import argparse
from gensim.models import Word2Vec
from datetime import datetime

def parse_args():
    # Parses the node2vec arguments.
    parser = argparse.ArgumentParser(description="Run learn embeddings.")
    parser.add_argument('--input', nargs='?', default=' ',
                        help='Input walks path')
    parser.add_argument('--output', nargs='?', default='emb/karate.emb',
                        help='Embeddings path')
    parser.add_argument('--model', nargs='?', default='emb/karate.emb',
                        help='Embeddings path')
    parser.add_argument('--dimensions', type=int, default=128,
                        help='Number of dimensions. Default is 128.')
    parser.add_argument('--window-size', type=int, default=10,
                        help='Context size for optimization. Default is 10.')
    parser.add_argument('--iter', default=5, type=int,
                        help='Number of epochs in SGD')
    parser.add_argument('--workers', type=int, default=8,
                        help='Number of parallel workers. Default is 8.')
    return parser.parse_args()

class Sentences(object):
    def __init__(self, fname):
        self.fname = fname

    def __iter__(self):
        try:
            file_loc = self.fname
            for line in open(file_loc, mode='r'):
                line = line.rstrip('\n')
                words = line.split(" ")
                yield words
        except Exception:
            print("Failed reading file:")
            print(self.fname)

def learn_embeddings(walks):

    # Learn embeddings by optimizing the Skipgram objective using SGD.
    sentences = Sentences(walks)
    model = Word2Vec(sentences, size=args.dimensions, window=args.window_size, min_count=0,
                     workers=args.workers, iter=args.iter, negative=25, sg=1)
    print("defined model using w2v")
    model.wv.save_word2vec_format(args.output)
    model.wv.save(args.model)
    print("saved model in word2vec format")
    return


def learn_embedding(args):

    # Pipeline for representational learning for all nodes in a graph.
    start_time = datetime.now()
    print('---- input parameters -----')
    print('iterations=', args.iter)
    print('window size=', args.window_size)
    print('dimensions=', args.dimensions)
    print('workers=', args.workers)
    print('--- run program ---')
    learn_embeddings(args.input)
    end_time = datetime.now()
    print('run time: ', (end_time-start_time))

def emb_main(args):
    args.input = 'data/walks.txt'
    args.output = 'data/emb.txt'
    args.model = 'data/emb.model'
    args.dimensions = 128
    args.iter = 5
    args.window_size = 10
    args.workers = 6
    learn_embedding(args)


if __name__ == '__main__':
    args = parse_args()
    emb_main(args)

0c767ea08fc7b1dbb7a72f23c38f178fc2c3cf27
<mask token>


def parse_args():
    parser = argparse.ArgumentParser(description='Run learn embeddings.')
    parser.add_argument('--input', nargs='?', default=' ', help=
        'Input walks path')
    parser.add_argument('--output', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--model', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--dimensions', type=int, default=128, help=
        'Number of dimensions. Default is 128.')
    parser.add_argument('--window-size', type=int, default=10, help=
        'Context size for optimization. Default is 10.')
    parser.add_argument('--iter', default=5, type=int, help=
        'Number of epochs in SGD')
    parser.add_argument('--workers', type=int, default=8, help=
        'Number of parallel workers. Default is 8.')
    return parser.parse_args()


class Sentences(object):

    def __init__(self, fname):
        self.fname = fname

    def __iter__(self):
        try:
            file_loc = self.fname
            for line in open(file_loc, mode='r'):
                line = line.rstrip('\n')
                words = line.split(' ')
                yield words
        except Exception:
            print('Failed reading file:')
            print(self.fname)


