def termdocumentmatrix(train_path, test_path, cnn=True):
tdm = textmining.TermDocumentMatrix()
tdm, train_labels = add_doc(tdm, train_path)
tdm, test_labels = add_doc(tdm, test_path)
tdm_rows = [x for x in tdm.rows()]
words = tdm_rows[0]
word_index_dict = {}
for i in range(0, len(words)):
word_index_dict[words[i]] = i
train_len, tdm_rows = add_senti_score(train_path, tdm_rows,
word_index_dict)
test_len, tdm_rows = add_senti_score(test_path, tdm_rows, word_index_dict)
train_tdm = np.asarray(tdm_rows[1:train_len + 1])
test_tdm = np.asarray(tdm_rows[train_len + 1:train_len + test_len + 1])
train_labels = np.asarray(train_labels)
if cnn:
train_tdm = reshapeX(train_tdm)
test_tdm = reshapeX(test_tdm)
train_labels = reshapeY(train_labels)
test_labels = np.asarray(test_labels)
return train_labels, train_tdm, test_labels, test_tdm, words
def term_document_matrix():
num_lines = 0
for line in fileinput:
num_lines = num_lines + 1
reading_file_info = [item.rstrip('\n') for item in fileinput]
tdm = textmining.TermDocumentMatrix()
for i in range(0, num_lines):
tdm.add_doc(reading_file_info[i])
tdm.write_csv('TermDocumentMatrix.csv', cutoff=1)
temp = list(tdm.rows(cutoff=1))
vocab = tuple(temp[0])
x = np.array(temp[1:])
mu = random((num_lines, 3))
fcm = p.FuzzyCMeans(x, mu, 2)
print fcm.mu
model = lda.LDA(n_topics=15, n_iter=50, random_state=1)
model.fit(x)
topic_word = model.topic_word_
n_top_words = 10
for i, topic_dist in enumerate(topic_word):
topic_words = np.array(vocab)[np.argsort(topic_dist)][:-n_top_words:-1]
fileoutput.write('Topic {}: {}\n'.format(i, ' '.join(topic_words)))
fileoutput.close()
def termdocumentmatrix_example():
path = "/Users/franciscojavierarceo/MyPrograms/Python/"
os.chdir(path)
# Create some very short sample documents
doc1 = 'John and Bob are brothers.'
doc2 = 'John went to the store. The store was closed.'
doc3 = 'Bob went to the store too.'
# Initialize class to create term-document matrix
tdm = textmining.TermDocumentMatrix()
# Add the documents
tdm.add_doc(doc1)
tdm.add_doc(doc2)
tdm.add_doc(doc3)
# Write out the matrix to a csv file. Note that setting cutoff=1 means
# that words which appear in 1 or more documents will be included in
# the output (i.e. every word will appear in the output). The default
# for cutoff is 2, since we usually aren't interested in words which
# appear in a single document. For this example we want to see all
# words however, hence cutoff=1.
tdm.write_csv('matrix.csv', cutoff=1)
print tdm
# Instead of writing out the matrix you can also access its rows directly.
# Let's print them to the screen.
for row in tdm.rows(cutoff=1):
print row
def termdoc(dicto):
docs = []
cuisines = ['cuisine']
# Add data to the TDM and remove unicode from cuisine names
for entry in dicto:
docs.append(dicto[entry])
cuisines.append(entry.encode('ascii', 'ignore'))
# Use of textmining library to obtain TDM
tdm = textmining.TermDocumentMatrix()
for doc in docs:
tdm.add_doc(doc)
matrix_file = 'matrix.csv'
# Remove 'matrix.csv' if it already exists
try:
os.remove(matrix_file)
except OSError:
pass
# Write frequencies of all ingredients in each cuisine
for row, cuisine in zip(tdm.rows(cutoff=1), cuisines):
with open(matrix_file, 'ab') as csvfile:
writer = csv.writer(csvfile)
writer.writerow([cuisine] + row)
def __GetDataSet__(self):
docTermMatrix = []
vocab = []
docIds = []
tdm = textmining.TermDocumentMatrix()
for i in range(len(self._docId_comma_documentsText_tuple_list)):
print i, " extraction.. "
docId, documentText = self._docId_comma_documentsText_tuple_list[i]
tdm.add_doc(documentText.lower())
# docIds.append(docId)
i = -1
for row in tdm.rows(cutoff=3):
row = row[0:18000]
i += 1
# First row of 'document-term matrix' is vocabulary
if i == 0:
for vocabWord in row:
vocab.append(vocabWord)
continue
print i, "Loading from tdm matrix.."
