def test_load_skipgram_model(self):
model = ft.load_model(skipgram_file, encoding='utf-8')
# Make sure the model is returned correctly
self.assertEqual(model.model_name, 'skipgram')
# Make sure all params loaded correctly
# see Makefile on target test-skipgram for the params
self.assertEqual(model.dim, 100)
self.assertEqual(model.ws, 5)
self.assertEqual(model.epoch, 1)
self.assertEqual(model.min_count, 1)
self.assertEqual(model.neg, 5)
self.assertEqual(model.loss_name, 'ns')
self.assertEqual(model.bucket, 2000000)
self.assertEqual(model.minn, 3)
self.assertEqual(model.maxn, 6)
self.assertEqual(model.lr_update_rate, 100)
self.assertEqual(model.t, 1e-4)
# Make sure the vector have the right dimension
self.assertEqual(len(model['the']), model.dim)
# Make sure we support unicode character
unicode_str = 'Καλημέρα'
self.assertTrue(unicode_str in model.words)
self.assertEqual(len(model[unicode_str]), model.dim)
def embed_titles(infile=TOPICS, outfile=EMBEDDED_TOPIC_TITLES_PATH):
'''
Vectorize topics with fasttext and save to file
ntopic=188, embeddings_dim=100
'''
with open(infile, "r") as f:
topics_json = json.load(f)
topic_titles = []
topic_vectors = []
model = fasttext.load_model(EMBEDDINGS_MODEL_PATH)
for i, topic in enumerate(topics_json):
title = topic['title']
# title = topic['title']
# title = topic['title']
# print title
topic_titles.append(title)
# embed titles with fasttext
topic_vectors.append(model[title])
assert len(topic_vectors) == len(topic_titles)
with open(outfile, 'w') as f:
pickle.dump(topic_vectors, f)
return topic_titles, topic_vectors
def embed_topics(infile=TOPICS, outfile=EMBEDDED_TOPICS_PATH):
'''
Vectorize topics with fasttext and save to file
ntopic=188, embeddings_dim=100
'''
with open(infile, "r") as f:
topics_json = json.load(f)
topic_vectors = []
model = fasttext.load_model(EMBEDDINGS_MODEL_PATH)
for i, topic in enumerate(topics_json):
title = topic['title']
description = topic['title']
narrative = topic['title']
joint = " ".join([title, description, narrative])
# embed topics with fasttext
topic_vectors.append(model[joint])
assert len(topic_vectors) == len(topics_json)
with open(outfile, 'w') as f:
pickle.dump(topic_vectors, f)
def load_model(cls):
"""
模型加载
"""
config = get_config()
model_path = '{}.bin'.format(config.get('train', 'model_path'))
if os.path.exists(model_path):
cls.__model = ft.load_model(model_path)
def __init__(self, model_path=EMBEDDINGS_MODEL_PATH,
embeddings_path=EMBEDDED_TOPICS_PATH):
# load fasttext model
self.model = fasttext.load_model(model_path)
# load topic titles
self.topic_titles = load_titles()
# load topic embeddings
with open(embeddings_path, 'r') as f:
self.topic_vectors = pickle.load(f)
def load(self):
if self.flavor == 'w2v':
self.model = Word2Vec.load(self.path)
self.model.init_sims(replace=True)
self.size = self.model.size
elif self.flavor == 'ft':
self.model = fasttext.load_model(self.path + '.bin')
self.size = self.model.dim
self.fitted = True
def loadfromfile(cls, repdir, word_model_file):
ont = pickle.load(open(repdir+'/ont.pickle',"rb" ))
class Config(object):
def __init__(self, d):
self.__dict__ = d
config = Config(json.load(open(repdir+'/config.json', 'r')))
#config = Config
#config.__dict__ = json.load(open(repdir+'/config.json', 'r'))
word_model = fasttext.load_model(word_model_file)
model = cls(config, ont, word_model)
model.load_params(repdir)
return model
def load(self, *args, **kwargs):
"""
Load dict of embeddings from file
Args:
fname: file name
"""
if self.load_path:
if self.load_path.is_file():
print("[loading embeddings from `{}`]".format(self.load_path))
model_file = str(self.load_path)
if self.emb_module == 'fasttext':
import fasttext as Fasttext
# model = Fasttext.load_model(model_file)
model = Fasttext.load_model(model_file)
elif self.emb_module == 'pyfasttext':
from pyfasttext import FastText as Fasttext
model = Fasttext(model_file)
else:
from gensim.models.wrappers.fasttext import FastText as Fasttext
model = Fasttext.load_fasttext_format(model_file)
elif isinstance(self.load_path, Path):
raise ConfigError("Provided `load_path` for {} doesn't exist!".format(
self.__class__.__name__))
else:
warn("No `load_path` is provided for {}".format(self.__class__.__name__))
if self.embedding_url:
try:
print('[trying to download a pretrained fasttext model from repository]')
local_filename, _ = urllib.request.urlretrieve(self.embedding_url)
with open(local_filename, 'rb') as fin:
model_file = fin.read()
mp = self.save_path
self.load_path = self.save_path
model = self.load()
print("[saving downloaded fasttext model to {}]".format(mp))
with open(str(mp), 'wb') as fout:
fout.write(model_file)
except Exception as e:
raise RuntimeError(
'Looks like the provided fasttext url is incorrect', e)
else:
raise FileNotFoundError(
'No pretrained fasttext model provided or provided "load_path" is incorrect.'
