class TF_Transformer(base.BaseEstimator, base.TransformerMixin):
def __init__(self):
self.cv_bi = CountVectorizer(min_df=2,max_df=0.7,ngram_range=(1,2))
self.tfidf_trans = TfidfTransformer()
self.SVD_trans = TruncatedSVD(n_components=300)
# X is a list of Fit_Review named tuples, y is none
def fit(self, X, y=None):
texts = [review.text for review in X]
counts = self.cv_bi.fit_transform(texts)
counts_tfidf = self.tfidf_trans.fit_transform(counts)
self.SVD_trans.fit(counts_tfidf)
return self
# X is a list of either Fit_Review or Prod_Corpus named tuples
def transform(self, X):
texts = [review.text for review in X]
counts = self.cv_bi.transform(texts)
counts_tfidf = self.tfidf_trans.transform(counts)
counts_trunc = self.SVD_trans.transform(counts_tfidf)
return counts_trunc
def check_webshell(clf,dir):
all=0
all_php=0
webshell=0
webshell_files_list = load_files_re(webshell_dir)
CV = CountVectorizer(ngram_range=(3, 3), decode_error="ignore", max_features=max_features,
token_pattern=r'\b\w+\b', min_df=1, max_df=1.0)
x = CV.fit_transform(webshell_files_list).toarray()
transformer = TfidfTransformer(smooth_idf=False)
transformer.fit_transform(x)
g = os.walk(dir)
for path, d, filelist in g:
for filename in filelist:
fulepath=os.path.join(path, filename)
t = load_file(fulepath)
t_list=[]
t_list.append(t)
x2 = CV.transform(t_list).toarray()
x2 = transformer.transform(x2).toarray()
y_pred = clf.predict(x2)
all+=1
if filename.endswith('.php'):
all_php+=1
if y_pred[0] == 1:
print "%s is webshell" % fulepath
webshell+=1
print "Scan %d files(%d php files),%d files is webshell" %(all,all_php,webshell)
def text_sentiment(docs_new):
docs_new=[docs_new]
twenty_train= load_files('./Sentiment') #the complete data is in this directory; like comp.graphics etc
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(twenty_train.data)
tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
# Fit a classifier on the training set
#clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)
#f = open('my_classifier.pickle', 'wb')
#pickle.dump(clf, f)
#f = open('my_classifier.pickle',)
#clf = pickle.load(f)
#f.close()
# save the classifier
#with open('my_sentiment.pkl', 'wb') as fid:
#cPickle.dump(clf, fid)
# load it again
with open('my_sentiment.pkl', 'rb') as fid:
clf = cPickle.load(fid)
X_new_counts = count_vect.transform(docs_new)
X_new_tfidf = tfidf_transformer.transform(X_new_counts)
predicted = clf.predict(X_new_tfidf)
return twenty_train.target_names[predicted]
def handle_doc(word_set,rs_path):
doc_dir = os.listdir(rs_path)
doc_matrix = []
doc_cat = []
for docs in doc_dir:
files = os.listdir(rs_path+docs)
print "start to handle the --> "+docs
for file_d in files:
d_path = rs_path+docs+'/'+file_d
#get the single file path
with open(d_path,'rb') as text_file:
str_tmp = ''
file_lines = text_file.readlines()
for line in file_lines:
pattern = r'''[a-zA-Z]+'''
tokens = nltk.regexp_tokenize(line,pattern)
for t in tokens:
if t.lower() in word_set:
str_tmp += t.lower()
str_tmp += ' '
doc_matrix.append(str_tmp)
doc_cat.append(cat_dic[docs])
text_file.close()
str_tmp = ''
for sw in word_set:
str_tmp += sw
str_tmp += ' '
doc_matrix.append(str_tmp)
doc_cat.append('NAN')
vectorizer = CountVectorizer()
doc_num = vectorizer.fit_transform(doc_matrix)
tfidf = TfidfTransformer()
doc_tfidf = tfidf.fit_transform(doc_num)
return doc_tfidf[:-1,:],doc_cat[:-1]
def bayes_tfidf(prefix, sufix, dic_fn):
"""
prefix example: ./data/single_label_sen/sen_spanish_protest
sufix example: pop_cat
"""
train_file = prefix + "_train.txt.tok"
test_file = prefix + "_test.txt.tok"
train_y_file = prefix + "_train." + sufix
test_y_file = prefix + "_test." + sufix
dic_cn = {k.strip(): i for i, k in enumerate(open(dic_fn))}
word_train_set = [l.strip().lower() for l in open(train_file)]
word_test_set = [l.strip().lower() for l in open(test_file)]
train_y = [dic_cn[l.strip()] for l in open(train_y_file)]
test_y = [dic_cn[l.strip()] for l in open(test_y_file)]
# construct the word count matrix
count_vect = CountVectorizer()
train_set_count = count_vect.fit_transform(word_train_set)
test_set_count = count_vect.transform(word_test_set)
# construct tfidf matrix
tfidf_transformer = TfidfTransformer()
train_set_x = tfidf_transformer.fit_transform(train_set_count)
test_set_x = tfidf_transformer.transform(test_set_count)
print "start the model"
test_score = bayes_experiment([train_set_x, train_y], [test_set_x, test_y])
return test_score
def get_feature_by_bag_tfidf():
global white_count
global black_count
global max_features
print "max_features=%d" % max_features
x=[]
y=[]
webshell_files_list = load_files_re(webshell_dir)
y1=[1]*len(webshell_files_list)
black_count=len(webshell_files_list)
wp_files_list =load_files_re(whitefile_dir)
y2=[0]*len(wp_files_list)
white_count=len(wp_files_list)
x=webshell_files_list+wp_files_list
y=y1+y2
CV = CountVectorizer(ngram_range=(2, 4), decode_error="ignore",max_features=max_features,
token_pattern = r'\b\w+\b',min_df=1, max_df=1.0)
x=CV.fit_transform(x).toarray()
transformer = TfidfTransformer(smooth_idf=False)
x_tfidf = transformer.fit_transform(x)
x = x_tfidf.toarray()
return x,y
def race_tfidf(data, can_be_noun_arg, stop_words):
print
data = data.groupby('race')['last']
data = dict(list(data))
docs = []
for k in data:
docs.append(' '.join(data[k]))
count_vectorizer = CountVectorizer(stop_words='english')
counts = count_vectorizer.fit_transform(docs)
#print counts.todense().shape
tfidf = TfidfTransformer(norm="l2", sublinear_tf='True')
tfidf.fit(counts)
#print "IDF:", tfidf.idf_.shape
tf_idf_matrix = tfidf.transform(counts)
freqs = {}
sorted_voc = sorted(count_vectorizer.vocabulary_.iteritems(), key=operator.itemgetter(1))
terms,_ = zip(*sorted_voc)
for i,k in enumerate(data.keys()):
# make list
row = np.array(tf_idf_matrix.todense()[i,:])[0].tolist()
freq = zip(terms, row)
freqs[k] = sorted(freq, reverse=True, key=lambda x: x[1])
print freqs[k][:5]
#print tf_idf_matrix.todense().shape
return freqs
def work_with_simple_bag_of_words():
count = CountVectorizer()
docs = np.array([
'The sun is shining',
'The weather is sweet',
'The sun is shining and the weather is sweet',
])
bag = count.fit_transform(docs)
print(count.vocabulary_)
print(bag.toarray())
np.set_printoptions(precision=2)
tfidf = TfidfTransformer(use_idf=True, norm='l2', smooth_idf=True)
print(tfidf.fit_transform(bag).toarray())
tf_is = 2
n_docs = 3
idf_is = np.log((n_docs+1) / (3+1))
tfidf_is = tf_is * (idf_is + 1)
print("tf-idf of term 'is' = %.2f" % tfidf_is)
tfidf = TfidfTransformer(use_idf=True, norm=None, smooth_idf=True)
raw_tfidf = tfidf.fit_transform(bag).toarray()[-1]
print(raw_tfidf)
l2_tfidf = raw_tfidf / np.sqrt(np.sum(raw_tfidf**2))
print(l2_tfidf)
def make_training_xy(self, data):
X = self.vectorizer.fit_transform(data.body)
if self.tfidf:
X = TfidfTransformer().fit_transform(X)
X = X.tocsc()
Y = normalize_scores(data.net, data.subreddit[0])
return X,Y
def test_tfidf_no_smoothing():
X = [[1, 1, 1],
[1, 1, 0],
[1, 0, 0]]
tr = TfidfTransformer(smooth_idf=False, norm='l2')
tfidf = tr.fit_transform(X).toarray()
assert_true((tfidf >= 0).all())
# check normalization
assert_array_almost_equal((tfidf ** 2).sum(axis=1), [1., 1., 1.])
