LogisticRegression.py

import random
import pandas as pd
import numpy as np
import matplotlib as nlp
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import warnings
warnings.filterwarnings('ignore')
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import RegexpTokenizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import ComplementNB
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn import metrics
from math import *
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report
from sklearn import tree
from sklearn.metrics import accuracy_score
from utils import clean_tweets, handle_emojis, evaluate_with_two_labels, clean_dataset

#Stop Words: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”) 
#that a search engine has been programmed to ignore,
#both when indexing entries for searching and when retrieving them as the result of a search query.


stopword = set(stopwords.words('english'))

tweets = clean_dataset()
tweets.drop(tweets[tweets.sentiment =='2'].index, inplace=True)
#print(tweets['sentiment'].head())


X = tweets.cleaned_tweets
y = tweets.sentiment
random_state = random.randint(10000,100000)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.05, random_state = 100)
vectoriser = TfidfVectorizer(ngram_range=(1,2), max_features=500000)
vectoriser.fit(X_train)
X_train = vectoriser.transform(X_train)
X_test  = vectoriser.transform(X_test)




def logistic_regression():
  
  LRmodel = LogisticRegression(C = 3, max_iter = 1000, n_jobs=-1)
  LRmodel.fit(X_train, y_train)
  train_acc =LRmodel.score(X_train,y_train)*100
  test_acc = LRmodel.score(X_test,y_test)*100
  #print ("Train accuracy ={:.2f}%".format(LRmodel.score(X_train,y_train)*100))
  #print ("Test accuracy ={:.2f}%".format(LRmodel.score(X_test,y_test)*100))
  return LRmodel, train_acc, test_acc
  
#nb;58, dt:65 lr:88
#two parameters: nb:82, dt:83

def fig_visualization_lr():
  return evaluate_with_two_labels(logistic_regression()[0], X_test, y_test)