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Final_Model.py
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Final_Model.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jan 30 02:59:27 2020
@author: Zohainus
"""
import pandas as pd
import nltk
import string
import re
import numpy as np
from nltk import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.porter import PorterStemmer
from nltk.stem import WordNetLemmatizer
stemmer = PorterStemmer()
lemmatizer = WordNetLemmatizer()
from sklearn.model_selection import StratifiedKFold,KFold
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer,TfidfTransformer
from sklearn.naive_bayes import MultinomialNB,GaussianNB,BernoulliNB
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,BaggingClassifier,GradientBoostingClassifier
from nltk.corpus import stopwords
import unicodedata
#from gensim.parsing.preprocessing import STOPWORDS
from string import punctuation
from sklearn.svm import LinearSVC,SVC
from sklearn.pipeline import Pipeline,FeatureUnion
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix,classification_report,accuracy_score
from nltk.stem.snowball import FrenchStemmer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import make_union,make_pipeline
from sklearn.feature_selection import SelectFromModel,VarianceThreshold, SelectPercentile,SelectKBest, f_classif,chi2
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.preprocessing import Imputer
from textblob import TextBlob
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
import csv
import nltk
nltk.download('wordnet')
nltk.download('stopwords')
#Read Dataset
#To see full tweet
pd.set_option("display.max_colwidth", 100)
dataset = pd.read_csv("C:\\Users\\xxx\\Desktop\\engtweets1.csv",encoding="latin-1", names = ["label","Tweets"]).astype(str)
#new_data = pd.read_csv("F:\\april2019.csv",encoding="latin-1", names = ["label","Tweets"]).astype(str)
def preprocess_text(text):
# Lowercase
text = text.lower()
words_seperated_by_space = text.split(" ")
words_seperated_by_space = [k.replace("\\xa0", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\xc2", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\n", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\r", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\xc8", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\x9b", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\x99", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\xc4", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\x83", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\"99%er\"", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\x99", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("2x80", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("cxf3", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("2x80", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("u0111", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("cixe2x80m", " ") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("don't", "dont") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("won't", "wont") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("can't", "cant") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("i\'m", "i am") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("ain't", "is not") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\'ll", "will") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\'t", "not") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\'ve", "have") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("'s", "is") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\'re", "are") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\'d", "would") for k in words_seperated_by_space]
words_seperated_by_space = [k.replace("\\ \\ ", " ") for k in words_seperated_by_space]