# the lack of smoothing make IDF fragile in the presence of feature with
# only zeros
X = [[1, 1, 0],
[1, 1, 0],
[1, 0, 0]]
tr = TfidfTransformer(smooth_idf=False, norm='l2')
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
1. / np.array([0.])
numpy_provides_div0_warning = len(w) == 1
in_warning_message = 'divide by zero'
tfidf = assert_warns_message(RuntimeWarning, in_warning_message,
tr.fit_transform, X).toarray()
if not numpy_provides_div0_warning:
raise SkipTest("Numpy does not provide div 0 warnings.")
def tfidf(fileList):
segPath = sys.path[0] + '/seg_result'
corpus = [] #存取文档的分词结果
for eachFile in fileList:
fileName = segPath + '/' + eachFile
f = open(fileName,'r+')
content = f.read()
corpus.append(content)
vectorizer = CountVectorizer() # 该类会将文本中的词语转换为词频矩阵，矩阵元素a[i][j] 表示j词在i类文本下的词频
transformer = TfidfTransformer() # 该类会统计每个词语的tf-idf权值,同时会使用默认的中文停用词
tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus)) # 第一个fit_transform是计算tf-idf，第二个fit_transform是将文本转为词频矩阵
word = vectorizer.get_feature_names() # 获取词袋模型中的所有词语
weight = tfidf.toarray() # 将tf-idf矩阵抽取出来，元素a[i][j]表示j词在i类文本中的tf-idf权重
#创建tfidf文件夹,保存tf-idf的结果
tfidfFilePath = os.getcwd() + '/tfidfFile'
if not os.path.exists(tfidfFilePath):
os.mkdir(tfidfFilePath)
for i in range(len(weight)):
print u"--------Writing all the tf-idf in the", i, u" file into ", tfidfFilePath + '/' + str(i) + '.txt', "--------"
name = tfidfFilePath + '/' + string.zfill(i, 5) + '.txt'
f = open(name,'w+')
for j in range(len(word)):
#f.write(word[j] + " " + str(weight[i][j]) + "\n")
#f.write(str(weight[i][j]) + "\n")
f.write(word[j] + "\n")
f.close()
def test_pickling_transformer():
X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
orig = TfidfTransformer().fit(X)
s = pickle.dumps(orig)
copy = pickle.loads(s)
assert_equal(type(copy), orig.__class__)
assert_array_equal(copy.fit_transform(X).toarray(), orig.fit_transform(X).toarray())
def test_tfidf_no_smoothing():
X = [[1, 1, 1],
[1, 1, 0],
[1, 0, 0]]
tr = TfidfTransformer(smooth_idf=False, norm='l2')
tfidf = tr.fit_transform(X).toarray()
assert_true((tfidf >= 0).all())
# check normalization
assert_array_almost_equal((tfidf ** 2).sum(axis=1), [1., 1., 1.])
# the lack of smoothing make IDF fragile in the presence of feature with
# only zeros
X = [[1, 1, 0],
[1, 1, 0],
[1, 0, 0]]
tr = TfidfTransformer(smooth_idf=False, norm='l2')
with warnings.catch_warnings(record=True) as w:
tfidf = tr.fit_transform(X).toarray()
assert_equal(len(w), 1)
# For Python 3 compatibility
if hasattr(w[0].message,'args') :
assert_true("divide by zero" in\
w[0].message.args[0])
else :
assert_true("divide by zero" in\
w[0].message)
class VectorModel(object):
def __init__(self , list_of_comments=None):
self.__list_of_comments = list_of_comments
self.__vectorizer = []
self.__corpus_simple_vector = []
self.__transformer = []
self.__corpus_tf_idf = []
#self.prepare_models()
def prepare_models(self):
self.__vectorizer = CountVectorizer()
vector = self.__vectorizer.fit_transform(self.__list_of_comments)
self.__corpus_simple_vector = vector.toarray()
self.__transformer = TfidfTransformer()
tfidf = self.__transformer.fit_transform(self.__corpus_simple_vector)
self.__corpus_tf_idf = tfidf.toarray()
return [self.__vectorizer , self.__corpus_simple_vector , self.__transformer , self.__corpus_tf_idf]
def set_models(self , vectorizer , transformer):
self.__vectorizer = vectorizer
self.__transformer = transformer
def get_comment_frequency_vector(self , comments):
vec_comments = []
for i in comments:
vec_comments.append(i)
vectores = self.__vectorizer.transform(vec_comments).toarray()
return vectores
def get_comment_tf_idf_vector(self , comments):
vector = self.get_comment_frequency_vector(comments)
result = self.__transformer.transform(vector).toarray()
return result
class UnitClassifier(Trainer):
def __init__(self, x, y, train_ratio):
super(UnitClassifier, self).__init__(x, y, train_ratio)
self._count_vec = CountVectorizer()
self._tfidf_transformer = TfidfTransformer()
def Fit(self):
x_count = self._count_vec.fit_transform(self._x_train)
self._tfidf_transformer.fit(x_count)
def Preprocess(self, x):
return self._tfidf_transformer.transform(self._count_vec.transform(x))
def Learn(self, x_train, y_train):
LOG.info('x_train.shape = %s', str(x_train.shape))
LOG.info('len(y_train) = %d', len(y_train))
clf = RandomForestClassifier(verbose=0, n_jobs=-1, n_estimators=20)
LOG.info('Training...')
clf.fit(x_train, y_train)
LOG.info('Done...')
return clf
def Eval(self):
LOG.info('Eval ...')
y_pred = self.Predict(self._x_test)
return {
'misclass': np.mean(y_pred != self._y_test),
'report': classification_report(self._y_test, y_pred,
target_names=self._model.classes_)
}
class CaloriesRegressor(Trainer):
def __init__(self, x, y, train_ratio):
super(CaloriesRegressor, self).__init__(x, y, train_ratio)
self._count_vec = CountVectorizer()
self._tfidf_transformer = TfidfTransformer()
def Fit(self):
x_count = self._count_vec.fit_transform(self._x_train)
self._tfidf_transformer.fit(x_count)
def Preprocess(self, x):
return self._tfidf_transformer.transform(self._count_vec.transform(x))
def Learn(self, x_train, y_train):
LOG.info('x_train.shape = %s', str(x_train.shape))
LOG.info('len(y_train) = %d', len(y_train))
clf = RandomForestRegressor(verbose=0, n_jobs=-1, n_estimators=100)
LOG.info('Training...')
clf.fit(x_train, y_train)
LOG.info('Done...')
return clf
def Eval(self):
LOG.info('Eval ...')
y_pred = self.Predict(self._x_test)
return {
'median_absolute_error':
median_absolute_error(self._y_test, y_pred),
'mean_squared_error': mean_squared_error(self._y_test, y_pred),
'explained_variance_score':
explained_variance_score(self._y_test, y_pred),
}
def load_dataset(prefix, sufix, dic_fn, vocab_fn='./data/english_review.trn-100000.vocab'):
train_file = prefix + "_train.txt.tok"
test_file = prefix + "_test.txt.tok"
train_y_file = prefix + "_train." + sufix
test_y_file = prefix + "_test." + sufix
dic_cn = {k.strip(): i for i, k in enumerate(open(dic_fn))}
word_train_set = [l.strip().lower() for l in open(train_file)]
word_test_set = [l.strip().lower() for l in open(test_file)]
train_y = [dic_cn[l.strip()] for l in open(train_y_file)]
test_y = [dic_cn[l.strip()] for l in open(test_y_file)]
vocab = [l.strip().lower().split("\t")[0] for l in open(vocab_fn)]
count_vect = CountVectorizer(vocabulary=vocab)
train_set_count = count_vect.fit_transform(word_train_set)
test_set_count = count_vect.transform(word_test_set)
tfidf_transformer = TfidfTransformer()
train_set_x = tfidf_transformer.fit_transform(train_set_count).toarray()
test_set_x = tfidf_transformer.transform(test_set_count).toarray()
train_shared_x, train_shared_y = shared_dataset([train_set_x, train_y])
test_shared_x, test_shared_y = shared_dataset([test_set_x, test_y])
return [(train_shared_x, train_shared_y), (test_shared_x, test_shared_y)]
def tfidf_score(train_set, test_set):
stopwords = nltk.corpus.stopwords.words('english')
vectorizer = TfidfVectorizer(min_df=1, stop_words=set(stopwords))
#Remove all the None Types from the input datasets
train_set = filter(None, train_set)
test_set = filter(None, test_set)
vectorizer.fit_transform(train_set)
#print "Word Index is {0} \n".format(vectorizer.vocabulary_)
smatrix = vectorizer.transform(test_set)
tfidf = TfidfTransformer(norm="l2")
tfidf.fit(smatrix)
#print "IDF scores:", tfidf.idf_
tf_idf_matrix = tfidf.transform(smatrix)
pairwise_similarity = tf_idf_matrix * tf_idf_matrix.T
msum = tf_idf_matrix.sum(axis=1)
cos_sum = pairwise_similarity.sum(axis=1)
mlist = msum.tolist()
cos_sim = cos_sum.tolist()
count = 0
tfidfscores = {}
for s in train_set:
tfidfscores[s] = []
tfidfscores[s].append(mlist[count][0])
tfidfscores[s].append(cos_sim[count][0])
count += 1
return tfidfscores
def getContextFeature(self):
import time
print 'start to get Context Feature'
start = time.time()
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
#when we meet the large corpus, need to input an iteration!
corpus = self.getIterText()
#transfer the text into word frequency matrix
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
tfidf=transformer.fit_transform(vectorizer.fit_transform(corpus))
print 'get word'
word=vectorizer.get_feature_names()
print 'get weight'
weight=tfidf
print 'weight type:', type(weight)
#print weight
end = time.time()
print 'total time: \t', end-start
return weight,word
def getTfidfData(dataTrain, dataTest, dataHold):
print dataTrain.target_names
count_vect = CountVectorizer(strip_accents='ascii', stop_words='english', max_features=len(dataTrain.target) * 2)
tfidf_transformer = TfidfTransformer(sublinear_tf=True)
X_counts = count_vect.fit_transform(dataTrain.data)
X_tfidf = tfidf_transformer.fit_transform(X_counts)
print X_tfidf.shape
Y_counts = count_vect.transform(dataTest.data)
Y_tfidf = tfidf_transformer.transform(Y_counts)
print Y_tfidf.shape
H_counts = count_vect.transform(dataHold.data)
H_tfidf = tfidf_transformer.transform(H_counts)
print 'feature selection using chi square test', len(dataTrain.target)
feature_names = count_vect.get_feature_names()
ch2 = SelectKBest(chi2, k='all')
X_tfidf = ch2.fit_transform(X_tfidf, dataTrain.target)
Y_tfidf = ch2.transform(Y_tfidf)
H_tfidf = ch2.transform(H_tfidf)
if feature_names:
# keep selected feature names
feature_names = [feature_names[i] for i
in ch2.get_support(indices=True)]
if feature_names:
feature_names = numpy.asarray(feature_names)
print 'important features'
print feature_names[:10]
return X_tfidf, Y_tfidf, H_tfidf
def tf_idf(seg_files):
seg_path = './segfile/'
corpus = []
for file in seg_files:
fname = seg_path + file
f = open(fname, 'r+')
content = f.read()
f.close()
corpus.append(content)
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
tfdif = transformer.fit_transform(vectorizer.fit_transform(corpus))
word = vectorizer.get_feature_names()
weight = tfdif.toarray()
save_path = './tfidffile'
if not os._exists(save_path):
os.mkdir(save_path)
for i in range(len(weight)):
print('--------Writing all the tf-idf in the', i, u' file into ', save_path + '/' + string.zfill(i, 5) + '.txt',
'--------')
f = open(save_path + '/' + string.zfill(i, 5) + '.txt', 'w+')
for j in range(len(word)):
f.write(word[j] + ' ' + str(weight[i][j]) + '\r\n')
f.close()
def test_classifiers():
print "running bayes classifier.."