' Please include "load_path" to json.')
return model
def test_load_cbow_model(self):
model = ft.load_model(cbow_file)
# Make sure the model is returned correctly
self.assertEqual(model.model_name, 'cbow')
# Make sure all params loaded correctly
# see Makefile on target test-cbow for the params
self.assertEqual(model.dim, 50)
self.assertEqual(model.ws, 5)
self.assertEqual(model.epoch, 1)
self.assertEqual(model.min_count, 3)
self.assertEqual(model.neg, 5)
self.assertEqual(model.loss_name, 'ns')
self.assertEqual(model.bucket, 2000000)
self.assertEqual(model.minn, 3)
self.assertEqual(model.maxn, 6)
self.assertEqual(model.lr_update_rate, 100)
self.assertEqual(model.t, 1e-4)
# Make sure the vector have the right dimension
self.assertEqual(len(model.get_vector('the')), model.dim)
def __init__(self, model_path):
self.model_path = model_path
self.model = fasttext.load_model(model_path)
self.n_labels = len(self.model.get_labels())
self.max_entropy = -1 * np.log(1.0 / self.n_labels)
def fasttext_predict(model_filepath, test_dataset):
classifier = fasttext.load_model(model_filepath, label_prefix='__label__')
result = classifier.test(test_dataset)
print(result)
return result
# -*- coding:utf-8 -*-
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
import fasttext
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
#load训练好的模型
classifier = fasttext.load_model('comment_code_fasttext.model.bin',
label_prefix='__label__')
result = classifier.test("test.txt")
print
print "precision:", (result.precision)
print(result.recall)
labels_right = []
texts = []
with open("test.txt") as fr:
lines = fr.readlines()
for line in lines:
if line == '\n':
continue
labels_right.append(
line.split("\t")[1].rstrip().replace("__label__", "").encode('utf-8'))
texts.append(line.split("\t")[0].decode("utf-8"))
# print labels
# print texts
# break
labels_predict = [e[0].encode('utf-8')
def load(self):
if os.path.exists(self.model_path + 'bin'):
return fasttext.load_model(self.model_path)
else:
return None
import numpy as np
import tensorflow as tf
import fasttext as ft
import math
import sys
from cnn_lstm_crf import CNN_BLSTM_CRF
Word2vec = ft.load_model('vi.bin')
def make_char_dictionary(data_path, dict_path):
### initialize dictionary set
char_dictionary = ['<UNK>', '<PAD>']
### make character dictionary
f = open(data_path, 'r')
for row in f:
row_split = row[:-1].split(' ')
for word in row:
for char in word:
char_dictionary.append(char)
f.close()
### remove duplicate characters
char_dictionary = list(set(char_dictionary))
### save character dictionary
f = open(dict_path, 'w')
for char in char_dictionary:
f.write(char + '\n')
f.close()
def load_dictionary(dict_path):
#train
#classfier=fasttext.supervised('news_fasttext_train.txt','news_fasttext.model',label_prefix='__label__')
#test
clf = fasttext.load_model('news_fasttext.model.bin',label_prefix = '__label__')
rel = clf.test('news_fasttext_test.txt')
print(rel.precision)
print(rel.recall)
'''
#测试
clf = fasttext.load_model('news_fasttext.model.bin')
text = [
'最高人民法宣宣布周某某因涉嫌贪污受贿，利用不正当手段为他人谋取各种利益等，判处其无期徒刑，剥夺政治权利终身。', '婚姻大事不必铺张浪费',
'小编祝大家新年快乐', '中国大陆多次强调，编排出武力夺取台湾',
'它被誉为天下第一果，补益气血，养阴生津，现在吃正应季! 六七月是桃子大量上市的季节，因其色泽红润，肉质鲜美，有个在实验基地里接受治疗的妹子。广受大众的喜爱。’'
]
label = clf.predict(text)
print(label)
'''
#训练词向量
model = fasttext.skipgram('news_fasttext_train.txt','model1')
import re
import numpy as np
import fasttext
model = fasttext.load_model("cc.vi.300.bin")
import torch
from collections import Counter
from typing import List
# Load PhoBERT-base in fairseq
from fairseq.models.roberta import RobertaModel
phobert = RobertaModel.from_pretrained('PhoBERT_base_fairseq',
checkpoint_file='model.pt')
phobert.eval() # disable dropout (or leave in train mode to finetune)
# Incorporate the BPE encoder into PhoBERT-base
from fairseq.data.encoders.fastbpe import fastBPE
from fairseq import options
parser = options.get_preprocessing_parser()
parser.add_argument('--bpe-codes',
type=str,
help='path to fastBPE BPE',
default="PhoBERT_base_fairseq/bpe.codes")
args = parser.parse_args()
phobert.bpe = fastBPE(args)
def align_bpe_to_words(roberta, bpe_tokens: torch.LongTensor,
other_tokens: List[str]):
"""
Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy).
