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from sklearn.model_selection import train_test_split | |
train, test = train_test_split(data, random_state=42, test_size=0.30, shuffle=True) | |
train_text = train['النص'].values.astype('U') | |
test_text = test['النص'].values.astype('U') |
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labels=["الجودة","السلوك","مراجعة","الاستخدام","السعر"] | |
y_train=train[labels] | |
y_test=test[labels] |
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def predict(text): | |
print("text -> ",text) | |
X=vectorizer.transform([text_s]) | |
X_pred=lp_classifier.predict(X).toarray() | |
return [data.columns[i] for i,x in enumerate(X_pred[0]) if x==1] | |
text="كان العمال في الورشة لطفاء وكان عملهن جيد جداً" | |
predict(test_txt) |
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import pysolr | |
from xml.etree import ElementTree | |
import argparse | |
skip = set(["shippingDetails", "priceDetail", "dailyOfferCategory", "stockInfo_availability", "abroad", | |
"imageDetail", "bigImageUrl", "specialOffer", "editorsChoice", "shippingDate", "shippingTime", "types", | |
"stockInfo", | |
"hasVariant", "member", "images", "shippingFee", "image", "globalTradeItemNumber", "quantity", | |
"soldQuantity", "variants", "subTitle" | |
, 'windowOptionFlag', 'productLine', 'affiliate']) |
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import argparse | |
import pandas as pd | |
import pymongo | |
from datetime import datetime | |
def args(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument("path", help="path to ebay index html") | |
parser.add_argument("mongodb",help="ip to mongodb",default="mongodb://localhost:27017/") | |
parser.add_argument("mongodb_prname",help="monogdb project name",default="shelock") |
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