# Remaiing rows are []
docTermMatrix.append(row)
print "Converting doc-term matrix to numpy array..."
return np.array(docTermMatrix), vocab, docIds
def cosine_similarity(list_of_file_paths):
# Create some very short sample documents
doc_list = [
filehandler.convert_to_txt(file_path)
for file_path in list_of_file_paths
]
# Initialize class to create term-document matrix
tdm = textmining.TermDocumentMatrix(
tokenizer=simple_tokenize_remove_our_stopwords)
for doc in doc_list:
tdm.add_doc(doc)
results = []
is_first_row1 = True
for row1 in tdm.rows(cutoff=1):
if is_first_row1:
is_first_row1 = False
continue
is_first_row2 = True
cols = []
for row2 in tdm.rows(cutoff=1):
if is_first_row2:
is_first_row2 = False
continue
cols.append(1 - spatial.distance.cosine(row1, row2))
results.append(cols)
return results
def termdocumentmatrix_example():
tdm = textmining.TermDocumentMatrix()
for i in range(0, len(vec_dictionary)):
tdm.add_doc(vec_dictionary[i][1])
for row in tdm.rows(cutoff=50):
D.append(row)
def termdocumentmatrix_example():
# Create some very short sample documents
tdm = textmining.TermDocumentMatrix()
mypath = "./corpus"
onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]
documents = open('documents.csv', 'w')
writer = csv.writer(documents)
writer.writerow(('document_name', 'content'))
pattern = r"(cid)+"
for f in onlyfiles:
if (f[-4:] == ".txt"):
doc = open(mypath + "/" + f, 'r')
txt = doc.read().replace(',', '')
txt = ' '.join(txt.split())
re.sub(pattern, "", txt)
writer.writerow((f, txt))
tdm.add_doc(txt)
doc.close
documents.close()
# Initialize class to create term-document matrix
# Add the documents
#tdm.add_doc(doc1)
#tdm.add_doc(doc2)
#tdm.add_doc(doc3)