Args:
def exportByDistance(action, modelFileExtension, modelsFolder, fromYear,
toYear, neighborsCount, fasttextPath):
"""
@param action:
@param modelFileExtension:
@param modelsFolder:
@param fromYear:
@param toYear:
@param neighborsCount:
@param fasttextPath:
@return:
@rtype: None
"""
fromYearFilename = fromYear + modelFileExtension
toYearFilename = toYear + modelFileExtension
modelA = fasttext.load_model(os.path.join(modelsFolder, fromYearFilename))
modelB = fasttext.load_model(os.path.join(modelsFolder, toYearFilename))
clearVectorModelA = {}
clearVectorModelB = {}
for label in modelA.get_labels():
clearVectorModelA[label] = modelA.get_word_vector(label)
for label in modelB.get_labels():
clearVectorModelB[label] = modelB.get_word_vector(label)
# alignedEmbeddingsB = vector.alignTwoEmbeddings(clearVectorModelA, clearVectorModelB)
results = {}
for word in modelA.words:
if word in modelB.words:
if action == 'getCD':
results[word] = vector.getCosineDistance(
clearVectorModelA[word], clearVectorModelB[word])
elif action == 'getCS':
results[word] = vector.getCosineSimilarity(
clearVectorModelA[word], clearVectorModelB[word])
if action == 'getCD':
sortedResults = sorted(results.items(),
key=lambda x: x[1],
reverse=True)
elif action == 'getCS':
sortedResults = sorted(results.items(), key=lambda x: x[1])
resultsPerPeriod = {}
for wordTuple in sortedResults[:50]:
word = wordTuple[0]
resultsPerPeriod[word] = {}
resultsPerPeriod[word][str(fromYear)] = getNeighboursForWord(
word, fromYearFilename, modelsFolder, fasttextPath, neighborsCount)
resultsPerPeriod[word][str(toYear)] = getNeighboursForWord(
word, toYearFilename, modelsFolder, fasttextPath, neighborsCount)
# print(resultsPerPeriod)
file.exportTextToFile(resultsPerPeriod, './shifts.json', True)
def __init__(self, model_path):
super(FeatureGenerator, self).__init__()
self.model = ft.load_model(model_path)
def __init__(self, dataset='yelp'):
acc_path = 'hoang/acc_' + str(dataset) + '.bin'
ppl_path = 'hoang/ppl_' + str(dataset) + '.bin'
self.classifier = fasttext.load_model(acc_path)
self.ppl_model = kenlm.Model(ppl_path)
self.dataset = dataset
INPUT_TXT = '/path/to/file.txt'
OUTPUT_PATH_SKIPGRAM = '/tmp/skipgram'
OUTPUT_PATH_CBOW = '/tmp/cbow'
# Learn the word representation using skipgram model
skipgram = fasttext.skipgram(INPUT_TXT, OUTPUT_PATH, lr=0.02, dim=300, ws=5,
epoch=1, min_count=5, neg=5, loss='ns', bucket=2000000, minn=3, maxn=6,
thread=4, t=1e-4, lr_update_rate=100)
# Get the vector of some word
print skipgram['word']
# Learn the word representation using cbow model
cbow = fasttext.cbow(INPUT_TXT, OUTPUT_PATH, lr=0.02, dim=300, ws=5,
epoch=1, min_count=5, neg=5, loss='ns', bucket=2000000, minn=3, maxn=6,
thread=4, t=1e-4, lr_update_rate=100)
# Get the vector of some word
print cbow['word']
# Load pre-trained skipgram model
SKIPGRAM_BIN = OUTPUT_PATH_SKIPGRAM + '.bin'
skipgram = fasttext.load_model(SKIPGRAM_BIN)
print skipgram['word']
# Load pre-trained cbow model
CBOW_BIN = OUTPUT_PATH_CBOW + '.bin'
cbow = fasttext.load_model(CBOW_BIN)
print cbow['word']
import json
import pickle
import fasttext
from elasticsearch import Elasticsearch
from kafka import KafkaConsumer
from tunga.preprocessing import normalization
import spacy
es = Elasticsearch()
model = fasttext.load_model("/home/burak/Desktop/cc.en.300.bin")
rfc = pickle.load(open("../random_forest.model", 'rb'))
nlp = spacy.load('../../../data/buyuk')
consumer = KafkaConsumer('crawled_tweets',
bootstrap_servers=['localhost:9091'])
print(consumer)
i = 1
for message in consumer:
data = json.loads(message.value)
tweet_text = data["tweet"]
tweet_text = tweet_text.lower()
tweet_text = tweet_text.strip()
tweet_text = tweet_text.replace("\n", " ")
tweet_text = normalization.remove_url(tweet_text)
tweet_text = normalization.remove_hashtag(tweet_text)
tweet_text = normalization.remove_emojis(tweet_text)
prediction = rfc.predict([model.get_sentence_vector(tweet_text)])[0]
if prediction == 0:
def __init__(self, path_fasttext_model):
self.fasttext_model = fasttext.load_model(path_fasttext_model)
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import textrank
import random
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import fasttext
import fasttext.util
import pickle
import os
#model = KeyedVectors.load("models/normalized.model")
fasttext.util.download_model('en', if_exists='ignore')
model = fasttext.load_model('cc.en.300.bin')
stop_words = set(stopwords.words('english'))
vocab = set(model.words)
cache={}
try:
with open("cache.pkl", "rb") as f:
cache=pickle.load(f)
except:
pass
def shuffleDict(dictionary):
keys = list(dictionary.keys())
random.shuffle(keys)
shuffled = {}
for key in keys:
from app import app, db
from app.parser import parse_entities
from app.models import Article, Entity
from app.entity import load_all_entities, clean_entity, get_entity_info
from app.summarize import summarize_text
from app.ner import extract_entities
from flask import request
import fasttext
import json
import requests
import re
from sacremoses import MosesTokenizer
import sentencepiece as spm
id_model = fasttext.load_model(r'/home/dion/Downloads/work/textonomy/backend/app/fasttext_w2v_indon.bin')
ms_model = fasttext.load_model(r'/home/dion/Downloads/work/textonomy/backend/app/fasttext_w2v_ms.bin')
ENT_DF = load_all_entities()
html = re.compile('<.*?>|<.*?>')
mtoken_source = MosesTokenizer(lang='id')
token_source = lambda text: mtoken_source.tokenize(re.sub(html, '', str(text)), return_str=True).strip().lower()
indon_sp = spm.SentencePieceProcessor()
indon_sp.load(r'/home/dion/Downloads/work/textonomy/backend/app/source.model')
eng_sp = spm.SentencePieceProcessor()
eng_sp.load(r'/home/dion/Downloads/work/textonomy/backend/app/target.model')
@app.route('/api/articles', methods=['GET'])
def get_all_articles():
def load_model(self):
loaded_model = fasttext.load_model('fasttext_sarcasm.ftz')
return loaded_model
import fasttext
import pandas as pd
import pdb
from tqdm import tqdm
model1 = fasttext.load_model("./model/fasttext_1.bin")
model2 = fasttext.load_model("./model/fasttext_2.bin")
model3 = fasttext.load_model("./model/fasttext_3.bin")
test = pd.read_csv("../../data/test.txt", header=None, sep='\t')
checking = pd.read_csv("./checking_sheet.csv")
answers = []
for sent in tqdm(test[0]):
checking['score'] = 0
ans = model1.predict(sent, k=10)
for i in range(10):
label = ans[0][i][9:]
score = ans[1][i]
checking.loc[checking['level1'] == label, 'score'] += score
ans = model1.predict(sent, k=10)
ans = model2.predict(sent, k=10)
for i in range(10):
label = ans[0][i][9:]
score = ans[1][i]
checking.loc[checking['level2'] == label, 'score'] += score
ans = model3.predict(sent, k=10)
for i in range(10):
label = ans[0][i][9:]
score = ans[1][i]
import fasttext
import numpy as np
import scipy
import nltk
PRETRAINED_MODEL_PATH = "vectors/english/cc.en.300.bin"
model = fasttext.load_model(PRETRAINED_MODEL_PATH)
def cos_similarity(sentence, word):
sent1_emb = model.get_sentence_vector(sentence)
sent2_emb = model.get_word_vector(word)
return (1 - scipy.spatial.distance.cosine(sent1_emb, sent2_emb))
good_barometer = "good"
bad_barometer = "bad"
test_good_sentence = "Wow, this is a really great sentence. I love it."
