def test_normalize():
""" Testing main function"""
text = "پاکستان ﻤﯿﮟ وسائل کی کوئی کمی نہیں ﮨﮯ۔"
expected = normalize(text)
assert isinstance(expected, str)
for char in expected:
if char == " ":
continue
assert char in URDU_ALL_CHARACTERS
def getTokensList(n):
wordsWoSW = []
words = []
words = normalize(n)
words = re.split('\W+', words)
for word in words:
if word in stop_words:
continue
wordsWoSW.append(word)
return wordsWoSW
def sorted_words():
"""Sorted the words.txt file"""
handler = open(file_name, encoding="utf8")
words_set = set()
for word in handler:
word = normalize(word.strip())
word = '_'.join(word.split())
words_set.add(word.strip())
handler.close()
print(F"Total words: {len(words_set)}")
words_set = sorted(words_set)
with open(file_name, 'w', encoding="utf8") as the_file:
for word in words_set:
the_file.write(word + '\n')
"""TF-IDF based similarity"""
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
from urduhack.preprocessing import remove_punctuation
from urduhack.tokenization.words import fix_join_words
from urduhack import normalize
import pandas as pd
documents = []
df = pd.read_csv("/Users/muhammadfahid/PycharmProjects/data_preprocess/islam-360/ahsanulbayan.db.csv")
for index, row in df.iterrows():
aya_no = row["Unnamed: 0"]
if isinstance(row['translation'], str):
translation = remove_punctuation(fix_join_words(normalize(row["translation"])))
documents.append(translation)
q1 = "کیا سود حرام ہے"
q2 = "عورت کے لئے فرائض"
query = normalize("یہود نصاری")
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform([query] + documents)
cosine_similarities = linear_kernel(vectors[0:1], vectors).flatten()
document_scores = [item.item() for item in cosine_similarities[1:]]
print(sorted(zip(document_scores, documents), reverse=True)[:10])
from urduhack import normalize
from arabic_reshaper import ArabicReshaper
from bidi.algorithm import get_display
from urduhack import stop_words,normalization
d = "F:\\Current Semester\\FYP\\OASRU_CLEN\\OASRU\\ResultScripts"
configuration = {
'delete_harakat': False,
'support_ligatures': True,
'RIAL SIGN': True, # Replace ر ي ا ل with ﷼
}
reshaper = ArabicReshaper(configuration=configuration)
text_to_be_reshaped = "ترجمان"
text_to_be_reshaped = normalize(text_to_be_reshaped)
text_to_be_reshaped = normalization.normalize_characters(text_to_be_reshaped)
text_to_be_reshaped = normalization.normalize_combine_characters(text_to_be_reshaped)
text_to_be_reshaped = normalization.punctuations_space(text_to_be_reshaped)
nlp = spacy.blank("ur")
reshaped_text = reshaper.reshape(text_to_be_reshaped)
doc = nlp(text_to_be_reshaped)
text = []
for each in doc:
if str(each) not in str(stop_words.STOP_WORDS):
#(each)
text.append(str(each))
reshaped_text = ""
for each in text:
from gensim.corpora import Dictionary
from gensim.similarities import SparseTermSimilarityMatrix, MatrixSimilarity
from urduhack.preprocessing import remove_punctuation
from urduhack.tokenization.words import fix_join_words
from urduhack import normalize
import pandas as pd
import numpy as np
z
documents = []
df = pd.read_csv("/Users/muhammadfahid/PycharmProjects/Islam-360/ahsanulbayan.db.csv")
for index, row in df.iterrows():
aya_no = row["Unnamed: 0"]
if isinstance(row['translation'], str):
translation = remove_punctuation(fix_join_words(normalize(row["translation"])))
documents.append(translation.split())
q1 = "کیا سود حرام ہے"
q2 = "بنی اسرائیل کے لیے دعوت"
q3 = "منافقانہ کردار کیا ہے"
q4 = "نماز کا حکم"
q5 = "میاں بیوی تعلقات"
search_terms = normalize(q5).split()
model_path = "/Users/muhammadfahid/PycharmProjects/Islam-360/rel_model-0.9.w2v"
model = Word2Vec.load(model_path)
similarity_index = WordEmbeddingSimilarityIndex(model.wv)
dictionary = Dictionary(documents+[search_terms])
tf_idf = TfidfModel(dictionary=dictionary)
from gensim.similarities import MatrixSimilarity
from urduhack.preprocessing import remove_punctuation
from urduhack.tokenization.words import fix_join_words
from urduhack import normalize
import pandas as pd
documents = []
df = pd.read_csv(
"/Users/muhammadfahid/PycharmProjects/Islam-360/ahsanulbayan.db.csv")
for index, row in df.iterrows():
aya_no = row["Unnamed: 0"]
if isinstance(row['translation'], str):
translation = remove_punctuation(
fix_join_words(normalize(row["translation"])))
documents.append(translation.split())
model_path = "/Users/muhammadfahid/PycharmProjects/Islam-360/rel_model-0.9.w2v"
dictionary = Dictionary(documents)
corpus = [dictionary.doc2bow(document) for document in documents]
lsi = LsiModel(corpus=corpus, id2word=dictionary, num_topics=50)
q1 = "کیا سود حرام ہے"
q2 = "بنی اسرائیل کے لیے دعوت"
q3 = "منافقانہ کردار کیا ہے"
vec_bow = dictionary.doc2bow(q2.split())
vec_lsi = lsi[vec_bow]
similarity_indexes = MatrixSimilarity(lsi[corpus])
from urduhack.tokenization.words import fix_join_words
from urduhack import normalize