# train_bayesian_classifier_from_scratch()
dataset = get_thing_from_file("training_dataset.txt")
print dataset.target_names
bayes = get_thing_from_file("bayes.txt")
bayes_model = bayes.fit(dataset.data, dataset.target)
bayes_model = get_thing_from_file("bayes_model.txt")
results = []
count = 0
url_arr = []
bayes_predicted = bayes_model.predict(dataset)
# for url in get_test_articles():
# url_arr.append(url)
article_arr = get_article_array(url_arr)
docs_new = ['God is love', 'OpenGL on the GPU is fast']
count_vect = CountVectorizer()
tfidf_trans = TfidfTransformer()
x_new_counts = count_vect.transform(docs_new)
x_new_horse = tfidf_trans.transform(x_new_counts)
predicted = bayes_model.predict(x_new_horse)
for doc, category in zip(docs_new, predicted):
print('%r => %s' % (doc, dataset.target_names[category]))
def tfidf(corpus, word_category, file_to_write):
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus))
weight = tfidf.toarray()
sum_weight = np.sum(weight, axis=0)
word = vectorizer.get_feature_names()
word_and_weight = []
for i in range(len(sum_weight)):
word_and_weight.append([word[i], sum_weight[i]])
word_and_weight.sort(key=lambda key: key[1], reverse=True)
f = open(file_to_write, "w+")
result = []
for j in range(len(word_and_weight)):
try:
f.write(
word_and_weight[j][0]
+ " "
+ str(word_and_weight[j][1])
+ " "
+ word_category[word_and_weight[j][0]]
+ "\n"
)
result.append([word_and_weight[j][0], word_and_weight[j][1], word_category[word_and_weight[j][0]]])
except:
continue
f.close()
return result
def extract_text_features(train_data, test_data):
"""
Returns one types of training and test data features.
1) Term Frequency times Inverse Document Frequency (tf-idf): X_train_tfidf, X_test_tfidf
Parameters
----------
train_data : List[str]
Training data in list. Will only take 30000 reviews for efficiency purposes
test_data : List[str]
Test data in list
Returns
-------
Tuple(scipy.sparse.csr.csr_matrix,.., list)
Returns X_train_tfidf, X_test_tfidf, vocab as a tuple.
"""
# set up a count vectorizer that removes english stopwords when building a term-doc matrix
count_vect = CountVectorizer(stop_words=set(stopwords.words('english')))
# build the term frequency per document matrix from a random sublist of 30,000 documents
train_counts = count_vect.fit_transform(random.sample(train_data, 30000))
test_counts = count_vect.transform(test_data)
tfidf_transformer = TfidfTransformer()
train_tfidf = tfidf_transformer.fit_transform(train_counts)
test_tfidf = tfidf_transformer.transform(test_counts)
vocab = count_vect.get_feature_names()
return (train_tfidf, test_tfidf, vocab)
def cal_product_description_tfidf():
#PART II compute the tf-idf for product description
print "\nBegins,compute the tf-idf for product description ..."
product_description_data = pd.read_csv('product_descriptions.csv')
print "\nMerge the product description into database..."
AllSet = pd.merge( AllSet , product_description_data, how='left', on='product_uid')
print "\nStemming the product description ..."
AllSet['product_description'] = AllSet['product_description'].map(lambda x: stem_process(x))
product_description=AllSet['product_description']
print "\nGet the (product description vocabulary)-(search term) frequency matrix..."
search_vect_descrip = CountVectorizer(stop_words='english', binary=True)# use binary value to indicate the frequency
search_vect_descrip.fit(product_description)#learn the vocabulary
search_descrip_fq_matrix = search_vect_descrip.transform(search_term) #get the (product description vocabulary)-(search term) frequency matrix
print "\nGet the (product description vocabulary)-(product_description) frequency matrix..."
description_vect = CountVectorizer(stop_words ='english')
description_vect.fit_transform(product_description)#learn the vocabulary
description_fq_matrix=description_vect.transform(product_description) #get the (product discription vocabulary)-(product_description) frequency matrix
print "\nGet the idf matrix..."
tfidf_transformer = TfidfTransformer(norm="l2",smooth_idf=True)
tfidf_transformer.fit(description_fq_matrix) # get idf for each vocabulary
tf_idf_descrip_matrix = tfidf_transformer.transform(description_fq_matrix) #get the idf matrix
print "\nCompute the result of tf-idf for product description ..."
tf_idf_descrip_result=[]#compute the result of tf-idf for product title
for index in range(tf_idf_descrip_matrix.shape[0]):
tf_idf_descrip_result.append((np.multiply(tf_idf_descrip_matrix[index], search_descrip_fq_matrix[index].transpose()))[0, 0])
pd.DataFrame({"id":AllSet['id'],"product_description_tfidf": tf_idf_descrip_result}).to_csv('product_description_tfidf.csv', index=False)
def LR_modeling(file_name, k, AUC=True, weight=False):
raw_data = pd.read_csv(file_name)
raw_data = raw_data.drop(['issue', 'field'], axis=1)
X = raw_data.drop('panelvote', axis=1)
y = raw_data['panelvote']
tfidf = TfidfTransformer(norm=u'l2', use_idf=True, smooth_idf=True, sublinear_tf=False)
X = tfidf.fit_transform(X.values)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.8, random_state=42)
lr = LogisticRegression(C=1)
lr.fit(X_train, y_train)
auc = np.mean(cross_validation.cross_val_score(lr, X, y, scoring="roc_auc"))
if AUC == True:
print "AUC for %s on the test data = %.3f" % (file_name, auc)
if weight == False:
top_positive, top_negative = get_top_k_nocoeff(lr.coef_[0], k)
return raw_data.columns[top_positive], raw_data.columns[top_negative]
else:
top_positive, top_negative = get_top_k(lr.coef_[0], k)
final_pos = {}
final_neg = {}
for i in top_positive.keys():
final_pos[raw_data.columns[i]] = top_positive[i]
for j in top_negative.keys():
final_neg[raw_data.columns[j]] = top_negative[j]
pos = sorted(final_pos.items(), key=operator.itemgetter(1), reverse=True)
neg = sorted(final_neg.items(), key=operator.itemgetter(1))
return pos, neg
def cal_product_title_tfidf():
#PART I compute the tf-idf for product title
print "\nBegins,compute the tf-idf for product title ..."
print "\nStemming product_title..."
AllSet['product_title'] = AllSet['product_title'].map(lambda x : stem_process(x))
product_title = AllSet['product_title']
print "\nGet the (product title vocabulary)-(search term) frequency matrix..."
search_vect_tittle = CountVectorizer(stop_words='english', binary=True)# use binary value to indicate the frequency
search_vect_tittle.fit(product_title)#learn the vocabulary
search_tittle_fq_matrix = search_vect_tittle.transform(search_term) #get the (product title vocabulary)-(search term) frequency matrix
print "\nGet the (product title vocabulary)-(product_title) frequency matrix"
title_vect = CountVectorizer(stop_words='english')
title_vect.fit_transform(product_title)#learn the vocabulary
title_fq_matrix = title_vect.transform(product_title) #get the (product title vocabulary)-(product_title) frequency matrix
print "\nGet the idf matrix"
tfidf_transformer = TfidfTransformer(norm="l2", smooth_idf=True)
tfidf_transformer.fit(title_fq_matrix) # get idf for each vocabulary
tf_idf_title_matrix = tfidf_transformer.transform(title_fq_matrix) #get the idf matrix
print "\nCompute the result of tf-idf for product title ..."
