Instruction stringlengths 362 7.83k | output_code stringlengths 1 945 |
|---|---|
Here is a snippet: <|code_start|> # Y_test = np.array([test_doc_labels[i] for i in test_doc_codes])
# # DBN
# X_train = np.array(load_marshal(args.train_doc_codes))
# Y_train = np.array(load_marshal(args.train_doc_labels))
# X_test = np.array(load_marshal(args.test_doc_codes))
# Y_test = np.arr... | results = retrieval_by_doclength(X_train, Y_train, X_test, Y_test, len_test, fraction=0.001, multilabel=args.multilabel) |
Given snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_doc_codes', type=str, help='path to the train doc codes file')
parser.add_argument('train_doc_labels', type=str, help='... | train_doc_codes = load_json(args.train_doc_codes) |
Next line prediction: <|code_start|> # Y_train = np.array([train_doc_labels[i] for i in train_doc_codes])
# X_test = np.r_[X_test]
# Y_test = np.array([test_doc_labels[i] for i in test_doc_codes])
# # DBN
# X_train = np.array(load_marshal(args.train_doc_codes))
# Y_train = np.array(load_marshal... | query_docs = load_corpus(args.query_info)['docs'] |
Continue the code snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--label', type=str, required=True, help='path to the input label file')
parser.add_argument('-c', '--corpus', type=str, required=True, help='path... | extract_labels(load_json(args.corpus)['docs'], args.label, args.output) |
Here is a snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--label', type=str, required=True, help='path to the input label file')
parser.add_argument('-c', '--corpus', type=str, required=True, help='path to the ... | extract_labels(load_json(args.corpus)['docs'], args.label, args.output) |
Predict the next line after this snippet: <|code_start|>from __future__ import absolute_import
def get_doc_codes(model, bow, vocab, avg=True):
vec = np.zeros(model.vector_size)
count = 0
for idx in bow:
word = vocab[int(idx)]
val = bow[idx]
if word in model:
vec += mode... | dump_json(doc_codes, output) |
Given snippet: <|code_start|> model.save(filepath, overwrite=True)
else:
if self.verbose > 0:
print('Epoch %05d: %s did not improve' %
(epoch, self.monitor))
else:
... | heatmap(weights.T, '%s_%s%s'%(self.filename, epoch, self.ext)) |
Using the snippet: <|code_start|> model.save_weights(filepath, overwrite=True)
else:
model.save(filepath, overwrite=True)
else:
if self.verbose > 0:
print('Epoch %05d: %... | weights = unitmatrix(weights, axis=0) # normalize |
Predict the next line for this snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--label', type=str, required=True, help='path to the input label file')
parser.add_argument('-c', '--corpus', type=str, required=Tru... | extract_labels(load_json(args.corpus)['docs'], load_json(args.label), args.output) |
Here is a snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--label', type=str, required=True, help='path to the input label file')
parser.add_argument('-c', '--corpus', type=str, required=True, help='path to the ... | extract_labels(load_json(args.corpus)['docs'], load_json(args.label), args.output) |
Based on the snippet: <|code_start|>
n_samples = X_docs.shape[0]
np.random.seed(0)
val_idx = np.random.choice(range(n_samples), args.n_val, replace=False)
train_idx = list(set(range(n_samples)) - set(val_idx))
X_train = X_docs[train_idx]
X_val = X_docs[val_idx]
del X_docs
# np.random.sh... | ae = AutoEncoder(n_vocab, args.n_dim, comp_topk=args.comp_topk, weights_file=args.load_weights) |
Given snippet: <|code_start|> X_val = X_docs[val_idx]
del X_docs
# np.random.shuffle(X_docs)
# n_val = args.n_val
## X_train = np.r_[X_docs[:-n_val]]
## X_val = np.r_[X_docs[-n_val:]]
# X_train = np.r_[X_docs[:-n_val]]
# del X_docs[:-n_val]
# X_val = np.r_[X_docs]
# del X_docs
... | if args.save_model: |
Here is a snippet: <|code_start|>'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
def train(args):
<|code_end|>
. Write the next line using the current file imports:
import timeit
import argparse
import numpy as np
from os import path
from autoencoder.core.ae import AutoEncoder, l... | corpus = load_corpus(args.input) |
Based on the snippet: <|code_start|>'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
def train(args):
corpus = load_corpus(args.input)
n_vocab, docs = len(corpus['vocab']), corpus['docs']
corpus.clear() # save memory
doc_keys = docs.keys()
X_docs = []
for k... | X_docs.append(vecnorm(doc2vec(docs[k], n_vocab), 'logmax1', 0)) |
Given snippet: <|code_start|>'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
def train(args):