<mask token>

<mask token>


def parse_args():
    parser = argparse.ArgumentParser(description='Run learn embeddings.')
    parser.add_argument('--input', nargs='?', default=' ', help=
        'Input walks path')
    parser.add_argument('--output', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--model', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--dimensions', type=int, default=128, help=
        'Number of dimensions. Default is 128.')
    parser.add_argument('--window-size', type=int, default=10, help=
        'Context size for optimization. Default is 10.')
    parser.add_argument('--iter', default=5, type=int, help=
        'Number of epochs in SGD')
    parser.add_argument('--workers', type=int, default=8, help=
        'Number of parallel workers. Default is 8.')
    return parser.parse_args()


class Sentences(object):

    def __init__(self, fname):
        self.fname = fname

    def __iter__(self):
        try:
            file_loc = self.fname
            for line in open(file_loc, mode='r'):
                line = line.rstrip('\n')
                words = line.split(' ')
                yield words
        except Exception:
            print('Failed reading file:')
            print(self.fname)


<mask token>


def emb_main(args):
    args.input = 'data/walks.txt'
    args.output = 'data/emb.txt'
    args.model = 'data/emb.model'
    args.dimensions = 128
    args.iter = 5
    args.window_size = 10
    args.workers = 6
    learn_embedding(args)


<mask token>

<mask token>


def parse_args():
    parser = argparse.ArgumentParser(description='Run learn embeddings.')
    parser.add_argument('--input', nargs='?', default=' ', help=
        'Input walks path')
    parser.add_argument('--output', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--model', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--dimensions', type=int, default=128, help=
        'Number of dimensions. Default is 128.')
    parser.add_argument('--window-size', type=int, default=10, help=
        'Context size for optimization. Default is 10.')
    parser.add_argument('--iter', default=5, type=int, help=
        'Number of epochs in SGD')
    parser.add_argument('--workers', type=int, default=8, help=
        'Number of parallel workers. Default is 8.')
    return parser.parse_args()


class Sentences(object):

    def __init__(self, fname):
        self.fname = fname

    def __iter__(self):
        try:
            file_loc = self.fname
            for line in open(file_loc, mode='r'):
                line = line.rstrip('\n')
                words = line.split(' ')
                yield words
        except Exception:
            print('Failed reading file:')
            print(self.fname)


def learn_embeddings(walks):
    sentences = Sentences(walks)
    model = Word2Vec(sentences, size=args.dimensions, window=args.
        window_size, min_count=0, workers=args.workers, iter=args.iter,
        negative=25, sg=1)
    print('defined model using w2v')
    model.wv.save_word2vec_format(args.output)
    model.wv.save(args.model)
    print('saved model in word2vec format')
    return


def learn_embedding(args):
    start_time = datetime.now()
    print('---- input parameters -----')
    print('iterations=', args.iter)
    print('window size=', args.window_size)
    print('dimensions=', args.dimensions)
    print('workers=', args.workers)
    print('--- run program ---')
    learn_embeddings(args.input)
    end_time = datetime.now()
    print('run time: ', end_time - start_time)


def emb_main(args):
    args.input = 'data/walks.txt'
    args.output = 'data/emb.txt'
    args.model = 'data/emb.model'
    args.dimensions = 128
    args.iter = 5
    args.window_size = 10
    args.workers = 6
    learn_embedding(args)


<mask token>

import argparse
from gensim.models import Word2Vec
from datetime import datetime


def parse_args():
    parser = argparse.ArgumentParser(description='Run learn embeddings.')
    parser.add_argument('--input', nargs='?', default=' ', help=
        'Input walks path')
    parser.add_argument('--output', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--model', nargs='?', default='emb/karate.emb',
        help='Embeddings path')
    parser.add_argument('--dimensions', type=int, default=128, help=
        'Number of dimensions. Default is 128.')
    parser.add_argument('--window-size', type=int, default=10, help=
        'Context size for optimization. Default is 10.')
    parser.add_argument('--iter', default=5, type=int, help=
        'Number of epochs in SGD')
    parser.add_argument('--workers', type=int, default=8, help=
        'Number of parallel workers. Default is 8.')
    return parser.parse_args()


class Sentences(object):