# Write out the matrix to a csv file. Note that setting cutoff=1 means
# that words which appear in 1 or more documents will be included in
# the output (i.e. every word will appear in the output). The default
# for cutoff is 2, since we usually aren't interested in words which
# appear in a single document. For this example we want to see all
# words however, hence cutoff=1.
tdm.write_csv('matrix.csv', cutoff=2)
def term_document_matrix():
num_lines = 0
for line in fileinput:
num_lines = num_lines + 1 #calculate the number of lines a text document
reading_file_info = [item.rstrip('\n') for item in fileinput]
tdm = textmining.TermDocumentMatrix(
) # creation of the list tdm for document matrix
for i in range(0, num_lines):
tdm.add_doc(
reading_file_info[i]
) # Add data to the matrix line by line tokenize is done by itself
tdm.write_csv(
'TermDocumentMatrix.csv', cutoff=1
) # csv document term matrix created by TermDocumentMatrix name
temp = list(
tdm.rows(cutoff=1)) #temp has all the rows of the document term matrix
vocab = tuple(temp[0]) # the row which have the each word of the document
x = np.array(
temp[1:]
) # starting from the second row of a matrix as initial is only the words
# cluster creation
mu = random(
(num_lines, 6)
) # generate the random number for cluster according to the number of data inserted in document matrix and 3 is the no of clusters should be created which can change
fcm = p.FuzzyCMeans(x, mu, 2) # create the clusters
num_arra = fcm.mu
summation = num_arra.sum(
axis=1
) # calculate the sum of each row of the document matrix as a numpy array
summation_vertical = summation[:,
None] #make the horizontal sum array to vertical array which is easy for furtur access
rows = num_arra.shape[
0] # give the number of rows of a n-dimension numpy array
columns = num_arra.shape[
1] # give the number of columns of a n-dimension numpy array
num_arra = num_arra.astype(
float) # change the int array to float to store the float value
for rows_count in range(0, rows): # run till the number of arrays
divide_sum = summation_vertical.item(
rows_count,
0) # give the item which divide the element for normailzation
for i in range(0, columns): # run till no of columns in a n-d array
replace_division = num_arra.item(
rows_count, i) / divide_sum #normalixe the existing value
num_arra[
rows_count,
i] = replace_division # replace the new value with exisitng value
print num_arra #give cluster array whose sum equal to 1 always
print num_arra.sum(axis=1)
# LDA implimentation
model = lda.LDA(n_topics=2, n_iter=10, random_state=2)
model.fit(x)
topic_word = model.topic_word_
n_top_words = 11
for i, topic_dist in enumerate(topic_word):
topic_words = np.array(vocab)[np.argsort(topic_dist)][:-n_top_words:-1]
fileoutput.write('Topic {}: {}\n'.format(i, ' '.join(topic_words)))
fileoutput.close()
def summarize(text):
# prepare text
lines = text.split('.')
clean_lines = [line.strip() for line in lines if line.strip()]
newtext = '\n'.join(clean_lines)
tdm = textmining.TermDocumentMatrix()
tdm.add_doc(newtext)
for index, row in enumerate(tdm.rows(cutoff=1)):
if index == 0: words = row
if index == 1: count = row
# filter stop words
text = open('stopwords.txt').read()
stopwords = textmining.simple_tokenize(text)
freq = [(w, count[index]) for index, w in enumerate(words)
if w not in stopwords]
freq.sort(reverse=True)
# Concordance
most_freq_words = freq[:10]
summary = []
h = histogram(lines, most_freq_words)
rowcount = threshold(h)
summary = [(index, line) for index, line in enumerate(lines)
if index < rowcount]
summary.sort()
ret = [line[1] for line in summary]
print '.'.join(ret)
return ret
def test_tdm_df(self):
tdm = txm.TermDocumentMatrix()
for doc in self.doclist:
tdm.add_doc(doc)
l = [r for r in tdm.rows(cutoff=1)]
df = pd.DataFrame(np.array(l[1:]), columns=l[0])
result = tdm_df(self.doclist, remove_punctuation=False)
assert_frame_equal(result, df)
def createTermDocM(C, name):
tdm = textmining.TermDocumentMatrix()
[tdm.add_doc(C[x]) for x in C]
tdm.write_csv('%s .csv' % name)
names = [x.encode('utf-8') for x in C]
f = open("authnames.txt", 'w')
f.writelines([name + '\n' for name in names])
f.close()
return (tdm)
def main():
