Is there a more efficient way to append lines from

Question

I'm trying to use this lda package to process a term-document matrix csv file with 39568 rows and 27519 columns containing counting/natural numbers only.

Problem: I'm getting a MemoryError with my approach to read the file and store it to a numpy array.

Goal: Get the numbers from the TDM csv file and convert it to numpy array so I can use the numpy array as input for the lda.

with open("Results/TDM - Matrix Only.csv", 'r') as matrix_file:
    matrix = np.array([[int(value) for value in line.strip().split(',')] for line in matrix_file])

I've also tried using the numpy append, vstack and concatenate and I still get the MemoryError.

Is there a way to avoid the MemoryError?

Edit:

I've tried using dtype int32 and int and it gives me:

WindowsError: [Error 8] Not enough storage is available to process this command

I've also tried using dtype float64 and it gives me:

OverflowError: cannot fit 'long' into an index-sized integer

With these codes:

fp = np.memmap("Results/TDM-memmap.txt", dtype='float64', mode='w+', shape=(len(documents), len(vocabulary)))
matrix = np.genfromtxt("Results/TDM.csv", dtype='float64', delimiter=',', skip_header=1)
fp[:] = matrix[:]

and

with open("Results/TDM.csv", 'r') as tdm_file:
    vocabulary = [value for value in tdm_file.readline().strip().split(',')]
    fp = np.memmap("Results/TDM-memmap.txt", dtype='float64', mode='w+', shape=(len(documents), len(vocabulary)))
    for idx, line in enumerate(tdm_file):
        fp[idx] = np.array(line.strip().split(','))

Other info that might matter

• Win10 64bit
• 8GB RAM (7.9 usable) | peaks at 5.5GB from more or less 3GB (around 2GB used) before it reports MemoryError
• Python 2.7.10 [MSC v.1500 32 bit (Intel)]
• Using PyCharm Community Edition 5.0.3

Answer 1

Since your word counts will be almost all zeros, it would be much more efficient to store them in a scipy.sparse matrix. For example:

from scipy import sparse
import textmining
import lda

# a small example matrix
tdm = textmining.TermDocumentMatrix()
tdm.add_doc("here's a bunch of words in a sentence")
tdm.add_doc("here's some more words")
tdm.add_doc("and another sentence")
tdm.add_doc("have some more words")

# tdm.sparse is a list of dicts, where each dict contains {word:count} for a single
# document
ndocs = len(tdm.sparse)
nwords = len(tdm.doc_count)
words = tdm.doc_count.keys()

# initialize output sparse matrix
X = sparse.lil_matrix((ndocs, nwords),dtype=int)

# iterate over documents, fill in rows of X
for ii, doc in enumerate(tdm.sparse):
    for word, count in doc.iteritems():
        jj = words.index(word)
        X[ii, jj] = count

X is now an (ndocs, nwords) scipy.sparse.lil_matrix, and words is a list corresponding to the columns of X:

print(words)
# ['a', 'and', 'another', 'sentence', 'have', 'of', 'some', 'here', 's', 'words', 'in', 'more', 'bunch']

print(X.todense())
# [[2 0 0 1 0 1 0 1 1 1 1 0 1]
#  [0 0 0 0 0 0 1 1 1 1 0 1 0]
#  [0 1 1 1 0 0 0 0 0 0 0 0 0]
#  [0 0 0 0 1 0 1 0 0 1 0 1 0]]

You could pass X directly to lda.LDA.fit, although it will probably be faster to convert it to a scipy.sparse.csr_matrix first:

X = X.tocsr()
model = lda.LDA(n_topics=2, random_state=0, n_iter=100)
model.fit(X)
# INFO:lda:n_documents: 4
# INFO:lda:vocab_size: 13
# INFO:lda:n_words: 21
# INFO:lda:n_topics: 2
# INFO:lda:n_iter: 100
# INFO:lda:<0> log likelihood: -126
# INFO:lda:<10> log likelihood: -102
# INFO:lda:<20> log likelihood: -99
# INFO:lda:<30> log likelihood: -97
# INFO:lda:<40> log likelihood: -100
# INFO:lda:<50> log likelihood: -100
# INFO:lda:<60> log likelihood: -104
# INFO:lda:<70> log likelihood: -108
# INFO:lda:<80> log likelihood: -98
# INFO:lda:<90> log likelihood: -98
# INFO:lda:<99> log likelihood: -99