test_bad_sentence = "This is terrible. I hate it."
good_good = cos_similarity(test_good_sentence, good_barometer)
good_bad = cos_similarity(test_good_sentence, bad_barometer)
bad_bad = cos_similarity(test_bad_sentence, bad_barometer)
bad_good = cos_similarity(test_bad_sentence, good_barometer)
print("How good is the test good sentence?", good_good)
print("How bad is the test good sentence?", good_bad)
print("Test good sentence is most likely '{}'.".format(
"good" if good_good > good_bad else "bad"))
print("How bad is the test bad sentence?", bad_bad)
def __init__(self, component_config: Optional[Dict[Text, Any]] = None) -> None:
super().__init__(component_config)
path = os.path.join(component_config["cache_dir"], component_config["file"])
self.model = fasttext.load_model(path)
from flask import render_template, request, jsonify, session
from pipeline import *
app = Flask(__name__)
store = RedisStore(redis.StrictRedis())
KVSessionExtension(store, app)
app.secret_key = 'PZ2HKD7WIAM1D708OE9I78KZ0'
data_path = os.path.join('..', 'project_historian')
models_path = os.path.join(data_path, 'models')
rss_path = os.path.join(data_path, 'rss_data')
model_path = os.path.join(models_path, 'fasttext_model.bin')
db_path = os.path.join(rss_path, 'rss_database.db')
model = fasttext.load_model(model_path)
print('- Model loaded successfully.')
@app.route('/')
def index():
return render_template('index.html')
@app.route('/query', methods=['POST'])
def query():
keywords = []
if 'keywords' not in request.form:
return None
t_keywords = request.form['keywords'].split(',')
keywords = ['_'.join(kw.strip().split()) for kw in t_keywords]
def initialization():
global logging_level
parser = argparse.ArgumentParser(
prog=os.path.basename(sys.argv[0]),
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=__doc__)
parser.add_argument('input',
nargs='?',
type=argparse.FileType('rt', errors="replace"),
default=io.TextIOWrapper(sys.stdin.buffer,
errors="replace"),
help="Tab-separated bilingual tagged file")
parser.add_argument('output',
nargs='?',
type=argparse.FileType('wt'),
default=sys.stdout,
help="Output of the classification")
parser.add_argument(
'--annotated_output',
default=False,
action='store_true',
help=
"Adds an extra column with each sentence's evaluation (\"keep\" if the sentence is good, otherwise the reason for rejecting"
)
#groupM = parser.add_argument_group('Mandatory')
#groupM.add_argument("-s", "--source_lang", type=str, required=True, help="Source language (SL) of the input")
#groupM.add_argument("-t", "--target_lang", type=str, required=True, help="Target language (TL) of the input")
groupO = parser.add_argument_group('Optional')
groupO.add_argument(
'--tmp_dir',
default=gettempdir(),
help=
"Temporary directory where creating the temporary files of this program"
)
groupO.add_argument('-b',
'--block_size',
type=int,
default=10000,
help="Sentence pairs per block")
groupO.add_argument('-p',
'--processes',
type=int,
default=max(1,
cpu_count() - 1),
help="Number of processes to use")
groupO.add_argument('--disable_lang_ident',
default=False,
action='store_true',
help="Don't apply rules that use language detecting")
groupO.add_argument('--disable_minimal_length',
default=False,
action='store_true',
help="Don't apply minimal length rule")
groupO.add_argument('--disable_porn_removal',
default=False,
action='store_true',
help="Don't apply p**n removal")
groupO.add_argument("-s",
"--source_lang",
type=str,
default=None,
help="Source language (SL) of the input")
groupO.add_argument("-t",
"--target_lang",
type=str,
default=None,
help="Target language (TL) of the input")
groupO.add_argument("--scol",
default=1,
type=check_positive,
help="Source sentence column (starting in 1)")
groupO.add_argument("--tcol",
default=2,
type=check_positive,
help="Target sentence column (starting in 1)")
groupO.add_argument("-S",
"--source_tokenizer_command",
default=None,
type=str,
help="Source language (SL) tokenizer full command")
groupO.add_argument("-T",
"--target_tokenizer_command",
default=None,
type=str,
help="Target language (TL) tokenizer full command")
#LM filtering
groupO.add_argument('--disable_lm_filter',
default=False,
action='store_true',
help="Don't apply LM filtering")
groupO.add_argument('--metadata',
type=argparse.FileType('r'),
default=None,
help="Training metadata (YAML file)")
groupO.add_argument('--lm_threshold',
type=check_positive_between_zero_and_one,
default=0.5,
help="Threshold for language model fluency scoring.")
#groupO.add_argument('--keep_lm_result',action='store_true', help="Add an additional column to the results with the language model fluency score.")