tf_idf_title_result = [] #compute the result of tf-idf for product title
for index in range(tf_idf_title_matrix.shape[0]):
tf_idf_title_result.append((np.multiply(tf_idf_title_matrix[index], search_tittle_fq_matrix[index].transpose()))[0, 0])
pd.DataFrame({"id": AllSet['id'],"product_title_tfidf": tf_idf_title_result}).to_csv('product_title_tfidf.csv', index=False)
return 0
def __init__(self, **kwargs):
pickle_loader = PickleLoader()
saved_pickles_path = kwargs.get("saved_pickles_path")
self.pickle_paths = PicklePaths(parent_dir=saved_pickles_path)
if self.pickle_paths.pickles_exist():
self.title_count_vectorizer = pickle_loader.load_pickle(
self.pickle_paths.TITLE_COUNT_VECTORIZER_PICKLE_PATH
)
self.title_tfidf_transformer = pickle_loader.load_pickle(
self.pickle_paths.TITLE_TFIDF_TRANSFORMER_PICKLE_PATH
)
self.title_classifier = pickle_loader.load_pickle(self.pickle_paths.TITLE_CLASSIFIER_PICKLE_PATH)
self.desc_count_vectorizer = pickle_loader.load_pickle(self.pickle_paths.DESC_COUNT_VECTORIZER_PICKLE_PATH)
self.desc_tfidf_transformer = pickle_loader.load_pickle(
self.pickle_paths.DESC_TFIDF_TRANSFORMER_PICKLE_PATH
)
self.desc_classifier = pickle_loader.load_pickle(self.pickle_paths.DESC_CLASSIFIER_PICKLE_PATH)
else:
# todo: don't forget lemmatization, stopwords. etc
self.title_count_vectorizer = CountVectorizer()
self.title_tfidf_transformer = TfidfTransformer()
self.desc_count_vectorizer = CountVectorizer()
self.desc_tfidf_transformer = TfidfTransformer()
self.title_classifier = None
self.desc_classifier = None
def estimation(file='song_text.txt', separator=u'--text--'):
arr = text_split_line(file, u'--text--')
dvect = data_vector(arr)
target = dvect[0]
text = dvect[1]
dic = dvect[2] # for converting target integer to artist name
# print (target)
# print (dic)
count_vect = CountVectorizer()
word_vect = count_vect.fit_transform(text)
tfidf_transformer = TfidfTransformer()
vect_tfidf = tfidf_transformer.fit_transform(word_vect)
machine = svm.SVC(probability=True) # one of the best for text, see tutorial working with text
machine.fit(vect_tfidf, target)
print (machine.score(vect_tfidf, target))
prediction = machine.predict(vect_tfidf) # accuracy test (tutorial)
print (u'model predictive accuracy: {:.1%}'
.format(np.mean(prediction == target)))
new_texts = [text[500], text[2345], text[-2], text[0], text[5893]]
new_data = count_vect.transform(new_texts)
new_tfidf = tfidf_transformer.transform(new_data)
prediction = machine.predict(new_tfidf)
for i in range(len(new_texts)):
print (u'{}\t=> {}'.format(new_texts[i].splitlines()[:2],
dic[prediction[i]]))
return
def use_pipeline_with_fs(self):
#####################
#Build a vectorizer / classifier pipeline that filters out tokens that are too rare or too frequent
#####################
pipeline = Pipeline([
('vect',
TfidfVectorizer(stop_words=stopwords, min_df=3, max_df=0.90)),
("selector", SelectPercentile()),
('clf', RandomForestClassifier()),
])
# Build a grid search to find the best parameter
# Fit the pipeline on the training set using grid search for the parameters
parameters = {
'vect__ngram_range': [(1, 1), (1, 2), (1, 3)],
'vect__use_idf': (True, False),
'clf__n_estimators': (10, 50, 100),
'clf__criterion': ("gini", "entropy"),
'clf__max_depth': (None, 2, 4),
'clf__min_samples_split': (2, 4, 6),
'selector__score_func': (chi2, f_classif),
'selector__percentile': (85, 95, 100),
}
#################
# Exhaustive search over specified parameter values for an estimator, use cv to generate data to be used
# implements the usual estimator API: when “fitting” it on a dataset all the possible combinations of parameter values are evaluated and the best combination is retained.
#################
cv = StratifiedShuffleSplit(y_train,
n_iter=5,
test_size=0.2,
random_state=42)
grid_search = GridSearchCV(pipeline,
param_grid=parameters,
cv=cv,
n_jobs=-1)
clf_gs = grid_search.fit(docs_train, y_train)
###############
# print the cross-validated scores for the each parameters set explored by the grid search
###############
best_parameters, score, _ = max(clf_gs.grid_scores_,
key=lambda x: x[1])
for param_name in sorted(parameters.keys()):
print("%s: %r" % (param_name, best_parameters[param_name]))
print("Score for gridsearch is %0.2f" % score)
#y_predicted = clf_gs.predict(docs_test)
###############
# run the classifier again with the best parameters
# in order to get 'clf' for get_important_feature function!
###############
ngram_range = best_parameters['vect__ngram_range']
use_idf = best_parameters['vect__use_idf']
score_func = best_parameters['selector__score_func']
percentile = best_parameters['selector__percentile']
# vectorisation
count_vect = CountVectorizer(stop_words=stopwords,
min_df=3,
max_df=0.90,
ngram_range=ngram_range)
X_CV = count_vect.fit_transform(docs_train)
# print number of unique words (n_features)
print("Shape of train data is " + str(X_CV.shape))
# tfidf transformation
tfidf_transformer = TfidfTransformer(use_idf=use_idf)
X_tfidf = tfidf_transformer.fit_transform(X_CV)
#################
# feature selection
#################
selector = SelectPercentile(score_func=score_func,
percentile=percentile)
combined_features = Pipeline([("vect", count_vect),
("tfidf", tfidf_transformer),
("feat_select", selector)])
X_features = combined_features.fit_transform(docs_train, y_train)
X_test_features = combined_features.transform(docs_test)
print("Shape of train data after feature selection is " +
str(X_features.shape))
print("Shape of test data after feature selection is " +
str(X_test_features.shape))
# run classifier on selected features
clf = RandomForestClassifier().fit(X_features, y_train)
# get the features which are selected and write to file
feature_boolean = selector.get_support(indices=False)
f = open(path_to_store_feature_selection_boolean_file, 'w')
for fb in feature_boolean:
f.write(str(fb) + '\n')
f.close()
##################
# get cross validation score
##################
scores = cross_val_score(clf,
X_features,
y_train,
cv=10,
scoring='f1_weighted')
print("Cross validation score: " + str(scores))
# Get average performance of classifier on training data using 10-fold CV, along with standard deviation
print("Cross validation accuracy: %0.2f (+/- %0.2f)" %
(scores.mean(), scores.std() * 2))
#################
# run classifier on test data
#################
y_predicted = clf.predict(X_test_features)
# print the mean accuracy on the given test data and labels
print("Classifier score on test data is: %0.2f " %
clf.score(X_test_features, y_test))
# Print and plot the confusion matrix
print(metrics.classification_report(y_test, y_predicted))
cm = metrics.confusion_matrix(y_test, y_predicted)
print(cm)
# import matplotlib.pyplot as plt
# plt.matshow(cm)
# plt.show()
return clf, count_vect
remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)
def LemNormalize(text):
return LemTokens(
nltk.word_tokenize(text.lower().translate(remove_punct_dict)))
LemVectorizer = CountVectorizer(tokenizer=LemNormalize, stop_words='english')
LemVectorizer.fit_transform(array)
print(LemVectorizer.vocabulary_)
tf_matrix = LemVectorizer.transform(array).toarray()
tfidfTran = TfidfTransformer(norm="l2")
tfidfTran.fit(tf_matrix)
print tfidfTran.idf_
tfidf_matrix = tfidfTran.transform(tf_matrix)
print("")
print(
"*************** printing tfidf_martrix ****************************************"
)
print("")
print tfidf_matrix.toarray()
cos_similarity_matrix = tfidf_matrix * tfidf_matrix.T
print("")
print(
"*************** printing cos_similarity_matrix *******************************"
)
print("")
def train_classifier(self):
# Get list of features
count_vect = CountVectorizer(stop_words=stopwords,
min_df=3,
max_df=0.90,
ngram_range=_ngram_range)
X_CV = count_vect.fit_transform(docs_train)
# print number of unique words (n_features)
print("Shape of train data is " + str(X_CV.shape))
# tfidf transformation###
tfidf_transformer = TfidfTransformer(use_idf=_use_idf)
X_tfidf = tfidf_transformer.fit_transform(X_CV)
# train the classifier
print("Fitting data ...")
