I have been trying to use python's NLP script with my QT GUI based C++ application. Basically in the application I am trying to access the NLP script through command line:
QString path = "D:/DS Project/Treegramming";
QString command("py");
QStringList params = QStringList() << "nlp.py";
params << text;
QProcess *process = new QProcess();
process->setWorkingDirectory(path);
process->start(command, params);
process->waitForFinished();
QString result = process->readAll();
The above is working perfectly. but the problem is, it is taking about 40-50 seconds to execute, as it is first training the model and then testing. But I want to train the model first and test it multiple times as we do in Jupyter Notebook. for that I made a separate function for testing and trying to access it with command line:
PS D:\DS Project\Treegramming> py nlp.py "test('it was amazing')"
but again this thing is executing the whole script first and then executing the function. is there anything I can do to solve this?
python script:
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 6 16:18:01 2019
@author: Muhammad Ahmed
"""
import nltk
import sys
import random
import re,string
from nltk.corpus import twitter_samples
from nltk.corpus import stopwords
from nltk.tag import pos_tag
from nltk.tokenize import word_tokenize
from nltk.corpus import twitter_samples
from nltk import classify
from nltk import NaiveBayesClassifier
from nltk import FreqDist
from nltk.stem.wordnet import WordNetLemmatizer
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
def lemmatize_sentence(tokens):
sentence = []
lematizer = WordNetLemmatizer()
for word, tag in pos_tag(tokens):
if tag.startswith('NN'):
pos = 'n'
elif tag.startswith('VB'):
pos = 'v'
else:
pos = 'a'
sentence.append( lematizer.lemmatize( word , pos ) )
return sentence
def remove_noise(tokens , stop_words = ()):
sentence = []
for token, tag in pos_tag( tokens ):
token = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+' , '',token)
token = re.sub("(@[A-Za-z0-9_]+)","",token)
if tag.startswith("NN"):
pos = 'n'
elif tag.startswith('VB'):
pos = 'v'
else:
pos = 'a'
lemmatizer = WordNetLemmatizer()
token = lemmatizer.lemmatize(token, pos)
if len(token) > 0 and token not in string.punctuation and token.lower() not in stop_words:
sentence.append( token.lower() )
return sentence
def get_all_words(tokens_list):
for tokens in tokens_list:
for token in tokens:
yield token
def get_tweets_for_model(tokens_list):
for tweets in tokens_list:
yield dict([token,True] for token in tweets)
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words( positive_cleaned_tokens_list )
all_neg_words = get_all_words( negative_cleaned_tokens_list )
freq_dis_pos = FreqDist( all_pos_words )
freq_dis_neg = FreqDist( all_neg_words )
positive_tokens_for_model = get_tweets_for_model(positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
pos_dataset = [(tweets,"Positive") for tweets in positive_tokens_for_model]
neg_dataset = [(tweets,"Negative") for tweets in negative_tokens_for_model]
dataset = pos_dataset + neg_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
def test( custom_tweet ):
custom_tokens = remove_noise(word_tokenize(custom_tweet))
res = classifier.classify(dict([token, True] for token in custom_tokens))
print(res)
f = open( "result.txt" , "w" )
f.write(res)
f.close()
eval( sys.argv[1] );
You need to create two python scripts:
To prevent repeating code, I will create a script for helpful functions and I will call it utils.py which should look like this:
import re
import string
from nltk.tag import pos_tag
from nltk.stem.wordnet import WordNetLemmatizer
def lemmatize_sentence(tokens):
sentence = []
lematizer = WordNetLemmatizer()
for word, tag in pos_tag(tokens):
if tag.startswith('NN'):
pos = 'n'
elif tag.startswith('VB'):
pos = 'v'
else:
pos = 'a'
sentence.append( lematizer.lemmatize( word , pos ) )
return sentence
def remove_noise(tokens , stop_words = ()):
sentence = []
for token, tag in pos_tag( tokens ):
token = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+' , '',token)
token = re.sub("(@[A-Za-z0-9_]+)","",token)
if tag.startswith("NN"):
pos = 'n'
elif tag.startswith('VB'):
pos = 'v'
else:
pos = 'a'
lemmatizer = WordNetLemmatizer()
token = lemmatizer.lemmatize(token, pos)
if len(token) > 0 and token not in string.punctuation and token.lower() not in stop_words:
sentence.append( token.lower() )
return sentence
def get_all_words(tokens_list):
for tokens in tokens_list:
for token in tokens:
yield token
def get_tweets_for_model(tokens_list):
for tweets in tokens_list:
yield dict([token,True] for token in tweets)
Then let's create the training script, I will call it train.py and it should look like this:
import random
import pickle
from utils import *
from nltk import FreqDist
from nltk.corpus import stopwords
from nltk import NaiveBayesClassifier
from nltk.corpus import twitter_samples
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words( positive_cleaned_tokens_list )
all_neg_words = get_all_words( negative_cleaned_tokens_list )
freq_dis_pos = FreqDist( all_pos_words )
freq_dis_neg = FreqDist( all_neg_words )
positive_tokens_for_model = get_tweets_for_model(positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
pos_dataset = [(tweets,"Positive") for tweets in positive_tokens_for_model]
neg_dataset = [(tweets,"Negative") for tweets in negative_tokens_for_model]
dataset = pos_dataset + neg_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
#### ADD THESE TO SAVE THE CLASSIFIER ####
with open("model.pickle", "wb") as fout:
pickle.dump(classifier, fout)
Finally, the test script test.py that should look like this:
import sys
import pickle
from nltk import classify
from nltk.tokenize import word_tokenize
from utils import remove_noise
#### ADD THESE TO LOAD THE CLASSIFIER ####
with open('model.pickle', 'rb') as fin:
classifier = pickle.load(fin)
def test( custom_tweet ):
custom_tokens = remove_noise(word_tokenize(custom_tweet))
res = classifier.classify(dict([token, True] for token in custom_tokens))
print(res)
f = open( "result.txt" , "w" )
f.write(res)
f.close()
eval( sys.argv[1] );
Now, run train.py once to train the Naive Bayes classifier that will create a new file called model.pickle that holds the trained classifier. Then run test.py from your C++ application on your custom tweet. test.py should loades the trained model model.pickle and use it on the given custom tweet.