words_seperated_by_space = [re.sub(" u ", "you", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("[!]+", "!", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('[?]+', "?", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('[.]+', ".", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('[\\\]+', "", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('[\']+', ".", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("(haha)+", "haha", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("(l(ol)+)", "lol ", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("(bw(a)+h)", "bwah", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("(bwa(h)+)", "bwah", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("(bwa(h)+)", "bwah", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("<[a-z]*>", "", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("[*****]+", "", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub("(xe2x80x(9|a6))", "", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('_', "", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('-', "", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('<>', "", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('g(rrr)+', "grr", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('u(mmm)+|u(mm)+', "umm", str(k)) for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('@[A-Za-z0-9]+',' ',str(k))for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('https?://[A-Za-z0-9./]+',' ',str(k))for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('#[A-Za-z0-9]+',' ',str(k))for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('\W', ' ', str(k))for k in words_seperated_by_space]
words_seperated_by_space = [re.sub('\s+', ' ', str(k))for k in words_seperated_by_space]
text = ' '.join(words_seperated_by_space)
# Remove HTML tags
text = re.sub(r'<[^>]*>', '', text)
# Remove twitter handlers, hashtags symbols and URLs
text = re.sub(r'@[\w_-]+', ' ', text)
text = re.sub('https?://[^ ]+', ' ', text)
text = re.sub('#', '', text)
text = re.sub('rt', '', text)
# Expand contractions
text = re.sub(r"i'm", " i am ", text)
text = re.sub(r" im ", " i am ", text)
text = re.sub(r"\: p", "", text)
text = re.sub(r" ive ", " i have ", text)
text = re.sub(r" he's ", " he is ", text)
text = re.sub(r" she's ", " she is ", text)
text = re.sub(r" that's ", " that is ", text)
text = re.sub(r" what's ", " what is ", text)
text = re.sub(r" where's ", " where is ", text)
text = re.sub(r" haven't ", " have not ", text)
text = re.sub(r" ur ", " you are ", text)
text = re.sub(r"\'ll", " will", text)
text = re.sub(r"\'ve", " have", text)
text = re.sub(r"\'re", " are", text)
text = re.sub(r"\'d", " would", text)
text = re.sub(r" won't ", " will not ", text)
text = re.sub(r" wouldn't ", " would not ", text)
text = re.sub(r" can't ", " cannot ", text)
text = re.sub(r" cannot ", " cannot ", text)
text = re.sub(r" don't ", " do not ", text)
text = re.sub(r" didn't ", " did not ", text)
text = re.sub(r" doesn't ", " does not ", text)
text = re.sub(r" isn't ", " is not ", text)
text = re.sub(r" it's ", " it is ", text)
text = re.sub(r" who's ", " who is ", text)
text = re.sub(r" there's ", " there is ", text)
text = re.sub(r" weren't ", " were not ", text)
text = re.sub(r" okay ", " o", text)
text = re.sub(r" you're ", " you are ", text)
text = re.sub(r" c'mon ", " come on ", text)
text = re.sub(r"in'", "ing", text)
text = re.sub(r"\'s", " s", text)
# Remove ponctuation and special chars except ! and ?
text = re.sub('[^a-zA-Z?!\s]', ' ', text)
# Lemmatize
lemmatizer = WordNetLemmatizer()
sentence = []
for word in text.split(' '):
sentence.append(lemmatizer.lemmatize(word))
# Rebuild sentences
text = ' '.join(sentence)
# Remove stopwords
stopWords = set(stopwords.words('english'))
sentence = []
for word in text.split(' '):
if word not in stopWords:
sentence.append(word)
# Rebuild sentences
text = ' '.join(sentence)
return text
# caling function
dataset['Tweets_clean'] = dataset['Tweets'].apply(preprocess_text)
dataset['Tweets_clean'].values.reshape(1,-1)
print (f'G = {len(dataset[dataset["label"]=="G"])}')
print (f'NG = {len(dataset[dataset["label"]=="NG"])}')
#new_data['Tweets_clean'] = new_data['Tweets'].apply(preprocess_text)
#print(new_data['Tweets_clean'])
# TFidfVectorization
tfidf_vect = TfidfVectorizer()
X = tfidf_vect.fit_transform(dataset['Tweets_clean'])
print(X.shape)
#X1 = tfidf_vect.transform(new_data['Tweets_clean']).toarray()
#print(X1.shape)
y = dataset['label']
print(dataset.groupby(['label']).size())
skf = StratifiedKFold(n_splits=10, random_state=18, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# Comparison of Classifiers
models = {
'LinearSVC':LinearSVC(),
'SVC':SVC(C = 10, gamma= 0.4, kernel = 'sigmoid') ,
#'SVC':SVC(C = 10, gamma= 0.2, kernel = 'linear') ,
#'SVC':SVC(C = 10, gamma= 0.4, kernel = 'poly') ,
'LogisticRegression':LogisticRegression(penalty='l2', dual=False, tol=0.0001, C= 0.5, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, verbose=5, warm_start=False, n_jobs=-1),
'DecisionTreeClassifier':DecisionTreeClassifier(),