corpus = load_corpus(args.input)
n_vocab, docs = len(corpus['vocab']), corpus['docs']
corpus.clear() # save memory
doc_keys = docs.keys()
X_docs = []
for k in doc... | X_docs.append(vecnorm(doc2vec(docs[k], n_vocab), 'logmax1', 0)) |
Given the following code snippet before the placeholder: <|code_start|>'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
def train(args):
corpus = load_corpus(args.input)
n_vocab, docs = len(corpus['vocab']), corpus['docs']
corpus.clear() # save memory
doc_keys = do... | X_docs_noisy = add_gaussian_noise(X_docs, 0.1) |
Continue the code snippet: <|code_start|>'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
def train(args):
corpus = load_corpus(args.input)
n_vocab, docs = len(corpus['vocab']), corpus['docs']
corpus.clear() # save memory
doc_keys = docs.keys()
X_docs = []
... | X_docs_noisy = add_masking_noise(X_docs, 0.01) |
Continue the code snippet: <|code_start|>'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
def train(args):
corpus = load_corpus(args.input)
n_vocab, docs = len(corpus['vocab']), corpus['docs']
corpus.clear() # save memory
doc_keys = docs.keys()
X_docs = []
... | X_docs_noisy = add_salt_pepper_noise(X_docs, 0.1) |
Here is a snippet: <|code_start|>
if args.noise:
# X_train_noisy = X_docs_noisy[:-n_val]
# X_val_noisy = X_docs_noisy[-n_val:]
X_train_noisy = X_docs_noisy[train_idx]
X_val_noisy = X_docs_noisy[val_idx]
print 'added %s noise' % args.noise
else:
X_train_noisy = X_t... | dump_json(dict(zip(doc_keys[train_idx].tolist(), train_doc_codes.tolist())), args.output) |
Based on the snippet: <|code_start|> # Y_train = load_pickle(args.train_doc_labels)
# X_val = np.array(load_pickle(args.val_doc_codes))
# Y_val = load_pickle(args.val_doc_labels)
# X_test = np.array(load_pickle(args.test_doc_codes))
# Y_test = load_pickle(args.test_doc_labels)
if args.multilabel... | results = multiclass_classifier(X_train, Y_train, X_val, Y_val, \ |
Predict the next line after this snippet: <|code_start|> Y_test = [test_doc_labels[i] for i in test_doc_codes]
# # DBN
# X_train = np.array(load_pickle(args.train_doc_codes))
# Y_train = load_pickle(args.train_doc_labels)
# X_val = np.array(load_pickle(args.val_doc_codes))
# Y_val = load_pickle(... | results = multilabel_classifier(X_train, Y_train, X_val, Y_val, \ |
Given the following code snippet before the placeholder: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_doc_codes', type=str, help='path to the train doc codes file')
parser.add_ar... | train_doc_codes = load_json(args.train_doc_codes) |
Continue the code snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.reuters import CorpusIterReuters
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus... | w2v = Word2Vec(args.n_dim, window=args.window_size, \ |
Predict the next line after this snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.reuters import CorpusIterReuters
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import Corpu... | save_w2v(w2v.model, args.save_model) |
Here is a snippet: <|code_start|>'''
from __future__ import absolute_import
# from autoencoder.datasets.reuters import CorpusIterReuters
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus
def train(args):
vocab = load_json(args.vo... | doc_codes = doc_word2vec(docs, revdict(vocab_dict), args.load_model, args.output, avg=True) |
Based on the snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.reuters import CorpusIterReuters
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus
de... | vocab = load_json(args.vocab) |
Given snippet: <|code_start|>@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.reuters import CorpusIterReuters
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus
def train(args):
vocab = load_j... | corpus = load_corpus(args.corpus[0]) |
Continue the code snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.reuters import CorpusIterReuters
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus... | corpus = CorpusIter20News(args.corpus[0], recursive=True, stem=True, with_docname=False) |
Here is a snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
def main():
parser T= argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, required=True, help='path to the input corpus dir')
parser.add_argument('-o', '--output', type=str, default='./', help='path to the o... | xml2text(args.input, args.output, white_list) |
Here is a snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
def main():
parser T= argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, required=True, help='path to the input corpus dir')