clf = RandomForestClassifier(n_estimators=_n_estimators,
criterion=_criterion,
max_depth=_max_depth,
min_samples_split=_min_samples_split).fit(
X_tfidf, y_train)
##################
# get cross validation score
##################
scores = cross_val_score(clf,
X_tfidf,
y_train,
cv=10,
scoring='f1_weighted')
print("Cross validation score: " + str(scores))
# Get average performance of classifier on training data using 10-fold CV, along with standard deviation
print("Cross validation accuracy: %0.2f (+/- %0.2f)" %
(scores.mean(), scores.std() * 2))
##################
# run classifier on test data
##################
X_test_CV = count_vect.transform(docs_test)
print("Shape of test data is " + str(X_test_CV.shape))
X_test_tfidf = tfidf_transformer.transform(X_test_CV)
y_predicted = clf.predict(X_test_tfidf)
# print the mean accuracy on the given test data and labels
print("Classifier score on test data is: %0.2f " %
clf.score(X_test_tfidf, y_test))
print(metrics.classification_report(y_test, y_predicted))
cm = metrics.confusion_matrix(y_test, y_predicted)
print(cm)
return clf, count_vect
def make_reuters_data(data_dir):
np.random.seed(1234)
from sklearn.feature_extraction.text import CountVectorizer
from os.path import join
did_to_cat = {}
cat_list = ['CCAT', 'GCAT', 'MCAT', 'ECAT']
with open(join(data_dir, 'rcv1-v2.topics.qrels')) as fin:
for line in fin.readlines():
line = line.strip().split(' ')
cat = line[0]
did = int(line[1])
if cat in cat_list:
did_to_cat[did] = did_to_cat.get(did, []) + [cat]
for did in list(did_to_cat.keys()):
if len(did_to_cat[did]) > 1:
del did_to_cat[did]
dat_list = [
'lyrl2004_tokens_test_pt0.dat', 'lyrl2004_tokens_test_pt1.dat',
'lyrl2004_tokens_test_pt2.dat', 'lyrl2004_tokens_test_pt3.dat',
'lyrl2004_tokens_train.dat'
]
data = []
target = []
cat_to_cid = {'CCAT': 0, 'GCAT': 1, 'MCAT': 2, 'ECAT': 3}
del did
for dat in dat_list:
with open(join(data_dir, dat)) as fin:
for line in fin.readlines():
if line.startswith('.I'):
if 'did' in locals():
assert doc != ''
if did in did_to_cat:
data.append(doc)
target.append(cat_to_cid[did_to_cat[did][0]])
did = int(line.strip().split(' ')[1])
doc = ''
elif line.startswith('.W'):
assert doc == ''
else:
doc += line
assert len(data) == len(did_to_cat)
x = CountVectorizer(dtype=np.float64,
max_features=2000).fit_transform(data)
y = np.asarray(target)
from sklearn.feature_extraction.text import TfidfTransformer
x = TfidfTransformer(norm='l2', sublinear_tf=True).fit_transform(x)
x = x[:10000]
y = y[:10000]
x = np.asarray(x.todense()) * np.sqrt(x.shape[1])
print('todense succeed')
p = np.random.permutation(x.shape[0])
x = x[p]
y = y[p]
print('permutation finished')
assert x.shape[0] == y.shape[0]
x = x.reshape((x.shape[0], x.size / x.shape[0]))
np.save(join(data_dir, 'reutersidf10k.npy'), {'data': x, 'label': y})
def loaddata(data):
data1 = jieba.cut(data)
data11 = ""
for item in data1:
data11 += item + " "
return data11
dataf = pda.read_sql(sql, conn)
dataf2 = dataf.T
title = dataf2.values[0]
content = dataf2.values[1]
train_text = []
for i in content:
thisdata = loaddata(str(i))
pat1 = "<[^>]*?>"
thisdata = re.sub(pat1, "", thisdata)
thisdata = thisdata.replace("\n", "").replace("\t", "")
train_text.append(thisdata)
count_vect = CountVectorizer()
train_x_counts = count_vect.fit_transform(train_text)
#tfidf模型
from sklearn.feature_extraction.text import TfidfTransformer
tf_ts = TfidfTransformer(use_idf=True).fit(train_x_counts)
train_x_tf = tf_ts.transform(train_x_counts)
from sklearn.cluster import KMeans
kms = KMeans(n_clusters=3)
y = kms.fit_predict(train_x_tf)
dataf["type"] = y
dataf.to_csv("./01聚类归类法结果.csv")
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.naive_bayes import MultinomialNB
from skmultilearn.problem_transform import LabelPowerset
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import svm
# LabelPowerset allows for multi-label classification
# Build a pipeline for multinomial naive bayes classification
text_clf = Pipeline([
('vect',
CountVectorizer(stop_words="english", lowercase=True,
ngram_range=(1, 1))),
('tfidf', TfidfTransformer(use_idf=True)),
('clf', LabelPowerset(svm.LinearSVC())),
])
voting_clf = Pipeline([
('vect',
CountVectorizer(stop_words="english", lowercase=True,
ngram_range=(1, 1))),
('tfidf', TfidfTransformer(use_idf=False)),
('clf', ClassifierChain(svm.LinearSVC())),
])
text_clf = text_clf.fit(X_train, y_train)
voting_clf = voting_clf.fit(X_train, y_train)
HashingVectorizer(ngram_range=(1, 3),
n_features=2**27,
dtype=np.float32,
norm='l2',
lowercase=False,
stop_words=stopwords)),
('drop_cols', DropColumnsByDf(min_df=2)),
]))
],
n_jobs=1)
sparse_merge = vectorizer.fit_transform(merge)
print(f'[{time() - start_time}] Merge vectorized')
print(sparse_merge.shape)
tfidf_transformer = TfidfTransformer()
X = tfidf_transformer.fit_transform(sparse_merge)
print(f'[{time() - start_time}] TF/IDF completed')
X_train = X[:nrow_train]
print(X_train.shape)
X_test = X[nrow_train:]
del merge
del sparse_merge
del vectorizer
del tfidf_transformer
gc.collect()
X_train, X_test = intersect_drop_columns(X_train, X_test, min_df=1)
y = dataemp["label"]
# In[ ]:
# Vectorizing the text data
# In[40]:
cv = CountVectorizer()
X = cv.fit_transform(X)
# Feeding the output of vectors into TFIDF transformer
# In[41]:
tfidf = TfidfTransformer()
tfidf.fit_transform(X)
# Splitting the training and test data in the ratio of 70% training data and 30% test data
# In[42]:
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=101)
# Naive Bayes Classification model
# In[43]:
print len(sent_list_old)
print sent_list_old
print sent_list_old[3]
for sentence in sent_list_old:
sent_list.append(clean_str(sentence))
print sent_list[3]
print len(sent_list)
vectorizer = CountVectorizer(min_df=1,
stop_words='english',
strip_accents='ascii')
count_vectorizer = vectorizer.fit_transform(sent_list)
transformer = TfidfTransformer(smooth_idf=True)
tfidf = transformer.fit_transform(count_vectorizer)
print count_vectorizer.shape
mat = tfidf.toarray()
km = SphericalKMeans(10)
clusters = km.fit(mat)
centroids = km.cluster_centers_
labels = km.labels_
summa_index = []
for c in range(10):
simi_list = []
max_simi = -1 * float("inf")
max_idx = -1
centroid = centroids[c, :]
@file: tf-idf-liiuxuejiang.py @time: 18-3-28 下午6:43 @contact: [email protected] ''' import jieba import jieba.posseg as pseg import os import sys from sklearn import feature_extraction from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import CountVectorizer if __name__ == "__main__": corpus = [ "我 来到 北京 清华大学", # 第一类文本切词后的结果，词之间以空格隔开 "他 来到 了 网易 杭研 大厦", # 第二类文本的切词结果 "小明 硕士 毕业 与 中国 科学院", # 第三类文本的切词结果 "我 爱 北京 天安门" ] # 第四类文本的切词结果 vectorizer = CountVectorizer( ) # 该类会将文本中的词语转换为词频矩阵，矩阵元素a[i][j] 表示j词在i类文本下的词频 transformer = TfidfTransformer() # 该类会统计每个词语的tf-idf权值 tfidf = transformer.fit_transform(vectorizer.fit_transform( corpus)) # 第一个fit_transform是计算tf-idf，第二个fit_transform是将文本转为词频矩阵 word = vectorizer.get_feature_names() # 获取词袋模型中的所有词语 weight = tfidf.toarray() # 将tf-idf矩阵抽取出来，元素a[i][j]表示j词在i类文本中的tf-idf权重 for i in range( len(weight)): # 打印每类文本的tf-idf词语权重，第一个for遍历所有文本，第二个for便利某一类文本下的词语权重 print(u"-------这里输出第", i, u"类文本的词语tf-idf权重------") for j in range(len(word)): print(word[j], weight[i][j])
def predict_posts(self):
docs_train, docs_test, y_train, y_test = train_test_split(
X, y, test_size=0.01, random_state=42)
print("Number of data point is " + str(len(y)))
###############
# uncomment either one of the below
# predict unlabelled tweet OR test classifier on gold standard
###############
# dataset_topredict = pd.read_csv(path_to_file_to_be_predicted, header=0, names=['tweets'])
dataset_topredict = pd.read_csv(path_to_gold_standard_file,
header=0,
names=['tweets', 'class'])
X_topredict = dataset_topredict['tweets']
y_goldstandard = dataset_topredict['class']
###############
# train classifier
###############
# Get list of features
count_vect = CountVectorizer(stop_words=stopwords,
min_df=3,
max_df=0.90,
ngram_range=_ngram_range)
X_CV = count_vect.fit_transform(docs_train)
# print number of unique words (n_features)
print("Shape of train data is " + str(X_CV.shape))
# tfidf transformation###
tfidf_transformer = TfidfTransformer(use_idf=_use_idf)
X_tfidf = tfidf_transformer.fit_transform(X_CV)
# train the classifier
print("Fitting data ...")
clf = RandomForestClassifier().fit(X_tfidf, y_train)
##################
# get cross validation score
##################
scores = cross_val_score(clf,
X_tfidf,
y_train,
cv=10,
scoring='f1_weighted')
print("Cross validation score: " + str(scores))
# Get average performance of classifier on training data using 10-fold CV, along with standard deviation
# the factor two is to signify 2 sigma, which is 95% confidence level
print("Cross validation accuracy: %0.2f (+/- %0.2f)" %
(scores.mean(), scores.std() * 2))
##################
# run classifier to predict tweets
##################
X_test_CV = count_vect.transform(X_topredict)
print("Shape of test data is " + str(X_test_CV.shape))
X_test_tfidf = tfidf_transformer.transform(X_test_CV)
y_predicted = clf.predict(X_test_tfidf)
##################
# run classifier on gold standard (tweets that were labelled by twitter insight)
##################
# print the mean accuracy on the given test data and labels
print("Classifier score on test data is: %0.2f " %
clf.score(X_test_tfidf, y_goldstandard))
print(metrics.classification_report(y_goldstandard, y_predicted))
cm = metrics.confusion_matrix(y_goldstandard, y_predicted)
print(cm)
##################
# write prediction results to file
##################
f = open(path_to_store_predicted_results, 'w')
for yp in y_predicted:
f.write(yp + '\n')
f.close()
from sklearn.datasets import fetch_20newsgroups
Training_Data = fetch_20newsgroups(subset='train', shuffle=True)
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
classificationText = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB())])
classificationText = classificationText.fit(Training_Data.data,
Training_Data.target)
# Performance measurement of NB Classifier
import numpy as np
Testing_Data = fetch_20newsgroups(subset='test', shuffle=True)
prediction_target = classificationText.predict(Testing_Data.data)
print("Accuracy in Categorization in percentage : ",
(np.mean(prediction_target == Testing_Data.target)) * 100)
def train_classifier_use_feature_selection(self):
# Get list of features
count_vect = CountVectorizer(stop_words=stopwords,
min_df=3,
max_df=0.90,
ngram_range=_ngram_range)
X_CV = count_vect.fit_transform(docs_train)
# print number of unique words (n_features)
print("Shape of train data is " + str(X_CV.shape))
# tfidf transformation###
tfidf_transformer = TfidfTransformer(use_idf=_use_idf)
X_tfidf = tfidf_transformer.fit_transform(X_CV)
#################
# feature selection
#################
selector = SelectPercentile(score_func=_score_func,
percentile=_percentile)
print("Fitting data with feature selection ...")
