def split_bson(input_bson_filename, output_bson_filename_1,
output_bson_filename_2, n, number_random_example):
data = bson.decode_file_iter(open(input_bson_filename, 'rb'))
random_items = random.sample(range(n), number_random_example)
random_items.sort()
print(random_items[0])
r_idx = 0
print(n)
with open(output_bson_filename_1, 'w+') as output:
for c, d in tqdm(enumerate(data), total=n):
if c != random_items[r_idx]:
continue
else:
# print("pick random item: {}".format(c))
# insert your code here.
output.write(BSON.encode(d))
r_idx = r_idx + 1
if r_idx == number_random_example:
break
r_idx = 0
data = bson.decode_file_iter(open(input_bson_filename, 'rb'))
with open(output_bson_filename_2, 'w+') as output:
for c, d in tqdm(enumerate(data), total=n):
if r_idx < number_random_example and c == random_items[r_idx]:
r_idx = r_idx + 1
continue
else:
output.write(BSON.encode(d))
print("Finish convert tfrecords with {} records".format(r_idx))
def large_social_networks_twitter():
# count = 0
degree_thrd = 3
index = []
G = nx.Graph()
with open('/mnt/wzhan139/cross media data/Twitter/twitter_followees.bson', "rb") as f:
data = bson.decode_file_iter(f, bson.CodecOptions(unicode_decode_error_handler="ignore"))
count = 0
for c, d in enumerate(data):
print("Reading node "+ str(c))
index.append(d['user_name'])
G.add_node(d['user_name'])
count += 1
with open('/mnt/wzhan139/cross media data/Twitter/twitter_followees.bson', "rb") as f:
data = bson.decode_file_iter(f, bson.CodecOptions(unicode_decode_error_handler="ignore"))
for c, d in enumerate(data):
print("Constructing graph in node " + str(c))
for j in range(len(d['followees'])):
if G.has_node(d['followees'][j]['screen_name']):
G.add_edge(d['user_name'], d['followees'][j]['screen_name'])
with open('/mnt/wzhan139/cross media data/Twitter/twitter_followers.bson', "rb") as f:
data = bson.decode_file_iter(f, bson.CodecOptions(unicode_decode_error_handler="ignore"))
for c, d in enumerate(data):
print("Constructing graph in node " + str(c))
for i in range(len(d['followers'])):
if G.has_node(d['followers'][i]['screen_name']):
G.add_edge(d['user_name'], d['followers'][i]['screen_name'])
G2 = nx.convert_node_labels_to_integers(G,label_attribute='old_label')
num_node = nx.adjacency_matrix(G2).shape[0]
sparsity = G2.number_of_edges() / num_node ** 2
print("no thredshold graph sparsity is " + str(sparsity))
print(nx.info(G2))
nx.write_gpickle(G2, "twitter.nothred.gpickle")
remove_node=[]
for n, d in G2.nodes(data=True):
if G2.degree(n)<degree_thrd:
remove_node.append(n)
G2.remove_nodes_from(np.asarray(remove_node))
G2 = nx.convert_node_labels_to_integers(G2)
num_node=nx.adjacency_matrix(G2).shape[0]
G3 = nx.from_scipy_sparse_matrix(sp.dia_matrix((np.ones(num_node), 0), shape=nx.adjacency_matrix(G2).shape))
G4=nx.compose(G2,G3)
nx.write_gpickle(G4, "twitter.gpickle")
nx.write_adjlist(G4, "twitter_adj")
nx.write_edgelist(G4, "twitter_edgelist")
sparsity = G4.number_of_edges()/num_node**2
print("sparsity is "+ str(sparsity))
print(nx.info(G4))
def load_train_data(path, cutoff, sample_file=0):
"""
:param path: path of input dataset
:param cutoff: how many lines you gonna read into memory
:param sample_file: 1 - using sample file
:return:
list of all category_ids, ids, images(binary), weights(1/n_imgs)
"""
NCORE = cpu_count()
all_categories = mp.Manager().list()
all_ids = mp.Manager().list()
all_imgs = mp.Manager().list()
all_weights = mp.Manager().list()
q = mp.Queue(maxsize=NCORE)
iolock = mp.Lock()
_, _, _, _, _, id2index = read_category(path)
pool = mp.Pool(NCORE,
initializer=process,
initargs=(q, iolock, all_ids, all_categories, all_imgs,
all_weights, id2index))
# process the file
if sample_file == 0:
data = bson.decode_file_iter(open(path + '/train.bson', 'rb'))
if sample_file == 1:
data = bson.decode_file_iter(open(path + '/train_example.bson', 'rb'))
it = 0
for c, d in enumerate(data):
if it >= cutoff:
break
q.put(d) # blocks until q below its max size
it = it + 1
# tell workers we're done
for _ in range(NCORE):
q.put(None)
pool.close()
pool.join()
# convert back to normal dictionary
all_categories = list(all_categories)
all_ids = list(all_ids)
all_imgs = list(all_imgs)
all_weights = list(all_weights)
return all_categories, all_ids, all_imgs, all_weights
def last_oplog_timestamp(self):
oplog = None
try:
if os.path.isfile(
self.lockfile): #Im firs looking for the .lock file
self.logger.info(
'Getting last bson-timestamp from previous oplog history file: "%s"'
% self.lockfile)
oplog = self.getLastTimestampSaved()
self.last_dump_ts = oplog
elif os.path.isfile(self.origin_dump_oplog):
self.logger.info(
'Getting last bson-timestamp from oplog file: "%s"' %
self.origin_dump_oplog)
oplog = open(self.origin_dump_oplog)
for change in bson.decode_file_iter(oplog):
self.last_dump_ts = change['ts']
oplog.close()
else:
raise Exception, 'Could not find an oplog.bson or history file: "%s"!' % self.origin_dump_oplog, None
except Exception, e:
self.logger.fatal(
'Failed to restore mongodump to destination! Error: "%s"' % e)
if oplog:
oplog.close()
exit(1)
def TestBatchGenerator(batch_size=1):
categories = categorydict()
count = 0
batchX = []
for sample in bson.decode_file_iter(
open(path.join(dataroot, testfile), 'rb')):
imgs = sample['imgs']
c = sample['_id']
for i in range(len(imgs)):
im = imgs[i]['picture']
im = imread(io.BytesIO(im))
im = cv2.resize(im,
None,
fx=0.5,
fy=0.5,
interpolation=cv2.INTER_AREA)
batchX.append(im)
batchY = c
count = count + 1
#print(count)
if count < batch_size:
pass
else:
yield np.asarray(batchX), np.asarray(batchY)
count = 0
batchX = []
def getTotalTrainImageCount():
count = 0
for sample in bson.decode_file_iter(
open(path.join(dataroot, trainfile), 'rb')):
imgs = sample['imgs']
count = count + len(imgs)
return count
def analyze(recording_file,
focus=None,
num_top=None,
new_aspects=None,
filter_stmt=None):
# Set up reporter
reporter = Reporter(filter_stmt=filter_stmt)
if new_aspects:
for new_aspect in new_aspects:
reporter.add_aspect_from_eval(*new_aspect)
plugins = load_plugins()
for aspect_class in plugins['aspects']:
reporter.add_aspect(aspect_class)
# Stream samples to reporter
for sample in decode_file_iter(recording_file):
reporter.add_sample(sample['t'], sample['o'])
echo('== Summary ==')
for stat, val in sorted(reporter.report.get_summary().items()):
echo(' %s = %s' % (stat, val))
echo()
reporter.report.print_top(focus=focus, num_top=num_top)
def merge_bson_unique(output, inputs, uniquefield):
uniquefieldset = set()
duplicatecount = 0
invalidcount = 0
totalcount = 0
#configure logging
logger = logging.getLogger(__name__)
logger.info('Creating your output file : %s', output)
with open(output, 'wb') as outputbson:
for bsonfile in inputs:
totalcount += 1
logger.info('Opening input file : %s', bsonfile)
with open(bsonfile, 'rb') as bsonfile_handle:
iterator = decode_file_iter(bsonfile_handle)
for singleobject in iterator:
if uniquefield not in singleobject:
invalidcount += 1
if singleobject[uniquefield] not in uniquefieldset:
outputbson.write(BSON.encode(singleobject))
uniquefieldset.add(singleobject[uniquefield])
else:
duplicatecount += 1
logging.info('Finished merging input file : %s', bsonfile)
logger.info('Finished merging all input files to path : %s', output)
logger.info('Duplicates: %s, Total: %s, Invalid: %s', duplicatecount,
totalcount, invalidcount)
def _get_data(self, file_path):
"""
This method imports the dummy dataset if it wasn't already loaded.