# Logging group
groupL = parser.add_argument_group('Logging')
groupL.add_argument('-q',
'--quiet',
action='store_true',
help='Silent logging mode')
groupL.add_argument('--debug',
action='store_true',
help='Debug logging mode')
groupL.add_argument('--logfile',
type=argparse.FileType('a'),
default=sys.stderr,
help="Store log to a file")
#groupL.add_argument('-v', '--version', action='version', version="%(prog)s " + __version__, help="show version of this script and exit")
args = parser.parse_args()
logging_setup(args)
logging_level = logging.getLogger().level
# Ensure that directory exists; if not, create it
if not os.path.exists(args.tmp_dir):
os.makedirs(args.tmp_dir)
#Try loading metadata for LM filtering and p**n removal
if not (args.disable_lm_filter
and args.disable_porn_removal) and args.metadata != None:
logging.info("Loading metadata info")
try:
args.metadata_yaml = yaml.safe_load(args.metadata)
args.metadata_yaml["yamlpath"] = os.path.dirname(
os.path.abspath(args.metadata.name))
if not ("source_lm" in args.metadata_yaml
and "target_lm" in args.metadata_yaml):
args.disable_lm_filter = True
logging.warning("LM file not present in metadata.")
if not ("porn_removal_file" in args.metadata_yaml):
args.disable_porn_removal = True
logging.warning(
"P**n removal classifier not present in metadata.")
else:
try:
args.porn_removal = fasttext.load_model(
os.path.join(args.metadata_yaml["yamlpath"],
args.metadata_yaml['porn_removal_file']))
except:
args.porn_removal = fasttext.load_model(
args.metadata_yaml['porn_removal_file'])
if "source_tokenizer_command" in args.metadata_yaml:
args.source_tokenizer_command = args.metadata_yaml[
"source_tokenizer_command"]
if "target_tokenizer_command" in args.metadata_yaml:
args.target_tokenizer_command = args.metadata_yaml[
"target_tokenizer_command"]
parser.set_defaults(**args.metadata_yaml)
except:
logging.warning("Error loading metadata.")
args.disable_lm_filter = True
args.disable_porn_removal = True
traceback.print_exc()
#sys.exit(1)
else:
if args.metadata == None:
logging.warning("Metadata file not provided.")
args.disable_lm_filter = True
args.disable_porn_removal = True
if (args.source_lang == None or args.target_lang == None):
if (args.metadata == None):
logging.error("No source or target languages provided.")
sys.exit(1)
else:
try:
if not "metadata_yaml" in args or args.metadata_yaml == None:
args.metadata_yaml = yaml.safe_load(args.metadata)
#args.metadata_yaml["yamlpath"] = os.path.dirname(os.path.abspath(args.metadata.name))
args.source_lang = args.metadata_yaml["source_lang"]
args.target_lang = args.metadata_yaml["target_lang"]
except:
traceback.print_exc()
logging.error(
"Error retrieving source or target languages from metadata."
)
sys.exit(1)
if args.disable_lm_filter:
logging.info("LM filtering disabled.")
if args.disable_porn_removal:
logging.info("P**n removal disabled.")
return args
def prediction(n_clicks, uploaded_filenames):
threshold = 0.80
global mapping_dict
print(n_clicks, uploaded_filenames)
if n_clicks is not None and uploaded_filenames is not None and threshold is not None:
page_text = ''
doc = fitz.open(".\\assets\\docs\\" + uploaded_filenames[0])
print(doc.pageCount)
for i in range(doc.pageCount):
page = doc.loadPage(i)
page_str = page.getText("text")
page_text = page_text + page_str
text = page_text.lower()
# /*********************Remove number*******************/
text = re.sub(r'\d+', ' ', text)
# /*****************Remove Punctuation****************/
text = re.sub(r'[^\w\s]', ' ', text)
# /*****************Remove \xa0****************/
text = re.sub(r'\xa0', '', text)
# /*****************Remove \x0c****************/
text = re.sub(r'\x0c', '', text)
# /*****************Remove stop words************/
token_text = word_tokenize(text)
tokens_without_sw = [word for word in token_text if not word in stop_words]
text_stem = [ps.stem(word) for word in tokens_without_sw]
text = (" ").join(text_stem)
# /***************Remove space line character*********/
text = text.replace('\n', ' ')
# /********************Remove duplicate space**********/
text = " ".join(text.split()) # /**********Common word removal************/
model = fasttext.load_model(join(project_root,'RTA_Future_Scanner.bin'))
predicted_label_1 = model.predict(text, k=-1)[0][0]
predicted_label_1_probab = model.predict(text, k=-1)[1][0]
predicted_label_2 = model.predict(text, k=-1)[0][1]
predicted_label_2_probab = model.predict(text, k=-1)[1][1]
predicted_label_3 = model.predict(text, k=-1)[0][2]
predicted_label_3_probab = model.predict(text, k=-1)[1][2]
predicted_label_4 = model.predict(text, k=-1)[0][3]
predicted_label_4_probab = model.predict(text, k=-1)[1][3]
predicted_label_1 = predicted_label_1.replace("__label__", '').replace("__n_", '')
predicted_label_2 = predicted_label_2.replace("__label__", '').replace("__n_", '')
predicted_label_3 = predicted_label_3.replace("__label__", '').replace("__n_", '')
predicted_label_4 = predicted_label_4.replace("__label__", '').replace("__n_", '')