# This file should only include rows of text. Be careful of mid-string
# linebreaks!
with open("train_plus_test_reviews.csv", "r") as f:
tdm = textmining.TermDocumentMatrix()
for line in f:
tdm.add_doc(line)
# Only include words which appear in 2+ documents
tdm.write_csv('matrix.csv', cutoff=2)
def texts2matrix(texts, titles, fname='dtm.csv'):
M = textmining.TermDocumentMatrix()
for text in texts:
M.add_doc(text)
M.write_csv(fname, cutoff=3)
tname = fname.split('.')[0] + '_filename.txt'
f = open(tname, 'w')
for i in titles:
f.write("%s\n" % i)
print "matrix saved as " + fname + " and filenames as " + tname
def termdocumentmatrix_example(inputPath, inputFile, outputPath, outputFile):
with open(inputPath + inputFile, 'rb') as f:
tdm = textmining.TermDocumentMatrix()
count = 1
for line in f:
vals = line.split('^')
try:
tdm.add_doc(vals[0])
except IndexError, e:
print str(count) + "th row data format error"
count = count + 1
def create_termdocument_matrix(self, tokenzied=False):
"""
creates a term document matrix of the frequencies of each term in each document held in self.docs
:return:
"""
self.tdm = tm.TermDocumentMatrix()
for doc in self.docs:
if not tokenzied:
self.tdm.add_doc(doc)
else:
self.tdm.add_tokenized_doc(doc)
def create_frequency_matrix(documents, cutoff=2, path_to_save=None):
# x = np.array([1, 1, 1, 2, 2, 2, 5, 25, 1, 1])
# y = np.bincount(x)
# ii = np.nonzero(y)[0]
# return zip(ii, y[ii])
tdm = textmining.TermDocumentMatrix()
for doc in documents:
tdm.add_doc(doc)
if not path_to_save == None:
tdm.write_csv(path_to_save, cutoff=cutoff)
return tdm.rows(cutoff=cutoff)
def prepare_data_2(clean_reviews=[]):
'''prepares reviews by creating the LDA corpus'''
tdm = textmining.TermDocumentMatrix()
for doc1 in clean_reviews :
tdm.add_doc(doc1)
temp = list(tdm.rows(cutoff=2))
vocab = tuple(temp[0])
X = np.array(temp[1:])
return X,vocab
def most_frequent_terms(*args):
tdm = textmining.TermDocumentMatrix(simple_tokenize_remove_our_stopwords)
for doc in args:
tdm.add_doc(doc)
freqs = []
for d in tdm.sparse:
f = [(freq, name) for (name, freq) in list(d.items())]
f.sort(reverse=True)
freqs.append(f)
return freqs
def tdm_df(doclist,
stopwords=[],
remove_punctuation=True,
remove_digits=True,
sparse_df=False):
'''
Create a term-document matrix from a list of e-mails.
Uses the TermDocumentMatrix function in the `textmining` module.
But, pre-processes the documents to remove digits and punctuation,
and post-processes to remove stopwords, to match the functionality
of R's `tm` package.
NB: This is not particularly memory efficient and you can get memory
errors with an especially long list of documents.
Returns a (by default, sparse) DataFrame. Each column is a term,
each row is a document.
'''
# Create the TDM from the list of documents.
tdm = txtm.TermDocumentMatrix()
for doc in doclist:
if remove_punctuation == True:
doc = doc.translate(None, string.punctuation.translate(None, '"'))
if remove_digits == True:
doc = doc.translate(None, string.digits)
tdm.add_doc(doc)
# Push the TDM data to a list of lists,
# then make that an ndarray, which then
# becomes a DataFrame.
tdm_rows = []
for row in tdm.rows(cutoff=1):
tdm_rows.append(row)
tdm_array = np.array(tdm_rows[1:])
tdm_terms = tdm_rows[0]
df = DataFrame(tdm_array, columns=tdm_terms)
