def update_reservation_logs(marketplace, asin, status, preemptible_code,
ip_address, pre_instance_name, zone, api_key,
chat_id):
global df_successfull_proxies
error_str = ""
if type(df_successfull_proxies) != type(None):
print(df_successfull_proxies.iloc[0])
error_str = "Error count: " + str(
df_successfull_proxies.iloc[0]["errorCount"]
) + " errors: " + ",".join(df_successfull_proxies.iloc[0]["errors"])
reservationdate = datetime.datetime.now()
df_reservation = pd.DataFrame({
"asin": [str(asin)],
"timestamp": [reservationdate],
"status": [str(status)],
"pree_id": [str(preemptible_code)],
"ip_address": [ip_address],
"error_log": [error_str]
})
df_reservation['timestamp'] = df_reservation['timestamp'].astype(
'datetime64')
# TODO: fix this error
try:
df_reservation.to_gbq(
"preemptible_logs.mba_detail_" + marketplace +
"_preemptible_%s_%s_%s" %
(reservationdate.year, reservationdate.month, reservationdate.day),
project_id="mba-pipeline",
if_exists="append")
except:
utils.stop_instance(pre_instance_name,
zone,
msg="Can not update big query reservation",
api_key=api_key,
chat_id=chat_id)
p_test_mean = p_test_all.mean(axis=2)
p_test_odds = np.ones(100000) * 1 / 9
for j in range(var_len):
if roc_auc_score(y_train, oof[:, j]) >= 0.500:
p_test_odds *= (9 * p_test_mean[:, j] / (1 - p_test_mean[:, j]))
p_test_odds = p_test_odds / (1 + p_test_odds)
sub1 = pd.read_csv("../input/sample_submission.csv.zip")
sub2 = pd.DataFrame({"ID_code": test.ID_code.values, "target": p_test_odds})
sub = pd.merge(sub1[["ID_code"]], sub2, how="left").fillna(0)
# save
sub.to_csv(SUBMIT_FILE_PATH, index=False, compression='gzip')
print(sub.target.describe())
os.system(f'gsutil cp {SUBMIT_FILE_PATH} gs://malware_onodera/')
os.system(f'cp LOG/log_{__file__}.txt LOG/log_{__file__}_{SEED}.txt')
os.system(f'gsutil cp LOG/log_{__file__}_{SEED}.txt gs://malware_onodera/')
"""
gsutil cp gs://malware_onodera/*.gz ../output/
gsutil cp gs://malware_onodera/*.txt LOG/
"""
#==============================================================================
utils.end(__file__)
utils.stop_instance()
def main(argv):
parser = argparse.ArgumentParser(description='')
parser.add_argument(
'marketplace',
help='Shortcut of mba marketplace. I.e "com" or "de", "uk"',
type=str)
parser.add_argument('--telegram_api_key',
default="",
help='API key of mba bot',
type=str)
parser.add_argument(
'--telegram_chatid',
default="",
help='Id of channel like private chat or group channel',
type=str)
parser.add_argument(
'--number_products',
default=10,
type=int,
help=
'Number of products/shirts that shoul be crawled. If 0, every image that is not already crawled will be crawled.'
)
parser.add_argument(
'--connection_timeout',
default=10.0,
type=float,
help=
'Time that the request operation has until its breaks up. Default: 10.0 sec'
)
parser.add_argument(
'--time_break_sec',
default=240,
type=int,
help=
'Time in seconds the script tries to get response of certain product. Default 240 sec'
)
parser.add_argument(
'--seconds_between_crawl',
default=20,
type=int,
help=
'Time in seconds in which no proxy/ip shoul be used twice for crawling. Important to prevent being blacklisted. Default 20 sec'
)
parser.add_argument(
'--preemptible_code',
default="0",
type=str,
help=
'Identifier of instance for pree logs. Default 0 which leads to GUID.')
parser.add_argument(
'--pre_instance_name',
default="",
type=str,
help=
'Name of instance. Important: if set, script will stop instance after successfull operation. Default "".'
)
parser.add_argument(
'--zone',
default="",
type=str,
help=
'Zone of instance. Must fit to close the instance correctly after successfull run. Default mayor zone of marketplace.'
