def preprocessTweetsAndSave(args, prep):
with open(args.tempFile, "w") as f:
for tweet in readFile(
args.filename,
columns=args.filenameColumns,
sep=args.filenameDelimiter)[args.dataColumnName].values:
# some tweets in the file reduced-tweets.parquet were None
if tweet is not None:
# tweets.append(prep.tokenize(tweet))
tweet = prep.replace_hashtags_URL_USER(tweet,
mode_URL="replace",
mode_Mentions="replace")
f.write((" ".join(prep.tokenize(tweet))) + "\n")
f.close()
retweeted_place_place_type, retweeted_created_at, retweeted_tweet_longitude, retweeted_tweet_latitude, \
retweeted_text)",
type=json.loads,
default={})
args = parser.parse_args()
if args.mode == "local":
# check from which source to read the data
if args.filename is not None:
print("Local mode: Read file..")
filtered_df = filter_dataframe(
readFile(args.filename,
columns=args.filenameColumns,
sep=args.filenameDelimiter),
args.configDict,
language=args.lang,
withRetweets=args.withRetweets,
withOriginalTweetOfRetweet=args.withOriginalTweetOfRetweet,
deleteDuplicates=True)
print("Save result to {} ..".format(args.saveResultPath))
savePandasDFtoFile(filtered_df, args.saveResultPath)
# Check if necessary arguments are given
elif args.localMongoDatabase is None and args.localMongoCollection is None:
sys.stderr.write(
"ERROR: A MongoDB database and collection need to be provided to extract the data"
)
editDistPos = 9
editSenDistPos =10
bleuPos = 11
metScores = []
terScores = []
mtBleuScores = []
mtNistScores = []
mtNormedNist = []
beerScores =[]
corpusEvalBleu = None
corpusEvalNist = None
try:
refList = rw.readFile(args.ref)
srcList = rw.readFile(args.src)
hypList = rw.readFile(args.hyp)
if len(refList) == len(hypList) and len(refList) == len(srcList):
pass
else:
raise UnboundLocalError
except IOError as io:
print('There was a problem Loading your named files...')
print(io)
print('Please check and try again...')
print('Exiting...')
sys.exit()
except UnboundLocalError as ule:
print('Your given files have different lengths, please check and try again')
parser.add_argument("-s", "--pathSave", help="Path to save personal database to (.parquet or .csv)")
parser.add_argument("-tn", "--columnNameTextData", help="Column name of the text data", default="tweet")
parser.add_argument("-unc", "--userNameColumn", help="Give the user_name column (default: user_screen_name)", default="user_screen_name")
args = parser.parse_args()
print("Load user classifier..")
#loaded_model = joblib.load(filename)
model_user_classif = joblib.load(args.pathUserClassifier)
print("Load tweet classifier..")
model_tweet_classif = joblib.load(args.pathTweetClassifier)
print("Load word embedding..")
wordEmbedding = FastText.load(args.pathWordEmbedding)
print("Load data..")
tweets = readFile(args.pathData)
print("Classify all tweets of an user and exclude all users with a mean score < {} ...".format(args.scorePersonalMinimum))
tweets_user_pers = score_users(tweets, model_user_classif, wordEmbedding, args.userNameColumn, score_personal_minimum=args.scorePersonalMinimum, textColumn=args.columnNameTextData)
print("Number tweets personal users:", len(tweets_user_pers))
print("Classify only personal tweets of personal users..")
tweets_personal = get_personal_tweets(tweets_user_pers, model_tweet_classif, wordEmbedding, textColumn=args.columnNameTextData)
print("Number personal tweets:", len(tweets_personal))
print(tweets_personal.head())
print("Save personal tweets to file {} ...".format(args.pathSave))
savePandasDFtoFile(tweets_personal, args.pathSave)
"--hyp",
help="the hypothesis file to be used (required)")
parser.add_argument("-s", "--src", help="the src file to be used (required)")
args = parser.parse_args()
outList = []
head = '<srcset setid="testName" srclang="any">\n'
docHead = '<doc sysid="src" docid="evaluation" genre="any" origlang="zh">\n'
pIn = '<p>\n'
pOut = '</p>\n'
docOut = '</doc>\n'
endFile = '</srcset>'
for fin in [args.ref, args.hyp, args.src]:
inList = rw.readFile(fin)
outList = []
outList.append(head)
outList.append(docHead)
outList.append(pIn)
for i, line in enumerate(inList):
outList.append('<seg id="{0}">{1}</seg>\n'.format(i + 1, line.strip()))
outList.append(pOut)
outList.append(docOut)
outList.append(endFile)
rw.writeFile(outList, fin.replace('.txt', '_mt_seg.txt'))
print('DONE')