# Remove stopwords from the dataset, manually.
# TermDocumentMatrix does not do this for us.
if len(stopwords) > 0:
for col in df:
if col in stopwords:
del df[col]
if sparse_df == True:
df.to_sparse(fill_value=0)
return df
def __init__(self):
self.docs = None
self.X = None
self.features = None
self.tdm = textmining.TermDocumentMatrix()
self.models_list = []
self.k_list = None
self.topics_n = None
self.topic_labels = []
self.models_matrix = None
self.cos_X = None
self.cos_list = []
self.ftps = []
def count_terms(site_dict):
terms_matrix = textmining.TermDocumentMatrix()
for site, text in site_dict.items():
terms_matrix.add_doc(text)
terms_df = pd.DataFrame(terms_matrix.rows())
terms_df.columns = terms_df.iloc[0]
terms_df = terms_df[1:]
terms_df.index = site_dict.keys()
terms_df.index.name = 'site'
terms_df = terms_df.T
terms_df.index.name = 'term'
terms_df = terms_df.reset_index()
return terms_df
def tdm_df(doclist):
tdm = textmining.TermDocumentMatrix()
if len(doclist) > 0:
for doc in doclist:
tdm.add_doc(doc)
tdm_rows, occurrence = [], []
for rows in tdm.rows():
tdm_rows.append(rows)
tdm_array = np.array(tdm_rows[1:])
tdm_terms = tdm_rows[0]
df = pd.DataFrame(tdm_array, columns=tdm_terms)
return df
def preprocess(inputFile,f_name):
# Read the text file
file = open(inputFile, 'r')
text = file.read()
text = text.replace('\n',' ')
#Number of words in the text
words_count = len(word_tokenize(strip_punctuation(text)))
# split in to sentences and store the sentences in a list
sentences = tokenize.sent_tokenize(text)
#Original Sentences
sentences_backup = list(sentences)
filtered_sentences = []
# Apply stop word removal to each sentence
stop_words = set(stopwords.words('english'))
for i in range(len(sentences_backup)):
temp = []
word_tokens = word_tokenize(strip_punctuation(sentences_backup[i]))
for w in word_tokens:
if w.lower() not in stop_words:
temp.append(w.lower())
filtered_sentences.append(temp)
tdm = textmining.TermDocumentMatrix()
for i in range(len(sentences)):
sent = " ".join(filtered_sentences[i])
tdm.add_doc(sent)
temp = list(tdm.rows(cutoff=1))
vocab = tuple(temp[0])
X = np.array(temp[1:],dtype = 'float64')
X1 = X.transpose()
fileObj = ".\\Pre_Processed\\"+f_name.replace('.txt','')+".csv"
np.savetxt(fileObj, X1, fmt='%1.5f', delimiter=",")
vocab1 = tuple(zip(vocab))
def termdocumentmatrix_example(xDIR):
# Initialize class to create term-document matrix
count = 0
tdm = textmining.TermDocumentMatrix()
for i in os.listdir(xDIR):
Res = tdm.add_doc(open(os.path.join(xDIR, i)).read())
# Write out the matrix to a csv file. Note that setting cutoff=1 means
# that words which appear in 1 or more documents will be included in
# the output (i.e. every word will appear in the output). The default
# for cutoff is 2, since we usually aren't interested in words which
# appear in a single document. For this example we want to see all
# words however, hence cutoff=1.
tdm.write_csv('/Users/XW/Desktop/datascience.stackexchange.com/answer.csv',
cutoff=1) #输出结果
def fitLDA(self, nTopics, nTopWords): #Fit LDA model
topicsList = []
tdm = textmining.TermDocumentMatrix(
tokenizer=textmining.simple_tokenize_remove_stopwords)
for index, row in self.typeData.iterrows():
if isinstance(row["Title/Description"], basestring):
tdm.add_doc(row["Title/Description"])
temp = list(tdm.rows(cutoff=1))
vocab = tuple(temp[0])
X = np.array(temp[1:])
self.model = lda.LDA(n_topics=nTopics, n_iter=500, random_state=1)
self.model.fit_transform(X)
topicWord = self.model.topic_word_ # model.components_ also works
topWords = nTopWords
for i, topic_dist in enumerate(topicWord):
topicWords = np.array(vocab)[np.argsort(topic_dist)][:-topWords:-1]
topicsList.append(topicWords)
return topicsList
def termdocumentmatrix_example(xDIR):
# Initialize class to create term-document matrix
count=0
tdm = textmining.TermDocumentMatrix()
for i in os.listdir(xDIR):
Res = tdm.add_doc(open(os.path.join(xDIR,i)).read())
# Write out the matrix to a csv file. Note that setting cutoff=1 means
# that words which appear in 1 or more documents will be included in
# the output (i.e. every word will appear in the output). The default
# for cutoff is 2, since we usually aren't interested in words which
# appear in a single document. For this example we want to see all
# words however, hence cutoff=1.
tdm.write_csv('/Users/Zhen/Desktop/Courses/BigData/stackexchange/topicModeling/result/matrix.csv',cutoff=1) #输出结果