"""
data_set = []
print("Reading BSON file...")
data = bson.decode_file_iter(open(file_path, 'rb'))
print("Starting processing...")
for c, d in enumerate(data):
# Store what product belongs to what
category_id = d['category_id']
for e, pic in enumerate(d['imgs']):
picture = imread(io.BytesIO(pic['picture']))
# Store image with its label
data_set.append((category_id, picture.reshape(1, -1)))
if c % 10 == 0 and c > 0:
print("Iteration {}".format(c))
# Convert image data to Pandas DataFrame
pd_data = pd.DataFrame.from_records(data_set)
pd_data.rename(columns={0: 'category_id', 1: 'image'}, inplace=True)
return pd_data
def __iter__(self):
for item in tqdm(decode_file_iter(open(self.fn, 'rb'))):
sentences = mongodoc2sentences(item, '../data/cases')
if sentences:
for sentence in sentences:
yield sentence.split()
self.count+=1
def main(args):
if os.path.exists(args.save_train_bson):
raise FileExistsError(args.save_train_bson)
if os.path.exists(args.save_val_bson):
raise FileExistsError(args.save_val_bson)
logging.info('aggregating id of products...')
product_ids = list()
with open(args.input_bson, 'rb') as reader:
data = bson.decode_file_iter(reader)
for x in tqdm(data, unit='products', total=args.num_products):
product_ids.append(x.get('_id'))
logging.info('shuffle train and val ids...')
num_val = int(len(product_ids) * args.val_ratio)
random.seed(args.random_seed)
random.shuffle(product_ids)
val_product_ids = set(random.sample(product_ids, num_val))
train_product_ids = set(product_ids) - val_product_ids
logging.info('writing {} products for validation: {}'.format(len(val_product_ids), args.save_val_bson))
encode_dict_list(products=products_iter(args.input_bson, val_product_ids),
output_bson_path=args.save_val_bson,
total=len(val_product_ids))
logging.info('writing {} products for training: {}'.format(len(train_product_ids), args.save_train_bson))
encode_dict_list(products=products_iter(args.input_bson, train_product_ids),
output_bson_path=args.save_train_bson,
total=len(train_product_ids))
def products_iter(input_bson_path, product_ids):
with open(input_bson_path, 'rb') as reader:
data = bson.decode_file_iter(reader)
for i, prod in tqdm(enumerate(data), unit='products', total=args.num_products, disable=True):
prod_id = prod.get('_id')
if prod_id in product_ids:
yield prod
def merge_bson_unique(output, inputs, uniquefield):
uniquefieldset = set()
duplicatecount = 0
invalidcount = 0
totalcount = 0
#configure logging
logger = logging.getLogger(__name__)
logger.info('Creating your output file : %s', output)
with open(output, 'wb') as outputbson:
for bsonfile in inputs:
totalcount += 1
logger.info('Opening input file : %s', bsonfile)
with open(bsonfile, 'rb') as bsonfile_handle:
iterator = decode_file_iter(bsonfile_handle)
for singleobject in iterator:
if uniquefield not in singleobject:
invalidcount += 1
if singleobject[uniquefield] not in uniquefieldset:
outputbson.write(BSON.encode(singleobject))
uniquefieldset.add(singleobject[uniquefield])
else:
duplicatecount += 1
logging.info('Finished merging input file : %s', bsonfile)
logger.info('Finished merging all input files to path : %s', output)
logger.info('Duplicates: %s, Total: %s, Invalid: %s', duplicatecount, totalcount, invalidcount)
def apply_bson_delta(left, patch, outfile):
"""Apply patch to bson file and output results file"""
if isinstance(left, io.IOBase):
leftf = left
else:
leftf = open(left, 'rb')
if isinstance(patch, io.IOBase):
deltaf = patch
else:
deltaf = open(patch, 'rb')
patchdata = bson.decode(patch.read())
patched = {'a': {}, 'c': {}, 'd': {}}
for r in patchdata['records']:
if 'patch' in r.keys():
o = r['patch']
else:
o = r['obj']
patched[r['mode']][r['uniqkey']] = o
for o in bson.decode_file_iter(leftf):
if o[patchdata['uniqkey']] in patched['d']:
continue
elif o[patchdata['uniqkey']] in patched['c']:
o = patched['c'][o[patchdata['uniqkey']]]
outfile.write(bson.BSON.encode(o))
else:
outfile.write(bson.BSON.encode(o))
for o in patched['a'].values():
outfile.write(bson.BSON.encode(o))
def test_generator(bson_file, batch_size):
product_cnt = 0
pic_cnt = 0
num_products = NUM_TEST_PRODUCTS
num_pics = NUM_TEST_PICS
prods = []
pics = []
with open(bson_file, 'rb') as bf:
data = bson.decode_file_iter(bf)
for prod in data:
product_id = prod['_id']
for picidx, pic in enumerate(prod['imgs']):
picture = load_img_array(io.BytesIO(pic['picture']))
pics.append(picture)
prods.append(product_id)
pic_cnt += 1
# guarantee pics in the same prod are in the same batch
if 0 <= (pic_cnt % batch_size) < 4 or pic_cnt == num_pics:
yield np.array(prods), np.array(pics)
prods = []
pics = []
product_cnt += 1
if product_cnt % 1000 == 0 or product_cnt == num_products:
print("converted {} products {} images".format(
product_cnt, pic_cnt))
def run():
for dataset_file_path, dataset_folder_path in zip(
(TRAIN_FILE_PATH, TEST_FILE_PATH),
(TRAIN_FOLDER_PATH, TEST_FOLDER_PATH)):
print("Processing {} ...".format(dataset_file_path))
with open(dataset_file_path, "rb") as dataset_file_object:
data_generator = bson.decode_file_iter(dataset_file_object)
for data in data_generator:
category_id = data.get("category_id", "dummy")
category_folder_path = os.path.join(dataset_folder_path,
str(category_id))
os.makedirs(category_folder_path, exist_ok=True)
product_id = data["_id"]
for picture_id, picture_dict in enumerate(data["imgs"]):
picture_content = picture_dict["picture"]
picture_file_path = os.path.join(
category_folder_path,
"{}_{}.jpg".format(product_id, picture_id))
with open(picture_file_path, "wb") as picture_file_object:
picture_file_object.write(picture_content)
print("All done!")