parser.add_argument('-o', '--output', type=str, default='./', help='path to the o... | white_list = load_json(args.whitelist) |
Given the code snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
def train_lda(corpus, vocab_dict, n_topics, n_iter, save_model):
lda = LdaModel(corpus, num_topics=n_topics, id2word=vocab_dict, \
passes=n_iter, minimum_probability=1e-3)
lda.sav... | dump_json(doc_codes, output) |
Continue the code snippet: <|code_start|>
def generate_doc_codes(model, corpus, output):
model.minimum_probability = 1e-3
n_topics = model.num_topics
doc_codes = {}
for key, doc_bow in corpus.iteritems():
code = np.zeros(n_topics)
for idx, val in model[doc_bow]:
code[idx] = v... | weights = unitmatrix(weights) # normalize |
Here is a snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from keras.optimizers import Adam
def retrieval(X_train, Y_train, X_test, Y_test, fractions=[0.01, 0.5, 1.0], multilabel=False):
db_size = len(X_train)
n_queries = len(X_test)
<|code_end|>
.... | X_train = unitmatrix(X_train) # normalize |
Given the following code snippet before the placeholder: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
cached_stop_words = init_stopwords()
class CorpusIter20News(object):
def __init__(self, corpus_path, recursive=False, stem=True, with_docname=False):
... | self.files = get_all_files(corpus_path, recursive) |
Continue the code snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
<|code_end|>
. Use current file imports:
import os
from random import shuffle
from collections import defaultdict
from ..preprocessing.preprocessing import get_all_files, init_stopwords, tiny... | cached_stop_words = init_stopwords() |
Given snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
cached_stop_words = init_stopwords()
class CorpusIter20News(object):
def __init__(self, corpus_path, recursive=False, stem=True, with_docname=False):
self.stem = stem
self.with_docna... | words = tiny_tokenize(text, self.stem, cached_stop_words) |
Based on the snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, required=True, help='path to the input source file')
parser.add_argument('--topn', type=int, default=25, help='keep only topn most... | labeldict = extract_labels(args.input, args.topn) |
Given the following code snippet before the placeholder: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, required=True, help='path to the input source file')
parser.add_argument('--topn', type=int, de... | dump_json(labeldict, args.output) |
Predict the next line after this snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, required=True, help='path to the input corpus file')
parser.add_argument('-o', '--output', type=str, default='... | construct_train_test_corpus(args.input, args.test_split, args.output, threshold=10, topn=2000) |
Given the code snippet: <|code_start|>'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
def train(args):
<|code_end|>
, generate the next line using the imports in this file:
import argparse
import timeit
import math
import numpy as np
from os import path
from autoencoder.preproces... | corpus = load_corpus(args.corpus) |
Predict the next line after this snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_doc_codes', type=str, help='path to the train doc code file')
parser.add_argument('train_doc... | train_doc_codes = load_file(train_doc_codes_path, True) |
Predict the next line after this snippet: <|code_start|> parser.add_argument('out_dir', type=str, help='path to the output dir')
args = parser.parse_args()
train_doc_codes_path = args.train_doc_codes
test_doc_codes_path = args.test_doc_codes
train_doc_codes = load_file(train_doc_codes_path, True)
... | dump_json(new_train_doc_codes, os.path.join(out_dir, 'new_' + os.path.basename(train_doc_codes_path))) |
Predict the next line for this snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_path', type=str, help='path to the train corpus file')
parser.add_argument('test_path', type=s... | docs = load_corpus(args.train_path)['docs'].items() |
Given the following code snippet before the placeholder: <|code_start|>
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_path', type=str, help='path to the train corpus file')
parser.add_argument('test_path', type=str, help... | train = corpus2libsvm(train_docs, doc_labels, os.path.join(args.out_dir, 'train.libsvm')) |
Predict the next line for this snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('doc_codes_file', type=str, help='path to the input corpus file')