'RandomForestClassifier':RandomForestClassifier(),
'GradientBoostClassifier':GradientBoostingClassifier(),
'KNN':KNeighborsClassifier(),
'NB':GaussianNB(),
'AdaBoost':AdaBoostClassifier(),
'BaggingClassifier':BaggingClassifier(),
'XGBClassifier':XGBClassifier(),
'LightGBM':LGBMClassifier()
}
f = open('FianlReportClassifier.txt','w')
for name, model in models.items():
clf = model
clf.fit(X_train.toarray(), y_train)
y_pred = clf.predict(X_test.toarray())
#y_pred = clf.predict(X1)
#print(y_pred)
print('Precision score of ' + name , precision_score(y_test,y_pred, pos_label="G"))
print('Recall score of ' + name , recall_score(y_test,y_pred, pos_label="G"))
print('F_measure of ' + name , f1_score(y_test,y_pred, pos_label="G"))
print('Accuracy score of ' + name , accuracy_score(y_test,y_pred))
print('Accuracy score of ' + name , accuracy_score(y_test,y_pred))
f.writelines('%s,%s\n,%s\n'%('Accuracy score of '+ name , accuracy_score(y_test, y_pred),classification_report(y_test, y_pred)))
print('Accuracy score of '+ name , accuracy_score(y_test, y_pred),'\n',classification_report(y_test, y_pred, average="binary", pos_label="G"))
print(confusion_matrix(y_test, y_pred))
# it is the code when i use another dataset to train the model. ok
'''
question1 = y_pred #question 1 data
question2 = new_data['Tweets_clean'] #question 2 data
df = pd.DataFrame(columns=["label", "Tweets"])
df["label"] = question1
df["Tweets"] = question2
df.to_csv("C:\\Users\\Zohainus\\Desktop\\newdata\\april2019.csv",index=False)
new_data_op = pd.read_csv("C:\\Users\\Zohainus\\Desktop\\newdata\\april2019.csv",encoding="latin-1", names = ["label","Tweets"],delimiter=',').astype(str)
#How many labels in dataset
print (f'G = {len(new_data_op[new_data_op["label"]=="G"])}')
print (f'NG = {len(new_data_op[new_data_op["label"]=="NG"])}')
'''
#pred = model.predict(X_test)
'''
# Construct the Confusion Matrix
label = ['G', 'NG']
cm = confusion_matrix(y_test, pred, label)
print(cm)
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm)
fig.colorbar(cax)
ax.set_xticklabels([''] + label)
ax.set_yticklabels([''] + label)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title()
plt.show()
'''
'''
# Confusion Matrix of LinearSvc
#import modules
import warnings
import pandas as pd
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
#ignore warnings
warnings.filterwarnings('ignore')
pred = clf.predict(X_test)
#Construct the Confusion Matrix
label = ['G', 'NG']
cm = confusion_matrix(y_test, pred, label)
print(cm)
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm)
fig.colorbar(cax)
ax.set_xticklabels([''] + label)
ax.set_yticklabels([''] + label)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title()
plt.show()
'''
'''
'''
#Exploring the dataset
# Sahpe of data
print (f"Input data has {len(dataset)} rows, {len(dataset.columns)}columns")
#How many labels in dataset
print (f'G = {len(new_data[new_data["label"]=="G"])}')
print (f'NG = {len(new_data[new_data["label"]=="NG"])}')
# Missing values in any row, ignore those
print (f"Number of missing labels = {dataset ['label'].isnull().sum() }")
print (f"Number of missing labels = {dataset ['Tweets'].isnull().sum() }")
'''
'''
# Confusion Matrix of LinearSvc
#import modules
import warnings
import pandas as pd
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
%matplotlib inline
#ignore warnings
warnings.filterwarnings('ignore')
model = LinearSVC()
model.fit(X_train, y_train)
pred = model.predict(X_test)
#Construct the Confusion Matrix
label = ['G', 'NG']
cm = confusion_matrix(y_test, pred, label)
print(cm)
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm)
fig.colorbar(cax)
ax.set_xticklabels([''] + label)
ax.set_yticklabels([''] + label)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title()
plt.show()
'''
# Hyper parameter Tunning
from sklearn.model_selection import GridSearchCV
# defining parameter range
param_grid = [
{'C': [10,100,100,1000,10000,100000,1000000],
'kernel': ['sigmoid', 'linear'],
'gamma': [0.4, 0.5]}
]
grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 2)
# fitting the model for grid search
grid.fit(X_train, y_train)
# print best parameter after tuning
print(grid.best_params_)
# print how our model looks after hyper-parameter tuning
print(grid.best_estimator_)
grid_predictions = grid.predict(X_test)
# print classification report
print(classification_report(y_test, grid_predictions))
print(accuracy_score(y_test,grid_predictions))