def random_sample_bson(input_bson_filename,
output_bson_filename,
n=100,
number_random_example=10):
data = bson.decode_file_iter(open(input_bson_filename, 'rb'))
random_items = random.sample(range(n), number_random_example)
random_items.sort()
r_idx = 0
with open(output_bson_filename, 'w+') as output:
for c, d in tqdm(enumerate(data), total=n):
if c != random_items[r_idx]:
continue
else:
# print("pick random item: {}".format(c))
# insert your code here.
output.write(BSON.encode(d))
r_idx = r_idx + 1
if r_idx >= number_random_example:
break
print("Finish convert tfrecords with {} records".format(r_idx))
def run_train_boson_to_image():
bson_file = '/media/ssd/data/kaggle/cdiscount/__download__/train.bson'
num_products = 7069896 # 7069896 for train and 1768182 for test
out_dir = CDISCOUNT_DIR + '/train'
os.makedirs(out_dir, exist_ok=True)
categories = pd.read_csv(CDISCOUNT_DIR + '/category_names.csv',
index_col='category_id')
for category in categories.index:
os.makedirs(out_dir + '/' + str(category), exist_ok=True)
with open(bson_file, 'rb') as fbson:
data = bson.decode_file_iter(fbson)
#num_products = len(list(data))
#print ('num_products=%d'%num_products)
#exit(0)
for n, d in enumerate(data):
print('%08d/%08d' % (n, num_products))
category = d['category_id']
_id = d['_id']
for i, pic in enumerate(d['imgs']):
img_file = out_dir + '/' + str(
category) + '/' + '%s-%d.jpg' % (str(_id), i)
#print(img_file)
with open(img_file, 'wb') as f:
f.write(pic['picture'])
def build_distance_matrix(filename):
#read measurements
measurements = []
#count = 300
with open(filename) as measurement_file:
iter = bson.decode_file_iter(measurement_file)
for document in iter:
#count -= 1
measurements.append(document)
#if count == 0:
# break
#compute distance matrix
distance_matrix = []
for i in range(0, len(measurements)):
distance_matrix.append([]);
for i in range(0, len(measurements) - 1):
distance_matrix[i].append(100.0)
for j in range(i + 1, len(measurements)):
distance = compute_distance(measurements[i], measurements[j])
distance_matrix[i].append(distance)
distance_matrix[j].append(distance)
distance_matrix[len(distance_matrix) - 1].append(100.0)
return measurements, distance_matrix
def parallel_map(func):
with Pool() as pool:
with path.open("rb") as file:
return [
res for res in pool.imap(
func, bson.decode_file_iter(file), chunksize=10000)
]
def get_single_item(idx):
data_bson = bson.decode_file_iter(open(train_example_bson, 'rb'))
prod_id = []
cat_id = []
img_arr = []
index = 0
for c, d in enumerate(data_bson):
product_id = d['_id']
category_id = d['category_id'] # This won't be in Test data
# prod_to_category[product_id] = category_id
for e, pic in enumerate(d['imgs']):
# array of image
# picture = imread(io.BytesIO(pic['picture']))
# bytes of image
picture = pic['picture']
if idx == index:
prod_id.append(product_id)
cat_id.append(category_id)
img_arr.append(picture)
index += 1
return (prod_id[0], cat_id[0], img_arr[0])
def test_backports(self):
doc = BSON.encode({"tuple": (1, 2)})
exp = {"tuple": [1, 2]}
options = CodecOptions(uuid_representation=ALL_UUID_REPRESENTATIONS[0],
tz_aware=False, document_class=dict)
self.assertEqual(
{"tuple": [1, 2]},
BSON.encode(
{"tuple": (1, 2)}, codec_options=options,
uuid_subtype=ALL_UUID_REPRESENTATIONS[1]).decode())
self.assertEqual(exp, doc.decode(
as_class=SON,
tz_aware=True,
uuid_subtype=ALL_UUID_REPRESENTATIONS[1],
codec_options=options))
self.assertEqual([exp], list(decode_iter(
doc,
as_class=SON,
tz_aware=True,
uuid_subtype=ALL_UUID_REPRESENTATIONS[1],
codec_options=options)))
self.assertEqual([exp], list(decode_file_iter(
StringIO(doc),
as_class=SON,
tz_aware=True,
uuid_subtype=ALL_UUID_REPRESENTATIONS[1],
codec_options=options)))
self.assertEqual([exp], decode_all(
doc, SON, True, ALL_UUID_REPRESENTATIONS[1], True, options))
def get_data(NCORE, bsonfile):
"""
given number of cores, and bsonfile location,
returns:
dataframe1: _id, category_id
dataframe2: _id, list of images
"""
q = mp.Queue(maxsize=NCORE)
iolock = mp.Lock()
manager = mp.Manager()
prod_to_category = manager.dict()
prod_to_images = manager.dict()
pool = mp.Pool(NCORE,
initializer=process,
initargs=(q, iolock, prod_to_category, prod_to_images))
data = bson.decode_file_iter(open(bsonfile, 'rb'))
for c, d in enumerate(data):
q.put(d) # blocks until q below its max size
# tell workers we're done
for _ in range(NCORE):
q.put(None)
pool.close()
pool.join()
prod_to_category = dict(prod_to_category)
prod_to_images = dict(prod_to_images)
prod_to_category = pd.DataFrame(list(prod_to_category.items()),
columns=['_id', 'category_id'])
prod_to_images = pd.DataFrame(list(prod_to_images.items()),
columns=['_id', 'images'])
return prod_to_category, prod_to_images
def run(self):
logging.info("Resolving oplog for host %s:%s to max timestamp: %s" % (self.host, self.port, self.max_end_ts))
try:
if self.dump_gzip:
tailed_oplog_fh = GzipFile(self.tailed_oplog_file)
mongodump_oplog_fh = GzipFile(self.mongodump_oplog_file, 'a+')
else:
tailed_oplog_fh = open(self.tailed_oplog_file)
mongodump_oplog_fh = open(self.mongodump_oplog_file, 'a+')
for change in decode_file_iter(tailed_oplog_fh):
if 'ts' in change:
ts = change['ts']
if ts > self.mongodump_oplog_last_ts or self.mongodump_oplog_last_ts is None:
if ts < self.max_end_ts:
mongodump_oplog_fh.write(BSON.encode(change))
self.changes += 1
self.last_ts = ts
elif ts > self.max_end_ts:
break
tailed_oplog_fh.close()