from urduhack.stop_words import STOP_WORDS
import pandas as pd
sentences = []
documents = []
df = pd.read_csv(
"/Users/muhammadfahid/PycharmProjects/data_preprocess/islam-360/ahsanulbayan.db.csv"
)
for index, row in df.iterrows():
aya_no = row["Unnamed: 0"]
if isinstance(row['translation'], str):
translation = replace_numbers(
remove_punctuation(fix_join_words(normalize(row["translation"]))))
documents.append(" ".join(
[word for word in translation.split() if word not in STOP_WORDS]))
sentences.append(translation)
keyword_processor = KeywordProcessor()
vectorizer = TfidfVectorizer()
results_kw = []
results_tfidf = []
query = normalize("کیا سود حرام ہے")
k_words = set()
for word in query.split():
if word not in STOP_WORDS:
k_words.add(word)
def MyWordCloudGen(imgpath, scriptpath, os):
# d = "F:\\Current Semester\\FYP\\OASRU_CLEN\\OASRU\\ResultScripts"
configuration = {
'delete_harakat': False,
'support_ligatures': True,
'RIAL SIGN': True, # Replace ر ي ا ل with ﷼
}
reshaper = ArabicReshaper(configuration=configuration)
scripts = os.listdir(scriptpath)
scripts.sort(key=lambda x: os.stat(os.path.join(scriptpath, x)).st_mtime)
print((scripts))
text_to_be_reshaped = open(path.join(scriptpath, scripts[1]),
encoding="UTF-8").read()
print(text_to_be_reshaped)
text_to_be_reshaped = normalize(text_to_be_reshaped)
text_to_be_reshaped = normalization.normalize_characters(
text_to_be_reshaped)
text_to_be_reshaped = normalization.normalize_combine_characters(
text_to_be_reshaped)
text_to_be_reshaped = normalization.punctuations_space(text_to_be_reshaped)
nlp = spacy.blank("ur")
reshaped_text = reshaper.reshape(text_to_be_reshaped)
doc = nlp(text_to_be_reshaped)
text = []
for each in doc:
if str(each) not in str(stop_words.STOP_WORDS):
#(each)
text.append(str(each))
reshaped_text = ""
for each in text:
reshaped_text = reshaped_text + " " + each
reshaped_text = reshaper.reshape(reshaped_text)
from bidi.algorithm import get_display
bidi_text = get_display(reshaped_text)
fontdir = "D:\\tarjumaan-master\\Urdu_fonts\\"
import os
plt.figure(figsize=(20, 15), dpi=200)
wordcloud = WordCloud(os.getcwd() + "\\Urdu_fonts\\" +
"DecoType Thuluth.ttf",
width=2000,
height=1500,
include_numbers=True,
stopwords=stop_words.STOP_WORDS,
min_font_size=30,
background_color="black",
margin=0,
max_words=200).generate(bidi_text)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig(imgpath + "\\image.png", format="png")
plt.show()
img = imgpath + "\\" + "image.png"
print(img)
print("Relative Path", os.path.relpath(img))
img = os.path.relpath(img)
return img
# line = line.strip().split()
# line = [token for token in line if token not in STOP_WORDS]
# DOCUMENTS.append(line)
DOCUMENTS = []
paths = Path('/home/ikram/workplace/datasets/translation_and_tafaseer/csvs'
).glob('*.csv')
for path in paths:
path_in_str = str(path)
print(path_in_str)
df = pd.read_csv(path_in_str)
for index, row in df.iterrows():
if isinstance(row['translation'], str):
translation = row['translation'].strip()
translation = replace_numbers(
remove_punctuation(fix_join_words(normalize(translation))))
translation = re.sub(" +", " ", translation)
DOCUMENTS.append(translation.split())
# DOCUMENTS = []
# df = pd.read_csv("/Users/muhammadfahid/PycharmProjects/data_preprocess/islam-360/ahsanulbayan.db.csv")
# for index, row in df.iterrows():
# if isinstance(row['translation'], str):
# translation = normalize(row['translation'])
# translation = translation.strip()
# translation = fix_join_words(translation)
# trans = remove_punctuation(translation)
# trans = re.sub(" +", " ", trans)
# trans = trans.split()
# DOCUMENTS.append(trans)
# if isinstance(row['tafseer'], str):
import pandas as pd
from urduhack.preprocessing import remove_punctuation, replace_numbers
from urduhack import normalize
from urduhack.tokenization import sentence_tokenizer
from urduhack.tokenization.words import fix_join_words
paths = Path('/home/ikram/workplace/datasets/translation_and_tafaseer/csvs'
).glob('*.csv')
all_sentences = "/home/ikram/workplace/projects/Islam-360/embedding/w2v/translation_sentences.txt"
file_open = open(all_sentences, "w", encoding="utf8")
for path in paths:
path_in_str = str(path)
print(path_in_str)
df = pd.read_csv(path_in_str)
for index, row in df.iterrows():
if isinstance(row['translation'], str):
translation = normalize(row['translation'])
translation = translation.strip()
translation = fix_join_words(translation)
for trans in sentence_tokenizer(translation):
trans = remove_punctuation(trans)
trans = re.sub(" +", " ", trans)
trans = replace_numbers(trans)
file_open.write(trans + "\n")
if isinstance(row['tafseer'], str):
sents = sentence_tokenizer(
fix_join_words(normalize(row['tafseer']).strip()))
for sent in sents:
sent = remove_punctuation(sent)
sent = re.sub(" +", " ", sent)
file_open.write(sent + "\n")
file_open.close()