parser.add_argument('doc_labels_fil... | reuters_visualize_pca_2d(load_json(args.doc_codes_file), load_json(args.doc_labels_file), classes_to_visual, args.output) |
Given the following code snippet before the placeholder: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('doc_codes_file', type=str, help='path to the input corpus file')
parser.add_argumen... | reuters_visualize_tsne(load_json(args.doc_codes_file), load_json(args.doc_labels_file), classes_to_visual, args.output) |
Given snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('doc_codes_file', type=str, help='path to the input corpus file')
parser.add_argument('doc_labels_file', type=str, help='path ... | reuters_visualize_pca_2d(load_json(args.doc_codes_file), load_json(args.doc_labels_file), classes_to_visual, args.output) |
Predict the next line after this snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_doc_codes', type=str, help='path to the train doc code file')
parser.add_argument('val_doc_c... | train_doc_codes = load_json(train_doc_codes_path) |
Given snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_doc_codes', type=str, help='path to the train doc code file')
parser.add_argument('val_doc_codes', type=str, help='path... | dump_json(train_doc_codes, os.path.join(out_dir, 'new_' + os.path.basename(train_doc_codes_path))) |
Predict the next line for this snippet: <|code_start|> parser.add_argument('-cv', '--cross_validation', type=int, help='k-fold cross validation')
args = parser.parse_args()
# autoencoder
# train_doc_codes = load_json(args.train_doc_codes)
# train_doc_labels = load_json(args.train_doc_labels)
# t... | results = neural_regression(X_new_train, Y_new_train, X_new_val, Y_new_val, \ |
Given the following code snippet before the placeholder: <|code_start|>
@author: hugo
'''
from __future__ import absolute_import
def main():
parser = argparse.ArgumentParser()
parser.add_argument('train_doc_codes', type=str, help='path to the train doc codes file')
parser.add_argument('train_doc_labels',... | X_train = np.array(load_pickle(args.train_doc_codes)) |
Given snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus
# from autoencoder.datasets.reuters import CorpusIterReuters
def train... | d2v = MyDoc2Vec(args.n_dim, window=args.window_size, \ |
Given snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus
# from autoencoder.datasets.reuters import CorpusIterReuters
def train... | save_doc2vec(d2v.model, args.save_model) |
Here is a snippet: <|code_start|>
def train(args):
vocab = load_json(args.vocab)
# import pdb;pdb.set_trace()
# load corpus
corpus = CorpusIter20News(args.corpus[0], recursive=True, stem=True, with_docname=True)
# corpus = CorpusIterMRD(args.corpus[0], load_json(args.docnames), stem=True, with_docna... | d2v = load_doc2vec(args.load_model) |
Continue the code snippet: <|code_start|>def train(args):
vocab = load_json(args.vocab)
# import pdb;pdb.set_trace()
# load corpus
corpus = CorpusIter20News(args.corpus[0], recursive=True, stem=True, with_docname=True)
# corpus = CorpusIterMRD(args.corpus[0], load_json(args.docnames), stem=True, wit... | doc_codes = predict(d2v, corpus_iter) |
Given the code snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus
# from autoencoder.datasets.reuters import CorpusIterReuters
... | vocab = load_json(args.vocab) |
Predict the next line for this snippet: <|code_start|> vocab = load_json(args.vocab)
# import pdb;pdb.set_trace()
# load corpus
corpus = CorpusIter20News(args.corpus[0], recursive=True, stem=True, with_docname=True)
# corpus = CorpusIterMRD(args.corpus[0], load_json(args.docnames), stem=True, with_do... | dump_json(doc_codes, args.output) |
Using the snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
# from autoencoder.datasets.movie_review_data import CorpusIterMRD
# from autoencoder.datasets.wiki10plus import CorpusIterWiki10plus
# from autoencoder.datasets.reuters import CorpusIterReuters
def t... | corpus = CorpusIter20News(args.corpus[0], recursive=True, stem=True, with_docname=True) |
Given the following code snippet before the placeholder: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-train', '--train_path', type=str, required=True, help='path to the training corpus')
parser.add_argument('-test', '--te... | train_corpus, test_corpus = construct_train_test_corpus(args.train_path, args.test_path, args.out_dir, threshold=args.threshold, topn=args.topn) |
Predict the next line after this snippet: <|code_start|>'''
Created on Dec, 2016
@author: hugo
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-train', '--train_path', type=str, required=True, help='path to the training corpus')