mongodump_oplog_fh.flush()
mongodump_oplog_fh.close()
except Exception, e:
logging.fatal("Resolving of oplogs failed! Error: %s" % e)
raise e
def run_make_train_summary():
bson_file = '/media/ssd/data/kaggle/cdiscount/__download__/train.bson'
num_products = 7069896 # 7069896 for train and 1768182 for test
out_dir = CDISCOUNT_DIR
id = []
num_imgs = []
category_id = []
with open(bson_file, 'rb') as fbson:
data = bson.decode_file_iter(fbson)
#num_products = len(list(data))
#print ('num_products=%d'%num_products)
#exit(0)
for n, d in enumerate(data):
print('\r%08d/%08d' % (n, num_products), flush=True, end='')
category_id.append(d['category_id'])
id.append(d['_id'])
num_imgs.append(len(d['imgs']))
print('')
#by product id
df = pd.DataFrame({
'_id': id,
'num_imgs': num_imgs,
'category_id': category_id
})
df.to_csv(
'/media/ssd/data/kaggle/cdiscount/__temp__/train_by_product_id.csv',
index=False)
t = df['num_imgs'].sum() #check :12371293
print(t)
#split by id --------------------------------------
id_random = list(id)
random.shuffle(id_random)
#make train, valid
num_train = int(0.8 * (num_products))
num_valid = num_products - num_train
#by id
file1 = CDISCOUNT_DIR + '/split/' + 'train_id_v0_%d' % (num_train)
file2 = CDISCOUNT_DIR + '/split/' + 'valid_id_v0_%d' % (num_valid)
id1 = id_random[0:num_train]
id2 = id_random[num_train:]
write_list_to_file(id1, file1)
write_list_to_file(id2, file2)
#summary ------------------------------------
g = (df.groupby('category_id').agg({
'_id': 'count',
'num_imgs': 'sum'
}).reset_index())
g.to_csv('/media/ssd/data/kaggle/cdiscount/__temp__/train_g.csv',
index=False)
def transfer_data(
): # A generator used to generating a batch of labels and images
#transfer all images to features
data = bson.decode_file_iter(open(
PIC_PATH, 'rb')) # bson.decode_file_iter is a generator
# full list of classes
length = len(data)
print length
char = input()
df_categories = pd.read_csv(LABEL_PATH, index_col='category_id')
category_classes = df_categories.index.values
category_classes = category_classes.reshape(category_classes.shape[0], 1)
# using just binarizer without endcoder to convert all unique category_ids to have a column for each class
lb = preprocessing.LabelBinarizer()
lb.fit(df_categories.index.values)
# Size of pictures is defined here instead read size of the first picture
n = 500 # Batch size
pix_x = 180
pix_y = 180
rgb = 3
X_ids = np.zeros((n, 1)).astype(int)
Y = np.zeros((n, 1)).astype(int) # category_id for each row
X_images = np.zeros((n, pix_x, pix_y, rgb)) # m images are 180 by 180 by 3
i = 0
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.7 # Restrict the growth of memory use, or memory will be used up
images = tf.placeholder(tf.float32, [n, pix_x, pix_y, rgb], name="images")
op = tf.image.resize_images(images, [MODEL_SIZE, MODEL_SIZE],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
batch_num = 0
with tf.Session(config=config) as sess:
for c, d in enumerate(data):
for e, pic in enumerate(d['imgs']):
if i == 0:
Y = np.zeros(
(n, 1)).astype(int) # category_id for each row
X_images = np.zeros(
(n, pix_x, pix_y, rgb)) # m images are 180 by 180 by 3
picture = imread(io.BytesIO(
pic['picture'])) # All images should be added.
Y[i] = d['category_id']
X_images[i] = picture
i += 1
if i == n:
batch_num += 1
i = 0
Y_flat = lb.transform(Y)
X_flat = sess.run(op, feed_dict={images: X_images})
Y = np.zeros(
(n, 1)).astype(int) # category_id for each row
X_images = np.zeros(
(n, pix_x, pix_y, rgb)) # m images are 180 by 180 by 3
yield X_flat, Y_flat
def from_bson(cls, x):
if six.PY2:
# Hack for python 2: may work in py3 too, but it's definitely not the standard way!
reader = bson.decode_file_iter(six.BytesIO(x))
event_dict = next(reader)
else:
event_dict = bson.BSON.decode(x)
return cls(**event_dict)
def __iter__(self):
with open(self._filename, 'rb') as f:
i = 1
for tweet in decode_file_iter(f):
if self._limit and i > self._limit:
raise StopIteration
if all(func(tweet) for func in self._filter_functions):
i += 1
yield tweet
def test_invalid_decodes(self):
# Invalid object size (not enough bytes in document for even
# an object size of first object.
# NOTE: decode_all and decode_iter don't care, not sure if they should?
self.assertRaises(InvalidBSON, list,
decode_file_iter(StringIO(b"\x1B")))
# An object size that's too small to even include the object size,
# but is correctly encoded, along with a correct EOO (and no data).
data = b"\x01\x00\x00\x00\x00"
self.assertRaises(InvalidBSON, decode_all, data)
self.assertRaises(InvalidBSON, list, decode_iter(data))
self.assertRaises(InvalidBSON, list, decode_file_iter(StringIO(data)))
# One object, but with object size listed smaller than it is in the
# data.
data = (b"\x1A\x00\x00\x00\x0E\x74\x65\x73\x74"
b"\x00\x0C\x00\x00\x00\x68\x65\x6C\x6C"
b"\x6f\x20\x77\x6F\x72\x6C\x64\x00\x00"
b"\x05\x00\x00\x00\x00")
self.assertRaises(InvalidBSON, decode_all, data)
self.assertRaises(InvalidBSON, list, decode_iter(data))
self.assertRaises(InvalidBSON, list, decode_file_iter(StringIO(data)))
# One object, missing the EOO at the end.
data = (b"\x1B\x00\x00\x00\x0E\x74\x65\x73\x74"
b"\x00\x0C\x00\x00\x00\x68\x65\x6C\x6C"
b"\x6f\x20\x77\x6F\x72\x6C\x64\x00\x00"
b"\x05\x00\x00\x00")
self.assertRaises(InvalidBSON, decode_all, data)
self.assertRaises(InvalidBSON, list, decode_iter(data))
self.assertRaises(InvalidBSON, list, decode_file_iter(StringIO(data)))
