordbag(x_train, x_test, y_train, y_test): print "NB and wordbag" gnb = GaussianNB() gnb.fit(x_train,y_train) y_pred=gnb.predict(x_test) print metrics.accuracy_score(y_test, y_pred) print metrics.confusion_matrix(y_test, y_pred) def do_svm_wordbag(x_train, x_test, y_train, y_test): print "SVM and wordbag" clf = svm.SVC() clf.fit(x_train, y_train) y_pred = clf.predict(x_test) print metrics.accuracy_score(y_test, y_pred) print metrics.confusion_matrix(y_test, y_pred) def get_features_by_wordbag_tfidf(): ham, spam=load_all_files() x=ham+spam y=[0]*len(ham)+[1]*len(spam) vectorizer = CountVectorizer(binary=True, decode_error='ignore', strip_accents='ascii', max_features=max_features, stop_words='english', max_df=1.0, min_df=1 ) print vectorizer x=vectorizer.fit_transform(x) x=x.toarray() transformer = TfidfTransformer(smooth_idf=False) print transformer tfidf = transformer.fit_transform(x) x = tfidf.toarray() return x,y def do_cnn_wordbag(trainX, testX, trainY, testY): global max_document_length print "CNN and tf" trainX = pad_sequences(trainX, maxlen=max_document_length, value=0.) testX = pad_sequences(testX, maxlen=max_document_length, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Building convolutional network network = input_data(shape=[None,max_document_length], name='input') network = tflearn.embedding(network, input_dim=1000000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.8) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') # Training model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch=5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=100,run_id="spam") def do_rnn_wordbag(trainX, testX, trainY, testY): global max_document_length print "RNN and wordbag" trainX = pad_sequences(trainX, maxlen=max_document_length, value=0.) testX = pad_sequences(testX, maxlen=max_document_length, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) # Network building net = tflearn.input_data([None, max_document_length]) net = tflearn.embedding(net, input_dim=10240000, output_dim=128) net = tflearn.lstm(net, 128, dropout=0.8) net = tflearn.fully_connected(net, 2, activation='softmax') net = tflearn.regression(net, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy') # Training model = tflearn.DNN(net, tensorboard_verbose=0) model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True, batch_size=10,run_id="spm-run",n_epoch=5) def do_dnn_wordbag(x_train, x_test, y_train, y_testY): print "DNN and wordbag" # Building deep neural network clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes = (5, 2), random_state = 1) print clf clf.fit(x_train, y_train) y_pred = clf.predict(x_test) print metrics.accuracy_score(y_test, y_pred) print metrics.confusion_matrix(y_test, y_pred) def get_features_by_tf(): global max_document_length x=[] y=[] ham, spam=load_all_files() x=ham+spam y=[0]*len(ham)+[1]*len(spam) vp=tflearn.data_utils.VocabularyProcessor(max_document_length=max_document_length, min_frequency=0, vocabulary=None, tokenizer_fn=None) x=vp.fit_transform(x, unused_y=None) x=np.array(list(x)) return x,y if __name__ == "__main__": print "Hello spam-mail" #print "get_features_by_wordbag" #x,y=get_features_by_wordbag() #x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.4, random_state = 0) #print "get_features_by_wordbag_tfidf" #x,y=get_features_by_wordbag_tfidf() #x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.4, random_state = 0) #NB #do_nb_wordbag(x_train, x_test, y_train, y_test) #show_diffrent_max_features() #SVM #do_svm_wordbag(x_train, x_test, y_train, y_test) #DNN #do_dnn_wordbag(x_train, x_test, y_train, y_test) print "get_features_by_tf" x,y=get_features_by_wordbag() x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.4, random_state = 0) #CNN do_cnn_wordbag(x_train, x_test, y_train, y_test) #RNN #do_rnn_wordbag(x_train, x_test, y_train, y_test)
自己写检测算法的时候也记得多个算法比较下