    def __init__(self, fname):
        self.fname = fname

    def __iter__(self):
        try:
            file_loc = self.fname
            for line in open(file_loc, mode='r'):
                line = line.rstrip('\n')
                words = line.split(' ')
                yield words
        except Exception:
            print('Failed reading file:')
            print(self.fname)


def learn_embeddings(walks):
    sentences = Sentences(walks)
    model = Word2Vec(sentences, size=args.dimensions, window=args.
        window_size, min_count=0, workers=args.workers, iter=args.iter,
        negative=25, sg=1)
    print('defined model using w2v')
    model.wv.save_word2vec_format(args.output)
    model.wv.save(args.model)
    print('saved model in word2vec format')
    return


def learn_embedding(args):
    start_time = datetime.now()
    print('---- input parameters -----')
    print('iterations=', args.iter)
    print('window size=', args.window_size)
    print('dimensions=', args.dimensions)
    print('workers=', args.workers)
    print('--- run program ---')
    learn_embeddings(args.input)
    end_time = datetime.now()
    print('run time: ', end_time - start_time)


def emb_main(args):
    args.input = 'data/walks.txt'
    args.output = 'data/emb.txt'
    args.model = 'data/emb.model'
    args.dimensions = 128
    args.iter = 5
    args.window_size = 10
    args.workers = 6
    learn_embedding(args)


if __name__ == '__main__':
    args = parse_args()
    emb_main(args)

import argparse
from gensim.models import Word2Vec
from datetime import datetime

def parse_args():
    # Parses the node2vec arguments.
    parser = argparse.ArgumentParser(description="Run learn embeddings.")
    parser.add_argument('--input', nargs='?', default=' ',
                        help='Input walks path')
    parser.add_argument('--output', nargs='?', default='emb/karate.emb',
                        help='Embeddings path')
    parser.add_argument('--model', nargs='?', default='emb/karate.emb',
                        help='Embeddings path')
    parser.add_argument('--dimensions', type=int, default=128,
                        help='Number of dimensions. Default is 128.')
    parser.add_argument('--window-size', type=int, default=10,
                        help='Context size for optimization. Default is 10.')
    parser.add_argument('--iter', default=5, type=int,
                        help='Number of epochs in SGD')
    parser.add_argument('--workers', type=int, default=8,
                        help='Number of parallel workers. Default is 8.')
    return parser.parse_args()

class Sentences(object):
    def __init__(self, fname):
        self.fname = fname

    def __iter__(self):
        try:
            file_loc = self.fname
            for line in open(file_loc, mode='r'):
                line = line.rstrip('\n')
                words = line.split(" ")
                yield words
        except Exception:
            print("Failed reading file:")
            print(self.fname)

def learn_embeddings(walks):

    # Learn embeddings by optimizing the Skipgram objective using SGD.
    sentences = Sentences(walks)
    model = Word2Vec(sentences, size=args.dimensions, window=args.window_size, min_count=0,
                     workers=args.workers, iter=args.iter, negative=25, sg=1)
    print("defined model using w2v")
    model.wv.save_word2vec_format(args.output)
    model.wv.save(args.model)
    print("saved model in word2vec format")
    return


def learn_embedding(args):

    # Pipeline for representational learning for all nodes in a graph.
    start_time = datetime.now()
    print('---- input parameters -----')
    print('iterations=', args.iter)
    print('window size=', args.window_size)
    print('dimensions=', args.dimensions)
    print('workers=', args.workers)
    print('--- run program ---')
    learn_embeddings(args.input)
    end_time = datetime.now()
    print('run time: ', (end_time-start_time))

def emb_main(args):
    args.input = 'data/walks.txt'
    args.output = 'data/emb.txt'
    args.model = 'data/emb.model'
    args.dimensions = 128
    args.iter = 5
    args.window_size = 10
    args.workers = 6
    learn_embedding(args)


if __name__ == '__main__':
    args = parse_args()
    emb_main(args)