# One object, sized correctly, with a spot for an EOO, but the EOO
# isn't 0x00.
data = (b"\x1B\x00\x00\x00\x0E\x74\x65\x73\x74"
b"\x00\x0C\x00\x00\x00\x68\x65\x6C\x6C"
b"\x6f\x20\x77\x6F\x72\x6C\x64\x00\x00"
b"\x05\x00\x00\x00\xFF")
self.assertRaises(InvalidBSON, decode_all, data)
self.assertRaises(InvalidBSON, list, decode_iter(data))
self.assertRaises(InvalidBSON, list, decode_file_iter(StringIO(data)))
def test_decode_file_iter(self):
expected, bson_data = self._generate_multidocument_bson_stream()
fileobj = tempfile.TemporaryFile()
fileobj.write(bson_data)
fileobj.seek(0)
for expected_doc, decoded_doc in zip(
expected, decode_file_iter(fileobj, self.codecopts)):
self.assertEqual(expected_doc, decoded_doc)
fileobj.close()
def test_date_filter_bson_date_filter_until(self):
self.setUp()
args = date_filter_bson.parse_args(['-i', os.path.dirname(os.path.abspath(__file__))+'/../test/test.bson', '-d2', '2016-01-18 00:00:00', '-o', os.path.dirname(os.path.abspath(__file__))+'/../test/output.bson'])
date_filter_bson.date_filter(args.output, args.input, args.dateone, args.datetwo)
count = 0
with open(os.path.dirname(os.path.abspath(__file__))+'/../test/output.bson', 'rb') as bsonfile_handle:
iterator = decode_file_iter(bsonfile_handle)
for line in iterator:
count+=1
self.assertEqual(count, 21)
#remove output self.tearDown
self.tearDown()
def filter_records(infile, year, month, day, tz):
"""
Takes in a file handle pointing at a BSON file, and a year, month, day, timezone.
Returns only those tweets which were sent on that date in that timezone.
"""
it = decode_file_iter(infile)
try:
for rec in it:
d = tweet_date(rec).astimezone(tz)
if d.year == year and d.month == month and d.day == day:
yield rec
except Exception as e:
print e
def merge_bson(output, inputs):
#configure logging
logger = logging.getLogger(__name__)
logger.info('Creating your output file : %s', output)
with open(output, 'wb') as outputbson:
for bsonfile in inputs:
logger.info('Opening input file : %s', bsonfile)
with open(bsonfile, 'rb') as bsonfile_handle:
iterator = decode_file_iter(bsonfile_handle)
for line in iterator:
outputbson.write(BSON.encode(line))
logger.info('Finished merging input file : %s', bsonfile)
logger.info('Finished merging all input files to path : %s', output)
def load(self):
try:
oplog = self.open()
logging.debug("Reading oplog file %s" % self.oplog_file)
for change in decode_file_iter(oplog, CodecOptions(unicode_decode_error_handler="ignore")):
if 'ts' in change:
self._last_ts = change['ts']
if self._first_ts is None and self._last_ts is not None:
self._first_ts = self._last_ts
self._count += 1
oplog.close()
except Exception, e:
logging.fatal("Error reading oplog file %s! Error: %s" % (self.oplog_file, e))
raise OperationError(e)
def get_iterator(self): tweet_parser = TweetParser() bson_handle = open(self.filepath, 'rb') for count, tweet in enumerate(bson.decode_file_iter(bson_handle)): if self.limit < count+1 and self.limit != 0: bson_handle.close() return elif tweet_parser.tweet_passes_filter(self.filter, tweet) \ and tweet_parser.tweet_passes_custom_filter_list(self.custom_filters, tweet): if self.should_strip: yield tweet_parser.strip_tweet(self.keep_fields, tweet) else: yield tweet bson_handle.close()
def convert_bson(self):
message = 'Converting BSON "{}" to language shelf #{}'.format(self.dataset, self.process_id)
bson_file = ProgressFile(self.bson_file, 'rb', message=message)
shelf_name = 'languages-' + self.process_id + '.shelf'
languages = shelve.open(self.path + shelf_name, writeback=True)
# Read every BSON object as an iterator to save memory.
for raw_json in bson.decode_file_iter(bson_file):
repository = raw_json['full_name'].encode('utf-8')
language = raw_json['language'].encode('utf-8') if raw_json['language'] is not None else ''
languages[repository] = language
languages.close()
bson_file.close()
self.cleanup(shelf_name)
def split_images(source, depth):
categories = get_categories(depth)
with open(source, 'rb') as data:
for entry in bson.decode_file_iter(data):
product_id = entry['_id']
category = categories[int(entry['category_id'])]
target = os.path.join('data', 'categories' + str(depth), category)
if not os.path.exists(target):
print target
os.makedirs(target)
for e, pic in enumerate(entry['imgs']):
picture = imread(io.BytesIO(pic['picture']))
picture_file = os.path.join(target, str(product_id) + '_' + str(e) + '.jpg')
imsave(picture_file, picture)
def read(self):
if os.path.isfile(self.oplog_file):
try:
logging.debug("Reading oplog file %s" % self.oplog_file)
if self.dump_gzip:
oplog = GzipFile(self.oplog_file)
else:
oplog = open(self.oplog_file)
for change in decode_file_iter(oplog):
if 'ts' in change:
self._last_ts = change['ts']
if self._first_ts is None and self._last_ts is not None:
self._first_ts = self._last_ts
self._count += 1
oplog.close()
except Exception, e:
logging.fatal("Error reading oplog file %s! Error: %s" % (self.oplog_file, e))
raise e
def test_basic_decode(self):
self.assertEqual({"test": u("hello world")},
BSON(b"\x1B\x00\x00\x00\x0E\x74\x65\x73\x74\x00\x0C"
b"\x00\x00\x00\x68\x65\x6C\x6C\x6F\x20\x77\x6F"
b"\x72\x6C\x64\x00\x00").decode())
self.assertEqual([{"test": u("hello world")}, {}],
decode_all(b"\x1B\x00\x00\x00\x0E\x74\x65\x73\x74"
b"\x00\x0C\x00\x00\x00\x68\x65\x6C\x6C"
b"\x6f\x20\x77\x6F\x72\x6C\x64\x00\x00"
b"\x05\x00\x00\x00\x00"))
self.assertEqual([{"test": u("hello world")}, {}],
list(decode_iter(
b"\x1B\x00\x00\x00\x0E\x74\x65\x73\x74"
b"\x00\x0C\x00\x00\x00\x68\x65\x6C\x6C"
b"\x6f\x20\x77\x6F\x72\x6C\x64\x00\x00"
b"\x05\x00\x00\x00\x00")))
self.assertEqual([{"test": u("hello world")}, {}],
list(decode_file_iter(StringIO(
b"\x1B\x00\x00\x00\x0E\x74\x65\x73\x74"
b"\x00\x0C\x00\x00\x00\x68\x65\x6C\x6C"
b"\x6f\x20\x77\x6F\x72\x6C\x64\x00\x00"
b"\x05\x00\x00\x00\x00"))))
def convert_bson(self):
output = open(self.path + self.dataset + '.json', 'wb')
message = 'Converting BSON "{}" and filtering fields'.format(self.dataset)
bson_file = ProgressFile(self.bson_file, 'rb', message=message)
Shelf.merge_shelves()
if os.path.isfile('languages.shelf'):
if self.path != "" and not os.path.isfile(self.path + 'languages.shelf'):
print("#{}. Copying languages shelf to local directory...".format(MPI.COMM_WORLD.rank))
shutil.copy('languages.shelf', self.path)
languages = shelve.open(self.path + 'languages.shelf', writeback=True)
else:
languages = {}
# Read every BSON object as an iterator to save memory.
for raw_json in bson.decode_file_iter(bson_file):
if not self.is_latin(raw_json['body']):
continue
preprocessed_json = {}
repository = str(re.search(r"repos/([^/]+/[^/]+)(/|$)", raw_json['url']).group(1))
raw_json['language'] = ''
if repository in languages:
raw_json['language'] = languages[repository]
for item in self.keep_fields:
preprocessed_json[item] = raw_json[item]
json.dump(preprocessed_json, output)
output.write('\n')
output.close()
bson_file.close()
# Don't move the file for now, since the commit comments only need to
# be on the worker nodes if we're running under MPI
self.cleanup()
def run(self):
try:
self.oplogs['backup'] = Oplog(self.mongodump_oplog['file'], self.do_gzip(), 'a+', self.flush_docs, self.flush_secs)
self.oplogs['tailed'] = Oplog(self.tailed_oplog['file'], self.do_gzip())
logging.info("Resolving oplog for %s to max ts: %s" % (self.uri, self.max_end_ts))
self.state.set('running', True)
self.state.set('first_ts', self.mongodump_oplog['first_ts'])
if not self.state.get('first_ts'):
self.state.set('first_ts', self.tailed_oplog['first_ts'])
for change in decode_file_iter(self.oplogs['tailed'], CodecOptions(unicode_decode_error_handler="ignore")):
self.last_ts = change['ts']
if not self.mongodump_oplog['last_ts'] or self.last_ts > self.mongodump_oplog['last_ts']:
if self.last_ts < self.max_end_ts:
self.oplogs['backup'].add(change)
self.changes += 1
elif self.last_ts > self.max_end_ts:
break
self.state.set('count', self.mongodump_oplog['count'] + self.changes)
self.state.set('last_ts', self.last_ts)
self.state.set('running', False)
self.exit_code = 0
except Exception, e:
raise Error("Resolving of oplogs failed! Error: %s" % e)
def parse_train_example():
# train_example_file = open(DATA_DIR + 'train_example.bson', 'rb')
# data = bson.loads(train_example_file.read())
#
train_example = bson.decode_file_iter(open(DATA_DIR + 'train_example.bson', 'rb'))
# data = bson.decode_document(open(DATA_DIR + 'train_example.bson', 'rb'))
data = []
for key, value in enumerate(train_example):
product_id = value['_id']
category_id = value['category_id'] # This won't be in Test data
# prod_to_category[product_id] = category_id
pics = []
for e, pic in enumerate(value['imgs']):
picture = imread(io.BytesIO(pic['picture']))
# do something with the picture, etc
# plt.imshow(picture)
# plt.title(category_id)
# plt.show()
pics.append(picture)
data.append((product_id, category_id, pics))
return data
#!/usr/bin/python3
# coding: utf-8
import bson # pip install pymongo 这样安装的附带包, 不能直接 pip install bson (这样出来是第三方的, 好多函数都没有)
##################################################################
## 写 bson 文件
post1 = {"author": "Mike", "text": "Another post!", "tags": ["bulk", "insert"], "date": 14}
post2 = {"author": "Jenny", "text": "Another post!", "tags": ["bulk", "insert"], "date": 14}
f = open('tmp.bson', 'wb')
f.write(bson.BSON.encode(post1))
f.write(bson.BSON.encode(post2))
f.close()
##################################################################
## 直接使用 Python 读取 bson 文件, bson 文件是 json 的二进制格式
items = list(bson.decode_file_iter(open('./tmp.bson', 'rb'))); print(len(items)) # 2
item = items[0]; print(type(items), type(item)) # <class 'list'> <class 'dict'>
print(item.keys()) # dict_keys(['author', 'text', 'tags', 'date'])
validation_data = val_gen,
validation_steps = num_val_images // batch_size + 1,
callbacks=[early_stopping, model_checkpoint])
print(history.history.keys())
# logging
logging.info('N. epochs == '+str(len(history.history['val_acc'])))
logging.info('Val accuracy == '+str(max(history.history['val_acc'])))
## Predict on Test-set
print(">>> Predicting on test-set ... ")
submission_df = pd.read_csv("data/sample_submission.csv")
print(submission_df.head())
test_datagen = ImageDataGenerator()
data = bson.decode_file_iter(open(test_bson_path, "rb"))
with tqdm(total=num_test_products) as pbar:
for c, d in enumerate(data):
product_id = d["_id"]
num_imgs = len(d["imgs"])
batch_x = np.zeros((num_imgs, 180, 180, 3), dtype=K.floatx())
for i in range(num_imgs):
bson_img = d["imgs"][i]["picture"]
# Load and preprocess the image.
img = load_img(io.BytesIO(bson_img), target_size=(180, 180))
x = img_to_array(img)
x = preprocess_image(x)
# = test_datagen.random_transform(x)
# = test_datagen.standardize(x)
# Add the image to the batch.
batch_x[i] = x
#!/usr/bin/env python
import bson
import bson.json_util
from urllib import urlopen
BUS='http://localhost:8000/test'
print "Connecting to %s and receiving objects..." % BUS
param = {
'heartbeat': 10,
'queue': {
'SYSTEM_ALERT': {
'seq': -1
}
}
}
ack = bson.BSON(urlopen(BUS + '/open', bson.BSON.encode(param)).read()).decode()
print bson.json_util.dumps(ack, indent=2)
for msg in bson.decode_file_iter(urlopen(BUS + '/stream/' + str(ack['sid']))):
print bson.json_util.dumps(msg, indent=2)
def build_load_files(self):
import bson
def yn(s):
"""
Return 'Y' if int(s) is True, or 'N' otherwise. Return an empty
string if s is None.