selector.fit(X_tfidf, y_train)
# get how many features are left after feature selection
X_features = selector.transform(X_tfidf)
print("Shape of array after feature selection is " +
str(X_features.shape))
clf = RandomForestClassifier(n_estimators=_n_estimators,
criterion=_criterion,
max_depth=_max_depth,
min_samples_split=_min_samples_split).fit(
X_features, y_train)
# get the features which are selected and write to file
feature_boolean = selector.get_support(indices=False)
f = open(path_to_store_feature_selection_boolean_file, 'w')
for fb in feature_boolean:
f.write(str(fb) + '\n')
f.close()
##################
# get cross validation score
##################
scores = cross_val_score(clf,
X_features,
y_train,
cv=10,
scoring='f1_weighted')
print("Cross validation score: " + str(scores))
# Get average performance of classifier on training data using 10-fold CV, along with standard deviation
print("Cross validation accuracy: %0.2f (+/- %0.2f)" %
(scores.mean(), scores.std() * 2))
####################
#test clf on test data
####################
X_test_CV = count_vect.transform(docs_test)
print("Shape of test data is " + str(X_test_CV.shape))
X_test_tfidf = tfidf_transformer.transform(X_test_CV)
# apply feature selection on test data too
X_test_selector = selector.transform(X_test_tfidf)
print("Shape of array for test data after feature selection is " +
str(X_test_selector.shape))
y_predicted = clf.predict(X_test_selector)
# print the mean accuracy on the given test data and labels
print("Classifier score on test data is: %0.2f " %
clf.score(X_test_selector, y_test))
print(metrics.classification_report(y_test, y_predicted))
cm = metrics.confusion_matrix(y_test, y_predicted)
print(cm)
return clf, count_vect
pickle.dump(nb, f)
f.close()
# importing classification report and confussion matrix
from sklearn.metrics import confusion_matrix,classification_report
print(confusion_matrix(y_test,predictions))
print('\n')
print(classification_report(y_test,predictions))
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import Pipeline
pipeline = Pipeline([
('bow', CountVectorizer()), # strings to token integer counts
('tfidf', TfidfTransformer()), # integer counts to weighted TF-IDF scores
('classifier', MultinomialNB()), # train on TF-IDF vectors w/ Naive Bayes classifier
])
X = text['review']
y = df['rating']
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3,random_state=101)
pipeline.fit(X_train,y_train)
predictions = pipeline.predict(X_test)
print("Actual Ratings(rating): ",end = "")
display(y_test[:15])
print("Predicted Ratings: ",end = "")
# print(predictions[:15])
twenty_train = fetch_20newsgroups(subset='train', shuffle=True)
print("lenth of the twenty_train--------->", len(twenty_train))
#print(twenty_train.target_names) #prints all the categories
print("***First Line of the First Data File***")
#print("\n".join(twenty_train.data[0].split("\n")[:5]))#prints first line of the first data file
#2 Extracting features from text files
from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(twenty_train.data)
print('dim=', X_train_counts.shape)
#3 TF-IDF
from sklearn.feature_extraction.text import TfidfTransformer
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
print(X_train_tfidf.shape)
# Machine Learning
#4 Training Naive Bayes (NB) classifier on training data.
from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)
# Building a pipeline: We can write less code and do all of the above, by building a pipeline as follows:
# The names ‘vect’ , ‘tfidf’ and ‘clf’ are arbitrary but will be used later.
# We will be using the 'text_clf' going forward.
from sklearn.pipeline import Pipeline
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()), ('clf', MultinomialNB())])
text_clf = text_clf.fit(twenty_train.data, twenty_train.target)
def tfidf_transformer(bow_matrix):
transformer = TfidfTransformer(norm='l2', smooth_idf=True, use_idf=True)
tfidf_matrix = transformer.fit_transform(bow_matrix)
return transformer, tfidf_matrix
def load_imdb(feature_type='tfidf'):
"""
Load IMDB data in several formats.
:param feature_type: feature type, default is 'tfidf', others are 'origin', 'tfidf-seq'
:return:
"""
data_dir = "../DeepForestTF_Data/"
if (os.path.exists(data_dir + "imdb_x_train.npy")
and os.path.exists(data_dir + "imdb_x_test.npy")
and os.path.exists(data_dir + "imdb_y_train.npy")
and os.path.exists(data_dir + "imdb_y_test.npy")):
x_train = np.load(data_dir + "imdb_x_train.npy")
x_test = np.load(data_dir + "imdb_x_test.npy")
y_train = np.load(data_dir + "imdb_y_train.npy")
y_test = np.load(data_dir + "imdb_y_test.npy")
x_train = x_train.reshape((x_train.shape[0], -1))
x_test = x_test.reshape((x_test.shape[0], -1))
return x_train, x_test, y_train, y_test
max_features = 0
if feature_type.startswith('tfidf'):
max_features = 5000
(x_train, y_train), (x_test,
y_test) = imdb.load_data(num_words=max_features)
else:
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=None)
if feature_type == 'origin':
max_len = 400
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
elif feature_type == 'tfidf':
from sklearn.feature_extraction.text import TfidfTransformer
transformer = TfidfTransformer(smooth_idf=True)
x_train_bin = np.zeros((len(x_train), max_features), dtype=np.int16)
x_test_bin = np.zeros((len(x_test), max_features), dtype=np.int16)
for i, x_i in enumerate(x_train):
x_train_bin[i, :] = np.bincount(x_i, minlength=max_features)
for i, x_i in enumerate(x_test):
x_test_bin[i, :] = np.bincount(x_i, minlength=max_features)
transformer.fit_transform(x_train_bin)
x_train = transformer.transform(x_train_bin)
x_test = transformer.transform(x_test_bin)
x_train = np.asarray(x_train.todense())
x_test = np.asarray(x_test.todense())
elif feature_type == 'tfidf-seq':
from sklearn.feature_extraction.text import TfidfTransformer
transformer = TfidfTransformer(smooth_idf=True)
transformer2 = TfidfTransformer(smooth_idf=True)
max_len = 400
n_train = len(x_train)
n_test = len(x_test)
x_train_bin = np.zeros((n_train, max_features), dtype=np.int16)
x_test_bin = np.zeros((n_test, max_features), dtype=np.int16)
for i, x_i in enumerate(x_train):
x_train_bin_i = np.bincount(x_i)
x_train_bin[i, :len(x_train_bin_i)] = x_train_bin_i
for i, x_i in enumerate(x_test):
x_test_bin_i = np.bincount(x_i)
x_test_bin[i, :len(x_test_bin_i)] = x_test_bin_i
x_train_tfidf = transformer.fit_transform(x_train_bin)
x_test_tfidf = transformer2.fit_transform(x_test_bin)
x_train_tfidf = np.asarray(x_train_tfidf.todense())
x_test_tfidf = np.asarray(x_test_tfidf.todense())
x_train_id = sequence.pad_sequences(x_train, maxlen=max_len)
x_test_id = sequence.pad_sequences(x_test, maxlen=max_len)
x_train = np.zeros(x_train_id.shape, dtype=np.float32)
x_test = np.zeros(x_test_id.shape, dtype=np.float32)
for i in range(n_train):
x_train[i, :] = x_train_tfidf[i][x_train_id[i]]
for i in range(n_test):
x_test[i, :] = x_test_tfidf[i][x_test_id[i]]
else:
raise ValueError('Unknown feature type: {}'.format(feature_type))
x_train = x_train[:, np.newaxis, :, np.newaxis].astype('float32')
x_test = x_test[:, np.newaxis, :, np.newaxis].astype('float32')
return x_train, x_test, y_train.astype('int8'), y_test.astype('int8')
file = os.path.join(args.train, "processed_data.csv")
# Loading Data
df = pd.read_csv(file, engine="python")
# isolating the target column (label)
y = df['LABEL']
X = df.drop(['LABEL'], axis=1)
# Splitting into train and test set
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.20,
random_state=42)
nb_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()), ('nb', MultinomialNB())])
parameters_ = {
'vect__ngram_range': [(1, 1), (1, 2), (2, 2)],
'tfidf__use_idf': (True, False),
'tfidf__norm': ('l1', 'l2'),
'nb__alpha': [1, 1e-1, 1e-2]
}
#cls_naivebayes_ = MultinomialNB()
clf = GridSearchCV(nb_clf, param_grid=parameters_, cv=5)
clf.fit(X_train["PROCESSED_REVIEW"].values, y_train)
# saving the model using joblib
joblib.dump(clf, os.path.join(args.model_dir, 'model.joblib'))
def input_fn(input_data, content_type='application/json'):
tf = cv.fit_transform(comments)
terms = cv.get_feature_names()
term_sums = tf.sum(axis=0)
term_counts = []
for i in range(len(terms)):
term_counts.append([terms[i], term_sums[0,i]])
def sortSecond(e):
return e[1]
term_counts.sort(key=sortSecond, reverse=True)
print("\nTerms with Highest Frequency:")
for i in range(50):
print('{:<15s}{:>5d}'.format(term_counts[i][0], term_counts[i][1]))
print("")
# Modify tf, term frequencies, to TF/IDF matrix from the data
print("Conducting Term/Frequency Matrix using TF-IDF")
tfidf_vect = TfidfTransformer(norm=None, use_idf=True) #set norm=None
tf = tfidf_vect.fit_transform(tf)
term_idf_sums = tf.sum(axis=0)
term_idf_scores = []
for i in range(len(terms)):
term_idf_scores.append([terms[i], term_idf_sums[0,i]])
term_idf_scores.sort(key=sortSecond, reverse=True)
# In sklearn, SVD is synonymous with LSA (Latent Semantic Analysis)
lda = LatentDirichletAllocation(n_components=n_topics, max_iter=max_iter,\
learning_method=learning_method, \
learning_offset=learning_offset, \
random_state=12345)
lda.fit_transform(tf)