predicted_label_1 = " ".join(re.findall('[3]*[A-Z][a-z]*', predicted_label_1))
predicted_label_1 = mapping_dict.get("".join(predicted_label_1.lower().split(" ")), predicted_label_1)
predicted_label_2 = " ".join(re.findall('[3]*[A-Z][a-z]*', predicted_label_2))
predicted_label_2 = mapping_dict.get("".join(predicted_label_2.lower().split(" ")), predicted_label_2)
predicted_label_3 = " ".join(re.findall('[3]*[A-Z][a-z]*', predicted_label_3))
predicted_label_3 = mapping_dict.get("".join(predicted_label_3.lower().split(" ")), predicted_label_3)
predicted_label_4 = " ".join(re.findall('[3]*[A-Z][a-z]*', predicted_label_4))
predicted_label_4 = mapping_dict.get("".join(predicted_label_4.lower().split(" ")), predicted_label_4)
Confidence_Score = '-'
j1 = ""
j2 = 0
j3 = ""
j4 = 0
j5 = ""
j6 = 0
if predicted_label_1_probab >= threshold:
Sample1 = []
Sample1.append(predicted_label_1)
Sample1.append(predicted_label_1_probab)
j1 = (Sample1[0])
j2 = round(Sample1[1], 2)
elif (predicted_label_1_probab + predicted_label_2_probab) >= threshold:
Sample1 = []
Sample1.append(predicted_label_1)
Sample1.append(predicted_label_1_probab)
Sample2 = []
Sample2.append(predicted_label_2)
Sample2.append(predicted_label_2_probab)
j1 = (Sample1[0])
j2 = round(Sample1[1], 2)
j3 = Sample2[0]
j4 = round(Sample2[1], 2)
elif (predicted_label_1_probab + predicted_label_2_probab + predicted_label_3_probab) >= threshold:
Sample1 = []
Sample1.append(predicted_label_1)
Sample1.append(predicted_label_1_probab)
Sample2 = []
Sample2.append(predicted_label_2)
Sample2.append(predicted_label_2_probab)
Sample3 = []
Sample3.append(predicted_label_3)
Sample3.append(predicted_label_3_probab)
j1 = (Sample1[0])
j2 = round(Sample1[1], 2)
j3 = Sample2[0]
j4 = round(Sample2[1], 2)
j5 = Sample3[0]
j6 = round(Sample3[1], 2)
else:
j1 = '-'
j2 = '-'
j3 = '-'
j4 = '-'
j5 = '-'
j6 = '-'
j2 = str(j2 * 100) + " %" if j2 != "-" else str(j2)
j4 = str(j4 * 100) + " %" if j4 != "-" else str(j4)
j6 = str(j6 * 100) + " %" if j6 != "-" else str(j6)
text_lem = [wn.lemmatize(word) for word in tokens_without_sw]
word_text = (" ").join(text_lem)
# /***************Remove space line character*********/
word_text = word_text.replace('\n', ' ')
# /********************Remove duplicate space**********/
word_text = " ".join(word_text.split())
tfidf_vectorizer = TfidfVectorizer()
top_unigram_words = wc(word_text, tfidf_vectorizer)
tfidf_vectorizer = TfidfVectorizer(ngram_range=(2, 2))
top_bigram_words = wc(word_text, tfidf_vectorizer)
tfidf_vectorizer = TfidfVectorizer(ngram_range=(3, 3))
top_trigram_words = wc(word_text, tfidf_vectorizer)
test = []
test = top_unigram_words
# + top_bigram_words + top_trigram_words
d = {}
for a, x in test:
d[a] = 10 * x
wordcloud = WordCloud(width=1450, height=700, background_color='white')
wordcloud.generate_from_frequencies(frequencies=d)
wordcloud.to_file(join(data_Path,'wc.png'))
# words = list(d.keys())
# weights = [round(each) for each in list(d.values())]
wordcloud_fig = word_cloud()
df = {
"Prediction": [j1, j3, j5],
"Probability": [j2, j4, j6],
}
df = pd.DataFrame(df)
df = df[df['Prediction'] != ""]
# fig.show()
print("reached here=====================================================1")
return df.to_dict('records'), [{"name": i, "id": i} for i in df.columns], wordcloud_fig, ""
import fasttext
model = fasttext.load_model(
'/users/aaronrank/developer/recipe-ai/recipeai/recipes/.ingredient_classifier_model'
)
def predict(text):
'''Returns label, confidence'''
clf = model.predict(text)
return clf[0][0], clf[1][0]
def prep_emb(fn, gen_emb, domain_emb, prep_dir, gen_dim=300, domain_dim=100):
text = []
with open(fn) as f:
for line in f:
ob = json.loads(line)
review = ob["text"]
token = word_tokenize(review)
text=text+token
vocab = sorted(set(text))
word_idx = {}
if os.path.exists(prep_dir+'word_idx.json'):
with io.open(prep_dir+'word_idx.json') as f:
prev_word = json.load(f)
else:
prev_word = {}
wx = 0
new_word = []
for word in vocab:
if word not in prev_word:
wx = wx+1
new_word.append(word)
word_idx[word] = wx+len(prev_word)
prev_word.update(word_idx)
if new_word == []:
return
# create embedding
embedding_gen=np.zeros((len(prev_word)+2, gen_dim) )
embedding_domain=np.zeros((len(prev_word)+2, domain_dim) )
if os.path.exists(prep_dir+'gen.vec.npy'):
gen_emb_prev=np.load(prep_dir+"gen.vec.npy")
embedding_gen[:gen_emb_prev.shape[0],:] = gen_emb_prev
if os.path.exists(prep_dir+'gen.vec.npy'):
domain_emb_prev=np.load(prep_dir+'restaurant_emb.vec.npy')
embedding_domain[:domain_emb_prev.shape[0],:] = domain_emb_prev
with open(gen_emb) as f:
# read the embedding .vec file
for l in f:
rec=l.rstrip().split(' ')
if len(rec)==2: #skip the first line.
continue
# if the word in word_idx, fill the embedding
if rec[0] in new_word:
embedding_gen[prev_word[rec[0]]] = np.array([float(r) for r in rec[1:] ])
with open(domain_emb) as f:
# read the embedding .vec file
for l in f:
# for each line, get the word and its vector
rec=l.rstrip().split(' ')
if len(rec)==2: #skip the first line.