"""
if s:
if int(s):
return "Y"
else:
return "N"
return ""
files = [x for x in sorted(self.get_file_list(self.arguments.path, self.file_filter)) if os.path.basename(x).lower() in BuilderClass.expected_files]
if self._widget:
self._widget.progressSet.emit(len(BuilderClass.expected_files), "")
self._widget.progressUpdate.emit(0)
self._corpus_id = 0
for i, filepath in enumerate(files):
filename = os.path.basename(filepath)
if filename == "wordforms.bson":
max_cache = 20000
self.table(self.corpus_table)._max_cache = max_cache
self._widget.progressSet.emit(4520596 // max_cache, "Loading {}".format(filename))
self._widget.progressUpdate.emit(0)
else:
self._widget.labelSet.emit("Loading {}".format(filename))
with open(filepath, "rb") as input_file:
for entry in bson.decode_file_iter(input_file):
self._entry = entry
if filename == "sources.bson":
self._source_id = len(self._source_dict) + 1
self._source_dict[str(entry["key"])] = self._source_id
d = {
self.source_id: self._source_id,
self.source_label: entry.get("title", ""),
self.source_year: entry.get("year", ""),
self.source_author: entry.get("author", ""),
self.source_key: entry.get("key", ""),
self.source_note: entry.get("note", "")}
self.table(self.source_table).add(d)
elif filename == "roots.bson":
self._root_id = len(self._root_dict) + 1
self._root_dict[str(entry["_id"])] = self._root_id
d = {self.root_id: self._root_id,
self.root_radicals: entry.get("radicals", ""),
self.root_type: entry.get("type", ""),
self.root_variant: entry.get("variant", 0),
self.root_alternatives: entry.get("alternatives", "")}
self.table(self.root_table).add(d)
elif filename == "lexemes.bson":
# Fix some spelling mistakes in the key names:
for x, correct in [("achaic", "archaic"),
("archaic ", "archaic"),
("adverbial ", "adverbial"),
("instransitive", "intransitive")]:
if x in entry.keys():
entry[correct] = entry[x]
self._lemma_id = len(self._lemma_dict) + 1
self._lemma_dict[str(entry["_id"])] = self._lemma_id
# get root id if possible, and also root radicals:
root_id = None
root = entry.get("root", "")
if root:
root_id = str(root.get("_id"))
root_radicals = root.get("radicals", "")
root_link = self._root_dict.get(root_id, 0)
# look up headword:
headword = None
headword_dict = entry.get("headword", "")
if headword_dict:
headword = headword_dict.get("lemma")
# fix 'verbalnoun':
verbal_noun = entry.get("verbalnoun", "")
if verbal_noun == "verbalnoun" or verbal_noun == "1":
verbal_noun = "N"
d = {
self.lemma_id: self._lemma_id,
self.lemma_label: entry.get("lemma", ""),
self.lemma_adjectival: yn(entry.get("adjectival")),
self.lemma_adverbial: yn(entry.get("adverbial")),
self.lemma_alternatives: ";".join(entry.get("alternatives", [])),
self.lemma_apertiumparadigm: entry.get("apertium_paradigm", ""),
self.lemma_archaic: yn(entry.get("archaic")),
self.lemma_created: entry.get("created", ""),
self.lemma_derived_form: entry.get("derived_form", 0),
self.lemma_ditransitive: yn(entry.get("ditransitive")),
self.lemma_features: entry.get("features"),
self.lemma_feedback: entry.get("feedback", ''),
self.lemma_form: entry.get("form", ''),
self.lemma_frequency: entry.get("frequency", 0),
self.lemma_gender: entry.get("gender", ""),
self.lemma_gloss: entry.get("gloss", ""),
self.lemma_headword: headword,
self.lemma_hypothetical: yn(entry.get("hypothetical")),
self.lemma_intransitive: yn(entry.get("intransitive")),
self.lemma_modified: entry.get("modified", ""),
self.lemma_notduplicate: yn(entry.get("not_duplicate")),
self.lemma_number: entry.get("number", ""),
self.lemma_onomastictype: entry.get("onomastic_type", ""),
self.lemma_participle: yn(entry.get("participle")),
self.lemma_pending: yn(entry.get("pending")),
self.lemma_pos: entry.get("pos",''),
self.lemma_radicals: root_radicals,
self.lemma_root_id: root_link,
self.lemma_transcript: entry.get("phonetic", ""),
self.lemma_verbalnoun: entry.get("verbalnoun")}
self.table(self.lemma_table).add(d)
elif filename == "wordforms.bson":
self._corpus_id += 1
# try to get source id at all costs:
source_id = None
source_list = entry.get("sources")
if source_list:
try:
source_id = self._source_dict[source_list[0]]
except KeyError:
for x in self._source_dict:
if self._source_dict[x] == source_list[0]:
source_id = self._source_dict[x]
break
else:
source_id = 0
# collapse the dictionaries behind subject,
# ind_obj, and dir_obj:
subj_dict = entry.get("subject")
l = []
if subj_dict:
l = [subj_dict["person"], subj_dict["number"]]
if "gender" in subj_dict:
l.append(subj_dict["gender"])
subj = "_".join(l)
ind_obj_dict = entry.get("ind_obj")
l = []
if ind_obj_dict:
l = [ind_obj_dict["person"], ind_obj_dict["number"]]
if "gender" in ind_obj_dict:
l.append(ind_obj_dict["gender"])
ind_obj = "_".join(l)
dir_obj_dict = entry.get("dir_obj")
l = []
if dir_obj_dict:
l = [dir_obj_dict["person"], dir_obj_dict["number"]]
if "gender" in dir_obj_dict:
l.append(dir_obj_dict["gender"])
dir_obj = "_".join(l)
d = {self.corpus_id: self._corpus_id,
self.corpus_adverbial: yn(entry.get("adverbial")),
self.corpus_alternatives: ";".join(entry.get("alternatives", [])),
self.corpus_archaic: yn(entry.get("archaic")),
self.corpus_aspect: entry.get("aspect", ""),
self.corpus_created: entry.get("created", ""),
self.corpus_dir_obj: dir_obj,
self.corpus_form: entry.get("form", ""),
self.corpus_full: entry.get("full", ""),
self.corpus_gender: entry.get("gender", ""),
self.corpus_generated: yn(entry.get("generated")),
self.corpus_gloss: entry.get("gloss", ""),
self.corpus_hypothetical: yn(entry.get("hypothetical")),
self.corpus_ind_obj: ind_obj,
self.corpus_lemma_id: self._lemma_dict.get(str(entry.get("lexeme_id"))),
self.corpus_modified: entry.get("modified", ""),
self.corpus_number: entry.get("number", ""),
self.corpus_pattern: entry.get("pattern", ""),
self.corpus_transcript: entry.get("phonetic", ""),
self.corpus_plural_form: entry.get("plural_form", ""),
self.corpus_polarity: entry.get("polarity", ""),
self.corpus_possessor: entry.get("possessor", ""),
self.corpus_source_id: source_id,
self.corpus_subject: subj,
self.corpus_word: entry.get("surface_form", "")}
self.table(self.corpus_table).add(d)
phon = entry.get("phonetic")
if self._widget and not self._corpus_id % max_cache:
self._widget.progressUpdate.emit(self._corpus_id // max_cache)
self.commit_data()
def test_backport_codec_options_uuid(self):
if not should_test_uuid:
raise SkipTest("No uuid module")
# Generated by the Java driver
from_java = b('bAAAAAdfaWQAUCBQxkVm+XdxJ9tOBW5ld2d1aWQAEAAAAAMIQkfACFu'
'Z/0RustLOU/G6Am5ld2d1aWRzdHJpbmcAJQAAAGZmOTk1YjA4LWMwND'
'ctNDIwOC1iYWYxLTUzY2VkMmIyNmU0NAAAbAAAAAdfaWQAUCBQxkVm+'
'XdxJ9tPBW5ld2d1aWQAEAAAAANgS/xhRXXv8kfIec+dYdyCAm5ld2d1'
'aWRzdHJpbmcAJQAAAGYyZWY3NTQ1LTYxZmMtNGI2MC04MmRjLTYxOWR'
'jZjc5Yzg0NwAAbAAAAAdfaWQAUCBQxkVm+XdxJ9tQBW5ld2d1aWQAEA'
'AAAAPqREIbhZPUJOSdHCJIgaqNAm5ld2d1aWRzdHJpbmcAJQAAADI0Z'
'DQ5Mzg1LTFiNDItNDRlYS04ZGFhLTgxNDgyMjFjOWRlNAAAbAAAAAdf'
'aWQAUCBQxkVm+XdxJ9tRBW5ld2d1aWQAEAAAAANjQBn/aQuNfRyfNyx'
'29COkAm5ld2d1aWRzdHJpbmcAJQAAADdkOGQwYjY5LWZmMTktNDA2My'
'1hNDIzLWY0NzYyYzM3OWYxYwAAbAAAAAdfaWQAUCBQxkVm+XdxJ9tSB'
'W5ld2d1aWQAEAAAAAMtSv/Et1cAQUFHUYevqxaLAm5ld2d1aWRzdHJp'
'bmcAJQAAADQxMDA1N2I3LWM0ZmYtNGEyZC04YjE2LWFiYWY4NzUxNDc'
'0MQAA')
data = base64.b64decode(from_java)