# I want to find articles related to the demonstrations in Hong Kong #So i wanted originally to just make my query 'Hong Kong' but I landed on: # 'hong', 'protests' and 'extradition' terms = ['hong', 'protests', 'extradition'] # After several tries with versatile letters and "hong kong", i just settled for # 'hong'. It's not like 'hong' og 'kong' is seperate words in english anyway term_idxs = [model_vect.vocabulary_.get(term) for term in terms] term_counts = [counts[idx] for idx in term_idxs] print(term_counts) #Here we get the term counts for each of the three words in our query from sklearn.feature_extraction.text import TfidfTransformer model_tfidf = TfidfTransformer() data_tfidf = model_tfidf.fit_transform(data_vect) data_tfidf # And then here I transform the count to a tfidf representation idfs = model_tfidf.idf_ term_idfs = [idfs[idx] for idx in term_idxs] term_idfs # And I get the individual weights for my selected terms df = pd.DataFrame(columns=['count', 'idf'], index=terms, data=zip(term_counts,term_idfs)) df # Here i use pandas to make a dataframe, that will let me compare my term counts with # my term weights. The one that have the largest difference is 'extradition', which i # guess means that it has a higher semantic value compared to how often it is represented
neg_word2vec_scores[i] += neg_min
#a_min = -min(diff)
#diff = [i + a_min for i in diff]
print X[0]
#pos_word2vec_scores, neg_word2vec_scores = np.array(pos_word2vec_scores), np.array(neg_word2vec_scores)
#diff = np.array(diff)
sentiment_prob_list = np.array(sentiment_prob_list)
X = np.array(X)
Y = np.array(Y)
print X.shape
print Y.shape
vectorizer = CountVectorizer(stop_words=stop)
transformer = TfidfTransformer()
#tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', \
# analyzer='word',token_pattern=r'\w{1,}',ngram_range=(1, 2), use_idf=1,smooth_idf=1,sublinear_tf=1)
trainVectorizerArray = vectorizer.fit_transform(X).toarray()
transformer.fit(trainVectorizerArray)
L = transformer.transform(trainVectorizerArray).toarray()
print L.shape
#tfv.fit(X)
#Z = tfv.transform(X)
#svd = TruncSVD(n_components = 25)
#totalsvd = svd.fit(Z)
#totalsvd = svd.fit_transform(Z)
#totalsvd = totalsvd[:,:] + 1
file_obj.close()
return bunch
#写入bunch对象
def writebunchobj(path,bunchobj):
file_obj = open(path, "wb")
pickle.dump(bunchobj,file_obj)
file_obj.close()
# 1. 读取停用词表
stopword_path = "train_word_bag/hlt_stop_words.txt"
stpwrdlst = readfile(stopword_path).splitlines()
# 2. 导入分词后的词向量bunch对象
path = "train_word_bag/train_set.dat" # 词向量空间保存路径
bunch = readbunchobj(path)
# 3. 构建tf-idf词向量空间对象
tfidfspace = Bunch(target_name=bunch.target_name,label=bunch.label,filenames=bunch.filenames,tdm=[],vocabulary={})
# 4. 使用TfidfVectorizer初始化向量空间模型
vectorizer = TfidfVectorizer(stop_words=stpwrdlst,sublinear_tf = True,max_df = 0.5)
transformer=TfidfTransformer() # 该类会统计每个词语的tf-idf权值
# 文本转为词频矩阵,单独保存字典文件
tfidfspace.tdm = vectorizer.fit_transform(bunch.contents)
tfidfspace.vocabulary = vectorizer.vocabulary_
# 创建词袋的持久化
space_path = "train_word_bag/tfdifspace.dat" # 词向量空间保存路径
writebunchobj(space_path,tfidfspace)
print "if-idf词向量空间创建成功！！！"
twenty_test = fetch_20newsgroups(subset='test',
categories=categories,
shuffle=True)
#twenty_train=fetch_20newsgroups(data_home='./scikit_learn_data',subset='train',shuffle=True)
#print(twenty_train)
#twenty_test=fetch_20newsgroups(data_home='./scikit_learn_data',subset='test',shuffle=True)
#print(twenty_train)
print("Number of Training Examples: ", len(twenty_train.data))
print("Number of Test Examples: ", len(twenty_test.data))
print(twenty_train.target_names)
from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_tf = count_vect.fit_transform(twenty_train.data)
from sklearn.feature_extraction.text import TfidfTransformer
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_tf)
X_train_tfidf.shape
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score
from sklearn import metrics
mod = MultinomialNB()
mod.fit(X_train_tfidf, twenty_train.target)
X_test_tf = count_vect.transform(twenty_test.data)
X_test_tfidf = tfidf_transformer.transform(X_test_tf)
predicted = mod.predict(X_test_tfidf)
print("Accuracy: ", accuracy_score(twenty_test.target, predicted))
print(
classification_report(twenty_test.target,
# load data.npy and target.npy
data = sp.load('data.npy')
target = sp.load('target.npy')
# vectorize method step1: count vectorizer (bag of words)
from sklearn.feature_extraction.text import CountVectorizer
countVector = CountVectorizer(stop_words=stopWords, decode_error='ignore')
trainCounts = countVector.fit_transform(data)
# .shape output format: (sample number, dict size)
# print(trainCounts.shape)
# vectorize method step2: TF-IDF vectorizer
from sklearn.feature_extraction.text import TfidfTransformer
tfTransformer = TfidfTransformer(use_idf=False).fit(trainCounts)
trainTF = tfTransformer.transform(trainCounts)
# print(trainTF.shape)
# build naive Bayes classifier
from sklearn.naive_bayes import MultinomialNB
# 1) use bag of words vector
naiveBayesClassifier = MultinomialNB().fit(trainCounts, target)
# 2) use TF-IDF vetor
# naiveBayesClassifier = MultinomialNB().fit(trainTF, target)
# print test results
predicted = naiveBayesClassifier.predict(tfTransformer.transform(countVector.transform(data)))
from sklearn import metrics
# print(metrics.classification_report(target, predicted))
import numpy as np
from numpy import *
from numpy import arange
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
corpus = []
for line in open('text3000.txt', 'r').readlines():
line = line.split('\t')
corpus.append(line[1].strip())
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus))
word = vectorizer.get_feature_names()
weight = tfidf.toarray()
print type(weight)
print len(weight)
print len(weight[0])
size = 3000
length = 200
preciseAll = 0
# resName = "euc60.txt"
# result = codecs.open(resName, 'w', 'utf-8')
for i in xrange(0, len(weight)):
d = dict()
for j in xrange(0, len(weight)):
# f.readline()
# print(f)
# np1 = np.loadtxt(f, dtype = 'str', delimiter = ',')
# np1 = np.loadtxt(filecp, dtype = 'str', delimiter = ',',skiprows=5)
df1 = pd.read_csv(filename, header=None, encoding="utf8")
np1 = np.array(df1)
data = np1[:, 0:-1]
label = np1[:, -1]
return data, label.astype('float32')
if __name__ == "__main__":
data, label = read_data("../input/data_clean.csv")
data_combine = np.hstack((data[:, 0], data[:, 1]))
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
label_up = np.hstack((label, label)).astype("float32")
similarity = np.zeros((data.shape[0], 1))
tfidf = transformer.fit_transform(vectorizer.fit_transform(data_combine))
question = csr_matrix(tfidf[0:data.shape[0]])
answer = csr_matrix(tfidf[data.shape[0]:])
data_sparse = hstack([question, answer])
plt.figure()
for model in ['logistic', 'linear', 'GBDT', 'NN']:
mse_train = []
mse_test = []
print(model)
for i in range(1, 100):
data_sparse_one = SelectKBest(chi2, k=i * 100).fit_transform(
data_sparse, label)
# print("precess finished")
for i in range(len(data['body'])):
if i in selected_indices:
bodyCommentsTest.append(data['body'][i])
scoreTest.append(data['score'][i])
else:
bodyCommentsTraining.append(data['body'][i])
scoreTraining.append(data['score'][i])
cv = CountVectorizer(stop_words='english',
strip_accents='ascii',
max_df=0.8,
ngram_range=(1, 5)) #, ngram_range=(1, 2)
bodyCommentsTraining = cv.fit_transform(bodyCommentsTraining)
bodyCommentsTest = cv.transform(bodyCommentsTest)
transformer = TfidfTransformer(norm='l2', use_idf=True, sublinear_tf=True)
bodyCommentsTraining = transformer.fit_transform(bodyCommentsTraining)
bodyCommentsTest = transformer.transform(bodyCommentsTest)
y_pred = KMeans().fit_predict(bodyCommentsTraining)
print y_pred
values = set()
for x in y_pred:
values.add(x)
temp = collections.Counter(y_pred)
maxValue = 0
class BotServer:
def __init__(self, file_path):
"""
Initialize corpus, bag-of-words, and TFIDF from CSV file at argument
file_path.
"""
processing = Processing()
# Read in FAQ data
self.faq = pd.read_csv(file_path, keep_default_na=False)
self.corpus = self.faq.question + ' ' + self.faq.answer
# Create BOW tranformer based on faq.question + faq.answer
self.bow_transformer = CountVectorizer(
analyzer=processing.text_process).fit(self.faq.question)
# Tranform faq.question itself into BOW
self.corpus_bow = self.bow_transformer.transform(self.faq.question)
# Create TFIDF transformer based on faq.question's BOW
self.tfidf_transformer = TfidfTransformer().fit(self.corpus_bow)
# Transform faq.question's BOW into TFIDF
self.corpus_tfidf = self.tfidf_transformer.transform(self.corpus_bow)
# Initialize search module
encoder, decoder, decoder_n_layers, self.voc = buildModels()
self.searcher = GreedySearchDecoder(encoder, decoder, decoder_n_layers)
# Set upload folder and output records folder
self.UPLOAD_FOLDER = '/app/records/in'
self.REC_RES_FOLDER = '/app/records/out'
# Set allowed extensions
self.ALLOWED_EXTENSIONS = {'wav'}
def tfidf_similarity(self, query):
"""
Returns (index, similarity value) of string argument query's most similar
match in FAQ, determined by cosine similarity.