parser.add_argument('-test', '--test_path', type=... | train_labels = generate_20news_doc_labels(train_corpus['docs'].keys(), os.path.join(args.out_dir, 'train.labels')) |
Using the snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
pattern = r'>([^<>]+)<'
prog = re.compile(pattern)
<|code_end|>
, determine the next line of code. You have imports:
import os
import re
import numpy as np
import pdb;pdb.set_trace()
from random i... | cached_stop_words = init_stopwords() |
Using the snippet: <|code_start|> self.stem = stem
self.train_docs = train_docs
self.with_docname = with_docname
self.files = get_all_files(corpus_dir, False)
def __iter__(self):
shuffle(self.files)
count = 0
for filename in self.files:
doc_name = ... | contents = tiny_tokenize_xml(contents, False, cached_stop_words) |
Predict the next line after this snippet: <|code_start|>@author: hugo
'''
from __future__ import absolute_import
pattern = r'>([^<>]+)<'
prog = re.compile(pattern)
cached_stop_words = init_stopwords()
class CorpusIterWiki10plus(object):
def __init__(self, corpus_dir, train_docs, stem=True, with_docname=False):... | words = tiny_tokenize(text, self.stem, cached_stop_words) |
Continue the code snippet: <|code_start|>'''
Created on Jan, 2017
@author: hugo
'''
from __future__ import absolute_import
pattern = r'>([^<>]+)<'
prog = re.compile(pattern)
cached_stop_words = init_stopwords()
class CorpusIterWiki10plus(object):
def __init__(self, corpus_dir, train_docs, stem=True, with_docn... | self.files = get_all_files(corpus_dir, False) |
Next line prediction: <|code_start|> corpus = {}
files = get_all_files(corpus_dir, False)
cached_stop_words = []
# cached_stop_words = init_stopwords()
count = 0
for filename in files:
try:
with open(filename, 'r') as fp:
text = fp.read().lower()
... | train_word_freq = count_words(train_data.values()) |
Based on the snippet: <|code_start|> cached_stop_words = []
# cached_stop_words = init_stopwords()
count = 0
for filename in files:
try:
with open(filename, 'r') as fp:
text = fp.read().lower()
# remove punctuations, stopwords and *unnecessary digits*, ... | train_docs, vocab_dict, train_word_freq = construct_corpus(train_data, train_word_freq, True, threshold=threshold, topn=topn) |
Next line prediction: <|code_start|> count = 0
for filename in files:
try:
with open(filename, 'r') as fp:
text = fp.read().lower()
# remove punctuations, stopwords and *unnecessary digits*, stemming
words = tiny_tokenize(text, stem, cached_stop... | dump_json(train_corpus, os.path.join(output, 'train.corpus')) |
Based on the snippet: <|code_start|> try:
word_count[vocab_dict[word]] = freq
except: # word is not in vocab, i.e., this word should be discarded
continue
docs[key] = word_count
return docs
def build_vocab(word_freq, threshold=5, topn=None, start_idx=... | dump_json(train_corpus, os.path.join(output, 'train.corpus')) |
Continue the code snippet: <|code_start|> # doc_name = os.path.basename(filename)
parent_name, child_name = os.path.split(filename)
doc_name = os.path.split(parent_name)[-1] + '_' + child_name
for i in range(len(words)):
# doc-word frequ... | corpus = load_json(corpus_path) |
Given the code snippet: <|code_start|> if freq < threshold:
return vocab_dict
vocab_dict[word] = idx
idx += 1
return vocab_dict
def construct_train_test_corpus(train_path, test_path, output, threshold=5, topn=None):
train_docs, vocab_dict, train_word_freq = co... | write_file(data, output) |
Using the snippet: <|code_start|> features: Number of neurons in each layer, and number of layers (given by
length of sequence) + one layer for softmax.
train: If model is being evaluated in training mode or not.
init_fn: Initialization function used for dense layers.
activation_fn: Activ... | kernel_init=init.sparse_init( |
Based on the snippet: <|code_start|> features = (32, 32),
train=True,
init_fn = flax.deprecated.nn.initializers.kaiming_normal,
activation_fn = flax.deprecated.nn.relu,
masks = None,
masked_layer_indices = None,
dropout_rate = 0.):
"... | masks = masked.generate_model_masks(depth, masks, |
Predict the next line for this snippet: <|code_start|># coding=utf-8
# Copyright 2022 RigL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | self._dataset = cifar10.CIFAR10Dataset( |
Predict the next line after this snippet: <|code_start|>#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable la... | dataset = dataset_factory.create_dataset( |
Next line prediction: <|code_start|># limitations under the License.