# Test decode_all.
docs = bson.decode_all(
data, dict, True, JAVA_LEGACY, True,
CodecOptions(SON, False, STANDARD))
for d in docs:
self.assertNotEqual(d['newguid'], uuid.UUID(d['newguidstring']))
encoded = [bson.BSON.encode(
doc, uuid_subtype=JAVA_LEGACY,
codec_options=CodecOptions(uuid_representation=STANDARD))
for doc in docs]
# Test decode.
docs2 = [e.decode(
uuid_subtype=JAVA_LEGACY, as_class=dict, tz_aware=True,
codec_options=CodecOptions(SON, False, STANDARD))
for e in encoded]
for d in docs2:
self.assertNotEqual(d['newguid'], uuid.UUID(d['newguidstring']))
# Test encode.
for i in range(len(docs)):
self.assertEqual(docs2[i]['newguid'], docs[i]['newguid'])
self.assertEqual(uuid.UUID(docs2[i]['newguidstring']),
uuid.UUID(docs[i]['newguidstring']))
# Test decode_iter
docs = bson.decode_iter(
data, dict, True, JAVA_LEGACY,
True, CodecOptions(SON, False, STANDARD))
for d in docs:
self.assertNotEqual(d['newguid'], uuid.UUID(d['newguidstring']))
# Test decode_file_iter
docs = bson.decode_file_iter(
StringIO(data), dict, True, JAVA_LEGACY,
True, CodecOptions(SON, False, STANDARD))
for d in docs:
self.assertNotEqual(d['newguid'], uuid.UUID(d['newguidstring']))
## 读数据库
import bson
import sys
from whoosh.index import create_in, open_dir
from whoosh.fields import TEXT, ID, STORED, KEYWORD, NUMERIC, Schema
from whoosh.qparser import QueryParser
from jieba.analyse import ChineseAnalyzer
import jieba
import pickle
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
analyzer = ChineseAnalyzer()
##################################################################
## 分析 bson 数据
# items = list(bson.decode_file_iter(open('./tmp.bson', 'rb'))); print(len(items)) # 14128; 读取 BSON 文件
items = list(bson.decode_file_iter(open('./tmp_news/sina.bson', 'rb'))); print(len(items)) # 140639; 读取 BSON 文件
print(items[0].keys()) # dict_keys(['_id', 'news_id', 'news_url', 'news_from', 'news_time2', 'news_channel', 'news_title', 'news_source', 'news_time', 'news_body', 'news_keywords', 'news_show', 'news_total'])
print(items[0]['news_id']) # fxzczfc6652525
print(items[0]['news_keywords']) # ['陕西', '公厕爆炸']
print(items[0]['news_show']) # 0
print(items[0]['news_time']) # 2017年05月17日23:48
print(items[0]['news_time2']) # 1495036111; 时间戳
print(set([len(item['news_title']) for item in items if item.get('news_title', 0) != 0]))
# {6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37}; 这些都是 str 长度
print(set([len(item['news_body']) for item in items])) # {0, 1, 2, 14, 15, 16, ..., 353, 982, 478, 1004}; 居然还能分成 1004 段...
print(set([len([''.join(item['news_body'])]) for item in items])) # {1}; 清净多了
print(set([str(item.keys()) for item in items])) # 好多缺失值
# {"dict_keys(['_id', 'news_id', 'news_url', 'news_from', 'news_channel', 'news_title', 'news_source', 'news_time', 'news_body', 'news_keywords', 'flag'])",
# "dict_keys(['_id', 'news_id', 'news_url', 'news_from', 'news_channel', 'news_title', 'news_source', 'news_time', 'news_body', 'news_keywords', 'flag', 'news_show', 'news_total'])",
# "dict_keys(['_id', 'news_id', 'news_url', 'news_from', 'news_channel', 'news_title', 'news_source', 'news_time', 'news_body', 'flag', 'news_show', 'news_total'])",
# "dict_keys(['_id', 'news_id', 'news_url', 'news_from', 'news_channel', 'news_title', 'news_time', 'news_body', 'news_keywords', 'flag', 'news_show', 'news_total'])",
while True:
if index >= len(success_bucket_floors):
break
if failure_rate < success_bucket_floors[index]:
break
index += 1
domain_map[fields[0]] = success_bucket_floors[index - 1]
#print "adding", fields[0], " ", success_bucket_floors[index-1]
#read measurements
error_map = {}
with open(sys.argv[1]) as measurement_file:
iter = bson.decode_file_iter(measurement_file)
for document in iter:
#get bucket map using error code
error_message = document["measurement_error_message"]
error_fields = error_message.split()
error_code = int(error_fields[0].replace("[","").replace("]",""))
if error_code not in error_map:
bucket_map = {}
for bucket_floor in success_bucket_floors:
bucket_map[bucket_floor] = 0
error_map[error_code] = bucket_map
bucket_map = error_map[error_code]
parser.add_argument("-n", "--num", dest="num", type=int, default=5, help="number of bson items")
parser.add_argument("--pprint", dest="pprint", action="store_true", default=False, help="prettified print")
parser.add_argument("-o", "--output", dest="output_file", type=str, help="output bson file")
parser.add_argument("input_file", type=str, help="input bson file")
args = parser.parse_args()
assert args.num > 0
assert args.input_file
if args.output_file:
out_file = open(args.output_file, "wb")
else:
out_file = None
with open(args.input_file, "rb") as in_file:
for i, entry in enumerate(bson.decode_file_iter(in_file)):
if i >= args.num:
break
if out_file is None:
if args.pprint:
pprint.pprint(entry)
else:
print entry
else:
out_file.write(bson.BSON.encode(entry))
if out_file:
out_file.close()
data_path = Path('data')
base_path = data_path / 'test'
base_path.mkdir(exist_ok=True)
n_cores = 12
prods = mp.Manager().dict()
q = mp.Queue(maxsize=n_cores)
iolock = mp.Lock()
pool = mp.Pool(n_cores, initializer=process, initargs=(q, iolock))
# process the file
data = bson.decode_file_iter(open(str(data_path / 'test.bson'), 'rb'))
for c, d in enumerate(data):
q.put(d)
# tell workers we're done
for _ in range(n_cores):
q.put(None)
pool.close()
pool.join()
# convert back to normal dictionary
prod_to_category = dict(prod_to_category)
prod_to_category = pd.DataFrame.from_dict(prod_to_category, orient='index')