"""
# Transform test question into BOW using BOW transformer
query_bow = self.bow_transformer.transform([query])
# Transform test question's BOW into TFIDF
query_tfidf = self.tfidf_transformer.transform(query_bow)
# Calculate cosine similarity and return maximum value with accompanying index
similarities = np.transpose(
cosine_similarity(query_tfidf, self.corpus_tfidf))
max_similarity = similarities.max()
max_index = np.argmax(similarities)
return max_index, max_similarity
def match_query(self, query):
"""
Prints most similar match in FAQ to user query.
"""
index, similarity = self.tfidf_similarity(query)
if similarity > 0.5:
response = self.faq.answer.iloc[index]
print(similarity)
else:
query = normalizeString(query)
output_words = evaluate(self.searcher, self.voc, query)
output_words[:] = [
x for x in output_words if not (x == 'EOS' or x == 'PAD')
]
response = ' '.join(output_words)
return response
def allowed_file(self, filename):
return '.' in filename and filename.rsplit(
'.', 1)[1].lower() in self.ALLOWED_EXTENSIONS
def get_duration(self, audio_name_only):
fname = os.path.join(self.REC_RES_FOLDER, audio_name_only)
audio = MP3(fname)
return round(audio.info.length)
def bot_dialog(self, request):
"""
Given the argument POST request, parse it according to form data,
and return a json response based on sklearn matching
within the FAQ.
"""
# Handle webhook request
req = request.form
msg_type = req.get('type')
if msg_type == "Text":
message = req.get('message')
response_text = self.match_query(message)
# Return json file as webhook response
messages = [{
"type": "Text",
"message": msg,
"fromBot": True
} for msg in response_text.split("\n\n")]
elif msg_type == "Audio":
respfilename = ''
record = request.files['record']
if record and self.allowed_file(record.filename):
filename = secure_filename(record.filename)
record.save(os.path.join(self.UPLOAD_FOLDER, filename))
list_records = []
durations = []
try:
r = spechrec.Recognizer()
with spechrec.AudioFile(
os.path.join(self.UPLOAD_FOLDER, filename)) as source:
# listen for the data (load audio to memory)
audio_data = r.record(source)
# recognize (convert from speech to text)
input_sentence = r.recognize_google(audio_data)
#searcher = GreedySearchDecoder(encoder, decoder,decoder_n_layers)
response_text = self.match_query(input_sentence)
for msg in response_text.split("\n\n"):
now = datetime.now()
respfilename = now.strftime("%d-%m-%Y-%H:%M:%S") + ".mp3"
engine = gTTS('' + response_text, lang='en')
engine.save(os.path.join(self.REC_RES_FOLDER,
respfilename))
list_records.append(respfilename)
durations.append(self.get_duration(respfilename))
except:
erreur = random.choice([
"Sorry, i did not understand you ,Please change the way you say it",
"please be a little simple in your discussion i m not a human",
"Sorry, get in mind that you are talking only with a computer "
])
print("" + erreur)
now = datetime.now()
respfilename = now.strftime("%d-%m-%Y-%H:%M:%S") + ".mp3"
engine = gTTS('' + erreur, lang='en')
engine.save(os.path.join(self.REC_RES_FOLDER, respfilename))
list_records.append(respfilename)
durations.append(self.get_duration(respfilename))
#Return json file as webhook response
messages = [{
"type":
"Audio",
"path":
"https://coronafaqsbot.herokuapp.com/records/" +
list_records[i],
"isLocal":
False,
"duration":
durations[i],
"fromBot":
True
} for i in range(len(list_records))]
return jsonify({"messages": messages})
# use all 25K words. Higher accuracy
movieVzer = CountVectorizer(min_df=2, tokenizer=nltk.word_tokenize)
# fit and tranform using training text
docs_train_counts = movieVzer.fit_transform(docs_train)
# 'screen' is found in the corpus, mapped to index 2290
print(movieVzer.vocabulary_.get('screen'))
# Likewise, Mr. Steven Seagal is present...
print(movieVzer.vocabulary_.get('seagal'))
print(docs_train_counts.shape)
# Convert raw frequency counts into TF-IDF values
movieTfmer = TfidfTransformer()
docs_train_tfidf = movieTfmer.fit_transform(docs_train_counts)
# Using the fitted vectorizer and transformer, tranform the test data
docs_test_counts = movieVzer.transform(docs_test)
docs_test_tfidf = movieTfmer.transform(docs_test_counts)
# Now ready to build a classifier.
# We will use Multinominal Naive Bayes as our model
# Train a Multimoda Naive Bayes classifier. Again, we call it "fitting"
clf = MultinomialNB()
clf.fit(docs_train_tfidf, y_train)
# Predict the Test set results, find accuracy
y_pred = clf.predict(docs_test_tfidf)
def train(self, method = "xgboost"):
# x_train, x_test, y_train, y_test = self.read_data()
corpus, label, numclass = self.feature(feature=self.feature_)
print "label: ", Counter(label)
print "numclass: ", numclass
print "------------------------------------------------"
x_train, x_test, y_train, y_test = train_test_split(corpus, label, test_size=0.2)
print "train num: ", len(y_train)
print "test num: ", len(y_test)
print "------------------------------------------------"
vectorizer = CountVectorizer()
tfidftransformer = TfidfTransformer()
tfidf = tfidftransformer.fit_transform(vectorizer.fit_transform(x_train))
weight = tfidf.toarray()
print "weight.shape: ", weight.shape
test_tfidf = tfidftransformer.transform(vectorizer.transform(x_test))
test_weight = test_tfidf.toarray()
print "test_weight.shape: ", test_weight.shape
print "------------------------------------------------\n"
# city count feature
x_train_count = np.array([self.city_count_feature(x) for x in x_train])
x_test_count = np.array([self.city_count_feature(x) for x in x_test])
assert len(weight) == len(x_train_count)
assert len(test_weight) == len(x_test_count)
weight = np.concatenate((weight, x_train_count), axis=1)
test_weight = np.concatenate((test_weight, x_test_count), axis=1)
# for i in range(len(weight)):
# weight[i] = np.append(weight[i], x_train_count[i])
# test_weight[i] = np.append(test_weight[i], x_test_count[i])
print "weight.shape: ", weight.shape
print "test_weight.shape: ", test_weight.shape
# sys.exit(0)
print "---------------------- train --------------------------"
xgbtrain = xgb.DMatrix(weight, label=y_train)
xgbtest = xgb.DMatrix(test_weight, label=y_test)
param = {'max_depth': 6, 'eta': 0.05, 'eval_metric': 'merror', 'silent': 1, 'objective': 'multi:softmax',
'num_class': numclass} # 参数
evallist = [(xgbtrain, 'train'), (xgbtest, 'test')]
num_round = 100 # 循环次数
bst = xgb.train(param, xgbtrain, num_round, evallist)
# save model
bst.save_model("./../model/TC{}.model".format(self.feature_))
preds = bst.predict(xgbtest)
print "------------------------------------------------"
count = 0
for i in range(80):
if preds[i] == y_test[i]:
count += 1
print "precision: ", count * 1.0 / len(y_test)
good_cases = []
bad_cases = []
valid_data = pd.read_csv(InputDataPath)
label_mapping_convert = self.caseAnalysis()
print "------------------ case analysis ------------------"
for i in range(len(y_test)):
# good case
if y_test[i] == preds[i]:
goodcase = {"case":"good"}
index = 0
while index < 399:
if valid_data["extract"][index].decode("utf-8") == x_test[i]:
break
index += 1
goodcase["index"] = index
for key, value in valid_data.iloc[index].items():
goodcase[key] = value
goodcase["label"] = y_test[i]
goodcase["predict"] = int(preds[i])
goodcase["predict_text"] = label_mapping_convert[preds[i]]
good_cases.append(goodcase)
# bad case
else:
badcase = {"case": "bad"}
index = 0
while index < 399:
if valid_data["extract"][index].decode("utf-8") == x_test[i]:
break
index += 1
badcase["index"] = index
for key, value in valid_data.iloc[index].items():
badcase[key] = value
badcase["label"] = y_test[i]
badcase["predict"] = int(preds[i])
badcase["predict_text"] = label_mapping_convert[preds[i]]
bad_cases.append(badcase)
print "------------------ good case ------------------"
print json.dumps(good_cases, ensure_ascii=False, encoding="utf-8", indent=4)
print "------------------ bad case ------------------"
print json.dumps(bad_cases, ensure_ascii=False, encoding="utf-8", indent=4)
print "------------------------------------------------"
print "precision: ", count * 1.0 / len(y_test)
def test_tfidf_no_smoothing():
X = [[1, 1, 1],
[1, 1, 0],
[1, 0, 0]]
tr = TfidfTransformer(smooth_idf=False, norm='l2')
tfidf = tr.fit_transform(X).toarray()
assert_true((tfidf >= 0).all())
# check normalization
assert_array_almost_equal((tfidf ** 2).sum(axis=1), [1., 1., 1.])
# the lack of smoothing make IDF fragile in the presence of feature with
# only zeros
X = [[1, 1, 0],
[1, 1, 0],
[1, 0, 0]]
tr = TfidfTransformer(smooth_idf=False, norm='l2')
# First we need to verify that numpy here provides div 0 warnings
with warnings.catch_warnings(record=True) as w:
1. / np.array([0.])
numpy_provides_div0_warning = len(w) == 1
with warnings.catch_warnings(record=True) as w:
tfidf = tr.fit_transform(X).toarray()
if not numpy_provides_div0_warning:
raise SkipTest("Numpy does not provide div 0 warnings.")
assert_equal(len(w), 1)
# For Python 3 compatibility
if hasattr(w[0].message, 'args'):
assert_true("divide by zero" in w[0].message.args[0])
else:
assert_true("divide by zero" in w[0].message)