)
print(os.getcwd())
print(argv)
# if python file path is in argv remove it
if ".py" in argv[0]:
argv = argv[1:len(argv)]
# get all arguments
args = parser.parse_args(argv)
marketplace = args.marketplace
api_key = args.telegram_api_key
chat_id = args.telegram_chatid
number_products = args.number_products
connection_timeout = args.connection_timeout
time_break_sec = args.time_break_sec
seconds_between_crawl = args.seconds_between_crawl
preemptible_code = args.preemptible_code
pre_instance_name = args.pre_instance_name
zone = args.zone
if zone == "":
zone = utils.get_zone_of_marketplace(marketplace)
ip_address = utils.get_extrenal_ip(pre_instance_name, zone)
if preemptible_code == "0":
preemptible_code = uuid.uuid4().hex
# get all arguments
args = parser.parse_args()
# get asins which are not already crawled
df_product_details_tocrawl = get_asin_product_detail_crawled(marketplace)
if len(df_product_details_tocrawl) == 0:
print("no data to crawl")
if pre_instance_name != "" and "pre" in pre_instance_name:
utils.stop_instance(pre_instance_name,
zone,
msg="No data to crawl",
api_key=api_key,
chat_id=chat_id)
return 0
#df_product_details = pd.DataFrame(data={"asin": ["B07RVNJHZL"], "url_product": ["adwwadwad"]})
df_product_details_tocrawl[
"url_product_asin"] = df_product_details_tocrawl.apply(
lambda x: "https://www.amazon." + marketplace + "/dp/" + x["asin"],
axis=1)
# if number_images is equal to 0, evry image should be crawled
if number_products == 0:
number_products = len(df_product_details_tocrawl)
reservationdate = datetime.datetime.now()
df_reservation = df_product_details_tocrawl.iloc[0:number_products][[
"asin"
]].copy()
df_reservation['status'] = "blocked"
df_reservation['pree_id'] = preemptible_code
df_reservation['ip_address'] = ip_address
df_reservation['error_log'] = ""
df_reservation['timestamp'] = reservationdate
df_reservation['timestamp'] = df_reservation['timestamp'].astype(
'datetime64')
try:
df_reservation.to_gbq(
"preemptible_logs.mba_detail_" + marketplace +
"_preemptible_%s_%s_%s" %
(reservationdate.year, reservationdate.month, reservationdate.day),
project_id="mba-pipeline",
if_exists="append")
except:
utils.stop_instance(pre_instance_name,
zone,
msg="Can not update big query reservation",
api_key=api_key,
chat_id=chat_id)
for j, product_row in df_product_details_tocrawl.iloc[
0:number_products].iterrows():
asin = product_row["asin"]
url_product = product_row["url_product"]
url_product_asin = product_row["url_product_asin"]
if True:
# try to get reponse with free proxies
response = get_response(
marketplace,
url_product_asin,
use_proxy=False,
connection_timeout=connection_timeout,
time_break_sec=time_break_sec,
seconds_between_crawl=seconds_between_crawl)
if response == None:
# update reservation logs with blacklist of ip
update_reservation_logs(marketplace, "blacklist", "blacklist",
preemptible_code, ip_address,
pre_instance_name, zone, api_key,
chat_id)
# if script is called by preemptible instance it should be deleted by itself
if pre_instance_name != "":
utils.stop_instance(
pre_instance_name,
zone,
msg="Response is none because of time break condition",
api_key=api_key,
chat_id=chat_id)
else:
assert response != None, "Could not get response within time break condition"
if response == 404:
crawlingdate = [datetime.datetime.now()]
df_product_details = pd.DataFrame(
data={
"asin": [asin],
"title": ["404"],
"brand": ["404"],
"url_brand": ["404"],
"price": ["404"],
"fit_types": [["404"]],
"color_names": [["404"]],
"color_count": [404],
"product_features": [["404"]],
"description": ["404"],
"weight": ["404"],
"upload_date_str": ["1995-01-01"],
"upload_date": ["1995-01-01"],
"customer_review_score": ["404"],
"customer_review_count": [404],
"mba_bsr_str": ["404"],
"mba_bsr": [["404"]],
"mba_bsr_categorie": [["404"]],
"timestamp": crawlingdate
})
# transform date/timestamo columns to datetime objects
df_product_details['timestamp'] = df_product_details[
'timestamp'].astype('datetime64')
df_product_details['upload_date'] = df_product_details[
'upload_date'].astype('datetime64')
df_product_details.to_gbq("mba_" + marketplace +
".products_details",
project_id="mba-pipeline",
if_exists="append")
update_reservation_logs(marketplace, asin, "404",
preemptible_code, ip_address,
pre_instance_name, zone, api_key,
chat_id)
print("No Match: Got 404: %s | %s of %s" %
(asin, j + 1, number_products))
continue
# save product detail page locally
with open("data/mba_detail_page.html", "w") as f:
f.write(response.text)
# transform html response to soup format
soup = BeautifulSoup(
utils.get_div_in_html(response.text, 'id="dp-container"'),
'html.parser')
# save html in storage
utils.upload_blob(
"5c0ae2727a254b608a4ee55a15a05fb7",
"data/mba_detail_page.html", "logs/" + marketplace +
"/product_detail/" + str(asin) + ".html")
else:
with open("data/mba_detail_page.html") as f:
html_str = f.read()
asin = "B086D9RL8Q"
soup = BeautifulSoup(
utils.get_div_in_html(html_str, 'id="dp-container"'),
'html.parser')
try:
df_product_details = get_product_detail_df(soup, asin,
url_product_asin,
marketplace, chat_id,
api_key)
except:
utils.send_msg(
chat_id,
"Error while trying to get information for asin: " + str(asin),
api_key)
continue
try:
df_product_details.to_gbq("mba_" + marketplace +
".products_details",
project_id="mba-pipeline",
if_exists="append")
except:
update_reservation_logs(marketplace, asin, "failure",
preemptible_code, ip_address,
pre_instance_name, zone, api_key, chat_id)
update_reservation_logs(marketplace, asin, "success", preemptible_code,
ip_address, pre_instance_name, zone, api_key,
chat_id)
print("Match: Successfully crawled product: %s | %s of %s" %
(asin, j + 1, number_products))
global df_successfull_proxies
if type(df_successfull_proxies) != type(None):
print(df_successfull_proxies.iloc[0])
#df_successfull_proxies.to_csv("data/successfull_proxies.csv")
# if script is called by preemptible instance it should be deleted by itself
if pre_instance_name != "" and "pre" in pre_instance_name:
utils.stop_instance(pre_instance_name,
zone,
msg="Success",
api_key=api_key,
chat_id=chat_id)
test = 0
def stop_instance(request,instance_id):
try:
utils.stop_instance(request, instance_id)
except Exception,e:
return HttpResponse(simplejson.dumps({"status":"error","message":e},ensure_ascii=False))