# Lint as: python3
"""Weight Symmetry: Train model with randomly shuffled sparse mask."""
# TODO: Refactor drivers to separate logic from flags/IO.
experiment_dir = '{}/{}/'.format(FLAGS.experiment_dir, work_unit_id)
logging.info('Saving exper... | base_model, _ = model_factory.create_model( |
Given the following code snippet before the placeholder: <|code_start|>
host_count = jax.host_count()
local_device_count = jax.local_device_count()
logging.info('Device count: %d, host count: %d, local device count: %d',
jax.device_count(), host_count, local_device_count)
if jax.host_id() == 0:
... | mask = mask_factory.create_mask(FLAGS.mask_type, base_model, mask_rng, |
Here is a snippet: <|code_start|> rng, ((input_shape, jnp.float32),),
num_classes=dataset.num_classes)
logging.info('Generating random mask based on model')
# Re-initialize the RNG to maintain same training pattern (as in prune code).
mask_rng = jax.random.PRNGKey(FLAGS.mask_randomseed)
mask = mas... | mask = masked.propagate_masks(mask) |
Using the snippet: <|code_start|> jax.device_count(), host_count, local_device_count)
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(experiment_dir)
dataset = dataset_factory.create_dataset(
FLAGS.dataset,
FLAGS.batch_size,
FLAGS.batch_size_test,
shuffl... | mask_stats = symmetry.get_mask_stats(mask) |
Continue the code snippet: <|code_start|> rng, ((input_shape, jnp.float32),),
num_classes=dataset.num_classes,
masks=mask)
if FLAGS.optimizer == 'Adam':
optimizer = flax.optim.Adam(
learning_rate=FLAGS.lr, weight_decay=FLAGS.weight_decay)
elif FLAGS.optimizer == 'Momentum':
optimiz... | trainer = training.Trainer( |
Next line prediction: <|code_start|> input_shape = (1,) + dataset.shape
base_model, _ = model_factory.create_model(
FLAGS.model,
rng, ((input_shape, jnp.float32),),
num_classes=dataset.num_classes)
logging.info('Generating random mask based on model')
# Re-initialize the RNG to maintain same ... | utils.dump_dict_json( |
Predict the next line for this snippet: <|code_start|>#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | fixed_param.main([]) |
Next line prediction: <|code_start|># coding=utf-8
# Copyright 2022 RigL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | dataset = dataset_factory.create_dataset( |
Here is a snippet: <|code_start|># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Weight Symmetry: Train model with randomly sampled sparse mask."""
experiment_dir =... | base_model, _ = model_factory.create_model( |
Given the following code snippet before the placeholder: <|code_start|> host_count = jax.host_count()
local_device_count = jax.local_device_count()
logging.info('Device count: %d, host count: %d, local device count: %d',
jax.device_count(), host_count, local_device_count)
if jax.host_id() == 0:
... | mask = mask_factory.create_mask(FLAGS.mask_type, base_model, mask_rng, |
Here is a snippet: <|code_start|> num_classes=dataset.num_classes,
masked_layer_indices=FLAGS.masked_layer_indices)
logging.info('Generating random mask based on model')