continue
# if the word in word_idx, fill the embedding
if rec[0] in new_word:
embedding_domain[prev_word[rec[0]]] = np.array([float(r) for r in rec[1:] ])
ftmodel = load_model(domain_emb+".bin")
for w in new_word:
if embedding_domain[word_idx[w] ].sum()==0.:
embedding_domain[word_idx[w] ] = ftmodel.get_word_vector(w)
with io.open(prep_dir+'word_idx.json', 'w') as outfile:
outfile.write(json.dumps(prev_word))
np.save(prep_dir+'gen.vec.npy', embedding_gen.astype('float32') )
np.save(prep_dir+'restaurant_emb.vec.npy', embedding_domain.astype('float32') )
'../../TSD/augmented_labels/data/normalized/transcripts/swedish/test.txt'
)
tags_test = prepare_data.load_tags(
'../../TSD/augmented_labels/data/normalized/ner/swedish/ner_test.txt')
# compare againt conventional NER
#features_test = prepare_data.load_features_combined('../augmented_labels/data/normalized/features/test.npy')
#target_test = prepare_data.load_transcripts('output/parliament/e2e_asr_combined.txt')
#tags_test = prepare_data.load_tags('output/parliament/conventional_ner.txt')
features_test = features_test[:50]
target_test = target_test[:50]
tags_test = tags_test[:50]
print('Loading embeddings...')
embeddings = fasttext.load_model('weights/embeddings/cc.sv.300.bin')
print('Done...')
tag2idx = {'O': 1, 'PER': 2, 'LOC': 3, 'ORG': 4}
idx2tag = {1: 'O', 2: 'PER', 3: 'LOC', 4: 'ORG'}
with open('weights/char2idx_swe.pkl', 'rb') as f:
char2idx = pickle.load(f)
with open('weights/idx2char_swe.pkl', 'rb') as f:
idx2char = pickle.load(f)
char2idx['~'] = len(char2idx) + 1
idx2char[len(idx2char) + 1] = '~'
char2idx_ctc = {}
idx2char_ctc = {}
# trans.overSampling(file_prefix + train_file, "0", "1")
# trans.overSampling(file_prefix + valid_file, "0", "1")
# trans.overSampling(file_prefix + test_file, "0", "1")
# else:
# trans.overSampling(file_prefix + train_file, "1", "2", "3")
# trans.overSampling(file_prefix + valid_file, "1", "2", "3")
# trans.overSampling(file_prefix + test_file, "1", "2", "3")
""" get and save the training model """
model = [None for i in range(4)]
# for i in range(1, 4):
# file_prefix = "villa/stage" + str(i) + "/"
# model[i] = anal.train(file_prefix + train_file, file_prefix + valid_file)
# model[i].save_model("villa/model_stage" + str(i) + "_jdComment.bin")
""" test the training model """
for i in range(1, 4):
model[i] = fasttext.load_model("villa/model_stage" + str(i) +
"_jdComment.bin")
# print(model[i].test("villa/stage" + str(i) + "/" + test_file))
tot = 0
bingo = 0
with open("villa/" + test_file) as infile:
for row in infile:
i = 9
tag = ""
while row[i] != ' ':
tag += row[i]
i += 1
res = anal.predictComment(model, row[i + 1:-2])
if res == tag:
bingo += 1
tot += 1
def load(self, path):
return fasttext.load_model(path)
def load(cls, load_dir, batch_size=4, gpu=False, embedder_only=True):
import fasttext
if os.path.isfile(load_dir):
return cls(model=fasttext.load_model(load_dir))
else:
logger.error(f"Fasttext model file does not exist at: {load_dir}")
def main():
data_path = '/Users/ruizhang/Documents/NLP_dataset/'
#############
#
############
# Load train set
train_file = data_path +'dbpedia_csv/train.csv'
df = pd.read_csv(train_file, header=None, names=['class', 'name', 'description'])
# Load test set
test_file = data_path + 'dbpedia_csv/test.csv'
df_test = pd.read_csv(test_file, header=None, names=['class', 'name', 'description'])
# Mapping from class number to class name
class_dict = {
1: 'Company',
2: 'EducationalInstitution',
3: 'Artist',
4: 'Athlete',
5: 'OfficeHolder',
6: 'MeanOfTransportation',
7: 'Building',
8: 'NaturalPlace',
9: 'Village',
10: 'Animal',
11: 'Plant',
12: 'Album',
13: 'Film',
14: 'WrittenWork'
}
df['class_name'] = df['class'].map(class_dict)
df.head()
#############
#
############
desc = df.groupby('class')
desc.describe().transpose()
# Transform datasets
df_train_clean = clean_dataset(df, True, False)
df_test_clean = clean_dataset(df_test, False, False)
# Write files to disk
train_file_clean = data_path + 'dbpedia.train'
df_train_clean.to_csv(train_file_clean, header=None, index=False, columns=['class', 'name', 'description'])
test_file_clean = data_path + 'dbpedia.test'
df_test_clean.to_csv(test_file_clean, header=None, index=False, columns=['class', 'name', 'description'])
# Train a classifier
output_file = data_path + 'dp_model'
classifier = fasttext.supervised(train_file_clean, output_file, label_prefix='__label__')
result = classifier.test(test_file_clean)
print('P@1:', result.precision)
print('R@1:', result.recall)
print('Number of examples:', result.nexamples)
sentence1 = ['Picasso was a famous painter born in Malaga, Spain. He revolutionized the art in the 20th century.']
labels1 = classifier.predict(sentence1)
class1 = int(labels1[0][0])
print("Sentence: ", sentence1[0])
print("Label: %d; label name: %s" % (class1, class_dict[class1]))
sentence2 = ['One of my favourite tennis players in the world is Rafa Nadal.']
labels2 = classifier.predict_proba(sentence2)
class2, prob2 = labels2[0][0] # it returns class2 as string
print("Sentence: ", sentence2[0])
print("Label: %s; label name: %s; certainty: %f" % (class2, class_dict[int(class2)], prob2))
sentence3 = ['Say what one more time, I dare you, I double-dare you m**********r!']
