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train_w2v.py
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train_w2v.py
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# coding: utf-8
from os import listdir, makedirs
from os.path import isfile, join, exists
import gc
from time import time
import pandas as pd
from gensim.models import Word2Vec, FastText
from constants import SMPL_PATH, ETL_PATH, TOKEN, HASH, SENT_IDX, EMB_PATH
from train_utils import parse_args, EpochSaver, EpochLogger
from utils import init_logging, log_args
class Sentences(object):
"""this is a memory friendly approach that streams data from disk."""
def __init__(self, input_dir, logger, use_file_cache=False, lowercase=False):
self.input_dir = input_dir
self.cache_dir = join(input_dir, "cache")
self.logger = logger
self.use_file_cache = use_file_cache
self.files = sorted(
[f for f in listdir(input_dir) if isfile(join(input_dir, f))]
)
self.cached_files = None
self.goodids = pd.read_pickle(join(ETL_PATH, "dewiki_good_ids.pickle"))
self.lowercase = lowercase
if use_file_cache:
self.init_file_cache()
def __iter__(self):
if self.use_file_cache:
for filename in self.cached_files:
gc.collect()
f = join(self.input_dir, "cache", filename)
self.logger.info(f)
ser = pd.read_pickle(f)
for sent in ser:
yield sent
else:
for filename in self.files:
ser = self.load(filename)
for sent in ser:
yield sent
def load(self, filename):
gc.collect()
f = join(self.input_dir, filename)
self.logger.info(f)
df = pd.read_pickle(f)
df = df[df.hash.isin(self.goodids.index)]
if self.lowercase:
df.token = df.token.str.lower()
df = df.groupby([HASH, SENT_IDX], sort=False)[TOKEN].agg(self.docs_to_lists)
return df
def init_file_cache(self):
if not exists(self.cache_dir):
makedirs(self.cache_dir)
self.logger.info("inititalizing file cache in " + self.cache_dir)
for filename in self.files:
ser = self.load(filename)
ser.to_pickle(
join(self.cache_dir, filename.split(".")[0] + "_cache.pickle")
)
self.cached_files = sorted(
[f for f in listdir(self.cache_dir) if isfile(join(self.cache_dir, f))]
)
@staticmethod
def docs_to_lists(token_series):
return tuple(token_series.tolist())
def main():
# --- argument parsing ---
(
model_name,
epochs,
min_count,
cores,
checkpoint_every,
cache_in_memory,
lowercase,
fasttext,
args,
) = parse_args(default_model_name="w2v_default", default_epochs=100)
# --- init logging ---
logger = init_logging(name=model_name, basic=True, to_file=True, to_stdout=False)
log_args(logger, args)
input_dir = join(SMPL_PATH, "dewiki")
model_dir = join(EMB_PATH, model_name)
if not exists(model_dir):
makedirs(model_dir)
logger.info("model dir: " + model_dir)
t0 = time()
if cache_in_memory:
# needs approx. 25GB of RAM
logger.info("cache data in memory")
sentences = [s for s in Sentences(input_dir, logger, lowercase=lowercase)]
else:
sentences = Sentences(
input_dir, logger, use_file_cache=True, lowercase=lowercase
)
gc.collect()
# Model initialization
logger.info("Initializing new model")
if fasttext:
model = FastText(
size=300,
window=5,
min_count=min_count,
sample=1e-5,
negative=5,
sg=1,
seed=42,
iter=epochs,
workers=cores,
min_n=3,
max_n=6,
)
else:
model = Word2Vec(
size=300,
window=5,
min_count=min_count,
sample=1e-5,
negative=5,
sg=1,
seed=42,
iter=epochs,
workers=cores,
)
logger.info("Building vocab")
model.build_vocab(sentences, progress_per=100_000)
# Model Training
epoch_saver = EpochSaver(model_name, model_dir, checkpoint_every)
epoch_logger = EpochLogger(logger)
logger.info("Training {:d} epochs".format(epochs))
model.train(
sentences,
total_examples=model.corpus_count,
epochs=model.epochs,
report_delay=60,
callbacks=[epoch_logger, epoch_saver],
)
# saving model
file_path = join(model_dir, model_name)
logger.info("Writing model to " + file_path)
model.callbacks = ()
model.save(file_path)
t1 = int(time() - t0)
logger.info(
"all done in {:02d}:{:02d}:{:02d}".format(t1 // 3600, (t1 // 60) % 60, t1 % 60)
)
if __name__ == "__main__":
main()