python - Is there a mechanism to load the complete Google Vectors in less memory and in optimized way? -


here code dealing with: 

import numpy np import theano import cpickle collections import defaultdict import sys, re import pandas pd import csv import getpass   def build_data_cv(datafile, cv=10, clean_string=true):     """     loads data , split 10 folds.     """     revs = []     vocab = defaultdict(float)      open(datafile, "rb") csvf:         csvreader=csv.reader(csvf,delimiter=',',quotechar='"')         first_line=true         line in csvreader:             if first_line:                 first_line=false                 continue             status=[]             sentences=re.split(r'[.?]', line[1].strip())             try:                 sentences.remove('')             except valueerror:                 none              sent in sentences:                 if clean_string:                     orig_rev = clean_str(sent.strip())                     if orig_rev=='':                             continue                     words = set(orig_rev.split())                     splitted = orig_rev.split()                     if len(splitted)>150:                         orig_rev=[]                         splits=int(np.floor(len(splitted)/20))                         index in range(splits):                             orig_rev.append(' '.join(splitted[index*20:(index+1)*20]))                         if len(splitted)>splits*20:                             orig_rev.append(' '.join(splitted[splits*20:]))                         status.extend(orig_rev)                     else:                         status.append(orig_rev)                 else:                     orig_rev = sent.strip().lower()                     words = set(orig_rev.split())                     status.append(orig_rev)                  word in words:                     vocab[word] += 1               datum  = {"y0":1 if line[2].lower()=='y' else 0,                   "y1":1 if line[3].lower()=='y' else 0,                   "y2":1 if line[4].lower()=='y' else 0,                   "y3":1 if line[5].lower()=='y' else 0,                   "y4":1 if line[6].lower()=='y' else 0,                   "text": status,                   "user": line[0],                   "num_words": np.max([len(sent.split()) sent in status]),                   "split": np.random.randint(0,cv)}             revs.append(datum)       return revs, vocab  def get_w(word_vecs, k=300):     """     word matrix. w[i] vector word indexed     """     vocab_size = len(word_vecs)     word_idx_map = dict()     w = np.zeros(shape=(vocab_size+1, k), dtype=theano.config.floatx)     w[0] = np.zeros(k, dtype=theano.config.floatx)     = 1     word in word_vecs:         w[i] = word_vecs[word]         word_idx_map[word] =         += 1     return w, word_idx_map  def load_bin_vec(fname, vocab):     """     loads 300x1 word vecs google (mikolov) word2vec     """     word_vecs = {}     open(fname, "rb") f:         header = f.readline()         vocab_size, layer1_size = map(int, header.split())         binary_len = np.dtype(theano.config.floatx).itemsize * layer1_size         line in xrange(vocab_size):             word = []             while true:                 ch = f.read(1)                 if ch == ' ':                     word = ''.join(word)                     break                 if ch != '\n':                     word.append(ch)             if word in vocab:                word_vecs[word] = np.fromstring(f.read(binary_len), dtype=theano.config.floatx)             else:                 f.read(binary_len)     return word_vecs  def add_unknown_words(word_vecs, vocab, min_df=1, k=300):     """     words occur in @ least min_df documents, create separate word vector.     0.25 chosen unknown vectors have (approximately) same variance pre-trained ones     """     word in vocab:         if word not in word_vecs , vocab[word] >= min_df:             word_vecs[word] = np.random.uniform(-0.25,0.25,k)             print word  def clean_str(string, trec=false):     """     tokenization/string cleaning datasets except sst.     every dataset lower cased except trec     """     string = re.sub(r"[^a-za-z0-9(),!?\'\`]", " ", string)     string = re.sub(r"\'s", " \'s ", string)     string = re.sub(r"\'ve", " have ", string)     string = re.sub(r"n\'t", " not ", string)     string = re.sub(r"\'re", " ", string)     string = re.sub(r"\'d" , " ", string)     string = re.sub(r"\'ll", " ", string)     string = re.sub(r",", " , ", string)     string = re.sub(r"!", " ! ", string)     string = re.sub(r"\(", " ( ", string)     string = re.sub(r"\)", " ) ", string)     string = re.sub(r"\?", " \? ", string) #    string = re.sub(r"[a-za-z]{4,}", "", string)     string = re.sub(r"\s{2,}", " ", string)     return string.strip() if trec else string.strip().lower()  def clean_str_sst(string):     """     tokenization/string cleaning sst dataset     """     string = re.sub(r"[^a-za-z0-9(),!?\'\`]", " ", string)     string = re.sub(r"\s{2,}", " ", string)     return string.strip().lower()  def get_mairesse_features(file_name):     feats={}     open(file_name, "rb") csvf:         csvreader=csv.reader(csvf,delimiter=',',quotechar='"')         line in csvreader:             feats[line[0]]=[float(f) f in line[1:]]     return feats  if __name__=="__main__":     w2v_file = sys.argv[1]     data_folder = sys.argv[2]     mairesse_file = sys.argv[3]     print "loading data...",     revs, vocab = build_data_cv(data_folder, cv=10, clean_string=true)     num_words=pd.dataframe(revs)["num_words"]      max_l = np.max(num_words)     print "data loaded!"      print "number of status: " + str(len(revs))     print "vocab size: " + str(len(vocab))     print "max sentence length: " + str(max_l)     print "loading word2vec vectors...",     w2v = load_bin_vec(w2v_file, vocab)     print "word2vec loaded!"      print "num words in word2vec: " + str(len(w2v))     add_unknown_words(w2v, vocab)     w, word_idx_map = get_w(w2v)     rand_vecs = {}     add_unknown_words(rand_vecs, vocab)     w2, _ = get_w(rand_vecs)     mairesse = get_mairesse_features(mairesse_file)     cpickle.dump([revs, w, w2, word_idx_map, vocab, mairesse], open("essays_mairesse.p", "wb"))     print "dataset created!"

the process after running getting killed. hence, started debugging find out culprit line f code. usual, thought came right. issue of process getting killed owing use of google news vector file. file not getting loaded , consuming hell lot memory, matter of concern me.

i looking solution can make process chunking google vectors 1 one , still better training result.

but of no use complete data in getting better resultant model of training.

kindly, suggest me can better result in lesser or optimized memory.


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