# Re-initialize the RNG to maintain same training pattern (as in prune code).
mask_rng = jax.random.PRNGKey(FLAGS.mask_randomseed)
... | mask = masked.propagate_masks(mask) |
Given snippet: <|code_start|>
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(experiment_dir)
dataset = dataset_factory.create_dataset(
FLAGS.dataset,
FLAGS.batch_size,
FLAGS.batch_size_test,
shuffle_buffer_size=FLAGS.shuffle_buffer_size)
logging.info('Training %s o... | mask_stats = symmetry.get_mask_stats(mask) |
Given the following code snippet before the placeholder: <|code_start|> rng, ((input_shape, jnp.float32),),
num_classes=dataset.num_classes,
masks=mask)
if FLAGS.optimizer == 'Adam':
optimizer = flax.optim.Adam(
learning_rate=FLAGS.lr, weight_decay=FLAGS.weight_decay)
elif FLAGS.optimi... | trainer = training.Trainer( |
Here is a snippet: <|code_start|> base_model, _ = model_factory.create_model(
FLAGS.model,
rng, ((input_shape, jnp.float32),),
num_classes=dataset.num_classes,
masked_layer_indices=FLAGS.masked_layer_indices)
logging.info('Generating random mask based on model')
# Re-initialize the RNG to... | utils.dump_dict_json( |
Given snippet: <|code_start|># coding=utf-8
# Copyright 2022 RigL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | return isinstance(layer, utils.PRUNING_WRAPPER) and layer.trainable |
Using the snippet: <|code_start|># coding=utf-8
# Copyright 2022 RigL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | dataset = dataset_factory.create_dataset( |
Based on the snippet: <|code_start|>
# Lint as: python3
"""Weight Symmetry: Train models with fixed param, but diff. depth and width."""
experiment_dir = path.join(FLAGS.experiment_dir, str(work_unit_id))
logging.info('Saving experimental results to %s', experiment_dir)
host_count = jax.host_count()
local_de... | features = mnist_fc.feature_dim_for_param( |
Using the snippet: <|code_start|>
host_count = jax.host_count()
local_device_count = jax.local_device_count()
logging.info('Device count: %d, host count: %d, local device count: %d',
jax.device_count(), host_count, local_device_count)
if jax.host_id() == 0:
summary_writer = tensorboard.Summa... | base_model, _ = model_factory.create_model( |
Using the snippet: <|code_start|>
rng = jax.random.PRNGKey(FLAGS.random_seed)
input_shape = (1,) + dataset.shape
input_len = functools.reduce(operator.mul, dataset.shape)
features = mnist_fc.feature_dim_for_param(
input_len,
FLAGS.param_count,
FLAGS.depth)
logging.info('Model Configurati... | mask = masked.shuffled_mask( |
Next line prediction: <|code_start|>
features = mnist_fc.feature_dim_for_param(
input_len,
FLAGS.param_count,
FLAGS.depth)
logging.info('Model Configuration: %s', str(features))
base_model, _ = model_factory.create_model(
MODEL,
rng, ((input_shape, jnp.float32),),
num_classes... | mask_stats = symmetry.get_mask_stats(mask) |
Next line prediction: <|code_start|> features=features, masks=mask)
if FLAGS.opt == 'Adam':
optimizer = flax.optim.Adam(
learning_rate=FLAGS.lr, weight_decay=FLAGS.weight_decay)
elif FLAGS.opt == 'Momentum':
optimizer = flax.optim.Momentum(
learning_rate=FLAGS.lr,
beta=FLAGS.mo... | trainer = training.Trainer( |
Given the code snippet: <|code_start|> if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(experiment_dir)
dataset = dataset_factory.create_dataset(
FLAGS.dataset,
FLAGS.batch_size,
FLAGS.batch_size_test,
shuffle_buffer_size=FLAGS.shuffle_buffer_size)
logging.info('Train... | model_param_count = utils.count_param(base_model, ('kernel',)) |
Based on the snippet: <|code_start|># Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
#... | _, initial_params = mnist_fc.MNISTFC.init_by_shape( |
Continue the code snippet: <|code_start|> invalid_masks = {
'MaskedModule_0': {
'kernel':
jnp.zeros((self._batch_size, 5 * 5 * 16))
}
}
with self.assertRaisesRegex(
ValueError, 'Mask is invalid for model.'):
mnist_fc.MNISTFC.init_by_shape(
... | total_size = utils.count_param(model, PARAM_COUNT_PARAM) |
Given the code snippet: <|code_start|># coding=utf-8
# Copyright 2022 RigL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | jnp.array], float], masked.MaskType] |
Continue the code snippet: <|code_start|># you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed ... | _, initial_params = mnist_cnn.MNISTCNN.init_by_shape( |
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