# Instead of writing out the matrix you can also access its rows directly.
# Let's print them to the screen.
for row in tdm.rows(cutoff=1):
print row
def construct_doc_term_matrix(tok_folders, indices):
'''
Take a list of path to folders where tok.mda file is stored,
and return
1. the numpy NumDoc x NumVocab matrix of doctermatrix
Document ordered by Folder order and then by CUSIP number
2. number of document in the last folder
Folders should not end with "/"
Folders should be ordered by time
'''
tm_tdm = textmining.TermDocumentMatrix()
document_count = 0
document_last_count = 0
for i in range(len(tok_folders)):
tok_folder = tok_folders[i]
tokfile_list = os.listdir(tok_folder)
tokfile_list.sort()
document_last_count = 0
index = indices[i]
for j in range(len(tokfile_list)):
if j not in index:
continue
tokfile_name = tokfile_list[j]
with open(tok_folder + "/" + tokfile_name) as tokfile:
line = tokfile.readline()
# in the origianl data, # refers to numbers
line = re.sub('[#]', 'number', line)
tm_tdm.add_doc(line)
document_count += 1
document_last_count += 1
np_tdm = 0
row_index = -1
vocab = []
for row in tm_tdm.rows(cutoff=1):
if row_index < 0:
np_tdm = np.zeros(shape=(document_count, len(row)))
vocab = row[:]
else:
np_tdm[row_index] = row
row_index += 1
return np_tdm, vocab, document_last_count
def tdm_df(doclist,
stopwords=[],
remove_punctuation=True,
remove_digits=True,
sparse_df=False):
"""
Create a term-document matrix from a list of e-mails.
Uses the TermDocumentMatrix function in the `textmining` module.
But, pre-processes the documents to remove digits and punctuation,
and post-processes to remove stopwords, to match the functionality
of R's `tm` package.
"""
tdm = txtm.TermDocumentMatrix()
for doc in doclist:
if remove_punctuation == True:
translator_pun = str.maketrans('', '', string.punctuation)
doc = doc.translate(translator_pun)
if remove_digits == True:
translator_digt = str.maketrans('', '', string.digits)
doc = doc.translate(translator_digt)
tdm.add_doc(doc)
# Push the TDM data to a list of lists,
# then make that an ndarray, which then
# becomes a DataFrame.
tdm_rows = []
for row in tdm.rows(cutoff=1):
tdm_rows.append(row)
tdm_array = np.array(tdm_rows[1:])
tdm_terms = tdm_rows[0]
df = DataFrame(tdm_array, columns=tdm_terms)
if len(stopwords) > 0:
for col in df:
if col in stopwords:
del df[col]
if sparse_df == True:
df.to_sparse(fill_value=0)
return df
def create_keyword_table():
with open(os.path.join(get_paths()['working_dir'], 'Paper.csv'),
'r',
encoding="utf-8") as paper_file:
paper = csv.reader(paper_file)
paper_column = paper.__next__()
tdm = textmining.TermDocumentMatrix()
papers = []
for x in paper:
papers.append(x)
tdm.add_doc(
re.sub(r"[^A-Za-z0-9 _]",
" ",
' '.join([str(x[1]), str(x[5])]),
flags=re.UNICODE))
keywords = []
cutoff = 30
stopwords = textmining.stopwords
stopwords.update(['key', 'words', 'keywords', 'keyword', 'word'])
for (paper_i, tdm_i) in zip(papers, tdm.sparse):
id_paper = paper_i[0]
year = paper_i[2]
id_conference = paper_i[3]
id_journal = paper_i[4]
paper_words = [
[id_paper, year, id_conference, id_journal, word]
for word in tdm_i.keys()
if tdm.doc_count[word] >= cutoff and word not in stopwords
]
keywords.extend(paper_words)
with open(os.path.join(get_paths()['working_dir'], 'keywords.csv'),
'w',
encoding="utf-8") as keyword_file:
keyword = csv.writer(keyword_file)
keyword.writerow(
('paperid', 'year', 'conferenceid', 'journalid', 'keyword'))
for x in keywords:
keyword.writerow(x)