def stem_using_stempel(self, stem_type="default", words=None):
if stem_type == "polimorf":
stemmer = StempelStemmer.polimorf()
else:
stemmer = StempelStemmer.default()
if words is None:
words = self.words
stem_words = [stemmer.stem(w) for w in words]
return stem_words
def get_java_stemmer(stemmer_table_fpath, jar_fpath):
os.environ['CLASSPATH'] = jar_fpath
from jnius import autoclass
FileInputStream = autoclass('java.io.FileInputStream')
StempelStemmer = autoclass(
'org.apache.lucene.analysis.stempel.StempelStemmer')
stemmerTrie = StempelStemmer.load(FileInputStream(stemmer_table_fpath))
return StempelStemmer(stemmerTrie)
def __init__(self):
self.DATASET_PATH = DATA_CONFIG["data_path"]
self.corpus = None
self.bayes = None
self.svm_model = None
self.stemmer = StempelStemmer.polimorf()
self.processor = BasicProcessor()
self.tfidf = TfidfVectorizer(max_features=5000)
self.encoder = LabelEncoder()
self.stop_words = self.read_file(
DATA_CONFIG["stopwords_path"]).split('\n')
self.codes_map = {
"POWTRZ":
"Wniosek o powtórzenie roku studiów/powtarzanie przedmiotu",
"PRZEP": "Wniosek o przepisanie oceny",
"WYKR": "Wniosek o wykreślenie z listy studentów",
"IPS": "Wniosek o Indywidualny Program Studiów",
"ECTS": "Wniosek o kontynuowanie studiów z deficytem punktów ECTS",
"INZ": "Rejestracja pracy inżynierskiej",
"DZIEKAN": "Podanie do dziekana",
"PRAKT": "Wniosek o zgodę na odbycie praktyki studenckiej",
"WARUN": "Wniosek o wpis warunkowy",
"REAKT": "Wniosek o reaktywację studiów",
"LIC": "Rejestracja pracy licencjackiej"
}
self.get_data()
self.train_model()
def add_keyword_if_not_exists(self, word):
if word[0] != "\"":
stemmer = StempelStemmer.polimorf()
word = stemmer.stem(word)
else:
word = word[1:-1]
if self.get_keyword(word) is None:
return Keyword(word=word).save()
else:
return self.get_keyword(word)
def cleanall(df):
#converting given variable to Series for further cleaning
df = pd.Series(df)
#making all letters lowercase, to avoid further issues regarding case sensivity
df = df.str.lower()
#declaring a function to find and remove certain patterns using regex
def remove_pattern(text,pattern):
#re.findall() finds the patterns, e.g. @user, and puts it in a list for further task
r = re.findall(pattern,text)
#re.sub() removes pattern from the sentences in the dataset
for i in r:
text = re.sub(i,"",text)
return text
#removing '@user' pattern through vectorize function
df = np.vectorize(remove_pattern)(df, "@[\w]*")
#removing 'https://t.co/' pattern through vectorize function
#since in our database there are tweets with links, we need to remove links as well
df = np.vectorize(remove_pattern)(df, "https://t.co/[\w]*")
df = pd.Series(df)
#replacing the symbols and punctuation with spaces, leaving all the polish special characters in place
df = df.str.replace("[^\w]", " ")
#getting rid of all the numbers in the tweets
df = df.str.replace("[0-9]", " ")
#removing '#hashtag' pattern, since in our database hashtags are rarely used and it makes more sense to just get rid of them
df = np.vectorize(remove_pattern)(df, "#[\w]*")
df = pd.Series(df)
#tokenizing the tweets
df = df.apply(lambda x: x.split())
#reading a .txt list of stopwords into a python list (stopwords were taken from https://github.com/bieli/stopwords)
with open('../lib/polishstopwords.txt', 'r') as stopwords:
stop = stopwords.read().splitlines()
#getting rid of the stop words
for i in range(len(df)):
df[i] = [j for j in df[i] if j not in stop]
#stemming the tweets (stripping the suffixes) using pystempel library
ps = StempelStemmer.polimorf()
df = df.apply(lambda x: [ps.stem(i) for i in x])
#Polish stemmer is defective and sometimes turns random words into NoneType, which prevents stiching the tokens back into strings
#we need to remove all NoneType objects in order to proceed
for i in range(len(df)):
df[i] = [j for j in df[i] if j]
#stiching the tweets back together
for i in range(len(df)):
df[i] = ' '.join(df[i])
return df
import sys
sys.path.insert(0, "../../VoiceAssistant")
# check operating system version
import platform
isLinux = 'Linux' == platform.system()
import tkinter as tk
from tkinter import font as tk_font
import command_manager
import threading
from UI.HomePage.home_page_layout import HomePage
from UI.Login.login_layout import LoginPage
from UI.Register.resgister_layout import RegisterPage
from stempel import StempelStemmer
stemmer = StempelStemmer.polimorf()
class Main(tk.Tk):
def __init__(self, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
self.title_font = tk_font.Font(family='Helvetica',
size=18,
weight="bold",
slant="italic")
container = tk.Frame(self)
container.pack(side="top", fill="both", expand=True)
container.grid_rowconfigure(0, weight=1)
container.grid_columnconfigure(0, weight=1)
with open('resources/generated/news_data.json', 'r') as f:
data = json.load(f)
all_words = []
all_articles = []
all_categories = []
for article in data['articles']:
art = str(article['description']).lower()
tokens = nltk.wordpunct_tokenize(art)
all_words.extend(tokens)
all_articles.append(art.lower())
all_categories.append(article['category'])
stemmer = StempelStemmer.default()
all_words = [stemmer.stem(word) for word in all_words]
all_words = list(set(all_words))
all_words.remove(None)
all_words = sorted(all_words)
with open('resources/generated/input_layer_words.txt', 'w') as datafile:
json.dump(all_words, datafile)
unique_categories = ['sports', 'health', 'business', 'entertainment', 'technology']
x = []
y = []
for article in all_articles:
def test_polimorf():
stemmer = StempelStemmer.from_file('../data/polimorf/stemmer_polimorf.tbl.gz')
assert stemmer.stem('jabłkami') == 'jabłko'
def __init__(self, text_df):
self.text_df = text_df
self.stemmer = StempelStemmer.default()
def get_python_stemmer(stemmer_table_fpath):
from stempel import StempelStemmer
return StempelStemmer.from_file(stemmer_table_fpath)
def get_stemmed_word(self, word):
stemmer = StempelStemmer.polimorf()
return stemmer.stem(word)
import spacy
import platform
import functools
import KeyExt.config
from keybert import KeyBERT
from string import punctuation
from nltk.stem import SnowballStemmer
from stempel import StempelStemmer
# Initialize all required stemmers once.
stemmers = {
'english': SnowballStemmer('english'),
'french': SnowballStemmer('french'),
'spanish': SnowballStemmer('spanish'),
'portuguese': SnowballStemmer('portuguese'),
'polish': StempelStemmer.default()
}
def load_models():
"""
Function which loads the english NLP model, and the Keybert model.
This needs to run once since all models need a few seconds to load.
"""
return (spacy.load('en_core_web_sm'),
KeyBERT('distiluse-base-multilingual-cased-v2'))
def preprocess(lis, language):
"""
Function which applies stemming to a