number_responses = 3
labels3 = classifier.predict_proba(sentence3, k=number_responses)
print("Sentence: ", sentence3[0])
for l in range(number_responses):
class3, prob3 = labels3[0][l]
print("Label: %s; label name: %s; certainty: %f" % (class3, class_dict[int(class3)], prob3))
# Load train set
train_file = data_path + 'amazon_review_polarity_train.csv'
df_sentiment_train = pd.read_csv(train_file, header=None, names=['class', 'name', 'description'])
# Load test set
test_file = data_path + 'amazon_review_polarity_test.csv'
df_sentiment_test = pd.read_csv(test_file, header=None, names=['class', 'name', 'description'])
# Transform datasets
df_train_clean = clean_dataset(df_sentiment_train, True, False)
df_test_clean = clean_dataset(df_sentiment_test, False, False)
# Write files to disk
train_file_clean = data_path + 'amazon.train'
df_train_clean.to_csv(train_file_clean, header=None, index=False, columns=['class', 'name', 'description'])
test_file_clean = data_path + 'amazon.test'
df_test_clean.to_csv(test_file_clean, header=None, index=False, columns=['class', 'name', 'description'])
dim = 10
lr = 0.1
epoch = 5
min_count = 1
word_ngrams = 2
bucket = 10000000
thread = 12
label_prefix = '__label__'
# Train a classifier
output_file = data_path + 'amazon_model'
classifier = fasttext.supervised(train_file_clean, output_file, dim=dim, lr=lr, epoch=epoch,
min_count=min_count, word_ngrams=word_ngrams, bucket=bucket,
thread=thread, label_prefix=label_prefix)
# Evaluate classifier
result = classifier.test(test_file_clean)
print('P@1:', result.precision)
print('R@1:', result.recall)
print('Number of examples:', result.nexamples)
class_dict = {
1: "Negative",
2: "Positive"
}
sentence1 = ["The product design is nice but it's working as expected"]
labels1 = classifier.predict_proba(sentence1)
class1, prob1 = labels1[0][0] # it returns class as string
print("Sentence: ", sentence1[0])
# print("Label: %s; label name: %s; certainty: %f" % (class1, class_dict[int(class1)], prob1))
sentence2 = ["I bought the product a month ago and it was working correctly. But now is not working great"]
labels2 = classifier.predict_proba(sentence2)
class2, prob2 = labels2[0][0] # it returns class as string
print("Sentence: ", sentence2[0])
# print("Label: %s; label name: %s; certainty: %f" % (class2, class_dict[int(class2)], prob2))
url = "https://twitter.com/miguelgfierro/status/805827479139192832"
response = urlopen(url).read()
title = str(response).split('<title>')[1].split('</title>')[0]
print(title)
# # Format tweet
# tweet = unescape(title)
# print(tweet)
#
# # Classify tweet
# label_tweet = classifier.predict_proba([tweet])
# class_tweet, prob_tweet = label_tweet[0][0]
# print("Label: %s; label name: %s; certainty: %f" % (class_tweet, class_dict[int(class_tweet)], prob_tweet))
wiki_dataset_original = data_path + 'enwik9'
wiki_dataset = data_path + 'text9'
if not os.path.isfile(wiki_dataset):
os.system("perl wikifil.pl " + wiki_dataset_original + " > " + wiki_dataset)
output_skipgram = data_path + 'skipgram'
if os.path.isfile(output_skipgram + '.bin'):
skipgram = fasttext.load_model(output_skipgram + '.bin')
else:
skipgram = fasttext.skipgram(wiki_dataset, output_skipgram, lr=0.02, dim=50, ws=5,
epoch=1, min_count=5, neg=5, loss='ns', bucket=2000000, minn=3, maxn=6,
thread=4, t=1e-4, lr_update_rate=100)
print(np.asarray(skipgram['king']))
print("Number of words in the model: ", len(skipgram.words))
# Get the vector of some word
Droyals = np.sqrt(pow(np.asarray(skipgram['king']) - np.asarray(skipgram['queen']), 2)).sum()
print(Droyals)
Dpeople = np.sqrt(pow(np.asarray(skipgram['king']) - np.asarray(skipgram['woman']), 2)).sum()
print(Dpeople)
Dpeople2 = np.sqrt(pow(np.asarray(skipgram['man']) - np.asarray(skipgram['woman']), 2)).sum()
print(Dpeople2)
print(len(skipgram.words))
targets = ['man', 'woman', 'king', 'queen', 'brother', 'sister', 'father', 'mother', 'grandfather', 'grandmother',
'cat', 'dog', 'bird', 'squirrel', 'horse', 'pig', 'dove', 'wolf', 'kitten', 'puppy']
classes = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
X_target = []
for w in targets:
X_target.append(skipgram[w])
X_target = np.asarray(X_target)
word_list = list(skipgram.words)[:10000]
X_subset = []
for w in word_list:
X_subset.append(skipgram[w])
X_subset = np.asarray(X_subset)
X_target = np.concatenate((X_subset, X_target))
print(X_target.shape)
X_tsne = TSNE(n_components=2, perplexity=40, init='pca', method='exact',
random_state=0, n_iter=200, verbose=2).fit_transform(X_target)
print(X_tsne.shape)
X_tsne_target = X_tsne[-20:, :]
print(X_tsne_target.shape)
plot_words(X_tsne_target, targets, classes=classes)
plot_words(X_tsne_target, targets, xlimits=[0.5, 0.7], ylimits=[-3.7, -3.6])
def __init__(self):
self.model = fasttext.load_model("model.ftz")
# _*_conding:utf8 _*_
import jieba
import re
import logging
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
import fasttext
import os
#######训练模型###########
model_path = './tmp/senti_model.model'
if os.path.exists(model_path + '.bin'):
classifier = fasttext.load_model(model_path + '.bin')
else:
train_file = './tmp/training.txt'
# model_path = './experiment/fasttext-classification/senti_model.model'
classifier = fasttext.supervised(train_file,
model_path,
label_prefix="__label__")
test_file = './tmp/test.txt'
result = classifier.test(test_file)
print(result.precision)
print(result.recall)
########加载停用词列表#######
stop_word_path = '../stopwords_cn.txt'
stop_word = []
with open(stop_word_path, 'r') as f:
for line in f.readlines():
stop_word.append(line.strip())
punction_list = list('、，。？！：；“”￥%&*@~#（）】【,.?!;:" "')
def __init__(self, model):
self.model = fasttext.load_model(model)
